From 2a781e762ab63ef0df76d00ddc5622d8126d8961 Mon Sep 17 00:00:00 2001 From: Bryan Weber Date: Sat, 19 Feb 2022 22:12:49 -0500 Subject: [PATCH 1/3] Blackify the notebooks --- electrochemistry/lithium_ion_battery.ipynb | 144 ++++--- ...lame_speed_with_convergence_analysis.ipynb | 328 +++++++++------ ...lame_speed_with_sensitivity_analysis.ipynb | 79 ++-- flames/twin_premixed_flame_axisymmetric.ipynb | 79 ++-- python_tutorial.ipynb | 74 ++-- reactors/1D_pfr_surfchem.ipynb | 380 ++++++++++-------- reactors/NonIdealShockTube.ipynb | 127 ++++-- .../batch_reactor_ignition_delay_NTC.ipynb | 61 ++- reactors/interactive_path_diagram.ipynb | 193 +++++---- reactors/stirred_reactor.ipynb | 98 ++--- thermo/flame_temperature.ipynb | 26 +- thermo/heating_value.ipynb | 42 +- 12 files changed, 929 insertions(+), 702 deletions(-) diff --git a/electrochemistry/lithium_ion_battery.ipynb b/electrochemistry/lithium_ion_battery.ipynb index 9b4efee..dec34cf 100644 --- a/electrochemistry/lithium_ion_battery.ipynb +++ b/electrochemistry/lithium_ion_battery.ipynb @@ -107,7 +107,8 @@ ], "source": [ "import cantera as ct\n", - "print('Runnning Cantera version: ' + ct.__version__)" + "\n", + "print(\"Runnning Cantera version: \" + ct.__version__)" ] }, { @@ -129,10 +130,10 @@ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", - "plt.rcParams['axes.labelsize'] = 16\n", - "plt.rcParams['xtick.labelsize'] = 12\n", - "plt.rcParams['ytick.labelsize'] = 12\n", - "plt.rcParams['figure.autolayout'] = True" + "plt.rcParams[\"axes.labelsize\"] = 16\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "plt.rcParams[\"figure.autolayout\"] = True" ] }, { @@ -152,14 +153,18 @@ }, "outputs": [], "source": [ - "input_file = '../data/lithium_ion_battery.yaml'\n", - "anode = ct.Solution(input_file, 'anode');\n", - "cathode = ct.Solution(input_file, 'cathode');\n", + "input_file = \"../data/lithium_ion_battery.yaml\"\n", + "anode = ct.Solution(input_file, \"anode\")\n", + "cathode = ct.Solution(input_file, \"cathode\")\n", "# The 'elde' electrode phase is needed as a source/sink for electrons:\n", - "elde = ct.Solution(input_file, 'electron');\n", - "elyte = ct.Solution(input_file, 'electrolyte');\n", - "anode_interface = ct.Interface(input_file, 'edge_anode_electrolyte', [anode, elde, elyte]);\n", - "cathode_interface = ct.Interface(input_file, 'edge_cathode_electrolyte', [cathode, elde, elyte]);" + "elde = ct.Solution(input_file, \"electron\")\n", + "elyte = ct.Solution(input_file, \"electrolyte\")\n", + "anode_interface = ct.Interface(\n", + " input_file, \"edge_anode_electrolyte\", [anode, elde, elyte]\n", + ")\n", + "cathode_interface = ct.Interface(\n", + " input_file, \"edge_cathode_electrolyte\", [cathode, elde, elyte]\n", + ");" ] }, { @@ -189,22 +194,23 @@ "# Array of lithium mole fractions in the anode:\n", "X_Li_an = np.arange(0.005, 0.995, 0.02)\n", "# Assume that the cathode and anode capacities are balanced:\n", - "X_Li_ca = 1. - X_Li_an;\n", + "X_Li_ca = 1.0 - X_Li_an\n", "\n", "# I_app = 0: Open circuit\n", - "I_app = 0.;\n", + "I_app = 0.0\n", "\n", "# At zero current, electrolyte resistance is irrelevant:\n", - "R_elyte = 0.;\n", + "R_elyte = 0.0\n", "\n", "# Temperature and pressure\n", - "T = 300 # K\n", + "T = 300 # K\n", "P = ct.one_atm\n", "\n", "F = ct.faraday\n", "\n", - "S_ca = 1.1167; # [m^2] Cathode total active material surface area\n", - "S_an = 0.7824; # [m^2] Anode total active material surface area" + "S_ca = 1.1167\n", + "# [m^2] Cathode total active material surface area\n", + "S_an = 0.7824; # [m^2] Anode total active material surface area" ] }, { @@ -222,7 +228,7 @@ }, "outputs": [], "source": [ - "phases = [anode, elde, elyte, cathode, anode_interface, cathode_interface];\n", + "phases = [anode, elde, elyte, cathode, anode_interface, cathode_interface]\n", "for ph in phases:\n", " ph.TP = T, P" ] @@ -242,10 +248,10 @@ }, "outputs": [], "source": [ - "def anode_curr(phi_l,I_app,phi_s,X_Li_an):\n", + "def anode_curr(phi_l, I_app, phi_s, X_Li_an):\n", "\n", " # Set the active material mole fraction\n", - " anode.X = 'Li[anode]:' + str(X_Li_an) + ', V[anode]:' + str(1 - X_Li_an)\n", + " anode.X = \"Li[anode]:\" + str(X_Li_an) + \", V[anode]:\" + str(1 - X_Li_an)\n", "\n", " # Set the electrode and electrolyte potential\n", " elde.electric_potential = phi_s\n", @@ -254,26 +260,27 @@ " # Get the net product rate of electrons in the anode (per m2^ interface)\n", " r_elec = anode_interface.get_net_production_rates(elde)\n", "\n", - " anCurr = r_elec*ct.faraday*S_an;\n", + " anCurr = r_elec * ct.faraday * S_an\n", " diff = I_app + anCurr\n", - " \n", + "\n", " return diff\n", "\n", - "def cathode_curr(phi_s,I_app,phi_l,X_Li_ca):\n", - " \n", + "\n", + "def cathode_curr(phi_s, I_app, phi_l, X_Li_ca):\n", + "\n", " # Set the active material mole fractions\n", - " cathode.X = 'Li[cathode]:' + str(X_Li_ca) + ', V[cathode]:' + str(1 - X_Li_ca)\n", + " cathode.X = \"Li[cathode]:\" + str(X_Li_ca) + \", V[cathode]:\" + str(1 - X_Li_ca)\n", "\n", " # Set the electrode and electrolyte potential\n", " elde.electric_potential = phi_s\n", " elyte.electric_potential = phi_l\n", - " \n", + "\n", " # Get the net product rate of electrons in the cathode (per m2^ interface)\n", " r_elec = cathode_interface.get_net_production_rates(elde)\n", - " \n", - " caCurr = r_elec*ct.faraday*S_an;\n", + "\n", + " caCurr = r_elec * ct.faraday * S_an\n", " diff = I_app - caCurr\n", - " \n", + "\n", " return diff" ] }, @@ -304,26 +311,26 @@ "# Initialize array of OCVs:\n", "E_cell_kin = np.zeros_like(X_Li_ca)\n", "\n", - "for i,X_an in enumerate(X_Li_an):\n", - " #Set anode electrode potential to 0:\n", + "for i, X_an in enumerate(X_Li_an):\n", + " # Set anode electrode potential to 0:\n", " phi_s_an = 0\n", " E_init = 3.0\n", - " \n", - " phi_l_an = fsolve(anode_curr,E_init,args=(I_app, phi_s_an, X_an))\n", - " \n", + "\n", + " phi_l_an = fsolve(anode_curr, E_init, args=(I_app, phi_s_an, X_an))\n", + "\n", " # Calculate electrolyte potential at cathode interface:\n", - " phi_l_ca = phi_l_an + I_app*R_elyte;\n", - " \n", + " phi_l_ca = phi_l_an + I_app * R_elyte\n", + "\n", " # Calculate cathode electrode potential\n", - " phi_s_ca = fsolve(cathode_curr,E_init,args=(I_app, phi_l_ca, X_Li_ca[i]))\n", - " \n", + " phi_s_ca = fsolve(cathode_curr, E_init, args=(I_app, phi_l_ca, X_Li_ca[i]))\n", + "\n", " # Calculate cell voltage\n", " E_cell_kin[i] = phi_s_ca - phi_s_an\n", - " \n", - " \n", + "\n", + "\n", "# Toc\n", "t1 = time.time()\n", - "print('{:d} cell voltages calculated in {:3.2f} seconds.'.format(i, t1 - t0))" + "print(\"{:d} cell voltages calculated in {:3.2f} seconds.\".format(i, t1 - t0))" ] }, { @@ -1130,19 +1137,19 @@ ], "source": [ "plt.figure()\n", - "plt.plot(100*X_Li_ca, E_cell_kin, color='b', linewidth=2.5)\n", + "plt.plot(100 * X_Li_ca, E_cell_kin, color=\"b\", linewidth=2.5)\n", "plt.ylim([2.5, 4.3])\n", - "plt.xlabel('Li Fraction in Cathode (%)', fontname='Times New Roman', fontsize=18)\n", - "plt.ylabel('Open Circuit Potential (V)', fontname='Times New Roman', fontsize=18)\n", + "plt.xlabel(\"Li Fraction in Cathode (%)\", fontname=\"Times New Roman\", fontsize=18)\n", + "plt.ylabel(\"Open Circuit Potential (V)\", fontname=\"Times New Roman\", fontsize=18)\n", "\n", "ax = plt.gca()\n", "\n", "for tick in ax.xaxis.get_major_ticks():\n", " tick.label1.set_fontsize(14)\n", - " tick.label1.set_fontname('Times New Roman')\n", + " tick.label1.set_fontname(\"Times New Roman\")\n", "for tick in ax.yaxis.get_major_ticks():\n", " tick.label1.set_fontsize(14)\n", - " tick.label1.set_fontname('Times New Roman')" + " tick.label1.set_fontname(\"Times New Roman\")" ] }, { @@ -1188,22 +1195,22 @@ "E_cell_therm = np.zeros_like(X_Li_ca)\n", "\n", "for i, X_an in enumerate(X_Li_an):\n", - " #Set anode electrode potential to 0:\n", - " anode.X = 'Li[anode]:' + str(X_an) + ', V[anode]:' + str(1 - X_an)\n", + " # Set anode electrode potential to 0:\n", + " anode.X = \"Li[anode]:\" + str(X_an) + \", V[anode]:\" + str(1 - X_an)\n", " dG_an = anode_interface.delta_gibbs[0]\n", - " n_charge = -1.\n", - " E_eq_an = -dG_an/n_charge/ct.faraday\n", - " \n", - " cathode.X = 'Li[cathode]:' + str(1. - X_an) + ', V[cathode]:' + str(X_an)\n", + " n_charge = -1.0\n", + " E_eq_an = -dG_an / n_charge / ct.faraday\n", + "\n", + " cathode.X = \"Li[cathode]:\" + str(1.0 - X_an) + \", V[cathode]:\" + str(X_an)\n", " dG_ca = cathode_interface.delta_gibbs[0]\n", - " n_charge = 1.\n", - " E_eq_ca = -dG_ca/n_charge/ct.faraday\n", - " \n", + " n_charge = 1.0\n", + " E_eq_ca = -dG_ca / n_charge / ct.faraday\n", + "\n", " E_cell_therm[i] = E_eq_ca - E_eq_an\n", - " \n", + "\n", "# Toc\n", "t1 = time.time()\n", - "print('{:d} cell voltages calculated in {:3.2f} seconds.'.format(i, t1 - t0))" + "print(\"{:d} cell voltages calculated in {:3.2f} seconds.\".format(i, t1 - t0))" ] }, { @@ -2012,21 +2019,28 @@ ], "source": [ "plt.figure()\n", - "plt.plot(100*X_Li_ca, E_cell_therm,color='b', linewidth=2.5)\n", - "plt.plot(100*X_Li_ca, E_cell_kin,linewidth=0., marker='o', markerfacecolor='none', markeredgecolor='r')\n", + "plt.plot(100 * X_Li_ca, E_cell_therm, color=\"b\", linewidth=2.5)\n", + "plt.plot(\n", + " 100 * X_Li_ca,\n", + " E_cell_kin,\n", + " linewidth=0.0,\n", + " marker=\"o\",\n", + " markerfacecolor=\"none\",\n", + " markeredgecolor=\"r\",\n", + ")\n", "plt.ylim([2.5, 4.3])\n", - "plt.xlabel('Li Fraction in Cathode (%)', fontname='Times New Roman', fontsize=18)\n", - "plt.ylabel('Open Circuit Potential (V)', fontname='Times New Roman', fontsize=18)\n", - "plt.legend(['Thermodynamic', 'Kinetic'])\n", + "plt.xlabel(\"Li Fraction in Cathode (%)\", fontname=\"Times New Roman\", fontsize=18)\n", + "plt.ylabel(\"Open Circuit Potential (V)\", fontname=\"Times New Roman\", fontsize=18)\n", + "plt.legend([\"Thermodynamic\", \"Kinetic\"])\n", "\n", "ax = plt.gca()\n", "\n", "for tick in ax.xaxis.get_major_ticks():\n", " tick.label1.set_fontsize(14)\n", - " tick.label1.set_fontname('Times New Roman')\n", + " tick.label1.set_fontname(\"Times New Roman\")\n", "for tick in ax.yaxis.get_major_ticks():\n", " tick.label1.set_fontsize(14)\n", - " tick.label1.set_fontname('Times New Roman')" + " tick.label1.set_fontname(\"Times New Roman\")" ] }, { diff --git a/flames/flame_speed_with_convergence_analysis.ipynb b/flames/flame_speed_with_convergence_analysis.ipynb index dc728c8..82ea973 100644 --- a/flames/flame_speed_with_convergence_analysis.ipynb +++ b/flames/flame_speed_with_convergence_analysis.ipynb @@ -77,17 +77,17 @@ "source": [ "# Import plotting modules and define plotting preference\n", "\n", - "plt.rcParams['axes.labelsize'] = 14\n", - "plt.rcParams['xtick.labelsize'] = 12\n", - "plt.rcParams['ytick.labelsize'] = 12\n", - "plt.rcParams['legend.fontsize'] = 10\n", - "plt.rcParams['figure.figsize'] = (8,6)\n", + "plt.rcParams[\"axes.labelsize\"] = 14\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "plt.rcParams[\"legend.fontsize\"] = 10\n", + "plt.rcParams[\"figure.figsize\"] = (8, 6)\n", "\n", "# Get the best of both ggplot and seaborn\n", - "plt.style.use('ggplot')\n", - "plt.style.use('seaborn-deep')\n", + "plt.style.use(\"ggplot\")\n", + "plt.style.use(\"seaborn-deep\")\n", "\n", - "plt.rcParams['figure.autolayout'] = True" + "plt.rcParams[\"figure.autolayout\"] = True" ] }, { @@ -111,17 +111,18 @@ " \"\"\"\n", " grids = list(grids)\n", " speeds = list(speeds)\n", + "\n", " def speed_from_grid_size(grid_size, true_speed, error):\n", " \"\"\"\n", " Given a grid size (or an array or list of grid sizes)\n", " return a prediction (or array of predictions)\n", - " of the computed flame speed, based on \n", + " of the computed flame speed, based on\n", " the parameters `true_speed` and `error`.\n", - " \n", + "\n", " It seems, from experience, that error scales roughly with\n", " 1/grid_size, so we assume that form.\n", " \"\"\"\n", - " return true_speed + error * np.array(grid_size)**-1.\n", + " return true_speed + error * np.array(grid_size) ** -1.0\n", "\n", " # Fit the chosen form of speed_from_grid_size, to the last four\n", " # speed and grid size values.\n", @@ -129,84 +130,126 @@ "\n", " # How bad the fit was gives you some error, `percent_error_in_true_speed`.\n", " perr = np.sqrt(np.diag(pcov))\n", - " true_speed_estimate = popt[0]\n", - " percent_error_in_true_speed = 100.*perr[0] / popt[0]\n", - " print(\"Fitted true_speed is {:.4f} ± {:.4f} cm/s ({:.1f}%)\".format(\n", - " popt[0]*100,\n", - " perr[0]*100,\n", - " percent_error_in_true_speed\n", - " ))\n", - " #print \"convergerce rate wrt grid size is {:.1f} ± {:.1f}\".format(popt[2], perr[2])\n", - " \n", - " # How far your extrapolated infinite grid value is from your extrapolated \n", + " true_speed_estimate = popt[0]\n", + " percent_error_in_true_speed = 100.0 * perr[0] / popt[0]\n", + " print(\n", + " \"Fitted true_speed is {:.4f} ± {:.4f} cm/s ({:.1f}%)\".format(\n", + " popt[0] * 100, perr[0] * 100, percent_error_in_true_speed\n", + " )\n", + " )\n", + " # print \"convergerce rate wrt grid size is {:.1f} ± {:.1f}\".format(popt[2], perr[2])\n", + "\n", + " # How far your extrapolated infinite grid value is from your extrapolated\n", " # (or interpolated) final grid value, gives you some other error, `estimated_percent_error`\n", - " estimated_percent_error = 100. * (\n", - " speed_from_grid_size(grids[-1], *popt) - true_speed_estimate\n", - " ) / true_speed_estimate\n", - " print(\"Estimated error in final calculation {:.1f}%\".format(estimated_percent_error))\n", + " estimated_percent_error = (\n", + " 100.0\n", + " * (speed_from_grid_size(grids[-1], *popt) - true_speed_estimate)\n", + " / true_speed_estimate\n", + " )\n", + " print(\n", + " \"Estimated error in final calculation {:.1f}%\".format(estimated_percent_error)\n", + " )\n", "\n", " # The total estimated error is the sum of these two errors.\n", - " total_percent_error_estimate = abs(percent_error_in_true_speed) + abs(estimated_percent_error)\n", + " total_percent_error_estimate = abs(percent_error_in_true_speed) + abs(\n", + " estimated_percent_error\n", + " )\n", " print(\"Estimated total error {:.1f}%\".format(total_percent_error_estimate))\n", - " \n", + "\n", " if plot:\n", - " plt.semilogx(grids,speeds,'o-')\n", - " plt.ylim(min( speeds[-5:]+[true_speed_estimate-perr[0]])*.95 ,\n", - " max( speeds[-5:]+[true_speed_estimate+perr[0]])*1.05 )\n", - " plt.plot(grids[-4:], speeds[-4:], 'or')\n", - " extrapolated_grids = grids + [grids[-1] * i for i in range(2,8)]\n", - " plt.plot(extrapolated_grids,speed_from_grid_size(extrapolated_grids,*popt),':r')\n", + " plt.semilogx(grids, speeds, \"o-\")\n", + " plt.ylim(\n", + " min(speeds[-5:] + [true_speed_estimate - perr[0]]) * 0.95,\n", + " max(speeds[-5:] + [true_speed_estimate + perr[0]]) * 1.05,\n", + " )\n", + " plt.plot(grids[-4:], speeds[-4:], \"or\")\n", + " extrapolated_grids = grids + [grids[-1] * i for i in range(2, 8)]\n", + " plt.plot(\n", + " extrapolated_grids, speed_from_grid_size(extrapolated_grids, *popt), \":r\"\n", + " )\n", " plt.xlim(*plt.xlim())\n", - " plt.hlines(true_speed_estimate, *plt.xlim(), colors=u'r', linestyles=u'dashed')\n", - " plt.hlines(true_speed_estimate+perr[0], *plt.xlim(), colors=u'r', linestyles=u'dashed', alpha=0.3)\n", - " plt.hlines(true_speed_estimate-perr[0], *plt.xlim(), colors=u'r', linestyles=u'dashed', alpha=0.3)\n", - " plt.fill_between(plt.xlim(), true_speed_estimate-perr[0],true_speed_estimate+perr[0], facecolor='red', alpha=0.1 )\n", + " plt.hlines(true_speed_estimate, *plt.xlim(), colors=\"r\", linestyles=\"dashed\")\n", + " plt.hlines(\n", + " true_speed_estimate + perr[0],\n", + " *plt.xlim(),\n", + " colors=\"r\",\n", + " linestyles=\"dashed\",\n", + " alpha=0.3\n", + " )\n", + " plt.hlines(\n", + " true_speed_estimate - perr[0],\n", + " *plt.xlim(),\n", + " colors=\"r\",\n", + " linestyles=\"dashed\",\n", + " alpha=0.3\n", + " )\n", + " plt.fill_between(\n", + " plt.xlim(),\n", + " true_speed_estimate - perr[0],\n", + " true_speed_estimate + perr[0],\n", + " facecolor=\"red\",\n", + " alpha=0.1,\n", + " )\n", "\n", - " above = popt[1]/abs(popt[1]) # will be +1 if approach from above or -1 if approach from below\n", + " above = popt[1] / abs(\n", + " popt[1]\n", + " ) # will be +1 if approach from above or -1 if approach from below\n", "\n", - " plt.annotate(\"\",\n", - " xy=(grids[-1], true_speed_estimate),\n", - " xycoords='data',\n", - " xytext=(grids[-1], speed_from_grid_size(grids[-1], *popt)),\n", - " textcoords='data',\n", - " arrowprops=dict(arrowstyle='|-|, widthA=0.5, widthB=0.5', linewidth=1,\n", - " connectionstyle='arc3',\n", - " color='black', shrinkA=0, shrinkB=0),\n", - " )\n", + " plt.annotate(\n", + " \"\",\n", + " xy=(grids[-1], true_speed_estimate),\n", + " xycoords=\"data\",\n", + " xytext=(grids[-1], speed_from_grid_size(grids[-1], *popt)),\n", + " textcoords=\"data\",\n", + " arrowprops=dict(\n", + " arrowstyle=\"|-|, widthA=0.5, widthB=0.5\",\n", + " linewidth=1,\n", + " connectionstyle=\"arc3\",\n", + " color=\"black\",\n", + " shrinkA=0,\n", + " shrinkB=0,\n", + " ),\n", + " )\n", "\n", - " plt.annotate(\"{:.1f}%\".format(abs(estimated_percent_error)),\n", - " xy=(grids[-1], speed_from_grid_size(grids[-1], *popt)),\n", - " xycoords='data',\n", - " xytext=(5,15*above),\n", - " va='center',\n", - " textcoords='offset points',\n", - " arrowprops=dict(arrowstyle='->',\n", - " connectionstyle='arc3')\n", - " )\n", + " plt.annotate(\n", + " \"{:.1f}%\".format(abs(estimated_percent_error)),\n", + " xy=(grids[-1], speed_from_grid_size(grids[-1], *popt)),\n", + " xycoords=\"data\",\n", + " xytext=(5, 15 * above),\n", + " va=\"center\",\n", + " textcoords=\"offset points\",\n", + " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"),\n", + " )\n", "\n", - " plt.annotate(\"\",\n", - " xy=(grids[-1]*4, true_speed_estimate-(above*perr[0])),\n", - " xycoords='data',\n", - " xytext=(grids[-1]*4, true_speed_estimate),\n", - " textcoords='data',\n", - " arrowprops=dict(arrowstyle='|-|, widthA=0.5, widthB=0.5', linewidth=1,\n", - " connectionstyle='arc3',\n", - " color='black', shrinkA=0, shrinkB=0),\n", - " )\n", - " plt.annotate(\"{:.1f}%\".format(abs(percent_error_in_true_speed)),\n", - " xy=(grids[-1]*4, true_speed_estimate-(above*perr[0])),\n", - " xycoords='data',\n", - " xytext=(5,-15*above),\n", - " va='center',\n", - " textcoords='offset points',\n", - " arrowprops=dict(arrowstyle='->',\n", - " connectionstyle='arc3')\n", - " )\n", + " plt.annotate(\n", + " \"\",\n", + " xy=(grids[-1] * 4, true_speed_estimate - (above * perr[0])),\n", + " xycoords=\"data\",\n", + " xytext=(grids[-1] * 4, true_speed_estimate),\n", + " textcoords=\"data\",\n", + " arrowprops=dict(\n", + " arrowstyle=\"|-|, widthA=0.5, widthB=0.5\",\n", + " linewidth=1,\n", + " connectionstyle=\"arc3\",\n", + " color=\"black\",\n", + " shrinkA=0,\n", + " shrinkB=0,\n", + " ),\n", + " )\n", + " plt.annotate(\n", + " \"{:.1f}%\".format(abs(percent_error_in_true_speed)),\n", + " xy=(grids[-1] * 4, true_speed_estimate - (above * perr[0])),\n", + " xycoords=\"data\",\n", + " xytext=(5, -15 * above),\n", + " va=\"center\",\n", + " textcoords=\"offset points\",\n", + " arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"),\n", + " )\n", "\n", " plt.ylabel(\"Flame speed (m/s)\")\n", " plt.xlabel(\"Grid size\")\n", " plt.show()\n", - " \n", + "\n", " return true_speed_estimate, total_percent_error_estimate" ] }, @@ -218,12 +261,12 @@ "source": [ "def make_callback(flame):\n", " \"\"\"\n", - " Create and return a callback function that you will attach to \n", + " Create and return a callback function that you will attach to\n", " a flame solver. The reason we define a function to make the callback function,\n", " instead of just defining the callback function, is so that it can store\n", - " a pair of lists that persist between function calls, to store the \n", + " a pair of lists that persist between function calls, to store the\n", " values of grid size and flame speed.\n", - " \n", + "\n", " This factory returns the callback function, and the two lists:\n", " (callback, speeds, grids)\n", " \"\"\"\n", @@ -236,16 +279,17 @@ " speeds.append(speed)\n", " grids.append(grid)\n", " print(\"Iteration {}\".format(len(grids)))\n", - " print(\"Current flame speed is is {:.4f} cm/s\".format(speed*100.))\n", + " print(\"Current flame speed is is {:.4f} cm/s\".format(speed * 100.0))\n", " if len(grids) < 5:\n", - " return 1.0 # \n", + " return 1.0 #\n", " try:\n", " extrapolate_uncertainty(grids, speeds)\n", " except Exception as e:\n", " print(\"Couldn't estimate uncertainty. \" + str(e))\n", - " return 1.0 # continue anyway\n", + " return 1.0 # continue anyway\n", " return 1.0\n", - " return callback, speeds, grids\n" + "\n", + " return callback, speeds, grids" ] }, { @@ -266,12 +310,12 @@ "To = 300\n", "Po = 101325\n", "\n", - "#Define the gas-mixutre and kinetics\n", - "#In this case, we are choosing a GRI3.0 gas\n", - "gas = ct.Solution('gri30.yaml')\n", + "# Define the gas-mixutre and kinetics\n", + "# In this case, we are choosing a GRI3.0 gas\n", + "gas = ct.Solution(\"gri30.yaml\")\n", "\n", - "# Create a stoichiometric CH4/Air premixed mixture \n", - "gas.set_equivalence_ratio(1.0, 'CH4', {'O2':1.0, 'N2':3.76})\n", + "# Create a stoichiometric CH4/Air premixed mixture\n", + "gas.set_equivalence_ratio(1.0, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po" ] }, @@ -298,11 +342,11 @@ "loglevel = 1\n", "\n", "# Define tight tolerances for the solver\n", - "refine_criteria = {'ratio':2, 'slope': 0.01, 'curve': 0.01}\n", + "refine_criteria = {\"ratio\": 2, \"slope\": 0.01, \"curve\": 0.01}\n", "flame.set_refine_criteria(**refine_criteria)\n", "\n", "# Set maxiumum number of grid points to be very high (otherwise default is 1000)\n", - "flame.set_max_grid_points(flame.domains[flame.domain_index('flame')], 1e4)" + "flame.set_max_grid_points(flame.domains[flame.domain_index(\"flame\")], 1e4)" ] }, { @@ -771,7 +815,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0*100))\n" + "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" ] }, { @@ -817,7 +861,9 @@ } ], "source": [ - "best_true_speed_estimate, best_total_percent_error_estimate = extrapolate_uncertainty(grids, speeds)\n", + "best_true_speed_estimate, best_total_percent_error_estimate = extrapolate_uncertainty(\n", + " grids, speeds\n", + ")\n", "\n", "best_true_speed_estimate" ] @@ -843,33 +889,45 @@ "source": [ "def analyze_errors(grids, speeds, true_speed):\n", " \"\"\"\n", - " If we assume that the final answer, with a very fine grid, \n", - " has actually converged and is is the \"truth\", then we can \n", + " If we assume that the final answer, with a very fine grid,\n", + " has actually converged and is is the \"truth\", then we can\n", " find out how large the errors were in the previous values,\n", - " and compare these with our estimated errors. \n", + " and compare these with our estimated errors.\n", " This will show if our estimates are reasonable, or conservative, or too optimistic.\n", " \"\"\"\n", " true_speed_estimates = [None for i in speeds]\n", " total_percent_error_estimates = [None for i in speeds]\n", " actual_extrapolated_percent_errors = [None for i in speeds]\n", " actual_raw_percent_errors = [None for i in speeds]\n", - " for i in range(3,len(grids)):\n", - " print(grids[:i+1])\n", - " true_speed_estimate, total_percent_error_estimate = extrapolate_uncertainty(grids[:i+1], speeds[:i+1], plot=False)\n", - " actual_extrapolated_percent_error = 100. * abs(true_speed_estimate - true_speed) / true_speed\n", - " actual_raw_percent_error = 100. * abs(speeds[i] - true_speed) / true_speed\n", - " print(\"Actual extrapolated error (with hindsight) {:.1f}%\".format(actual_extrapolated_percent_error))\n", - " print(\"Actual raw error (with hindsight) {:.1f}%\".format(actual_raw_percent_error))\n", + " for i in range(3, len(grids)):\n", + " print(grids[: i + 1])\n", + " true_speed_estimate, total_percent_error_estimate = extrapolate_uncertainty(\n", + " grids[: i + 1], speeds[: i + 1], plot=False\n", + " )\n", + " actual_extrapolated_percent_error = (\n", + " 100.0 * abs(true_speed_estimate - true_speed) / true_speed\n", + " )\n", + " actual_raw_percent_error = 100.0 * abs(speeds[i] - true_speed) / true_speed\n", + " print(\n", + " \"Actual extrapolated error (with hindsight) {:.1f}%\".format(\n", + " actual_extrapolated_percent_error\n", + " )\n", + " )\n", + " print(\n", + " \"Actual raw error (with hindsight) {:.1f}%\".format(actual_raw_percent_error)\n", + " )\n", "\n", " true_speed_estimates[i] = true_speed_estimate\n", " total_percent_error_estimates[i] = total_percent_error_estimate\n", " actual_extrapolated_percent_errors[i] = actual_extrapolated_percent_error\n", " actual_raw_percent_errors[i] = actual_raw_percent_error\n", " print()\n", - " \n", - " plt.loglog(grids, actual_raw_percent_errors,'o-', label='raw error')\n", - " plt.loglog(grids, actual_extrapolated_percent_errors,'o-', label='extrapolated error')\n", - " plt.loglog(grids, total_percent_error_estimates,'o-', label='estimated error')\n", + "\n", + " plt.loglog(grids, actual_raw_percent_errors, \"o-\", label=\"raw error\")\n", + " plt.loglog(\n", + " grids, actual_extrapolated_percent_errors, \"o-\", label=\"extrapolated error\"\n", + " )\n", + " plt.loglog(grids, total_percent_error_estimates, \"o-\", label=\"estimated error\")\n", " plt.ylabel(\"Error in flame speed (%)\")\n", " plt.xlabel(\"Grid size\")\n", " plt.legend()\n", @@ -877,12 +935,16 @@ " plt.gca().get_yaxis().set_major_formatter(matplotlib.ticker.PercentFormatter())\n", " plt.show()\n", " flame.get_refine_criteria()\n", - " \n", - " data = pd.DataFrame(data={'actual error in raw value': actual_raw_percent_errors,\n", - " 'actual error in extrapolated value':actual_extrapolated_percent_errors,\n", - " 'estimated error':total_percent_error_estimates}, \n", - " index=grids)\n", - " display(data)\n" + "\n", + " data = pd.DataFrame(\n", + " data={\n", + " \"actual error in raw value\": actual_raw_percent_errors,\n", + " \"actual error in extrapolated value\": actual_extrapolated_percent_errors,\n", + " \"estimated error\": total_percent_error_estimates,\n", + " },\n", + " index=grids,\n", + " )\n", + " display(data)" ] }, { @@ -1140,7 +1202,7 @@ "metadata": {}, "outputs": [], "source": [ - "refine_criteria = {'ratio':3, 'slope': 0.1, 'curve': 0.1}" + "refine_criteria = {\"ratio\": 3, \"slope\": 0.1, \"curve\": 0.1}" ] }, { @@ -1465,14 +1527,14 @@ ], "source": [ "# Reset the gas\n", - "gas.set_equivalence_ratio(1.0, 'CH4', {'O2':1.0, 'N2':3.76})\n", + "gas.set_equivalence_ratio(1.0, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po\n", "\n", "# Create a new flame object\n", "flame = ct.FreeFlame(gas, width=width)\n", "\n", "flame.set_refine_criteria(**refine_criteria)\n", - "flame.set_max_grid_points(flame.domains[flame.domain_index('flame')], 1e4)\n", + "flame.set_max_grid_points(flame.domains[flame.domain_index(\"flame\")], 1e4)\n", "\n", "callback, speeds, grids = make_callback(flame)\n", "flame.set_steady_callback(callback)\n", @@ -1483,7 +1545,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0*100))" + "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" ] }, { @@ -1696,7 +1758,7 @@ "flame = ct.FreeFlame(gas, width=width)\n", "flame.get_refine_criteria()\n", "refine_criteria = flame.get_refine_criteria()\n", - "refine_criteria.update({'prune':0})\n", + "refine_criteria.update({\"prune\": 0})\n", "refine_criteria" ] }, @@ -1961,14 +2023,14 @@ } ], "source": [ - "gas.set_equivalence_ratio(1.0, 'CH4', {'O2':1.0, 'N2':3.76})\n", + "gas.set_equivalence_ratio(1.0, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po\n", "\n", "# Create a new flame object\n", "flame = ct.FreeFlame(gas, width=width)\n", "\n", "flame.set_refine_criteria(**refine_criteria)\n", - "flame.set_max_grid_points(flame.domains[flame.domain_index('flame')], 1e4)\n", + "flame.set_max_grid_points(flame.domains[flame.domain_index(\"flame\")], 1e4)\n", "\n", "callback, speeds, grids = make_callback(flame)\n", "flame.set_steady_callback(callback)\n", @@ -1979,7 +2041,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0*100))" + "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" ] }, { @@ -2132,7 +2194,7 @@ "metadata": {}, "outputs": [], "source": [ - "refine_criteria = {'ratio':3, 'slope': 0.1, 'curve': 0.1}" + "refine_criteria = {\"ratio\": 3, \"slope\": 0.1, \"curve\": 0.1}" ] }, { @@ -2457,14 +2519,14 @@ ], "source": [ "# Reset the gas\n", - "gas.set_equivalence_ratio(1.0, 'CH4', {'O2':1.0, 'N2':3.76})\n", + "gas.set_equivalence_ratio(1.0, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po\n", "\n", "# Create a new flame object\n", "flame = ct.FreeFlame(gas, width=width)\n", "\n", "flame.set_refine_criteria(**refine_criteria)\n", - "flame.set_max_grid_points(flame.domains[flame.domain_index('flame')], 1e4)\n", + "flame.set_max_grid_points(flame.domains[flame.domain_index(\"flame\")], 1e4)\n", "\n", "callback, speeds, grids = make_callback(flame)\n", "flame.set_steady_callback(callback)\n", @@ -2475,7 +2537,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0*100))" + "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" ] }, { @@ -2674,7 +2736,7 @@ "outputs": [], "source": [ "# Tight criteria\n", - "refine_criteria = {'ratio':2, 'slope': 0.01, 'curve': 0.01}" + "refine_criteria = {\"ratio\": 2, \"slope\": 0.01, \"curve\": 0.01}" ] }, { @@ -3175,14 +3237,14 @@ ], "source": [ "# Reset the gas\n", - "gas.set_equivalence_ratio(1.0, 'H2', {'O2':1.0, 'N2':3.76})\n", + "gas.set_equivalence_ratio(1.0, \"H2\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po\n", "\n", "# Create a new flame object\n", "flame = ct.FreeFlame(gas, width=width)\n", "\n", "flame.set_refine_criteria(**refine_criteria)\n", - "flame.set_max_grid_points(flame.domains[flame.domain_index('flame')], 1e4)\n", + "flame.set_max_grid_points(flame.domains[flame.domain_index(\"flame\")], 1e4)\n", "\n", "callback, speeds, grids = make_callback(flame)\n", "flame.set_steady_callback(callback)\n", @@ -3193,7 +3255,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0*100))" + "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" ] }, { @@ -3225,7 +3287,9 @@ ], "source": [ "# get a new best true speed estimate\n", - "best_true_speed_estimate, best_total_percent_error_estimate = extrapolate_uncertainty(grids, speeds)" + "best_true_speed_estimate, best_total_percent_error_estimate = extrapolate_uncertainty(\n", + " grids, speeds\n", + ")" ] }, { @@ -3513,7 +3577,7 @@ "metadata": {}, "outputs": [], "source": [ - "refine_criteria = {'ratio':3, 'slope': 0.1, 'curve': 0.1}" + "refine_criteria = {\"ratio\": 3, \"slope\": 0.1, \"curve\": 0.1}" ] }, { @@ -3874,14 +3938,14 @@ ], "source": [ "# Reset the gas\n", - "gas.set_equivalence_ratio(1.0, 'H2', {'O2':1.0, 'N2':3.76})\n", + "gas.set_equivalence_ratio(1.0, \"H2\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po\n", "\n", "# Create a new flame object\n", "flame = ct.FreeFlame(gas, width=width)\n", "\n", "flame.set_refine_criteria(**refine_criteria)\n", - "flame.set_max_grid_points(flame.domains[flame.domain_index('flame')], 1e4)\n", + "flame.set_max_grid_points(flame.domains[flame.domain_index(\"flame\")], 1e4)\n", "\n", "callback, speeds, grids = make_callback(flame)\n", "flame.set_steady_callback(callback)\n", @@ -3892,7 +3956,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0*100))" + "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" ] }, { diff --git a/flames/flame_speed_with_sensitivity_analysis.ipynb b/flames/flame_speed_with_sensitivity_analysis.ipynb index 1bb64cb..043c3aa 100644 --- a/flames/flame_speed_with_sensitivity_analysis.ipynb +++ b/flames/flame_speed_with_sensitivity_analysis.ipynb @@ -55,17 +55,17 @@ "%matplotlib notebook\n", "import matplotlib.pylab as plt\n", "\n", - "plt.rcParams['axes.labelsize'] = 14\n", - "plt.rcParams['xtick.labelsize'] = 12\n", - "plt.rcParams['ytick.labelsize'] = 12\n", - "plt.rcParams['legend.fontsize'] = 10\n", - "plt.rcParams['figure.figsize'] = (8,6)\n", + "plt.rcParams[\"axes.labelsize\"] = 14\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "plt.rcParams[\"legend.fontsize\"] = 10\n", + "plt.rcParams[\"figure.figsize\"] = (8, 6)\n", "\n", "# Get the best of both ggplot and seaborn\n", - "plt.style.use('ggplot')\n", - "plt.style.use('seaborn-deep')\n", + "plt.style.use(\"ggplot\")\n", + "plt.style.use(\"seaborn-deep\")\n", "\n", - "plt.rcParams['figure.autolayout'] = True" + "plt.rcParams[\"figure.autolayout\"] = True" ] }, { @@ -81,17 +81,17 @@ "metadata": {}, "outputs": [], "source": [ - "#Inlet Temperature in Kelvin and Inlet Pressure in Pascals\n", - "#In this case we are setting the inlet T and P to room temperature conditions\n", + "# Inlet Temperature in Kelvin and Inlet Pressure in Pascals\n", + "# In this case we are setting the inlet T and P to room temperature conditions\n", "To = 300\n", "Po = 101325\n", "\n", - "#Define the gas-mixutre and kinetics\n", - "#In this case, we are choosing a GRI3.0 gas\n", - "gas = ct.Solution('gri30.yaml')\n", + "# Define the gas-mixutre and kinetics\n", + "# In this case, we are choosing a GRI3.0 gas\n", + "gas = ct.Solution(\"gri30.yaml\")\n", "\n", - "# Create a stoichiometric CH4/Air premixed mixture \n", - "gas.set_equivalence_ratio(1.0, 'CH4', {'O2':1.0, 'N2':3.76})\n", + "# Create a stoichiometric CH4/Air premixed mixture\n", + "gas.set_equivalence_ratio(1.0, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po" ] }, @@ -324,7 +324,7 @@ "source": [ "flame.solve(loglevel=loglevel, auto=True)\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0*100))\n", + "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))\n", "\n", "# Note that the variable Su0 will also be used downstream in the sensitivity analysis" ] @@ -1141,9 +1141,9 @@ "source": [ "plt.figure()\n", "\n", - "plt.plot(flame.grid*100, flame.T, '-o')\n", - "plt.xlabel('Distance (cm)')\n", - "plt.ylabel('Temperature (K)');" + "plt.plot(flame.grid * 100, flame.T, \"-o\")\n", + "plt.xlabel(\"Distance (cm)\")\n", + "plt.ylabel(\"Temperature (K)\");" ] }, { @@ -1970,13 +1970,13 @@ "\n", "plt.figure()\n", "\n", - "plt.plot(flame.grid*100, X_CH4, '-o', label=r'$CH_{4}$')\n", - "plt.plot(flame.grid*100, X_CO2, '-s', label=r'$CO_{2}$')\n", - "plt.plot(flame.grid*100, X_H2O, '-<', label=r'$H_{2}O$')\n", + "plt.plot(flame.grid * 100, X_CH4, \"-o\", label=r\"$CH_{4}$\")\n", + "plt.plot(flame.grid * 100, X_CO2, \"-s\", label=r\"$CO_{2}$\")\n", + "plt.plot(flame.grid * 100, X_H2O, \"-<\", label=r\"$H_{2}O$\")\n", "\n", "plt.legend(loc=2)\n", - "plt.xlabel('Distance (cm)')\n", - "plt.ylabel('MoleFractions');" + "plt.xlabel(\"Distance (cm)\")\n", + "plt.ylabel(\"MoleFractions\");" ] }, { @@ -1995,7 +1995,9 @@ "outputs": [], "source": [ "# Create a dataframe to store sensitivity-analysis data\n", - "sensitivities = pd.DataFrame(data=[], index=gas.reaction_equations(range(gas.n_reactions)))" + "sensitivities = pd.DataFrame(\n", + " data=[], index=gas.reaction_equations(range(gas.n_reactions))\n", + ")" ] }, { @@ -2025,18 +2027,18 @@ "outputs": [], "source": [ "for m in range(gas.n_reactions):\n", - " gas.set_multiplier(1.0) # reset all multipliers \n", - " gas.set_multiplier(1+dk, m) # perturb reaction m \n", - " \n", + " gas.set_multiplier(1.0) # reset all multipliers\n", + " gas.set_multiplier(1 + dk, m) # perturb reaction m\n", + "\n", " # Always force loglevel=0 for this\n", - " # Make sure the grid is not refined, otherwise it won't strictly \n", + " # Make sure the grid is not refined, otherwise it won't strictly\n", " # be a small perturbation analysis\n", " flame.solve(loglevel=0, refine_grid=False)\n", - " \n", + "\n", " # The new flame speed\n", " Su = flame.velocity[0]\n", - " \n", - " sensitivities[\"baseCase\"][m] = (Su-Su0)/(Su0*dk)\n", + "\n", + " sensitivities[\"baseCase\"][m] = (Su - Su0) / (Su0 * dk)\n", "\n", "# This step is essential, otherwise the mechanism will have been altered\n", "gas.set_multiplier(1.0)" @@ -2929,13 +2931,16 @@ "# For plotting, collect only those steps that are above the threshold\n", "# Otherwise, the y-axis gets crowded and illegible\n", "sensitivitiesSubset = sensitivities[sensitivities[firstColumn].abs() > threshold]\n", - "indicesMeetingThreshold = sensitivitiesSubset[firstColumn].abs().sort_values(ascending=False).index\n", - "sensitivitiesSubset.loc[indicesMeetingThreshold].plot.barh(title=\"Sensitivities for GRI 3.0\",\n", - " legend=None)\n", + "indicesMeetingThreshold = (\n", + " sensitivitiesSubset[firstColumn].abs().sort_values(ascending=False).index\n", + ")\n", + "sensitivitiesSubset.loc[indicesMeetingThreshold].plot.barh(\n", + " title=\"Sensitivities for GRI 3.0\", legend=None\n", + ")\n", "plt.gca().invert_yaxis()\n", "\n", - "plt.rcParams.update({'axes.labelsize': 20})\n", - "plt.xlabel(r'Sensitivity: $\\frac{\\partial\\:\\ln{S_{u}}}{\\partial\\:\\ln{k}}$');\n", + "plt.rcParams.update({\"axes.labelsize\": 20})\n", + "plt.xlabel(r\"Sensitivity: $\\frac{\\partial\\:\\ln{S_{u}}}{\\partial\\:\\ln{k}}$\");\n", "\n", "# Uncomment the following to save the plot. A higher than usual resolution (dpi) helps\n", "# plt.savefig('sensitivityPlot', dpi=300)" diff --git a/flames/twin_premixed_flame_axisymmetric.ipynb b/flames/twin_premixed_flame_axisymmetric.ipynb index 119615e..e946bc1 100644 --- a/flames/twin_premixed_flame_axisymmetric.ipynb +++ b/flames/twin_premixed_flame_axisymmetric.ipynb @@ -53,14 +53,14 @@ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", - "plt.rcParams['figure.autolayout'] = True\n", + "plt.rcParams[\"figure.autolayout\"] = True\n", "\n", - "plt.rcParams['axes.labelsize'] = 14\n", - "plt.rcParams['xtick.labelsize'] = 12\n", - "plt.rcParams['ytick.labelsize'] = 12\n", - "plt.rcParams['legend.fontsize'] = 10\n", - "plt.rcParams['figure.facecolor'] = 'white'\n", - "plt.rcParams['figure.figsize'] = (8,6)" + "plt.rcParams[\"axes.labelsize\"] = 14\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "plt.rcParams[\"legend.fontsize\"] = 10\n", + "plt.rcParams[\"figure.facecolor\"] = \"white\"\n", + "plt.rcParams[\"figure.figsize\"] = (8, 6)" ] }, { @@ -83,15 +83,15 @@ "\n", "# Define the gas-mixutre and kinetics\n", "# In this case, we are choosing a GRI3.0 gas\n", - "gas = ct.Solution('gri30.yaml')\n", + "gas = ct.Solution(\"gri30.yaml\")\n", "\n", "# Create a CH4/Air premixed mixture with equivalence ratio=0.75\n", - "gas.set_equivalence_ratio(0.75, 'CH4', {'O2':1.0, 'N2':3.76})\n", + "gas.set_equivalence_ratio(0.75, \"CH4\", {\"O2\": 1.0, \"N2\": 3.76})\n", "gas.TP = To, Po\n", "\n", "# Set the velocity of the reactants\n", "# This is what determines the strain-rate\n", - "axial_velocity = 2.0 # in m/s\n", + "axial_velocity = 2.0 # in m/s\n", "\n", "# Done with initial conditions\n", "# Compute the mass flux, as this is what the Flame object requires\n", @@ -111,7 +111,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "def derivative(x, y):\n", " \"\"\"Differentiation function for data that has variable grid spacing.\n", " Used here to compute normal strain-rate.\n", @@ -120,12 +119,13 @@ "\n", " dx = np.diff(x)\n", " dy = np.diff(y)\n", - " dydx[0:-1] = dy/dx\n", + " dydx[0:-1] = dy / dx\n", "\n", - " dydx[-1] = (y[-1] - y[-2])/(x[-1] - x[-2])\n", + " dydx[-1] = (y[-1] - y[-2]) / (x[-1] - x[-2])\n", "\n", " return dydx\n", "\n", + "\n", "def computeStrainRates(oppFlame):\n", " # Compute the derivative of axial velocity to obtain normal strain rate\n", " strainRates = derivative(oppFlame.grid, oppFlame.velocity)\n", @@ -141,22 +141,25 @@ "\n", " return strainRates, strainRatePoint, K\n", "\n", + "\n", "def computeConsumptionSpeed(oppFlame):\n", "\n", " Tb = max(oppFlame.T)\n", " Tu = min(oppFlame.T)\n", " rho_u = max(oppFlame.density)\n", "\n", - " integrand = oppFlame.heat_release_rate/oppFlame.cp\n", + " integrand = oppFlame.heat_release_rate / oppFlame.cp\n", "\n", " I = np.trapz(integrand, oppFlame.grid)\n", - " Sc = I/(Tb - Tu)/rho_u\n", + " Sc = I / (Tb - Tu) / rho_u\n", "\n", " return Sc\n", "\n", + "\n", "# This function is called to run the solver\n", - "def solveOpposedFlame(oppFlame, massFlux=0.12, loglevel=0,\n", - " ratio=2, slope=0.3, curve=0.3, prune=0.05):\n", + "def solveOpposedFlame(\n", + " oppFlame, massFlux=0.12, loglevel=0, ratio=2, slope=0.3, curve=0.3, prune=0.05\n", + "):\n", " \"\"\"\n", " Execute this function to run the Oppposed Flow Simulation This function\n", " takes a CounterFlowTwinPremixedFlame object as the first argument\n", @@ -676,7 +679,7 @@ "\n", "print(\"Peak temperature: {0:.1f} K\".format(T))\n", "print(\"Strain Rate: {0:.1f} 1/s\".format(K))\n", - "print(\"Consumption Speed: {0:.2f} cm/s\".format(Sc*100))" + "print(\"Consumption Speed: {0:.2f} cm/s\".format(Sc * 100))" ] }, { @@ -1496,17 +1499,19 @@ "source": [ "plt.figure()\n", "\n", - "plt.plot(oppFlame.grid*100, oppFlame.velocity, 'r-o', lw=2)\n", - "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1]*100)\n", - "plt.xlabel('Distance (cm)')\n", - "plt.ylabel('Axial Velocity (m/s)')\n", + "plt.plot(oppFlame.grid * 100, oppFlame.velocity, \"r-o\", lw=2)\n", + "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1] * 100)\n", + "plt.xlabel(\"Distance (cm)\")\n", + "plt.ylabel(\"Axial Velocity (m/s)\")\n", "\n", "# Identify the point where the strain rate is calculated\n", - "plt.plot(oppFlame.grid[strainRatePoint]*100, oppFlame.velocity[strainRatePoint],'gs')\n", - "plt.annotate('Strain-Rate point',\n", - " xy=(oppFlame.grid[strainRatePoint]*100, oppFlame.velocity[strainRatePoint]),\n", - " xytext=(0.001, 0.1),\n", - " arrowprops={'arrowstyle':'->'});" + "plt.plot(oppFlame.grid[strainRatePoint] * 100, oppFlame.velocity[strainRatePoint], \"gs\")\n", + "plt.annotate(\n", + " \"Strain-Rate point\",\n", + " xy=(oppFlame.grid[strainRatePoint] * 100, oppFlame.velocity[strainRatePoint]),\n", + " xytext=(0.001, 0.1),\n", + " arrowprops={\"arrowstyle\": \"->\"},\n", + ");" ] }, { @@ -2317,10 +2322,10 @@ "source": [ "plt.figure()\n", "\n", - "plt.plot(oppFlame.grid*100, oppFlame.T, 'b-s', lw=2)\n", - "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1]*100)\n", - "plt.xlabel('Distance (cm)')\n", - "plt.ylabel('Temperature (K)');" + "plt.plot(oppFlame.grid * 100, oppFlame.T, \"b-s\", lw=2)\n", + "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1] * 100)\n", + "plt.xlabel(\"Distance (cm)\")\n", + "plt.ylabel(\"Temperature (K)\");" ] }, { @@ -3143,13 +3148,13 @@ "\n", "plt.figure()\n", "\n", - "plt.plot(oppFlame.grid*100, X_CH4, 'c-o', lw=2, label=r'$CH_{4}$')\n", - "plt.plot(oppFlame.grid*100, X_CO2, 'm-s', lw=2, label=r'$CO_{2}$')\n", - "plt.plot(oppFlame.grid*100, X_H2O, 'g-<', lw=2, label=r'$H_{2}O$')\n", + "plt.plot(oppFlame.grid * 100, X_CH4, \"c-o\", lw=2, label=r\"$CH_{4}$\")\n", + "plt.plot(oppFlame.grid * 100, X_CO2, \"m-s\", lw=2, label=r\"$CO_{2}$\")\n", + "plt.plot(oppFlame.grid * 100, X_H2O, \"g-<\", lw=2, label=r\"$H_{2}O$\")\n", "\n", - "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1]*100)\n", - "plt.xlabel('Distance (cm)')\n", - "plt.ylabel('Mole Fractions')\n", + "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1] * 100)\n", + "plt.xlabel(\"Distance (cm)\")\n", + "plt.ylabel(\"Mole Fractions\")\n", "\n", "plt.legend(loc=2);" ] diff --git a/python_tutorial.ipynb b/python_tutorial.ipynb index c4fdc0a..d74fc4a 100644 --- a/python_tutorial.ipynb +++ b/python_tutorial.ipynb @@ -46,7 +46,7 @@ "metadata": {}, "outputs": [], "source": [ - "gas1 = ct.Solution('gri30.yaml')" + "gas1 = ct.Solution(\"gri30.yaml\")" ] }, { @@ -188,12 +188,12 @@ "metadata": {}, "outputs": [], "source": [ - "gas1.TP = 1200, 101325 # temperature, pressure\n", - "gas1.TD = 1200, 0.0204723 # temperature, density\n", - "gas1.HP = 1.32956e7, 101325 # specific enthalpy, pressure\n", - "gas1.UV = 8.34619e6, 1/0.0204723 # specific internal energy, specific volume\n", - "gas1.SP = 85227.6, 101325 # specific entropy, pressure\n", - "gas1.SV = 85227.6, 1/0.0204723 # specific entropy, specific volume" + "gas1.TP = 1200, 101325 # temperature, pressure\n", + "gas1.TD = 1200, 0.0204723 # temperature, density\n", + "gas1.HP = 1.32956e7, 101325 # specific enthalpy, pressure\n", + "gas1.UV = 8.34619e6, 1 / 0.0204723 # specific internal energy, specific volume\n", + "gas1.SP = 85227.6, 101325 # specific entropy, pressure\n", + "gas1.SV = 85227.6, 1 / 0.0204723 # specific entropy, specific volume" ] }, { @@ -292,7 +292,7 @@ "metadata": {}, "outputs": [], "source": [ - "gas1.X = 'CH4:1, O2:2, N2:7.52'" + "gas1.X = \"CH4:1, O2:2, N2:7.52\"" ] }, { @@ -309,7 +309,7 @@ "outputs": [], "source": [ "phi = 0.8\n", - "gas1.X = {'CH4':1, 'O2':2/phi, 'N2': 2*3.76/phi}" + "gas1.X = {\"CH4\": 1, \"O2\": 2 / phi, \"N2\": 2 * 3.76 / phi}" ] }, { @@ -356,7 +356,7 @@ } ], "source": [ - "gas1.TPX = 1200, 101325, 'CH4:1, O2:2, N2:7.52'\n", + "gas1.TPX = 1200, 101325, \"CH4:1, O2:2, N2:7.52\"\n", "gas1()" ] }, @@ -423,7 +423,7 @@ "metadata": {}, "outputs": [], "source": [ - "gas1.TPX = None, None, 'CH4:1.0, O2:0.5'" + "gas1.TPX = None, None, \"CH4:1.0, O2:0.5\"" ] }, { @@ -446,7 +446,7 @@ "metadata": {}, "outputs": [], "source": [ - "Xmajor = gas1['CH4','O2','CO2','H2O','N2'].X" + "Xmajor = gas1[\"CH4\", \"O2\", \"CO2\", \"H2O\", \"N2\"].X" ] }, { @@ -462,7 +462,7 @@ "metadata": {}, "outputs": [], "source": [ - "major = gas1['CH4','O2','CO2','H2O','N2']\n", + "major = gas1[\"CH4\", \"O2\", \"CO2\", \"H2O\", \"N2\"]\n", "cp_major = major.partial_molar_cp\n", "wdot_major = major.net_production_rates" ] @@ -496,7 +496,7 @@ "metadata": {}, "outputs": [], "source": [ - "ct.add_directory('/usr/local/cantera/my_data_files')" + "ct.add_directory(\"/usr/local/cantera/my_data_files\")" ] }, { @@ -514,9 +514,9 @@ "metadata": {}, "outputs": [], "source": [ - "gas2 = ct.Solution('diamond.yaml', 'gas')\n", - "diamond = ct.Solution('diamond.yaml', 'diamond')\n", - "diamond_surf = ct.Interface('diamond.yaml', 'diamond_100', [gas2, diamond])" + "gas2 = ct.Solution(\"diamond.yaml\", \"gas\")\n", + "diamond = ct.Solution(\"diamond.yaml\", \"diamond\")\n", + "diamond_surf = ct.Interface(\"diamond.yaml\", \"diamond_100\", [gas2, diamond])" ] }, { @@ -562,7 +562,7 @@ "metadata": {}, "outputs": [], "source": [ - "g = ct.Solution('gri30.yaml')" + "g = ct.Solution(\"gri30.yaml\")" ] }, { @@ -578,7 +578,7 @@ "metadata": {}, "outputs": [], "source": [ - "g?" + "?g" ] }, { @@ -815,7 +815,7 @@ "metadata": {}, "outputs": [], "source": [ - "g.species_index?" + "?g.species_index" ] }, { @@ -833,7 +833,7 @@ }, "outputs": [], "source": [ - "g.T?" + "?g.T" ] }, { @@ -849,7 +849,7 @@ "metadata": {}, "outputs": [], "source": [ - "g.__class__.T?" + "?g.__class__.T" ] }, { @@ -904,9 +904,9 @@ "metadata": {}, "outputs": [], "source": [ - "g = ct.Solution('gri30.yaml')\n", - "g.TPX = 300.0, ct.one_atm, 'CH4:0.95, O2:2, N2:7.52'\n", - "g.equilibrate('TP')" + "g = ct.Solution(\"gri30.yaml\")\n", + "g.TPX = 300.0, ct.one_atm, \"CH4:0.95, O2:2, N2:7.52\"\n", + "g.equilibrate(\"TP\")" ] }, { @@ -922,8 +922,8 @@ "metadata": {}, "outputs": [], "source": [ - "g.TPX = 300.0, ct.one_atm, 'CH4:0.95, O2:2, N2:7.52'\n", - "g.equilibrate('HP')" + "g.TPX = 300.0, ct.one_atm, \"CH4:0.95, O2:2, N2:7.52\"\n", + "g.equilibrate(\"HP\")" ] }, { @@ -944,8 +944,8 @@ "metadata": {}, "outputs": [], "source": [ - "g.TPX = 300.0, ct.one_atm, 'CH4:0.95, O2:2, N2:7.52'\n", - "g.equilibrate('HP')" + "g.TPX = 300.0, ct.one_atm, \"CH4:0.95, O2:2, N2:7.52\"\n", + "g.equilibrate(\"HP\")" ] }, { @@ -1274,7 +1274,7 @@ "rr = g.reverse_rates_of_progress\n", "for i in range(g.n_reactions):\n", " if g.is_reversible(i) and rf[i] != 0.0:\n", - " print(' %4i %10.4g ' % (i, (rf[i] - rr[i])/rf[i]))" + " print(\" %4i %10.4g \" % (i, (rf[i] - rr[i]) / rf[i]))" ] }, { @@ -1321,7 +1321,7 @@ } ], "source": [ - "g = ct.Solution('gri30.yaml')\n", + "g = ct.Solution(\"gri30.yaml\")\n", "r = g.reaction(2) # get a Reaction object\n", "r" ] @@ -1410,7 +1410,11 @@ } ], "source": [ - "II = [i for i, r in enumerate(g.reactions()) if 'CO' in r.reactants and 'CO2' in r.products]\n", + "II = [\n", + " i\n", + " for i, r in enumerate(g.reactions())\n", + " if \"CO\" in r.reactants and \"CO2\" in r.products\n", + "]\n", "\n", "for i in II:\n", " print(g.reaction(i).equation)" @@ -1429,8 +1433,8 @@ "metadata": {}, "outputs": [], "source": [ - "g.TPX = 300, 101325, {'CH4':0.6, 'O2':1.0, 'N2':3.76}\n", - "g.equilibrate('HP')" + "g.TPX = 300, 101325, {\"CH4\": 0.6, \"O2\": 1.0, \"N2\": 3.76}\n", + "g.equilibrate(\"HP\")" ] }, { @@ -1484,7 +1488,7 @@ } ], "source": [ - "g.TP = g.T-100, None\n", + "g.TP = g.T - 100, None\n", "g.net_rates_of_progress[II]" ] }, diff --git a/reactors/1D_pfr_surfchem.ipynb b/reactors/1D_pfr_surfchem.ipynb index 60281a6..ad69b7a 100644 --- a/reactors/1D_pfr_surfchem.ipynb +++ b/reactors/1D_pfr_surfchem.ipynb @@ -27,9 +27,11 @@ "import numpy as np\n", "from scikits.odes import dae\n", "import cantera as ct\n", + "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "print('Runnning Cantera version: ' + ct.__version__)" + "\n", + "print(\"Runnning Cantera version: \" + ct.__version__)" ] }, { @@ -52,12 +54,12 @@ "metadata": {}, "outputs": [], "source": [ - "#import the SiF4 + NH3 reaction mechanism\n", - "mech = '../data/SiF4_NH3_mec.yaml'\n", - "#import the models for gas and bulk\n", - "gas, bulk_Si, bulk_N = ct.import_phases(mech, ['gas', 'SiBulk', 'NBulk'])\n", - "#import the model for gas-Si-N interface\n", - "gas_Si_N_interface = ct.Interface(mech, 'SI3N4', [gas,bulk_Si,bulk_N])" + "# import the SiF4 + NH3 reaction mechanism\n", + "mech = \"../data/SiF4_NH3_mec.yaml\"\n", + "# import the models for gas and bulk\n", + "gas, bulk_Si, bulk_N = ct.import_phases(mech, [\"gas\", \"SiBulk\", \"NBulk\"])\n", + "# import the model for gas-Si-N interface\n", + "gas_Si_N_interface = ct.Interface(mech, \"SI3N4\", [gas, bulk_Si, bulk_N])" ] }, { @@ -82,11 +84,13 @@ "gas_Si_N_interface.TP = T0, p0\n", "gas_Si_N_interface.coverages = \"HN_NH2(S): 1.0\"\n", "D = 5.08e-2 # diameter of the tube [m]\n", - "Ac = np.pi * D**2/4 # cross section of the tube [m^2]\n", + "Ac = np.pi * D**2 / 4 # cross section of the tube [m^2]\n", "mu = 5.7e-5 # kg/(m-s) dynamic viscosity\n", "perim = np.pi * D # perimeter of the tube\n", "# calculate the site fractions of surface species at the entrance of the tube at steady state\n", - "gas_Si_N_interface.advance_coverages(100.0) # Here we assume after 100s, the system reaches the steady state\n", + "gas_Si_N_interface.advance_coverages(\n", + " 100.0\n", + ") # Here we assume after 100s, the system reaches the steady state\n", "Zk_0 = gas_Si_N_interface.coverages\n", "N = gas.n_species # number of gas species\n", "M = gas_Si_N_interface.n_species # number of surface species" @@ -136,61 +140,68 @@ "outputs": [], "source": [ "def residual(z, vec, vecp, result):\n", - " \"\"\" we create the residual equations for the problem\n", - " vec = [u, rho, Yk, p, Zk]\n", - " vecp = [dudz, drhodz, dYkdz, dpdz, dZkdz]\n", + " \"\"\"we create the residual equations for the problem\n", + " vec = [u, rho, Yk, p, Zk]\n", + " vecp = [dudz, drhodz, dYkdz, dpdz, dZkdz]\n", " \"\"\"\n", " # temporary variables\n", " u = vec[0] # velocity\n", " rho = vec[1] # density\n", - " Y = vec[2:2+N] # vector of mass fractions of all gas species\n", - " p = vec[2+N] # pressure\n", - " Z = vec[3+N:] # vector of site fractions of all surface species \n", + " Y = vec[2 : 2 + N] # vector of mass fractions of all gas species\n", + " p = vec[2 + N] # pressure\n", + " Z = vec[3 + N :] # vector of site fractions of all surface species\n", "\n", " dudz = vecp[0] # velocity spatial derivative\n", " drhodz = vecp[1] # density spatial derivative\n", - " dYdz = vecp[2:2+N] # mass fraction spatial derivative\n", - " dpdz = vecp[2+N] # pressure spatial derivative\n", + " dYdz = vecp[2 : 2 + N] # mass fraction spatial derivative\n", + " dpdz = vecp[2 + N] # pressure spatial derivative\n", "\n", " # Use unnormalized mass fractions to avoid over-constraining the system\n", " gas.set_unnormalized_mass_fractions(Y)\n", - " gas.TP = T0,p\n", + " gas.TP = T0, p\n", "\n", - " bulk_Si.TP = T0,p\n", - " bulk_N.TP = T0,p\n", + " bulk_Si.TP = T0, p\n", + " bulk_N.TP = T0, p\n", "\n", " # Use unnormalized site fractions (coverages) to avoid over-constraining the system\n", " gas_Si_N_interface.set_unnormalized_coverages(Z)\n", - " gas_Si_N_interface.TP = T0,p\n", + " gas_Si_N_interface.TP = T0, p\n", "\n", " # temporary variables (based on the given state)\n", " coverages = gas_Si_N_interface.coverages # site fraction vector\n", - " sdot_g = gas_Si_N_interface.get_net_production_rates(\"gas\") # heterogeneous production rate of gas species\n", + " sdot_g = gas_Si_N_interface.get_net_production_rates(\n", + " \"gas\"\n", + " ) # heterogeneous production rate of gas species\n", " sdot_s = gas_Si_N_interface.get_net_production_rates(\"SI3N4\")\n", " wdot_g = gas.net_production_rates # homogeneous production rate of gas species\n", " W_g = gas.molecular_weights # vector of molecular weight of gas species\n", "\n", " # mass continuity equation\n", - " result[0] = u*drhodz + rho*dudz - perim*np.sum(sdot_g*W_g)/Ac\n", + " result[0] = u * drhodz + rho * dudz - perim * np.sum(sdot_g * W_g) / Ac\n", "\n", " # conservation of species\n", " for k in range(N):\n", - " result[1+k] = (rho*u*Ac*dYdz[k] + Y[k]*perim*np.sum(sdot_g*W_g)\n", - " - wdot_g[k]*W_g[k]*Ac\n", - " - sdot_g[k]*W_g[k]*perim)\n", + " result[1 + k] = (\n", + " rho * u * Ac * dYdz[k]\n", + " + Y[k] * perim * np.sum(sdot_g * W_g)\n", + " - wdot_g[k] * W_g[k] * Ac\n", + " - sdot_g[k] * W_g[k] * perim\n", + " )\n", " # conservation of momentum\n", - " result[1+N] = 2*rho*u*dudz + np.power(u,2)*drhodz + dpdz + 32*u*mu/D**2 \n", + " result[1 + N] = (\n", + " 2 * rho * u * dudz + np.power(u, 2) * drhodz + dpdz + 32 * u * mu / D**2\n", + " )\n", "\n", " # equation of state\n", - " result[2+N] = gas.density - rho\n", + " result[2 + N] = gas.density - rho\n", "\n", " # algebraic constraints\n", " for j in range(M):\n", - " result[3+N+j] = sdot_s[j]\n", + " result[3 + N + j] = sdot_s[j]\n", "\n", " # replace the constraint with the condition sum(Zk) = 1 for the largest site fraction species\n", " index = np.argmax(coverages)\n", - " result[3+N+index] = np.sum(coverages) - 1" + " result[3 + N + index] = np.sum(coverages) - 1" ] }, { @@ -241,28 +252,32 @@ "u0 = 11.53 # m/s initial velocity of the flow\n", "W = gas.molecular_weights\n", "W_avg = gas.mean_molecular_weight\n", - "sdot_g = gas_Si_N_interface.get_net_production_rates(\"gas\") # heterogeneous production rate of gas species\n", + "sdot_g = gas_Si_N_interface.get_net_production_rates(\n", + " \"gas\"\n", + ") # heterogeneous production rate of gas species\n", "wdot = gas.net_production_rates # homogeneous molar production rate\n", "################### a #########################\n", - "a = np.zeros((3+N,3+N))\n", - "a[0,:] = np.hstack((rho0, u0, np.zeros(1+N)))\n", + "a = np.zeros((3 + N, 3 + N))\n", + "a[0, :] = np.hstack((rho0, u0, np.zeros(1 + N)))\n", "for i in range(N):\n", - " a[1+i,2+i] = rho0*u0*Ac\n", - "a[1+N,:] = np.hstack((2*rho0*u0, u0**2, np.zeros(N), 1))\n", + " a[1 + i, 2 + i] = rho0 * u0 * Ac\n", + "a[1 + N, :] = np.hstack((2 * rho0 * u0, u0**2, np.zeros(N), 1))\n", "coef = np.zeros(N)\n", "for j in range(N):\n", - " coef[j] = gas.P/W[j]/np.power(np.sum(gas.Y/W),2)\n", - "a[2+N,:] = np.hstack((0, ct.gas_constant*T0, coef, -W_avg))\n", + " coef[j] = gas.P / W[j] / np.power(np.sum(gas.Y / W), 2)\n", + "a[2 + N, :] = np.hstack((0, ct.gas_constant * T0, coef, -W_avg))\n", "################### b ###########################\n", - "b = np.zeros(3+gas.n_species)\n", - "b[0] = perim*np.sum(sdot_g[:N]*W)/Ac\n", + "b = np.zeros(3 + gas.n_species)\n", + "b[0] = perim * np.sum(sdot_g[:N] * W) / Ac\n", "for i in range(gas.n_species):\n", - " b[1+i] = (wdot[i]*W[i]*Ac\n", - " + sdot_g[i]*W[i]*perim\n", - " - gas.Y[i]*perim*np.sum(sdot_g[:N]*W))\n", - "b[1+gas.n_species] = -32*u0*mu/D**2\n", - "b[2+gas.n_species] = 0\n", - "part_vecp0 = np.linalg.solve(a,b)\n", + " b[1 + i] = (\n", + " wdot[i] * W[i] * Ac\n", + " + sdot_g[i] * W[i] * perim\n", + " - gas.Y[i] * perim * np.sum(sdot_g[:N] * W)\n", + " )\n", + "b[1 + gas.n_species] = -32 * u0 * mu / D**2\n", + "b[2 + gas.n_species] = 0\n", + "part_vecp0 = np.linalg.solve(a, b)\n", "\n", "vecp0 = np.hstack((part_vecp0, np.zeros(M)))\n", "vec0 = np.hstack((11.53, gas.density, gas.Y, gas.P, Zk_0))" @@ -320,8 +335,8 @@ ], "source": [ "solver = dae(\n", - " 'ida',\n", - " residual, \n", + " \"ida\",\n", + " residual,\n", " first_step_size=1e-16,\n", " atol=1e-8, # absolute tolerance for solution\n", " rtol=1e-8, # relative tolerance for solution\n", @@ -330,12 +345,12 @@ " # algebraic variables must be defined. These algebraic variables are\n", " # denoted by the position (index) in the state vector y. All these\n", " # indexes have to be specified in the 'algebraic_vars_idx' array.\n", - " algebraic_vars_idx=[np.arange(3+N,3+N+M,1)], \n", + " algebraic_vars_idx=[np.arange(3 + N, 3 + N + M, 1)],\n", " max_steps=5000,\n", - " old_api=False # Forces use of new api (namedtuple)\n", + " old_api=False, # Forces use of new api (namedtuple)\n", ")\n", "\n", - "times = np.arange(0,0.7,0.01)\n", + "times = np.arange(0, 0.7, 0.01)\n", "solution = solver.solve(times, vec0, vecp0)\n", "print(solution)" ] @@ -367,54 +382,60 @@ ], "source": [ "# plot velocity of gas along the flow direction\n", - "f, ax = plt.subplots(3,2, figsize=(9,9), dpi=96)\n", - "ax[0,0].plot(times, solution.values.y[:,0], color='C0')\n", - "ax[0,0].set_xlabel('Distance (m)')\n", - "ax[0,0].set_ylabel('Velocity (m/s)')\n", + "f, ax = plt.subplots(3, 2, figsize=(9, 9), dpi=96)\n", + "ax[0, 0].plot(times, solution.values.y[:, 0], color=\"C0\")\n", + "ax[0, 0].set_xlabel(\"Distance (m)\")\n", + "ax[0, 0].set_ylabel(\"Velocity (m/s)\")\n", "\n", "# plot gas density along the flow direction\n", - "ax[0,1].plot(times, solution.values.y[:,1], color='C1')\n", - "ax[0,1].set_xlabel('Distance (m)')\n", - "ax[0,1].set_ylabel('Density ($\\mathregular{kg/m^3}$)')\n", - "ax[0,1].ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) # scientific notation\n", + "ax[0, 1].plot(times, solution.values.y[:, 1], color=\"C1\")\n", + "ax[0, 1].set_xlabel(\"Distance (m)\")\n", + "ax[0, 1].set_ylabel(\"Density ($\\mathregular{kg/m^3}$)\")\n", + "ax[0, 1].ticklabel_format(\n", + " axis=\"y\", style=\"sci\", scilimits=(-2, 2)\n", + ") # scientific notation\n", "\n", "# plot major and minor gas species separately\n", "minor_idx = []\n", "major_idx = []\n", - "for i,name in enumerate(gas.species_names): \n", - " mean = np.mean(solution.values.y[:,2+i])\n", + "for i, name in enumerate(gas.species_names):\n", + " mean = np.mean(solution.values.y[:, 2 + i])\n", " if mean <= 0.01:\n", - " minor_idx.append(i) \n", + " minor_idx.append(i)\n", " else:\n", " major_idx.append(i)\n", "\n", "# plot minor species\n", "for i in minor_idx:\n", - " style = '-' if i < 10 else '--' \n", - " ax[1,0].plot(times, solution.values.y[:,2+i], label=gas.species_names[i], linestyle=style)\n", - "ax[1,0].legend(fontsize=7, loc='upper right')\n", - "ax[1,0].set_xlabel('Distance (m)')\n", - "ax[1,0].set_ylabel('Mass Fraction')\n", - "ax[1,0].ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) # scientific notation\n", + " style = \"-\" if i < 10 else \"--\"\n", + " ax[1, 0].plot(\n", + " times, solution.values.y[:, 2 + i], label=gas.species_names[i], linestyle=style\n", + " )\n", + "ax[1, 0].legend(fontsize=7, loc=\"upper right\")\n", + "ax[1, 0].set_xlabel(\"Distance (m)\")\n", + "ax[1, 0].set_ylabel(\"Mass Fraction\")\n", + "ax[1, 0].ticklabel_format(\n", + " axis=\"y\", style=\"sci\", scilimits=(-2, 2)\n", + ") # scientific notation\n", "\n", "# plot major species\n", "for j in major_idx:\n", - " ax[1,1].plot(times,solution.values.y[:,2+j], label=gas.species_names[j])\n", - "ax[1,1].legend(loc='best')\n", - "ax[1,1].set_xlabel('Distance (m)')\n", - "ax[1,1].set_ylabel('Mass Fraction')\n", + " ax[1, 1].plot(times, solution.values.y[:, 2 + j], label=gas.species_names[j])\n", + "ax[1, 1].legend(loc=\"best\")\n", + "ax[1, 1].set_xlabel(\"Distance (m)\")\n", + "ax[1, 1].set_ylabel(\"Mass Fraction\")\n", "\n", "# plot the pressure of the gas along the flow direction\n", - "ax[2,0].plot(times, solution.values.y[:,2+N], color='C2')\n", - "ax[2,0].set_xlabel('Distance (m)')\n", - "ax[2,0].set_ylabel('Pressure (Pa)')\n", - "\n", - "# plot the site fraction of the surface species along the flow direction \n", - "for i,name in enumerate(gas_Si_N_interface.species_names):\n", - " ax[2,1].plot(times, solution.values.y[:,3+N+i], label=name)\n", - "ax[2,1].legend()\n", - "ax[2,1].set_xlabel('Distance (m)')\n", - "ax[2,1].set_ylabel('Site Fraction')\n", + "ax[2, 0].plot(times, solution.values.y[:, 2 + N], color=\"C2\")\n", + "ax[2, 0].set_xlabel(\"Distance (m)\")\n", + "ax[2, 0].set_ylabel(\"Pressure (Pa)\")\n", + "\n", + "# plot the site fraction of the surface species along the flow direction\n", + "for i, name in enumerate(gas_Si_N_interface.species_names):\n", + " ax[2, 1].plot(times, solution.values.y[:, 3 + N + i], label=name)\n", + "ax[2, 1].legend()\n", + "ax[2, 1].set_xlabel(\"Distance (m)\")\n", + "ax[2, 1].set_ylabel(\"Site Fraction\")\n", "f.tight_layout(pad=0.5)" ] }, @@ -458,12 +479,12 @@ "source": [ "############################### initial conditions ##################################################################\n", "# import the SiF4 + NH3 reaction mechanism\n", - "mech = '../data/SiF4_NH3_mec.yaml'\n", + "mech = \"../data/SiF4_NH3_mec.yaml\"\n", "# import the models for gas and bulk\n", - "gas, bulk_Si, bulk_N = ct.import_phases(mech,['gas','SiBulk','NBulk'])\n", + "gas, bulk_Si, bulk_N = ct.import_phases(mech, [\"gas\", \"SiBulk\", \"NBulk\"])\n", "\n", "# import the model for gas-Si-N interface\n", - "gas_Si_N_interface = ct.Interface(mech, 'SI3N4',[gas,bulk_Si,bulk_N])\n", + "gas_Si_N_interface = ct.Interface(mech, \"SI3N4\", [gas, bulk_Si, bulk_N])\n", "T0 = 1713 # K\n", "p0 = 2 * ct.one_atm / 760.0 # Pa ~2Torr\n", "gas.TPX = T0, p0, \"NH3:6, SiF4:1\"\n", @@ -472,7 +493,7 @@ "gas_Si_N_interface.TP = T0, p0\n", "gas_Si_N_interface.coverages = \"HN_NH2(S): 1.0\"\n", "D = 5.08e-2 # diameter of the tube [m]\n", - "Ac = np.pi * D**2/4 # cross section of the tube [m]\n", + "Ac = np.pi * D**2 / 4 # cross section of the tube [m]\n", "mu = 5.7e-5 # kg/(m-s) dynamic viscosity\n", "perim = np.pi * D # perimeter of the tube\n", "# calculate the site fractions of surface species at the entrance of the tube at steady state\n", @@ -480,74 +501,85 @@ "Zk_0 = gas_Si_N_interface.coverages\n", "######################################## IDA solver ###################################################################\n", "def residual(z, vec, vecp, result):\n", - " \"\"\" we create the residual equations for the problem\n", - " vec = [u, rho, Yk, p, Zk, T]\n", - " vecp = [dudz, drhodz, dYkdz, dpdz, dZkdz, dTdz]\n", + " \"\"\"we create the residual equations for the problem\n", + " vec = [u, rho, Yk, p, Zk, T]\n", + " vecp = [dudz, drhodz, dYkdz, dpdz, dZkdz, dTdz]\n", " \"\"\"\n", " # temporary variables\n", " u = vec[0] # velocity\n", " rho = vec[1] # density\n", - " Y = vec[2:2+N] # vector of mass fractions of all gas species\n", - " p = vec[2+N] # pressure\n", - " Z = vec[3+N:-1] # vector of site fractions of all surface species\n", + " Y = vec[2 : 2 + N] # vector of mass fractions of all gas species\n", + " p = vec[2 + N] # pressure\n", + " Z = vec[3 + N : -1] # vector of site fractions of all surface species\n", " T = vec[-1] # temperature\n", - " \n", + "\n", " dudz = vecp[0] # velocity spatial derivative\n", " drhodz = vecp[1] # density spatial derivative\n", - " dYdz = vecp[2:2+N] # mass fraction spatial derivative\n", - " dpdz = vecp[2+N] # pressure spatial derivative\n", + " dYdz = vecp[2 : 2 + N] # mass fraction spatial derivative\n", + " dpdz = vecp[2 + N] # pressure spatial derivative\n", " dTdz = vecp[-1] # temperature spatial derivative\n", - " \n", + "\n", " h = gas.enthalpy_mass # enthalpy of gas species per mass\n", " h_Si = bulk_Si.enthalpy_mass # enthalpy of Si per mass\n", " h_N = bulk_N.enthalpy_mass # enthalpy of N per mass\n", - " \n", + "\n", " # initial conditions\n", " gas.set_unnormalized_mass_fractions(Y)\n", " gas.TP = T, p\n", - " \n", + "\n", " bulk_Si.TP = T, p\n", " bulk_N.TP = T, p\n", " gas_Si_N_interface.set_unnormalized_coverages(Z)\n", " gas_Si_N_interface.TP = T, p\n", - " \n", + "\n", " # temporary variables (based on the current system state)\n", " coverages = gas_Si_N_interface.coverages # site fraction vector\n", - " sdot_g = gas_Si_N_interface.get_net_production_rates(\"gas\") # heterogeneous production rate of gas species\n", + " sdot_g = gas_Si_N_interface.get_net_production_rates(\n", + " \"gas\"\n", + " ) # heterogeneous production rate of gas species\n", " sdot_s = gas_Si_N_interface.get_net_production_rates(\"SI3N4\")\n", " wdot_g = gas.net_production_rates # homogeneous production rate of gas species\n", " W_g = gas.molecular_weights # vector of molecular weight of gas species\n", " W_Si_b = bulk_Si.molecular_weights\n", " W_N_b = bulk_N.molecular_weights\n", - " sdot_Si = gas_Si_N_interface.get_net_production_rates(\"SiBulk\") # bulk production rate\n", + " sdot_Si = gas_Si_N_interface.get_net_production_rates(\n", + " \"SiBulk\"\n", + " ) # bulk production rate\n", " sdot_N = gas_Si_N_interface.get_net_production_rates(\"NBulk\")\n", "\n", " # mass continuity equation\n", - " result[0] = u*drhodz+rho*dudz-perim*np.sum(sdot_g*W_g)/Ac\n", + " result[0] = u * drhodz + rho * dudz - perim * np.sum(sdot_g * W_g) / Ac\n", " # conservation of species\n", " for k in range(gas.n_species):\n", - " result[1+k] = (rho*u*Ac*dYdz[k] + Y[k]*perim*np.sum(sdot_g*W_g)\n", - " - wdot_g[k]*W_g[k]*Ac\n", - " - sdot_g[k]*W_g[k]*perim)\n", + " result[1 + k] = (\n", + " rho * u * Ac * dYdz[k]\n", + " + Y[k] * perim * np.sum(sdot_g * W_g)\n", + " - wdot_g[k] * W_g[k] * Ac\n", + " - sdot_g[k] * W_g[k] * perim\n", + " )\n", " # conservation of momentum\n", - " result[1+gas.n_species] = 2*rho*u*dudz + np.power(u,2)*drhodz + dpdz + 32*u*mu/D**2\n", + " result[1 + gas.n_species] = (\n", + " 2 * rho * u * dudz + np.power(u, 2) * drhodz + dpdz + 32 * u * mu / D**2\n", + " )\n", "\n", " # equation of state\n", - " result[2+gas.n_species] = gas.density - rho\n", + " result[2 + gas.n_species] = gas.density - rho\n", "\n", " # algebraic constraints\n", " for j in range(M):\n", - " result[3+N+j] = sdot_s[j]\n", - " \n", + " result[3 + N + j] = sdot_s[j]\n", + "\n", " # replace the constraints with the condition sum(Zk) = 1 for the largest site fraction species\n", " index = np.argmax(coverages)\n", - " result[3+N+index] = np.sum(coverages) - 1\n", - " \n", + " result[3 + N + index] = np.sum(coverages) - 1\n", + "\n", " # energy equation\n", - " result[3+N+M] = (rho*u*Ac*gas.cp*dTdz\n", - " + Ac*np.sum(wdot_g*W_g*h)\n", - " + perim*np.sum(h*sdot_g*W_g)\n", - " + perim*(sdot_Si[0]*W_Si_b*h_Si + sdot_N[0]*W_N_b*h_N))" + " result[3 + N + M] = (\n", + " rho * u * Ac * gas.cp * dTdz\n", + " + Ac * np.sum(wdot_g * W_g * h)\n", + " + perim * np.sum(h * sdot_g * W_g)\n", + " + perim * (sdot_Si[0] * W_Si_b * h_Si + sdot_N[0] * W_N_b * h_N)\n", + " )" ] }, { @@ -565,7 +597,9 @@ "u0 = 11.53 # m/s initial velocity of the flow\n", "W = gas.molecular_weights\n", "W_avg = gas.mean_molecular_weight\n", - "sdot_g = gas_Si_N_interface.get_net_production_rates(\"gas\") # heterogeneous production rate of gas species\n", + "sdot_g = gas_Si_N_interface.get_net_production_rates(\n", + " \"gas\"\n", + ") # heterogeneous production rate of gas species\n", "sdot_s = gas_Si_N_interface.get_net_production_rates(\"SI3N4\")\n", "sdot_Si = gas_Si_N_interface.get_net_production_rates(\"SiBulk\")\n", "sdot_N = gas_Si_N_interface.get_net_production_rates(\"NBulk\")\n", @@ -577,29 +611,33 @@ "W_Si = bulk_Si.molecular_weights\n", "W_N = bulk_N.molecular_weights\n", "################### a #########################\n", - "a = np.zeros((4+N,4+N))\n", - "a[0,:] = np.hstack((rho0, u0, np.zeros(2+N)))\n", + "a = np.zeros((4 + N, 4 + N))\n", + "a[0, :] = np.hstack((rho0, u0, np.zeros(2 + N)))\n", "for i in range(N):\n", - " a[1+i,2+i] = rho0*u0*Ac\n", - "a[1+N,:] = np.hstack((2*rho0*u0, u0**2, np.zeros(N), 1,0))\n", + " a[1 + i, 2 + i] = rho0 * u0 * Ac\n", + "a[1 + N, :] = np.hstack((2 * rho0 * u0, u0**2, np.zeros(N), 1, 0))\n", "coef = np.zeros(N)\n", "for j in range(N):\n", - " coef[j] = gas.P/W[j]/np.power(np.sum(gas.Y/W),2)\n", - "a[2+N,:] = np.hstack((0, ct.gas_constant*T0, coef, -W_avg, 0))\n", - "a[3+N,:] = np.hstack((np.zeros(3+N), rho0*u0*Ac*gas.cp))\n", + " coef[j] = gas.P / W[j] / np.power(np.sum(gas.Y / W), 2)\n", + "a[2 + N, :] = np.hstack((0, ct.gas_constant * T0, coef, -W_avg, 0))\n", + "a[3 + N, :] = np.hstack((np.zeros(3 + N), rho0 * u0 * Ac * gas.cp))\n", "################### b ###########################\n", - "b = np.zeros(4+gas.n_species)\n", - "b[0] = perim*np.sum(sdot_g*W)/Ac\n", + "b = np.zeros(4 + gas.n_species)\n", + "b[0] = perim * np.sum(sdot_g * W) / Ac\n", "for i in range(N):\n", - " b[1+i] = (wdot[i]*W[i]*Ac\n", - " + sdot_g[i]*W[i]*perim\n", - " - gas.Y[i]*perim*np.sum(sdot_g*W))\n", - "b[1+gas.n_species] = -32*u0*mu/D**2\n", - "b[2+gas.n_species] = 0\n", - "b[3+gas.n_species] = (- Ac*np.sum(wdot*W*h)\n", - " - perim*np.sum(h*sdot_g*W)\n", - " - perim*np.sum(sdot_Si[0]*W_Si*h_Si + sdot_N[0]*W_N*h_N))\n", - "part_vecp0 = np.linalg.solve(a,b)\n", + " b[1 + i] = (\n", + " wdot[i] * W[i] * Ac\n", + " + sdot_g[i] * W[i] * perim\n", + " - gas.Y[i] * perim * np.sum(sdot_g * W)\n", + " )\n", + "b[1 + gas.n_species] = -32 * u0 * mu / D**2\n", + "b[2 + gas.n_species] = 0\n", + "b[3 + gas.n_species] = (\n", + " -Ac * np.sum(wdot * W * h)\n", + " - perim * np.sum(h * sdot_g * W)\n", + " - perim * np.sum(sdot_Si[0] * W_Si * h_Si + sdot_N[0] * W_N * h_N)\n", + ")\n", + "part_vecp0 = np.linalg.solve(a, b)\n", "\n", "vecp0 = np.hstack((part_vecp0[:-1], np.zeros(M), part_vecp0[-1]))\n", "vec0 = np.hstack((11.53, gas.density, gas.Y, gas.P, Zk_0, T0))" @@ -612,14 +650,14 @@ "outputs": [], "source": [ "solver = dae(\n", - " 'ida',\n", - " residual, \n", + " \"ida\",\n", + " residual,\n", " atol=1e-8, # absolute tolerance for solution\n", " rtol=1e-8, # relative tolerance for solution\n", - " algebraic_vars_idx=[np.arange(3+N,3+N+M,1)], \n", + " algebraic_vars_idx=[np.arange(3 + N, 3 + N + M, 1)],\n", " max_steps=5000,\n", " one_step_compute=True,\n", - " old_api=False\n", + " old_api=False,\n", ")\n", "\n", "time = []\n", @@ -653,61 +691,65 @@ } ], "source": [ - "f, ax = plt.subplots(4,2, figsize=(9,12), dpi=96)\n", + "f, ax = plt.subplots(4, 2, figsize=(9, 12), dpi=96)\n", "\n", "# plot gas velocity along the flow direction\n", - "ax[0,0].plot(time, solution[:,0], color='C0')\n", - "ax[0,0].set_xlabel('Distance (m)')\n", - "ax[0,0].set_ylabel('Velocity (m/s)')\n", + "ax[0, 0].plot(time, solution[:, 0], color=\"C0\")\n", + "ax[0, 0].set_xlabel(\"Distance (m)\")\n", + "ax[0, 0].set_ylabel(\"Velocity (m/s)\")\n", "\n", "# plot gas density along the flow direction\n", - "ax[0,1].plot(time, solution[:,1], color='C1')\n", - "ax[0,1].set_xlabel('Distance (m)')\n", - "ax[0,1].set_ylabel('Density ($\\mathregular{kg/m^3}$)')\n", - "ax[0,1].ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) # scientific notation\n", + "ax[0, 1].plot(time, solution[:, 1], color=\"C1\")\n", + "ax[0, 1].set_xlabel(\"Distance (m)\")\n", + "ax[0, 1].set_ylabel(\"Density ($\\mathregular{kg/m^3}$)\")\n", + "ax[0, 1].ticklabel_format(\n", + " axis=\"y\", style=\"sci\", scilimits=(-2, 2)\n", + ") # scientific notation\n", "\n", "# plot major and minor gas species separately\n", "minor_idx = []\n", "major_idx = []\n", - "for i,name in enumerate(gas.species_names): \n", - " mean = np.mean(solution[:,2+i])\n", + "for i, name in enumerate(gas.species_names):\n", + " mean = np.mean(solution[:, 2 + i])\n", " if mean <= 0.01:\n", - " minor_idx.append(i) \n", + " minor_idx.append(i)\n", " else:\n", " major_idx.append(i)\n", "\n", "# plot minor gas species along the flow direction\n", "for i in minor_idx:\n", - " style = '-' if i < 10 else '--'\n", - " ax[1,0].plot(time, solution[:,2+i], label=gas.species_names[i], linestyle=style)\n", - "ax[1,0].legend(fontsize=7.5, loc='best')\n", - "ax[1,0].set_xlabel('Distance (m)')\n", - "ax[1,0].set_ylabel('Mass Fraction')\n", - "ax[1,0].ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) # scientific notation\n", + " style = \"-\" if i < 10 else \"--\"\n", + " ax[1, 0].plot(time, solution[:, 2 + i], label=gas.species_names[i], linestyle=style)\n", + "ax[1, 0].legend(fontsize=7.5, loc=\"best\")\n", + "ax[1, 0].set_xlabel(\"Distance (m)\")\n", + "ax[1, 0].set_ylabel(\"Mass Fraction\")\n", + "ax[1, 0].ticklabel_format(\n", + " axis=\"y\", style=\"sci\", scilimits=(-2, 2)\n", + ") # scientific notation\n", "\n", "# plot major gas species along the flow direction\n", "for j in major_idx:\n", - " ax[1,1].plot(time, solution[:,2+j], label=gas.species_names[j])\n", - "ax[1,1].legend(fontsize=8, loc='best')\n", - "ax[1,1].set_xlabel('Distance (m)')\n", - "ax[1,1].set_ylabel('Mass Fraction')\n", + " ax[1, 1].plot(time, solution[:, 2 + j], label=gas.species_names[j])\n", + "ax[1, 1].legend(fontsize=8, loc=\"best\")\n", + "ax[1, 1].set_xlabel(\"Distance (m)\")\n", + "ax[1, 1].set_ylabel(\"Mass Fraction\")\n", "\n", "# plot the pressure of the gas along the flow direction\n", - "ax[2,0].plot(time, solution[:,2+N], color='C2')\n", - "ax[2,0].set_xlabel('Distance (m)')\n", - "ax[2,0].set_ylabel('Pressure (Pa)')\n", + "ax[2, 0].plot(time, solution[:, 2 + N], color=\"C2\")\n", + "ax[2, 0].set_xlabel(\"Distance (m)\")\n", + "ax[2, 0].set_ylabel(\"Pressure (Pa)\")\n", "\n", "# plot the site fraction of the surface species along the flow direction\n", - "for i,name in enumerate(gas_Si_N_interface.species_names):\n", - " ax[2,1].plot(time, solution[:,3+N+i], label=name)\n", - "ax[2,1].legend(fontsize=8)\n", - "ax[2,1].set_xlabel('Distance (m)')\n", - "ax[2,1].set_ylabel('Site Fraction')\n", + "for i, name in enumerate(gas_Si_N_interface.species_names):\n", + " ax[2, 1].plot(time, solution[:, 3 + N + i], label=name)\n", + "ax[2, 1].legend(fontsize=8)\n", + "ax[2, 1].set_xlabel(\"Distance (m)\")\n", + "ax[2, 1].set_ylabel(\"Site Fraction\")\n", "\n", "# plot the temperature profile along the flow direction\n", - "ax[3,0].plot(time, solution[:,-1], color='C3')\n", - "ax[3,0].set_xlabel('Distance (m)')\n", - "ax[3,0].set_ylabel('Temperature (K)')\n", + "ax[3, 0].plot(time, solution[:, -1], color=\"C3\")\n", + "ax[3, 0].set_xlabel(\"Distance (m)\")\n", + "ax[3, 0].set_ylabel(\"Temperature (K)\")\n", "f.tight_layout(pad=0.5)" ] } diff --git a/reactors/NonIdealShockTube.ipynb b/reactors/NonIdealShockTube.ipynb index 56b6559..b009a4f 100644 --- a/reactors/NonIdealShockTube.ipynb +++ b/reactors/NonIdealShockTube.ipynb @@ -56,7 +56,8 @@ "import time\n", "\n", "import cantera as ct\n", - "print('Runnning Cantera version: ' + ct.__version__)" + "\n", + "print(\"Runnning Cantera version: \" + ct.__version__)" ] }, { @@ -68,10 +69,10 @@ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", - "plt.rcParams['axes.labelsize'] = 16\n", - "plt.rcParams['xtick.labelsize'] = 12\n", - "plt.rcParams['ytick.labelsize'] = 12\n", - "plt.rcParams['figure.autolayout'] = True" + "plt.rcParams[\"axes.labelsize\"] = 16\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "plt.rcParams[\"figure.autolayout\"] = True" ] }, { @@ -91,7 +92,7 @@ "metadata": {}, "outputs": [], "source": [ - "gas = ct.Solution('../data/WangMechanismRK.yaml')" + "gas = ct.Solution(\"../data/WangMechanismRK.yaml\")" ] }, { @@ -108,14 +109,14 @@ "outputs": [], "source": [ "# Define the reactor temperature and pressure:\n", - "reactorTemperature = 1000 # Kelvin\n", - "reactorPressure = 40.0*101325.0 # Pascal\n", + "reactorTemperature = 1000 # Kelvin\n", + "reactorPressure = 40.0 * 101325.0 # Pascal\n", "\n", "# Set the state of the gas object:\n", "gas.TP = reactorTemperature, reactorPressure\n", "\n", "# Define the fuel, oxidizer and set the stoichiometry:\n", - "gas.set_equivalence_ratio(phi=1.0, fuel='c12h26', oxidizer={'o2':1.0, 'n2':3.76})\n", + "gas.set_equivalence_ratio(phi=1.0, fuel=\"c12h26\", oxidizer={\"o2\": 1.0, \"n2\": 3.76})\n", "\n", "# Create a reactor object and add it to a reactor network\n", "# In this example, this will be the only reactor in the network\n", @@ -171,22 +172,26 @@ "estimatedIgnitionDelayTime = 0.005\n", "t = 0\n", "\n", - "counter = 1;\n", - "while(t < estimatedIgnitionDelayTime):\n", + "counter = 1\n", + "while t < estimatedIgnitionDelayTime:\n", " t = reactorNetwork.step()\n", - " if (counter%20 == 0):\n", + " if counter % 20 == 0:\n", " # We will save only every 20th value. Otherwise, this takes too long\n", " # Note that the species concentrations are mass fractions\n", " timeHistory.loc[t] = reactorNetwork.get_state()\n", - " counter+=1\n", + " counter += 1\n", "\n", "# We will use the 'oh' species to compute the ignition delay\n", - "tau = ignitionDelay(timeHistory, 'oh')\n", + "tau = ignitionDelay(timeHistory, \"oh\")\n", "\n", "# Toc\n", "t1 = time.time()\n", "\n", - "print('Computed Ignition Delay: {:.3e} seconds. Took {:3.2f}s to compute'.format(tau, t1-t0))\n", + "print(\n", + " \"Computed Ignition Delay: {:.3e} seconds. Took {:3.2f}s to compute\".format(\n", + " tau, t1 - t0\n", + " )\n", + ")\n", "\n", "# If you want to save all the data - molefractions, temperature, pressure, etc\n", "# uncomment the next line\n", @@ -1007,23 +1012,37 @@ ], "source": [ "plt.figure()\n", - "plt.plot(timeHistory.index, timeHistory['oh'],'-o',color='b',markersize=4)\n", - "plt.xlabel('Time (s)',fontname='Times New Roman')\n", - "plt.ylabel('$\\mathdefault{OH\\, mass\\, fraction,}\\, Y_{OH}}$',fontname='Times New Roman')\n", + "plt.plot(timeHistory.index, timeHistory[\"oh\"], \"-o\", color=\"b\", markersize=4)\n", + "plt.xlabel(\"Time (s)\", fontname=\"Times New Roman\")\n", + "plt.ylabel(\n", + " \"$\\mathdefault{OH\\, mass\\, fraction,}\\, Y_{OH}}$\", fontname=\"Times New Roman\"\n", + ")\n", "\n", "# Figure formatting:\n", - "plt.xlim([0,0.00075])\n", + "plt.xlim([0, 0.00075])\n", "ax = plt.gca()\n", - "font = plt.matplotlib.font_manager.FontProperties(family='Times New Roman',size=14)\n", - "ax.annotate(\"\",xy=(tau,0.005), xytext=(0,0.005),arrowprops=dict(arrowstyle=\"<|-|>\",color='r',linewidth=2.0),fontsize=14,)\n", - "plt.annotate('Ignition Delay Time (IDT)', xy=(0,0), xytext=(0.00004, 0.00525), family='Times New Roman',fontsize=16);\n", + "font = plt.matplotlib.font_manager.FontProperties(family=\"Times New Roman\", size=14)\n", + "ax.annotate(\n", + " \"\",\n", + " xy=(tau, 0.005),\n", + " xytext=(0, 0.005),\n", + " arrowprops=dict(arrowstyle=\"<|-|>\", color=\"r\", linewidth=2.0),\n", + " fontsize=14,\n", + ")\n", + "plt.annotate(\n", + " \"Ignition Delay Time (IDT)\",\n", + " xy=(0, 0),\n", + " xytext=(0.00004, 0.00525),\n", + " family=\"Times New Roman\",\n", + " fontsize=16,\n", + ")\n", "\n", "for tick in ax.xaxis.get_major_ticks():\n", " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname('Times New Roman')\n", + " tick.label1.set_fontname(\"Times New Roman\")\n", "for tick in ax.yaxis.get_major_ticks():\n", " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname('Times New Roman')" + " tick.label1.set_fontname(\"Times New Roman\")" ] }, { @@ -1048,17 +1067,35 @@ "outputs": [], "source": [ "# Make a list of all the temperatures we would like to run simulations at\n", - "T = [1800, 1600, 1400, 1200, 1100, 1075, 1050, 1025, 1000, 975, 950, 925, 900, 850, 825, 800,\n", - " 750, 700]\n", + "T = [\n", + " 1800,\n", + " 1600,\n", + " 1400,\n", + " 1200,\n", + " 1100,\n", + " 1075,\n", + " 1050,\n", + " 1025,\n", + " 1000,\n", + " 975,\n", + " 950,\n", + " 925,\n", + " 900,\n", + " 850,\n", + " 825,\n", + " 800,\n", + " 750,\n", + " 700,\n", + "]\n", "\n", - "# Set the initial guesses to a common value. We could probably speed up simulations \n", - "# by tuning this guess, but as seen in the figure above, the 'extra' time after igntion \n", + "# Set the initial guesses to a common value. We could probably speed up simulations\n", + "# by tuning this guess, but as seen in the figure above, the 'extra' time after igntion\n", "# does not add many data points or simulation steps. The time savings would be small.\n", "estimatedIgnitionDelayTimes = np.ones_like(T) * 0.005\n", "\n", "# Now create a DataFrame out of these\n", - "ignitionDelays = pd.DataFrame(data={'T':T})\n", - "ignitionDelays['ignDelay'] = np.nan" + "ignitionDelays = pd.DataFrame(data={\"T\": T})\n", + "ignitionDelays[\"ignDelay\"] = np.nan" ] }, { @@ -1102,9 +1139,9 @@ "for i, temperature in enumerate(T):\n", " # Set up the gas and reactor\n", " reactorTemperature = temperature\n", - " reactorPressure = 40.0*101325.0\n", + " reactorPressure = 40.0 * 101325.0\n", " gas.TP = reactorTemperature, reactorPressure\n", - " gas.set_equivalence_ratio(phi=1.0, fuel='c12h26', oxidizer={'o2':1.0, 'n2':3.76})\n", + " gas.set_equivalence_ratio(phi=1.0, fuel=\"c12h26\", oxidizer={\"o2\": 1.0, \"n2\": 3.76})\n", " r = ct.Reactor(contents=gas)\n", " reactorNetwork = ct.ReactorNet([r])\n", "\n", @@ -1121,12 +1158,14 @@ " timeHistory.loc[t] = r.get_state()\n", " counter += 1\n", "\n", - " tau = ignitionDelay(timeHistory, 'oh')\n", + " tau = ignitionDelay(timeHistory, \"oh\")\n", " t1 = time.time()\n", "\n", - " print(f'Computed Ignition Delay: {tau:.3e} seconds for T={temperature}K. Took {t1 - t0:3.2f}s to compute')\n", + " print(\n", + " f\"Computed Ignition Delay: {tau:.3e} seconds for T={temperature}K. Took {t1 - t0:3.2f}s to compute\"\n", + " )\n", "\n", - " ignitionDelays.at[i, 'ignDelay'] = tau" + " ignitionDelays.at[i, \"ignDelay\"] = tau" ] }, { @@ -1937,27 +1976,29 @@ "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", - "ax.semilogy(1000/ignitionDelays['T'], ignitionDelays['ignDelay'],'o-',color='b')\n", - "ax.set_ylabel('Ignition Delay (s)',fontname='Times New Roman',fontsize=16)\n", - "ax.set_xlabel(r'$\\mathdefault{1000/T\\, (K^{-1})}$', fontsize=16,fontname='Times New Roman')\n", + "ax.semilogy(1000 / ignitionDelays[\"T\"], ignitionDelays[\"ignDelay\"], \"o-\", color=\"b\")\n", + "ax.set_ylabel(\"Ignition Delay (s)\", fontname=\"Times New Roman\", fontsize=16)\n", + "ax.set_xlabel(\n", + " r\"$\\mathdefault{1000/T\\, (K^{-1})}$\", fontsize=16, fontname=\"Times New Roman\"\n", + ")\n", "\n", "# Add a second axis on top to plot the temperature for better readability\n", "ax2 = ax.twiny()\n", "ticks = ax.get_xticks()\n", "ax2.set_xticks(ticks)\n", - "ax2.set_xticklabels((1000/ticks).round(1))\n", + "ax2.set_xticklabels((1000 / ticks).round(1))\n", "ax2.set_xlim(ax.get_xlim())\n", - "ax2.set_xlabel('Temperature (K)',fontname='Times New Roman',fontsize=16);\n", + "ax2.set_xlabel(\"Temperature (K)\", fontname=\"Times New Roman\", fontsize=16)\n", "\n", "for tick in ax.xaxis.get_major_ticks():\n", " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname('Times New Roman')\n", + " tick.label1.set_fontname(\"Times New Roman\")\n", "for tick in ax.yaxis.get_major_ticks():\n", " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname('Times New Roman')\n", + " tick.label1.set_fontname(\"Times New Roman\")\n", "for tick in ax2.xaxis.get_major_ticks():\n", " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname('Times New Roman')" + " tick.label1.set_fontname(\"Times New Roman\")" ] } ], diff --git a/reactors/batch_reactor_ignition_delay_NTC.ipynb b/reactors/batch_reactor_ignition_delay_NTC.ipynb index 2ca11b7..2fc6b53 100644 --- a/reactors/batch_reactor_ignition_delay_NTC.ipynb +++ b/reactors/batch_reactor_ignition_delay_NTC.ipynb @@ -30,7 +30,8 @@ "import time\n", "\n", "import cantera as ct\n", - "print('Runnning Cantera version: ' + ct.__version__)" + "\n", + "print(\"Runnning Cantera version: \" + ct.__version__)" ] }, { @@ -49,13 +50,13 @@ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", - "plt.rcParams['axes.labelsize'] = 18\n", - "plt.rcParams['xtick.labelsize'] = 12\n", - "plt.rcParams['ytick.labelsize'] = 12\n", - "plt.rcParams['figure.autolayout'] = True\n", + "plt.rcParams[\"axes.labelsize\"] = 18\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "plt.rcParams[\"figure.autolayout\"] = True\n", "\n", - "plt.style.use('ggplot')\n", - "plt.style.use('seaborn-pastel')" + "plt.style.use(\"ggplot\")\n", + "plt.style.use(\"seaborn-pastel\")" ] }, { @@ -84,7 +85,7 @@ } ], "source": [ - "gas = ct.Solution('../data/seiser.yaml')" + "gas = ct.Solution(\"../data/seiser.yaml\")" ] }, { @@ -1207,10 +1208,22 @@ "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"$Y_{OH}$\")\n", "\n", - "plt.xlim([0,0.05])\n", - "plt.arrow(0, 0.008, tau, 0, width=0.0001, head_width=0.0005,\n", - " head_length=0.001, length_includes_head=True, color=\"r\", shape=\"full\")\n", - "plt.annotate(r\"$Ignition Delay: \\tau_{ign}$\", xy=(0,0), xytext=(0.01, 0.0082), fontsize=16);" + "plt.xlim([0, 0.05])\n", + "plt.arrow(\n", + " 0,\n", + " 0.008,\n", + " tau,\n", + " 0,\n", + " width=0.0001,\n", + " head_width=0.0005,\n", + " head_length=0.001,\n", + " length_includes_head=True,\n", + " color=\"r\",\n", + " shape=\"full\",\n", + ")\n", + "plt.annotate(\n", + " r\"$Ignition Delay: \\tau_{ign}$\", xy=(0, 0), xytext=(0.01, 0.0082), fontsize=16\n", + ");" ] }, { @@ -1246,8 +1259,12 @@ "estimated_ignition_delay_times[-2:] = 100\n", "\n", "# Now create a SolutionArray out of these\n", - "ignition_delays = ct.SolutionArray(gas, shape=T.shape, extra={\"tau\": estimated_ignition_delay_times})\n", - "ignition_delays.set_equivalence_ratio(1.0, fuel=\"nc7h16\", oxidizer={\"o2\": 1.0, \"n2\": 3.76})\n", + "ignition_delays = ct.SolutionArray(\n", + " gas, shape=T.shape, extra={\"tau\": estimated_ignition_delay_times}\n", + ")\n", + "ignition_delays.set_equivalence_ratio(\n", + " 1.0, fuel=\"nc7h16\", oxidizer={\"o2\": 1.0, \"n2\": 3.76}\n", + ")\n", "ignition_delays.TP = T, reactor_pressure" ] }, @@ -1323,7 +1340,11 @@ " tau = time_history[i_ign]\n", " t1 = time.time()\n", "\n", - " print('Computed Ignition Delay: {:.3e} seconds for T={}K. Took {:3.2f}s to compute'.format(tau, state.T, t1-t0))\n", + " print(\n", + " \"Computed Ignition Delay: {:.3e} seconds for T={}K. Took {:3.2f}s to compute\".format(\n", + " tau, state.T, t1 - t0\n", + " )\n", + " )\n", "\n", " ignition_delays.tau[i] = tau" ] @@ -2341,17 +2362,17 @@ "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", - "ax.semilogy(1000/ignition_delays.T, ignition_delays.tau, 'o-')\n", - "ax.set_ylabel('Ignition Delay (s)')\n", - "ax.set_xlabel(r'$\\frac{1000}{T (K)}$', fontsize=18)\n", + "ax.semilogy(1000 / ignition_delays.T, ignition_delays.tau, \"o-\")\n", + "ax.set_ylabel(\"Ignition Delay (s)\")\n", + "ax.set_xlabel(r\"$\\frac{1000}{T (K)}$\", fontsize=18)\n", "\n", "# Add a second axis on top to plot the temperature for better readability\n", "ax2 = ax.twiny()\n", "ticks = ax.get_xticks()\n", "ax2.set_xticks(ticks)\n", - "ax2.set_xticklabels((1000/ticks).round(1))\n", + "ax2.set_xticklabels((1000 / ticks).round(1))\n", "ax2.set_xlim(ax.get_xlim())\n", - "ax2.set_xlabel(r'Temperature: $T(K)$');" + "ax2.set_xlabel(r\"Temperature: $T(K)$\");" ] } ], diff --git a/reactors/interactive_path_diagram.ipynb b/reactors/interactive_path_diagram.ipynb index 08a0a40..0420364 100644 --- a/reactors/interactive_path_diagram.ipynb +++ b/reactors/interactive_path_diagram.ipynb @@ -18,9 +18,10 @@ "outputs": [], "source": [ "from IPython.display import Image, display\n", - "from ipywidgets import widgets,interact\n", + "from ipywidgets import widgets, interact\n", "import cantera as ct\n", "import numpy as np\n", + "\n", "%matplotlib inline\n", "import matplotlib\n", "from matplotlib import pyplot as plt\n", @@ -43,7 +44,7 @@ }, "outputs": [], "source": [ - "gas = ct.Solution('gri30.yaml')" + "gas = ct.Solution(\"gri30.yaml\")" ] }, { @@ -66,7 +67,7 @@ }, "outputs": [], "source": [ - "gas.TPX = 1550., 6.5*ct.one_atm,'CH4:3.29, C2H6:0.21, O2:7 , Ar:89.5'" + "gas.TPX = 1550.0, 6.5 * ct.one_atm, \"CH4:3.29, C2H6:0.21, O2:7 , Ar:89.5\"" ] }, { @@ -102,7 +103,7 @@ }, "outputs": [], "source": [ - "reactor = ct.IdealGasConstPressureReactor(gas, energy='on')\n", + "reactor = ct.IdealGasConstPressureReactor(gas, energy=\"on\")\n", "reactor_network = ct.ReactorNet([reactor])\n", "reactor_network.atol = 1e-12\n", "reactor_network.rtol = 1e-12" @@ -124,15 +125,15 @@ "outputs": [], "source": [ "profiles = defaultdict(list)\n", - "time =0\n", + "time = 0\n", "steps = 0\n", "while time < residence_time:\n", - " profiles['time'].append(time)\n", - " profiles['pressure'].append(gas.P)\n", - " profiles['temperature'].append(gas.T)\n", - " profiles['mole_fractions'].append(gas.X)\n", + " profiles[\"time\"].append(time)\n", + " profiles[\"pressure\"].append(gas.P)\n", + " profiles[\"temperature\"].append(gas.T)\n", + " profiles[\"mole_fractions\"].append(gas.X)\n", " time = reactor_network.step()\n", - " steps += 1\n" + " steps += 1" ] }, { @@ -164,32 +165,35 @@ } ], "source": [ - "@interact( plot_step=widgets.IntSlider(value=100,min=0,max=steps,step=10),\n", - " threshold=widgets.FloatSlider(value=0.005,min=0,max=1),\n", - " details = widgets.ToggleButton(),\n", - " species = widgets.Dropdown(options=gas.element_names,\n", - " value='C',\n", - " description='Element',\n", - " disabled=False,))\n", - "def plot_reaction_path_diagrams(plot_step, threshold,details,species):\n", - " P = profiles['pressure'][plot_step]\n", - " T = profiles['temperature'][plot_step]\n", - " X = profiles['mole_fractions'][plot_step]\n", - " time= profiles['time'][plot_step]\n", - " gas.TPX = T,P,X\n", + "@interact(\n", + " plot_step=widgets.IntSlider(value=100, min=0, max=steps, step=10),\n", + " threshold=widgets.FloatSlider(value=0.005, min=0, max=1),\n", + " details=widgets.ToggleButton(),\n", + " species=widgets.Dropdown(\n", + " options=gas.element_names,\n", + " value=\"C\",\n", + " description=\"Element\",\n", + " disabled=False,\n", + " ),\n", + ")\n", + "def plot_reaction_path_diagrams(plot_step, threshold, details, species):\n", + " P = profiles[\"pressure\"][plot_step]\n", + " T = profiles[\"temperature\"][plot_step]\n", + " X = profiles[\"mole_fractions\"][plot_step]\n", + " time = profiles[\"time\"][plot_step]\n", + " gas.TPX = T, P, X\n", " print(\"time = {:.2g} s\".format(time))\n", - " \n", + "\n", " diagram = ct.ReactionPathDiagram(gas, species)\n", " diagram.threshold = threshold\n", "\n", " diagram.show_details = details\n", - " dot_file = 'reaction_paths.dot'\n", - " png_file = 'reaction_paths.png'\n", + " dot_file = \"reaction_paths.dot\"\n", + " png_file = \"reaction_paths.png\"\n", " diagram.write_dot(dot_file)\n", - " subprocess.run(f'dot {dot_file} -Tpng -o{png_file} -Gdpi=100'.split())\n", + " subprocess.run(f\"dot {dot_file} -Tpng -o{png_file} -Gdpi=100\".split())\n", " img = Image(filename=png_file)\n", - " display(img)\n", - " " + " display(img)" ] }, { @@ -210,7 +214,7 @@ "source": [ "C2H6_stoichiometry = np.zeros_like(gas.reactions())\n", "for i, r in enumerate(gas.reactions()):\n", - " C2H6_moles = r.products.get('C2H6',0) - r.reactants.get('C2H6',0)\n", + " C2H6_moles = r.products.get(\"C2H6\", 0) - r.reactants.get(\"C2H6\", 0)\n", " C2H6_stoichiometry[i] = C2H6_moles\n", "C2H6_reaction_indices = C2H6_stoichiometry.nonzero()[0]" ] @@ -230,19 +234,22 @@ }, "outputs": [], "source": [ - "profiles['C2H6_production_rates'] = []\n", - "for i in range(len(profiles['time'])):\n", - " X = profiles['mole_fractions'][i]\n", - " t = profiles['time'][i]\n", - " T = profiles['temperature'][i]\n", - " P = profiles['pressure'][i]\n", + "profiles[\"C2H6_production_rates\"] = []\n", + "for i in range(len(profiles[\"time\"])):\n", + " X = profiles[\"mole_fractions\"][i]\n", + " t = profiles[\"time\"][i]\n", + " T = profiles[\"temperature\"][i]\n", + " P = profiles[\"pressure\"][i]\n", " gas.TPX = (T, P, X)\n", - " C2H6_production_rates = (gas.net_rates_of_progress * # [kmol/m^3/s]\n", - " C2H6_stoichiometry *\n", - " gas.volume_mass # Specific volume [m^3/kg].\n", - " ) # overall, mol/s/g (g total in reactor, same basis as N_atoms_in_fuel)\n", - " \n", - " profiles['C2H6_production_rates'].append(C2H6_production_rates[C2H6_reaction_indices])\n" + " C2H6_production_rates = (\n", + " gas.net_rates_of_progress\n", + " * C2H6_stoichiometry # [kmol/m^3/s]\n", + " * gas.volume_mass # Specific volume [m^3/kg].\n", + " ) # overall, mol/s/g (g total in reactor, same basis as N_atoms_in_fuel)\n", + "\n", + " profiles[\"C2H6_production_rates\"].append(\n", + " C2H6_production_rates[C2H6_reaction_indices]\n", + " )" ] }, { @@ -274,39 +281,48 @@ } ], "source": [ - "@interact(annotation_cutoff=widgets.FloatSlider(value=1e-2,min=1e-2,max=4,steps=10),\n", - " profiles=widgets.fixed(profiles))\n", - "def plot_instantaneous_fluxes(profiles,annotation_cutoff):\n", + "@interact(\n", + " annotation_cutoff=widgets.FloatSlider(value=1e-2, min=1e-2, max=4, steps=10),\n", + " profiles=widgets.fixed(profiles),\n", + ")\n", + "def plot_instantaneous_fluxes(profiles, annotation_cutoff):\n", " profiles = profiles\n", - " fig = plt.figure(figsize=(6,6),dpi=100)\n", - " plt.plot(\n", - " profiles['time'],\n", - " np.array(profiles['C2H6_production_rates']))\n", + " fig = plt.figure(figsize=(6, 6), dpi=100)\n", + " plt.plot(profiles[\"time\"], np.array(profiles[\"C2H6_production_rates\"]))\n", "\n", - " for i, C2H6_production_rate in enumerate(np.array(profiles['C2H6_production_rates']).T):\n", + " for i, C2H6_production_rate in enumerate(\n", + " np.array(profiles[\"C2H6_production_rates\"]).T\n", + " ):\n", " peak_index = abs(C2H6_production_rate).argmax()\n", - " peak_time = profiles['time'][peak_index]\n", + " peak_time = profiles[\"time\"][peak_index]\n", " peak_C2H6_production = C2H6_production_rate[peak_index]\n", " reaction_string = gas.reaction_equations(C2H6_reaction_indices)[i]\n", "\n", - " if abs(peak_C2H6_production)>annotation_cutoff:\n", - " plt.annotate((reaction_string).replace('2','$_2$').replace('<=>','='),\n", - " xy=(peak_time, peak_C2H6_production),\n", - " xytext=(peak_time*2, (peak_C2H6_production +\n", - " 0.003*(peak_C2H6_production/abs(peak_C2H6_production)) * \\\n", - " (abs(peak_C2H6_production)>0.005) * \\\n", - " (peak_C2H6_production<0.06) )\n", - " ),\n", - " arrowprops=dict(arrowstyle=\"->\",\n", - " color='black',\n", - " relpos=(0,0.6),\n", - " linewidth=2,),\n", - " horizontalalignment='left',\n", - " )\n", + " if abs(peak_C2H6_production) > annotation_cutoff:\n", + " plt.annotate(\n", + " (reaction_string).replace(\"2\", \"$_2$\").replace(\"<=>\", \"=\"),\n", + " xy=(peak_time, peak_C2H6_production),\n", + " xytext=(\n", + " peak_time * 2,\n", + " (\n", + " peak_C2H6_production\n", + " + 0.003\n", + " * (peak_C2H6_production / abs(peak_C2H6_production))\n", + " * (abs(peak_C2H6_production) > 0.005)\n", + " * (peak_C2H6_production < 0.06)\n", + " ),\n", + " ),\n", + " arrowprops=dict(\n", + " arrowstyle=\"->\",\n", + " color=\"black\",\n", + " relpos=(0, 0.6),\n", + " linewidth=2,\n", + " ),\n", + " horizontalalignment=\"left\",\n", + " )\n", "\n", - "\n", - " plt.xlabel('Time (s)',fontsize=16)\n", - " plt.ylabel('Net rates of C2H6 production',fontsize=16)\n", + " plt.xlabel(\"Time (s)\", fontsize=16)\n", + " plt.ylabel(\"Net rates of C2H6 production\", fontsize=16)\n", " plt.tight_layout()\n", " plt.show()" ] @@ -327,10 +343,13 @@ "outputs": [], "source": [ "from scipy import integrate\n", - "integrated_fluxes = integrate.cumtrapz(np.array(profiles['C2H6_production_rates']), \n", - " np.array(profiles['time']), \n", - " axis=0,\n", - " initial=0)" + "\n", + "integrated_fluxes = integrate.cumtrapz(\n", + " np.array(profiles[\"C2H6_production_rates\"]),\n", + " np.array(profiles[\"time\"]),\n", + " axis=0,\n", + " initial=0,\n", + ")" ] }, { @@ -362,27 +381,29 @@ } ], "source": [ - "@interact(annotation_cutoff=widgets.FloatLogSlider(value=1e-5,min=-5,max=-4,base=10, step=0.1),\n", - " profiles=widgets.fixed(profiles),\n", - " integrated_fluxes=widgets.fixed(integrated_fluxes))\n", - "def plot_integrated_fluxes(profiles,integrated_fluxes,annotation_cutoff):\n", + "@interact(\n", + " annotation_cutoff=widgets.FloatLogSlider(\n", + " value=1e-5, min=-5, max=-4, base=10, step=0.1\n", + " ),\n", + " profiles=widgets.fixed(profiles),\n", + " integrated_fluxes=widgets.fixed(integrated_fluxes),\n", + ")\n", + "def plot_integrated_fluxes(profiles, integrated_fluxes, annotation_cutoff):\n", " profiles = profiles\n", " integrated_fluxes = integrated_fluxes\n", - " fig = plt.figure(figsize=(6,6),dpi=100)\n", - " plt.plot(\n", - " profiles['time'],\n", - " integrated_fluxes)\n", - " final_time = profiles['time'][-1]\n", + " fig = plt.figure(figsize=(6, 6), dpi=100)\n", + " plt.plot(profiles[\"time\"], integrated_fluxes)\n", + " final_time = profiles[\"time\"][-1]\n", " for i, C2H6_production in enumerate(integrated_fluxes.T):\n", " total_C2H6_production = C2H6_production[-1]\n", " reaction_string = gas.reaction_equations(C2H6_reaction_indices)[i]\n", "\n", - " if abs(total_C2H6_production)>annotation_cutoff:\n", - " plt.text(final_time*1.06, total_C2H6_production, reaction_string)\n", + " if abs(total_C2H6_production) > annotation_cutoff:\n", + " plt.text(final_time * 1.06, total_C2H6_production, reaction_string)\n", "\n", - " plt.xlabel('Time (s)',fontsize=16)\n", - " plt.ylabel('Integrated net rate of progress',fontsize=16)\n", - " plt.title(\"Cumulative C2H6 formation\",fontsize=16)\n", + " plt.xlabel(\"Time (s)\", fontsize=16)\n", + " plt.ylabel(\"Integrated net rate of progress\", fontsize=16)\n", + " plt.title(\"Cumulative C2H6 formation\", fontsize=16)\n", " plt.tight_layout()\n", " plt.show()" ] diff --git a/reactors/stirred_reactor.ipynb b/reactors/stirred_reactor.ipynb index b6ffaaf..bfb328e 100644 --- a/reactors/stirred_reactor.ipynb +++ b/reactors/stirred_reactor.ipynb @@ -59,13 +59,13 @@ "%matplotlib widget\n", "import matplotlib.pyplot as plt\n", "\n", - "plt.style.use('ggplot')\n", - "plt.style.use('seaborn-pastel')\n", + "plt.style.use(\"ggplot\")\n", + "plt.style.use(\"seaborn-pastel\")\n", "\n", - "plt.rcParams['axes.labelsize'] = 18\n", - "plt.rcParams['xtick.labelsize'] = 14\n", - "plt.rcParams['ytick.labelsize'] = 14\n", - "plt.rcParams['figure.autolayout'] = True" + "plt.rcParams[\"axes.labelsize\"] = 18\n", + "plt.rcParams[\"xtick.labelsize\"] = 14\n", + "plt.rcParams[\"ytick.labelsize\"] = 14\n", + "plt.rcParams[\"figure.autolayout\"] = True" ] }, { @@ -100,7 +100,7 @@ } ], "source": [ - "gas = ct.Solution('../data/galway.yaml')" + "gas = ct.Solution(\"../data/galway.yaml\")" ] }, { @@ -119,18 +119,18 @@ "source": [ "# Inlet gas conditions\n", "reactorTemperature = 925 # Kelvin\n", - "reactorPressure = 1.046138*ct.one_atm # in atm. This equals 1.06 bars\n", - "inlet_concentrations = {'NC7H16': 0.005, 'O2': 0.0275, 'HE': 0.9675}\n", - "gas.TPX = reactorTemperature, reactorPressure, inlet_concentrations \n", + "reactorPressure = 1.046138 * ct.one_atm # in atm. This equals 1.06 bars\n", + "inlet_concentrations = {\"NC7H16\": 0.005, \"O2\": 0.0275, \"HE\": 0.9675}\n", + "gas.TPX = reactorTemperature, reactorPressure, inlet_concentrations\n", "\n", "# Reactor parameters\n", "residenceTime = 2 # s\n", - "reactorVolume = 30.5*(1e-2)**3 # m3\n", + "reactorVolume = 30.5 * (1e-2) ** 3 # m3\n", "\n", "# Instrument parameters\n", "\n", - "# This is the \"conductance\" of the pressure valve and will determine its efficiency in \n", - "# holding the reactor pressure to the desired conditions. \n", + "# This is the \"conductance\" of the pressure valve and will determine its efficiency in\n", + "# holding the reactor pressure to the desired conditions.\n", "pressureValveCoefficient = 0.01\n", "\n", "# This parameter will allow you to decide if the valve's conductance is acceptable. If there\n", @@ -182,15 +182,17 @@ "fuelAirMixtureTank = ct.Reservoir(gas)\n", "exhaust = ct.Reservoir(gas)\n", "\n", - "stirredReactor = ct.IdealGasReactor(gas, energy='off', volume=reactorVolume)\n", + "stirredReactor = ct.IdealGasReactor(gas, energy=\"off\", volume=reactorVolume)\n", "\n", - "massFlowController = ct.MassFlowController(upstream=fuelAirMixtureTank,\n", - " downstream=stirredReactor,\n", - " mdot=stirredReactor.mass/residenceTime)\n", + "massFlowController = ct.MassFlowController(\n", + " upstream=fuelAirMixtureTank,\n", + " downstream=stirredReactor,\n", + " mdot=stirredReactor.mass / residenceTime,\n", + ")\n", "\n", - "pressureRegulator = ct.PressureController(upstream=stirredReactor,\n", - " downstream=exhaust,\n", - " master=massFlowController)\n", + "pressureRegulator = ct.PressureController(\n", + " upstream=stirredReactor, downstream=exhaust, master=massFlowController\n", + ")\n", "\n", "reactorNetwork = ct.ReactorNet([stirredReactor])" ] @@ -222,16 +224,16 @@ "\n", " # We will store only every 10th value. Remember, we have 1200+ species, so there will be\n", " # 1200 columns for us to work with\n", - " if(counter%10 == 0):\n", + " if counter % 10 == 0:\n", " # Extract the state of the reactor\n", " timeHistory.append(stirredReactor.thermo.state, t=t)\n", - " \n", + "\n", " counter += 1\n", "\n", "# Stop the stopwatch\n", "toc = time.time()\n", "\n", - "print('Simulation Took {toc-tic:3.2f}s to compute, with {counter} steps')" + "print(\"Simulation Took {toc-tic:3.2f}s to compute, with {counter} steps\")" ] }, { @@ -281,9 +283,9 @@ ], "source": [ "plt.figure()\n", - "plt.semilogx(timeHistory.t, timeHistory('CO').X,'-o')\n", - "plt.xlabel('Time (s)')\n", - "plt.ylabel(r'Mole Fraction : $X_{CO}$');" + "plt.semilogx(timeHistory.t, timeHistory(\"CO\").X, \"-o\")\n", + "plt.xlabel(\"Time (s)\")\n", + "plt.ylabel(r\"Mole Fraction : $X_{CO}$\");" ] }, { @@ -389,7 +391,7 @@ } ], "source": [ - "expData = pd.read_csv('../data/zhangExpData.csv')\n", + "expData = pd.read_csv(\"../data/zhangExpData.csv\")\n", "expData.head()" ] }, @@ -467,25 +469,27 @@ "\n", " # We will use concentrations from the previous iteration to speed up convergence\n", " gas.TPX = reactorTemperature, reactorPressure, concentrations\n", - " \n", - " stirredReactor = ct.IdealGasReactor(gas, energy='off', volume=reactorVolume)\n", - " massFlowController = ct.MassFlowController(upstream=fuelAirMixtureTank,\n", - " downstream=stirredReactor,\n", - " mdot=stirredReactor.mass/residenceTime)\n", - " pressureRegulator = ct.PressureController(upstream=stirredReactor, \n", - " downstream=exhaust, \n", - " master=massFlowController)\n", + "\n", + " stirredReactor = ct.IdealGasReactor(gas, energy=\"off\", volume=reactorVolume)\n", + " massFlowController = ct.MassFlowController(\n", + " upstream=fuelAirMixtureTank,\n", + " downstream=stirredReactor,\n", + " mdot=stirredReactor.mass / residenceTime,\n", + " )\n", + " pressureRegulator = ct.PressureController(\n", + " upstream=stirredReactor, downstream=exhaust, master=massFlowController\n", + " )\n", " reactorNetwork = ct.ReactorNet([stirredReactor])\n", - " \n", + "\n", " # Re-run the isothermal simulations\n", " tic = time.time()\n", " reactorNetwork.advance(maxSimulationTime)\n", "\n", " toc = time.time()\n", - " print(f'Simulation at T={reactorTemperature}K took {toc-tic:3.2f}s to compute')\n", - " \n", + " print(f\"Simulation at T={reactorTemperature}K took {toc-tic:3.2f}s to compute\")\n", + "\n", " concentrations = stirredReactor.thermo.X\n", - " \n", + "\n", " tempDependence.append(stirredReactor.thermo.state)" ] }, @@ -529,16 +533,16 @@ ], "source": [ "plt.figure()\n", - "plt.plot(tempDependence.T, tempDependence('NC7H16').X, 'r-', label='$nC_{7}H_{16}$')\n", - "plt.plot(tempDependence.T, tempDependence('CO').X, 'b-', label='CO')\n", - "plt.plot(tempDependence.T, tempDependence('O2').X, 'k-', label='O$_{2}$')\n", + "plt.plot(tempDependence.T, tempDependence(\"NC7H16\").X, \"r-\", label=\"$nC_{7}H_{16}$\")\n", + "plt.plot(tempDependence.T, tempDependence(\"CO\").X, \"b-\", label=\"CO\")\n", + "plt.plot(tempDependence.T, tempDependence(\"O2\").X, \"k-\", label=\"O$_{2}$\")\n", "\n", - "plt.plot(expData['T'], expData['NC7H16'],'ro', label=r'$nC_{7}H_{16} (exp)$')\n", - "plt.plot(expData['T'], expData['CO'],'b^', label='CO (exp)')\n", - "plt.plot(expData['T'], expData['O2'],'ks', label='O$_{2}$ (exp)')\n", + "plt.plot(expData[\"T\"], expData[\"NC7H16\"], \"ro\", label=r\"$nC_{7}H_{16} (exp)$\")\n", + "plt.plot(expData[\"T\"], expData[\"CO\"], \"b^\", label=\"CO (exp)\")\n", + "plt.plot(expData[\"T\"], expData[\"O2\"], \"ks\", label=\"O$_{2}$ (exp)\")\n", "\n", - "plt.xlabel('Temperature (K)')\n", - "plt.ylabel(r'Mole Fractions')\n", + "plt.xlabel(\"Temperature (K)\")\n", + "plt.ylabel(r\"Mole Fractions\")\n", "\n", "plt.xlim([650, 1100])\n", "plt.legend(loc=1);" diff --git a/thermo/flame_temperature.ipynb b/thermo/flame_temperature.ipynb index e7576ce..4b04c4e 100644 --- a/thermo/flame_temperature.ipynb +++ b/thermo/flame_temperature.ipynb @@ -44,19 +44,19 @@ "outputs": [], "source": [ "# Get all of the Species objects defined in the GRI 3.0 mechanism\n", - "species = {S.name: S for S in ct.Species.listFromFile('gri30.yaml')}\n", + "species = {S.name: S for S in ct.Species.listFromFile(\"gri30.yaml\")}\n", "\n", "# Create an IdealGas object with species representing complete combustion\n", - "complete_species = [species[S] for S in ('CH4','O2','N2','CO2','H2O')]\n", - "gas1 = ct.Solution(thermo='IdealGas', species=complete_species)\n", + "complete_species = [species[S] for S in (\"CH4\", \"O2\", \"N2\", \"CO2\", \"H2O\")]\n", + "gas1 = ct.Solution(thermo=\"IdealGas\", species=complete_species)\n", "\n", "phi = np.linspace(0.5, 2.0, 100)\n", "T_complete = np.zeros(phi.shape)\n", "for i in range(len(phi)):\n", " gas1.TP = 300, ct.one_atm\n", - " gas1.set_equivalence_ratio(phi[i], 'CH4', 'O2:1, N2:3.76')\n", - " gas1.equilibrate('HP')\n", - " T_complete[i] = gas1.T " + " gas1.set_equivalence_ratio(phi[i], \"CH4\", \"O2:1, N2:3.76\")\n", + " gas1.equilibrate(\"HP\")\n", + " T_complete[i] = gas1.T" ] }, { @@ -79,12 +79,12 @@ "outputs": [], "source": [ "# Create an IdealGas object including incomplete combustion species\n", - "gas2 = ct.Solution(thermo='IdealGas', species=species.values())\n", + "gas2 = ct.Solution(thermo=\"IdealGas\", species=species.values())\n", "T_incomplete = np.zeros(phi.shape)\n", "for i in range(len(phi)):\n", " gas2.TP = 300, ct.one_atm\n", - " gas2.set_equivalence_ratio(phi[i], 'CH4', 'O2:1, N2:3.76')\n", - " gas2.equilibrate('HP')\n", + " gas2.set_equivalence_ratio(phi[i], \"CH4\", \"O2:1, N2:3.76\")\n", + " gas2.equilibrate(\"HP\")\n", " T_incomplete[i] = gas2.T" ] }, @@ -887,11 +887,11 @@ } ], "source": [ - "plt.plot(phi, T_complete, label='complete combustion', lw=2)\n", - "plt.plot(phi, T_incomplete, label='incomplete combustion', lw=2)\n", + "plt.plot(phi, T_complete, label=\"complete combustion\", lw=2)\n", + "plt.plot(phi, T_incomplete, label=\"incomplete combustion\", lw=2)\n", "plt.grid(True)\n", - "plt.xlabel('Equivalence ratio, $\\phi$')\n", - "plt.ylabel('Temperature [K]');" + "plt.xlabel(\"Equivalence ratio, $\\phi$\")\n", + "plt.ylabel(\"Temperature [K]\");" ] } ], diff --git a/thermo/heating_value.ipynb b/thermo/heating_value.ipynb index c996410..d25a053 100644 --- a/thermo/heating_value.ipynb +++ b/thermo/heating_value.ipynb @@ -35,18 +35,19 @@ ], "source": [ "import cantera as ct\n", - "gas = ct.Solution('gri30.yaml')\n", + "\n", + "gas = ct.Solution(\"gri30.yaml\")\n", "\n", "# Set reactants state\n", - "gas.TPX = 298, 101325, 'CH4:1, O2:2'\n", + "gas.TPX = 298, 101325, \"CH4:1, O2:2\"\n", "h1 = gas.enthalpy_mass\n", - "Y_CH4 = gas['CH4'].Y[0] # returns an array, of which we only want the first element\n", + "Y_CH4 = gas[\"CH4\"].Y[0] # returns an array, of which we only want the first element\n", "\n", "# set state to complete combustion products without changing T or P\n", - "gas.TPX = None, None, 'CO2:1, H2O:2' \n", + "gas.TPX = None, None, \"CO2:1, H2O:2\"\n", "h2 = gas.enthalpy_mass\n", "\n", - "print('LHV = {:.3f} MJ/kg'.format(-(h2-h1)/Y_CH4/1e6))" + "print(\"LHV = {:.3f} MJ/kg\".format(-(h2 - h1) / Y_CH4 / 1e6))" ] }, { @@ -81,8 +82,10 @@ "h_gas = water.h\n", "\n", "# Calculate higher heating value\n", - "Y_H2O = gas['H2O'].Y[0]\n", - "print('HHV = {:.3f} MJ/kg'.format(-(h2-h1 + (h_liquid-h_gas) * Y_H2O)/Y_CH4/1e6))" + "Y_H2O = gas[\"H2O\"].Y[0]\n", + "print(\n", + " \"HHV = {:.3f} MJ/kg\".format(-(h2 - h1 + (h_liquid - h_gas) * Y_H2O) / Y_CH4 / 1e6)\n", + ")" ] }, { @@ -114,29 +117,32 @@ ], "source": [ "def heating_value(fuel):\n", - " \"\"\" Returns the LHV and HHV for the specified fuel \"\"\"\n", + " \"\"\"Returns the LHV and HHV for the specified fuel\"\"\"\n", " gas.TP = 298, ct.one_atm\n", - " gas.set_equivalence_ratio(1.0, fuel, 'O2:1.0')\n", + " gas.set_equivalence_ratio(1.0, fuel, \"O2:1.0\")\n", " h1 = gas.enthalpy_mass\n", " Y_fuel = gas[fuel].Y[0]\n", "\n", " # complete combustion products\n", - " Y_products = {'CO2': gas.elemental_mole_fraction('C'),\n", - " 'H2O': 0.5 * gas.elemental_mole_fraction('H'),\n", - " 'N2': 0.5 * gas.elemental_mole_fraction('N')}\n", + " Y_products = {\n", + " \"CO2\": gas.elemental_mole_fraction(\"C\"),\n", + " \"H2O\": 0.5 * gas.elemental_mole_fraction(\"H\"),\n", + " \"N2\": 0.5 * gas.elemental_mole_fraction(\"N\"),\n", + " }\n", "\n", " gas.TPX = None, None, Y_products\n", - " Y_H2O = gas['H2O'].Y[0]\n", + " Y_H2O = gas[\"H2O\"].Y[0]\n", " h2 = gas.enthalpy_mass\n", - " LHV = -(h2-h1)/Y_fuel\n", - " HHV = -(h2-h1 + (h_liquid-h_gas) * Y_H2O)/Y_fuel\n", + " LHV = -(h2 - h1) / Y_fuel\n", + " HHV = -(h2 - h1 + (h_liquid - h_gas) * Y_H2O) / Y_fuel\n", " return LHV, HHV\n", "\n", - "fuels = ['H2', 'CH4', 'C2H6', 'C3H8', 'NH3', 'CH3OH']\n", - "print('fuel LHV (MJ/kg) HHV (MJ/kg)')\n", + "\n", + "fuels = [\"H2\", \"CH4\", \"C2H6\", \"C3H8\", \"NH3\", \"CH3OH\"]\n", + "print(\"fuel LHV (MJ/kg) HHV (MJ/kg)\")\n", "for fuel in fuels:\n", " LHV, HHV = heating_value(fuel)\n", - " print('{:8s} {:7.3f} {:7.3f}'.format(fuel, LHV/1e6, HHV/1e6))" + " print(\"{:8s} {:7.3f} {:7.3f}\".format(fuel, LHV / 1e6, HHV / 1e6))" ] } ], From f1ffefc61b6d89c47a675376bab0ede151961391 Mon Sep 17 00:00:00 2001 From: Bryan Weber Date: Sun, 20 Feb 2022 20:41:36 +0000 Subject: [PATCH 2/3] PEP8 the variable names --- electrochemistry/lithium_ion_battery.ipynb | 1643 +---- ...lame_speed_with_convergence_analysis.ipynb | 546 +- ...lame_speed_with_sensitivity_analysis.ipynb | 2485 +------ flames/twin_premixed_flame_axisymmetric.ipynb | 2621 +------- python_tutorial.ipynb | 939 +-- reactors/1D_pfr_surfchem.ipynb | 16 +- reactors/NonIdealShockTube.ipynb | 2026 ------ .../batch_reactor_ignition_delay_NTC.ipynb | 1962 +----- reactors/interactive_path_diagram.ipynb | 25 +- reactors/nonideal_shock_tube.ipynb | 360 + reactors/stirred_reactor.ipynb | 236 +- thermo/equations_of_state.ipynb | 5858 +---------------- thermo/flame_temperature.ipynb | 808 +-- thermo/heating_value.ipynb | 55 +- 14 files changed, 1607 insertions(+), 17973 deletions(-) delete mode 100644 reactors/NonIdealShockTube.ipynb create mode 100644 reactors/nonideal_shock_tube.ipynb diff --git a/electrochemistry/lithium_ion_battery.ipynb b/electrochemistry/lithium_ion_battery.ipynb index dec34cf..eaf5c21 100644 --- a/electrochemistry/lithium_ion_battery.ipynb +++ b/electrochemistry/lithium_ion_battery.ipynb @@ -108,14 +108,14 @@ "source": [ "import cantera as ct\n", "\n", - "print(\"Runnning Cantera version: \" + ct.__version__)" + "print(f\"Runnning Cantera version: {ct.__version__}\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": true + "tags": [] }, "outputs": [], "source": [ @@ -127,7 +127,7 @@ "import time\n", "\n", "# Plotting:\n", - "%matplotlib notebook\n", + "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams[\"axes.labelsize\"] = 16\n", @@ -149,7 +149,7 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "tags": [] }, "outputs": [], "source": [ @@ -187,7 +187,7 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": true + "tags": [] }, "outputs": [], "source": [ @@ -208,8 +208,9 @@ "\n", "F = ct.faraday\n", "\n", - "S_ca = 1.1167\n", "# [m^2] Cathode total active material surface area\n", + "S_ca = 1.1167\n", + "\n", "S_an = 0.7824; # [m^2] Anode total active material surface area" ] }, @@ -224,7 +225,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": true + "tags": [] }, "outputs": [], "source": [ @@ -242,16 +243,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { - "collapsed": true + "tags": [] }, "outputs": [], "source": [ "def anode_curr(phi_l, I_app, phi_s, X_Li_an):\n", "\n", " # Set the active material mole fraction\n", - " anode.X = \"Li[anode]:\" + str(X_Li_an) + \", V[anode]:\" + str(1 - X_Li_an)\n", + " anode.X = f\"Li[anode]:{X_Li_an}, V[anode]:{1 - X_Li_an}\"\n", "\n", " # Set the electrode and electrolyte potential\n", " elde.electric_potential = phi_s\n", @@ -260,8 +261,8 @@ " # Get the net product rate of electrons in the anode (per m2^ interface)\n", " r_elec = anode_interface.get_net_production_rates(elde)\n", "\n", - " anCurr = r_elec * ct.faraday * S_an\n", - " diff = I_app + anCurr\n", + " anode_current = r_elec * ct.faraday * S_an\n", + " diff = I_app + anode_current\n", "\n", " return diff\n", "\n", @@ -269,7 +270,7 @@ "def cathode_curr(phi_s, I_app, phi_l, X_Li_ca):\n", "\n", " # Set the active material mole fractions\n", - " cathode.X = \"Li[cathode]:\" + str(X_Li_ca) + \", V[cathode]:\" + str(1 - X_Li_ca)\n", + " cathode.X = f\"Li[cathode]:{X_Li_ca}, V[cathode]:{1 - X_Li_ca}\"\n", "\n", " # Set the electrode and electrolyte potential\n", " elde.electric_potential = phi_s\n", @@ -278,8 +279,8 @@ " # Get the net product rate of electrons in the cathode (per m2^ interface)\n", " r_elec = cathode_interface.get_net_production_rates(elde)\n", "\n", - " caCurr = r_elec * ct.faraday * S_an\n", - " diff = I_app - caCurr\n", + " cathode_current = r_elec * ct.faraday * S_an\n", + " diff = I_app - cathode_current\n", "\n", " return diff" ] @@ -293,14 +294,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "49 cell voltages calculated in 0.61 seconds.\n" + "49 cell voltages calculated in 3.82e-02 seconds.\n" ] } ], @@ -330,7 +331,7 @@ "\n", "# Toc\n", "t1 = time.time()\n", - "print(\"{:d} cell voltages calculated in {:3.2f} seconds.\".format(i, t1 - t0))" + "print(f\"{i:d} cell voltages calculated in {t1 - t0:3.2e} seconds.\")" ] }, { @@ -342,796 +343,19 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. 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IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], + "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], @@ -2029,18 +472,16 @@ " markeredgecolor=\"r\",\n", ")\n", "plt.ylim([2.5, 4.3])\n", - "plt.xlabel(\"Li Fraction in Cathode (%)\", fontname=\"Times New Roman\", fontsize=18)\n", - "plt.ylabel(\"Open Circuit Potential (V)\", fontname=\"Times New Roman\", fontsize=18)\n", + "plt.xlabel(\"Li Fraction in Cathode (%)\", fontsize=18)\n", + "plt.ylabel(\"Open Circuit Potential (V)\", fontsize=18)\n", "plt.legend([\"Thermodynamic\", \"Kinetic\"])\n", "\n", "ax = plt.gca()\n", "\n", "for tick in ax.xaxis.get_major_ticks():\n", " tick.label1.set_fontsize(14)\n", - " tick.label1.set_fontname(\"Times New Roman\")\n", "for tick in ax.yaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(14)\n", - " tick.label1.set_fontname(\"Times New Roman\")" + " tick.label1.set_fontsize(14)" ] }, { @@ -2057,7 +498,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2071,9 +512,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.9.10" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/flames/flame_speed_with_convergence_analysis.ipynb b/flames/flame_speed_with_convergence_analysis.ipynb index 82ea973..0bfa7cb 100644 --- a/flames/flame_speed_with_convergence_analysis.ipynb +++ b/flames/flame_speed_with_convergence_analysis.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -66,7 +66,7 @@ "import scipy\n", "import scipy.optimize\n", "\n", - "print(\"Running Cantera Version: \" + str(ct.__version__))" + "print(f\"Running Cantera Version: {ct.__version__}\")" ] }, { @@ -122,7 +122,7 @@ " It seems, from experience, that error scales roughly with\n", " 1/grid_size, so we assume that form.\n", " \"\"\"\n", - " return true_speed + error * np.array(grid_size) ** -1.0\n", + " return true_speed + error / np.array(grid_size)\n", "\n", " # Fit the chosen form of speed_from_grid_size, to the last four\n", " # speed and grid size values.\n", @@ -131,30 +131,24 @@ " # How bad the fit was gives you some error, `percent_error_in_true_speed`.\n", " perr = np.sqrt(np.diag(pcov))\n", " true_speed_estimate = popt[0]\n", - " percent_error_in_true_speed = 100.0 * perr[0] / popt[0]\n", + " percent_error_in_true_speed = perr[0] / popt[0]\n", " print(\n", - " \"Fitted true_speed is {:.4f} ± {:.4f} cm/s ({:.1f}%)\".format(\n", - " popt[0] * 100, perr[0] * 100, percent_error_in_true_speed\n", - " )\n", + " f\"Fitted true_speed is {popt[0] * 100:.4f} ± {perr[0] * 100:.4f} cm/s \"\n", + " f\"({percent_error_in_true_speed:.1%})\"\n", " )\n", - " # print \"convergerce rate wrt grid size is {:.1f} ± {:.1f}\".format(popt[2], perr[2])\n", "\n", " # How far your extrapolated infinite grid value is from your extrapolated\n", " # (or interpolated) final grid value, gives you some other error, `estimated_percent_error`\n", " estimated_percent_error = (\n", - " 100.0\n", - " * (speed_from_grid_size(grids[-1], *popt) - true_speed_estimate)\n", - " / true_speed_estimate\n", - " )\n", - " print(\n", - " \"Estimated error in final calculation {:.1f}%\".format(estimated_percent_error)\n", - " )\n", + " speed_from_grid_size(grids[-1], *popt) - true_speed_estimate\n", + " ) / true_speed_estimate\n", + " print(f\"Estimated error in final calculation {estimated_percent_error:.1%}\")\n", "\n", " # The total estimated error is the sum of these two errors.\n", " total_percent_error_estimate = abs(percent_error_in_true_speed) + abs(\n", " estimated_percent_error\n", " )\n", - " print(\"Estimated total error {:.1f}%\".format(total_percent_error_estimate))\n", + " print(f\"Estimated total error {total_percent_error_estimate:.1%}\")\n", "\n", " if plot:\n", " plt.semilogx(grids, speeds, \"o-\")\n", @@ -174,14 +168,14 @@ " *plt.xlim(),\n", " colors=\"r\",\n", " linestyles=\"dashed\",\n", - " alpha=0.3\n", + " alpha=0.3,\n", " )\n", " plt.hlines(\n", " true_speed_estimate - perr[0],\n", " *plt.xlim(),\n", " colors=\"r\",\n", " linestyles=\"dashed\",\n", - " alpha=0.3\n", + " alpha=0.3,\n", " )\n", " plt.fill_between(\n", " plt.xlim(),\n", @@ -212,7 +206,7 @@ " )\n", "\n", " plt.annotate(\n", - " \"{:.1f}%\".format(abs(estimated_percent_error)),\n", + " f\"{abs(estimated_percent_error):.1%}\",\n", " xy=(grids[-1], speed_from_grid_size(grids[-1], *popt)),\n", " xycoords=\"data\",\n", " xytext=(5, 15 * above),\n", @@ -237,7 +231,7 @@ " ),\n", " )\n", " plt.annotate(\n", - " \"{:.1f}%\".format(abs(percent_error_in_true_speed)),\n", + " f\"{abs(percent_error_in_true_speed):.1%}\",\n", " xy=(grids[-1] * 4, true_speed_estimate - (above * perr[0])),\n", " xycoords=\"data\",\n", " xytext=(5, -15 * above),\n", @@ -278,8 +272,8 @@ " grid = len(flame.grid)\n", " speeds.append(speed)\n", " grids.append(grid)\n", - " print(\"Iteration {}\".format(len(grids)))\n", - " print(\"Current flame speed is is {:.4f} cm/s\".format(speed * 100.0))\n", + " print(f\"Iteration {len(grids)}\")\n", + " print(f\"Current flame speed is is {speed * 100:.4f} cm/s\")\n", " if len(grids) < 5:\n", " return 1.0 #\n", " try:\n", @@ -528,7 +522,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -815,7 +809,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" + "print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")" ] }, { @@ -883,7 +877,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -895,27 +889,24 @@ " and compare these with our estimated errors.\n", " This will show if our estimates are reasonable, or conservative, or too optimistic.\n", " \"\"\"\n", - " true_speed_estimates = [None for i in speeds]\n", - " total_percent_error_estimates = [None for i in speeds]\n", - " actual_extrapolated_percent_errors = [None for i in speeds]\n", - " actual_raw_percent_errors = [None for i in speeds]\n", + " true_speed_estimates = np.full_like(speeds, np.NaN)\n", + " total_percent_error_estimates = np.full_like(speeds, np.NaN)\n", + " actual_extrapolated_percent_errors = np.full_like(speeds, np.NaN)\n", + " actual_raw_percent_errors = np.full_like(speeds, np.NaN)\n", " for i in range(3, len(grids)):\n", " print(grids[: i + 1])\n", " true_speed_estimate, total_percent_error_estimate = extrapolate_uncertainty(\n", " grids[: i + 1], speeds[: i + 1], plot=False\n", " )\n", " actual_extrapolated_percent_error = (\n", - " 100.0 * abs(true_speed_estimate - true_speed) / true_speed\n", + " abs(true_speed_estimate - true_speed) / true_speed\n", " )\n", - " actual_raw_percent_error = 100.0 * abs(speeds[i] - true_speed) / true_speed\n", + " actual_raw_percent_error = abs(speeds[i] - true_speed) / true_speed\n", " print(\n", - " \"Actual extrapolated error (with hindsight) {:.1f}%\".format(\n", - " actual_extrapolated_percent_error\n", - " )\n", - " )\n", - " print(\n", - " \"Actual raw error (with hindsight) {:.1f}%\".format(actual_raw_percent_error)\n", + " \"Actual extrapolated error (with hindsight) \"\n", + " f\"{actual_extrapolated_percent_error:.1%}\"\n", " )\n", + " print(f\"Actual raw error (with hindsight) {actual_raw_percent_error:.1%}\")\n", "\n", " true_speed_estimates[i] = true_speed_estimate\n", " total_percent_error_estimates[i] = total_percent_error_estimate\n", @@ -923,11 +914,16 @@ " actual_raw_percent_errors[i] = actual_raw_percent_error\n", " print()\n", "\n", - " plt.loglog(grids, actual_raw_percent_errors, \"o-\", label=\"raw error\")\n", + " plt.loglog(grids, actual_raw_percent_errors * 100, \"o-\", label=\"raw error\")\n", + " plt.loglog(\n", + " grids,\n", + " actual_extrapolated_percent_errors * 100,\n", + " \"o-\",\n", + " label=\"extrapolated error\",\n", + " )\n", " plt.loglog(\n", - " grids, actual_extrapolated_percent_errors, \"o-\", label=\"extrapolated error\"\n", + " grids, total_percent_error_estimates * 100, \"o-\", label=\"estimated error\"\n", " )\n", - " plt.loglog(grids, total_percent_error_estimates, \"o-\", label=\"estimated error\")\n", " plt.ylabel(\"Error in flame speed (%)\")\n", " plt.xlabel(\"Grid size\")\n", " plt.legend()\n", @@ -938,9 +934,10 @@ "\n", " data = pd.DataFrame(\n", " data={\n", - " \"actual error in raw value\": actual_raw_percent_errors,\n", - " \"actual error in extrapolated value\": actual_extrapolated_percent_errors,\n", - " \"estimated error\": total_percent_error_estimates,\n", + " \"actual error in raw value\": actual_raw_percent_errors * 100,\n", + " \"actual error in extrapolated value\": actual_extrapolated_percent_errors\n", + " * 100,\n", + " \"estimated error\": total_percent_error_estimates * 100,\n", " },\n", " index=grids,\n", " )\n", @@ -949,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1030,14 +1027,12 @@ }, { "data": { - "image/png": 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\n", 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B73T9fv31V0WtViv/93//pxw4cEA5evSo8umnn1Z/D9asWaMAyrvvvqvEx8crH330keLr61vnKKq6ZGVlKTqdTundu7fSs2fPGs+VlZUp0dHRSkxMjPLrr78qJ0+eVLZv3668/PLLyg8//FBvzZfqg6MoDXtP6/qeTpw4UQkPD1d+//13Zd++fcr111+vdOrUqcYovFOnTin79u1TPv744+o+Ivv27VNycnIuWtOlhISEKK6ursqzzz6rHDt2TPniiy8UZ2dn5T//+U+NferqfzV9+nQlNDRU2bp1qxIbG6vcdtttipubW60+OH9+7d13311jRFJr/N04R0ZRNZwEnHZIrVYrixYtuuR+dQUcRVGU33//XenZs6fi4OCgdO7cWfn222+V8PDwGgHn559/Vrp27aro9foGDxN3d3evNUz8z879ctc1nPWcTz/9tNYw9XP/NmzYUL3fiBEjag3DPHr0qHLNNdcojo6Oiqenp3LvvfcqxcXFNfYBLmv46Icffqj07dtXcXV1VZydnZWYmBhl+fLl1c/XN0y8a9euik6nUwICApR//etfdQ4Tf/TRRxVPT0/FxcVFmT17tlJSUlLjOAsWLFB69eqlODg4KB4eHsqAAQOU999/v95an332WaUhn3su9T7V93369NNPlcjISEWn0ymdO3euFbTP/Yz8+d+F36dz+1z4vWyIX375RRk0aJBiMBgUNzc3ZeTIkUpCQkL18y+99JISEBCgODs7K7fddpvy3nvvNTjgKIqi3HTTTQqg/Pe//631XHZ2tnLfffcpAQEB1d/Tm266Sdm7d2+9xwsJCalx4a7Ppd7Tur6nhYWFyt13360YjUbF0dFRue6662qFyFmzZtX5vbjwezpr1qzLnsbh3DDxO++8U3F1dVWMRqPy6KOP1ugwX1/AOXPmjHLDDTcorq6uSlBQkPL+++/X2cn4UgFHUVrf78Y58fHxCqBs3br1krW0dypFacTMbaJVCgoKYsKECcybN6/ZJ91ra841a2/atKnRt60aY+TIkURERLBgwQKb1WArCxcu5Mknn+TYsWMNuvUoms/w4cOJiopi/vz5DX5NaGgof/nLX3j66aebsbK2a/HixcyaNYuTJ0+2qtm6bUGubu3Q448/zsKFC3FwcJAe+Jdp1apVzJw506bhpr1btWoVr732moQbG8vLy+PYsWO8/PLLti6lXYiNjcXBwYE777yzwYNE2jtpwWmnysvLSU9Px9PTUzqptULtuQVHtG7SgnNlKioqSEtLw9vbW/5mN5AEHCGEEEK0OXKLSgghhBBtjgQcIYQQQrQ57Wom4z9P5HUp3t7eZGdnN1M1QgghROthr9fE+ibMlBYcIYQQQrQ5EnCEEEII0eZIwBFCCCFEm9Ou+uAIIYRo/RRFwWQyYbVaL7pKvGhaGRkZlJeX2+TciqKgVqsxGAwN/p63+YCze/du9uzZw5w5c2xdihBCiCZgMpnQ6XRotW3+EmZXtFpto1Zbb6zKykpMJhOOjo4N2r/N/3TExMQQExNj6zKEEEI0EavVKuGmHdJqtZfVgiR9cIQQQrQqcluq/bqc770EHCGEEEK0ORJwhBBCCNHmyE1MIYQQbdqOI9ks35RGblEFnq56bhoWyMAo7yY5tqIo1SN8WkJlZWWN/kd/ftzQ17UH7eurFUII0a7sOJLNkt9OUVFpBSC3qIIlv50CuOKQk5KSwh133MFVV13Fnj17WLhwIe+99x4HDhzAZDIxfvx4Hn30Ufbt28e8efNYsGABv/76Kw888ABHjhzBarUyatQotm3bVuO4OTk5PPHEE6SlpQHw/PPP079/f9544w0yMjJISUnB09OTsLCwGo+ffPJJ/v73v5Obm4unpydvvfUWgYGBPPzww3h4eHDo0CF69OjBs88+24h3svWRgNMIRTt3kffjSiy5eWg8jRgnTsB1QH9blyWEEO3Gsg3JpGaW1vt84pliKi1KjW0VlVYW/5rE5oN1r6sU5OvE1FEdL3rehIQE3nzzTV555RUAHn/8cYxGIxaLhalTp3L48GF69OjBoUOHANixYwddunThwIEDVFZW0qdPn1rH/Pe//80999zDgAEDSEtL4/bbb+ePP/4A4ODBg/zwww84Ojryxhtv1Hg8a9YsJk+ezK233spXX33FM888w8KFC6u+/sREli1bZtPh3bYiAecKFe3cRc4XS1EqzABYcvPI+WIpgIQcIYSwE38ON5fa3lBBQUH069ev+vHKlSv54osvsFgsZGRkcOLECbp160ZoaCgnTpxg//793HvvvWzfvh2LxcKAAQNqHXPTpk0cP368+nFxcTHFxcUAXHvttTXmf7nw8Z49e1iwYAEAt9xyCy+99FL1fjfccEO7DDcgAeeK5f24sjrcnKNUmMn7caUEHCGEaCGXaml58qMD5BZV1Nru6arnH1O7XvF5nZycqv8/OTmZ+fPns3r1ajw8PHj44YcxmUwADBw4kPXr16PVahk2bBgPP/wwVquVZ555ptYxrVYrK1asqHMiuwvPV9fjC104lPpi+7V1bX4U1e7du5k/f36TH9eSm1fvdnNmForSuE8HQgghGu+mYYHotTUvdXqtmpuGBTbZOYqKinB0dMTNzY2srCw2bNhQ/dzAgQNZsGAB/fr1w8vLi7y8POLj4+nSpUut44wYMYJFixZVPz53e+tSYmJi+PHHHwH4/vvv62wdao/afAtOc81krPE01htyUp99AY3RA0NkJI6dIzF0jkDr7S2TUwkhRAs715G4uUZRAURHR9O9e3dGjRpFx44d6d//fCt+nz59yM7OZtCgQQB069aNzMzMOq8HL774Iv/6178YM2YMlZWVDBw4kNdee+2S53/xxRf5+9//zocffljdyViASmlHTQ2nT5++rP29vb3Jzq67E9qf++AAqPQ63G8Yh8bBgOn4ccqOx2MtKgJAYzTi2DkCQ+dIDJGRaL29JPAIIcQVKC0tbde3XmxFq9VSWVlp0xrq+t4HBATUuW+bb8FpLuf62dQ3ispt+FAURcGcnoHp+AlMx09QGneE4h27gKoWIMfIyKrA0yUSnZeXzb4WIYQQoq2RFpyLuFgLzpWoCjzpmI6doOzECUzH47Ge7SGv9fTEcK6Fp3NndF6eTXZeIYRoS6QFxzakBUfUS6VSoe/QAX2HDriNHF4VeM6kV9/OKo2No3j7TgC0Xp5nw05VPx6tpwQeIYQQoqEk4NiQSqVCH9ABfUAH3EaOQLFaMZ9Jp+zcLa2DhyjetgMArZdX9e0sx8hItJ5GG1cvhBBC2C8JOHZEpVajDwxAHxiA+6izgef0marAc+IEpQcPUrxtOwBab28MnSNw7Ny5apSWUQKPEEIIcY4EHDumUqvRBwWiDwrE/eqRKFYrFadPYzoeX9XCs/8gxVvPBh4f76ph6V2qbmtpPTxsW7wQQghhQ9LJ+CKaupNxU1OsVirSTleN0jrXabmsDACtj09V2Ik8F3jcbVytEEI0jdbWyTglJYXdu3czadKkZj/X5MmTeeaZZ+jVq1e9+3z88cfccccddc6YXJ+tW7cyf/58Pvvss6Yo84pJJ+N2QqVW4xAchENwEO6jR50NPGlnh6XHU7JnH0WbtwKg9fWpvp1liJTAI4RoP3ZnxrI6aQN55QUYHdwZHzqKGN8eLXb+lJQUfvjhhzoDTmVlJVpty16KFyxYwC233HJZAacxLBZLjfWw/vy4Po19byTgtCFVgScYh+Bg3EdfXRV4UtOq5+Ep2bOXos1bAND5+mKobuGJQOsugUcI0fbszoxl2YnVmK1Vk7LmlRew7MRqgEaFnO+++46FCxdSUVFBnz59eOWVV4iNjeXRRx9l1apVWK1Wxo8fzwcffMDLL79MfHw811xzDVOmTMHd3Z1169ZRXl5OaWkpixYtYvbs2RQUFFBZWcljjz3GddddR0pKCtOnT6dPnz7ExcXRqVMn3nnnHRwdHdm0aRMvvvgiFouFXr168corr+Dg4FCjxieeeIIDBw5gMpkYP348jz76KJ988gkZGRlMmTIFo9HIt99+yx9//MF///tfKioqCAkJ4a233sLZ2ZkNGzbw7LPP4unpSY8edb9XFouFl19+mW3btlFRUcGsWbOYMWMGW7du5c0338TPz4+4uDhefvnlGo9/+eUXnnzySQ4ePIhGo+HZZ59lyJAhLFu2rMZ7880331zx96jNB5zdu3ezZ88e5syZY+tSWpxKrcahYzAOHYNxH3M28KSkYjpxgrLj8RTv2kPRprOBx88Xw4UtPO5uNq5eCCEu7fuEX0kryaj3+VOFqVQqlhrbzFYzXx1fybb0fXW+JtDZj5vDr6v3mCdOnGDFihUsX74cnU7Hk08+yffff8+UKVO45ppreP311zGZTNx888107dqVf/3rX3z44YcsXrwYgGXLlrFnzx7Wrl2L0WiksrKSTz75BFdXV3Jzc5kwYQLXXnstAAkJCbzxxhv079+fv//973z22WfceeedPPLIIyxbtozw8HAeeughFi9ezD333FOjzscffxyj0YjFYmHq1KkcPnyYu+++m48++ohvvvkGT09PcnNz+d///seyZctwcnJi3rx5fPTRR9x///3885//5Ouvv6ZTp07cd999db4XS5cuxdXVlZ9++ony8nJuuukmRowYAcD+/ftZv349HTt2ZOvWrTUef/jhhwCsW7eO+Ph4pk2bxqZNmwBqvDeN0eYDTnOtRdUaqdRqHEI64hDSEfcxo1EsluoWnrLjxynetZuiTZsB0Pn7V0886BgZgcZNAo8QovX5c7i51PaG2Lx5M7GxsYwbNw4Ak8mEt3fV2laPPPII48aNw2Aw8OKLL9Z7jOHDh1dfwBVF4dVXX2XHjh2oVCrS09PJysoCqvqXnFvb6uabb2bhwoUMGzaMjh07Eh4eDsCUKVP47LPPagWclStX8sUXX2CxWMjIyODEiRN069atxj579uzh+PHjTJw4EQCz2Uy/fv2Ij4+nY8eOhIWFAXDLLbfwxRdf1Po6/vjjD44cOcLq1VWtYkVFRZw8eRKdTkfv3r3p2PH8au8XPt61axezZ88GICIigqCgIBITE2u9N43R5gOOqJ9KozkfeK45G3hSUqvn4SnesYuijWcDTwf/84uHRkagcXO1cfVCCMFFW1oAnt/5DnnlBbW2Gx3c+VvPmVd0TkVRmDJlCk8++WSt5/Lz8yktLaWyspLy8vJ6O0NfuP37778nJyeHn3/+GZ1Ox8CBAykvLweotWahSqWiIWODkpOTmT9/PqtXr8bDw4OHH34Yk8lU59cyfPhw3n///RrbDx061OD1El966SVGjhxZY9vWrVtrfe0XPr7Y19BUHcjVl95FtBcqjQaH0BA8rh2D/1/vJ+SN1wh4/B8YJ01EazRSvGMnmQsWkvz4v0h94WWyv/qakr37sJxdUFQIIezN+NBR6NS6Gtt0ah3jQ0dd8TGHDh3KqlWrqkfZ5uXlkZqaCsBjjz3GP//5TyZNmsTcuXMBcHFxoaSkpN7jFRUV4e3tjU6nY8uWLdXHAkhLS2P37t0A/Pjjj/Tv35+IiAhSUlI4efIkUNUf6Nxq5Rce09HRETc3N7KystiwYUP1cy4uLhSfXSaoX79+7Nq1q/pYZWVlJCQkEBERQXJyMklJSQAsX768ztpHjBjB4sWLMZur+jglJCRQWlp6yfdw4MCB/PDDD9WvSUtLq26RairSgiPqVRV4QnEIDYVrx6BYLJQnp1R3Wi7evoOiP6rumeoCOpxt3anqtKxxcbFt8UIIwfmOxE05iqpz58489thjTJs2DUVR0Gq1zJ07l23btqHVapk0aRIWi4WJEyeyefNmBg4ciEajYcyYMdx66624/2lQx80338ysWbO4/vrriY6OJiIiovq5yMhIvvnmG5544gk6derErFmzMBgMvPnmm8yZM6e6k/GMGTNqHDM6Opru3bszatQoOnbsWH2bC2D69Onccccd+Pr68u233/LWW2/x4IMPUlFRAVSFtPDwcF5//XVmzpyJp6cnAwYM4NixY7Xei9tvv52UlBTGjh2Loih4enqycOHCS76Hs2bN4oknnmD06NFoNBreeuutWp2kG0vmwbkIe58Hx9YUi4XyU8nVgceUkIhy9hdEFxhwdrX0qk7LGhdnG1crhGgrWts8OFcqJSWFWbNmsX79eluXAshim6IdUWk0GMI6YQjrBGOvPR94jh2n7EQ8RVu3Ufj7HwDoAwOqFw81REagcZbAI4QQovlIC85FSAtO4yiVldUtPGXHT1CekIhiNoNKVRV4qlt4JPAIIRquvbTg2JvW1oIjAeciJOA0LaWykvKkU9WjtMoTT14QeAKrh6UbIiLQOMsfLyFE3STg2IYEHDsmAce+KGZzVeA5EV878AQFnr2dFYkhMhyN/DETQpwlAcc2JODYMQk49k0xmzElnarutFyeeBKlsvJs4AnC0DkCxy6dMUSEoz67hkrRzl3k/bgSS24eGk8jxokTcB3Q/xJnEkK0ZhJwbEMCjh2TgNO6WM1myk8mnV0tPb5m4AkOQu3qiunYcbjgF06l1+E1fZqEHCHaMAk4ttHaAo5M9Cfsllqnw7FzJMYbxtHhkYfo+Obr+D/yEB7jxqJ2cMAUd7hGuAFQKszk/bjSRhULIURty5YtIz09vfrxo48+yvHjxxt93HOrlF+uhx9+mFWrVjX6/PZOhomLVuNc4HHsHAnAyfv/Vud+ltw8yo4cxdClMyq1ZHgh2jtb38r+5ptv6Nq1K/7+/gD897//bZLjngs4kyZNapLjNURlZSVarbbexw19XUuQgCNaLY2nEUtuXu0nVCrS35mHxt0dlwExuAwcgD6w7iZMIUTbVrRzFzlfLEWpqFpKwJKbR84XSwEaFXK+++47Fi5cSEVFBX369OGVV14B4B//+AcHDx5EpVIxdepUAgICOHDgAH/9618xGAysWLGCGTNm8Mwzz9CrVy8iIyO588472bRpE+7u7jzxxBPMnTuXtLQ0nn/+ea699lpSUlJ46KGHqpdAeOmll+jfvz8vv/wy8fHxXHPNNUyZMoW7776bl19+mW3btlFRUcGsWbOYMWMGiqLw9NNPs2XLFoKDg+v9mpKSknjqqafIycnB0dGR//znP0RERPDwww/j4eFBXFwc3bt3Jy8vDw8PDw4dOkSPHj245ZZbeOKJJzCZTISEhPDGG2/g4eHB5MmT6devH7t37+aaa66pd0Xy5iIBR7RaxokTavzhgqo+OJ5Tb0VjcKB4xy4K1m2gYM069EFBuAzqj3NMDFp3WRldiLYi5+vvqLhg7aY/M51MqvNWdvbnX1K8eWudr9EHBeF16y31HvPEiROsWLGC5cuXo9PpePLJJ/n+++/p0qUL6enp1TMPFxQU4O7uzqJFi6oDzZ+VlpYyePBgnnrqKe6++25ef/11li5dyvHjx3n44Ye59tpr8fb2ZunSpRgMBhITE3nwwQf5+eef+de//sWHH37I4sWLAViyZAmurq789NNPlJeXc9NNNzFixAgOHTpEQkIC69atIysri1GjRjF16tRatTz22GO8+uqrhIWFsXfvXp588km++eYbABITE/n2229RFIWHH36YxMREli1bVr0ExYsvvsjgwYP5z3/+w5tvvskLL7wAQGFhId99912972VzkoAjWq1zn77qa3p27tsHS1ERxbv3UrxjJ7nf/kDu9z/iGNUVl4EDcOrVA7Veb8svQQjR3OrrFNuIzrKbN28mNjaWcePGAWAymfD29uaaa64hOTmZp59+mtGjRzNixIhLHkuv1zNqVNXCn127dkWv16PT6YiKiqpedNNsNvPUU09x+PBh1Go1iYmJdR7rjz/+4MiRI6xevRqoWnDz5MmTbN++nZtuugmNRoO/vz9Dhgyp9dqSkhL27NnDnDlzqredW5sK4IYbbkCj0VR3Mj73uLCwkIKCAgYPHgzAlClTahzjxhtvvOR70Fwk4IhWzXVA/4s2M2tcXXEfNQL3USOoOJNO8Y6dFO/cTdbCRagMBpz79sZl4AAMEeHSX0eIVuhiLS0AyU/9u85b2RpPIx3+/n9XdE5FUZgyZQpPPvlkrefWrFnD77//zqJFi1i5ciVvvvnmRY+l1WpRqVQAqNXq6gUn1Wp1dZj4+OOP8fHxYc2aNVitVsLCwuo93ksvvcTIkSNrbFu3bl31OepjtVpxc3NjzZo1dT7/55FLDR3FZsvRbm3+L/ru3buZP3++rcsQdkDfwR/Pm24k+KXn8H/4bzj36U3Jnn2kv/UOKc88R+6Pq6hIz7B1mUKIJmScOAGVXldjm0qvwzhxwhUfc+jQoaxatap6GpG8vDxSU1PJzc3FarUyfvx4/vnPfxIbGwuAs7MzxcXFV3y+wsJCfH19UavVfPfdd1gsFgBcXFwoKSmp3m/EiBEsXrwYs7nqtn1CQgKlpaUMGjSIH3/8EYvFQkZGBlu31r415+rqSnBwMCtXVo1CVRSFuLi4S9bm5uaGu7s7O3bsAKr6Jg0aNOiKv9am1OZbcGJiYoiJibF1GcKOqNRqHLt0xrFLZ7xum0Lp/oMU79hJwa+/UfDLrziEhuAycADOMX3RuLjYulwhRCNc6lb2lejcuTOPPfYY06ZNQ1EUtFotc+fOxWAw8Pe//x2r1QpQ3cJz66238sQTT1R3Mr5cs2bN4t5772XVqlUMGTKkulUkKiqqug/Mrbfeyl/+8hdSUlIYO3YsiqLg6enJwoULuf7669myZQujR48mLCys3gDy3nvv8eSTT/K///2PyspKJk6cSHR09CXre/vtt6s7GXfs2PGSrVYtRSb6uwiZ6K99qSwooGTXbop37KIiNQ3Uapx6ROMyYABOPaJR6XSXPogQotnJRH+20dom+mvzLTjNaXdmLKuTNpBXXoDRwZ3xoaOI8e1h67LEFdK6u+M+ZjTuY0ZTkZpG8Y5dFO/cRemBWNROTjj364vLwP44hHW65P1sIYQQtiUtOBdxsRac3ZmxLDuxGrP1/BBlnVrH1MjxEnLaEMVioezYcYp37KR03wEUsxmtjzcuAwfgMqA/Oh9vW5coRLsjLTi2IS047cTqpA01wg2A2WpmddIGCThtiEqjwalbFE7dorBOM1Gybz/FO3aRv/pn8lf9hEN4WFV/nX59ZMVzIVpIO/pcLv7kcr73EnCuUF55wWVtF62f2mDAdfAgXAcPojI3l+KduynesZOcL78i9+tvcerZA5eB/XGM7oZKo7F1uUK0WeeGULf01P/CtiorK1FfxnQecovqIi52i+r5ne/UG2Y6e3Sir080Pb264qRzvOw6ReuhKAoVySkUb99J8e49WIuLUbu44BLTD5dBA9B3DJb+OkI0MUVRMJlMWK1W+f1qQQ4ODpSXl9vk3IqioFarMRgMtb7n9d2ikoBzEZfbB0er0tLVGEZ6aRbZpjw0KjVdjeH09Ymmu1cXHDQya25bplgslMUdpnjHLkoOxkJlJTp/f1wG9sdlQH+0nkZblyiEEFfMXkcWS8Ch6YeJ1zeKSlEUUorPsC8rjn1Zh8mvKESn1hLtGUkfn2iijBHoNTLkuC2zlJRSsncfxTt2Up6QCCoVhs6RuAzsj3Of3qgNBluXKIQQl0UCjh2zxTw4VkUhqTCFvVlx7M8+QrG5BAeNnh5eXejrE00XjzA0aumv0ZaZs7Kqh5xXZmWj0ulw6t0Ll0EDcOzaRZaIEEK0ChJw7JitJ/qzKFbi85PYlxXHgZyjlFWacNI60su7K318oolwD0GtkotdW6UoCuWJJ6tuYe3eg7WsDI27Gy79++MysD/6oEAAinbuatJZV4UQoilIwLFjtg44F6q0WjiWl8DerDhic45RYTXjpnOht083+vhEE+oaKJ3n2jDFbKY0No7inTspjY0DqxV9UCC6Dh0o3V813845Kr0Or+nTJOQIIWxKAo4ds6eAc6EKi5nDuSfYmxXH4dwTVCoWjA7u9PHpRl+faAKd/SXstGGW4mJKdu+heMcuypNO1bmPxtNIx7kvtHBlQghxngQcO2avAedCpspyYnOOsS8rjqP5iVgVKz6OnvT1iaaPTzT+Tj4tWo9oWSfv/1u9z3X64N0WrEQIIWpqbQFHZkmyMwatA/39etLfrycl5lIOZh9lb1YcvyVv4tfkTQQ4+9LHJ5o+3tF4O8qw47ZG42nEkptXe7uHR8sXI4QQrZi04FyEPaXVgooiDmQdYV92HCcLUwEIcQ2kj080vb2j8HBws3GFoikU7dxFzhdLUSpqLgOicnIi4JGHqjsiCyFES7Ona+KF5BYVrTvgXCjXlM++rMPsy4ojtSQdFRDmHkJfn2h6eXXFRe9s6xJFI/x5FJXr0CEUbdyMtawM37vvxKlHd1uXKIRoh+z1migBh7YTcC6UUZrNvqzD7M06RGZZDmpUdDaG0ccnmh5eXXDSyoRybUFlfj4Z739ERWoqnpNvxm3UCOl4LoRoUfZ6TZSAQ9sMOOcoisKZ0kz2ZsWxLyuOHFM+GpWGKM9w+npHE+3VWZaKaOWs5eVkfbqY0gMHcR0+FK9bJ8uinkKIFmOv10QJOLTtgHMhRVFILj5dNXty1mEKKorQq3VEe3Wmr080UcZwtGrpX94aKVYreT+upOC3tThGdcX3nrtQO8qCrkKI5mev10QJOLSfgHMhq6KQWJDM3qw4DmQfoaSyFIPGgZ5nZ0/u7B4qS0W0QkVbtpH95Vfo/Hzxe2AOOm9vW5ckhGjj7PWaKAGH9hlwLmSxWjheULVUxMHso5gs5ThrnejlHUVfn2jC3Duiln4dNlffIq5/VnbsOJkffQJqNX733YMhPMwG1Qoh2gt7vSZKwEECzoXM1kqOnl0qIi7nOBVWM+56V3p7Vy0VEeIaIJ1YbWB3ZizLTqzGbD0/TFyn1jE1cnydIceckUn6vA+x5OXhPWM6LgNiWrJcIUQ7Yq/XRAk4SMCpT7mlgrjcE+zLiuNwbjwWxYKXwYM+PtH09Ymmg5OvhJ0W8vzOd8grL6i13ejgzrMDHqrzNZbiEjI/WoDpRDwe46/HY/z18v0SQjQ5e70mykzGol4OGj19z4aZ0koTh3KOsTcrjvUpW1mbsgU/R+/qsOPr5GXrctucoopijuUncjQvsc5wA9S7HUDj4oz/Qw+S/cVS8lf/jDkjE++Z01HrdM1VshBC2L0Wa8H55Zdf+P3330lOTmbIkCE8+OCD1c/FxsbyySefkJ2dTWRkJA888AA+PlVrLm3evJnFixej0+l44IEHiI6OBiA9PZ333nuPF154AbVa3aAapAXn8hRXlHAgp2qpiMSCUyhAkLN/1VIRPt3wNHjYusRWqdJaSWJhCkfzEjiWl0haSQYAzlonKqzmGrenzlGj4p7u04gyhtd7XEVRKPh1DXk/rsQhrBN+c+5B4+babF+HEKJ9sddros1vUe3YsQOVSsWBAweoqKioDjiFhYX87W9/47777qNfv34sW7aMo0ePMnfuXCwWC3/96195+eWXSUxM5Msvv+SNN94A4JVXXuGWW26hc+fODa5BAs6Vyy8vZH/2EfZlxXGqKA2AUNegqqUifKJw18uFtD6KopBRls2xvKpWmoSCU1RYzWhUajq5BdPFGEZXj3ACXfzZm3WoVh8crUqDo9ZAkbmEfj7duSnsWlwvMlt1yd59ZC36HI2bK34P3oe+Q4eW+DKFEG2cvV4TbX6LauDAgQAkJiaSk5NTvX3nzp0EBwczePBgAKZMmcLdd99NWloazs7OeHp6YjQa6dGjBxkZVZ90t2/fjqen52WFG9E4Hg5ujAwcyMjAgeSY8s7OnhzHD4m/sjzxNyLOLhXR07srzjonW5drcyXmMo7nJ1aFmvxE8ssLAfBx9GSgX2+6GsMIdw/BoHWo8bpzHYn/PIqqt3cUa1K2sDZlM0fyEpgYdg0DfHvW2dfGuW8ftJ6eZHzwEadffxPfe+7CqVtU83/RQghhR2zeByclJYWQkJDqxwaDAX9/f1JSUhgwYADFxcXk5ORw8uRJgoODMZlMfPfdd/z73/+2YdXtm5fByJjgIYwJHkJ6aVb1UhHL4lfzTcLPdPU4v1TEny/gbZXFauFUURpH8xI4mp9IStFpFMCgcaCLRyeuDR5GF2MYXg24rRfj26POEVPXh4ygj3c3lsWvZunxFezOOMitkePxcfSsta9DaAgBj/+DjPfnkzHvQ7xunYzbiGFN8JUKIUTrYPOAYzKZcHOruRK2k5MTJpMJtVrNX/7yF9588020Wi1z5sxh2bJlXH/99SQnJ/Ptt9+i1WqZMWMGHTt2rHXstWvXsnbtWgBeffVVvC9zMjStVnvZr2lvvPGme8co7lBuJrkgjW2pe9meto8vjv+ITq2lt383BgX1pbdfNA7atrVUREZxNrGZR4nNPEpc1nFMleWoUBHhGcKkrmPp4duVMGPHJp1I0dvbm24du7AhaStfHVrJ63s/YlLX6xgXeTXaP5/H2xufV14i4e13yfnqa7SFhQTPnIFK07A+a0IIcaHWdk20ecAxGAyUlZXV2FZaWorBULVIZI8ePejRo+rT7KlTp0hMTGTGjBk8+OCDvPDCC+Tk5DB//nzmzp1b69hjxoxhzJgx1Y8v996hvd5vtFfOGBjjfxWj/QaTVJTGvrNLRew6fRC9WkcPry708YmmqzG89sW4FTBVlnM8/yTHzt56yjblAVVDuPt4R9PVGEakR6fzC5wqkJeb1yy19HLtSmjfQL5P+IWvD69iU9JOpkbeQKhbYK19jXffieLhTsZPv1CYnILv3XeiNsgirEKIy2Ov10Sb98GpT3BwMH/88Uf1Y5PJREZGBsHBwTX2UxSFhQsXMnv2bAoLC7Farfj4+ODh4cGpU6daumxxESqVik5uQXRyC+KmsGtIuGCpiD1Zh3DUGujlVbVURIRHKBqVfbYoWBUrKcVnqkc7JRWmYkVBr9YR6RHKiMCBdDWG4W3wtMm8M+4OrszuNoXYnGN8G/8z/zuwkKEB/RkfMqrGrUGVWo3XlFvQ+fmRs+wbzvz3LfwemIPWs/atLSGEaCtaLOBYLBYsFgtWqxWr1UpFRQUajYYBAwbw+eefs337dvr27cu3335LSEgIgYE1P4muW7eO0NBQQkNDsVgsVFRUkJqaSnZ2Nn5+fi31ZYjLpFapifQIJdIjlMnhYzmWf5J9WXHsyz7M9oz9uOic6X12qYhQt2CbLxWRV15wdrRTAsfzT1JaaUIFBLl0YHTwVXTxCCfULciuWqB6eHUh0j2U1Ukb2Hx6F7HZx5gccT3dvWp2wncbPhSttxeZHy/k9Gv/xe/+e3EIDbVN0UII0cxabJj4119/zbfffltj2+TJk7n11ls5ePAgCxcuJCsrq3oeHF9f3+r9CgsLef7553nxxRdxcqoaobNp0yYWL16MXq/n/vvvp3v37nWed/fu3ezZs4c5c+bIMHE7YrZWciQ3nr1Zh4jLPYHZWomH3o0+PlVLRQS7dGiRVpFySwUJBac4mld12ymjrOr77a53pYsxjC4eYXTx6ITLRYZl25OThaksO7GK9NIsentHMSn8ulpD+CvOnCFj3nwshYX43DkD5759bFStEKI1sddros3nwbEHEnDsk6mynLjc4+zNiuNoXgIWxYq3wXh+qQhn30sfpIGsisLpkozqVprEwhQsigWdWku4ewhdPMLoagzD38mn1S53UGm1sD51K78lb0Kr1nJjpzEM8u9To3XMUlRExocfU554EuPECbhfd02r/XqFEC3DXq+JEnCQgNMalJrLOJhzjH1ZcRzPP4mCQgcnn7OzJ0fXOST6UgorijmWl1i9HEKxuQSADk6+dDWG09UYRie3YPSatrW0QWZpDl/Hrya+4BRhbh2ZGjkeP6fzIyCsZjPZn39Jya7duAwagPf0aai0Nu+WJ4SwU/Z6TZSAgwSc1qaoopgD2WeXiihMBiDYpQN9faLp7dMNo4M7uzNja02K18s7isSC5OpAc/rsUgguOqeqW05nbz25O7T92ZcVRWFHxgFWnFxDucXMNWfnL9KqtdXP5//0C/mrfsIQGYHvvX9B49I6bscJIVqWvV4TJeAgAac1yysvYH/WEfZmHSKl+AwAPgZPcsvzsSjW6v1UqFABVpTqpRC6GsPpYgwj0Nnf5p2YbaWoopgfEn9jb1Ycfo7eTI0cT5j7+bmjinftIXvxEjRGI34PzEHvLx33hRA12es1sd0GHOlk3PZkleWyLyuOX5I3Yr0g3JzjoNEzs+vNRLiH4KBpW5MLNtbh3BN8E/8zeeUFXOXfjxs6XV09b48p8SQZH3wEFgu+c/6CYxdZCkUIcZ69XhPbbcC5kASctuXhTS/W+9zbw55pwUpal3JLBT+d+p2NaTtx1TtzS/hYenp1RaVSYc7JIWPefMwZGXjffhuuQwbbulwhhJ2w12tifQHHPmdYE6IBjA7ul7VdVHHQ6JkUdi2P9L4LV50znx75lk+OfE1+eSE6Ly8C/vkIjl27kL3kS3K/X45ird1KJoQQ9k4Cjmi1xoeOQqeuOfJJp9YxPnSUjSpqXTq6BvD3Pn/hxk6jOZaXyCt7PmDT6V1gcMDvgTm4jhhGwZp1ZH70CdbycluXK4QQl0VuUV2EvTbHifPqGkVV10rc4uKyy/L4Jv4njuUnEuIayNTI8QQ4+1Gw4Q9yv/kOfVAQfg/ci9bDw9alCiFsxF6vidIHBwk4QlyMoijsyYrlh4Q1lFlMjA66ims7DsN8+BiZCz5FbTDg98AcHDoGX/pgQog2x16vie22D87u3buZP3++rcsQwu6pVCpifHvyZMz99PPpzpqUzby+dz5pQc4EPPoIqNWceeNtSg4ctHWpQghxSdKCcxH2mlaFaAnH8hL5Ov4nckx5DPTrzXivgRQvWEz5qWQ8b56I2+irZXkHIdoRe70myi0qJOAIcbkqLGZ+Td7IhtRtOOmcmBR8NUE/7aF0735ch16F1223otLYz8rqQojmY6/XxHZ7i0oIceX0Gh0TOo3mH33+gqeDO58nrmT5IEf014ygaPNW0t99H0tJqa3LFEKIWqQF5yIulVZ3HMlm+aY0cosq8HTVc9OwQAZGede7vxCtmVWxsun0LlYnbQBgSmFHvFfvROfthd+D96Hz8bFxhUKI5iQtOO3EjiPZLPntFLlFFQDkFlWw5LdT7Dhif998IZqCWqVmROBAnux3PxEeoXzhksAf40IxFxVx+rU3MJ2It3WJQghRTQLOFVq+KY2KypozvFZUWlm+Kc1GFQnRMowGd+7pNpVZXW8mwdPK4tFOlDqoOPO/9yjavsPW5QkhBABaWxfQ3C5cbLMpnWu5aeh2IdoSlUpFH59ouniEsSJpHYsc9jBxiwU+W4I5Iwudny95K1dhyc1D42nEOHECrgP627psIUQ70uYDTkxMDDExMU1+XE9XfZ1hxtNVVq8W7YeTzpHbIm8gxqcHX7uupPvGU/T45VcUlQrV2e59ltw8spZ8CSAhRwjRYuQW1RW6aVggem3Nt0+jVnHTsEAbVSSE7UR4hPDPmPvQ3jIOk+58uDlHZa4k4/vvbVSdEKI9koBzhQZGeXPHtSHVLTZ6rRqLVcHJoc03iglRJ51ay7hOo3Aw1z0wU11Q3MIVCSHaM7kaN8LAKO/qYeEVZguvLz3KJz8l8q/p3fA1GmxcnRC2UeSkxq3UWud2IYRoKfIXp4nodRrumxiBWgUfrIjHVGGxdUlC2MT+GB/Mf5rc2KKq2i6EEC1FAk4T8nZ34J4bwjmTU8biX0/SjuZQFKJa9OiJbBjkQaGTGgWo0KrQKOCudsQqvxNCiBbS5gNOS68mHhXizqRhQew5nsdvu9Nb7LxC2IsY3x70u/ZWlk8N553bfVk2PZycYA+6b0zit9+/kOAvhGgRF12qobCwkI0bN7J3715OnTpFaWkpTk5OhISE0Lt3b0aOHImbm1tL1tsoLbXYpqIofLwqgb0n8njo5s50C3W/7GMI0ZZUlpRw4uWXUIpLSJwxkvH9JslK5EK0Mq1tqYZ6A86XX37Jpk2b6NOnD926dSMwMBBHR0fKyspIS0vj8OHD7Nu3j6FDhzJ9+vRmLb6ptORq4qYKC68tPUJBcQX/uiMab3eHKzqOEG1FRUYmp159lUK9ldOzruWGqOsl5AjRirSZgPPzzz8zZswYdDpdvQetqKhg/fr1jB07tmmqbGYtGXAAMvNMvPLFYbzcHHhsWlf0Os2lXyREG1Z27Din33mPFF8tRXdczw1hYyTkCNFKtLaAU28fnOuvv/6i4QZAr9e3mnBjC75GA3eNCyM1q5TP1yRJ3wPR7jl26YzP9GmEpJux/LiGn079Lr8XQohmcVmdjEtLS/nyyy959dVXWbhwIbm5uc1VV5vRI8yDG4cEsvNILuv3Zdi6HCFszu2qwbiNGU2vE2VkrP2NX5M32rokIUQbdFkB55NPPsFgMHD99ddjMBh48803m6uuNmXswA70jvDg299TOJZSaOtyhLA5z0k34tizOyP3FnNk+xp+S95k65KEEG3MRQPOokWLKCsrq36cnZ3NTTfdRK9evbj55ptJS0tr9gLbArVKxZ1jw/A1Gvh4ZQK5heW2LkkIm1Kp1fjOvhN9YCA3bC1m+8G1rEvZauuyhBBtyEUDTnh4OM899xxbt1b94Rk4cCCPPfYY77zzDo8//jgjRoxokSLbAkcHDfdPjMBssfLhinjMlbWnsheiPVEbHPC//170Bicmbypl7dE1bEjdbuuyhBBtxEXnwYGqfjdfffUVZ86cYfbs2VitVpKTk/H19SUiIqKl6mwSLT2Kqi774/P44Md4ror2ZuZ1oTKCRLR7ppNJpL/1Dvk+jnw+zIEbI8cyInCArcsSQvxJmxlFdY6TkxN33XUX06ZN44MPPmDjxo3ExMS0mnDT0jMZX0rvCCPjBwWwNS6bjQeybF2OEDZn6BSK98zpuJ8u5JYDKn5I+IXNp3fbuiwhRCt30dXE8/Ly+OGHH8jMzCQoKIjHHnuMLVu28NRTTzF16lRiYmJaqs4rFhMTY3d13nBVAKcySli2IZlAH0ciAl1tXZIQNuUS0w9zegas/pnxxlC+Vf2MWqXmqg59bV2aEKKVumgLzptvvlljrpuFCxcyduxYnnrqKbZu3cqrr77aIkW2NWqVirvHheHlpmf+ygTyiytsXZIQNucx/nqcY/oSsf0UI/KMfB2/mu3p+21dlhCilbpowElNTeW2226jd+/e3HrrraSmpgLg4eHBQw89xIQJE1qkyLbIyaDlvokRlFdYmL8inkqLdDoW7ZtKpcJ7xnQcQkLos+4k/Sv9WXZiJTszDti6NCFEK3TRgDNixAhefPFFli5dyty5cxk5cmSN56Ojo5uztjYv0NuJWWM7kXim6naVEO2dWq/H9757UDs7M/S3U3TXBbL0+Ar2ZMbaujQhRCtzyVFU8fHxZGZmEhwcTHBwcEvV1SzsYRRVXb7fmMKvu9KZcW0oQ3v4NPv5hLB3FalpnP7vm2j9/FhxrQ8nStOY0XUSfX3kQ5UQttLmRlFFRERw1VVXtfpwY89uGhpEtxA3lq47xckzxbYuRwib0wcF4nvXnZhTUpm0x0In1yCWHP2BA9lHbF2aEKKVqDfgPPnkk2zbto3Kyso6n6+srGTr1q3861//arbi2gu1WsXd48Nxd9bx4Yp4CkvMti5JCJtz6tkDz0kTMe07wLRTRkLcAvns6PfE5hyzdWlCiFag3ltUqampLFu2jMOHD9OpUycCAgIwGAyYTCbOnDlDYmIi3bt3Z8qUKQQFBbV03VfEXm9RnZOSWcprS48Q6u/MI5M7o9Fc1lJhQrQ5iqKQveRLirdux2Pm7SxyPEJq8RnuippCtFdnW5cnRLvS2m5RXbIPTn5+PgcPHiQ5OZmSkhKcnZ0JCQmhZ8+euLu7N0uxzcXeAw7AziM5fPJTIlf38WXq1SEtem4h7JFSWUn6O/MoP5mE8aE5LCjezOmSTP7S7VaiPFvHhKNCtAVtLuC0Ja0h4AB8/Xsy6/ZkMPv6Tgzq5t3i5xfC3liKSzj9+n+xmsrx/MdfmZ+2mvTSLO6Jvo0uxjBblydEu9DaAo7cA7FDtwwPpnOwK0vWJJGcUWLrcoSwOY2LM34PzIHKSgo+WsScyMn4Onmz4PAyjueftHV5Qgg71OYDjr2tRdUQGrWKe24Ix8WxqtNxcal0OhZC7++P7z13YU7PoGTxMu7vNg1vg5GP474iPv+UrcsTQtiZNh9wYmJimDNnjq3LuGxuTjruuzGCghIzH69OxGJtN3cShaiXY1RXvG6dTNmhOCpWreGBHjPwdPDgo7ilJBbIZJlCiPPafMBpzUL9nZk+JpSjyYUs35Rq63KEsAtuI4bhNnIEhes2oOzcz4M9Z+Dh4Mb8uKWcLJTfEyFElXpXE1+/fn2DDnD11Vc3WTGitqu6e3Mqo4TfdqfT0c+J/l29bF2SEDbnOXkS5sxMcpZ+jb+PDw/2mMF7sYuZf+hL7u8xnRDXQFuXKISwsXoDzqZNm6r/X1EUjh07hoeHB15eXuTk5JCfn0/Xrl0l4LSAKSODScksZfGvSXTwciTIx8nWJQlhUyqNBt+/zOb0f94k86NPCHjsHzzYYwbvHlzMB7Ff8GCPGQS7drB1mUIIG2rQMPGFCxfi5+fH+PHjq7f99NNPpKenc9dddzVrgU2ptQwTr0tBiZmXl8Sh1aj51/RuODvWm02FaDfM2dmcfu0NNE5OdHjsHxRqzLx7cDEmi4kHeswgyMXf1iUK0WbY0zXxQo0aJr5p0yauv/76GtvGjh1bo5VHNC93Zx33Toggr6iCT35KxCqdjoVA5+2N35y/YM7NJfPjT/DQufBgzxnoNXo+iF3C6ZIMW5cohLCRBgUcDw8Pdu/eXWPb7t27cXNza5aiRN3CA1y4bXRH4pIKWLE1zdblCGEXDBHheE+/DdOx4+R89Q2eDu78tccMtGot8w4u4UxJpq1LFELYQIPuc8yePZs33niDFStW4OXlRXZ2Nqmpqfz9739v7vrEnwzv6cup9BJ+3nGGED9n+kQabV2SEDbnOmgg5vQMCn5dg87fD+/Ro/hrz6o+OfNil/DXnjPwd/KxdZlCiBbU4KUaCgsL2b9/P7m5uRiNRvr27Yurq2tz19ekWnMfnAuZK6288fVRTmeX8eT0bnTwcrR1SULYnGK1kvnxQkoPHMTvgTk4dY8mozSb9w4uRoWKv/acia+TjEIU4krZ6zWx0Us1uLm50a1bN7p168aIESNaXbhpS3RaNXMmRKDXqfngx3jKyittXZIQNqdSq/G5cwb6oCAyP1lERdpp/Jy8ebDHDKxYmRf7OVllubYuUwjRQhoUcLKzs3nmmWd45JFHePHFFwHYvn07H374YbMWJ+pndNVz74QIsgrK+fTnk1jbz5qpQtRL7eCA3wP3onbQk/H+fCyFRfg7V82TU2mtZN7Bz8kuy7N1mUKIFtCggPPRRx/Rp08fPvvsM7Taqm47PXv25ODBg81anLi4zkGu3DoymAMJ+fy0/fJuvwnRVmk9PPC7fw6WoiIy5n+M1Wymg7MvD/S4gwqrmXmxn5Nryrd1mUKIZtaggBMfH89NN92EWn1+dycnJ0pLS5utMNEwI3v7MqibF6u2nuZgQr6tyxHCLjiEdMTnzhmUJ54ke8lSFEUh0MWf+3tMx2QpZ17s5+SVF9i6TCFEM2pQwHF3dyc9Pb3GttTUVLy9vZulKNFwKpWK6WNCCfZ14pOfEsnINdm6JCHsgnPfPhhvvIGSnbso+OU3AIJdOnB/9+mUmMuYd/Bz8ssLbVylEKK5NCjgTJgwgddee40NGzZgtVrZvHkzb731FhMnTmzu+kQD6HVq7psYgVaj4oMVJzBVWGxdkhB2wX3stTgP6E/eilWU7N0HQEfXAO7rfjtF5hLmxX5OQUWRjasUQjSHBgWcq6++munTp7N9+3a8vLzYuHEjU6dOZdiwYc1dn2ggLzcH7rkhnPRcE5/9cpIGjv4Xok1TqVR43zENh7BOZC36nPJTyQCEugUxJ/p2CsqLmHfwc4oqim1cqRCiqTV4Hpy2oK3Mg3Mxa3an8+0fKUwaGsTYgbLYoBAAlsIiTr/2XxSLhYAnHkXr4QFAQsEp5h9aiqfBg7/2mIGL3tm2hQphx+z1mtioeXAURWHt2rW88MILPProowAcPnyYrVu3Nl2FzWT37t3Mnz/f1mW0mDH9/OjfxZPlm1OJS5JOlEIAaNxc8XtgDlaTiYz3P8JaXg5AuHsI90TfRo4pj3mxSyg2y8AJIdqKBgWcZcuWsWHDBkaPHl2d3ry8vPjxxx+btbimEBMTw5w5c2xdRotRqVTMuC6UQB9HFqxOICtfOh0LAaAPDMD37jupSE0la9HnKFYrAJEeofyl21SyTbl8ELuEEnOZjSsVQjSFBgWcP/74g8cff5whQ4agUqkA8PX1JTNTFrGzRw46DffdGAHAhyviKTdLp2MhAJx6dMfzlkmU7j9A3opV1du7GMO4q9utpJdm8+GhLyitlA8GQrR2DVps02q1YjAYamwzmUy1tgn74eNh4O5x4bz3/XE+/y2Ju8eFVYdTIdozt6tH1liY03XQQACijOHc1W0KCw9/zX/3foxVsZJfUYjRwZ3xoaOI8e1h48qFEJejQS04ffr0YfHixZjNZqCqT86yZcvo169fsxYnGqd7J3cmDg1k19Fc1u3NsHU5QtgFlUqF121TMHTpTPYXX2GKT6h+LtozkmEd+pNbnk9+RdUcOXnlBSw7sZrdmbG2KlkIcQUaFHBmzpxJbm4ud955J6WlpcycOZOsrCymT5/e3PWJRho7oAN9I41890cKR5NlUjMhAFQaDb733IXW00jG/AWYLxgZciDnaK39zVYzq5M2tGSJQohGuqxh4gUFBWRlZeHt7Y3H2WGWrUl7GCZeF1OFhVe/PExRaSVP3dENTzcHW5ckhF0wZ2Ry+vU30Li7EfDPv6N2dOThTS/Wu//bw55pweqEsC/2ek1s1DBxgJKSEg4ePMjhw4eJjY2luFgmxmotDHoN998YSaVF4YMV8VSYrbYuSQi7oPPzxfeeuzBnZJK54FMUiwWjg3ud+9a3XQhhnxoUcA4dOsSDDz7Izz//THx8PL/88gt//etfiY2Ve9KthZ+ngbvHhZGcUcqX65JkpmMhznLs2gWvabdSdvgIud/9wPjQUejUulr7+Tt6y++NEK1Ig0ZRffLJJ9x7771cddVV1du2bdvGJ598wttvv91ctYkm1jPcgxsGB7Bq22lC/JwZ1cfP1iUJYRfchg7BnJ5B4boNdPb3Z2rX8axO2kBeeQFGB3d8HT05kp/A2tQtXBM81NblCiEaoEEBJy8vj0GDBtXYNmDAgHY1Q3BbMX5wAMkZpXz9ewpBPk5EBrnauiQh7ILnzTdhzsgkZ9k3RP/1fmIGPFT9nFVR+OLYclYnbcBZ68RVHfrasFIhREM06BbV8OHD+eWXX2ps++233xg+fHizFCWaj1ql4q5xnfBxd+CjlfHkFVXYuiQh7IJKrcb3rlno/P1I/+Ajkp94mpP3/43kp/5Nya7d3N75RroZI/gmfjX7sw7bulwhxCU0aBTVM888Q3x8PO7u7nh6epKbm0tBQQGRkZE1Jo97/vnnm7XYxmqvo6jqcjqnjFe/OEyAtyP/uLUrOm2D+5sL0ablr9tA3rff19im0uvwmj4Nh369+eDQFyQXpXFv9DS6GMNsVKUQLc9er4n1jaJqUMD5/fffG3SSkSNHXk5NLU4CTk17T+Qyf0UCw3r6cMc1obYuRwi7kPzUv7Hk5tXarvE00nHuC5RWmnjv4Gdkl+XxQI8ZhLoF2qBKIVqevV4T6ws4DeqDY+/BRVyZvpGejB1Qyi87zxDi58ywnj62LkkIm6sr3Fy43UlrYE7323nnwGd8FLeUh3rOwt9ZfneEsDcNui+xefNmUlNTgapWkGeffZbnn3+etLS0Zi1ONL+JQwLpFurGV+tPkXha5jYSQuNpvOR2d70r93efjlal4YNDX5Brym+h6oQQDdWggLNs2TJcXFwAWLx4MeHh4URFRbFgwYJmLU40P7VaxV/GhePhomf+yngKSsy2LkkImzJOnIBK/6d5cLQajBMn1Njk7Wjkvh63U2E188GhLyiqKGnBKoUQl9KggFNYWIiHhwcVFRUcO3aMadOmMXnyZJKSkpq5PNESnB213D8xglKThY9WxlNpkZmORfvlOqA/XtOnnW+xUatROTrh0q/20PAAZz/u6XYb+eWFzI/7ElNleQtXK4SoT4MCjpubG+np6ezfv5/w8HB0Ol31yuKibQjycWLmdaHEpxXzze8pti5HCJtyHdCfjnNfoNMH7+J33z0oRUUUbd5S575h7sHMjprM6ZJMFhxehtla2cLVCiHq0qCAc8stt/D444/zwQcfcOONNwIQGxtLSEhIsxYnWlb/rl5c08+P3/dnsi3O/nrKC2ELjt2jMURGkLfqZ6wmU537dPOMZHrnG4kvOMVnR7/DokgrqBC21uDVxMvLq5peHRyqVqIuKChAUZRWtaq4DBO/NItV4X/fHSMhrZjHpkUR4uds65KEsLnypCROv/YGHuPGYpwwvt79Np3exXcJvzDArxfTIifUmCdMiNbOXq+JjV5N3MHBoTrcALi7u7eqcCMaRqNWce/4cNycdHzwYzxFpXIrUgiH0FCc+/WlYO16KvML6t1vWEB/xnYczs6MA6w4uVYW5xTChmT6WlGLi5OO+ydGUFxm5uNVCVis8kdaCOPECSgWC3mrVl90v+s6DmdYh/5sSNvOutStLVSdEOLPJOCIOnX0c2b6mFCOpRTx/UbpdCyEzscbtxHDKN66nYrTZ+rdT6VSMSn8Ovr6RLMqaT3bzuxtwSqFEOdIwBH1Ghztzag+vqzdk8HOIzm2LkcIm/O4fixqg4Hc5Ssuup9apeL2zhOJMkbwdfxPsjinEDbQ4ICTmprKt99+Wz25X1paGqdOnWq2woR9mDIimIhAFxb/lkRKZqmtyxHCpjQuzrhfdw1lsYcoO37iovtq1RpmR00m1C2Qz48t51heYgtVKYSABgacbdu28dxzz5Gbm8umTZsAMJlMLF68uFmLE7an0aiZMyECZ4OGD1fEU1Imc3yI9s1t1Ag0RiO53y9HsV58OLheo+Oebrfh6+jFJ4e/5lSRLG8jREtpUMD5+uuvefrpp7n33ntRq6teEhIS0mQzGf/yyy888cQT3H777cybN696e3Z2Nk899RSzZ8+uFabmzp1LQkJCk5xfXJybs477bowgv7iCBasTsEqnY9GOqfV6jDeOp+JUMiV79l1yfyedI/f1uB1XvTMfHVpKemlWC1QphGhQwCkoKKg1qZ9KpWqyOR6MRiM333wzo0aNqrF9+fLljBgxgnnz5rFr167qQLN161b8/PwIDw9vkvOLS+vUwYVpV4dw+FQhP25JtXU5QtiUy4D+6IMCyftxJUoDZnU/tzinWqXmg1hZnFOIltCggBMWFsbGjRtrbNuyZQsRERFNUsTAgQMZMGAArq6uNbZnZmbSvXt3nJycCA8PJyMjg9LSUpYvX860adOa5Nyi4Yb29GF4Tx9+2ZnOnuO5ti5HCJtRqdUYJ02kMieHwo2bG/Qab0dP7ut+O+WWCj449AXFsjinEM1K25CdZs+ezUsvvcT69espLy9n7ty5nD59mqeffrpZiwsODubgwYO4u7uTkJDAzTffzLJlyxg3bhzOzpeeYXft2rWsXbsWgFdffRVvb+/LOr9Wq73s17R1D0wxkp63k8W/JhEV3oGOfq6XfpEQbdHwYZT9sYmCX34j5IZxaBvwN8nb25t/uszhta0f8Mmxb3hy6F9x0hlaoFghGq+1XRMva6mGPXv2kJ2djZeXF/369cNgaNpfzK+++oqcnBwefPBBAIqLi/n44485ffo0I0aMIDo6msWLF/OPf/yDTz75hNzcXAYPHszYsWMbdHxZqqFp5BdXMHfJYQx6NU/e3g0nQ4NyshBtTnlKKqdfeR33a0bjOWlig193OPcECw5/TbhbR+7tPg2dWn6HhP2z12tikyzVcNVVV3HjjTcyZMiQJg83dXFxceGRRx7hP//5D+PGjWPhwoXMnj2b5cuXExwczDPPPMOaNWtITZU+IS3Jw0XPnBvCyS6oYOHPiVhlOnrRTjkEB+EyoD+F63+nMrfht227eUZye+cbOVGQxOKj38vinEI0gwZ9bMjOzuabb74hKSkJ059W0/3f//7XLIX92dq1a4mMjKRjx44kJyczfvx4tFotwcHBJCcnExQU1CJ1iCoRQa5MHdWRpetOsXrbaSZcFWjrkoSwCeON4ynZs5e8FavxuXNGg18X49uDEnMZPyT+yrITq2RxTiGaWIMCzptvvklAQAC33norer2+yYuwWCxYLBasVitWq5WKigo0Gg0ajQaoGsX166+/MnfuXAB8fX2Ji4ujS5cuJCYmMmHChHqPvXv3bvbs2cOcOXOavO72bkQvH5LSS1i17TQdfZ3oFWG0dUlCtDitpyduV4+kYM063EaPwiG44R+2RgQOoLSylF+TN+Gic+LGTmOasVIh2pcG9cGZNWsWn376afUcOE3t66+/5ttvv62xbfLkydx6660AvPfee/Tr14/BgwcDVS1Kb775JmfOnGHUqFHMnDmzQeeRPjhNz1xp5T9fHSEjr5wnp0fh7+lo65KEaHGW0lJS//08Dh074v/Qg5f1WkVR+C7hFzaf2c2E0NGMDr6qmaoUonHs9ZpYXx+cBgWcd955h6uvvpru3bs3eWEtSQJO88gtLOflJYdxdtTyxO3dcHTQ2LokIVpcwbr15H77A35/ewCnblGX9VqrorDk2A/szYpjauQNDPbv00xVCnHl7PWa2KiAU1xczNNPP42fnx/u7u41nnvggQeapsIWIAGn+RxLKeTtb47RM9yDOTdGoJa+BKKdUcxmUp+fi9rRQMCTj6G6zBbvSquFBYeXcSwvkTujbqGX9+WFJCGam71eExs1iur9999HrVYTGBiIp6dnjX9CAHQJduOWEcHsj8/nl51nbF2OEC1OpdNhnDiBitQ0infuuuzXn1ucM8Q1kMVHf+B43slmqFKI9qNBnYwPHTrE/PnzcXRsff0rpJNxyxnd149T6SWs2JxGR18nunfysHVJQrQo5359KFi3nrwVq3Hu2wf1ZQ7KcNDouSf6Nt47+BmfHPmaB3vMoKNr3Z9OhRAX16AWnJCQEIqKipq7lmYRExMj4aaFqFQqZlwbSqCPI5+sTiQr33TpFwnRhqjUajwnTcSSl0fh7xsv/YI6OOscua/7dFx0Tsw/9CUZpfZ3S0CI1qBBfXC++uortm3bxsiRI2v1wbn66qubrbimJn1wWkZ2QTkvL4nDw0XP47dH4aCTTsftzY4j2SzflEZuUQWernpuGhbIwKjWM8V7Y6XP+5DyhESCXngWjcull3CoS1ZZLu8cWIRGpeH/et2J0eB+6RcJ0Yzs9ZrYqD44x44dw9PTk4MHD7Jp06Ya/4T4M293B/4yPpzTOWUs/jWJBq4GItqIHUeyWfLbKXKLKgDILapgyW+n2HHE/v4wNhfPSROxmkzk//zLFR/D5+zinCZLuSzOKcQVaPBaVG2BtOC0rF92nuGHTancMjyIa/t3sHU5ogUUl5p5btEhisoqaz3nbNBw17hw3Jy0uDnrcHXUotE0z9xa9iBryZcUb99J0LNPo/O58tarhIJkPjz0Bf5OPjzYYwYGrUMTVilEw9nrNbFRw8QvpChKjU/kzTX5X3OQgNOyFEXho1UJ7DuRx//d0pmoEGlibysURSG7oJyUrFJSMs//yy82X9ZxnA1a3Jy1uDnpcHPS4eqkxdVZV/343HOuTjp02tbztwagMr+A1H8/j1OvHvjePbtRx4rLOc4nh78m3D1EFucUNmOv18RGBZzc3Fw++eQTjhw5QklJzWbSZcuWNU2FzeTCUVQScFqeqcLCa18eoaDEzL/u6Ia3u3z6bG0sFiunc0ykZpWSnFlKamYpKVmllJVbAFCrwN/TkWBfJ4J9nfh11xmKSmu34Lg765gzIZzC0koKS80UlpgpKjVXPa7+fzOmiroXnnR00FSHILcLQpDrBQGparsWfSP7fTVVH6K8FavI//lXAh5/FIfQkEbVtDvzIEuO/UhPry7MipqMRtW6Ap9o/ez1mtiogPPqq6/i4ODApEmTePbZZ3n++ef55ptv6NOnD2PGtJ61UyTg2EZmnomXvziMt7sDj90WhV4nf5jtVVm5hbRzQebsf8/klFFpqfozodeqCfRxpKOvE0G+TnT0dSLAy6nG9/RcH5yKyvNBRa9Vc8e1IQ0KCRVmK4WlZwNPSVUAOhd+qoLQ2YBUaqbUZKnzGA46dXUIcnU63wp0LgBVBaOq5w16dY1FLhtb/4WsZWWk/PsF9B388X/koUYvpvlH2k5+SPyVQX69mRp5gyzOKVqUvV4T6ws4DWrnPH78OO+//z4GgwGVSkVoaCj3338/Tz/9dKsKOMI2fI0G7h4XxrwfTrBkTRKzr+8kf5jtQEFxBclnW2NSzrbMZOaXVz/v4qgl2NeJq/v6EexTFWZ8jQbU6ot/786FgCttAdHr1Hi7OzSota/SYj0feEqqQk9RyfkAVFhqJjO/nIS0YorLKqnr05xOqzofgJx0HEsprBFuACoqrSzflHbZAUft6Ihx/PXkLPuGskNxOPVo3HI3IwIHUGIu5beUTTjpHGVxTiEuokEBR61WV6/s7ezsTGFhIY6OjuTm5jZrcaLt6BHmwYSrAlmxNY1Qf2eu7utn65LaDauikJVXTnJmSY3bTIUX3Ebydnego68Tg6K9q1pnfJzwcNFdcRAdGOXdIsPCtRo1Rlc9RtdLT6hnsSoUl9W8HVajhajETG5ROeXmum+R5RZVsGz9KQJ9nAjycSTAy7FBt8Jchw2hYMPv5P7wI47dolBpGnf77PqQEZRUlrI+dRvOWidZnFOIejQo4ERERLBv3z4GDBhAr169eOutt9Dr9YSHhzd3faINuX5QB05llPDN78kE+TjSOdjN1iW1OeZKK2nZZaRmnr/NlJpVWn3RVqtVBHgZiO7kQbCvI8G+zgT7OOLo0PY7rWrUKtyddbg76y6635MfHage4v7n1285lF39XqpU4OthIMjHsSr0eFf918tNXyMYqjQaPG+6kcyPPqF42w5chzYukKhUKm4JH0upuYyVSetw1jkySBbnFKKWBvXBKSkpQVEUXFxcqKioYMWKFZhMJsaPH4/RaGyJOpuE9MGxvbJyC698cZjS8kqeuiO6QZ+825uGdnAtMVVWd/hNORto0nPKsJ79jTbo1QT5OFV3/g32caKDl2OrG43U0i7WB6d/Vy9yCspJzSqrDo9p2WVkXXBrz6DXVIUeb6fq8BPgZSDvnXeozMkh6Pl/o3ZofGd7WZxTtDR7vSY22TDx1kwCjn1IzynjlS8P4+/pyKNTu8oF9wL1XVxvGhaIt5tDdatMSmYpOYXnWxncnXXng8zZzr9e7g6yqvsVutxRVKYKC6ezz4WeMtKyq/5rqjjfCTpak8eEYyvJ6DUM/egxBHk74e3RuO9RuaWC92OXkFqczqjAQezJOkReeQFGB3fGh44ixrfHFR9biD+z12viZQechg7/njp16pVX1QJkmLh92n8ijw9WxDOkuzczrg2VTsdn1Xd75BwVVZ22LwwzwT5OuF3itotoeYqikFtUQWpmKanZZaRlldJ564/4F6QyP2QSpVpHHHRqArwdCfJxIvDsf4Mu85ZhibmM1/bMp9Bcc71AnVrH1MjxEnJEk7HXa+Jlj6LKyclptmJaUkxMDDExMbYuQ/xJ70gj4wZ14KftZwj1d2Z4L19bl2RzFWbrRcPNY9OiCPR2xKCXtb1aA5VKhZebA15uDvSKqLqVX9H/DtJefJmHvFNIG3AtaVlV4WfP8Vw2HTzf2uPlpifQ24lAn/Ohx9ej7hFszjpH6vp8YLaaWZ20QQKOaLfqDThhYWGMHTsWgPT0dPz9/VusKNE+TBgcSHJGKV+tTybQx4nwABdbl2QTmXkmNh7IZGtc/Z+MPF317fb9aUv0/n64Dh1C0eYtDBg/Bn2Pqsn/FEUhv9hc1acnq4zU7KpbkYdO5lf3qdJpVQR4nW3t8TnX6uOEi6OWgoqiOs+XV17QUl+aEHan3oCzdOnS6oDz+OOP89lnn7VYUaJ9UKtV3DUujFe+OMz8FfE8dUc33F3aR6dji1XhYEI+fxzI5MipQtRqFb0jPPD1cGD93sw6++CItsE4fizFO3aS9+NK/Ob8Bahq7Tk33L1HmEf1vuZKK2dyykjLLiPlbPg5kJDPlkPnw7CHiw4l3IDKwVTrXKoKx2b/eoSwV/UGHH9/fxYvXkxQUBCVlZWsX7++zv2uvvrqZitOtH3OBi33T4zgtS+PMH9lAn+/tQvaNrwAY35xBZtjs9h0MIv8YjNGFx03XhXI0B7e1eEuwNuxSZYJEPZJ4+aG+7VjyF+5GlNCIobwsHr31WnVdPRzpqOfM4PPblMUhcLSStKySqtDz+6Uzug6HUKlOR+MFQUqUjtxIrWIED9nmUFctDv1djI+ffo0K1asICsri7i4OKKi6h6C+OyzzzZrgU1JOhnbr93Hcvl4VQIjevly+5jGrdljbxRF4WhyERsPZLI/Pg+rAt1C3BjRy5ce4R5oLjEzsGh7rOXlpP77BbTeXnR49JFGd7J/8qMDFOiS0AYfR6U3QaUeNBUope6UH+2PGh3BPo6EBbgQ1sGF8EAXPF310rlfXBZ7vSY2apj4Cy+8wL///e8mL6qlScCxb99tTOG3XenMvDaUIT18bF1Oo5WYKtkWl83GA1lk5JlwNmi4qrsPw3v64Gs02Lo8YWOFm7eQ88VX+N57N859ejfqWHVOL+CVhTZ8L376QCIrRpJ0upyk9JLqfdyddWcDjzPhAS509HOWKRvERdnrNVHmwUECjr2zWBXe/e44J9KK+OdtXQn1b52dapPSS9h4IJOdR3MxV1rp1MGZEb186dfZU24TiGqKxULa3FdRLBaC/v1Uo5dwqGvuHo3XaZYc+5Foz0juipoCqEnLKiXhdDGJp4tJPFNCdkHVJIUatYqOfk5VLTwBLoQFuMhEnKIGe70mttuAI/PgtC7FZZW8vCQOq1XhXzOicXNqHfO7VJgt7Dqayx8HMjmVUYpeq2ZAlBcjevnQ0c/Z1uUJO1Uae4iM9+fjddsU3EYMb5ZzbD69m28TfqafT3emd7mp1sSCBSVmTp4urgo9Z4o5lVGCubLqsmB0OdvKE1AVeoJ9ndp0HzlxcfZ6TWy3AedCEnBah+SMEl7/6gid/F14eHJnNHb8BzU9t4yNB7LYFpdNabmFDl4GRvTyZVA3r3axvpNoHEVRSH/rHSrOpBP84rOoDc1z63JNymZWJ23gKv9+TIm4/qJ9byotVlKzSkk8XVLd0nNufiatRkWInzNhHZwJC3QhvINLuxn5KOz3migBBwk4rcn2w9l8+vNJRvf149ZRHW1dTg0Wi5X9Z4d4H0suQqNW0SfSyIjevkQGukjHTXFZypOSOP3aG3iMG4txwvhmO8/Kk+tYl7qV0UFXMaHT6Mt6bX5xBYnnWnlOF5OcWUqlperS4eWmJ6yDS3VLT7CPo11/KBFXzl6viZc9k3FdCgoKMJlqzrXg5+d35VUJUY9B3bw5lVHKur0ZdPRzYlA32w+TziuqYNPBLDbHZlFQYsbTVc/EoYEM7e4jSyWIK+YQGopzv74UrF2P67ChaD3cm+U8N4ReTVllOetSt+KoNTAmeEiDX+vhoqdvZ0/6dvYEqubnScksJfFMVeg5kVbErmO5QNXQ9hA/p6rbWmeDj/x+CFtoUMDZv38/H3zwAfn5+bWea+iaVUJcrsnDg0jNLGXJmlMEeDnapC+LVVE4eqqQPw5kcjAhH0WB6E7u3NHLl+6d3OucOl+Iy2WcOIGS/QfIW7Uanztub5ZzqFQqJkdcT7mlnFVJ6zFoHBgacGXL2Oi06uoWmzH9qrblFpaTeKakuqVn3Z4MfrOmA+Dt7kBYgHN1B+ZAHyeZHkE0uwbdovrb3/7GhAkTGDlyJHp9673fKreoWp/CUjMvfx6HWq3iX3dE4+LYMv1aisvODfHOJDO/HBdHLUO6ezOspw8+HjLEWzS9nG++o3DDHwQ+/ST6gA7Ndh6L1cLCI99wOPcE07tMJMa3Z7Ocp8JsJTmzpHq0VsLpYgpLzAA46NSE+J8PPGEdnHH504CCy13RXTQ/e70mNqoPzuzZs1m4cGGr71sgAad1Skov5j9fHSUy0JW/3dK52T75KYrCyTMl/HEgk93Hcqm0KIQHuDCity99I40yR4hoVpbiElL//TwOEeH4PzCnWc9ltlby0aGlJBSc4q5uU+ju1aVZzwdVv185hRVnA09VK09qZmn1Wlu+Rofqvjylpkp+2n6m1pIld1wbIiHHhuz1mtiogPP5558TGBjY6pdlkIDTem05lMXiX5O4tr8/twwPbtJjl5st7DxSNcQ7JbMUB52aQd28GN7LlyAfpyY9lxAXk//rGvKWr8D/kYdw7BzZrOcyVZbz/qElnC7O4N7oaXQ2dmrW89WlwmwhKaO0KvSc/VdUVlnv/h4uOl69t1er/7DdWtnrNbFRAeff//438fHx+Pj44OHhUeO5559/vkkKbAkScFq3L9cm8ceBLO65IZyYLp6NPt7pnDL+2J/J9sM5mCosBHo7MqKXLwO7eWHQN27SNSGuhLWigtTnXkLj5krAY/9ApW7eVsMScxnvHfyMHFM+D/S4g1C3oGY936UoikJ2QTlPfxJb7z56rRpfowN+RgO+RgN+F/xzbqFb2O2VvV4TGzWK6uqrr261rTcXTvQnWrdbR3UkNauMz345SQdPA4FX0LpSabGy70QeGw9kcTy1CK1GRd/ORkb08iU8QIZ4C9tS6/UYbxxP9mdLKNmzD5f+/Zr1fM46R+7rMZ13D3zG/Lil/K3nTAKcbTcyVqVS4eNhwNNVXz33zoWcDBoGd/MmI89ESmYp+07kVd/igqrFe/2MDvh51gw/vh4O6HXyoaW9kXlwLsJe02p7VlBcwdwlh9Hr1Dw5vRvOhoZ9YsstLGfjwSy2xGZRWFqJt7sDw3r6MKS7N66tZLZk0T4oViunX3kda5mJoGefQqVr/p/PHFM+7xxYhFWx8lCvWfg4ejX7OS+mzrW16uiDU2mxkl1QTmZeORl5JjLzTGSc/ZdfbK5xTKOr/myrj8P54GM04O2ml3l7Gsher4mXfYtq48aNDB9eNXX4+vXr6z1wa2rZkYDTNiSkFfHG18eICnHjwZsi6x2qbVUUDicV8Mf+LGJP5oMCPcI8GNHbh26h7rWmrBfCXpQePkLGu+/jOflm3EePapFzppdm8e6Bxeg1Oh7qNQujQ/PMx9NQjR1FZaqwkJVfXh14qsNPronSckv1fmq1Ch93hxrB51z48XDRSavuBez1mnjZt6i2bNlSHXA2bdpU74FbU8ARbUN4oCtTr+7Il2tP8eGKE6RkltX4I9gtxJ2th7LZeDCL7IJyXJ20jB3QgWE9ffByc7B1+UJcklO3KAxRXcn/+RdcBg9E49T8nd39nXy4r8ftzDv4OR/EfsHfes7CVW+7ddS6FZ2kw6mVWHLz0HgaMfaeADQ84Bj0GoJ9nQj2rfneKYpCSVnl2eBTs+XnaHJh9TpcUDWc3dfjbKuP54W3vKS/T2sgt6guwl7Tqqj6I/XG10c5kVpcY7taBQqgKBAZ5MqIXj70iTTKAoGi1SlPSeH0K//B/ZrReE6a2GLnTShI5sNDX+Dr6MWDPWfipG35eZ+Kdu4i54ulKBXnbzOp9Dq8pk/DdUD/ZjuvVVHIL6ogI6+8xu2uzDwT2QXlNfr7uDhq8fVoX/197PWa2CRLNQhhL1QqFdkFtTshWpWqT11P3N6NAG9HG1QmRNNwCA7GZUAMhet/x23EMLSejR852BDh7h25K2oKCw4v4+O4r7iv++04aFp2gte873+sEW4AlAozucu+Revhgd7fD7Wra5PfPlKrVHi6OeDp5kBUiFuN58719/lz+DlyqpBtcTk19j3X38fP6FAj/Hi5O1xyHi+Z4LDpSMARrVZeHaMsAMrNVgk3ok0w3ngDJXv2kbdiNT53zmix80Z5RjCjyyQ+O/o9Cw9/wz3RU9Gqm/dyYS0vp2TvPoq2bMNSUFD3PqWlpL/1DgBqJyd0/n7o/P3Q+/tX/7/Wy6tZhtdrNWr8PR3x96z9t8VUYakOPec6PGfkmdh1NLfO/j7V4eeC217uzjp2Hs2p0bk6t6iCJb+dApCQcwUk4IhWq76hpJ6urXc5ESEupPX0xO3qkRSsWYfb6FE4BLfcPDW9fbphslTw1YmVfH70B2ZG3YJG1bTBQVEUKk4lU7R1G8W79qCYTGh9fVA7OmItK6u1v8bDHe8Z0zGnZ2BOT8ecnkFpbBzFW7dX76PSatH6+aL390Pn54fO3x9dBz90vr6om2mpIYNeQ0c/51rr5V2sv8+R5IJa/X0qLQoWa81eIxWVVpZvSpOAcwUuGXCsViuHDx+ma9euaLWSh4T9uGlYYJ1DSW8aFmjDqoRoWu7XXUPRlq3k/fAj/g892KLnHuTfG5OlnOWJv7HsxCpui5zQJKMPLSUlFO/YRfHWbVSknUal0+Hctw+uQwbjEBFO8a7ddfbBMU6aiFO3KOgWVet4VaGn6l9Fejrlp1Io2bu/qkMegEqF1tOzjlYffzQuzdOZWqVS4eKkw8VJR3iga43nzvf3OR9+1u/NqPM4uUUVFBRX4O4iH94uxyUTi1qt5vXXX2fx4sUtUY8QDXbuE43crxZtmcbJCY/rryP32x8oPXyk6gLfgkYGDqSs0sSvyRsxaByYFHbtFfV9UaxWTMdPULRlK6X7D6JUVqIP6YjXtKm49O+H2vH8rZ9zHYnzfrxgFNXECfV2MNY4O6MJD8MQHlZju9VspjIjk4qMDMxn0jFnVAUg0/ETKObz4Unt4nJB6PGtavXx90NrNDbbbNI1+/tUbdt/Iq/OVmmAx+YfIMTPiR5hHvQM9yDY10mmuriEBo2ieuWVV7jlllvo3LlzS9TUbGQUlRCiNVLMZlKfn4va0UDAk481+xIOtc6vKCxPXMMfp3dwXcdhXB8yssGvrczLo2jbDoq3bqcyJwe1oyMuAwfgMmQQDkG2WRpCsVqpzM2t0eJz7raXtaS0ej+VXo/Oz7fqVlcHf/R+Va0/Ol+fZpmAsb4JDscP6oACxCYWkHi6GAVwd9ZVhZ0wd7qGuOHQAiO37PWa2Ki1qBYsWMCWLVuIiYnBy8urRnqfOnVq01XZzCTgCCFaq+Jde8hauAjvWXfgOmhgi5/fqih8dWIlOzMOMLHTNYwKGlTvvorFQunBWIq2bqMs7ggoCoYunXG9ajBOvXs2W1+YpmApKjobejKqWnzOVIWfytzc8zupVGi9vav6+Zy9zXXu1ldj5yy61CiqolIzh04WEJuYT1xSIaYKC1qNiq4d3aoDj2czzfdlr9fERgWc999/v97nHnjggSuvqoVJwBFCtFaK1crp19/AUlhE0HNP2yQkWBUrnx39ngPZR7gt8gYG+fep8XxFegbFW7dRtH0n1qIiNO7uuAwehOtVA9H5+LR4vU3JWlFRfYurRqtPZhZUnl8BXePmVh12dH5+6DtUhR+Nh0eTD2uvtFiJTyvmYEI+BxPzycovByDIx5EeYR70CPOgk79zvbO9Xy57vSY2KuC0ZhcutikBRwjRmpUdO0762+9inDQRj2vH2KSGSquFBYeXcSwvgZldb6aXW0T18O7yhERQq3Hq0R3XIYNx7BaFStM2J707R7FYqMzJxZyeXtXqc8EIrwtHgqkcHM6HHv+qW146v7O3uy54j4p27mpw36MadSgKGXkmYhMLOJiQT3xaEVYFXB21dA9zp0eYB91C3HF0uPLvx5VcE6/067kcjQ44Z86cYcuWLeTm5uLp6cmQIUPo0KFDkxbZ3CTgCCFau/R5H1KekEjQC8822+ifSymvrOCrDR/jefAU0SkWVOUV6Hx9cRkyGJeBA9C6u136IG2coihYCovO3+bKOHvbKz0dS17++R3VanQ+3uj8/VGsVsoOHwHL+blzrnQG5xJTJYeTCjiYmM+hkwWUmixo1Coig1zpGe5OzzAPfDwub5bqy70mttSM1I0KOLt37+bdd9+lb9+++Pj4kJ2dzZ49e/jb3/5GTExMkxXZ3CTgCCFau4rTZ0h76RXcrh6J1+SbW/Tc54Z3F23dhjntNJUaFfEhBjpfM5GwXlfJwpQNZDWZMGdkXnCrKx1zeibm9PS6X6DRYIiMQOPmisb17L9z/+/mVv3/9bWWWawKiaeLOZiYT2xiPmdyTAD4exqq++2EB7pecpbly70mJj/1byy5ebW/HE8jHee+0ODjXEqjlmpYunQp//znP+nevXv1tri4OBYuXNiqAo4QQrR2+oAOuFw1iMLfN+I2Yjg6n+adFuFiw7vpFcU3J5bxe+lm/locRrBr62rVtxW1wYBDSEccQjrW2H7y/r/V/QKLBaW8nPKEbCxFRSgVdQ8lVzs7oXF1uyD8nA9BgW6udOzoysTugeRa9RxKLuFgYj7r92awZnc6Tg4aojtVtexEh7o3yWKidYWbi21vag36CnJzc4mKqjn3QteuXcnJyannFUIIIZqL8YbxlOzcTd6KlfjePbtZzlFreLeTE65Dh9Qa3v1Ajzv434FFfHioagVyf+fW3ZnYljSexnpbPAIe+0f1Y6upHEtRIZbCIixFZ/8VFp1/XFhIeUoKlsIiFJOpznOFOzrS2dUVlasLxWoDWaU6Undp2L1DzyatAaO/FyERfkRFBdDB3+2KWucu9vW0hAYFnNDQUFauXMlNN91UvW3VqlWEhoY2U1lCCCHqo/Vwx33M1eT//Cvlo6/GITSkSY5b3/Bu48QbcOrdC3Udc794OLjxQI87ePfAZ3xw6Ase6jULL0PLXMDaGuPECXXP4DxxQo391AYH1AafBo1Ms1ZUYCkqxnouEBVeEIjObnMuysFQWERg6fk5gDgD7INy4Lhah9XRGUdvIwYPV7RubmhcXc62EJ1tMTp3m8xgqA5Djt2jKd64uVZNjt2jr+j9uVwN6oOTlpbGa6+9Rnl5OV5eXuTk5ODg4MBjjz1GkI0maroS0gdHCNFWWMvKSPn3C+g7+OP/yEON6v9S//DuQQ2+BXa6JIP3Di7GUevIQ71m4a53vfSLRC0tMeqoPkpl5QXhp4jCjFzOpGSTeyaX8vwCHM1luFhNuFnL0ZnLqOsnTqXTVd8eq0g7XWPG6HNaqg/OJQPOubWoIiIiSEpKqh5FFRER0erWppKAI4RoSwp/30jOsm/we2AOTj26X/oFF7hw9e6mGt6dVJjG+7Gf42nw4G89Z+Ksa9ykd8J+VJgtnM5XsWV/MgcT8ykoKsfZUk6kEaK81IS6gbtiwlpUXN0yVHbkaL3H6/TBu01WW6NGUc2cObNNrEUlAUcI0ZYoFgupL8xFpdEQ+NQTlwwl1at3b9lG8e6q1bubenj3ifwk5h/6kgBnPx7ocQcGbfPMqita3rlroqIopGSWVs25k5hPUnoJAEZXPT3PzrnTJdiN9Oees/9RVFFRURw/frzVr0UlhBBtiUqjwfOmG8n86BOKt+3AdehVde735+HdKp0O5359cL2qavXuphzeHekRyp1Rt7Dw8DcsOLyMe6Onodc0/bpNwnZUKhUd/Zzp6OfM+MEBFJSYOZRYNZvy9sM5/HEgC51WzUDPPgzM+wOdcn5eH7NKQ0H/UXS8yPGbrE5Zi6p+0oIjhLB3iqJw5r9vUXHmDGqDAUteflXfjQk3oHV3o2jrthrDu12HDMYlpubq3c1hT2YsS44tp5tnJHdFTUGjbtszGrcHDbkmmiutnEgt4mBiPhv2ZRJVmMjI3H24VZZQqHXmd88+JPtE8uZf+zZZXY1qwamoqKB//6pOTrkXLjgmhBDCplQqFYauXShPPImlrGpIsCU3j+zPPgeod3h3c+vn2wOTpYJv4n9iyfEfmdHlJtSqll0FXbQ8nVZNt1B3uoW6s2FfJkfcwjjiFlZzp3JL3S9uYpcMOFarFS8vL26++WZ0zbA8vBBCiMYp3r6jzu1qZ2eCX3mxzuHdLWFIh36UVZpYlbQeg8aBWyPGyWzHosVcMk6r1Wp+++03NG18wTQhhGit6psZ1lpSYrNwc86Y4CGMCR7CtvS9rExaRxtf31lcwNlQd26ob3tTa1B74fDhw1mzZk1z1yKEEOIK1DczbEvNGHsp40NGMbRDDOtTt7E2ZYutyxEtZOrVHWutb6VRq5h6dUt0MW5gH5z4+Hh++eUXVqxYUauT8fPPP99sxQkhhLi0hs6AaysqlYqbw8dispSz+tQGDFoHhgW0zOR1wnYGRlVNErl8Uxq5RRV4uuq5aVhg9fbm1qCAM3r0aEaPHt3ctQghhLgC52a6tdUMuA2hVqmYFjkBk6Wc7xJ+waBxoL9fT1uXJZrZwCjvFgs0f9agYeKt2e7du9mzZw9z5syRYeJCCGFjZmslHx1aSkLBKe6MmkxP7662Lkk0kL1eE+sbJn7RPjgLFy6s8Xj9+vU1Hv/3v/9tZFnNLyYmhjlz5ti6DCGEEIBOreUv0VMJdg3gs6Pfcywv0dYliTbqogHnjz/+qPH4888/r/E4Nja26SsSQgjRpjlo9NwbPQ1fRy8+Ofw1JwtTbV2SaIMuGnDa+N0rIYQQNuKsc+T+HtNx07vyUdxS0orTbV2SaGMuGnBkQiYhhBDNxU3vwgM9puOg1vPBoS/JKsuxdUmiDbnoKCqLxcKhQ4eqH1ut1lqPhRBCiCvlafDg/h7TeffgZ7wf+wUP9ZyF0eBu67JEG3DRUVQPPvjgJQ8wb968Ji2oOckoKiGEsE+pxWd47+DnuOqdeajnLFz1LrYuSfyJvV4T6xtF1eaHiV9IAo4QQtivxIIUPji0BB9HL/7acyZOWoOtSxIXsNdrogQcJOAIIYS9O5KXwIK4rzA6uFOpWMgvL8To4M740FHE+PawdXntmr1eE69oHhwhhBCiJUUZwxnSIYZsUx755YUA5JUXsOzEanZnytQkouEk4AghhLArsTnHam0zW82sTtpgg2pEayUBRwghhF3JKy+4rO1C1EUCjhBCCLtidKh7mLgKFdvT92NtP11HRSNIwBFCCGFXxoeOQqfW1dimVWnxMhj56sRK3j24SGY+Fpd00Yn+hBBCiJZ2brTU6qQN5JUXVI+i6uvTnV2ZB1mRuJY39i1gWMAArg8ZgUHrYOOKhT2SYeIXYa9D4oQQoj0rMZexOmkD29L34Kp34aZO19DHJ1qWF2pm9npNlGHiQggh2gRnnSO3Ro7jkd534653ZfGxH3g/dgkZpfZ38RW2IwFHCCFEq9TRNYBHet/F5PDrSSk+w+t757MqaT0VFrOtSxN2QPrgCCGEaLXUKjVDA2Lo5d2VFSfXsTZlC3syD3FL+HV09+pi6/KEDUkLjhBCiFbPVe/C9C4T+VvPmTho9Cw4/DUfx31FjinP1qUJG5FOxhdhrx2qhBBC1M9itfDH6Z38cuoPFBSuCR7K1UGD0arlpkVj2Os1sb5OxvLdFkII0aZo1BquDhpMX59ofkj8jZ9O/c6uzINMDr+eLsYwW5cnWojcohJCCNEmeTi4MTtqMnO6346iwAeHvmDRke+qF/EUbZvcoroIe22OE0IIcXnM1krWp25lbcoW1Co1YzuOYHhAfzRqja1LazXs9Zoo8+AIIYRot3RqLdd1HM7jfe8j3K0jP55cw3/3LSCxINnWpYlmIgFHCCFEu+HtaOSe6Nu4K2oKZRYT7xz8jC+Pr6CoosTWpYkmJp2MhRBCtCsqlYqe3l3pYgzjt+RN/J62ndicY9wQejWD/fugVsln/7bAbr6LxcXF/Oc//2HGjBk88MADbN68GYDs7GyeeuopZs+ezeLFi2u8Zu7cuSQkJNiiXCGEEK2cg0bPhE6j+Wffewly9ueb+J94e/+npBSdsXVpognYTcBZsGABWq2Wjz/+mIceeoiPP/6YlJQUli9fzogRI5g3bx67du2qDjRbt27Fz8+P8PBwG1cuhBCiNfN38uGBHncwo8tN5JcX8ub+BXwb/zOl5jJblyYawS4CjslkYseOHUydOhWDwUDXrl2JiYlh48aNZGZm0r17d5ycnAgPDycjI4PS0lKWL1/OtGnTbF26EEKINkClUtHPtwdPxtzPsIABbDmzh5f3vM/OjAO0o8HGbYpd9ME5c+YMarW6xlCvkJAQDh8+THBwMAcPHsTd3Z2EhARuvvlmli1bxrhx43B2dr7ocdeuXcvatWsBePXVV/H29r6surRa7WW/RgghROt2r/90rssfwaf7v+HL4yvYkxPHnb0nE+xW93Dk9qK1XRPtIuCYTCacnJxqbHNycsJkMjFp0iQ+/vhj1q1bx3XXXYfFYiE5OZkpU6bwv//9j9zcXAYPHszYsWNrHXfMmDGMGTOm+vHljt+31zH/QgghmpczBh6IvoOdGftZcXIdT617nRGBgxgbMhwHjd7W5dmEvV4T7XqpBoPBQFlZzXudZWVlGAwGXFxceOSRRwCwWq08++yz3HPPPSxfvpzg4GAefPBBHn/8cbp3705QUJAtyhdCCNEGqVUqBvn3obtXF1YlrWdD2jb2Zh1iUti19PKOQqVS2bpEcRF20QenQ4cOWCwWzpw533P91KlTBAcH19hv7dq1REZG0rFjR5KTkwkPD0er1RIcHExyskzWJIQQoum56Jy4LfIGHu41GxedE4uOfseHh74kqyzH1qWJi7CLgGMwGBg4cCDLli3DZDJx9OhRdu3axfDhw6v3KSgo4Ndff+XWW28FwNfXl7i4OEwmE4mJifj5+dmqfCGEEO1AqFsQf+/zF24Ou45TRWm8umc+PyX9ToXFbOvSRB3sZi2q4uJi3n//fWJjY3FxcWH69OkMHTq0+vn33nuPfv36MXjwYKCqP82bb77JmTNnGDVqFDNnzqzzuLt372bPnj3MmTNH1qISQgjRJAoqiliRuJY9WYfwMnhwc/hYoj0jbV1Ws7LXa2J9fXDsJuC0BAk4QgghmtKJ/CS+jf+ZjLJsenh1YVLYtXgaPGxdVrOw12uiBBwk4AghhGh6lVYLv6dt57fkTSgoXNdxOCMDB6FtYyuV2+s10a5HUQkhhBCtlVatYUzwEPr5dOeHxF9ZlbSenRkHmBIxjkiPUFuX127ZRSdjIYQQorUzGty5q9ut3Bt9GxbFyrzYz/n86A8UVBTZurR2qc234FzYyVgIIYRobt08I4lwD2Vd6hbWpmwlLvcE14eMZGhADBpZqbzFSB+ci7DX+41CCCFah6yyHL5L+JWjeQkEOvsxJWIcoW6tc1Jae70m1tcHR6KkEEII0Ux8HL2YEz2N2VGTKTaX8vaBT/nqxCpKzKW2Lq3Na/O3qIQQQghbUqlU9PKOoqsxnF+TN/J72g4OZh9lQqfRDPTrjVqWfGgW0oIjhBBCtAAHjZ4bO43hn33uoYOTD8tOrOJ/Bz4ltfjMpV8sLlub74MjMxkLIYSwN4qisDszlh9PrqXEXMqwgP5cHzKCuNwTrE7aQF55AUYHd8aHjiLGt4etywXs95ooE/0hnYyFEELYl9JKEz8lbWDLmd04qB0wK5VYFEv18zq1jqmR4+0i5NjrNVE6GQshhBB2xklrYHLE9fy9919qhRsAs9XM6qQNNqqudZOAI4QQQthYsGuHWuHmnLzyghaupm2QgCOEEELYAaOD+2VtFxcnAUcIIYSwA+NDR6FT62ptD3EJoB11l20yEnCEEEIIOxDj24OpkeOrW2w8HNwIdQ1if84Rlp1YhcVa9y0sUbc2P9GfrEUlhBCitYjx7VFjxJSiKPx86nd+S9lMXnkhs6MmY9A62LDC1kOGiV+EvQ6JE0II0b5sT9/P1/Gr8XP05t7ut9mkX469XhNlmLgQQgjRSg3y782c6GnklRfw9v5PSS1Ot3VJdk8CjhBCCNEKdDGG8VCvWahUKt49+BmHc+NtXZJdk4AjhBBCtBIBzn480usufAyeLIj7ii1n9ti6JLslAUcIIYRoRdwdXPlbr1l09Yzgm/ifWHFyLdb20522wSTgCCGEEK2Mg0bP3d1uZUiHfqxP3cbio99RYTHbuiy70uaHiQshhBBtkUalZnL49XgbjPx4ci0FFUXc3W0qLjonW5dmF9p8C87u3buZP3++rcsQQgghmpxKpWJU0GDu7HoLqcXpvL3/U7LKcmxdll2QeXAuwl7H/AshhBB/drIwlQVxywCFu7vdSph7xyY9vr1eE2UeHCGEEKIN6+QWxCO9Z+Okc2Re7BL2ZsXZuiSbkoAjhBBCtBHejp483Gs2Ia4BLD76PWtTtrTbhTol4AghhBBtiLPOift73EEfn2hWJa3n6/ifsChWW5fV4mQUlRBCCNHG6NRaZnSZhLfBgzUpW8grL+DOrre0q4U6pQVHCCGEaIPUKhXjQ69mauQNHM9L5J2Dn5FfXmjrslqMBBwhhBCiDRvs34d7uk8jx5THW/sXktZOFuqUgCOEEEK0cVHGcP6v152oUPHOwc840g4W6pSAI4QQQrQDAc5+PNx7Nt4GIx/HfcXWM3ttXVKzavMBR2YyFkIIIap4OLjxt56z6GIM5+v41aw8ua7NLtQpMxlfhL3O2iiEEEI0hkWx8l38z2xN30sf727c3mUiOvXFB1bb6zWxvpmMZZi4EEII0c5oVGqmRIzDy2BkZdI68iuKuLvbrW1qoc42f4tKCCGEELWpVCpGB1/FrK43k1J0mv8d+JSsslxbl9VkJOAIIYQQ7Vgfn2ge6DGDUnMZb+9fyMnCFFuX1CQk4AghhBDtXJh7MA/3vgtHrSPzDn7O/qzDti6p0STgCCGEEAIfR08e7j2bYNcAFh39jvWpW1v1Qp0ScIQQQggBgIvOiQd63EFv726sOLmOb+J/brULdcooKiGEEEJU06m1zOx6M15JHqxL3UpeeQGzut5s67Ium7TgCCGEEKIGtUrFhE6jmRIxjmN5Cbx78DNyy/JtXdZlkYAjhBBCiDoN6dCPv0TfRrYpj+f+eIvTJRm2LqnBJOAIIYQQol7dPCN4qOcsFEXhfwcWcTQvwdYlNUibDziyFpUQQgjROIEu/jw38u94GTz46NBStqfvs3VJlyRrUV2Eva67IYQQQrQ0b29vUtPTWHT0O47mJXBN8BCuDxmFWqWyaV31rUXV5ltwhBBCCNE0DFoH7uk2lUH+fViTsoUlx36g0lpp67LqJMPEhRBCCNFgGrWGqRHj8TYYWZW0nvzyIu7uNgVnO1uoU1pwhBBCCHFZVCoVY4KHMLPLJE4VpfH2gU/JtrOFOiXgCCGEEOKK9PXtzoM97qDEXMZb+z8lqTDV1iVVk4AjhBBCiCsW5t6Rh3vNxlHr8P/t3WtMVGcex/HfDAOcIBM7TkSLq4KbFV5sg61YL/W+1qQhWRovYMqm66YNqYsa28RoU/vC2qhJ+0YrKl7StQ3x0klaHI3dZN1OilIb3aA1oQ3bxPESMQYKtXQYBmbYF01nO0WxCp4znPl+XjHPPJznP4cXz4/nOeeMqi9/qEutX1tdkiQCDgAAGKScLK/WFf1N40aM1T++9umzG19Y/kWdXGQMAAAGLTtjhP7+xF9U21ynuiv/Ulu4QxPc43TqakDt3d/LkzlSJXkLVJzzhCn1EHAAAMCQyEhL118Ll+pE8LT+feMLOVr+oz79tJLT3v29jv73pCSZEnLYogIAAEPG6XDoz/mLlOUy4uHmZz2xHp0MfmZOHaaMAgAAUkqoN3zX9vbu700Zn4ADAACGnCdz5AO1DzUCDgAAGHIleQvkcqQltKU701WSt8CU8Qk4AABgyBXnPKE//W5W/LUnc6TK/1DCXVQAAGB4K/D8Xv+8Xq9Vf6xQgWeSqWOzggMAAGzH9gHnwoULqqmpsboMAABgIttvURUXF6u4uNjqMgAAgIlsv4IDAABSDwEHAADYDgEHAADYDgEHAADYDgEHAADYDgEHAADYDgEHAADYDgEHAADYjqOvr6/P6iIAAACGEis4A9i4caPVJSCJ8RUgg5MK52+4fcZkqtfKWswa+1GO8yiOPdzmRAIO8JCmTp1qdQnDWiqcv+H2GZOpXitrMWvsRzlOMv0trULAAR4S33E2OKlw/obbZ0ymeq2sxayxH+U4yfS3tAoBZwCLFi2yugQAAJLCcJsTucgYAADYDis4AADAdgg4AADAdlxWFzDcNDc369ChQ3K5XPJ4PFq9erVcLk4jACD1dHR06N1331VaWpqcTqfWrl0rj8djdVmSuAbngX333XfKzs5WRkaGDh8+rPz8fM2YMcPqsgAAMF0sFpMkOZ1OBQIBtbW1aenSpRZX9ROWHh7QqFGj4j+npaXJ4XBYWA0AANZxOv9/pUtXV5fGjx9vYTWJUjbgfPrppwoEArp27ZqeeeYZVVVVxd/r7OzUnj179NVXX8ntduuFF17Q7NmzE37/9u3bamxs1JIlS8wuHQCAITWYOTEYDGrfvn368ccftWnTJivKv6uUDTgej0dLlizRpUuXFIlEEt47cOCAXC6X9u/fr2AwqG3btmnixInxZBoKhVRdXa01a9Zw/Q0AYNgbzJyYl5enrVu3qqGhQR9//LEqKyut+Aj9pOxdVNOnT9fTTz8tt9ud0B4Oh/Xll1+qvLxchmGosLBQxcXF+vzzzyVJ0WhUO3bs0PLly5Wbm2tF6QAADKmHnRN7enrifbOyspSZmWlq3QNh+eFXWlpa5HQ6E8LLxIkT1dTUJEk6e/asvv32W/l8Pvl8Pi1evFizZs2yqlwAAB6Z+82JV65cUW1trZxOp9LT07Vq1SqrSu2HgPMr4XBYWVlZCW1ZWVkKh8OSpLlz52ru3LlWlAYAgKnuNydOnjxZmzdvtqK0+0rZLap7MQxDXV1dCW1dXV0yDMOiigAAsMZwnhMJOL/y+OOPKxqNqqWlJd529erVpLr1DQAAMwznOTFlA040GlUkElEsFlMsFlMkElE0GpVhGJo+fbqOHj2qcDisb775RufPn2dbCgBgW3acE1P2ScbHjh2Tz+dLaFu2bJnKysrU2dmp3bt36/Lly8rOzlZFRUW/5+AAAGAXdpwTUzbgAAAA+0rZLSoAAGBfBBwAAGA7BBwAAGA7BBwAAGA7BBwAAGA7BBwAAGA7BBwAAGA7BBwAAGA7BBwASW/fvn39nrL6S2VlZbp169YDHbO+vl5vv/32YEsDkKR4kjEAU509e1YnT57U9evXlZmZqZycHM2bN0+LFy+Ww+F4qGOWlZVp586dGjt27BBXC2C4clldAIDU4ff7dfz4cb300ksqKiqSYRgKBoPy+/1auHCh0tPT+/1OLBaT08liM4AHQ8ABYIpQKKRjx46pqqpKM2bMiLfn5+dr7dq18dfV1dXKyMhQa2urmpqatH79etXX18vr9WrFihWSpOPHj+vEiRNyOBwqLy8fcNxAICCfz6c7d+7I7XZrxYoVmjNnjgKBgE6fPq0tW7aorq4uYQust7dXs2fPVlVVlUKhkA4dOqTGxkY5HA4tWLBAZWVlhC4gyRFwAJiiublZPT09mjZt2n37njlzRq+//ro2bNig3t5e1dfXx9+7ePGi/H6/3nzzTeXk5KimpuaexwmHw3r//fe1bds25ebmqr29XZ2dnf36lZaWqrS0VJLU2tqqN954QzNnzpQk7dq1S4899ph27typ7u5ubd++XV6vV88+++yDngIAJuJfEACm+HkFJS0tLd62adMmrVy5UhUVFWpqaoq3T5s2TYWFhXI6ncrIyEg4TkNDg+bPn68JEybIMAwtX758wHEdDoeuXbumSCQij8ej8ePH37NvJBLRO++8o+eee05PPfWUOjo6dPHiRa1cuVKGYWjkyJEqKSlRQ0PDQ54FAGZhBQeAKdxut3744QdFo9F4yPn5LqZXXnlFv7zfwev13vM47e3tmjRpUvz16NGj79nXMAytW7dOfr9fe/fuVUFBgV588UWNGzfurv337Nmj3NxcPf/885J+Ws2JRqOqrKyM9+nr6xuwPgDJgYADwBSTJ09Wenq6zp8/n3ANzt0MdDeVx+NRW1tb/HVra+uAx5oyZYqmTJmiSCSiI0eOqKamRm+99Va/fp988olu3rypLVu2xNu8Xq9cLpcOHjyYsPIEIPmxRQXAFCNGjNCyZct08OBBnTt3TuFwWLFYTMFgUN3d3b/5ODNnzlQgENCNGzfU3d2tjz766J59Ozo6dOHCBYXDYblcLhmGcdeLgxsbG3Xq1CmtX78+YUvM4/GoqKhIH3zwgUKhkGKxmG7dupWwnQYgObGCA8A0paWlGjVqlOrq6rRr1y5lZmZqzJgxqqioUEFBwW86xpNPPqmSkhJt3rxZTqdT5eXlOnPmzF379vX1ye/367333pPD4VBeXp5efvnlfv0aGhp0584dvfrqq/G2OXPmqLKyUqtXr1Ztba1ee+01dXV1acyYMfELkgEkLx70BwAAbIctKgAAYDsEHAAAYDsEHAAAYDsEHAAAYDsEHAAAYDsEHAAAYDsEHAAAYDsEHAAAYDv/A8tex4EV3P2NAAAAAElFTkSuQmCC\n", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -1094,8 +1089,8 @@ " \n", " 172\n", " 0.348239\n", - " 7.890168\n", - " 22.423467\n", + " 7.890169\n", + " 22.423468\n", " \n", " \n", " 226\n", @@ -1155,7 +1150,7 @@ "32 NaN NaN \n", "58 NaN NaN \n", "105 5.915084 17.124319 \n", - "172 0.348239 7.890168 \n", + "172 0.348239 7.890169 \n", "226 0.581112 11.113093 \n", "289 0.718922 5.454031 \n", "369 0.650537 1.753594 \n", @@ -1170,7 +1165,7 @@ "32 NaN \n", "58 NaN \n", "105 39.468954 \n", - "172 22.423467 \n", + "172 22.423468 \n", "226 14.217672 \n", "289 5.491014 \n", "369 1.457227 \n", @@ -1198,7 +1193,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1207,7 +1202,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -1364,14 +1359,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -1545,12 +1530,12 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" + "print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1603,14 +1588,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -1673,7 +1656,7 @@ " \n", " 67\n", " 0.111825\n", - " 22.012257\n", + " 22.012258\n", " 27.776320\n", " \n", " \n", @@ -1705,7 +1688,7 @@ "31 NaN NaN \n", "42 5.935871 31.438978 \n", "53 0.519098 12.655752 \n", - "67 0.111825 22.012257 \n", + "67 0.111825 22.012258 \n", "85 0.298172 6.420185 \n", "113 0.975078 1.057040 \n", "142 1.867447 3.323424 \n", @@ -1740,7 +1723,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1749,7 +1732,7 @@ "{'ratio': 10.0, 'slope': 0.8, 'curve': 0.8, 'prune': 0}" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1764,7 +1747,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -1966,14 +1949,12 @@ }, { "data": { - "image/png": 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\n", 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HNC6Pn4JD2llcRESkKSjgNAIt+CciItK0Qgo4Ho+HnTt3snnzZnbu3InHo56JE2kXb8ZmMbJDA41FRESahPlEhatXr+bjjz9mw4YNmEwmYmJicDqdeL1e+vTpw8UXX8ygQYPC1dYWI7DgnwKOiIhIkzhuwPntb39LbGwsWVlZ/OIXv8ButwfKSktL+fbbb/nkk0945513ePLJJ8PS2JYk02HhvT2HcFb7iInSk0AREZFwOm7AmThxIp06daq3LCkpiaysLLKysti5c2ejNa4ly3RE4wfyit30SrU2dXNERERaleN2LRwv3JxqvdamS/LhgcYt/zHVrvnvYB5xKW0HDcY84lJ2zX+nqZskIiJyQiE9O3n//ffJy8sD4LvvvuNXv/oVd911F1u2bGnMtrVoNouR9glmdrTwmVS75r9D31lP4zhYiBE/joOF9J31tEKOiIg0ayEFnA8++IC2bdsCsHDhQq644gquvfZa5s+f36iNa+kyHdFsL3Lj97fcBf+6zP8LVk9wSLN6qugy/y9N1CIREZEfF1LAqaysxGaz4XQ6ycvL47LLLmPEiBHs2bOnsdvXomU6LJRV+Sgqb7k7i9sPFh33vKvaF+bWiIiIhOaE08QPS05OZsuWLezatYtevXphNBqprKzEaAx9dlB5eTmzZ89m3bp1xMfHM3bsWLKysk74mieeeIJvv/2WhQsXYjKZTvk6TeXoBf9S4kP6UTc7JW0cOA4W1jlfFO/gobf3cP4ZsYzoHt9iP5+IiESmkP5Wuvnmm/nzn/+M2WzmgQceAGrWyOnatWvIbzRv3jzMZjNz584lLy+PqVOnkpGRQXp6er31ly9fjs9Xt4fgZK/TlNLaRBFtNrC92M3QLrFN3ZxTsuOWScTNejroMZXLHM36m26nb1oMn20p5z+by+nX0cqI7vH0So3GYDA0YYtFRER+5BHV3r17ARg4cCBz5szhpZdeIjMzE4BzzjmHhx56KKQ3cblcrFq1ijFjxmC1WunZsyeDBw9m2bJl9davrKxkyZIljBs37rSu09SMRgOdk1v2gn9xN1zBixffQWFCCj4MFLVJYf0dD3HmL69nYlYy065OY1SfBLYXupnxaSG/+2Avn28tp8qjx1ciItJ0TtiDM3XqVAAGDBjAwIED6d27N2ZzzUsO/zsUBQUFGI1G0tLSAucyMjLYuHFjvfUXLFjAxRdfTGJi4mldJycnh5ycHACmTZsWtFhhuPTuWMW7a/YTm5BItLnlLfi3cmchn/e6gOv++AvaJFlJAM46qtxuh8yOKYwb5mPl1gN88E0Rf/+ylLe/OcTI3nYu6+egbYKlqZovIifJbDY3yXelSEM7YUqZOXMm+/btY/Xq1bz//vvMnDmTHj16MHDgQAYMGEBycnJIb+JyubDZbEHnbDYbLperTt1t27axZcsWbr31VoqLi0/5OgDZ2dlkZ2cHjktKSkJqb0NKi/Xh9cE32/bRNSU67O9/upZuLKJTUhQx/kpKSipPWLdfO+h7cTLbitx8uqWM99cW8t7aQs7qEMPIHnH0aKfHVyLNnd1ub5LvSpFTldq5c73nf7Qbpl27dlx22WVcdtlluN1u1q9fz5o1a3j77beJiYlhwIABXHTRRUG9KseyWq04nc6gc06nE6s1eIVfn8/HvHnzuPXWWwODik/lOs1JF8eRBf9aWsApLPewo9jNdf3bhPwag8FA15RouqZEU1Lp4fPvyln2fQVrf3DSoU0UI3rGMbSzrUX2ZomISMtxUlNfLBYLgwYNCmywuWvXLtasWcPOnTtPGHDat2+P1+uloKCA9u3bA5Cfn19nYLDT6WT79u3MmDEDIDDIeNKkSdx///106dIlpOuEk6GsjKhNm3D36wf1BK02MSaSY01sL6oC4sPfwNPwVX5Nj83gDNuP1Kyf3Wbmmv6JjOqTQG5+JTlbynl9VSlvrTnIsK6xXNQtjuQ4zb4SEZGGF/Kv0VVVVeTn57Nly5bAP5WVlfz0pz/lnHPOOeFrrVYrQ4cOZdGiRbhcLjZv3kxubi7Dhw8Pqmez2ZgzZw7PPPMMzzzzDI8++igA06dPp1u3biFfJ5wsubnYf/Uror77DgDTrl1EL10KRz02y3REs6MFDjT+Mr+SMxwWHKcZQixmI+efEcf/u6wdD17cll6p0XyyqYxH3y1g9rIituxzNdpiiHMXLODC0aO54IYbeHnBgjrlh8rK+Pl99zHyxhu54IYbeOPddwEoKi3lpxMmcOHo0Xz42WeB+uPvv5+9hXWnzYuISPMS0t9cn3/+OX/9618xm81YLMEDRmfPnh3SG91+++3MmjWLiRMnEhcXx8SJE0lPT6eoqIjJkyczY8YMHA5H0MBit7smFLRp0ybwyOp412kq7qFDKXn+eap79QLAmpND/Isvsu8//8FvtRK1di0jN2/j6+hBlFZ6SLK1jB6LgoPV/FBazY2DEhvsmgaDge5to+neNpriiiOPr1bvctIxMYoRPWoeX1ka6PHV5u+/5//eeYd/zZ+PJSqKsXffTXZWFplH7Z/2tzffpHtmJq899xxFpaUMu/Zarr3sMt756CNGX3EFV//kJ4y9+24uu+giPl62jL49e5KaktIg7RMRkcYT0t+2f//733nggQfo16/fKb9RXFxcvdPKHQ4Hr7/+er2vadu2LYsXLw7pOk3FHxuL+/zzA8cVY8dSdfbZ+GuDWsz773P2p5/hm/A3dhS5ab/pPxhcLpxXXdVELQ5Nbn4lBmDQKT6e+jHJsWauHZDIFX0TWJVXyX+2lPPaqlL+sfYgw86I5cLucdhjTy8Mbt2xg0F9+mCLiQHgnIED+fCzz7jzllsCdQxAeUUFfr+fyspKEhMSMJtMRJnNuKqqqHK7MRgMeDwe5i5YwPzax6ciItK8hfSrstlspnfv3o3dlsgQHY3nzDMDh4cefZTC+a9hNhnYXuTG+q9/EfPPfwbKY//6V2JqH4s0F36/ny/zKuneLprEmLqDvRuSxWxkWNc4fnd5O6Zkp9C9bTQfbSrj0X8W8JflRWzdX3XKj696dO3K/9asoeTAASqdTj5dsYI9+/YF1ZkwZgxbd+yg/09+wkVjxvDklCkYjUauufRSlv73v4y9+26m/PKXvPrmm9wwalQgLImISPMW0q/IY8aM4bXXXuP6668nISGhsdsUWUwmjOkd6GTfx/YiNweeew5DeXmgOHrZMjxduuD86U8BSHj8cdznnIPr0kubqsXsKq1mX5mHS3qHb1C0wWCgRzsrPdpZKSr3sPS7cpZvK+frnU46JUUxokc8Z3e2EWUKfZp59y5duPOWWxhzxx3E2mz07t69zuy8pf/9L2f26MGSOXPI++EHxtxxB0MHDCAhPp6/P/88AAcOHeLFV1/lr3/6Ew88+SQHy8qYdPPNDD6NHk0REWlcIQWctLQ0Fi9ezL///e86ZYsWLWrwRkWiTIeFZVsr8PjBHH8kOJS8+ipUV9ccVFUR9d13eGpXi8btxj5pEhXjx1MVxoHUX+ZXYjLAwPSm6a1wxJm5fmAiV/ZLYNWOSv6zpYxX/1fCkjUHGN41jgu7x5JkM7Nr/jt0mf8X7AeLKGnjYMctk0i/5eqga429+mrGXl1z7qkXXyStbdug8jfefZe7br0Vg8FAl/R0OqWl8X1eHgP69AnU+fPcudx72228/dFH9OvVi2svvZTx99/PWy+/3Ng/ChEROUUhBZwXXniB4cOHc95559UZZCyhyUyOJmdzObsPVJNhP+ZnGBVV8+/oaIoXLIDaRzLGkhIwGqF2cTzTzp0k3XcfB3/9a6oHDYKqKgxeL35bw42T8fv95OZV0ru9lbjoxn089WOizUaGd4tjWNdYNu+r4tMt5Xz47SH+vfEQ1+z8gjFvvxDYI8txsJC4WU+zHoJCTlFJCQ67nR8KCvjXp5/y/quvBr1Hh9RUvvjyS84ZMIDC4mK25efTqUOHQPn2nTvZV1jIeYMG8e2WLVitVgwGA1XuljcrTkSkNQkp4JSXlzNmzBitQnsajt5ZvE7AOVbtz9mXmkrJvHlHTldV4enSBV/tMurRq1aR+MADFL/2Gp5evTDu24dp/36qe/Y8EppO0vYiNyWVXq4+K/TF/RqbwWCgV6qVXqlWCss8LN1azmUvzw/aABTA6qmiy/y/4Dkq4Nz24IOUHjxIlNnM1EceITEhgflLlgBwy/XXM3niRO793e+4aPRo/MCv77mH5KSkwOunvfQSj9x5JwDXXHoptz7wAPMWLuTBSZMa/XOLiMipM/hDGME5f/58OnfuzAUXXBCONjWavatXN9l7+/1+HvzHHnq1t3LbeaFtcfFjTHl5WD/+mMqf/Qx/TAy2118n4bnn2P/JJ/jsdqJWr8a8fXvNjK0QA88bX5Xy+dZy/nx9B2Kimu9qw20HDcZI3VvXh4H9X3/VBC0SiQzaqkFamtSBA+s9H1IPzvfff89HH33EP/7xjzobYD7xxBOn3bjWwGAw0KWBF/zzdu5MxS9+ETh2jRoV1MNj/c9/iPngA5zXXQeAbdEiTLt2UTZlSr3X8/n8fJVfSb8OMc063ACUtHHgOFh3wb2SNo4maI2IiDQ3IQWckSNHMnLkyMZuS8TLdFhY+4OT8ipvo4xv8dntuLOyAsdlU6ZQMWHCkTE8BQWYt28PlLd57DEADj71FAB5m3dTVulhSEYSzd2OWyYRN+vpoMdULnN0zUDjJmyXiIg0DyEFnAsvvLCRm9E6HB6Hs6PITd8OYZihZDDgO2rH97L77gsq9nTtGhjQDND9wbt5LD6N1JteAMCyciXezp3xnmCfsaaSfsvVrIcfnUUlIiKt03GfQ3z1VWjjGEKtJ9A52YLBANuLm8cMnIoJE6i47TYAPD4/C4dcz7aRV9Ts9O3xkDRlCrY33qip7PcTN3MmUd9804QtDpZ+y9V4Pv2I/V9/hefTj44fbswtY3sMERFpOMf95l+xYgULFy4kKyuL3r17k5aWRkxMDE6nk4KCAjZu3Mjy5cvJyMhg8ODB4WxzixVtNtIxMYrtzXDjzU0FLj7uNpy7Lqgdw2I0Ujx/Pv7aHdKNJSXELlqENy2N6rPOwlhSQvLPfsahBx6gasQIcLkwb9uG54wz6t1VPazMZvB4wGDAlJSEr7y8nuHIIiISyY4bcO6991527tzJJ598wosvvsj+/fsDZampqQwYMID77ruvSTe6bIm6JFvIza/E5/djbEbT7nPzK7FZDPRuXxtOjEY83boFyn3Jyexbvhy83poTVVW4+/fHV7vxZNR335F8662UzphB1fDhmHbtIuadd6i8/np87duH7XMYY2PxV1fjNxoxJyXhOXDgSJtFRKTVOGHffadOnbit9hFGVVUVFRUVxMbGEh0dHZbGRaJMRzTLvq9g3yEP7duc2lo1Da3a62fNLieDMn5kKwSTqeYfwNe+PQf/+MdAkadzZ0qfeQZ3374AmLdvJ/bvf8c5ahQA0Z9+Svzzz1P64ot4O3bEWFiI8cCBmlWbTQ034NpgseB3uzElJuIpKQkaYyQiIq1HyHOBo6OjsdvtCjen6egF/5qL9XucuDx+zj6NncP9CQlUjRiBv3aRvKoLLmDfF1/g7dw5UO7p3h1v7aDnmA8+wHHjjRgqKwGwfPEFcX/5y5FtK06BoXaVbWNsLN7SUgy6V0VEWq3mvdhJBGqXYMZmMTSrcTi5eZXERxvp0a6BA0FUVM1WE4B78GAOPP001O7G7fzJTyh95hn8tftyWdauJeattwIDguNnzMA+fnzgUubvv8e0c+cJ385st9eEHIMBU0KCem9ERFoxTS8JM6PBQJfk6GYTcFzVPtbtdnFeZiwmY/jGBPnat6fqqLE55XfdRfkvfxlYs8fTpUtQ/fjnn8e4fz/FtbO6bAsW4LdacV57bU0Fj4fq4uLT6gESEZHIoYDTBDIdFt7fcAhXtQ9rE68Y/M1uJ26vnyGdG27DzlN21HYSztodwA8ru/tuDGVlgePoZcvwJyRQcfXVjL//fj7YswdPz54c/P3vAYidOxdPZiZVtQtUGgsK8CUlNf0MLxERCQsFnCaQ6bDg90NesZueqU37F25ufiVJNhNdU5r3LvGHZ3RdOHo0W7ZtO1IwZAgAFoBt2+CDDwA402xm1XXXBQKOY8wYnFdcQdlDDwGQ+NBDuEaOxPWTn9S8/ssva7a5qJ0VJiIiLdtxA86vfvWrkC4we/bsBmtMa9El+fBA46YNOBVVPjbscTGyR3yzmrJ+IksXLw469nq9dDrnHHbn5tapW+bz1fyH38+hhx/Ge3hJA7cb0w8/YDh4EABDeTn2X/2KQ/feS+XPf46hogL7+PFUTJyI65JLwOkk5sMPcZ99Nt6OHY+M7WkhPzMRkdbouAHn7rvvDvz3999/z+eff85ll11GSkoKhYWF/Pvf/2b48OFhaWSkiY02kZpgZntx086kWvNDJV4fDDmN2VPNWu0AZwwGXLXT1QGwWChesCBw6I+OpnjePHzt2tVUr6rC27kzvtoB0Ka9e2nzxz9y4Kmn8HbsiGnHDpJ//nMOPvUUVcOHY9y3D9tbb+G88sqaEFVVhaGyEn9iokKQiEgTOW7A6d27d+C/X3nlFX79619jr92lGmDAgAE89dRTXHnllY3bwgjVxWFhwx4Xfr8fQxP9JZibV0lKnJkMe/NYj6fJREVRPWBA4NBnt3PgmWcCx95Ondj//vuBGV/+mBic116Lp7ZHyLxrF7GvvkrVuefiTU/H8vXX2O++m+J586geMADzt98S+8YblN1xB7727TEWF2Pcv79m1WdL8340KCLSUoU0wrWkpATrMYMzrVYrJSUljdKo1iAzOZoyl4+iiqZZZfeQy8umfVUMybA1WcBqMUwmfO3b44+LA2pmgJXdfz/e2ple7sGD2bdyJdX9+gHgzcjg0AMP4KldA8hUXEzU2rWBy0UvW4bj5psxFhfXHH/yCfbbbsNw4ABQMyU++j//0YwwEZHTEFLAGTx4MNOnT2fdunX88MMPfPPNN/zpT39i0KBBjd2+iNXUC/59vbMSvx/O7hyGXc1bA7M5sCKzt0MHKseOPbLo4fDhFL33XmDLiqrzz6f02WfxOWr3/TKZ8JtM+GNjAbDm5JD48MOBS8fOm0fKqFGBLScsK1diO+oRm6G8XGFIROQYIc2imjhxIm+++SZz586lpKQEu93OOeecww033NDY7YtYHRKjsJgM7ChyM7RzbNjfPzfPSVobMx0S9Ygk3Hxt21LVtm3guGrEiJoNS2tVjBuHKzs7MG3ek5mJ64ILAgHKunQp0Z9/TuXYsQDE//nPRP/3vxR++CEAtsWLMZaUUD5pEgCm/Hz8FktY9wQTEWlqIQUci8XCuHHjGDduXGO3p9UwGQ10TrY0yYJ/JZUethZWcVW/hLC/t/w4f3w8ntrxPlA3AB169FEM990XOHaNHEn1mWcGjs3ffYdpz57AccL06RjKyyl57TUA4qdNA6uVstprWP73P3wJCXiOGncnItLShbwOzrp161ixYgUHDx7kkUceYdu2bTidTvr06dOY7YtomQ4Ln2wuo9rrP/Emlw3sq3wnEMGzpyKdwYDfduT/nfv884OKD/3mN0HH5ZMmYXC5jrzc78d/1DYWCc88g+eMM2q20gDsEyZQ3acPZfffD9T0CHm6dMFdu+YQTmdgyw0RkeYqpDE4H374IXPnzqV9+/Zs2rQJqOnVeaN22Xw5NZkOC14f7CwJby9Obn4lGfYo2iW08tlTrUR1v364zz47cHzo0Ucpmzw5cFz63HOU3XVXUP2jt8qIe/FFopctqznw+2l7ySXEPf984DjhqaewrFwZODZ//31gE1URkaYSUsD517/+xW9/+1uuvvpqjLVri3To0IE9R3WDy8nr4qjZ3HJHcfgCzv4yD3nFbvXeSIA3PR1vp06B47L77sN5zTWB4/2ffFKzTxiAz0fF7bfjPuccAAxOJ9Gff455x46a4/JyHGPG1GycChjKykiaNOlIAHI6iV62LDCDTESksYQUcJxOJ47DMz5qeTwezGbt9HA6EmNMJMeawjqTKje/5jfrwQo4Eqro6MAUeUwmKm65JdAj5LfZKPz3v6msHZ/nN5s5MHUqVVlZABgqKjBUVWGonQFm/uEHkiZPxvL11zXH27bhuOoqLLUrURsLC7EtWoRx//6a9/N64fCK1CIiJyGkgNOrVy/eeeedoHMffvghZx41sFFOTZcwDzTOza+ka4qF5FiFU2kEMTG4LrkksEaQLzWVkr/9japhwwDwpKdTPH8+VbXjefwmE9VnnlmzESo1A6QTnn4a0969QM0A6Hbnnou59tG4efNm4p97DmNREVDTQ2Tcv18hSETqCCngTJgwgS+//JI777wTl8vFvffey//+9z9uueWWxm5fxMt0RFNc4eWAs/EX/Nt9oJrdB6r1eEqajtVKdZ8+gTWCvJ07c/Cpp/B07QqA+9xz2f/xx1T37AnUBKSKm2/Gm5oKgDkvD9vixYE1gayffELbyy7DWFhYc/z++ySPGRPYed6yciXxTz8NVTW9pOatW4n+9NNAIDKUldUssHjUoGsRiQwh/RqflJTE1KlT2bZtG4WFhSQnJ9O1a9fAeBw5dYcX/NtRVMWA9MYNHl/lV2IwwKBOCjjSTBmN+JKTA4eeM86g/Kh98VyXXhrYAR7APXAgBx97LPAaf0IC3vR0/LUrr5u3byfmX/+i7IEHALB+/DGx8+ezb9UqAGL/9jdiFy5k33//W3M8bx7RX3xByauv1tT/4APM27ZRfs89AER98w3GQ4cCPVKG0tKahRoTtOSCSHMT8nMKr9dLdXU1fr+f7t2746qddnrsFg5ycjrZLZiMNTuLN2bA8fv9fJlfSc920bSJMTXa+4g0uqO2FvF27oyzdksMqFk1uuqoTYArb76ZyptvDhxXjBtXE5Bqr1F14YU1O8TX8qWk4MnMDBxHbdmCJTc3EHBsixYRtXFjIOC0mToV8/btFC1ZAkDCH/6A8eDBwF5mtr//HXw+Kn/+c6CmR4moqMCUe2NhIX6rNbDPmYg0nJACzs6dO5k+fTpRUVEUFxdz3nnnsXHjRj7//HMmHzXd9ETKy8uZPXs269atIz4+nrFjx5JVOxDxaCtWrGDx4sUcOHCAqKgo+vfvz4QJE7DVrvvx+OOPs3Xr1kDvkd1uZ+bMmaF+3mYnymSgU1Ljj8PZWVrN/jIPl/bWF6m0Xv7ERDyJiYHj6n79AnuIATivugrnVVcFjg+vBRQ4fuABDBUVgePKa6/FeOhQ4NjbsWNgPBGAZd068HgCASduzhz8cXGBgJM0eTJeu50DtdPuk+68E09GBmUPPVRT//nn8XbqhPPqq4Gafct8qalU9+0LgLGgAH98/JFB4CISEFLAmTt3LmPGjGH48OHceuutQM1u43PmzAn5jebNm4fZbGbu3Lnk5eUxdepUMjIySK/dkfmwHj168OSTT5KQkIDL5eLll1/mjTfeYMKECYE6EyZMYOTIkSG/d3OX6bCw/PsKvD4/JmPjLPiXm1eJyQgD07VAm8ip8iUnw1GP0A5Plz+sYvz4oOPDiycGjv/0J/B4AsflEyYEHqcBVHfvjq9du8CxZfVqqt1HfvlJmD6dqhEjAgHHMXYszssuCwQix3XX4bzyykA7Eh5/nKoLLqDqoovA7yfmn/+kum/fmp3sfT7MW7bgTU2tGRPl9we1TaSlC2kQzQ8//MCw2i7Zw6xWK253aL0OLpeLVatWMWbMGKxWKz179mTw4MEsO7x42FEcDgcJRz3PNhqN7Nu3L6T3aam6OCy4vX52H2icDRN9fj+5+ZWc2d5KbLQeT4k0FV9KStCeYFUjRuA+77zAcfm991J5442B45JXX6VsypTAcfHf/075L34ROD704IO4Lr205sDvx33WWXjT0mqOq6uxrF4d2LbD4HLR5sknif7ii5rjigocN99MTO0eZsbSUlLPOQfj66/XHBcV4bj22ppB2bXHiQ89RNSaNTWvLy0l9uWXMR1eA6msjOjPPjuyxpHLhWnXLjhqFW2RcAqpByclJYXt27dzxhlnBM59//33pNbObPgxBQUFGI1G0g7/wQMyMjLYuHFjvfU3b97M1KlTcTqdREdHM+WoP+AACxYsYMGCBaSlpXHjjTced7p6Tk4OOTk5AEybNg273R5Se8NtoCkWVpSwz2mmfyO0cfOeCkoqvfwsqwN2e9KPv6AF8dbOpmmu/29FGtSx9/lR44sAeO45zMDh0Xy+L77AClgBfD7cX3xBdGws0YmJEBtL9Zw5WLt3x2q3Q1QUnvvvxzhoUM2fJ68XQ58+xHXsSKzdDmVlROXlYTIY8NvtGPbuJWrOHKwDB+IfNAjDzp1ETZlC9V//ir9bNwy5uUSNHk31a6/hHzYMw3//i/nWW/G8/jr+IUMwfP01piefxPv00/i7d8ewYQPG11/He8890KEDbNuG8fPP8V17LSQmwt69GLZvxz9wIFitUFlZs21IUhJowovUI6SAM2bMGKZNm8bFF1+Mx+Ph7bff5pNPPuGXh1c3/REulyswhuYwm80WGKh8rJ49ezJ//nxKSkrIyckhJSUlUDZu3Dg6duyI2WxmxYoVTJ8+naeffrresJWdnU12dnbguKSkJKT2hpvZ7yfeamT9zlKGdGj4P6g560uJMhnomuhttj+DU3U44ETa5xJpFDExNVPkD/95GTy45t+Hj8eNw2631/x5Mpng978/Uh4fD4sXHzlOTYVVq2oGbJeUQEoK5gUL8Kal4S8pwZiYiOWJJ3C3a4evpAST1Ypt9GgqY2LwlpQQVV5OnM3GIacTb0kJ0du2kfDZZxwcMwZvTAzWlStJfPJJCgcNwuvzEfPhh7T5/e/Z//77+Nq3J2bJEtpMncr+jz7Cl5JCzJIlxM+aReHbb+Nv0wbre+9he/NNSubMgZgYoj/9FGtODgd//3swm7H8979Y1qyh/Fe/AoOBqPXrMeXn47riCgBM+fkYS0up7t8fqOmxMng8+A7/feT3Bw14l6aTetREg6OF9LfpoEGDePTRRzl06BC9e/emsLCQKVOmcNZZZ4X05larFafTGXTO6XT+6Awsu91O//79gwYRd+vWjZiYGKKiorjwwgvp0aMHa2q7TFsqg8FApsPCjkYYaOz1+flqZyX9OlixRum3HBFpQGZzTRACiInB06NHYEaYLzkZ1xVX4KtdBd+bkUHZffcFHqFV9+1L6Ysv4q0dh1k1bBiFH32Et/YvK9dPfsK+Tz8N1K/KyqL45ZcDSwJU9+/PoYcewlc7pMGbkYHzJz8JjGnyW63427SpaSNgKioiatOmQHsta9cS+/e/B0KK9ZNPSJg+PfDRbG+8QdJRk2jiZ80iuXbFboCEJ57AUTv4GyD+z38m6aglDWLnziV+2rTAccybb2J77bXAsfXf/8b6r38Fji3//S+WL7888qPdtAnztm2BY2NBAcajf5Fzu7XA5Y8IeZp4ZmYmmUdNnzwZ7du3x+v1UlBQQPva58/5+fl1BhjXx+fzsbd2VdP6GAyGoJ2RW6rM5Gi++cFFRZW3QcfJfLe/ijKXT4v7iUjLYjbXBJRavuTk4DWSunYNLBAJ4B4y5MiO90DVxRdTdfHFgePK0aOpHD06cFz+q19RPmnSkeOJE6m46aYj9W+6CdcllwSOnaNG4R406Mj1s7KClhTwHjW2CsBYURE0w87y9dcYDx4MzKiLeestDD4frssvByBu7lz80dGBbVDaPPUUvqQkSg/PsJs8GW9aGgf+/GcAHDfdRHXXrhysDWXJP/sZ7r59AwPOk+66C3f//lTcfnvN9X7zG9wDBuC87joA4p99Fnf//lTVTtiJnTuX6rPOCrx/zJIlVJ95Jp5evcDvJ3rpUjxnnFGzb53XS9TGjXjbt68JsF4vpn378CUm4rfZanq33G6IimrSx4chBRyPx8Nbb73FihUrKC0tJSkpifPOO49rr70Wi8Xyo6+3Wq0MHTqURYsWMWnSJPLy8sjNzeUPf/hDnbrLly+nV69eJCcnU1RUxMKFC+lbO2OgoqKCrVu30rt3b0wmEytXrmTTpk2MP2bmQksUWPCv2E2ftIab6ZSbV0m02UDfNK1XJCIS5KhHTP74+KD1iLydOgVtQlvdvz9HTwOpOmr4A9QEoqOV3Xdf0PHBo3pzAEpfeimwIjfAgalTg3pkDj766JHeMaD8jjuCZtxVjB0btCRB1bnn4jmq08BntwctH2Davz8ocEV//jn+2NhAwImbO/fIPnNeL22mTqXsl7+sCTjV1SRNmULZnXdSMWECBqeT5PHjOTR5MpU334zh0CFSrrySQw89ROWYMRiLimh76aUc/PWvcV57LU3F4A+h+2P27Nns2bOHa6+9lpSUFAoLC3nnnXdo164dd9xxR0hvVF5ezqxZs1i/fj1xcXGMGzeOrKwsioqKmDx5MjNmzMDhcLBw4UI+//xzKioqiI2NZcCAAYwdO5b4+HgOHTrE1KlT2b17N0ajkQ4dOjBmzBj6HbWOxYnsXb06pHpNwVXt4543d3NFnwR+2q/Nj78gBB6vnwf+sYd+aVZuOz/5x1/QAnm9Xjqdcw67azdrFJHTExiDI62L318TsEwm8PsxFhfXPOaLi6tZUmDrVnx2e80YpOpqLLm5NSGwY0dwuYj5+GPcffvi7dIFQ3k5tjffpOq88/D06NHoTU8dOLDe8yEFnAkTJvDCCy8QGxsbOFdeXs7dd9/N3/72t4ZrZSNrzgEH4IkP9tImxsR9I1J+vHII1u128sLSIu6+0EG/DpG5/o0CjkjDUsCRluZ4ASekh2OJiYlU1W5Wd5jb7SYpKbKmHDe1TIeFHcVV+BpoTFFuXiU2i5HeqXo8JSIirUtIPTjvvPMOX3zxBZdeeinJyckUFxfz73//m/PPP5+uRw3y6tOnT6M29nS5D0+JrOXMzsY5ejQ4ndjvvbdOfecVV+D86U8xlJaS9PDDdcorr78e1yWXYNy7l8T/9//qlFfcfDNVw4djysujzVNP1Skvv+023EOHYt6yhYRnn+Wg08veQ9V0TrYQbTZSduedVJ91FlHffEP8Sy/Vef2hBx7A06MHllWriHvllaAyn9/PQwNuo+PA7kx0b6iZLXCMA7//Pb7UVKwff4ytdi+do5VOn44/KYmYd98l5v3365SXzJwJMTHELF5MTO16Q0HlL78MgO2117DWLi52mD86mtIXXgBqBrdFH9MD42vTJrCfT9wLL2BZvz6o3Nu2LQf/8Ae8Xi+Z55xD2YABQeWeTp049JvfADX7A5l37gwqr+7ePbCAWpvf/AbT/v1B5e6+fQObPCY++CDGgweDyquGDKFi4kQAku6+G8MxvwC4srICgwntRy3Mdlhzu/eOdTr3HsDBxx7D27kz0cuWRey9BxD/pz8R9d13QeUt/d4z3ngjRSNGtKh77/+Kizk7NpZuVqvuvRZ8753q957lq6/q1IUQBxl/8sknALz99tt1zh8uMxgMvPjii6FcTo7DGlUz4M1V7Sc65Plt9auo8uH2wpDONvjux+u3FAO+/ZaNh9dPql2BFSD666+D6vX67js+rf2DLiKR5cLRo9ly1BTqILWzhAB6W62sOc5CsBL5QurBiRTNfQyOz+/nvjd3MyTDxs+Gnt7KvH9ZXsTW/VU8c00axkba30pEIk9LHIMzYcoUrrv8ckaNGNHUTZEmcFpjcI61YcMGNm3adFoNkrqMBgNdHNHsKD69Bf9c1T7W7XYxqJNN4UZERFqlkALO7373OzZv3gzUjMeZOXMmzz33HP/4xz8atXGtUWayhR8OVFPlOfUVKtf+4KTa6+dsLe4nIiKtVEgBZ9euXXTv3h2A//znP/zud7/jj3/8Y2D8jTScTIcFvx/yTqMXJze/ErvNRGbKjy/CKCIiEolCCjiHh+kc3jKhY8eOOBwOKioqGq9lrVSX2hWNt5/ivlQVVV6+LXAxJMOGURvBiYhIKxXSXJ0ePXrw17/+ldLSUobU7vWxd+9e4o9a1loaRly0ibbx5lMOOKt3OfH6amdPiYiItFIh9eDceeed2Gw2MjIyGF27WdmePXu4vHaTMGlYNTuLV53SJqK5+ZW0jTfTKSmqEVomIiLSMoTUgxMfH8/YsWODzg08zrQsOX2ZDgv/21FJSYWX5LjQF8Q56PSyeV8Vo85MwKDHUyIi0oo13T7mclyZjmjg5MfhfL2zEr9fj6dEREQUcJqhDolRWEwGthdX/Xjlo+TmV9IhMYq0Nno8JSIirZsCTjNkNhrIsFtOqgenuMLD94VuhmjtGxERkZMLOD6fj9LS0sZqixwl02FhZ4mbam9oA42/yq8EUMAREREhxIBTUVHBzJkzGTduHPfccw8AX331FW+88UajNq41y3RY8PhgV2lovTi5+ZV0TrbQNv40d+kUERGJACEFnLlz52Kz2Zg1axZmc81foN27d2flypWN2rjW7GQGGu87VE1+SbV6b0RERGqF9Ov++vXrmTNnTiDcACQkJHDw4MFGa1hrl2gzYbeZQgo4ubWPpwZnxDR2s0RERFqEkHpwbDYbZWVlQeeKiopISkpqlEZJjS4OC9uLfnwmVW5+Jd1SorHb9HhKREQEQgw4I0eO5Nlnn2XDhg34/X6+++47XnrpJS6++OLGbl+rlumIprjCy0Gn97h1dh9ws+eghyGd1XsjIiJyWEi/8l911VVERUXxyiuv4PV6mT17NtnZ2dqqoZFl1m68uaPITf/0+gPMl3lODAYY1Enjb0RERA4LKeAYDAZGjRrFqFGjGrs9cpROSVGYjLC9qKregOP3+8nNr6RXu2gSrKYmaKGIiEjzFPKgjf3797Nz505cLlfQ+aysrAZvlNSwmI2kJ1nYXlz/QOP8kmoKyz2M6qNd3UVERI4WUsB5++23WbJkCenp6VgslsB5g8GggNPIuiRbWLm9Aq/Pj8kYvIFmbn4lJiMMSNfjKRERkaOFFHDef/99pk+fTseOHRu7PXKMTIeFz74rZ8/BatKTjoRLX+3jqT5pVmwW7bghIiJytJD+ZoyLiyMlJaWx2yL1OLzg345j1sPZVuimtNKrxf1ERETqEVLAGT9+PHPmzGHbtm0UFRUF/SONKyXORFy0sc6Cf7n5lVhMBs7qoOnhIiIixwrpEZXH42HdunWsWLGiTtmiRYsavFFyhMFgIPOYBf+8Pj9f7aykXwcr1ig9nhIRETlWSAFn3rx53HTTTZx//vlBg4wlPDId0azb7aLS7cNmMbJlXxVlLh9DOuvxlIiISH1C+vXf5/Nx0UUXYbVaMRqNQf9I4wss+Fc7XfzL/EqsZgN90/R4SkREpD4hJZQrr7ySd955B7/f39jtkXp0TrZgoGbBv2qvnzW7KhmQHkOUyfCjrxUREWmNQnpE9eGHH3LgwAHefvtt4uLigspmz57dKA2TI2KijKS1iWJ7kZuNBS4q3X7NnhIRETmBkALO3XfffdpvVF5ezuzZs1m3bh3x8fGMHTu23kUCV6xYweLFizlw4ABRUVH079+fCRMmYLPZTuo6kSbTYeHrXU5sUZXEWoz0am9t6iaJiIg0WyEFnN69e5/2G82bNw+z2czcuXPJy8tj6tSpZGRkkJ6eHlSvR48ePPnkkyQkJOByuXj55Zd54403mDBhwkldJ9L0/DKH8f/4K46yIooSHORXTiL9lqubulkiIiLNUsh7UeXl5bFp0ybKysqCxuKMGTPmR1/rcrlYtWoVzz77LFarlZ49ezJ48GCWLVvGuHHjguo6HI6gY6PRyL59+076OpFk1/x3uOT/nsPqqZkq3vZQIQmznmY9KOSIiIjUI6SAk5OTw/z58+nXrx9r166lf//+rFu3jsGDB4f0JgUFBRiNRtLS0gLnMjIy2LhxY731N2/ezNSpU3E6nURHRzNlypRTuk5OTg45OTkATJs2DbvdHlJ7mxvz/L8Ews1hVk8VXeb/hYTJE5qoVSISicxmc4v7rrRYLMTHxbW4dkvjCing/POf/+Sxxx6jV69e3HrrrTz44IOsWbOm3oX/6uNyuQJjaA6z2Wx1diY/rGfPnsyfP5+SkhJycnIC20Sc7HWys7PJzs4OHJeUlITU3uam7cH6V4y2Hyxifwv9TCLSPNnt9hb3Xel2uykrL29x7ZaGkdq5c73nQ5omfujQIXr16gXUrKzr8/kYMGAAX3/9dUhvbrVacTqdQeecTidW64kHytrtdvr378/MmTNP6zotXUkbx0mdFxERae1CCjh2u539+/cD0L59e7766is2bdqE2RzaEJ727dvj9XopKCgInMvPzw9pYLDP52Pv3r2nfZ2WbMctk3CZo4POuczR7LhlUhO1SEREpHkLKeBcddVV7N69G4Drr7+eF154gd///vfccMMNIb2J1Wpl6NChLFq0CJfLxebNm8nNzWX48OF16i5fvpyioiL8fj+FhYUsXLiQvn37nvR1Ikn6LVez/o6HKGqTgg8DRW1SWH/HQxpgLCIichwG/yksT+zxePB4PCf1aKi8vJxZs2axfv164uLiGDduHFlZWRQVFTF58mRmzJiBw+Fg4cKFfP7551RUVBAbG8uAAQMYO3Ys8fHxJ7xOKPauXn2yH1VEpFVpiWNwJkyZwnWXX86oESOauinSBFIHDqz3/HEDjs/nC+nCLWk/KgUcEZETU8CRluZ4Aee4g2huuummkC68aNGiU2uRiIiISCM5bsB58cUXw9kOERERkQZz3ICTkpLCgQMHSExMDGNzRERERE7fCQfQ3HvvvUHHf/rTnxq1MSIiIiIN4YQB59jxx99++22jNkZERESkIZww4BgMhnC1Q0RERKTBnHApYq/Xy4YNGwLHPp8v6BigT58+jdMyERERkVN0woDTpk0bZs+eHTiOi4sLOjYYDJptJSIiIs3OCQPOSy+9FK52iIiIiDSYlrMMsYiIiEiIFHBEREQk4ijgiIiISMRRwBEREZGIo4AjIiIiEUcBR0RERCKOAo6IiIhEHAUcERERiTgKOCIiIhJxFHBEREQk4ijgiIiISMRRwBEREZGIo4AjIiIiEUcBR0RERCKOAo6IiIhEHAUcERERiTgKOCIiIhJxFHBEREQk4ijgiIiISMRRwBEREZGIo4AjIiIiEUcBR0RERCKOAo6IiIhEHAUcERERiTjmcL1ReXk5s2fPZt26dcTHxzN27FiysrLq1Fu6dCkffvghe/fuJSYmhqysLG666SZMJhMAjz/+OFu3bsVorMlmdrudmTNnhutjiIiISAsQtoAzb948zGYzc+fOJS8vj6lTp5KRkUF6enpQPbfbzfjx4+nWrRuHDh1i+vTpxMXFcfXVVwfqTJgwgZEjR4ar6SIiItLChOURlcvlYtWqVYwZMwar1UrPnj0ZPHgwy5Ytq1P3kksuoVevXpjNZux2O8OGDWPz5s3haKaIiIhEiLD04BQUFGA0GklLSwucy8jIYOPGjT/62o0bN9bp5VmwYAELFiwgLS2NG2+8kTPPPLPe1+bk5JCTkwPAtGnTsNvtp/EpREQi3+FfLlsSi8VCfFxci2u3NK6wBByXy4XNZgs6Z7PZcLlcJ3zdZ599xvbt25k0aVLg3Lhx4+jYsSNms5kVK1Ywffp0nn76aVJTU+u8Pjs7m+zs7MBxSUnJaX4SEZHIZrfbW9x3pdvtpqy8vMW1WxpGaufO9Z4PyyMqq9WK0+kMOud0OrFarcd9zZdffsmCBQt47LHHSEhICJzv1q0bMTExREVFceGFF9KjRw/WrFnTaG0XERGRlicsAad9+/Z4vV4KCgoC5/Lz8+s8ejps7dq1zJkzh4cffphOnTqd8NoGgwG/39+g7RUREZGWLWw9OEOHDmXRokW4XC42b95Mbm4uw4cPr1N3w4YNPP/88zzwwAN07do1qKyiooK1a9fidrvxer0sX76cTZs20b9//3B8DBEREWkhwjZN/Pbbb2fWrFlMnDiRuLg4Jk6cSHp6OkVFRUyePJkZM2bgcDh46623qKysZOrUqYHX9urVi8ceewyv18uiRYvYvXs3RqORDh068OCDDwYNXhYREREx+FvR8529q1c3dRNERJq1ljjIeMKUKVx3+eWMGjGiqZsiTSB14MB6z2urBhEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBxzuN6ovLyc2bNns27dOuLj4xk7dixZWVl16i1dupQPP/yQvXv3EhMTQ1ZWFjfddBMmk+mkriMiIiKtV9gCzrx58zCbzcydO5e8vDymTp1KRkYG6enpQfXcbjfjx4+nW7duHDp0iOnTpxMXF8fVV199UtcRERGR1issj6hcLherVq1izJgxWK1WevbsyeDBg1m2bFmdupdccgm9evXCbDZjt9sZNmwYmzdvPunriIiISOsVlh6cgoICjEYjaWlpgXMZGRls3LjxR1+7cePGQO/MyV4nJyeHnJwcAKZNm4bdbj+djyEiEvEO/3LZklgsFuLj4lpcu6VxhSXguFwubDZb0DmbzYbL5Trh6z777DO2b9/OpEmTTuk62dnZZGdnB45LSkpOpfkiIq2G3W5vcd+VbrebsvLyFtduaRipnTvXez4sj6isVitOpzPonNPpxGq1Hvc1X375JQsWLOCxxx4jISHhlK8jIiIirU9YAk779u3xer0UFBQEzuXn5x93YPDatWuZM2cODz/8MJ06dTrl64iIiEjrFLYenKFDh7Jo0SJcLhebN28mNzeX4cOH16m7YcMGnn/+eR544AG6du16ytcRERGR1its08Rvv/12Zs2axcSJE4mLi2PixImkp6dTVFTE5MmTmTFjBg6Hg7feeovKykqmTp0aeG2vXr147LHHTngdERERkcMMfr/f39SNCJe9q1c3dRNERJq1ljjIeMKUKVx3+eWMGjGiqZsiTSB14MB6z2urBhEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk4CjgiIiIScRRwREREJOIo4IiIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk45nC9UXl5ObNnz2bdunXEx8czduxYsrKy6tTbuXMnr7/+Otu3b6esrIzFixcHlT/++ONs3boVo7Emm9ntdmbOnBmWzyAiIiItQ9gCzrx58zCbzcydO5e8vDymTp1KRkYG6enpwQ0ymzn33HO55JJLeOaZZ+q91oQJExg5cmQ4mi0iIiItUFgeUblcLlatWsWYMWOwWq307NmTwYMHs2zZsjp109LSGDFiRJ3gIyIiIhKqsPTgFBQUYDQaSUtLC5zLyMhg48aNp3S9BQsWsGDBAtLS0rjxxhs588wz662Xk5NDTk4OANOmTcNut5/S+4mItBZms7nFfVdaLBbi4+JaXLulcYUl4LhcLmw2W9A5m82Gy+U66WuNGzeOjh07YjabWbFiBdOnT+fpp58mNTW1Tt3s7Gyys7MDxyUlJSffeBGRVsRut7e470q3201ZeXmLa7c0jNTOnes9H5ZHVFarFafTGXTO6XRitVpP+lrdunUjJiaGqKgoLrzwQnr06MGaNWsaqqkiIiISAcIScNq3b4/X66WgoCBwLj8/v0HG2RgMBvx+/2lfR0RERCJH2Hpwhg4dyqJFi3C5XGzevJnc3FyGDx9ep67f78ftduPxeICarsfq6moAKioqWLt2LW63G6/Xy/Lly9m0aRP9+/cPx8cQERGRFiJs08Rvv/12Zs2axcSJE4mLi2PixImkp6dTVFTE5MmTmTFjBg6Hg8LCQu66667A626++WZSUlJ46aWX8Hq9LFq0iN27d2M0GunQoQMPPvhg0OBlEREREYO/FT3f2bt6dVM3QUSkWWuJg4wnTJnCdZdfzqgRI5q6KdIEUgcOrPe8tmoQERGRiBO2R1TNQdQ33wQd+1JS8KalgddL1IYNdep727XDl5oK1dVE1bNmjzctDV9KCrhcRG3ZUre8Y0d8yckYKisxb91ap9zTqRP+pCQM5eWYt22rW965M/42bTAcPIg5L69u+Rln4I+Lw1Bainnnzrrl3brht9kwFhdj+uGHOuXVPXqA1YqxsBDTnj11y3v3hqgojHv3Ytq3r255nz5gMmHaswdjYWHd8rPOAsC0axfGY38jNBqp7tu3pjw/H+OBA0HF/qgoPL1715Tv2IHx0KHgcosFT69eAJi3bcNQXh5cHhODp3v3mvLvvsNwzCw+f1wcnjPOqCnftAmD2x1U7ktIwNulS035xo0YaseBBcoTE/FmZAAQtX49+HzB5XY73tpB9Mfed6B7T/de8733qP1/05LuPWNt3ahvvtG914LvvVP+3jtOD06rCji+YxaB8qWk1PwwvV589dzogXK3G9/evccvdzrx1XOj+9q2xZeSgqGiAl9xcZ1yf9u2NV8EZWX4Skvrfb0/KQlDdDS+Y250AF+7dvjj4zFGReE75kYPlMfGgsmEobKy3nJiYgAw1LMmka9dO7BYwOutc6MDNZ/dZKop83rrLz/OtTGZjpRXVIDxmM5EiyVQbiwrw2c+5la1WgPlvoMHMVgsQcV+m+1IeXFxnc/vT0gIlPsLC/Ef00Z/YuKR8n378B/7RWC3H7n+nj11Pn/g3gB8u3bV+fi693TvQfO892jXDl9MTLO+9y666iq2HBOuPqhnCELPTp1Y9vzzdV6ve6953nun+713rFY1BmdPfX+YRUQkwOFwUFRU1NTNOCUGgwGj0YjP59PyIa3I8SYataoeHBERiVx+vx+z2Yz3qJ4Fn8+H75hHKdI6aJCxiIhEDK/XGwg0Ho9H4aYVU8AREZGI4fF4MBqNeDweoqKiMB47zkVaDf2fFxGRiHJ4/E11dTUGgwHzsYN1pVVoVYOMRUSk9fF6vVRVVWG1WtWj04ro/3SYzJkzp6mb0KJE4s+rJXym5tLGpmhHON6zMd6joa/5yCOPNOj1mgOTyURMTIxmVoWouXwPnC4FnDAZNGhQUzehRYnEn1dL+EzNpY1N0Y5wvGdjvEdz+X/W3BkMBkwmU1M3o0WIlHtKj6hERCTgkUceYdq0aU3dDJHTph4cEREJyM7ObuomiDQIBRwREQmIxIDTuXNn+vbtS//+/Rk8eDAA33zzDeeeey59+/blyiuv5FA920IATJgwgbZt29KnT5+g8w8//DD9+vXj5z//eeDc66+/zsyZMxvvg8hJUcAREZGI99lnn7F27Vq++uorAG6//XamTZvG+vXrueaaa3jmmWfqfd348eP56KOPgs4dPHiQlStXsm7dOrxeL+vXr8fpdPLqq69yxx13NPpnkdAo4IiISKuzZcsWhg8fDsDFF1/MW2+9VW+94cOHYz9mo2aj0Yjb7cbv9+N0OomKiuKZZ57hnnvuISoqqtHbLqFRwBERkRP67rvv+PWvf83vfvc7nnvuOTweT1M36aQYDAYuueQSBg0axMsvvwxAnz59ePfddwF488032VXP7tfHEx8fz3XXXceAAQPo0qULbdq0ITc3l6uuuqpR2i+nRrOoRETkhEpKSoiLi8NisbBw4UK6dOnCOeec09TNCtmePXtIS0tj//79XHzxxbzwwgu0bduWe+65h+LiYn7605/y/PPPU1xcXO/r8/LyuOKKK9iwYUO95bfffjt33nknX3/9NR9//DH9+vXjN7/5TWN+JAmBenBEROSE7HY7FosFqFk0z2AwNHGLTk5aWhoAbdu25ZprruHLL7+kZ8+efPzxx3z99dfcdNNNnHHGGad07TVr1gDQvXt3XnvtNRYvXsyGDRvYunVrg7VfTo026BARaSU++ugjli5dys6dOzn//PO58847A2Xl5eXMnj2bdevWER8fz9ixY8nKygp6/f79+1mzZg3XXnttuJt+yioqKvD5fMTHx1NRUcHHH3/M//t//4/9+/fTtm1bfD4ff/jDH5g0adIpXf+3v/0tL7/8MtXV1Xi9XqBmjE5lZWVDfgw5BerBERFpJZKSkrj22mu56KKL6pTNmzcPs9nM3Llzueeee5g7d27QuJTKykpeeukl7r777ha1eeW+ffvIysrirLPO4uyzz2bUqFFceumlLFy4kO7du9OzZ0/S0tK49dZbgZrHWZdffnng9TfddBPnnnsuW7ZsoWPHjrzyyiuBsnfeeYchQ4aQlpZGYmJiYNq5wWDgrLPOCvtnlWAagyMi0sq88cYbFBcXB3pwXC4Xt956K88++2zgcc4LL7yA3W5n3LhxeL1enn76aa688so668GINFfqwRERaeUKCgowGo2BcAOQkZER6MFZsWIF33//PUuWLOHxxx9n5cqVTdVUkZC1nH5GERFpFC6XC5vNFnTOZrPhcrmAmrVgDq8ZI9JSqAdHRKSVs1qtOJ3OoHNOpxOr1dpELRI5fQo4IiKtXPv27fF6vRQUFATO5efnk56e3oStEjk9CjgiIq2E1+vF7Xbj8/nw+Xy43W68Xi9Wq5WhQ4eyaNEiXC4XmzdvJjc3V4+lpEXTLCoRkVZi8eLFLFmyJOjc9ddfz+jRoykvL2fWrFmsX7+euLg4xo0bV2cdHJGWRAFHREREIo4eUYmIiEjEUcARERGRiKOAIyIiIhFHAUdEREQijgKOiIiIRBwFHBEREYk4CjgiIiIScRRwREREJOIo4IhIs/fyyy/XWYH3aKNHj2bv3r0ndc3ly5fzhz/84XSbJiLNlFYyFpGwWrFiBR988AG7du0iOjqatm3bcsEFF3DJJZdgMBhO6ZqjR4/m+eefJzU1tYFbKyItlbmpGyAircd7773Hu+++y2233cZZZ52F1WolLy+P9957jxEjRhAVFVXnNT6fD6NRnc0icnIUcEQkLCorK1m8eDF33nkn55xzTuB8ly5duOeeewLHL730EhaLhaKiIjZu3MiDDz7I8uXLSU5O5sYbbwTg3Xff5f3338dgMDBmzJgTvu/SpUtZsmQJhw4dIj4+nhtvvJFhw4axdOlS/vOf//Dkk0/yz3/+M+gRmMfjISsrizvvvJPKykrmz5/PmjVrMBgMXHTRRYwePVqhS6SZU8ARkbD47rvvqK6uZsiQIT9a94svvuDRRx/l4YcfxuPxsHz58kDZ2rVree+99/jtb39L27ZtmTNnznGv43K5+Nvf/sbUqVNJS0ujtLSU8vLyOvWuuuoqrrrqKgCKior49a9/zbnnngvAiy++SGJiIs8//zxVVVVMmzaN5ORkLr744pP9EYhIGOlXEBEJi8M9KCaTKXDuN7/5DePHj2fcuHFs3LgxcH7IkCH07NkTo9GIxWIJus7KlSu58MIL6dSpE1arlRtuuOGE72swGNi5cydut5ukpCTS09OPW9ftdvPMM89w2WWXMXDgQA4cOMDatWsZP348VquVNm3aMGrUKFauXHmKPwURCRf14IhIWMTHx1NWVobX6w2EnMOzmCZNmsTR8x2Sk5OPe53S0lIyMzMDxykpKceta7Vaue+++3jvvff4y1/+Qo8ePfj5z39Ohw4d6q0/e/Zs0tLSuPrqq4Ga3hyv18svfvGLQB2/33/C9olI86CAIyJh0b17d6KiosjNzQ0ag1OfE82mSkpKori4OHBcVFR0wmv179+f/v3743a7eeONN5gzZw6///3v69R755132LNnD08++WTgXHJyMmazmVdeeSWo50lEmj89ohKRsIiNjeX666/nlVde4X//+x8ulwufz0deXh5VVVUhX+fcc89l6dKl/PDDD1RVVfHmm28et+6BAwf46quvcLlcmM1mrFZrvYOD16xZw4cffsiDDz4Y9EgsKSmJs846i9dee43Kykp8Ph979+4NepwmIs2TenBEJGyuuuoq7HY7//znP3nxxReJjo6mXbt2jBs3jh49eoR0jQEDBjBq1CieeOIJjEYjY8aM4Ysvvqi3rt/v57333uOFF17AYDDQuXNnbr/99jr1Vq5cyaFDh5g8eXLg3LBhw/jFL37BXXfdxf/93/9x//3343Q6adeuXWBAsog0X1roT0RERCKOHlGJiIhIxFHAERERkYijgCMiIiIRRwFHREREIo4CjoiIiEQcBRwRERGJOAo4IiIiEnEUcERERCTi/H/FttQPnImgwQAAAABJRU5ErkJggg==\n", 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\n", 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GUFBQwCOPPMIbb7xBQkICACtWrODPf/4zpaWldOzYkbvuusvbS9NQO77IX7WqyT6fiEhz0trn4HQZNIhVixYRFRkZ6FCkiaX07Vvveb8lOM2BEhwRaS2U4CjBaS0aSnC0VYOIiIgEHSU4IiIiEnSU4IiIiEjQUYIjIiIiQUcJjoiIiAQdJTgiIiISdJTgiIiISNBRgiMiIiJBRwmOiIiIBB0lOCIiIhJ0lOCIiIhI0FGCIyIiIkFHCY6IiIgEHSU4IiIiEnSU4IiIiEjQUYIjIiIiQUcJjoiIiAQdJTgiIiISdJTgiIiISNBRgiMiIiJBRwmOiIiIBB2TYRhGoIPwF1e/frWOHZmZOG65BRwO4h96qE59xzXX4Lj2WkxFRcQ9+WSd8oqbbsJ5+eWY8/OJ/e1v65SX33YblYMGYcnJIeaVV+qUl911F67+/bH++CPRf/hDnfJD999P1TnnEPLDD0S99Vad8tLHHqO6a1dCV6wg8t1365SXPPMM7g4dCFu6lIg//7lOefHvfocnJQXbl19iX7CgTnnRpEkYcXGEf/IJ4f/4R53ywilTIDyc8PnzCc/Orlv+zjsA2D/4ANu339YqM8LCKHrzTQAiZs4kbOXKWuWemBiKX3sNgMg33yR03bpa5e6kJEpefhmAqP/3/wjZsqVWeXX79pQ+9xwA0S+/jHXnzlrlVWeeyaHHHwfA/NRThBUUEG7+Od939epF2dixAMSOG4e5pKTW9ZXnnUf56NEAxI0di6mysla5c8AAKm6/HYD4e+6p873Rs6dnDyDmueew7N9fq7yxnr34+Hi46aY635vW8ux1uegifurenWiLpVa5nr2mf/bAvz/3Qr//vk5dAGu9Z0WCWJ8NG9j4v//B3LkNV/rf/+i6bBlL5s/3X2Aicsr6bNjARqcTbrzRey5xzZo69bqOGcOSjz7yY2QSKK2qByd/1apAhyDN0Iuvv05KYiL3jRgR6FBEGk18fDyFhYWBDkOkyaX07Vvvec3BERERkaCjBEdERESCjhIcERERCTpKcERERCToKMERERGRoKMER0RERIKOEhwREREJOkpwREREJOgowREREZGgowRHREREgo5Pe1FVV1ezd+9eKioqsNvtpKWlYbWe3DZWZWVlTJ8+nbVr1xIVFcWwYcMYMGBAnXpLlixh+vTphIaGes899dRT9OzZE4AXX3yRrVu3Yj68MWJ8fDxTpkw5qVhEREQkuB03S1m1ahVffvkl69evx2KxEB4ejsPhwO12c9ZZZzF06FDOPfdcn240a9YsrFYrM2fOJCcnhwkTJpCRkUF6enqdumeeeSbjx49vsK1Ro0YxZMgQn+4rIiIirU+DCc7zzz9PREQEAwYM4J577iE+Pt5bVlRUxIYNG/jqq69YuHDhcZMRAKfTyYoVK/jDH/6AzWajW7du9OvXj6VLlzJ8+PDG+zQiIiIiHCfBGT16NO3bt6+3LC4ujgEDBjBgwAB27tx5wpvk5eVhNptJS0vznsvIyGDjxo311s/JyeGuu+4iMjKSgQMHcv3112OxWLzlc+bMYc6cOaSlpXHrrbd6h6+OlZ2dTXZ2NgATJ06slaSJHGGz2Qi32/V8SFCxWq16pqVVazDBaSi5OZV6TqcTu91e65zdbsfpdNap2717d/7whz+QkJDA7t27eeONN7BYLFx//fUADB8+nHbt2mG1Wlm+fDmTJk3i1VdfJSUlpU5bmZmZZGZmeo8LCwt9+kzSujidThwVFXo+JKjEx8frmZZWIaVDh3rP+/QW1T/+8Q9ycnIA2LJlC7/5zW944IEH+PHHH326uc1mw+Fw1DrncDiw2Wx16iYnJ5OUlITZbKZ9+/bcdNNN/Oc///GWd+nShfDwcEJCQrj00kvp2rUrq1ev9ikOERERaR18SnA+++wzkpKSAJg7dy7XXHMNN9xwA7Nnz/bpJqmpqbjdbvLy8rzncnNz651gfCyTyXTCcsMwfIpDREREmo6pqAjb4sWYDx4EIOSHH4icNi0gsfiU4Bx5PdzhcJCTk8OVV17JZZddxt69e326ic1mo3///sybNw+n08nmzZtZuXIlgwYNqlN39erVFBcXA7Bnzx7+9re/0a9fPwDKy8tZs2YNLpcLt9vNsmXL2LRpE7179/bt04qIiMjJ8XigqgoAU2kp9jlzsOzYAYB12zYSr7yS0O++qznevZvYZ54h5PAcW8Nux+RyBSRsnxazadOmDT/++CO7du2ie/fumM1mKioqvGvR+OLuu+9m2rRpjB49msjISEaPHk16ejoFBQU88sgjvPHGGyQkJLBu3TqmTZuG0+kkJibGO8kYwO12M2/ePPbs2YPZbKZt27aMGzeu1uRlERER8ZFhYCotrfkyJgacTiJnzcJ1/vm4zj8fc0EBiddcQ+m4cThuvBGT00n0H/5AyTPP4OjUCU98PK7zz8cTEwNAVZcuFMyfT3XbtgBUd+nCoYcfDshHMxk+jO+sWrWKGTNmYLVaeeyxx+jUqRPffvstS5cu5ZlnnvFHnI0if9WqQIcgzdCLr79OSmIi940YEehQRBqNJhnLEaZDhzC5XHjatAHDIHriRFxnnYXzl78Et5vkCy+k/M47KRszpuZ44EDK7r2X8jvuALebyLfeonLwYKp69QKPB1NJCUZsLJxgCom/pPTtW+/54/bg5Ofnk5KSQt++fZkxY0atsgsuuIALLrig8SIUERGRk+d2Y6qowIiKAsD+wQcYERE4brwRgISsLFznnUfJSy+ByUTIhg14jiwhYLFQ+uSTVHft6j3e9+23cGSExmKh7MEHf76X2YwRF+evT3ZajpvgTJgwAYA+ffrQt29fevTo4d2i4WS3ahAREZFTVFUFISEANZN4i4qouPVWAOLvuw/DYqHo7bcBCPv2WzwJCd4E59CDD+JOTPQ2dfDPf67V9JF6Xicx/aQ5O26WMmXKFPbt28eqVav4xz/+wZQpU+jatSt9+/alT58+tGnTxl9xioiIBC/D8A75hPzvf4Rs2ULFr38NQPT48YSuWkXBxx8DEPbNN1h37PAmOBU331xz/WFFM2bUGj5y/uIX/voUzcoJu2GSk5O58sorufLKK3G5XKxbt47Vq1fz8ccfEx4eTp8+fRg8eLAm+oqIiPjAsmMHYd9/X5OYmExEzJ5NxOzZ7M/OBrOZsG+/JWLePCqyssBspvLii3EftahuyYsventzAJyXX177Bs1kbkygndQ4U2hoKOeee653g81du3axevVqdu7cqQRHREQEwDAw5+fjSUiAkBBCv/uOyHfeoWjyZIzYWMJWriT61VdxDhmCp00bqjp3xnHNNeBygc1G+V13Ufab33iHiiovu4zKo9sPDQ3Ix2ppfE5wKisryc/Pr7O9wrXXXtvoQYmIiLQU5gMHsH3+Oc6hQ/GkphK2ZAlxjz9OwQcfUN2zJ4SEYISFYS4rwx0bi+Oqq2qSm8MTfV0XX4zr4ou97RmRkYH6KEHFpwTnm2++4b333sNqtRJ6TOY4ffr0JglMRESkWfB4sOzZgxERgSc+HsuePcQ++SRl995L5cCBmIuLiZ4yBXe7dlSmplJ11lmUPPMMnuRkAFznnYfrvPO8zRlRUd43nqTp+JTg/PnPf+axxx7j7LPPbup4REREAquqiog//Ymq7t1xXXghptJSEn/1K0offpiKESPwxMXhiY7GODwPprpjR/b9618Y0dEAeBIT676ZJH7n07tgVquVHj16NHUsIiIifmHJycG6fbv3OH7kSCLffLPmwGol4k9/IvT77wEwYmMp/t3vqLz00ppju52iadNwHVkLzmr1JjfSfPiU4GRlZfHBBx9Qeng5ZxERkWbvqFenw+fPx/7hh97j2CeeIPKtt7zHVT174j6yAbTJxP7PP6ds7FhvufPqq38ulxbBpyGqtLQ05s+fz+LFi+uUzZs3r9GDEhEROSlVVVjy8ryvU0e/9BLWHTsonD0bgLAVKzC5XFQMHw5A6VNP1ep1OfT447Xbs9n8E7c0GZ8SnDfffJNBgwZx0UUX1ZlkLCIi4m/m/fsJWb+eyssuAyD6//0/bF98wf5//QvMZlz9+uE+avmS4tdeq7VCb1UD+xdJ8PApwSkrKyMrKwuTFg8SEZEAsOTmYlu8mIoRIzDCw7F9+SXRb7zB/sWL8SQkUHHttVT26wceD5jNOK++unYDQbL9gPjOp//jl156KUuXLm3qWERERICaScAxzz2HJScHAGtuLpHvvOOdGOy84goK5szBExsLQHXPnlQOHQraJ1EO8+lJ2LZtG1988QUfffQRsYcfpiNeeumlpohLRESCncdTs4lkWBjm/Hxin3iC8lGjat5WMpkI/f57LNdcg7tDByr792f/N99gRETUXJqYiOeoDSRFjuVTgjNkyBCGDBnS1LGIiEgQMxUVYXI68aSmgsNB0tVXU37bbZSPGoUnPh4jIgLjcA+Mu317Dnzxxc8Xh4VhhIUFKHJpiXxKcC49/O6/iIiIr0LWr4fKSqrOPRcMg4Sbb6ZywABKX3wRwsOpuPFGqnr2rKkcGkrR0Svja86nnKYG5+B8f3iBoxPxtZ6IiAS3sKVLsc+f7z2Oev11oqZOrTkwmSh9+umaHbIPK7v/flz9+/s7TGklGuzBWb58OXPnzmXAgAH06NGDtLQ0wsPDcTgc5OXlsXHjRpYtW0ZGRgb9+vXzZ8wiItIM2L74grDvvqPkd78DIOxf/yJ01SoqbrkFgNJnn8Vz1FozlZrqIH7UYILz0EMPsXPnTr766iumTp3K/v37vWUpKSn06dOHhx9+mHSt7CgiErRMDkfN3BezGduiRUTOmEHB/Pk1E4MLCrDu2AEuF4SGcuixxzDsdu+11WecEcDIpbU77hyc9u3bc9dddwFQWVlJeXk5ERERhGmil4hI8HG7sf70E+7UVIyICML++U9in3qKgvnzcXfogCc+nqoePTCXleEJC6PittuouO027+VGZGQAgxepzeeVj8LCwoiPj1dyIyISREzFxZhKSgAIWbuWhKws7yaT1WeeSfnIkRiHty1wXXABJRMm4GnTJmDxivhKSzuKiLQ2lZVAzWvbSZdfjv3jjwGo6t6d4vHjqTrrLADc7dpR9pvf4ElJCVioIqdKCY6ISGthGMTffjvRkybVHMbFceixx6gcOLCm3GbDedVV6qGRoKA1rUVEgljkjBlYf/yR4tdfB5OJyiFDcCckeMuPfm1bJJgowRERCSKh332HfeFCeOcdADyRkXhiYrybUJbfcUdgAxTxkwYTnN/85jc+NTD96JUnRUTEr8x5edg/+ojyYcMw4uIwl5Rg3b4dz/79EBpKxfDhgQ5RJCAaTHDGjh3r/Xrbtm188803XHnllSQmJnLgwAEWL17MoEGD/BKkiIgc5nIR9t13VHfqhLt9e8xFRUTMno2rTx9cF12E84orcF55JfHx8VBYGOhoRQKmwQSnR48e3q/fffddnn322Zq/MIf16dOHV155hV/+8pdNG6GISCtnPnAAk8OBu317TA6Hd9ftsvvuo7pbN/Z/9RVGTMzhynp3RAR8fIuqsLAQ2+F1EI6w2WwU6l8HIiKNzzAwFRV5v24zYgSRb71VcxgTw8H/+z/KDi/Citn8c3IjIl4+TTLu168fkyZN4sYbbyQ+Pp6DBw+ycOFCzj333KaOT0SkdXC7wWIBIPbJJ7Hs2sXBuXPBZKLk+edxp6Z6q1Yf1cMuIvXzKcEZPXo0f/3rX5k5cyaFhYXEx8dzwQUXcPPNNzd1fCIiQS/8r38l8t13OfDppxASguPqqzGXlIBhgMmE6+KLAx2iSIvjU4ITGhrK8OHDGa7Z+CIipy1k/XqiXn+d4t//Hk9qKu6MDCoHDcJUXo4RG0vlJZcEOkSRFs/ndXDWrl3L8uXLKSkp4amnnmL79u04HA7OOrykt4iI1M9UVETke+/hHDqUqrPPxhMZiamyEnNhIZ7UVFznn4/r/PMDHaZIUPEpwfn8889ZtGgRQ4YM4T//+Q9Q06vz/vvv8/LLL/t0o7KyMqZPn87atWuJiopi2LBhDBgwoE69JUuWMH36dEJDQ73nnnrqKXr27HlS7YiIBIzHg+3LL/HExeHq3x/DZiP800+pbt+eqrPPxt2hAwc//DDQUYoENZ8SnEWLFvH888+TlJTE3//+dwDatm3L3r17fb7RrFmzsFqtzJw5k5ycHCZMmEBGRgbp6el16p555pmMHz/+tNsREfEXy+7dmPfto+rcc8FkIvLtt6nq2hVX//4QHs7+r76CkJBAhynSavj0mrjD4SDhqL1LAKqrq7FafRvhcjqdrFixgqysLGw2G926daNfv34sXbr0pIJtrHZERE6b243lp5+8h1GvvUbMSy95JwYXTp9OySuv/FxfyY2IX/mUoXTv3p2FCxdyww03eM99/vnn3mGjE8nLy8NsNpOWluY9l5GRwcaNG+utn5OTw1133UVkZCQDBw7k+uuvx2KxnHQ72dnZZGdnAzBx4sRaCxWKHGGz2Qi32/V8yIlVVEB4OJhMWF57DfOsWVStWgUREZiefRaP3U78kZ24A/w8Wa1WPdPSqvmU4IwaNYpJkybx9ddf43Q6eeihh7Db7Tz55JM+3cTpdGK322uds9vtOJ3OOnW7d+/OH/7wBxISEti9ezdvvPEGFouF66+//qTaAcjMzCQzM9N7rIUJpT5OpxNHRYWeD6nf4R6ZsKVLiX3ySQo+/BB3p05YL7kEa7t2OEtLobISkpJq6jeT5yg+Pl7PtLQKKR061HvepwQnLi6OCRMmsH37dg4cOECbNm3o3LkzZh+XBLfZbDgcjlrnHA5HndWRAZKTk71ft2/fnptuuolPPvmE66+//qTaERE5Hea8POIee4yy0aOpHDyYqm7dqLj5ZggLA6C6c2eqO3cOcJQi0hCfNy1xu91UVVVhGAZnnnkmLperwZ6TY6WmpuJ2u8nLy/Oey83N9WlisMlkapR2RESOy+0m+pVXCP/oIwA8CQl44uMxDr/R6UlK4tCjj+Ju2zaQUYqIj3xKcHbu3MlDDz3EjBkzmD59OgAbN270fn0iNpuN/v37M2/ePJxOJ5s3b2blypX17ka+evVqiouLAdizZw9/+9vf6Nev30m3IyJyIrbFi7HPn19zYLFg/eknLPv21RyHhFA0dapWERZpoXxKcGbOnElWVhaTJ0/2vjnVo0cPNm/e7PON7r77blwuF6NHj2bKlCmMHj2a9PR0CgoKGDFiBAUFBQCsW7eOcePGMWLECCZMmMD555/P9ddff8J2REROxJKTg+3TT73HYUuWEH7UceE771D2m98EIjQRaWQ+zcHZvXs3AwcOrHXOZrPhcrl8vlFkZCRPPPFEnfMJCQn86U9/8h7ffvvt3H777SfdjojIiYR/8gkRf/4zlZdeihEVRemzz2JERPxc4aghcRFp2XzqwUlMTGTHjh21zm3bto2UlJQmCUpEpDGYSkqInjAB6+GlJMpHjeLAZ59hREUBYERGKqkRCVI+9eBkZWUxceJEhg4dSnV1NR9//DFfffUV9957b1PHJyJy6iwWwpYsoeqMM6ju0QMjMrImqRGRoOdTD865557L008/TWlpKT169ODAgQM8/vjjnHPOOU0dn4jISQn/6CNiH30UDAMjMpIDCxfiuOWWQIclIn7m827inTp1olOnTk0Zi4jIqamqAqu1ZrjJ7YbqakwVFTXza8LDAx2diASATwlOdXU1f/vb31i+fDlFRUXExcVx0UUXccMNN9Ta9VtExN8se/cSd//9HBo7lsrLLsNx0004br450GGJSID5lODMnDmTvXv3MnLkSBITEzlw4AALFy5k1qxZjBkzpqljFBGpzTAwFRdjxMXhTkqiuksX78RhTRoWEfAxwVm5ciVvvvkmEYdfp2zXrh1dunRh7NixTRqciEh9ol59lbDlyyn4298gJITiV18NdEgi0sz4lODExsZSWVnpTXAAXC4XcXFxTRaYiMjRLD/9hDstDcLCqBw8GHdGRqBDEpFmzKcEZ9CgQbzyyiv84he/oE2bNhw8eJDFixczaNAg1q9f76131llnNVmgItJ6WX76iYRbbuHQI49QMWwYrvPPx3X++YEOS0SaMZ8SnK+++gqAjz/+uM75I2Umk4mpU6c2cngi0lqZiooI2boV1/nn4+7YkdInnsCZmRnosESkhfApwXnrrbeaOg4RkVqiJ00i7L//Zf+iRWCz6c0oETkpPi30d6z169ezadOmxo5FRFozlwv7X/6CubAQgLIxYzj47rtgswU4MBFpiXxKcF544QXvzuELFy5kypQpTJ48mY8++qhJgxOR1sOSl0fU669jW7wYAHf79rg7dgxwVCLSUvmU4OzatYszzzwTgK+//poXXniB3//+9975NyIipyJs2TIi3n8fAHdGBgXz5lFx660BjkpEgoFPCY5hGADk5+cDNevgJCQkUF5e3nSRiUjQC/33vwn/7DNwuQBqemy0UJ+INAKfEpyuXbvy3nvv8ac//YnzzjsPqEl2oo6sHCoi4gNzXh6xDz2Edds2AMruv5+CuXNBW76ISCPzKcG5//77sdvtZGRkcMvhXXn37t3LVVdd1aTBiUiQ8HgAMMLDsf70E5Zdu2qOIyIgJCSQkYlIkPLpNfGoqCiGDRtW61zfvn2bJCARCS6R06Zh3bKF4smTMWJjKfj4Y7BYAh2WiAS5U3pNXETkeEzl5XB47p4nNhZPcjJUVdUUKrkRET9QgiMijcq6eTOJ11xD6L//DUDFsGGUPv20hqJExK+U4IjI6auqwrJ3LwDVZ5yBc/Dgml4bEZEA8WkOzhEej4eSkpIWu4t4yA8/1Dr2JCbW7E7sdhNy1KahR7iTk/GkpEBVFSEbN9YtT0vDk5gITichP/5Yt7xdOzxt2mCqqMC6dWud8ur27THi4jCVlWHdvr1ueYcOGDExmEpKsObk1C0/4wyMyEhMRUVYd+6sW96lC4bdjvngQSy7d9cpr+raFWw2zAcOeH851Srv0QNCQjDn52PZt69u+VlngcWCZe9ezAcO1C0/5xwALLt2eVen9TKbqerVq6Y8NxdzcXGtYiMkhOoePWrKf/oJc2lp7fLQUKq7dwfAun07prKy2uXh4VQfXrvJumULJoejdnlkJNVnnFETSmEh5qqqWs+HJzrau8icdeNGTEeGV46Ux8Z6d7MOWbfOO4nWWx4fjzs9vab8mOcOgu/Zi5wyBcv+/ZSMHw9mM4cef1zPHid+9qybNmE6/Ir8EXr29HMP9Oyd1LPXwJxgnxKc8vJyZs2axX/+8x+sVit/+tOf+P7779m2bRu3tqBFuTzx8bWPExNrvpluN556HnRvucuF5/AaQPWWOxx46nnQPUlJeBITMZWX4zl4sE65kZRU84Pg0CE8RUX1Xm/ExWEKC8NzzIMO4ElOxoiKwhwSgueYB91bHhEBFgumiop6ywkPB8DkdNZfHhoKbnedBx2o+ewWS02Z211/eQNtY7H8XF5eDuZjOhNDQ73l5kOH8FiPeVRtNm+5p6QE0zGvGRt2+8/lBw/W+fxGdLS33AgPr6l/1PNhxMb+XL5vH8axPwji439uf+/eOp/f+2wAnsNvDNVb3lKfvaQkQtato/qMM/CUleG84QaorMSTkFBTrmevpvxEz96BAxjHxNhoz15CQp2febXKW+qzp597Nfdpzs8egfm5dyyTcWQVv+OYPHkyERER3HTTTTz66KO8//77lJaW8txzz/HHP/7xhDdpLvbW8w0Veemll0hOTua+++4LdCgtRuh//0vC9ddT9PrrOLKyAh2O1CMhIYGCgoJAhyHS5NLS0uo979McnHXr1jFy5MhaQ1PR0dGUlJQ0TnQi0uxZt24lLDsbANd551H01ls4fvWrwAYlItIAn4ao7HY7hw4dqpXgFBQUtNi5OCJy8qJ/+1usO3ey/7LLwGxWciMizZpPPThDhgzhD3/4A+vXr8cwDLZs2cJbb73F0KFDmzo+EQkQc2EhUa+8gunwRMiSCRMo+PTTuvMGRESaIZ96cK677jpCQkJ49913cbvdTJ8+nczMTG3VIBLEzHl5RM6YQdXZZ+O85hrcHToEOiQREZ/5lOCYTCauvvpqrr766qaOR0QCyP5//4e5uJiyhx+mumdP9q1Y4X0rQkSkJfF5HZz9+/ezc+dOnMe8VjZgwIBGD0pE/MgwwGQCata2sOzfX7O+hdms5EZEWiyfEpyPP/6YBQsWkJ6eTuhR792bTCYlOCItmHX9emIff5yit9/G3aEDJa+8UrMGyOGER0SkpfIpwfnHP/7BpEmTaNeuXVPHIyL+UFkJYWE1C7J5PJgPHKiZYxMWFujIREQahU8JTmRkJImJiU0di4j4QeyYMZhcLopmzcKTnMyBxYvVYyMiQcenBOfOO+9kxowZXH311cTExNQqSzi8NLuINF/mffu8m19W9elTs8z8kbk3Sm5EJAj5lOBUV1ezdu1ali9fXqds3rx5Pt2orKyM6dOns3btWqKiohg2bNgJ5++89NJLbNiwgblz52KxWAB48cUX2bp1K+bDa3HEx8czZcoUn2IQaY3Cli4lfsQIDs6bh+uCCygfPTrQIYmINDmfEpxZs2bx61//mosvvrjWJOOTMWvWLKxWKzNnziQnJ4cJEyaQkZFB+uGdR4+1bNkyPMfsVHrEqFGjGDJkyCnFIdIamEpKaubVdO6M67zzKB89murDuwSLiLQGPi1J6vF4GDx4MDabDbPZXOuPL5xOJytWrCArKwubzUa3bt3o168fS5curbd+RUUFCxYsYPjw4b5/EhGpYRi0ycoi7sEHwTAwwsMpfe457xCViEhr4FMPzi9/+UsWLlzI9ddfj+kUxuvz8vIwm821dvzMyMhg48aN9dafM2cOQ4cOJTY2tsHyOXPmkJaWxq233krPnj3rrZednU324c0BJ06c2Ojzhd5ZuJ6v/rsLjwFmEww9P517fnVWo95Dml54eDgREREtez5ZdTXmv/0Nz003gcWC6dVXoU0bEvRyQKtltVpb9jMtcpp8SnA+//xziouL+fjjj4mMjKxVNn369BNe73Q6sdvttc7Z7fY6iwYCbN++nR9//JGRI0dy8ODBOuXDhw+nXbt2WK1Wli9fzqRJk3j11VdJqWdBsszMTDIzM73HBQUFJ4zVV3Oyf+KbH35uz2PA4hW7cDodDMvUUEBL4nA4KC8vb9Tnw99sixcTP2oUB91uKi+/HM4+u6agBX8mOT0JCQkt+pkW8dXRnSdH8ynBGTt27Gnd3Gaz4XA4ap1zOBzYbLZa5zweD7NmzWLkyJHeScXH6tKli/frSy+9lOXLl7N69WquvPLK04rxZC1bW/8PjmVrC5TgSNMzDMKWLQOnk8rLL8c5dCgH586lcuDAQEcmItIs+JTg9OjR47RukpqaitvtJi8vj9TUVAByc3PrTDB2OBzs2LGDN954A8A7yfi+++7j0UcfpXv37nXaNplMGIZxWvGdCk8Dt2zovEhji3rtNQyLpabHxmymctCgQIckItJs+LwXVU5ODps2beLQoUO1EoqsrKwTXmuz2ejfvz/z5s3jvvvuIycnh5UrV/Lyyy/Xqme325kxY4b3uKCggGeeeYZJkyYRHR1NeXk5W7dupUePHlgsFr777js2bdrEnXfe6evHaDRmU/3JjFlLikgTsfz0E1F//CMlL7+MERFRs72C5liIiNTLpwQnOzub2bNnc/bZZ7NmzRp69+7N2rVr6devn883uvvuu5k2bRqjR48mMjKS0aNHk56eTkFBAY888ghvvPEGCQkJtSYWu1wuAGJiYrBYLFRUVDBv3jz27NmD2Wymbdu2jBs3rsHxt6Y08OyEWnNwjj4v0hTMhYXYFi2iIisL1wUX4G7bNtAhiYg0WybDh/GdsWPHMmbMGLp3787IkSN5//33Wb16NcuXL+eBBx7wR5yNYu/evY3a3pzsn1j6QwEGNYvBDjo7QfNvWqCXXnqJ5ORk7rvvvkCHUpthEP3yy3giIyl75BEATGVlGMdM9BepjyYZS2vRUCeHTwvZlJaWeue/mEwmPB4Pffr04X//+1/jRdgCDcvsyLhbuwHwm2s7K7mRxuF21/zXZMK8fz+Wo35JKbkREfGNTwlOfHw8+/fvB2omDH///fds2rQJq9XnKTxBKz3JjskEufsqAh2KBIHQpUtJuvBCzId7G4unTKHk978PcFQiIi2PTxnKddddx549e0hKSuKmm27i9ddfp7q6mpEjRzZ1fM1eaIiFtDbh5O4rD3Qo0lK53ZjKyzGio3F36kR1ly6YKg4nzD6uFi4iIrX5lOBceuml3q/79OnD+++/T3V1dZ11bFqrjOQI1v1UjGEYp7TSs7RiHg8J111HdceOFL/5Ju527Sj88MNARyUi0uI1+M9Dj8fT4B+z2UxoaGiDm2G2Nu2T7RyqqKbokCvQoUgLYd26teYLsxnHr36Fc+jQwAYkIhJkGuzB+fWvf+1TA/PmzWu0YFqqDikRQM08nPjosABHI81d+IIFxD30EAcWLaLqnHMov/vuQIckIhJ0Gkxwpk6d6s84WrS2CXbMZhO5+8rp0yUu0OFIM2TJycFUWUl11644r7iCkt/+luozzwx0WCIiQavBBCcxMZHi4uIGd/SWn4WGmElrY9NEY6mf202bW26hunNnCufMwYiKovzeewMdlYhIUDvuKxoPPfRQreP/9//+X5MG05JlJEeQm18RkH2xpPkxlZQQ8d57YBhgsVD8xz9SfHiPNRERaXrHTXCO/WW9YcOGJg2mJctIiaDcWc3BUk00FrAtXkzM888TsmoVAK4LLsCTnBzgqEREWo/jviauV559l5FsB2DnvnISYjTRuNVxu7H/5S94EhJwXnEFjhtvpOqss6ju0SPQkYmItErHTXDcbjfr16/3Hns8nlrHAGeddVbTRNbCtE2wYzk80bjvmfGBDkf8zWQiYvZsqrp0wXnFFWCxKLkREQmg4yY4MTExTJ8+3XscGRlZ69hkMultq8NCrGbaJoSTm68tG1qLkP/9j8i33qJo2jSw2Tg4dy6eeCW3IiLNwXETnLfeestfcQSF9skRrNpaqBWNg51hgMmEyekkZMMGrLm5VHftiqdNm0BHJiIih2mjm0aUkWKnwummoKQy0KFIU3C5iBs9msg336w5vPhi9n/7LdVduwY4MBEROZYSnEbUIfnnFY0leJgcjpovQkMxbDaMsKMmkYeEBCYoERE5LiU4jSgtIRyrxaQF/4JI+EcfkXT++ZgLCgAofvNNLdInItICKMFpRFbL4YnGSnBaNFN5OaaiIgBc55xD5dChoI1lRURaFCU4jSwjJYKd+yrwaEXjlsnpJOmSS4ieMAEA9xlnUPz663iSkgIcmIiInAwlOI0sIzkCR6WbA8WaaNxiGAbWXbtqvrbZODR2LBVZWYGNSURETosSnEaWcXii8U4NU7UY4atXE/V//4d12zYAKu64g6pzzw1wVCIicjqOuw6OnLy0Nraaicb55ZzXTeuiNEeXXXYZP/74Y53zj15ySa3jrl278s9//tNfYYmISCNSgtPILBYz6Ul2vSrejP3zn/8k4u23sWVnc3Du3FqveoeFheF2u4GazWY9Ho92iBcRaYE0RNUE2idFsHN/uSYaN2OepCTcycmYqqtrnT+S3Hg8Hu/XIiLS8ijBaQIZKXacLg/7i5yBDkWOYt20ibClSwFw3HADxVOnsvvd+YT26ENK23aE9ujDnnfnATX7rFmtVvXeiIi0UEpwmoBWNG6GDIOYZ58l+oUX4HDPzJ63PqDXay+RULIfMwYJJfvp/ttxHPrvD7jdbtxuNyEhIdpXTESkBVKC0wRS2oQTYjVrwb/mwOWq+WMyUfzmmxycNw8sFgA6TpuMrbr26/y26kpixj6AyWTCMAyqqqqwWCxYDl8jIiItgxKcJmAxm0hPDCc3XwlOQLlctMnKIua3vwXA3bZtrQX74ksO1HtZfM7WWkNT1dXVGIahJEdEpAVRgtNEMlIi2LW/Ao9HczgCJjSUyoEDqbzwwjpFTpebguiEei8rjEmsc06TjkVEWhYlOE0kIzmCyioP+1roROM9U2fXnnw7dXagQ/KNYRAxaxbWrVsBKHv0UZzXXVerSlW1h2l/38oHF9+G0xpWq8xpDeOnMQ/7K1oREWkiSnCaSIZ3onHLG6baM3V2ncm3vV57qUUkOeaiIiLffBP7nDn1lrs9BrM+286POw/R8ZE7WTfuBQpikvBgoiAmiXXjXqDtA3f4OWoREWlsJqMVvQe7d+9ev93L4zF46M1VDOiVQNZlGX67b2MI7dGHhJL9dc4XxCTh2rg6ABGdmDk/H09yMphMWHbvxt22LRzz9pPHMPhgcQ7/3lDALYPTGdI3JUDRijS9hIQECgoKAh2GSJNLS0ur97x6cJqI2WyifXLLXNG4wcm3JQeoqvb4OZoTs27ZQtIll2CfOxcAd7t2dZIbwzBY8M0u/r2hgGsuTPM5udmzZw833XQTl1xyCYMHD2bWrFnesvfee4+BAwcyePBgXn755ZO69ve//z2ZmZk8+OCD3nMLFiyoVUdERE6d37ZqKCsrY/r06axdu5aoqCiGDRvGgAEDjnvNSy+9xIYNG5g7d673DZZTaSdQ2idH8O3aA7g9BhZzy1lLpTAmsf4enKgEnpn5A5f1TeaSc5Kw25rHTh/VnTtTfvvtOI/ZS+pon6/I4+v/7WNwnySuubD+bL8+VquVF154gV69elFWVsYvfvELBg0axIEDB1i8eDHZ2dmEhYXV+y/lhq5NSUnh+++/Jzs7mwceeIBNmzbRoUMH5s+fz4cffnhK3wMREanNbz04s2bNwmq1MnPmTB588EFmzpzJrl27Gqy/bNkyPJ66vQUn204gZSTbcVV7yC90BDqUk/LTmIfrnXy75o77aZdkZ+G3e3h65g8s+GYXxWWugMRoLigg9tFHMRUXg9nMoWefxdO2bb11l6zZz9+X7+GCHm24ZXD7k1q4Lzk5mV69egEQGRlJly5dyM/P54MPPuD+++8nLKzm+5SQUPeNrIauNZvNVFVVYRgGTqeTkJAQ3n77be666y5CjtoXS0RETp1fEhyn08mKFSvIysrCZrPRrVs3+vXrx9LDy+Yfq6KiggULFjB8+PDTaifQvCsa57esYaq2D9zB/JsfYX9UYq3Jt2c9PZqHbuzKcyN60qtTLNn/y+eZmWv5YPFPfk/iLLm52BYtInTt2uPW+++mg/zl61zOOSOW2y/vgPk0ViXetWsX69evp0+fPuzYsYP//ve/XHPNNdx4442sWbPG52sjIyO56qqruPzyy0lPTycqKoo1a9ZwxRVXnHJsIiJSm1/GGPLy8jCbzbUmAmVkZLBx48Z668+ZM4ehQ4cSGxt7Wu0EWlK8jbCQmhWNLzqr/jVXmiO328NnHS5m1xtXM/LKTgAc3TeSnmTn7qvP4FcD2vHV9/ksX3+A79YXcE7nWH5xfiodUyObLDZLbi7ujAyqzj2XfStWYMTENFh33Y5i3v/iJ7qkRzH6mjOwWE49ny8vL2f06NG89NJLREVF4Xa7KSkp4dNPP2XNmjXcd999/Pvf/663d+jYawHGjBnDmDFjAHj88ccZN24cc+bM4ZtvvqF79+48/PDDpxyriIj4KcFxOp3Y7fZa5+x2O05n3TVitm/fzo8//sjIkSM5ePDgKbcDkJ2dTXZ2NgATJ06sdxihqZ3RLoa9B10Bufep+mFbARWVbi45N+O4cSckQLcz2nL71ZUs+i6XL/6zk4lzNtGzYzy/uqQTfc5MaNR9nMzz5mEZNYrqr7/GuOCCmgAasPGnQt75dDsdU6N4ftT52G2nPvRTVVXFHXfcwW233cbtt98OQPv27bn11ltJTExk6NChWK01f5WO/X7Vd+3R1qxZg81m47zzzmP8+PF8/fXX3HbbbRQVFdGlS5dTjlnEarW2qJ87Io3NLwmOzWbD4ag9hOFwOLDZbLXOeTweZs2axciRI+tdFt/Xdo7IzMwkMzPTexyIVybT4kP55of97Nt/oMVMNP7m+1xCrWbaxZl8/p4N7RvPwLNi+HbdAbK/z+f3//c97RLDueK8VM7tGt8on910/vlEPvgghzIy4Dhx7dxXzh/m/0h8dCi/ubYTFWUlVJSd2j0Nw+Chhx4iIyOD2267zfv9uOyyy1i0aBE9e/Zk+/bt3iT76O9XQ9ce7ZlnnuHVV18lPz8fp9NJQUEBVVVV7N27l7i4uFMLWgS9Ji6tR0BfE09NTcXtdpOXl+c9l5ubS3p6eq16DoeDHTt28MYbbzB69GiefvppAO677z42bdrkczvNSUZyBFXVBnkHW8ZEY49hsGZbET07xhAacnKPhy3UQua5Kbx899nc+YuOuD0G7y7awfPvruVfq/fhqjqFrQ6qq4l47z2orsaIiuLQY4/BcSbi5hc6+OPftmAPs/DQjWcSZT+9SbsrV67kb3/7G9999x1Dhw5l6NChfP3119x6663s3LmTyy67jDFjxjB58mRMJhP5+fmMGDHiuNce8cUXX9C7d29SUlKIiYnh3HPPZciQIZhMJnr27HlacYuItHZ+W+hv8uTJQE2ykpOTw4QJE3j55ZdrJSeGYVBSUuI9Ligo4JlnnuHtt98mOjoaq9XqUzsN8edCf0fsK3Ty2/fXcfvlHbi4V909jpqbHXvLmDR3E6Ou6kT/7m1Oqy2PYbBuezGLV+azfW8ZkeFWBvdJ4tLeyUSG+9Z5GPbVV7S5804K33sP5wkm4RaWVvLqXzZTXe1h3K3dSY6vv2dPpDVQD460FgFf6O/uu+/G5XIxevRopkyZwujRo0lPT6egoIARI0ZQUFCAyWQiNjbW+yc6OhqAmJgY7xyHhtpprhLjwrCFWlrMlg2rtxVhNpvo1bHhybu+MptMnNM5jid+3Z1xWd3omBrBp9/t5el3fmDev3ZSWFp5wjYqhw7lwKJFJ0xuDlVUMXnBFhyVbh688UwlNyIirZy2avCD1+dvprLKw9PDewTk/r4yDIPfvreOhJgwHrqpa5PcY09BBV+uzOe/mwsBOL9bPJefl0LbhJ8nj5sPHCD2wQcpGT8ed+fOJ2zTUenm9fmbySt08NCNXenSLqpJYhdpSdSDI61FwHtwWrP2yRHsPlBBtbv5bXNwtL0HHewvrqRPl6ab3No2wc7IKzvx+7t6cWnvJFZtKeJ3szcw9eMtbNt9CABTWRnWnBwsR821aoirysO0hVvZXeDg3l92VnIjIiKAH7dqaM0yku1Uuw32Fjhof3jxv+Zo9dYiTMA5nZv+7Z346DCyBrfn6gvSWLJmH/9ctZ8Z768gsXNbrjgvhV5LlmAOCztuG263h5n/2M7W3YcYdVUnenWKbfK4RUSkZVCC4wcdUg6vaLyvolknOGu2FtMpLZKYCP9tFxAZbuWaC9tyVVw5idf+mjmXjWTa3stIbWPjivNSOa9bPNZ6FujzGAazF+ewdkcxw4ZkcP5pTogWEZHgoiEqP0iICcMeZmFnM55oXFBSya4DFU06PHU85s6dqLr1Fq546lZGXdUJs8nE/33xE8/NWkv2//JxutzsmTqb0B59SGnbjtAefbB99BHXXdyWS3onBSRmERFpvtSD4wcmk4n2yRHkNOMEZ/XWIgB6d471633DvvwS1wUXYERHU/q73wHQn5rJxxtySlj833z+umQXB9/5kN988Ra26po3r5JKD/DAV9NYe04iXHCHX2MWEZHmTz04fpKRbGfPAQdV1c1zovGabUW0SwwnMdZ/r1eb9+4l/t57iZoypU6ZyWTirI6xPJbVjaeGdWf40j95k5sjbNWVdJo+2U/RiohIS6IEx08ykiNwewz2NsMVjUvLq9i+p4zefphcfDRPWhoH58yhdNy449brmBpJQmn9r7vGlxxoitBERKSFU4LjJxlHJhrnN79hqjXbizDAP/NvPB6iX3iB0OXLAXBdeCE0sJfY0Qpj6l8FuqHzIiLSuinB8ZM20aFE2JrnisZrthaTGBtG24TwJr+XqayMsGXLCPv3v0/qup/GPIzTWvu1cac1jJ/GPNyI0YmISLDQJGM/MZlMZCRHkJtfEehQanFUVrN5ZymX9U3GZGq63c5NDgdGWBhGdDQFn3yCERl5Ute3feAO1gEdp00mvuQAhTGJ/DTmYdo+oAnGIiJSlxIcP2qfHMGX3+dTVe0hxNo8Os/W7SjB7THo05RvTzmdtMnKwnXOOZSOH3/Syc0RbR+4A9cDd5B/5LjxIhQRkSDTPH7LthIZKXY8HoPdB5pPL87qbUVER4TQMe3Ukg6f2GxUXnQRrosuarp7iIiIHEU9OH7UIfnnFY07pjZhQuEjV5WHDT+V0L9HG8xNMDxl2bEDQkJwp6dz6KmnGr19ERGRhijB8aO4qFAiw63NZkXjTbklVFZ56NMUr4e73cSPHIkRG0vBwoXQhPN7REREjqUEx4+8E42bSYKzelsx9jALZ6Y3wQ7cFgvFb7yBJzpayY2IiPid5uD4WUaKnb0FDlxVgV3R2O0xWLu9iF6dYuvdzPJU2RYvJnzePACq+vbF3blzo7UtIiLiKyU4fpaRFIHHIOATjbfuPkS5003vLrGN16hhYP/wQyLmzAG3u/HaFREROUkaovIz74rG+8rp1JRvLp3A6q1FhFjN9OwQ0zgNVlVBSAhFb78NhgEWS+O0KyIicgrUg+NnsZEhRNutAZ2H4zEM1mwromeHaMJCTj8RiZo0ifiRI8HlwrDbMSIiGiFKERGRU6ceHD8zmUy0T44gd1/ghqhy88spLqtqtM013e3bYy4qUq+NiIg0G+rBCYCM5AjyDjqorArMPJU124owm02c3Sn21BuprMT6448AVPz615RMnKgER0REmg0lOAGQkRKBYcCu/f7vxTEMg9Vbi+naLoqI8FPvwIt59lkSbrgBU0lJI0YnIiLSODREFQAZyXagZqJx57ZNsAbNceQVOtlX5GRI3+TTaqfswQepHDgQI6aRJimLiIg0IvXgBEBsZCgxESHsDMA8nDVbiwA45xQ217Ts2UPEjBlAzbwb53XXNWZoIiIijUYJToBkJEeQm+//N6lWby2iY2oEsZGhJ32t/cMPiZo8GXNeXhNEJiIi0niU4ARIRoqd/EInTpf/JhofLK1k5/4K+nQ5ybenDAOAQ489xoEvvsCTmtoE0YmIiDQeJTgBkpEcgYF/Jxqv2VYzPHUym2uGLl1KwrXXYjr8Grg7I6OpwhMREWk0SnACpH3yzysa+8vqrcWkJYSTFGfz/SKzGTweTFVVTReYiIhII1OCEyAxESHERYb4bR7OoYoqtu055HPvjcnhAMA1YAAFn36KJympKcMTERFpVEpwAsifKxr/sL0Yw8Cn+TfWrVtJuugiwrKza06Y9ZiIiEjLot9cAZSREsG+IieOyqafaLx6axFtokNplxh+wrruhARc/fpR3bVrk8clIiLSFJTgBFDG4Xk4O/c37TCVo9LN5p2l9OkSh8lkarCeqawM3G6MuDiKZs7EnZ7epHGJiIg0FSU4AeRd0biJ5+Gs/6mYardx/Pk3VVXE33YbsQ8/3KSxiIiI+IO2agigKHsI8VGhTb6i8eqtRUTZrXRKi2y4UkgIzl/8Anfbtk0ai4iIiD/4LcEpKytj+vTprF27lqioKIYNG8aAAQPq1Fu+fDnz58+nuLiYkJAQevfuzahRo7Dba3o7XnzxRbZu3Yr58MTX+Ph4pkyZ4q+P0egyUiKa9FXxqmoP638q4fzubTCb6xmeMgzMhYV42rSh/L77miwOERERf/JbgjNr1iysViszZ84kJyeHCRMmkJGRQfox8zy6du3K+PHjiY6Oxul08s477/CXv/yFUaNGeeuMGjWKIUOG+Cv0JpWRbGf11iIqnNXYbY3/v2PzzlIqqzz0bmB4KvKtt4h4910OLFqkFYpFRCRo+GUOjtPpZMWKFWRlZWGz2ejWrRv9+vVj6dKldeomJCQQHR39c4BmM/v27fNHmAHx80TjphmmWr21CFuohW7t69+13Dl0KBU334wnJaVJ7i8iIhIIfunBycvLw2w2k5aW5j2XkZHBxo0b662/efNmJkyYgMPhICwsjMcff7xW+Zw5c5gzZw5paWnceuut9OzZs0njb0reFY3zy+nWPvoEtU+O22Pww/Zizu4Ug9VSO5c179mDp21bqrt25dAzzzTqfUVERALNLwmO0+n0zqE5wm6343Q6663frVs3Zs+eTWFhIdnZ2SQmJnrLhg8fTrt27bBarSxfvpxJkybx6quvklJPD0R2djbZhxermzhxIgkJCY34qRpHApAUF05ecXWjx7dhx0HKHNUM6tu+VtumH37AOmgQ7qlT8YwY0aj3FJHmwWq1NsufeSL+4pcEx2az4Ti89P8RDocDm+34eyLFx8fTu3dvpkyZwqRJkwDo0qWLt/zSSy9l+fLlrF69miuvvLLO9ZmZmWRmZnqPCwoKTudjNJl2iTa27ixq9PiWfJ9LiNVEehtz7bYTE4m6+27K+/fH00y/JyJyehISEprtzzyRxnT06NDR/DIHJzU1FbfbTV5envdcbm5unQnG9fF4POTn5zdYbjKZMAyjUeIMlIzkCApKKil3VDdam4ZhsHpbMT0yYrCFWgAw5+VhqqiA0FAOPf00nvj4RrufiIhIc+KXBMdms9G/f3/mzZuH0+lk8+bNrFy5kkGDBtWpu2zZMgoKCjAMgwMHDjB37lx69eoFQHl5OWvWrMHlcuF2u1m2bBmbNm2id+/e/vgYTSajCXYWz91XQdEh189vT1VX0+a224i7+25o4QmhiIjIifjtNfG7776badOmMXr0aCIjIxk9ejTp6ekUFBTwyCOP8MYbb5CQkMDu3bv58MMPKS8vJyIigj59+jBs2DAA3G438+bNY8+ePZjNZtq2bcu4ceMa7J5qKdofXtF4574KenSIaZQ212wrwmyCs8+IrTlhtVL65JMYUVFwnO0aREREgoHJaOnjOydh7969gQ6hQc+9u5b0RDv3Xtu5Udp78f11REeG8Oj1nbFu20Z1t26N0q6ItAyagyOtRUDn4MiJZSQ33orG+Qcd5BU66dM5jqjXXyfh6qux7N7dKG2LiIi0BNqLqplon2zn+x8LKauoItIeclptrd5WBEDvznGUp4/CnZaGu127xghTRESkRVAPTjPR4chE40ZY0XjNtmIurcwlLjIET0ICFbfddtptioiItCRKcJqJIxONc/NPb5iqsLQSyw9reXTaw0S8/35jhCYiItLiaIiqmQgPs5IcZzvteThrthWzPakTO1+aQOivb2qk6ERERFoWJTjNSPtkO9v2lJ3y9dYff2TH9/tJTYgl5M4RtJrX40RERI6hIapmJCM5gqJDLkrLq07+YrebmLtHc/PMF+nTObbRYxMREWlJlOA0Ixkpp7GiscXC0sd+z9TM39Cni7ZgEBGR1k0JTjPSPsmOiZoVjX1lqqgg7J//BGCJkUJ5py6kJ9lPcJWIiEhwU4LTjNhCLSTHn9xE48ipU4kfOZLqbTvYmFtC785xmLQVg4iItHKaZNzMZCRH8OOuUp/rH3rwQVznn88P7liq3Qfp3SWuCaMTERFpGdSD08y0T7ZTXFZFSZnruPXCFy4EhwNsNiovvZTV24qICrfSOS3SP4GKiIg0Y0pwmhnvisbHmYdj3biR2AceIGL2bACqqj2s21HMOZ1jMZs1PCUiIqIhqmamXZIdk6nmTaqzz4itt051jx4c/OtfcZ13HgA/7irF6fLQu7OGp0REREA9OM2OLdRCSgMTjUP//W9C1q0DwHXhhWCtyU9Xby3GFmqmW/tov8YqIiLSXKkHpxnKSI5gY24phmH8/EaUx0PM889jhIdT8MkncPi8x2Pww7YienWMJcSqfFVERASU4DRLGckR/GfjQYrLqoiLCq05aTZz8M9/Bo/Hm9wAbN9bxiFHtd6eEhEROYr+yd8M1VrRuKqK8I8+AsPAk5KCJy2tVt3VW4uwWkyc1TEmEKGKiIg0S0pwmqH0xHBMppoVjcM//pi4sWMJ/fe/69QzDIM124ronhGNLdQSgEhFRESaJw1RNUOhIRbS2oSTu68cx803405NxXXRRXXq7dpfwcFSF1dfkFZPKyIiIq2XenCaqbPNJRRt34MBuAYOrLfO6m1FmExwTgOvk4uIiLRW6sFpjgyDW977Hb8oKqNodDbxseH1VluztZgu7aKItIf4OUAREZHmTT04zZHJxO7fjufdS0aRe8BZb5V9hU72HnTQR4v7iYiI1KEEp5kx798PQOyAfqzvcE6DO4uv3lYEQO/Osf4KTUREpMVQgtOMWNevJ+nCCwlfuJDQEDNpbWzk5tef4KzZVkRGsp346DA/RykiItL8KcFpRtydOlExYgSVgwYBNQv+5e6rwDCMWvWKDrn4Ka9ci/uJiIg0QAlOc+DxQFUVht1O6Ysv4omPB2oW/Ct3VnOw1FWr+prDw1N9Nf9GRESkXkpwmoHIN98k4aabMJWV1TqfkWwHqDMPZ822YlLibaS0qf/tKhERkdZOCU4zUN2xI1Vdu2JERNQ63zbBjsVsYudRCU65o5otu0rprd4bERGRBmkdnEAyDDCZcF57Lc5rr61THGI10zYhnNz8Cu+5tTuK8RjQR/NvREREGqQenEBxuYi//XZsixcft1pGSgS5+8u9E41Xby0iLirUO3wlIiIidSnBCRBzaSnmwkJw1r+Q3xHtk+1UON0UlFRSWeVmY24JfTrHYjKZ/BSpiIhIy6MhqgDxJCRQ8MknYDn+LuAdkmvm5eTuq8BsgqpqQ6+Hi4iInIB6cPzMsm0b0c89V9Nzc4LkBiAtIRyrxURufjmrtxURYbPSuW2UHyIVERFpufzWg1NWVsb06dNZu3YtUVFRDBs2jAEDBtSpt3z5cubPn09xcTEhISH07t2bUaNGYbfbT6qd5irs228J//RTyu6/H09q6gnrWy01E4237y1j70EHfbvEYTFreEpEROR4/JbgzJo1C6vVysyZM8nJyWHChAlkZGSQnp5eq17Xrl0ZP3480dHROJ1O3nnnHf7yl78watSok2qnuaq4804cv/oVRmysz9dcuPZfDP1kFgmHCjgYnUDO/Y/Q9oE7mi5IERGRFs4vQ1ROp5MVK1aQlZWFzWajW7du9OvXj6VLl9apm5CQQHR09M8Bms3s27fvpNtpbsIXLsS6eTPASSU3e6bO5sZ5r5N06ABmDBJLD9DrtZfYM3V2E0UqIiLS8vmlBycvLw+z2UxaWpr3XEZGBhs3bqy3/ubNm5kwYQIOh4OwsDAef/zxU2onOzub7OxsACZOnEhCQkJjfaST43QSMmkSRr9+VM+de1KXhk6bjK26stY5W3UlHadNJvrFxxozShEJIlarNXA/80SaAb8kOE6n0zuH5gi73Y6zgVeku3XrxuzZsyksLCQ7O5vExMRTaiczM5PMzEzvcUFBwel8jNNiXrgQIzQU4yRjSCk5UO/5+JID5Afw84hI85aQkBDQn3ki/nJ0p8fR/DJEZbPZcDgctc45HA5sNttxr4uPj6d3795MmTLltNoJGI8H2xdfgGHgSU7GiDv517sLYxJP6ryIiIj4KcFJTU3F7XaTl5fnPZebm+vTxGCPx0N+fv5ptxMI4QsXEn/XXYQtW3bKbfw05mGc1rBa55zWMH4a8/BpRiciIhK8/NaD079/f+bNm4fT6WTz5s2sXLmSQYMG1am7bNkyCgoKMAyDAwcOMHfuXHr16nXS7TQHjl/9ioPvv0/lwIGn3EbbB+5g3bgXKIhJwoOJgpgk1o17QW9RiYiIHIfJOLLJURMrKytj2rRprFu3jsjISIYPH86AAQMoKCjgkUce4Y033iAhIYG5c+fyzTffUF5eTkREBH369GHYsGFERUUdtx1f7N27tyk/opc5Px8jLOyUhqRERBqD5uBIa9HQHBy/JTjNgV8SHMOgzU03YS4p4cCXX4JZi0WLiP8pwZHWoqEER3tRNTaTidLnnsN88KCSGxERkQBRgtOITEVFGHFxVPXpE+hQREREWjV1MTQS69atJF94IeEffxzoUERERFo9JTiNxJ2aiuO666i88MJAhyIiItLqaYjqdBkGeDwYkZGUTJoU6GhEREQE9eCctoj336dNVhamQ4cCHYqIiIgcpgTnNHliYvAkJmJERAQ6FBERETmsVa2DIyIiIq2DenD8ZMaMGYEOoVkLpu9Pc/4sgY7N3/dvyvs1RduN2eZTTz3VaG1J8xbov9fNlRIcPzn33HMDHUKzFkzfn+b8WQIdm7/v35T3a4q2A/3/R1omPTf10xCViEgQeuqpp5g4cWKgwxAJGPXgiIgEoczMzECHIBJQ6sERERGRoKMeHBEREQk6SnBEREQk6GirBhGRVmLLli3Mnj0bq9VKXFwcDzzwAFarfg1IcNIcHBGRVqKwsJDIyEhCQ0OZO3cuHTt25IILLgh0WCJNQqm7iEgrER8f7/3aYrFgMpkCGI1I01KCIyLSwnzxxRcsWbKEnTt3cvHFF3P//fd7y8rKypg+fTpr164lKiqKYcOGMWDAgFrX79+/n9WrV3PDDTf4O3QRv1GCIyLSwsTFxXHDDTfwww8/4HK5apXNmjULq9XKzJkzycnJYcKECWRkZJCeng5ARUUFb731FmPHjtX8GwlqeotKRKSF6d+/P+effz5RUVG1zjudTlasWEFWVhY2m41u3brRr18/li5dCoDb7WbKlCncfPPNpKWlBSJ0Eb9RgiMiEiTy8vIwm821kpeMjAx27doFwPLly9m2bRsLFizgxRdf5LvvvgtUqCJNTv2TIiJBwul0Yrfba52z2+04nU4ABg0axKBBgwIRmojfqQdHRCRI2Gw2HA5HrXMOhwObzRagiEQCRwmOiEiQSE1Nxe12k5eX5z2Xm5vrnWAs0poowRERaWHcbjculwuPx4PH48HlcuF2u7HZbPTv35958+bhdDrZvHkzK1eu1LCUtEpayVhEpIWZP38+CxYsqHXupptu4pZbbqGsrIxp06axbt06IiMjGT58eJ11cERaAyU4IiIiEnQ0RCUiIiJBRwmOiIiIBB0lOCIiIhJ0lOCIiIhI0FGCIyIiIkFHCY6IiIgEHSU4IiIiEnSU4IiIiEjQUYIjIs3eO++8U2fl3qPdcsst5Ofnn1Sby5Yt4+WXXz7d0ESkmdJKxiLiV8uXL+ezzz5j165dhIWFkZSUxCWXXMLll1+OyWQ6pTZvueUW/vjHP5KSktLI0YpIS2UNdAAi0np8+umnfPLJJ9x1112cc8452Gw2cnJy+PTTT7nssssICQmpc43H48FsVmeziJwcJTgi4hcVFRXMnz+f+++/nwsuuMB7vmPHjjz44IPe47feeovQ0FAKCgrYuHEj48aNY9myZbRp04Zbb70VgE8++YR//OMfmEwmsrKyjnvfJUuWsGDBAkpLS4mKiuLWW29l4MCBLFmyhK+//prx48fz97//vdYQWHV1NQMGDOD++++noqKC2bNns3r1akwmE4MHD+aWW25R0iXSzCnBERG/2LJlC1VVVZx33nknrPvtt9/y9NNP8+STT1JdXc2yZcu8ZWvWrOHTTz/l+eefJykpiRkzZjTYjtPp5P3332fChAmkpaVRVFREWVlZnXrXXXcd1113HQAFBQU8++yzXHjhhQBMnTqV2NhY/vjHP1JZWcnEiRNp06YNQ4cOPdlvgYj4kf4JIiJ+caQHxWKxeM8999xz3HnnnQwfPpyNGzd6z5933nl069YNs9lMaGhorXa+++47Lr30Utq3b4/NZuPmm28+7n1NJhM7d+7E5XIRFxdHenp6g3VdLhevvfYaV155JX379qW4uJg1a9Zw5513YrPZiImJ4eqrr+a77747xe+CiPiLenBExC+ioqI4dOgQbrfbm+QceYvpvvvu4+j3Hdq0adNgO0VFRXTq1Ml7nJiY2GBdm83Gww8/zKeffsrbb79N165duf3222nbtm299adPn05aWhq/+tWvgJreHLfbzT333OOtYxjGceMTkeZBCY6I+MWZZ55JSEgIK1eurDUHpz7He5sqLi6OgwcPeo8LCgqO21bv3r3p3bs3LpeLv/zlL8yYMYPf/e53deotXLiQvXv3Mn78eO+5Nm3aYLVaeffdd2v1PIlI86chKhHxi4iICG666Sbeffdd/vOf/+B0OvF4POTk5FBZWelzOxdeeCFLlixh9+7dVFZW8te//rXBusXFxXz//fc4nU6sVis2m63eycGrV6/m888/Z9y4cbWGxOLi4jjnnHP44IMPqKiowOPxkJ+fX2s4TUSaJ/XgiIjfXHfddcTHx/P3v/+dqVOnEhYWRnJyMsOHD6dr164+tdGnTx+uvvpqXnrpJcxmM1lZWXz77bf11jUMg08//ZQ333wTk8lEhw4duPvuu+vU++677ygtLeWRRx7xnhs4cCD33HMPDzzwAB9++CGPPvooDoeD5ORk74RkEWm+tNCfiIiIBB0NUYmIiEjQUYIjIiIiQUcJjoiIiAQdJTgiIiISdJTgiIiISNBRgiMiIiJBRwmOiIiIBB0lOCIiIhJ0/j/yWDmdUeEDDgAAAABJRU5ErkJggg==\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -2041,7 +2020,7 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" + "print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")" ] }, { @@ -2537,72 +2516,49 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" + "print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[23, 23, 31, 42]\n", - "Fitted true_speed is 25.7317 ± 15.5178 cm/s (60.3%)\n", - "Estimated error in final calculation 36.1%\n", - "Estimated total error 96.4%\n", - "Actual extrapolated error (with hindsight) 31.4%\n", - "Actual raw error (with hindsight) 5.9%\n", - "\n", - "[23, 23, 31, 42, 53]\n", - "Fitted true_speed is 32.7812 ± 11.6625 cm/s (35.6%)\n", - "Estimated error in final calculation 10.1%\n", - "Estimated total error 45.7%\n", - "Actual extrapolated error (with hindsight) 12.7%\n", - "Actual raw error (with hindsight) 0.5%\n", - "\n", - "[23, 23, 31, 42, 53, 67]\n", - "Fitted true_speed is 45.7925 ± 6.3741 cm/s (13.9%)\n", - "Estimated error in final calculation -13.9%\n", - "Estimated total error 27.8%\n", - "Actual extrapolated error (with hindsight) 22.0%\n", - "Actual raw error (with hindsight) 0.1%\n", - "\n", - "[23, 23, 31, 42, 53, 67, 85]\n", - "Fitted true_speed is 35.1215 ± 1.3939 cm/s (4.0%)\n", - "Estimated error in final calculation 5.9%\n", - "Estimated total error 9.8%\n", - "Actual extrapolated error (with hindsight) 6.4%\n", - "Actual raw error (with hindsight) 0.3%\n", + "[19, 19, 22, 26]\n", + "Fitted true_speed is 18.4813 ± 30.7668 cm/s (166.5%)\n", + "Estimated error in final calculation 94.3%\n", + "Estimated total error 260.8%\n", + "Actual extrapolated error (with hindsight) 50.8%\n", + "Actual raw error (with hindsight) 8.0%\n", "\n", - "[23, 23, 31, 42, 53, 67, 85, 113]\n", - "Fitted true_speed is 37.9278 ± 0.3533 cm/s (0.9%)\n", - "Estimated error in final calculation -0.4%\n", - "Estimated total error 1.3%\n", - "Actual extrapolated error (with hindsight) 1.1%\n", - "Actual raw error (with hindsight) 1.0%\n", + "[19, 19, 22, 26, 32]\n", + "Fitted true_speed is 33.9553 ± 20.0669 cm/s (59.1%)\n", + "Estimated error in final calculation 9.8%\n", + "Estimated total error 68.9%\n", + "Actual extrapolated error (with hindsight) 9.5%\n", + "Actual raw error (with hindsight) 2.8%\n", "\n", - "[23, 23, 31, 42, 53, 67, 85, 113, 142]\n", - "Fitted true_speed is 38.7784 ± 0.2004 cm/s (0.5%)\n", - "Estimated error in final calculation -1.6%\n", - "Estimated total error 2.2%\n", - "Actual extrapolated error (with hindsight) 3.3%\n", - "Actual raw error (with hindsight) 1.9%\n", + "[19, 19, 22, 26, 32, 36]\n", + "Fitted true_speed is 56.6805 ± 9.9888 cm/s (17.6%)\n", + "Estimated error in final calculation -26.2%\n", + "Estimated total error 43.9%\n", + "Actual extrapolated error (with hindsight) 51.0%\n", + "Actual raw error (with hindsight) 9.6%\n", "\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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sLNTW1mLlypUYPnx4j0atDmimuwpGzAW6GBV1s876HajVaubq6srmzJnDLl68yJKSklhcXByztrbWuxY+ZMgQ9tBDD7Hi4mLdKJyb+7uUlZUxOzs7tnjxYpaZmcni4+PZsGHDWFxcXJc1dbePTX5+PktPT2fffvutrh9Ceno6q66u1u3z0UcfMSsrK7ZlyxaWnZ3NVqxYwYRCoV7/o5uvvavVahYVFcVGjhzJTp8+zU6dOsWio6PZ6NGj76gfyHfffcf++9//soyMDJaXl8fWr1/PuFwuO3z4MGOsYx+bs2fPMh6Px55//nmWnZ3NDhw4wHx8fPT6NyxdupTZ2dmxWbNmsczMTHbixAkWFBSk19dk8+bNTCAQsLVr17LMzEyWk5PDdu7cyZ588skua+1OH5vunqeQkBD22Wef6W4vX76cubm5sR07drBr166xgwcPsoCAAHb//ff37ITe5Pfff2dcLpf94x//YGfPnmU5OTns+++/143aOnToEAPAPvvsM3blyhX23//+l7m5uXU6KqozlZWVTCAQsBEjRrDhw4fr3dfS0sLCw8NZTEwM+/3339m1a9fYqVOn2FtvvcV27tzZZc2362PDGGM7d+5kPB6PffzxxywnJ4d98MEHjMfjsd9++023z2effcZCQkJ0ty9evMjEYjFbunQpO3/+PMvJyWHPP/884/F47NSpU7d8vtsBwBwdHdlnn33GLl26xD799FPG4/HYr7/+qrdPZ58/48aNY1FRUSwjI4OlpKSwe+65p8PnSmePvfvuu/VGGC1fvpx5eHiwzZs3s8uXL7OMjAy2fv169s4773RZd3f62BQUFDA7Ozu2fPlydv78ebZ7927m5OTEVqxYodvn9OnTLCQkRG+UaHc+Z9rRqCjDULAhjMvlso0bN952v86CDWOMHT9+nA0fPpyJRCIWHBzMtm3b1qGT34EDB1hoaCgTCoXdHu5tb2/fYbj3zdo7Gd5uqObSpUs7DDnv7HHvvvsu8/HxYUKhkEVERLCDBw92OM7Nw4ZLSkrY/Pnzma2tLbOzs2MLFy7sULOfn1+Phkhv376djRkzhjk4ODArKysWHh7OvvvuO939txvu7eLiwp5++ulOh3u/9957zMPDg4nFYjZ79uwOnUp37tzJRo8ezaysrJidnR2LiIhga9as6bLW77//vltDk7tzngDo/d00NTWxl156iQUEBDCRSMR8fHzYX//6V71A2v531NPh9QcPHmSjR49mYrGYSSQSNmnSJHb16lXd/evWrWNSqZTZ2Niwv/zlL+zzzz/vdrBhjLHZs2czAOz999/vcF9VVRV7+umnmVQqZQKBgEmlUjZ79myWlpbW5fG6+zf0/fffs6CgICYQCFhwcHCHL/7Vq1ezm3/THjt2jE2cOJE5ODgwiUTCRo0axXbv3q23z8SJE285bLwzANhHH33EZs2axaysrJiHhwf7z3/+02GfzoLNxYsX2YQJE5i1tTULDAxk27dv77Tz8O2CjUqlYu+++y4LCQlhAoGAOTs7swkTJnTZwZexzv+dd+bkyZNszJgxTCQSMXd3d7Zy5Uq9Tr5d/W3e7nOm3eHDhxkAVlJScttayHUcxmgWsYHO29sbM2fOxBdffNEnk+UNNDKZDM7OztiwYQMWL15ssjqWLVuGoqKiTvu69GcbNmzAqlWrcPHiRTg4OJi6HIvl5+eHp59+GqtWrer2YzgcDrZs2YKHH37YiJVZJsYY1q1bhzfeeAMtLS20hEoP0LcYwYoVK7BhwwaIRKJuTb9Oeubw4cMYNWqUSUONJdu3bx/effddCjVGdO7cOYhEIrz44oumLmVA2LdvH0QiEdasWYOXX36ZQk0PUYsNAaBdcqCsrAxOTk6ws7MzdTnECCy1xYaYL2qxMUxzczMqKyvh7u5OHYYNQMGGEEIIIRaDLkURQgghxGJQsCGEEEKIxaAeSW1unoyLEEIIIabX04krqcWGEEIIIRaDgg0hhBBCLAYFG0IIIYRYDOpjQwghxKwwxiCXy6HRaO54FW7SPzDGwOVyIRaL7/g9p2BDCCHErMjlcggEAppxd4BRqVSQy+V3PCnhgL4UlZKSgm+++cbUZRBCCLmBRqOhUDMA8fl8aDSaOz4OzTzchoZ7E0KIeZDJZLC2tjZ1GcQEOnvvabg3IYQQQgYsCjaEEEIIsRh0EZMQQki/djq7Crvii1HTqICTnRCzx3thVJhLrxybMaYbsdMXVCqVXv+im29393EDGZ0FQggh/dbp7Cr88Ec+FCptp9OaRgV++CMfAAwON4WFhXj44YcxduxYpKamYsOGDfj8889x9uxZyOVyzJgxAy+99BLS09PxxRdf4LvvvsPvv/+OZ555BtnZ2dBoNJg8eTJOnjypd9zq6mqsXLkSxcXFAIA1a9YgNjYWH3zwAcrLy1FYWAgnJycMGjRI7/aqVavwwgsvoKamBk5OTvjoo4/g5eWF5557Dg4ODjh//jyGDRuG1atX38GZtBwUbAghhJitrccKUFQh6/L+3NImqNT6Y2AUKg02/56HhHNVnT7G280aiyb73vJ5r169ig8//BBvv/02AGDFihVwdHSEWq3GokWLkJWVhWHDhuH8+fMAgNOnTyMkJARnz56FSqVCZGRkh2P+61//whNPPIGRI0eiuLgYDz74IE6cOAEAOHfuHHbu3AkrKyt88MEHereXLl2K+fPnY+HChfj555/x+uuvY8OGDdrXn5uLrVu3gsfj3fL1DCQUbIyk8UwyanfvhbqmFjwnRzjOmgm7kbGmLosQQizKzaHmdtu7y9vbG9HR0brbe/fuxY8//gi1Wo3y8nJcvnwZQ4YMgb+/Py5fvoyMjAw8+eSTOHXqFNRqNUaOHNnhmPHx8bh06ZLudlNTE5qamgAA06ZN05u/5cbbqamp+O677wAA8+bNw7p163T73X///RRqbkLBxggazySj+sefwBRKAIC6phbVP/4EABRuCCGkB27XsrLqv2dR06josN3JTogXF4Ua/Lw3DjkuKCjAN998g/3798PBwQHPPfcc5HI5AGDUqFE4evQo+Hw+xo8fj+eeew4ajQavv/56h2NqNBrs2bOn0wnobh7ifKvh7jfOzEvD4juiUVFGULt7ry7UtGMKJWp37TFRRYQQYplmj/eCkK//VSbkczF7vFevPUdjYyOsrKwgkUhQWVmJY8eO6e4bNWoUvvvuO0RHR8PZ2Rm1tbW4cuUKQkJCOhxn4sSJ2Lhxo+52+2Ws24mJicHu3bsBADt27Oi0NYhcRy02RqCuqe18e20dKr7dAJuoSFgNHQKuSNTHlRFCiGVp7yBsrFFRABAeHo6hQ4di8uTJ8PX1RWzs9Zb3yMhIVFVVYfTo0QCAIUOGoKKiotP1jtauXYt//vOfmDJlClQqFUaNGoV33333ts+/du1avPDCC/j66691nYdJ12jm4Ta9OfNwwav/6jTccEQicIRCaBobwREKYT00HDbRkbAaGg6uUNhrz08IIf0ZzTw8cPXGzMPUYmMEjrNm6vWxAQCOUADnBxfBNiYa8stX0Jyajub0DDSnpWtDzrBw2ERFaVtyKOQQQgghBqEWmza9vVZUd0ZFMbVaG3LSMtCcngFNUxM4IiGshw7VtuSEU8ghhAw81GIzcPVGiw0FmzamXgTzeshJR3P62eshZ9hQbZ8cCjmEkAGCgs3ARcGmF5k62NyIqdWQX7p8vSWnuRkckUgbcqIjYTUkjEIOIcRiUbAZuCjY9CJzCjY3uh5y2lpy2kPO8GGwiRqhbckRCExdJiGE9BoKNgMXBZteZK7B5kbtIacpNQ2yjHPakCMW67fkUMghhPRzFGwGrt4INjRBXz/C4fFgFRYK14cfhO+7b8L9b8/AJjoSLVlZqPj6WxS88k9UfL8JzWczoVEqb39AQgghd6ywsBA7d+7sk+eaP38+zp49e8t9vv32W7S0tPTouElJSXjkkUfupDSzQcO9+ykOjwfrIWGwHhIGtngRWi5eQnNqOmQZZ9F8JkXbkjN8GGyiImE9JBQcaskhhFiolIpM7M87htrWejiK7DHDfzJi3Ib12fO3B5s5c+Z0uE+lUoHP79uv2u+++w7z5s3rdOkGY1Cr1XrrVd18uyvGOjcUbCyAXsh5cBFaci6iOS0dsoxzaD6TDI5YDJsIbcixCqOQQwixHCkVmdh6eT+UGm0rdW1rPbZe3g8AdxRutm/fjg0bNkChUCAyMhJvv/02MjMz8dJLL2Hfvn3QaDSYMWMGvvrqK7z11lu4cuUKpk6digULFsDe3h5HjhxBa2srZDIZNm7ciEcffRT19fVQqVR45ZVXcM8996CwsBAPPfQQIiMjceHCBQQEBODTTz+FlZUV4uPjsXbtWqjVakRERODtt9+G6KbZ6leuXImzZ89CLpdjxowZeOmll7B+/XqUl5djwYIFcHR0xLZt23DixAm8//77UCgU8PPzw0cffQQbGxscO3YMq1evhpOTE4YN6/xcqdVqvPXWWzh58iQUCgWWLl2KJUuWICkpCR9++CHc3d1x4cIFvPXWW3q3Dx48iFWrVuHcuXPg8XhYvXo1xo0bh61bt+qdm19//dXg96grFGwsDIfHg3X4EFiHD+nQktN0uj3kDNf2yQkNoZBDCDFrO67+juLm8i7vz28ogoqp9bYpNUr8fGkvTpald/oYLxt3zB18T5fHvHz5Mvbs2YNdu3ZBIBBg1apV2LFjBxYsWICpU6fiP//5D+RyOebOnYvQ0FD885//xNdff43NmzcDALZu3YrU1FQcPnwYjo6OUKlUWL9+Pezs7FBTU4OZM2di2rRpAICrV6/igw8+QGxsLF544QVs2rQJy5Ytw/PPP4+tW7di8ODB+Pvf/47NmzfjiSee0KtzxYoVcHR0hFqtxqJFi5CVlYXHHnsM//3vf/Hrr7/CyckJNTU1+OSTT7B161ZYW1vjiy++wH//+1/89a9/xcsvv4xffvkFAQEBePrppzs9Fz/99BPs7Ozw22+/obW1FbNnz8bEiRMBABkZGTh69Ch8fX2RlJSkd/vrr78GABw5cgRXrlzB4sWLER8fDwB658YYKNhYMA6ffz3k3NSS03T6DLhWVtrLVdFtLTl93FxKCCF36uZQc7vt3ZGQkIDMzEzcd999AAC5XA4XF+3aU88//zzuu+8+iMVirF27tstjTJgwQffFzRjDO++8g9OnT4PD4aCsrAyVlZUAtB1j29eemjt3LjZs2IDx48fD19cXgwcPBgAsWLAAmzZt6hBs9u7dix9//BFqtRrl5eW4fPkyhgwZordPamoqLl26hFmzZgEAlEoloqOjceXKFfj6+mLQoEEAgHnz5uGHH37o8DpOnDiB7Oxs7N+vbQVrbGzEtWvXIBAIMGLECPj6Xl99/cbbycnJePTRRwEAgYGB8Pb2Rm5ubodzYwz0TTZAcPh8WA8Nh/XQcLAHVZ2HnIhhsImO0rbkUMghhJiBW7WsAMCaM5+itrW+w3ZHkT3+NtywzrCMMSxYsACrVq3qcF9dXR1kMhlUKhVaW1u7HL114/YdO3aguroaBw4cgEAgwKhRo9Da2goAHRbL5HA46M5g5YKCAnzzzTfYv38/HBwc8Nxzz0Eul3f6WiZMmIAvv/xSb/v58+c7XaizM+vWrcOkSZP0tiUlJXV47TfevtVrMPaINxoVNQC1hxzXRx6G73/egvszT8E6YhhkZzNR/sXXKHjln6jc/ANk5y+AqVSmLpcQQro0w38yBFz9S+oCrgAz/CcbfMy4uDjs27cPVVVVAIDa2loUFRUBAF555RW8/PLLmDNnDt58800AgK2tLZqbm7s8XmNjI1xcXCAQCJCYmKg7FgAUFxcjJSUFALB7927ExsYiMDAQhYWFuHbtGgBtf5/21cNvPKaVlRUkEgkqKytx7Ngx3X22trZoamoCAERHRyM5OVl3rJaWFly9ehWBgYEoKChAXl4eAGDXrl2d1j5x4kRs3rwZyraRtlevXoVMJrvtORw1apRupNjVq1dRXFysa4EyNvpZPsBx+HxYDxsK62FDwZRKbUtOqnYywKaTp7UtOSMitJMBUksOIcTMtHcQ7s1RUcHBwXjllVewePFiMMbA5/Px5ptv4uTJk+Dz+ZgzZw7UajVmzZqFhIQEjBo1CjweD1OmTMHChQthb2+vd7y5c+di6dKluPfeexEeHo7AwEDdfUFBQfj111+xcuVKBAQEYOnSpRCLxfjwww/x1FNP6ToPL1myRO+Y4eHhGDp0KCZPngxfX1/d5SwAeOihh/Dwww/Dzc0N27Ztw0cffYRnn30WCoUCgDacDR48GP/5z3/wyCOPwMnJCSNHjkROTk6Hc/Hggw+isLAQ06dPB2MMTk5O2LBhw23P4dKlS7Fy5Urcfffd4PF4+Oijjzp0fjYWmqCvTX+YoK8vXQ85aWg+mwkml4NrbQ3rEcO1o6tCQ8DpxnA+QgjpqYEyQV9hYSGWLl2Ko0ePmroUs9EbE/TRz2/SKY5AoGvJcVEq0ZKdg6bUdDSnZaAp6RS4NtawjojQdjwOCaaQQwghxCwM6BablJQUpKam4qmnnqIWm27SKJVoycpBc1oaZOfOa1tyKOQQQnrRQGmxIR3RWlG9iIJNz2lDTrZ2dNXZTLDWVm3IGREB2+goiIODKOQQQnqMgs3ARcGmF1GwuTMahaIt5GRAdq495NjAJjICNlGRFHIIId1GwWbgomDTiyjY9B5dyElNhywzE6xVAa6tLWzaRldRyCGE3AoFm4GLgk0vomBjHNdDThpkmeevh5z2lpygQAo5hBA9FGwGLgo2vYiCjfFpFAq0XMjStuScp5BDCOlcfws2W7duxcSJE+Hh4QEAeOmll/Dkk08iODj4jo5bWFiIlJSUTlcNv5XnnnsOU6ZMwf33339Hz28KNNyb9CtcoRA2kSNgEzlCG3LOZ6E5LR1Np5PRGJ8Irp2d9nJVdFvI4dLE2ISQ22s8k4za3XuhrqkFz8kRjrNmwm5k7O0f2Et+/fVXhIaG6oLN+++/3yvHLSwsxM6dO3scbO6ESqUC/4aJWG++3d3HmZJ5VEEGHK5QCJuoEbCJag85F9pCzhk0xidoQ05kBGyioyAOHEwhhxDSqcYzyaj+8ScwhXbKf3VNLap//AkA7ijcbN++HRs2bIBCoUBkZCTefvttAMCLL76Ic+fOgcPhYNGiRZBKpTh79iz+7//+D2KxGHv27MGSJUvw+uuvIyIiAkFBQVi2bBni4+Nhb2+PlStX4s0330RxcTHWrFmDadOmobCwEH//+991SxWsW7cOsbGxeOutt3DlyhVMnToVCxYswGOPPYa33noLJ0+ehEKhwNKlS7FkyRIwxvDaa68hMTERPj4+Xb6mvLw8vPrqq6iuroaVlRXee+89BAYG4rnnnoODgwPOnz+PYcOGoba2Vu/2vHnzsHLlSsjlcvj5+eGDDz6Ag4MD5s+fj+joaKSkpGDq1KldrhDe1yjYEJPThpxI2ERFQtPaqrtc1XTyNBr/TABPYgfrESO0LTkUcggZUKp/2Q7FDWsr3Ux+LQ+4aU07plCiasv/0JSQ1OljhN7ecF44r8tjXr58GXv27MGuXbsgEAiwatUq7NixAyEhISgrK9PNFFxfXw97e3ts3LhRF2RuJpPJMGbMGLz66qt47LHH8J///Ac//fQTLl26hOeeew7Tpk2Di4sLfvrpJ4jFYuTm5uLZZ5/FgQMH8M9//hNff/01Nm/eDAD44YcfYGdnh99++w2tra2YPXs2Jk6ciPPnz+Pq1as4cuQIKisrMXnyZCxatKhDLa+88greeecdDBo0CGlpaVi1ahV+/fVXAEBubi62bt0KHo+H5557Tu/2lClTsHbtWowZMwbvvfcePvzwQ7zxxhsAgIaGBmzfvr3Lc2kKFGyIWeGKRPoh53wWmtLS0HTyFBr/jNeGnMgR2j45FHIIIV0t1HsHC/gmJCQgMzMT9913HwBALpfDxcUFU6dORUFBAV577TXcfffdmDhx4m2PJRQKMXmydkHO0NBQCIVCCAQChIWF6RbDVCqVePXVV5GVlQUul4vc3NxOj3XixAlkZ2dj//79ALQLYV67dg2nTp3C7NmzwePx4OHhgXHjxnV4bHNzs25C2nbta0cBwP333w/eDX0c2283NDSgvr4eY8aMAQAsWLBA7xgPPPDAbc9BX6NgQ8wWVySCTXQkbKK1IUd2/oK2JSfpFBpPxIMnkcC6/XLV4EEUcgixQLdqWQGAglf/BXVNbYftPCdHeL7wD4OekzGGBQsWYNWqVR3uO3ToEI4fP46NGzdi7969+PDDD295LD6fDw6HAwDgcrm6hSC5XC5UbeHr22+/haurKw4dOgSNRoNBgwZ1ebx169Zh0qRJetuOHDmie46uaDQaSCQSHDp0qNP7b+6w293O2+bYyZu+CUi/wBWJYBsdBfcnH4Pvf96G62PLIBoUgKakUyj78BMU/vN1VG/9FfLLV8A0GlOXSwjpI46zZoIjFOht4wgFcJw10+BjxsXFYd++faiqqgIA1NbWoqioCDU1NdBoNJgxYwZefvllZGZmAgBsbGzQ1NRk8PM1NDTAzc0NXC4X27dvh1qtBgDY2tqiublZt9/EiROxefNmKJXa/kRXr16FTCbD6NGjsXv3bqjVapSXlyMpqeMlODs7O/j4+GDv3r0AtOHtwoULt61NIpHA3t4ep0+fBqDtezR69GiDX2tfoBYb0u9wxSLYxkTDNiYaGnkrZJnn0ZyWjsbEk2g4/id49hLYRGpbekSDAqglhxAL1t5BuDdHRQUHB+OVV17B4sWLwRgDn8/Hm2++CbFYjBdeeAGath9P7S06CxcuxMqVK3Wdh3tq6dKlePLJJ7Fv3z6MGzdO1woSFham6+OycOFCPP744ygsLMT06dPBGIOTkxM2bNiAe++9F4mJibj77rsxaNCgLoPH559/jlWrVuGTTz6BSqXCrFmzEB4eftv6Pv74Y13nYV9f39u2UpkazWPThuax6f80crku5LSczwJTqcCzt28bfUUhh5D+or/NY0N6D03Q14so2FgWXchJTUfLhbaQ4+CgnUcnOhKiAH8KOYSYKQo2AxcFm15EwcZyaVpaIMu8gOa0NLRcyL4ectpbcijkEGJWKNgMXBRsehEFm4FBG3K0LTmyrGxApQLPsb0lJwoifz8KOYSYWHNzM2xsbExdBjGBzt57CjYGomAz8GhaWiA7p+2Tcz3kOOq35NxmCCUhpPe1tLRAIBCYzRT9pG+oVCoolUpYWVnpbadgYyAKNgObpqUFzecyIUtNhyw7h0IOISbEGINcLodGo6F/dwMEYwxcLhdisbjDe07BxkAUbEi79pDTnJqOlqxsQK0Gz8lRNyOyyN+PPmwJIaSPULAxEAUb0hm1TAZZe8jJzgHUavCdnLQhJ3oEhH4UcgghxJgo2BiIgg25HbVMBtnZTO3oquyLN4WcSAj9fCnkEEJIL6NgYyAKNqQn1M0yyM6d004GmJUDaDTgO7eHnCgIfX0o5BBCSC+gYGMgCjbEUOrm5raWnLbLVRoN+M7O2gU8oyIp5BBCyB2gYGMgCjakN+hCTmoaWnIuakOOi4tudBWFHEII6RkKNgaiYEN6mzbknNN2PL4x5LS35Ph4U8ghhJDboGBjIAo2xJjUTe0hJw0tFy9pQ46ry/WOx94UcgghpDMUbAxEwYb0FXVTE2QZbR2PdSHHVXu5KjoKQm8vCjmEENKGgo2BKNgQU1A3NaE54yya0zIgvzHkRLe15HhRyCGEDGwUbAxEwYaYmi7kpKZrQw5j4Lu5wiYqSjsZIIUcQsgARMHGQBRsiDlRNzaiOUPbJ0d+6TLAGARubrqOxwIvKYUcQsiAQMHGQBRsiLlSNzSi+WxbS057yHF3g010lDbkSD0p5BBCLBYFGwNRsCH9gbqhse1yVRrkl69oQ46Hu26BTgo5hBBLQ8HGQBRsSH+jbmhAc/pZNKel3xByPK6PrpJ6mrpEQgi5YxRsDETBhvRnXYac9skAKeQQQvopCjYGomBDLIWqvgGyjAxtn5wrV7Uhx9Pj+mSAnhRyCCH9BwUbA1GwIZZIVV8PWXtLji7keF5vyfH0MHWJhBBySxRsDETBhlg6bcjJQFNqOlqv5mpDjtSzbXTVCAg9KOQQQswPBRsDUbAhA4mqrh7N6RloTrsh5HhJdaOrhB7upi6REEIAULAxGAUbMlCp6urQnHZDyAEg9JLCOioSttFRELi7mbhCQshARsHGQBRsCOk65OgmA6SQQwjpYxRsDETBhhB9qtpa7RDy1DS05l4DAAi9vXSjqwRuFHIIIcZHwcZAFGwI6ZqqpvZ6nxxdyPG+vnaVm6uJKySEWCoKNgaiYENI92hDTjqa0zKuhxwf7+vLOlDIIYT0Igo2BqJgQ0jPqWpqrvfJuZYHABD6+MAmeoQ25LhSyCGE3BkKNgaiYEPIndGFnNR0tOblAWgPOW0tOa4upi2QENIvUbAxEAUbQnqPsroGsvT0tpCTDwAQ+vroJgMUuFDIIYR0j9kGm4MHD+L48eMoKCjAuHHj8Oyzz+ruy8zMxPr161FVVYWgoCA888wzcG1rwmaM4ccff8TRo0cBAHfddRceeughcDgcqNVqfPbZZ8jIyEBwcDCef/55WFlZAQB27NgBoVCI+++/v1v1UbAhxDiU1dWQpWWgKTUNivwCAIDQz/f66CpnZxNXSAgxZz0NNlwj1dGBo6Mj5s6di8mTJ+ttb2howPvvv49FixZhw4YNGDRoED7++GPd/YcPH0ZycjLee+89vP/++0hNTcWhQ4cAAKdPnwYArF+/HlZWVrrtFRUVSE1Nxb333ts3L44Q0iWBszPsp94Nr5Uvw3vtajjOmQUAqN25G0Wv/RvF77yH+kNHoKyuNnGlhBBLwO+rJxo1ahQAIDc3F9U3fICdOXMGPj4+GDNmDABgwYIFeOyxx1BcXAwvLy+cOHECM2fOhHPbr7qZM2fiyJEjmDZtGioqKhAeHg4ej4fw8HAUFGh/DW7YsAFLliwBj8frq5dHCOkGgYsLHKZNgcO0KVBWVek6Htfs2IWaHbsg8veDTVQkrKMiIXB2MnW5hJB+qM+CTVcKCwvh5+enuy0Wi+Hh4YHCwkJ4eXl1uN/Pzw+FhYUAAF9fX5w4cQKTJ0/GhQsXEBYWhjNnzkAikSA0NPSWz3v48GEcPnwYAPDOO+/Aha75E9K3XFyA0FDgwb9AXl6O2lOnUZN0ShdybIIC4TRmFBxHj4aIOh4TQrrJ5MFGLpdDIpHobbO2toZcLtfdb21t3eE+xhgiIyORnZ2NlStXIigoCOPGjcMbb7yB1157DT/99BNycnLg4+ODZcuWgc/Xf6lTpkzBlClTdLerqqqM+CoJIbfE40Ewbizcx42FsrJSN7qqcPOPKNz8I0T+/m2jq0aA70QtOYQMJD3tY2PyYCMWi9HS0qK3TSaTQSwWd3p/S0sLxGIxOBwOAOChhx7CQw89BADYsmULpk6diqtXryI3Nxf//ve/8c033+Do0aOYNm1aH70iQsidELi6wuGeqXC4Z2pbyNGOrqrZvhM123dCFNAWciIjwXdyNHW5hBAzY/Jg4+PjgxMnTuhuy+VylJeXw8fHR3d/Xl4eAgMDAQB5eXm6+25UUFCAS5cu4aGHHsKePXsQEBAADoeDwYMHIz8/v29eDCGkV2lDzjQ43DMNyoq2kJOWjpptO1GzbSdEgwLaZjweAb4jhRxCSB+OilKr1VAoFNBoNNBoNFAoFFCr1Rg5ciQKCgpw6tQpKBQKbNu2DX5+fvDy8gIATJgwAfv370dNTQ1qamqwb98+TJw4Ue/YjDGsX78ey5YtA5fLhZubGy5evAiVSoWsrCy40WJ9hPR7AjdXOEyfBq9/roD3mtfh+MD9YAolarbtQOE//4WS9z5E/ZFjUNXWmrpUQogJ9dk8Nr/88gu2bdumt23+/PlYuHAhzp07hw0bNqCyslI3j017GGmfx+bIkSMAgLvvvls3j027o0ePIjc3F48//jgA6Oa3SU9PR3BwMF544QXd/DZdoXlsCOmflBUVaE7VtuQoiooBAKLBg6635Dg4mLZAQsgdMdsJ+swdBRtC+j9leUVbn5w0KIq1/6Yp5BDSv1Gw6YGUlBSkpqbiqaeeomBDiIVRlJXr+uQoi0sADkfbJyc6CjaRI8B3sDd1iYSQbqBgYyAKNoRYLkVZWdsQ8jQoS0q1IWfwINhGR8I6cgT49hRyCDFXFGwMRMGGkIFBUVp2vSWnLeSIAwdrZzyOHAG+veT2ByGE9BkKNgaiYEPIwKMLOanpUJZSyCHEHFGwMRAFG0IGNkVpqW50lbK07HrIiY6E9QgKOYSYCgUbA1GwIYS0U5SUXm/JKWsLOUGB2tFVkRHgSSjkENJXKNgYiIINIeRmjDEo20NOWjqUZeXXQ050FGxGRIAnsTN1mYRYNAo2BqJgQwi5Fb2Qk5oGZXmFNuQEB2nXrhoRAZ4dhRxCehsFGwNRsCGEdJcu5KSmaS9XVVDIIcRYKNj0AE3QRwi5U4wxKItLrvfJqagAuFxtyImKhM2I4RRyCLkDFGwMRMGGEHKnGGNQFBejOTUDzWlpUFVUXg857S05tramLpOQfoWCjYEo2BBCehNjDIqiYl1LjqqyLeSEBMM2KhLWIyLAs7UxdZmEmD0KNgaiYEMIMRZdyElNQ3NaOlSVVQCXC6uQYNhER8E6YjiFHEK6QMHGQBRsCCF9QRtyinSTAepCTmiIdsZjCjmE6KFgYyAKNoSQvsYYg6Kw6PrlqqobQk50W8ixoZBDBrZeDTYNDQ34888/kZaWhvz8fMhkMlhbW8PPzw8jRozApEmTILGQGTgp2BBCTIkxBkVB4fWQU119Q8hpu1xlY23qMgnpc70WbP73v/8hPj4ekZGRGDJkCLy8vGBlZYWWlhYUFxcjKysL6enpiIuLw0MPPdQrxZsSBRtCiLnQDzlpUFXXADze9Zac4ddDTuOZZNTu3gt1TS14To5wnDUTdiNjTfwKCOk9vRZsDhw4gClTpkAgEHT5YIVCgaNHj2L69Ok9q9IMUbAhhJgjXchpmwxQVdMWcsJCwHNwRPPpM2BKpW5/jlAA54cWU7ghFoP62PQATdBHCOlPGGNQ5Bdc73hcU9PpfjwnR/i++UYfV0eIcRg12MhkMuzatQsFBQVwc3PD7Nmz4eTk1OMizREFG0JIf8IYQ94zf+/yfttxYyDy84XI1xdCqSc4t2h9J8ScGTXYfPbZZ/Dy8sLgwYNx4cIFZGVlYd26dT0u0hxRsCGE9DcFr/4L6prajnfw+eAKhdDIZNrbPB6EUk8IfX0g8vWFyI/CDuk/ehps+Le6c+PGjVi0aBGsrKwAAFVVVXj22WfB5XIREhKCQ4cOGV4pIYSQO+I4ayaqf/wJTNGxj41tbAxU1dVQFBSite0/WfpZNCWe1O54c9jx9YHQS0phh/R7t2yxiY+Px759+zBr1iyMHTsWv/32G44ePQpfX19cvXoVkZGRWLZsWR+WazzUYkMI6Y96MiqKMdYh7CgKCqBpbmvZ4XIh9JJS2CFmpdcvRclkMvz8888oLS3Fo48+Co1Go+tjExgYeEfFmhMKNoSQgYgxBlVNDRT5BV2HHakUQr/rYUfgJQWXwg7pI0brY5Obm4vvv/8eYWFhmD9/PoRCoUEFmisKNoQQonU97BSitaCg67Dj69PWQZnCDjGeXg02tbW12LlzJyoqKuDt7Y1Zs2YhMTERR44cwaJFixATE3PHBZsLCjaEENK1HoWdtsBDYYf0hl4NNq+//jpCQkIwdOhQnD9/HtXV1fjHP/6Buro6bN68GTKZDCtXrrzjos0BBRtCCOmZm8OOtu/OrcKODwReXhR2SI/0arB59NFH8e2334LP50OhUODVV1/Fe++9p7v/woULCA8PN7xaM0LBhhBC7hyFHdLbejXYbNy4EdeuXUNoaChycnIwcuRIzJgx446LNEcUbAghxDg6DzuF0DQ3a3fgctuGnvtS2CEd9Hrn4StXrqCiogI+Pj7w8fG5o+LMDS2pQAghpqENO7Vto7FuFXbaJxWksDNQ0VpRBqJgQwghpqULOwUFaM2nsEO0ei3YrFq1Cg888ABiY2PB53ecoFilUuHMmTPYt28f3nrrLcOqNSMUbAghxPzohZ2CQt18O52HnfbRWBR2LEmvBZuioiJs3boVWVlZCAgIgFQqhVgshlwuR2lpKXJzczF06FAsWLAA3t7evVK8KVGwIYSQ/qFD2CkoRGt+QZdhR+jrC6E3hZ3+qtcvRdXV1eHcuXMoKChAc3MzbGxs4Ofnh+HDh8Pe3v6OijUnFGwIIaT/6jTsFBRC09Sk3aGzsOMlBdfCJpu1RNTHxkAUbAghxLIwxqCurUVr/i3CjqcHhG2zJ1PYMU8UbAxEwYYQQixft8NOW+dkCjumR8HGQBRsCCFkYNKFnba+OrcMO74+EPpR2OlLFGwMRMGGEEJIO/2wU6jru9Nl2PH10XZQprDT6yjYGKi3g01KRSb25x1DbWs9HEX2mOE/GTFuw3r1OQghhPQdCjudM/b3Xa8Fm6NHj3brAHfddVePntBc9WawSanIxNbL+6HUKHXbBFwBFgXNoHBDCCEWpDthR+DpoZ1Q0ALDTl983/U02HScea9NfHy87v8zxnDx4kU4ODjA2dkZ1dXVqKurQ2hoqMUEm960P++Y3psMAEqNEvvzjlGwIYQQC8LhcMB3cgLfyQk2IyIAtIedOrQWFOjCjizzPJpOntI+SBd2tDMo9+ewY47fd10Gm9WrV+v+/4YNGxAbG6u3AOZvv/2GsrIy41bXT9W21vdoOyGEEMuhDTuO4Ds53ibsXEDTydPaB+mFnRsmFTTjsKPUqMzy+67LYHOj+Ph4rF+/Xm/b9OnT8dhjj2H58uVGKawv3LgIZm9yFNl3+aZ+lLEB46UxGOEyBHxut04/IYSQfs7Swk6FrBobc7Z3eb+jyHQT+Hbrm9XBwQEpKSkYOXKkbltKSgokEonRCusLMTExiImJ6fXjzvCf3Mk1Rz4inMOQ31SMHy7uxq7cQxjjEYmxntEm/QMghBBiGrcLO+1LRZhb2EmtyMQvV34Dn8PDZK/RSChN7dDHZob/5D6r52bdGhV17tw5fPDBB/Dx8YGzszOqqqpQVFSEF154AREREX1Rp9H11agoDWO4XHcN8SXJuFBzGQAwzDkYcdJYBNn7g8Ph9GodhBBC+rcOYad9bawbOyh7uEN04wzKRgg7CrUSO64exKnyDAyS+OCR0LlwEEn6z6iomzU0NCAjIwM1NTVwdHREVFQU7OzsDCrSHJliHptqeR2SSlNxqiwdzaoWuFu5IE4ag1i34RDzRX1eDyGEkP6h07BTUAhNY6N2h/aw07bi+Z2GndLmCmzK2YFyWSWm+MRhut9E8DjcXnxFXTPqPDZVVVWoqalBcHBwjwszd6acoE+pUSG98gLiS5JR2FQKEU+IWLfhiJPGwMPa1WR1EUII6T8YY1DX1enNnqwXdjgc3WUs7ZIRtw87jDGcKT+LbVcPQMQTYUnIbIQ4DuqjV6RllGBTVVWFTz75BHl5eQCALVu24NSpU8jIyMDTTz9tUKHmxlxmHs5vLEZ8STLSK7OgZmoE2ftjvDQW4c7BfZaOCSGEWAZd2CkohKJtfaxbhh1fHwh9vMEVCiFXtWLb1QNIqchEkL0/Hg6dDXth31+pMUqweeuttxAaGorZs2fjsccew/fffw+ZTIaXXnoJX375pcHFmhNzCTbtmhTNOFmejqTSNNS21sNBJME4j2iM9oiEndDG1OURQgjpp/TCTlt/nZvDDsfdBVftWlEgYRg0JAbjou8DXyTu9HiNZ5JRu3sv1DW14Dk5wnHWTNiNjO21enttgr4bXblyBStXrgSXe73FwNraGjKZrGfVkW6zFdpgqk8c7vIeiwvVl5BQmoL9+cdwsOBPRLoOQZxnLPzspNTZmBBCSI9wOBzwHR3Bd3SETcRwAPqXsa5mJaP6ag48C1UYJFcDqcdQ+MNxCDw8dCuet7fsNGecRfWPP4EptKOi1DW1qP7xJwDo1XDTE90KNvb29igrK9NLTUVFRXBxcTFaYUSLx+FiuEsohruEolxWhYTSFJwpP4uUikz42HoiThqLSJchEPIEpi6VEEJIP8XhcKC0s8KvosvI8ClF6PAoxAQ9ACuZ8nrLTkEBZBey0XTqTPuDtP9pNHrHYgolanfvNVmw6dalqKNHj2L37t2YPXs2Nm7ciCeeeAI7d+7E7NmzMX78+L6o0+jM7VLUrchVrUipyERCaTLKZFWw5lthtMcIjPOMgbPYwdTlEUII6WcKGkuwKWcHauV1mOF/FyZ7jwG3kysCN1/GqvvtYJfHDPjqs16pzWijos6cOYMjR46gsrISLi4umDJlit6Eff1dfwo27RhjuFKfj4SSZGRWXwQDwxCnIIyXxiLYYVCnf5SEEEJIO8YYTpScwd5rhyER2uGR0LkIkHh3+/EFr/4L6praDtt5To7wffONXqnRqMO9LVl/DDY3qmttQFJpKpLK0tGkbIarlRPiPGMQ6x4Ba37nHb4IIYQMXM3KFvx0aQ/O11zCUOdgLA56ADYCqx4do/FMsl4fGwDgCAVwfmhxr12KMkqwYYzhyJEjSEpKQkNDA95//31kZWWhrq4OY8eONbhYc9Lfg007lUaFs1XZiC9JQV5jEYRcAWLchiFOGgOpjbupyyOEEGIGrjUUYlPODjQqmvBAwBRMkI40eDBKvxwVtXXrVmRmZuK+++7Dt99+CwBwdnbGpk2bLCbYWAo+l49ot2GIdhuGwqZSJJSkILniHJLK0jBY4os4aQyGO4eCx+WZulRCCCF9TMMYjhYl4be8Y3AUO+AfEY/C165nweFmdiNjTdZRuDPdCjYnTpzAu+++C4lEgu+++w4A4ObmhoqKCqMWR+6Mj60nFgfPxAMBd+N0+VkklqZgU84O2AvtMMYjCmM8I00y2RIhhJC+16hoxo+XdiOn9ipGuIRhUdD9sLLArgrdCjYajQZisf6Ll8vlHbYR82QjsMZd3mMwyWsUsmuvIr4kGQcLTuCPwnhEuIRhvGcMAiQ+NCcOIYRYqCt1+dh8cQdkyhYsCLwPYz2iLPYzv1vBJjIyEps3b8bSpUsBaPvcbN26FdHR0UYtzthSUlKQmpqKp556ytSl9Akuh4twpyCEOwWhsqUaCaWpOF2WgfTKC5DauGO8Zwyi3IZCxOvdFWEJIYSYhoZp8EdBAn4v+BMuVo54KnwxvGw9TF2WUXWr87BMJsPnn3+Os2fPQqVSQSgUYvjw4fi///s/WFn1rAe1ubKUzsM91apWIK3iPOJLk1HSXAErvhgj3SMQ5xkDVysnU5dHCCHEQPWKRvyQswuX6/MQ4zYMCwLv65c/XI063Lu+vl43j42Dg0NPazNrAzXYtGOM4VpDIeJLknG2OgcapkGYYyDipDEIcwykOXEIIaQfuVibiy0Xd6FV3Yr5g+/FSPeIfnvpyWjBprm5GWlpaaitrYWjoyMiIyNha2trUJHmaKAHmxvVKxpxsjQNSWVpaFA0wVnsiHGe0RjlPqLHcxwQQgjpO2qmwcH84zhcmAh3a1csC50HDxtXU5d1R4wSbM6fP4/3338fUqkULi4uqK6uRnFxMV588UUMGzbM4GLNCQWbjtQaNc5V5yC+JAW5DQUQcPmIch2K8dIYeNt6mro8QgghN6htrceWnJ3IbSjEaI9IzB10j0WsI2iUYPP8889jwYIFenPWnDx5Elu3bsXHH3/c4yLNEQWbWytpLkd8SQpSKzKh0CgRIPFGnGcMIlyGgE9z4hBCiEldqLmMHy/uhpqpsTDwPkS7WUajA2CkYLNs2TJs2LABXC5Xt02tVuOxxx7Dxo0be1ykOaJg0z0ylRxnys8ioSQFVfIa2AlsMMYjEmM9o+Egkpi6PEIIGVBUGjX25x3FseJT8LJxx7KweXC1cjZ1Wb3KKMFmw4YN8PDwwH333afbduDAAZSWlmL58uU9r9IMUbDpGQ1juFiXi4SSZGTVXAYHHAxzDkGcNBaB9n79tpMaIYT0F9XyWmzK2YGCxhLEecZg1qCpEHC7NYtLv2KUYPP666/jypUrsLe3h5OTE2pqalBfX4+goCC9L7A1a9b0vGIzQcHGcNXyWiSWpuJUWQZkqhZ4WLu2LcA5vF8OLSSEEHN3tiobP13aCwBYHDwTES5hJq7IeIwSbI4fP96tg02aNKlHT25OKNjcOYVaifTKC4gvTUZRUxnEPBFi3YcjzjMG7tYupi6PEEL6PaVGhT25hxFfmgxfWymWhs2Fs9jR1GUZlVHnsbFkFGx6D2MM+Y3FiC9JRkZVFtRMgxCHQYiTxiDcKQhcDvf2ByGEEKKnsqUam7J3oKi5DJO8RuN+/7sGxOANowSbhIQE+Pv7w9vbGyUlJfjmm2/A5XLx+OOPw8vLy+BizQkFG+NoVDThZFk6kkrTUKdogKPIHuM8ozHafQRshTamLo8QQvqF1Irz+OXKfvA4PDwY/ACGOgebuqQ+Y5Rg87e//Q1r166Fg4MD3nnnHUilUojFYmRnZ2P16tUGF2tOKNgYl5ppcL76IhJKUnC5Pg98Dg+RruEYL42Fr13P/mgJIWSgUKiV2JH7O06VpSNA4oNHQufAUWRv6rL6VE+DTbe6Tzc0NMDBwQEKhQIXL17Eiy++CB6Ph8cee8ygIsnAw+NwEeEShgiXMJQ1VyKhNAXJFeeQXHEOvrZSxEljEOkabpE9+gkhxBBlzZXYlLMdpbJKTPEZh3v9JoFHl/Jvq1vfIhKJBGVlZSgoKMDgwYMhEAjQ2tpq7NqIhfKwccX8wHtxv/9dSK44h/iSZPzv0h7szj2M0R4jMM4zGk5iB1OXSQghJnO6/Cy2XzkAIU+Ap4Y+iDDHwaYuqd/oVrCZN28eVqxYAS6Xi+effx4AkJmZCT8/P6MWRyybmC/CeGks4jxjcLkuDwmlyThadBJHi04i3DkI4z1jEewQQHPiEEIGjFa1Ar9e+Q0pFZkIsvfHw6GzYS+0M3VZ/Uq3R0W1t9CIRCIA2pW+GWMWs8o39bExD7XyeiSVpSKpNB3NKhncrJwR5xmDke4REPNFpi6PEEKMpqS5HBuzt6OypQb3+E7ANN84GkUKGu5tMAo25kWpUSGjMgsJpSnIbyyGkCvQzYnjaeNm6vIIIaTXMMaQVJaGnVd/h7XACktC5iDIwd/UZZkNCjYGomBjvgoaS5BQmoK0ivNQMTUC7f0wXhqLoc4h1JGOENKvyVWt+PnyPmRUZSHUcTAeCp4FO5oKQw8FGwNRsDF/TUoZTpWlI7E0FbWt9bAX2mGcZzTGeETCTmhr6vIIIaRHChtLsSlnO2rkdbjPfzLu8h4LLvUp7ICCTQ+kpKQgNTUVTz31FAWbfkTDNMiquYL4kmRcrMsFj8PFCJchiJPGwt/OizobE0LMGmMMf5YkY8+1Q7AT2mJp6FwESHxMXZbZMlqwKSoqwqlTp1BXV4fHH38cxcXFUKlUFjMyioJN/1Qhq0ZCaQrOlJ+FXN0KbxsPxEljEeUaDiFPYOryCCFET7OyBT9d3oPz1ZcQ7hSEB4MfgI3A2tRlmTWjBJuTJ09i/fr1GDlyJBITE7Fp0yZcvXoV//vf//D6668bXKw5oWDTv7WqFUipyERCSTJKZZWw5osxyj0S4zyj4WJl2QvEEUL6h2sNRdicswMNikbMDJiCidKR1MLcDUaZefiXX37Ba6+9Bn9/f5w8eRIA4Ofnh7y8vB4XSIgxiHhCjPOMxliPKOQ2FCC+JBknik/hePFJhDkFIc4zBqGOg+n6NSGkz2kYw7Gik9ifdxSOYnv8I+JRWkrGiLoVbOrr6ztccuJwOJQ0idnhcDgYbO+HwfZ+qGttwMmyNCSVpuG/NT/BReyEOGk0RrpFwFpgZepSCSEDQJOiGT9e2oPs2iuIcAnDX4LuhxVfbOqyLFq3gs2gQYPw559/YuLEibptiYmJCAwMNFphhNwpB5EE9/pNwlSf8ThXlY340hTsyj2E/XnHEOM2DHGeMfCy9TB1mYQQC3W1Ph+bc3aiWSnD/MH3YpxnNDUI9IFu9bEpLi7GunXr4ObmhsuXLyM8PBwlJSV47bXX4Onp2Rd1Gh31sRkYiprKkFCagtSKTCg1KgyS+CDOMwbDXcLA5/JMXR4hxAJomAaHCxNxIP8EXKwcsTR0HrzpR5TBjDYqqrW1FampqaiqqoKzszOio6MhFltOcxoFm4FFpmzB6fKzSChNQbW8FhKBLcZ4RmGsRxTsRbQuCyHEMA2KJvxwcRcu1V1DtOtQLAi8j5aDuUM0j42BKNgMTBrGkFN7FQklyciuvQIOh4vhzqEYL43BIIkvNRsTQrrtYm0utlzchVZ1K+YPvhcj3SPoM6QXGCXYVFVV4ddff0VeXh7kcrnefZ988knPKjRTFGxIVUsNEktTcbo8AzKVHJ7WboiTxiDGbRhEPKGpyyOEmCk10+Bg/gkcLkyAu7ULlobOozXtepFRgs0///lPSKVSjBkzBkKh/gf8sGHDelahmaJgQ9op1EqkVZ5HfEkyipvLIeaJMNI9AnGeMXCzdjZ1eYQQM1LX2oDNOTuR21CA0e4jMHfwdJoctJcZJdgsXboU33//Pbhcy11wkIINuRljDHmNRYgvScbZqmyomQahjoMR5xmDIU6B4NICnIQMaBdqLuN/F3dDqVFhYdAMxLhZxg99c2OUCfqio6ORlZWFoUOHGlQUIf0Rh8NBgMQHARIfNCiadHPifJe1FU4iB4zzjMZojxE0HTohA4xao8a+vGM4VnwSUht3LAudR625ZqRbLTZNTU147bXX4O7uDnt7e737nnnmGaMV15eoxYZ0h1qjRmb1RcSXpuBqfT4EXD4iXcMx3jMWPnaWMfUBIaRr1fI6bM7ZgfzGYsR5xmDWoKkQcLvVRkAMZJQWmy+//BJcLhdeXl4d+tgQMpDwuDyMcB2CEa5DUNpcgfiSFKRUnMOZ8rPws/PCeGkMRrgMAZ8+6AixOOeqcvDT5b1gjGFZ6DyMcB1i6pJIJ7rVYvPII4/gm2++gZWV5U5DTy02xFAtKjnOlJ9DQmkyKltqYCuwxhiPSIz1jIajyP72ByCEmDWVRoXd1w4jviQZPraeWBo6jxbX7UNGabHx8/NDY2OjRQcbQgxlxRdjotdIjJfG4nLdNcSXJONwYRIOFyZhmHMw4qSxCLL3p/ksCOmHKltqsClnO4qayjDRaxRm+t9Ns5SbuW612Pz88884efIkJk2a1KGPzV133WW04voStdiQ3lQtr0NSaSpOlaWjWdUCdysXxEljEOs2nGYhJaSfSKs4j61X9oPH4eLB4Acw1DnE1CUNSEYZ7r1mzZou71u9enWPntBcUbAhxqDUqJBeeQHxJckobCqFiCdErNtwxElj4GHtauryCCGdUKiV2Jn7B06WpSFA4o1HQubCUUyXlU2FllQwEAUbYmz5jcWIL0lGemUW1EyNIHt/jJfGItw5GDyaE4cQs1Amq8Sm7B0olVXgbu+xuM9vEnh06cmkjB5sGGO48SGWMmkfBRvSV5oUzThZno6k0jTUttbDQSTBOI9ojPaIhJ3QxtTlETJgnSk/i21XDkDIE+ChkNkIcxxs6pIIjBRsampqsH79emRnZ6O5uVnvvq1bt/asQjNFwYb0NTXT4EL1JSSUpuBS3TXwODxEug5BnGcs/Oyk1NmYkD7SqlZg25UDSK44h0B7PywJmQN7kZ2pyyJtjBJs3nnnHYhEIsyZMwerV6/GmjVr8OuvvyIyMhJTpkwxuFhzQsGGmFK5rAoJpSk4U34WrWoFfGw9ESeNRaTLEFp3hhAjKmkux8bs7ahsqcY03wm4x3c8LZdiZowSbJYvX44vv/wSYrEYy5Ytw8aNG3WzEX/88ceG1mpWKNgQcyBXtSKlIhMJpckok1XBmm+F0R4jMM4zBs5iB1OXR4jFYIzhVFk6duT+DiueGA+HzkawQ4CpyyKdMMo8NlwuFzyetvOUjY0NGhoaYGVlhZqamp5XSAjpkpgvQpw0BuM8o3GlPh8JJck4XnQKx4pOYohTEMZLYxHsMAhcukxFiMHkqlZsvbIf6ZUXEOIwCA+HzKb+bRakW8EmMDAQ6enpGDlyJCIiIvDRRx9BKBRi8GDqWEWIMXA4HAQ5+CPIwR91rQ1IKk1FUlk6Lpz/H1ytnBDnGYNY9whY88WmLpWQfqWwqRSbsrejRl6H+/3vwl3eY+mHgoXp1qWo5uZmMMZga2sLhUKBPXv2QC6XY8aMGXB0tIxppelSFDF3Ko0KZ6uyEV+SgrzGIgi5AsS4DUOcNAZSG3dTl0eIWWOMIb4kGbuvHYadwAaPhM7BIHtfU5dFuoHmsTEQBRvSnxQ2lSKhJAVpleeh1KgwWOKL8dJYDHMOoTk3CLmJTNmCny/vxbnqiwh3CsKDwQ/ARmBt6rJIN/VasOnuMO5Fixb16AnNFQUb0h81K2U4XX4WiaUpqJbXwV5ohzEeURjjGQl7IQ1XJSSvoQibcnagXtGImf53Y5LXKJpKoZ/ptc7D1dXVd1yMuUtJSUFqaiqeeuopU5dCiEFsBNa4y3sMJnmNQnbtVcSXJONgwQn8URiPCJcwjPeMQYDEhz7IyYCjYQzHi09hX95ROAgl+EfEMvjZeZm6LNIHumyxOXjwIKZPnw4AKCsrg4eHR58W1teoxYZYisqWaiSUpuJ0WQbk6lZIbdwxXhqLaNehNCcOGRCalDL87+JuZNVeQYRzKBYFz6SO9v1Yr12KWrp0KTZt2tTh/1sqCjbE0rSqFUirOI/40mSUNFfAii/GKPcRGOcZDVcrJ1OXR4hRXK0vwOacHWhSyjBn0DSM84ymFst+rtcuRXl4eGDz5s3w9vaGSqXC0aNHO93vrrvu6lmFhJA+IeIJMcYzCqM9InGtoRDxJcn4s+QMjhefQphjIOKkMQhzDKShrsQiaJgGhwsTcSD/BFysHPF8+KPwtvU0dVnEBLpssSkpKcGePXtQWVmJCxcuICwsrNMDrF692qgF9hVqsSEDQb2iESdL05BUloYGRROcxY6I84zGSPcRsBFYmbo8QgzSqGjClou7cKnuGqJdh2JB4H0Q80WmLov0EqMM937jjTfwr3/9y+Ci+gMKNmQgUWvUOFedg/iSFOQ2FEDA5SPadSjipDH0K5f0K5dqr2HLxZ2Qq1sxb/B0jHIfQZeeLAzNY2MgCjZkoCppLkd8SQpSKzKh0CgRIPFGnGcMIlyGgE9z4hAzpWYa/J7/Jw4VxsPNygXLwubB08bN1GURI6BgYyAKNmSgk6nkOFN+FgklKaiS18BOYIMxHpEY6xkNB5HE1OURolPX2oAtOTtxtaEAI90jMG/wdIh4QlOXRYyEgo2BKNgQoqVhDBfrcpFQkoysmsvggINhLqGI84xBoL0fNfMTk8qquYwfL+6BUqPEgsD7EOs+3NQlESOjYGMgCjaEdFQtr0ViaSpOlWVApmqBh7Vr2wKcw+kXMulTao0a+/OP4WjRSUht3LA0dB7crV1MXRbpA0YNNvX19ZDL5Xrb3N0tY/E9CjaEdE2hViK98gLiS5NR1FQGMU+EWPfhiPOMoS8XYnQ18jpsytmB/MZijPWIxuxBU2myyQHEKMEmIyMDX331Ferq6jrc1901pcwdBRtCbo8xhvzGYsSXJCOjKgtqpkGIwyDESWMQ7hQELodr6hKJhTlXlYOfLu8FYwyLgmYg0jXc1CWRPmaUYPO3v/0NM2fOxKRJkyAUWmbzMwUbQnqmUdGEk2XpSCpNQ52iAY4ie4zzjMZoj0jY0srJ5A6pNCrsuXYEf5acgY+tJ5aGzoULzZg9IBkl2Dz66KPYsGGDRXcapGBDiGHUTIPz1ReRUJKCy/V54HN4iHQNx3hpLHztevaBRAgAVLXUYFPODhQ2lWKidBRmBtwFPrfLifKJhTNKsNmyZQu8vLwsevkECjaE3Lmy5koklKYgueIcWtUK+NpKESeNQaRrOAT0xUS6Ib3yAn6+vA88DheLgx/AMOcQU5dETMwoweZf//oXrly5AldXVzg4OOjdt2bNmh49obmiYENI75GrWpFccQ7xJcmoaKmGDd8aoz20C3A6iR1MXR4xQwq1Erty/0BSWRr87bzxSOgc+lshAIwUbI4fP97lfZMmTerRE5orCjaE9D7GGC7X5SGhNBmZ1ZcAAOHOQRjvGYtghwCLvrxNuq9cVoVNOdtR0lyBu73H4j6/SeDRrNekDc1jYyAKNoQYV628HkllqUgqTUezSgY3K2fEecZgpHsELVg4gCWXn8OvV36DgCvAwyGzEOYUaOqSiJnptWDz559/YsKECQCAo0ePdnkAS+l3Q8GGkL6h1KiQUZmFhNIU5DcWQ8gV6ObEobV+Bo5WtQLbrx7EmfKzGCzxxZLQObR0B+lUT4NNl735EhMTdcEmPj6+ywNYSrAhhPQNAZePWPfhiHUfjoLGEiSUpuB0WQYSS1MRaO+H8dJYDHUOAY/mxLFYJc3l2JS9AxUtVbjHdzym+U6g95v0GroU1YZabAgxnSalDKfK0pFYmora1no4CCUY6xmFMR6RsBPamro80ksYYzhVnoEdVw/CiifGwyGzEewYYOqyiJmjPjYGomBDiOlpmAZZNVcQX5KMi3W54HG4GOEyBHHSWPjbeVFn435MrmrFL1f2I63yAoIdArAkZDaFVtItFGwMRMGGEPNSIatGQmkKzpSfhVzdCm8bD8RJYxHlGk7rBPUzRU2l2Ji9A9XyWtznNwl3+4wDl0Iq6SYKNgaiYEOIeWpVK5BSkYmEkmSUyiphzRdjlHskxnlGw8XK0dTlkVtgjCGhNAW7cg/BVmCNR0LnYrC9r6nLIv1MrwcbjUaDrKwshIaGgs+33JlDKdgQYt4YY8htKEB8STLOVeWAgSHMKQhxnjEIdRxMLQBmRqaS4+dLe3GuOgdDnILwYPADtIYYMYhRWmweeeQRbN682eCi+gMKNoT0H3WtDThZloak0jQ0KpvhInZCnDQaI90iYC2wMnV5A15eQzE25+xAnaIBM/3vwkSv0RQ8icGMEmzefvttzJs3D8HBwQYXZu4o2BDS/6g0apyrykZ8aQquNRRCwOUjxm0Y4jxj4GXrYeryBhzGGI4Xn8LevKOwF9phaeg8+Eu8TF0W6eeMEmy+++47JCYmIiYmBs7OznojExYtWtTzKs0QBRtC+reipjIklKYgtSITSo0KgyQ+iJPGYrhzKPg0Pb/RNStl+N+lPbhQcxnDnUPxl6D7qfWM9AqjBJsvv/yyy/ueeeaZHj2huaJgQ4hlkClbcLr8LBJKU1Atr4VEYIsxnlEY6xEFe5GdqcuzSLn1BdicsxONymbMHjQVcZ4xNDSf9BoaFWUgCjaEWBYNY8ipvYqEkmRk114Bh8PFcOdQjJfGYJDEl754e4GGMRwpSsSBvONwEjtiadhc+Nh6mrosYmGMFmxKS0uRmJiImpoaODk5Ydy4cfD0tJw/YAo2hFiuqpYaJJam4lR5BlpUcnhau2G8NBbRbkMh4glNXV6/1Khowg8Xd+NiXS4iXcOxKHAGLWZKjMIowSYlJQWfffYZoqKi4OrqiqqqKqSmpuJvf/sbYmJiDC7WnFCwIcTyKdRKpFWeR3xJMoqbyyHmiTDKfQTipNFwtXI2dXn9xqW6a/ghZxda1HLMHTwdo91HUAsYMRqjBJsXX3wRjz76KIYOHarbduHCBWzYsAEffPBBz6s0QxRsCBk4GGPIayxCfEkyzlZlQ800CHUcjDjPGAxxCgSXFmTslIZp8HvBn/ijIB6uVs5YFjYPUht3U5dFLFyvre59o5qaGoSFheltCw0NRXV1dY+ejBBCzAGHw0GAxAcBEh80KJp0c+J8l7UVTiIHjPOMxmiPEbChCeV06lsbsfniTlytz8dI9wjMGzydLuMRs9StYOPv74+9e/di9uzZum379u2Dv7+/kcoihJC+IRHa4h7fCZjiPQ6Z1RcRX5qCvXlHcLDgBCJdwzHeMxY+dpbTn9AQ2TVX8MPF3VBqlHgw+AGMdI8wdUmEdKlbl6KKi4vx7rvvorW1Fc7OzqiuroZIJMIrr7wCb2/vvqjT6OhSFCGkXWlzBeJLUpBScQ4KjRJ+dl4YL43BCJch4HMtd2mZm6k1avyWfxxHipLgae2GZWHz4G7tYuqyyABjtLWiAgMDkZeXpxsVFRgYaFFrR1GwIYTcrEUlx5nyc0goTUZlSw1sBdYY4xGJsZ7RcBTZm7o8o6qV12NTzg7kNRZhrEcUZg+aRquqE5OgtaIMRMGGENIVDWO4XHcN8SXJuFBzGQAwzDkEcdIYBNn7W9yIoPPVF/G/S3ugZhosCrofUa7hpi6JDGBG6TwcFhaGS5cuWfRaUYQQ0hUuh4MQx0EIcRyEankdkkpTcaosHeeqc+Bu5YI4aQxi3Yb3+3lcVBo19l47ghMlp+Fj64lHQufC1crJ1GUR0iO0VlQbarEhhPSEUqNCeuUFxJcko7CpFCKeELFuwxEnjYGHtaupy+uxqpZabMrZjsKmUkyQjsQDAXcPqP5ExHwZpcVGoVAgNjYWgHboNyGEDHQCLh8j3SMw0j0C+Y3FiC9JxsmydCSUpiDI3h/jpbEIdw4Grx/MiZNRmYWfL+8Dh8PB8rAFGO4SauqSCDFYtzoP//rrr5g7dy4EAsvtOEYtNoSQO9WkaMbJ8nQklaahtrUeDiIJxnlEY7RHJOyENqYurwOlRoVduX8gsTQVfnZeWBo6F05iB1OXRYgeo3Qefuyxx/Dtt9+CyzX/Xx6GomBDCOktaqbBhepLSChNwaW6a+BxeIh0HYI4z1j42UnNorNxuawKm3J2oKS5HHd5j8EMv8ngcXmmLouQDowSbDZt2gQPDw/cc889Bhdm7ijYEEKMoVxWhYTSFJwpP4tWtQI+tp6Ik8Yi0mWIyYZPp1Scwy+Xf4OAy8dDIbMwxCnIJHUQ0h1GCTavv/46rly5Aicnpw6dh9esWdPzKs0QBRtCiDHJVa1IqchEQmkyymRVsOZbYbTHCIzzjIFzH13+aVUrsOPq7zhdnoFBEl88EjoHDiJJnzw3IYYySrA5fvx4l/dNmjSpR09orijYEEL6AmMMV+rzkVCSjMzqi2BgGOIUhPHSWAQ7DALXSJepSpsrsClnO8plVZjqMx73+E3oFx2bCTFKsDG2gwcP4vjx4ygoKMC4cePw7LPP6u7LzMzE+vXrUVVVhaCgIDzzzDNwddUOpUxISMDmzZshEAjwzDPPIDxcO4lUWVkZPv/8c7zxxhvd7hdEwYYQ0tfqWhuQVJqKpLJ0NCmb4WrlhDjPGIx0j4AVX9wrz8EYw+nyDGy/ehBinggPh8xGiOOgXjk2IX2hp8Hmlt/6GzZs0Lt99OhRvdvvv/9+j56sK46Ojpg7dy4mT56st72hoQHvv/8+Fi1ahA0bNmDQoEH4+OOPAQBqtRo//vgj3n33XSxfvlyv1u+//x6PPPKIRXd2JoT0fw4iCe7zn4x/j/w7loTMhg3fGjtz/8Dq0x/jl8v7UdJcfkfHl6ta8cPFXfj58j4ESHzwctSTFGqIxbvlPDYnTpzA8uXLdbe3bNmCu+66S3c7MzOzV4oYNWoUACA3NxfV1dW67WfOnIGPjw/GjBkDAFiwYAEee+wxFBcXw8bGBk5OTnB0dMSwYcNQXq79ADh16hScnJxolmRCSL/B5/IR7TYM0W7DUNhUioSSFCRXnENSWRoGS3wxXhqLYc4hPRq1VNRUhk0521HVUov7/CZhis84cOnSExkAbhlsTH2VqrCwEH5+frrbYrEYHh4eKCwsxMiRI9HU1ITq6mpcu3YNPj4+kMvl2L59O/71r3+ZsGpCCDGcj60nFgfPxAMBd+N0+VkklqZgY8522AvtMMYjCmM9oyAR2nb5eMYYEktTsSv3D9gIrPF/w5dgsL1fl/sTcqdOZ1dhV3wxahoVcLITYvZ4L4wKM90q8LcMNqaea0Eul0Mi0e+xb21tDblcDi6Xi8cffxwffvgh+Hw+nnrqKWzduhX33nsvCgoKsG3bNvD5fCxZsgS+vr4djn348GEcPnwYAPDOO+/AxcV0bwIhhNzMBYCfpy/mR8zA2fJsHMqNx8GCEzhUmIBYrwhMDYhDsPMgJBWl4pcL+1DdUgsnKwc4iCTIrStAhHsYno5+GHairkMQIXfqz/Ri/HgoH61KDQCgplGBHw/lw87WDhMivUxS0y2DjVqtxvnz53W3NRpNh9vGJBaL0dLSordNJpNBLNZ2qhs2bBiGDRsGAMjPz0dubi6WLFmCZ599Fm+88Qaqq6vxzTff4M033+xw7ClTpmDKlCm621VVVUZ8JYQQYjgfvjuWB89HpU81EkpTcbo0A6eK0uAglKBR2Qw1UwMAalrqUNNShyiXcDwcNAetjXK0NspNXD2xZFsO5OhCTbtWpQZbDuRgiE/vLArbq2tF2dvb46uvvtLdtrW11bt9c2tKb/Px8cGJEyd0t+VyOcrLy+Hj46O3H2MMGzZswKOPPoqGhgZoNBq4urrCwcEB+fn5Rq2REEL6iquVM+YMmob7/CYhreI8tl09ADXr+APzWmOR0YaNE9KOMYaaRkWn93W1vS/cMth88cUXfVKEWq2GWq2GRqOBRqOBQqEAj8fDyJEjsWXLFpw6dQpRUVHYtm0b/Pz84OWl37x15MgR+Pv7w9/fH2q1GgqFAkVFRaiqqoK7u3ufvAZCCOkrIp4QYzyjsPXK/k7vr22t7+OKyEBztbgR2/8s6vJ+JzthH1ajzyzWpN++fTu2bdumux0fH4/58+dj4cKFePHFF7FhwwZ89tlnCAoKwj/+8Q+9xzY0NODAgQNYu3YtAIDH42H58uVYs2YNhEIh/vrXv/bpayGEkL7iKLLvNMQ4iuxNUA0ZCMqqW7AzoQgZV+ogsRFg7FBnpOTUQqG63nIo5HMxe7xp+tcAZjJBnzmgCfoIIf1NSkUmtl7eD6VGqdsm4AqwKGgGYtyGmbAyYmnqmxTYe7IEiZmVEAq4mBbriSnR7hAJeEYfFdUvZx42BxRsCCH9UUpFJvbnHUNtaz0cRfaY4T+ZQg3pNXKFGn8kl+FQShlUGoaJEa6YMVoKO+u+W8CVgk0PpKSkIDU1FU899RQFG0IIIaSNWq3Bn+cqsf9kCRpbVIgOdsTsOG+4OfbOUh89QcHGQBRsCCGEDHSMMaRdqsWuhCJU1LUi2McO8yZ4w9/DdPMh9epwb0IIIYQMDJcKG7D9zyLklTVD6mKF/5sThKEB9iafrLenKNgQQgghA1hxlQw744uQmVsPR1sBHrnHH2OGuIDL7V+Bph0FG0IIIWQAqm1UYG9SMZIuVEEs5GHOeG/cFekOoaB/L5ZKwcZIzG1RMEIIIQQAWlpVOHimFEfSysEYcHeUO+4dJYWtlWVEAst4FWbmdHYVfvgjXzdhUU2jAj/8oV3agcINIYQQU1CqNDhxtgK/nSpFs1yFkWFOmDXOGy72vbOmk7mgYGMEu+KL9WZhBACFSoNd8cUUbAghhPQpDWNIyanBroQiVDcoEOYnwdzx3vB1tzF1aUZBwcYIbrUoWHGVDFJnq37Xy5wQQkj/k51fjx1/FqGgQgYfV2s8PM8fQ/wte8mNAR1sbpygrzc52Qm7DDdvbLoAiTUfob4S7X9+EjhLLKsZkBBCiGkVVsiw489CZOU3wFkixKP3BmBkmPOAWPWdJuhr05sT9N3cxwbQLgo2K84LYiEPOQUNyCloQKNMBQBwdRBdDzo+drDtw6mqCSGEWI7qhlbsSSzG6axqWIl5uG+UFJNGuEHA778jnWjmYQP19szDtxsVxRhDSXULcgoakJ3fgMtFjZArtEHIx9Uaob52CPWTINDLDmIhr1drI4QQYlmaW1Q4cLoExzIqAGhHOk0f6Qlrcf+/MEPBxkCmXlJBrWHIK2vWtebkljRBpWbgcjkY5GmDUF8JwnwlCPC0AY/Xf5M3IYSQ3qNUaXA0vRwHT5eipVWN0eEueGCsFE4W1MWBgo2BTB1sbqZQqnGluAnZbUGnsFwGBkAk4CLI20536crL1WpAXDMlhBBynUbDcDq7GrsTi1HbqMDQAHvMHe8NL1drU5fW6yjYGMjcgs3NmltUuFjUgJz8RuQUNKC8Vg4AsLXiI8RHe9kqzFcCF3sRjbgihBALxRjDhTztSKfiqhb4uVtj3gQfhPhKTF2a0VCwMZC5B5ub1TYqdJetcgoaUNekBAA4S4TXOyL7SiCxoY7IhBBiCfLKmrHjz0JcLGyEi70Is+O8EB3iZPGt9hRsDNTfgs2NGGMor5HrLltdKmyErFUNAJC6WOlCTrC3HaxE1BGZEEL6k8o6OXYnFCP5Yg1srfiYMVqKCRGu4A+Q/pYUbAzUn4PNzTQahoKKZt1lqysljVCqGLgcwN/DBqF+2qAzyNO2Xw8BJIQQS9YkU2L/6VKcyKgAl8vBlGh33BPrOeB+oFKw6YEbJ+izpGBzM6VKg6slTbrLVnllzWAMEPC5CPSy1bXo+LpZ99tl6gkhxFIolGocTivH72fK0KpUY9xQV8wcK4WDrdDUpZkEBRsDWXKwuVlLqwqXCht1l65Kq7Udka3FPIT4tPfPsYO7o5g6IhNCSB9RaxhOnq/C3pPFqGtSImKwA2aP94bU2crUpZkUBRsDDaRgc7P6JgVyChuRk68NOu3LQTjYCrTz57RduhqovxYIIcSYGGM4d7UOOxOKUFotR4CnDeZP8EGgt52pSzMLFGwMNJCDzY0YY6isa9W15lwsaESzXLv0g4eT+HpHZB872FjAjJaEEGJKuSVN2P5nIa4UN8HdUYzZ470QGehIreU3oGBjIAo2ndMwhqIKWVv/nEZcLmqEQqUBhwP4udtol37wlWCw1A5CAXVEJoSQ7iivkWNXQhHSLtdCYs3H/WO9EDfUhWaW7wQFGwNRsOkelVqD3NJm3WWra2XN0GgY+DwOBkttdZeufN1twKOOyIQQoqehWYl9J0sQn1kJAY+DqTEemBrjQWsC3gIFGwNRsDGMXKHG5SJtR+SLBQ0oqmwBAIiFPO2MyG2XrjydqSMyIWTgkivUOJRShkMpZVCqNBg/3A33j5HSJKrdQMHGQBRsekeDTImLbZetcgoaUFXfCgCQ2Ah0l63CfCUWtUAbIYR0Ra3WICGzCvtOFqNBpkJUkCNmx3nD3Uls6tL6DQo2BqJgYxxV9a26y1Y5hQ1olGk7Irs5iHStOSE+drC1pl8thBDLwRhD+pVa7IovRnmtHIFetpg3wQeDpLamLq3foWBjIAo2xscYQ0lVi97SD61KDTgAvN2sdUEnyNsWIgFdbyaE9E9Xihqx/c9C5JY2w9NZjDnjfTB8kD1djjcQBRsDUbDpe2q1BnllzbrLVldLmqDWMPC4HAR42ujmzwnwsKGRAoQQs1da3YKd8UU4e7UODrYCzBzrhTHhLjSQ4g5RsOmBgbKkQn+hUKpxpbgJ2W2XrgorZGAARAIugryvd0T2crWy+NVsCSH9R12TAnuTSpB4vhIiARf3jPTElCh3CKnluVdQsDEQBRvz09SiwqVCbcjJLmhARa22I7KdFR8hvteXfnB1oE54hJC+19Kqxu/JpTicWg6NhmFihCvuGy2FHfUZ7FUUbAxEwcb81TS06i5b5RQ0oL5ZCQBwlgh1rTmhvhIaPkkIMSqVWoM/z1Zi/6kSNLWoEBvihFlxXvQjy0go2BiIgk3/whhDWY1cd9nqUlEjWlrVAAAvFyu9pR9o4itCSG/QMIbUizXYlVCMqvpWhPjaYe54H/h72Ji6NItGwcZAFGz6N7WGoaC8Wbf0w5XiRqjUDFwOEOBpq7tsFeBpCwGfOiITQnrmYkEDtv9ZiPxyGbxdrTBnvA/C/SU00qkPULAxEAUby6JQanC1pEl32Sq/vBmMAQI+F0Fe15d+8Hazpo7IhJAuFVfKsCO+COev1cPRTohZ47wwKswZXBrp1Gco2BiIgo1lk8lVuFTU1j8nvwGlNXIAgI2YhxCf6/1z3BxF9AuMEIKahlbsSSrBqQtVsBLxMH2UJyaPcKfFfk2Ago2BKNgMLHVNCt1lq5yCBtQ2KgAAjrYChPrZI9TXDmG+EtjbCk1cKSGkL8nkKhw4U4pj6eVgDJg8wg33jpLCxopv6tIGLAo2BqJgM3AxxlBRd33ph4uFDWiWazsiezqJEdK2vlWwjx2sxfThRoglUqo0OJ5Rgd9Ol6BFrsaoIc54YJwXnGldO5OjYGMgCjaknYYxFFXIdEs/XClqgkKlAYcD+Lnb6C5bBXpRR2RC+jsNYziTXY09icWoblBgiL8Ec8f7wMfN2tSlkTYUbAxEwYZ0RanS4Fppk+6y1bXSJmgYwOdxMLi9I7KvBH7uNtShkJB+JCuvHjv+LEJhpQy+btaYO8EHYX4SU5dFbkLBxkAUbEh3yRVqXC5q1M2hU1zVAgCwEvEQ3L70g58Enk5i6ohMiBkqKG/GjvgiZOc3wFkixOw4b8SEOtEISTNFwcZAFGyIoRpkSlxsu2yVU9CIqnrt0g/2NoIbZkS2gxNdqyfEpKrqW7E7sQhnsmtgI+bhvtFSTIxwo0vKZo6CTQ/QIpjEGCrr5LrLVhcLGtDYogIAuDmKdEEnxEcCWxplQUifaGpR4bfTJTiRUQEOB7g7yh33xHrSYIB+goKNgSjYEGPQMIaSqhbdRIGXChvRqtSAA8DHzVp32SrQyxYiWgmYkF6lUGpwNL0cB8+UQq5QY2y4C2aO9YKjHU3j0J9QsDEQBRvSF9RqDa6VXV/6IbekCWoNA4/LwSCpLcLaLlv5e9iAx6PmcUIModEwnMyqwt7EYtQ2KTFskD3mjPeGlwuNdOqPKNgYiIINMYVWpRqXi64v/VBUIQMDIBJwEexjp7t05eViRR2RCbkNxhjOX6vHjvgilFS1wN/DBvMmeCPYh0Y69WcUbAxEwYaYg6YWFS4WNuiCTkWttiOynTUfoe1LP/hJ4GJPHZEJuVFeWRO2/1mES4WNcHMQYXacN6KCHekHgQWgYGMgCjbEHNU0tOomCswpaERDsxIA4GIvQqjv9RYdO2uBiSslxDQq6+TYGV+E1Eu1sLPiY8YYKcYPdwWfLuVaDAo2BqJgQ8wdYwylNfIbln5ohFyhXfrB29VKt/RDkLcdxELqiEwsW4NMif0nS/DnuUrwuRxMjfHA1BgPWInob9/SULAxEAUb0t+oNQwF5c26Fp2rxU1QqRm4XA4CPGx0l60GedrQr1diMVqVahxOLccfyaVQKDWIG+aK+8dIacFaC0bBxkAUbEh/p1BqcLWkUXfZKr+8GYwBQj4Xgd7Xl37wdrOmGVZJv6PWMCSer8S+pBLUNysxItABc+K84eFsZerSiJFRsDEQBRtiaWRyFS4VNiK7baLA0ho5AMBGzEfIDf1z3BxE1MGSmC3GGM5ercPO+CKU1cgxWGqLuRO8EehlZ+rSSB+hYGMgCjbE0tU2Kq6PuMpvQG2TtiOyo51QO3+OnwShPnbUpE/MxtWSJmw/UYirJU1wdxRj7nhvRAQ6UBAfYCjYGIiCDRlIGGOoqL0+4upiYQNkcm1HZE9n8Q1LP9jBSkTTzpO+VVbTgp3xRci4UgeJjQAzx0gxbpgreFwKNAMRBRsDUbAhA5lGw1BYKdONuLpc3ASlSgMOB/Bv74jsK8FgqS0tGEiMpr5ZiX0ni5FwrhICPhf3xHpiSow7LTcywFGwMRAFG0KuU6o0yC1t0l22yitrhoYBAj4Hg6V2CPW1Q5ifBL5uNuDSr2hyh+QKNf5ILsPh1DIo1QwThrtixhgpJDQ/EwEFG4NRsCGkay2talwu0o64yi5oQElVCwDAWsTTW/rBw0lM/R9It6nVGsRnVmHfyWI0ylSIDnbE7DhvuDmKTV0aMSMUbAxEwYaQ7mtoVuqWfcgpaEB1gwIA4GAr0IWcUF8JraJMOsUYQ9rlWuxKKEJFbSuCve0wd4I3AjxtTV0aMUMUbHogJSUFqampeOqppyjYEGIgxhiq6ts6IudrZ0RualEBANwdxdqlH/wkCPGWwMaKOiIPdJeKGrHjz0JcK22G1NkKcyd4Y2iAPbX0kS5RsDEQBRtCeoeGMRRXtuhacy4XNaJVqQEHgI+7tW6iwEAvWwipU+iAUVLVgh3xhcjMrYeDrQAPjPXCmHAX6qNFbouCjYEo2BBiHCq1Bnml15d+uFbaDLWGgc/jYJCnrXb+HF8J/D1saDivBaptVGBvUjGSLlRBJODh3pGeuCvKjUIt6TYKNgaiYENI32hVqnG5qAk5+fXIKWhEYaUMACAWchHkre2IHOYngdTZii5P9GMtrSocPFOGI2nlYIxh4gg33DdKClu6HEl6iIKNgSjYEGIaTTIlLhY26i5dVdS1AgAk1nyE3NAR2cVeZOJKSXcoVRr8ea4C+0+WolmuwshQJ8yK86b3jxiMgo2BKNgQYh6qG1p1EwXmFDSgQabtiOxiL9K15oT42MGO5jgxKxrGkJJTg92Jxaiqb0WorwTzJnjD193G1KWRfo6CjYEo2BBifhhjKK2Wa+fPyW/ApaJGyBXapR+8Xa10rTlB3nYQC6nPhqlk5zdgR3whCspl8Ha1wtwJPhjiJ6FLiaRXULAxEAUbQsyfWsOQX9asa825WtIElZqBy+VgkOf1pR8CPG3A59HSD8ZWWCHDjvhCZOU1wMlOiFlxXhgZ5gwuBRrSiyjYGIiCDSH9j0KpwdWSRmS3XboqKJeBARAJuAj0al/6wR5erlb0ZduLahpasTuxGKezqmEl5uG+UVJMGuFG64gRo6BgYyAKNoT0f81yFS61dUTOzm9Aea0cAGBrxUfIDUs/uDqI6DKJAZrlKhw4XYpj6eUAgLui3DF9pCdsxDTSiRgPBRsDUbAhxPLUNir0ln6oa1ICAJzshAhrmz8nxFcCexvqiHwrSpUGx9LLceBMKVrkaowOd8YDY73gJKGRTsT4KNgYiIINIZaNMYbyWjlyChqRnV+PS4WNkLVqOyJLna20Sz/4ShDsYwcrEbVAAIBGw3A6uxp7EotR06hAuL895k7whrertalLIwMIBRsDUbAhZGDRaBgKKmS61pwrxY1Qqhi4HMDPw0a39MMgqe2A6zvCGMOFvAbsjC9EUWULfN2tMW+CD0J9JaYujQxAFGwMRMGGkIFNqdIgt6RJt/RDflkzNAwQ8DkIlNrpln7wdbO26PWN8subsePPIuQUNMDFXoTZcV6IDnGiztfEZCjYGIiCDSHkRi2tKlwqatK26OQ3oKS6BQBgLeIh2Of60g/ujmKL6IhcVd+KXQlFSM6pga0VHzNGSzEhwpWGzROTo2BjIAo2hJBbqW9W4uINHZGrGxQAAAdbgW60VaivBI52QhNX2jNNMiV+O12K4xkV4HI5mBLtjntiPaifETEbFGwMRMGGENJdjDFU1bfq5s/JKWhEs1y79IO7o1jXmhPsY2e2Q6EVSjWOpJXj4JkytCrVGDfUBfeP8ep3wYxYPgo2BqJgQwgxlIYxFFe26FpzLhU2QqHSgMMBfN2sda05gV52EApMe2lHo2FIulCFvUnFqGtSYvggB8yZ4A2ps5VJ6yKkKxRsDETBhhDSW1RqDa6VXl/6Ibe0GRoNA5/HwWCprS7o+HnYgNdHHZEZY8jMrceO+EKUVssR4GmDeRN8EORt1yfPT4ihKNgYiIINIcRY5Ao1rhRfX/qhqFLbEVks5CHY2w6hfnYI87WHp7NxOiJfK23C9j+LcLmoEW6OIsyJ80ZkkKNFdHomlo+CTQ+kpKQgNTUVTz31FAUbQkifaZQpcbFt6YecggZU1rUCACQ2AoS2L/3gJ4HzHc7sW14rx66EIqRdqoWdNR8zx3ghbpgLeDTSifQjFGwMRMGGEGIqVfWteks/NMq0HZFdHUS6iQJDfOxga929pR8aZErsP1mCP89VQsDjYGqMB6bGeEAs5BnzZRBiFBRsDETBhhBiDhhjKKlu0S3kebmoEXKFBgDg42qtXfrBT9sRWSzk4XR2FXbFa5c8cLQTwN/DBll5DVCqNBg/3BUzxnjRWlikX6NgYyAKNoQQc6TWMOSV3dARuaQJKjUDj8uBs0SI6gYF1Br9j3E/d2ssv28QPJxopBPp/3oabMxzggVCCCEAAB5XO5JqsNQWM0ZLoVCqcaVYu/TDkdTyDqEGABplKgo1ZMCiHmSEENKPCAU8DPG3x7wJPp2GGgCoaVT0cVWEmA8KNoQQ0k85dTFLcFfbCRkIKNgQQkg/NXu8F4R8/Y9xIZ+L2eO9TFQRIaZHfWwIIaSfGhXmAgC6UVFOdkLMHu+l207IQESjotrQqChCCCHE/PR0VBRdiiKEEEKIxaBgQwghhBCLQcGGEEIIIRaDgg0hhBBCLAYFG0IIIYRYDAo2hBBCCLEYFGwIIYQQYjEo2BBCCCHEYlCwIYQQQojFoGBDCCGEEItBwYYQQgghFoPWiiKEEEKIxaAWGyP75ptvTF0CuQm9J6ZB59246Pz2HjqXhjGX80bBxsiio6NNXQK5Cb0npkHn3bjo/PYeOpeGMZfzRpeiCCGEEGIxqMWGEEIIIRaDgg0hhBBCLAYFm35KJpNh1apVWLJkCQoKCkxdzoBH74fx0Tk2DTrvvYfOpWF6et4o2PRTQqEQq1atwujRo01dCgG9H32BzrFp0HnvPXQuDdPT80bBpp/i8/mQSCSmLoO0offD+Ogcmwad995D59IwPT1vfCPWYraUSiW+++47ZGZmoqmpCR4eHli8eDEiIyO7fExiYiK2bduGqqoqODg44JlnnkFYWNgd1XHw4EEcP34cBQUFGDduHJ599lm9+5uamvDVV1/h3LlzsLOzw4MPPoi4uLg7ek5z1dP3pKKiAuvXr8elS5fA5/MxevRoLFu2DDwe747qoPfkuk8//RTnz59Ha2srHBwc8MADD+Duu+++4+MOxHPcnXNpyOdST9zqvPeXc27I32RpaSleeukljBo1Cn//+9/vuIb++Pfb0/PW37/vBmSwUavVcHZ2xr///W+4uLggPT0dH330Ed5//324ubl12P/cuXP48ccf8dxzzyEwMBB1dXWdHpcxhry8PAQEBOhtz8vLg6+vL7hc/QYyR0dHzJ07F2fPnoVCoehwvO+++w58Ph/ffvst8vLy8Pbbb8PPzw8+Pj6Gv3gz1dP3ZP369ZBIJPjmm28gk8mwdu1a/P7777jvvvv09qP3xHBz5szBX//6VwgEAhQXF+Pf//43AgICMGjQIN0+PT2/wMA8x905lz35N9Db572/nPPunMebrV+/HoMHD+7y/oHwGdGT82YJ33cD8lKUWCzGwoUL4ebmBi6Xi+joaLi5uSE3N7fT/X/55RfMnz8fwcHB4HK5cHJygpOTU4f9KisrsW7dOmRkZOi2ZWdnY+3atSgqKuqw/6hRozBy5EjY2dl1uE8ul+P06dNYtGgRxGIxQkNDERMTgz///NPwF27GevqeVFRUYMyYMRAKhXBwcMCIESM6Pcf0nhjOx8cHAoEAAMDhcMDhcFBWVqa3T0/PLzAwz3F3zmVP/g305nnvT+e8O+fxRomJibC2tsbQoUO73GcgfEb05LxZwvfdgGyxuVldXR1KS0s7TYYajQZXr15FTEwM/va3v0GpVCI2NhZLliyBUCjU29fNzQ0vvvgi3n//fbzwwgsQi8V4//338be//Q2+vr49qqm0tBRcLhdSqVS3zc/PD1lZWbrbb7/9NvLy8lBSUoKpU6di0qRJPXvhZuxW7wkA3HvvvUhKSkJ4eDiam5uRkZGBRYsWddivL98TS3w/vvvuOxw/fhwKhQIBAQGIiorSu783zy9g2ef4dufyZrf6NzCQP2u6ex5lMhl++eUXvP766zh69GiXxxso57I7581Svu8GfLBRqVT47LPPMHHiRHh5eXW4v66uDmq1GqdOncIbb7wBHo+H9957D9u3b8fixYs77D9kyBD8/e9/x4cffggul4unnnoKI0aM6HFdcrkc1tbWetusra0hl8t1t1etWtXj4/YHt3tPAO15PnLkCJYuXQqNRoOJEyciNja2y3374j2xxPfj8ccfx/Lly3Hp0iVcuHABfH7Hj4zeOr+AZZ/j7pzLdt39NzAQP2u6ex63bt2KyZMnw8XF5bbHHAjnsjvnzVK+7wbkpah2Go0Gn3/+Ofh8PpYvX97pPu0pdfr06XB0dIREIsGMGTOQnp7e5XFdXFzA4/HAGIOrq6tBtYnFYrS0tOhta2lpgVgsNuh4/UV33hONRoM333wTo0aNwpYtW7B+/Xo0Nzfjxx9/7PK49J4YjsvlIjQ0FNXV1fjjjz863ac3zi9g+ee4O+eyO/8G2g3Uv+vbnce8vDxkZmbi/vvv7/YxB8K5vN15s5TvuwEbbBhj+Prrr1FfX48XX3yxy9Rva2sLZ2dncDicbh23rKwM69atw0MPPYQnnngCb7/9NgoLC3tcn6enJ9RqNUpLS3Xb8vPzza4zX2/q7nvS1NSE6upqTJ8+HQKBAHZ2dpg0aVKX//joPekdGo0G5eXlHbb31vkFBs457upcdvffAEB/10DX5/HChQuorKzEX//6VzzxxBPYu3cvTp8+jRUrVnR6nIF2Lrs6b5byfTdgg823336L4uJirFixosO1w5tNmjQJBw8eRH19PZqamvDbb791en2ypqYGa9euxdy5czFp0iSMHj0aS5Yswbp16zr9I1Kr1VAoFNBoNNBoNFAoFFCr1QC0CXbUqFHYunUr5HI5cnJykJycjAkTJvTOCTBD3X1PJBIJ3Nzc8Mcff0CtVqO5uRknTpyAn59fh33pPTFMfX09EhMTIZfLodFokJGRgcTExA6dMHt6foGBd467ey6B7v8b6M3z3l/OeU/O45QpU/DZZ5/hvffew3vvvYepU6ciKioKr776aod9Lf0zoifnDbCM77sBubp3ZWUlnn32WQgEAr0haU8++STGjx+Pt956C6GhoZg7dy4A7fXujRs3IiEhAQKBAGPGjMHDDz/c4YNHqVQiNTW1w+yIycnJGDZsWIdmtV9++QXbtm3T2zZ//nwsXLgQgLZl4ssvv0RmZiZsbW3x0EMPmXw+BGPp6XuSl5eHjRs3Ij8/H1wuF+Hh4Xjsscdgb2+vd1x6TwzT0NCADz74APn5+WCMwcXFBffeey+mTJmit19Pzy8w8M7x7c5l+9/2+PHjb/lv4Ea9fd77wznv7nls/4y40S+//IKysrJO57Gx9M+Inp43S/i+G5DBhhBCCCGWacBeiiKEEEKI5aFgQwghhBCLQcGGEEIIIRaDgg0hhBBCLAYFG0IIIYRYDAo2hBBCCLEYFGwIIYQQYjEo2BBCCCHEYlCwIYSYvf/+978dZi290cKFC1FWVtajY8bHx2PdunV3WhohxMzQzMOEkD6VmJiI/fv3o7CwECKRCG5ubpg4cSKmTZvW7cX3brZw4UJ8+umn8PDw6OVqCSH9TddLxxJCSC/bu3cv9uzZg8ceewwREREQi8XIy8vD3r17cdddd0EgEHR4jEaj0Vs7iRBCboWCDSGkT8hkMvzyyy949tln9RbOCwgI0Fuc8IsvvoBQKERVVRWysrLw8ssvIz4+Hs7OzvjLX/4CANizZw/27dsHDoeDRYsW3fJ5jx8/jm3btqGhoQF2dnb4y1/+gvHjx+P48eM4cuQI1q5di927d+td6lKpVIiLi8Ozzz4LmUyGTZs2IT09HRwOB5MnT8bChQspbBFipijYEEL6xKVLl6BUKhEbG3vbfRMSErBq1SqsWLECKpUK8fHxuvsyMjKwd+9evP7663Bzc8M333zT5XHkcjm+//57vP3225BKpaitrUVTU1OH/WbNmoVZs2YBAKqqqvDqq69izJgxAIDPP/8cDg4O+PTTT9Ha2op33nkHzs7OmDp1ak9PASGkD9BPDkJIn2hvMeHxeLptr732GpYtW4aHHnoIWVlZuu2xsbEIDQ0Fl8uFUCjUO05SUhImTZoEX19fiMViLFiw4JbPy+FwUFBQAIVCAUdHR/j4+HS5r0KhwHvvvYd7770XUVFRqKurQ0ZGBpYtWwaxWAx7e3vMmDEDSUlJBp4FQoixUYsNIaRP2NnZobGxEWq1Whdu2kclPf3007hxHIOzs3OXx6mtrcWgQYN0t11dXbvcVywW47nnnsPevXvx9ddfIyQkBI888gi8vLw63f+rr76CVCrF7NmzAWhbb9RqNZ588kndPoyxW9ZHCDEtCjaEkD4RHBwMgUCA5ORkvT42nbnV6ChHR0dUV1frbldVVd3yWCNGjMCIESOgUCjw888/45tvvsEbb7zRYb9du3ahpKQEa9eu1W1zdnYGn8/H+vXr9VqaCCHmiy5FEUL6hI2NDebPn4/169fj1KlTkMvl0Gg0yMvLQ2tra7ePM2bMGBw/fhxFRUVobW3Fr7/+2uW+dXV1SElJgVwuB5/Ph1gs7rTTb3p6Og4cOICXX35Z79KXo6MjIiIisHnzZshkMmg0GpSVleldNiOEmBdqsSGE9JlZs2bByckJu3fvxueffw6RSAR3d3c89NBDCAkJ6dYxIiMjMWPGDKxZswZcLheLFi1CQkJCp/syxrB371589tln4HA48Pf3x+OPP95hv6SkJDQ0NOD555/XbRs/fjyefPJJ/N///R9+/PFHvPDCC2hpaYG7u7uuozEhxPzQBH2EEEIIsRh0KYoQQgghFoOCDSGEEEIsBgUbQgghhFgMCjaEEEIIsRgUbAghhBBiMSjYEEIIIcRiULAhhBBCiMWgYEMIIYQQi/H/fOQHbEcMvTAAAAAASUVORK5CYII=\n", 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" ], "text/plain": [ - " actual error in raw value actual error in extrapolated value \\\n", - "23 NaN NaN \n", - "23 NaN NaN \n", - "31 NaN NaN \n", - "42 5.935871 31.438978 \n", - "53 0.519098 12.655752 \n", - "67 0.111825 22.012257 \n", - "85 0.298172 6.420185 \n", - "113 0.975078 1.057040 \n", - "142 1.867447 3.323424 \n", + " actual error in raw value actual error in extrapolated value \\\n", + "19 NaN NaN \n", + "19 NaN NaN \n", + "22 NaN NaN \n", + "26 8.020085 50.757467 \n", + "32 2.821832 9.527453 \n", + "36 9.634620 51.022939 \n", "\n", - " estimated error \n", - "23 NaN \n", - "23 NaN \n", - "31 NaN \n", - "42 96.383332 \n", - "53 45.697973 \n", - "67 27.776320 \n", - "85 9.826740 \n", - "113 1.341516 \n", - "142 2.159880 " + " estimated error \n", + "19 NaN \n", + "19 NaN \n", + "22 NaN \n", + "26 260.769151 \n", + "32 68.874022 \n", + "36 43.855803 " ] }, "metadata": {}, @@ -2731,7 +2663,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -2741,7 +2673,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -2864,14 +2796,12 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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PHuBfG84SEVHTiLYODnkuTZUen9pWJY7ELVcl+tzCfa3hsvYh6Binwrq9Z9G/WwT85NbtSMxcKp6IqMX4W4ma5MSZCrx2flXie65Pwu0j27G4aSZBEDB+cDwqqg3Y/Hv9RS+lubmImDABit27RUpHROTZ+JuJHGKxWPDLb+fw3g/H4e8nw7OTk9E/hT0NLdUhLgg9OoRiw/58VNX8s02DKSICQnU1JFVVIqYjIvJcvEVFDdp3rBirduahtFKPMJUcqgAZzhRq0atzGO66Jgn+fva7u1PzjB0Uj1e/SsfPv53DxKsSrQf9/VG8fr24wYiIPBh7cMjOvmPFWLoxG6WV1lk8ZVUGnCnUol9yOKaP6sDippXFqv1xZYoa2w8VoqSitn6j2Qxpbq44wYiIPBgLHLKzamce9Eaz3fG/c6u45YKT3DTAuhLn2j159Y6HPvkkIsaNA0wmMWIREXksFjhkp67nxtHj1HLhwX4YlhqNvRklyCuqsR2vmTQJlc8/D3jPjipERC7BAofshAc1vP5KY8epdVx/RRso/aRYueufW1L6/v2hHTcOkHG4HBFRU7DAITtjB8dBesFqxAqZBGMHc48kZwr0l+HavjE4clqDk7mV/zRotfD/7jtIz5wRLxwRkYdhgUN2+iWrER3qB8n5qyM8SIE7rmmLfsmcFu5sI3pFIyRQjh935sJy/raUpKwMoU8/Df+1a0VOR0TkOdjvTXZ0ehMKNbUYkRqNCXXTlsklFHIpRg2IxTebsnH4VDl6dgyDOTYWRWlpMHbqJHY8IiKPwR4csnP8TCWMJgu6cW8pUQzsHonoMCVW7cqF2WztxTF27gxwBhsRkcNY4JCdjMxy+Mkl6BinEjuKT5JKBIwZFIdzJTr8evSfjTgDvvoKYQ88IGIyIiLPwQKH6rFYLEjP1KBrYjD3mBJRr05haBcTiLW786A3WNckErRaCFVVgFYrcjoiIvfH32BUT0GpDiUVenRLChE7ik+r24izrMqAbYesG3FWT5+O0qVLAX9/kdMREbk/FjhUT3qWBgDQnQWO6LokBiOlXTDW/3YONTqjbQyOoNGwF4eI6BJY4FA96ZkatAlXIiLYT+woBGD84ATU6EzYsP8cAECanY3oXr0QsGKFyMmIiNwbCxyyqTWY8HduJW9PuZGEqABc0TUcm/8oRHmVHqbERFQ/+CD0ffqIHY2IyK2xwCGb4znW6eG8PeVeRg+Mg9lswbpfzwKCgMqnnoKxa1exYxERuTUWOGSTnqk5Pz08SOwo9C+RoUoMvjwSu48UIb/UOvZGmpkJ5erVIicjInJfLHAIgHV6eEaWBl0SOD3cHd14ZSzkMglW78oDAKg+/RShs2ZB4GBjIqIG8TcZAQAKynQo1tTy9pSbCg6Q4+o+Mfjj7zJknqtC5aOPonDnTlg4ZZyIqEEscAiA9fYUAA4wdmNX94lBkL8MP+7MhSkmBuboaLEjERG5LRY4BADIyNQgJlwJdQinh7srpUKKG/rH4sSZSmRkVUBSWIiwBx6AYtcusaMREbkdFjgEvcGEE5we7hGG9IiEOsQPK3eegTEoCPL0dEjPnhU7FhGR22GBQ7bdw7u3Y4Hj7mRSCUYPjENukRYHsmpQuGMHtBMnih2LiMjtsMAhpGdqoJBJ0Cme08M9Qd+u4YiP9Mfq3XkwWqzHhNJScUMREbkZFjg+rm738C6JQZwe7iEkgoBxgxNQrKnFjsNFCJ49G1HXXAMYjWJHIyJyGzKxA5C4CstqUaypxdV9YsSOQk3QrV0wOicE4ee9ZzF86DCY2rSxFjgyvqWJiAD24Pi89KxyANw93NMIgoDxg+NRqTViraIDqqdPB5RKsWMREbkNFjg+Lj1Tg+gwTg/3REltVOjVKQybDuSjolIHv02bIM3NFTsWEZFbYIHjw/QGE06cqWTvjQcbMygOBqMZ2zdlIHzqVAR8953YkYiI3AJv2Psw2/RwFjgeKybcHwO6R2J9RjGGf/UdAgf2FTsSEZFbYA+OD+P0cO8w6spYSCQCvquOBuRyseMQEbkFFjg+LCOL08O9QViQAiN6ReG3v0pRvXQ5Qp58UuxIRESi4282H1VQpkNReS26cfVir3Bt3zYIUEpx/Ne/IE9Ph1BVJXYkIiJRscDxURnndw/n+BvvEKCU4bor2mBxu5HY/ekyWFQqsSMREYmKBY6PqpseHhnKtVO8xbCe0QgJVuLHXXmw1NYCtbViRyIiEg0LHB+kN5hxIreCu4d7GYVcgpsGxKHi5Bmoe/dBwPffix2JiEg0LHB80IncChiMnB7ujfp3U8MvoQ12dB2K2i5dxY5DRCQaFjg+KD1TA7lMgs6cHu51pBIBY4ck4IMrp2Crfzux4xARiYYFjg/KyNSgSwKnh3urHh1C0SFWhe2bjkH6ywax4xARiYK/4XxMYZkOheW1vD3lxQRBwLjB8bhhy1JEPPAAhIoKsSMREbkcCxwfk87p4T6hU3wQjo2/E09PeQ9V8gCx4xARuRwLHB+TkaVBVJgfp4f7gGGj++BkcDzW/3ZO7ChERC7HAseH6A1mHD9Tge5cvdgnxEUGYGhbBTr8dw5qN20TOw4RkUuxwPEhJ3IrYTBa0C0pVOwo5CLXXpWEfif34tSGfWJHISJyKRY4PiQjsxxymcDp4T4kXB2M/324Eh/EDsfZEq3YcYiIXIYFjg9Jz9KgS0IwFHL+s/uS6wYkwk8uxbrNp8SOQkTkMvxN5yOKynUoLOPu4b5IFSDH09k/46GX78Dp7FKx4xARuQQLHB/B6eG+Lf6Ggdh72VCs25oJi8UidhwiIqeTiR2AXCM9U4OoUD9EhXF6uC+yDB+G6vBuyNicjSOnNbi8Q6jYkYiInIo9OD6gbno4dw/3bYO6R6BfVRa2rfsDZjN7cYjIu7HA8QF108N5e8q3yTXleP6zp9B3ywrsO1YidhwiIqfiLSof8M/08GCxo5CIzBERKPm/L7DntArFu/PQp0s4N1wlIq/Fn24+ID1Lg87xnB5OgGH4cNxwdReUVuqx/XCh2HGIiJyGv/G8XN30cN6eojo9sg/j6QNf4ee956CtNYodh4jIKVjgeLm66eEcYEx15MeP44oTeyGUlWHD/nyx4xAROYVDBY7RaEROTg7++usv5OTkwGjkX32eIj1Tg8hQP0RzejidV33XXSj5bS+69myLzb8XQFOlFzsSEVGru+gg4z/++AMbN25Eeno6pFIp/P39odVqYTKZ0L17d1x99dXo3bu3q7JSExmMZhw/U4mB3dViRyF34ucHABgzIBZBPy5HRN+pUFcUozQkEpkPPY64R6aIHJCIqOUaLXBeeuklBAYGYtCgQZg+fTrCw8NtbWVlZcjIyMCmTZuwatUqzJkzxyVhqWms08PNHH9DdoTSUnQZOgzdysshN1t7ZNWaQqj+OxtHABY5ROTxBEsj67bn5OQgMTHxkk/g6HmucPbsWbEjuJXvt+Zgx+FCzHs4FQq5VOw4HkutVqO4uFjsGK3LYkF4UkcoDTq7puKQKOiPHhQhFJG4vPK97gNiY2MbPN7oGBxHixZ3KW7IXkamBp0TgljckD1BgMJQ22BTuKbIxWGIiFqfQwv9rVu3Dt27d0e7du1w4sQJzJs3D1KpFDNmzECXLl0ceqEPPvgA6enpqK2tRWhoKEaPHo0RI0Y0+nqrV6+GXq9Hv379MG3aNMjlcse/KkJRuQ4FZToM7RkpdhRyU6UhkVBr7NfCKQ3hNUNEns+hWVQ//fQToqKiAADffvstRo0ahfHjx+PLL790+IXGjRuHjz76CF9++SWefvppfPfddzh9+rTdeYcOHcLq1avx8ssv46OPPkJhYSGWLVvm8OuQlW338Hah4gYht5X50OMwCvV793QyP2Q+9Lg4gYiIWpFDBU5NTQ0CAgKg1WqRlZWF66+/HsOHD2/SmJeEhARbL4wgCBAEAfn59mtwbN++HcOGDUNCQgJUKhVuvvlmbNu2zeHXIauMLA3UIX6ICvMTOwq5qbhHpuDo/TOhCQyFGUBhUCRW3vYkBxgTkVdw6BZVREQEjh8/jjNnziA5ORkSiQQ1NTWQSJq2TuCSJUuwbds26PV6JCUloVevXnbn5Obmom/fvrbHbdu2hUajQWVlJYKCguqdm5aWhrS0NADA3LlzoVZzOjQA6A0mHD9TieG94xEZydsNLSWTybz22lLPfx2Y/zqMAL5e9if2pJ/DSGUQQlQsjMn3ePN73Rc5VODccccdeO+99yCTyfDkk08CsK6R07Fjxya92H333Yd7770XJ06cQEZGBmQy+5fX6XQICAiwPa77WKvV2hU4I0eOxMiRI22POfrd6miWBnqDGR3b+PF70gp8YWZF4Kef4q70v7C97W34Ie0Yxg6KFzsSkcv5wnvdGzU2i+qiBU5+fj5iYmLQq1cvfPLJJ/Xa+vfvj/79+zc5iEQiQdeuXbFjxw5s3LgRN9xwQ712pVKJmpoa22OtVgsA8Pf3b/Jr+ar0TA1kUgFdEoIufTIRAElFBVTVGvRJUmHboUJc2zcG/n4O/f1DROSWLvoT7M033wQApKamolevXkhJSbH1ujTU+9IUZrMZBQUFdsfj4+ORnZ2NAQMGAACys7MREhJi13tDjUvP1KBzPKeHk+Mqn3gCkEhwTUE19i89iq2HCnFDv4b/KiIi8gQXHUQzf/58PP/884iOjsa6detw//334+2330ZaWhpKSkocfhGNRoPdu3dDp9PBbDbj0KFD2L17N7p372537tChQ7Flyxbk5uaiqqoKK1aswFVXXdXkL8xXFWtqUVCm4+aa1DTnx9O1k9RgvPY4Nv9egFqDSeRQRETN1+hKxg3R6/U4cuQIDh48iIMHD8Lf3x+pqakYNmxYo/fAAKCiogLvvvsusrOzYbFYoFarcf3112PkyJEoLi7GzJkzMW/ePNvgruaug8OVjIFthwrx7eZszL6nO2LCeVuvNfjSffmwBx6AbMdO3HrXJxh7TUeM6BUjdiQil/Gl97o3aaz+aFKBc6EzZ87g4MGDiIqKatZ4nNbGAgf4cOUJnCvR4bWpl0EQBLHjeAVf+qEnPXMG0Ovx1gEDisp1eG3q5ZDLmjZbkshT+dJ73Zs0a5Dxv9XW1iI/Px86Xf29a0aPHt2yZNRqDEYzjudU4spuahY31CymhAQAwA1SDRZ+n469R0sw+HIuNUBEnsehAmf79u34/PPPIZPJoFAo6rUtWrTIKcGo6f7OrYSeu4dTK7jix8+QvOxHvBL2AQZ0V0MqYcFMRJ7FoQJn6dKlePLJJ3H55Zc7Ow+1QHrW+enhiZxxRi1juOwy1A4qRFlpDX4/XoorkiPEjkRE1CQO3VyXyWRISUlxdhZqoYxMDTrFB8GP08OphWqvvhqK999EREwo1u87B3Pzh+oREYnCoQJn0qRJ+Oqrr1BRUeHsPNRMxZpa5JfqeHuKWo1EEDAxshID1n+NP0+Wix2HiKhJHLpFFRsbi2XLlmHDhg12bd9//32rh6Kmy7DtHs4Ch1pPn6O7cMUfa/DGltHo0XEAB68TkcdwqMBZsGABhgwZggEDBtgNMib3kJ6pQUSwAtHhSrGjkBepfuQR7L1yDNL3aXAsuwIpLKCJyEM4dIuqqqoKkyZNQmJiImJiYur9R+IzGM04fqYC3ZNC+Bc2tS5/f/Tu3xGhgTL89tPvYqchInKYQwXOVVddhR07djg7CzXTybxK1BrM6JYUKnYU8kJymQRPnlyLx/87DTmHT4kdh4jIIQ7dojp58iR++eUX/PjjjwgNDa3XNnv2bGfkoiao2z28K6eHk5NEPDAZy0qAv0/o8XAPsdMQEV2aQwXOiBEjMGLECGdnoWbKyNSgUxynh5PzSDt2gPH++/Dn7jzkFFQjMTpQ7EhERBflUIHD3bzdV0lFLc6V6jDwMi6nT851Vc8o5P2wAaF3vw2s+R/gwAa4RERiaXQMzoEDBxx6AkfPI+dIr5sezvVvyMkClDL0SVBCdSYbpUczxY5DRHRRjfbg7N69G99++y0GDRqElJQUxMbGwt/fH1qtFufOncPRo0exc+dOtG3bFn369HFlZvqXjPPTw2M4PZxcoOP0CZhpbo/UczLczbE4ROTGGi1wHnvsMeTk5GDTpk348MMPUVhYaGuLiYlBamoqHn/8cSSc332YXM9gNOOvnAr0S4ng9HByiaAAOQaktsHOA3m4TfcX/MbeIHYkIqIGXXQMTmJiIqZOnQoAqK2tRXV1NQIDA+Hn5+eScHRxJ/OqUGswc/Vicqlr+sQg6LPPkPT+EhR22ghjt25iRyIisuPQIGMA8PPzY2HjZjIyy8/vHh4sdhTyIWFBCmhuvQ1zwmMxoW0nsLwmInfk0EJ/5J7SszToGBcEpYLTw8m1Rgxsh/3temHT7wWAwSB2HCIiOyxwPFRpRS3OlXD3cBJHVJgSfbuEo/LHnxF55QBI8vPFjkREVA8LHA9VNz28GwscEsl1/dogK7gNzsYkQdDrxY5DRFQPCxwPlZ6pQXiQAm04PZxEEqcOQHTvZDx9/bOojokTOw4RUT2NDjJ+8MEHHXqCRYsWtVoYcozRdH56eDKnh5O4ru/XBof/V45fd53ETYd/RtUjj3CFYyJyC40WODNmzLB9fPLkSWzfvh3XX389IiMjUVRUhA0bNmDIkCEuCUn11U0P5+0pEltSGxWSE4Nxbt02BP3wHvR9+kA/eLDYsYiIGi9wUlJSbB9/9tlneOGFFxAeHm47lpqaijfeeAM33XSTcxOSnfRMDaQSAV05PZzcwPX92+C97FR0+vhH9B3cV+w4REQAHByDU1paCqWy/lgPpVKJ0tJSp4Sii8vI1KBjvIrTw8ktdI4PQoe4IPyYp4DJZIbk3DmxIxEROVbg9OnTB2+99Rb+/PNP5Obm4vDhw3jnnXfQu3dvZ+ejC5RW1OJsiZarF5PbEAQB1/drg9JKPTKXrkV0//5Q7N4tdiwi8nEOrWQ8bdo0/PDDD1i8eDFKS0sRHh6O/v3745ZbbnF2PrpAelbd7uGh4gYh+pfuSSFIiAzAt9o4pE6bBmNystiRiMjHOVTgKBQKTJ48GZMnT3Z2HrqEjEwNwoIUaBPB6eHkPup6cT5ddwpbb3kIff41Xo+ISAwOr4Pz559/YtGiRZg7dy4A4NSpU0hPT3daMLJnNJlxLLsC3ZNCOD2c3E5qpzBEhymxft9ZSHJyEDZtGlc4JiLROFTgrF+/HosXL0abNm1w7NgxANZene+++86p4ai+U9w9nNyYRCLguivaILdIixM5Gij27YM8I0PsWETkoxwqcH7++We89NJLGDt2LCQS66fExcXh7NmzTg1H9dmmh7fl9HByT/2SwxERrMCKHCny9+1D7YgRYkciIh/lUIGj1WqhVqvrHTMajZDJHBrCQ63Euns4p4eT+5JKJbimbxucPleNE0XWXcYVu3dzx3EicjmHCpzk5GSsWrWq3rH169ejW7duzshEDSitqMXZYi1XLya3N7C7GsEBMqzfdw7ygwehnjgRAf/7n9ixiMjHOFTg3Hvvvfjtt9/w8MMPQ6fT4bHHHsPevXsxZcoUZ+ej8zKyKgBYp+MSuTO5TIKRfWJwLKcCJ2I6oXThQtTceqvYsYjIxzh0jyksLAxvvvkmTp06haKiIkRERKBjx4628TjkfOmZ5QhTyREb4S92FKJLGtojCr/8dg7r951D0tgxAABBq4XFzw/gzw0icgGHf9KYTCYYDAZYLBZ07twZer0eOp3OmdnovLrdw7slhXJ6OHkEpUKK4anROHyqHHlFNRDKy6G+8UaoFiwQOxoR+QiHCpycnBw89thj+OSTT7Bo0SIAwNGjR20fk3OdOlsFnd7M21PkUYanRsNPLsH6387BEhKC2oEDYUhNFTsWEfkIhwqcxYsXY9KkSXj//fdtM6dSUlLw119/OTUcWaVnaiDh7uHkYQL9ZRjaIwoBK1dC3i0VAZ9/AcsDjyHvwy8Bi0XseETk5RwqcHJzczF48OB6x5RKJfR6vVNCUX0Zmdbp4f5+nB5OniX1jzQ8smkhIjVFkMACtaYQPd96GcoR1wJms9jxiMiLOVTgREZG4vTp0/WOnTx5EjExMU4JRf8oq9Qjr5i7h5Nn6rr4AyiNtfWOyc1GqE79DaGmRqRUROQLHJpFNWnSJMydOxdXX301jEYjVq5ciU2bNuH+++93dj6fl5F5fvfw9ixwyPOEa4oaPC4z6mFRqVychoh8iUM9OL1798Zzzz2HiooKpKSkoKioCE899RR69Ojh7Hw+Lz1Lw+nh5LFKQyIbOR4FQaNByHPPQSgrc3EqIvIFDu+10L59e7Rv396ZWegCpvO7h/fpEs7p4eSRMh96HKr/zq53m0on80PmQ4+jXVYW/H/4Abphw1B7zTUipiQib+RQgWM0GrFixQrs3r0bZWVlCAsLw4ABAzB+/HgoFApnZ/RZ1unhJk4PJ48V98gUHAGQtPB9hGuKUBysxteD7sDgSbfAEBmAwn37YI6IEDsmEXkhh6eJp6en45577sGbb76Je+65B8eOHcOSJUucnc+ncXo4eYO4R6ZAf/Qg8vNyUbL/NxzsPRKLVp9EtdZoK24U+/dDfuSIyEmJyJs4VODs378fzz77LFJTUxEfH4/U1FTMmjUL+/fvd3Y+n5aRxenh5F1CVArcP7ojyir1WPLTKZjNFsBgQOijjyLojTfEjkdEXsShAic0NBS1tfWneur1eoSFhTklFFmnh+cWcXo4eZ8OsSrcNqItjmZXYNWuXEAuR+kXX6Dsk0/EjkZEXsShMThDhgzBG2+8geuuuw4REREoKSnBhg0bMGTIEKSnp9vO6969u9OC+pqMrPPTwzn+hrzQ4MsjkVNQjQ3785EYHYg+XbtaG0wmyDMyYLj8cnEDEpHHc6jA2bRpEwBg5cqVdsfr2gRBwIcfftjK8XxXRqYGoSo5YtWcHk7eadLwROQVa/HlL5mICVciPjIAQe++C9XHH6Nw+3aYEhLEjkhEHsyhAuejjz5ydg76F5PJjKPZFejTJYzTw8lryaQS3H9TB7zxzVEsWn0Sz09OgWTqVBjbt4cpPl7seETk4Rwag3Oh9PR0HDt2rLWz0HmnzlVDpzehG29PkZcLUSlw/03/DDo2hoVDO2ECIAjWBQC5KScRNZNDBc5//vMf287hq1atwvz58/H+++/jxx9/dGo4X5WRWQ6JREByIgsc8n7t/zXoeOWuXACA9NQpRA0ZAv/vvxc5HRF5KocKnDNnzqBz584AgM2bN+M///kPXn/9ddv4G2pd6ZkadIzl9HDyHYMvj8SQyyOxcX8+9v9VAlO7dtDddBMMffqIHY2IPJRDY3As57uJ8/PzAQDx5++PV1dXOymW7yqvsk4PHzeYYxDIt9QNOv5qQxZiwpOBf6+LYzYDkmbdUSciH+XQT4wuXbrg888/x9dff42+ffsCsBY7QUFBTg3ni2y7h3P8DfmYukHHAUopPl5jXekYFguCX30VIU8/LXY8IvIwDhU4Dz/8MAICAtC2bVtMnDgRAHD27FnccMMNTg3ni9KzrNPD4zg9nHxQ3aDj8qrzKx1bAItSCYtSCZhMYscjIg/i0C2qoKAg3H777fWO9erVyymBfJnJbMGx7Ar06sTp4eS72seqcNvwtvh6UxZW7srFzbNmAXw/EFET8aa2Gzl9tgraWu4eTjTo8kgM6XF+0PHxUgCA9PRphM6YAWi1IqcjIk/AAseN1O0entyWu4cTTRqWiA6xKny1IQtnCmsgy8qC39atkJ06JXY0IvIALHDcSHpmOTrEquDv59CdQyKvJpNKcP/ojrZBxyVXDkHh3r0wcs87InJAkwocs9mMsrIyZ2XxaXXTw7l7ONE/QgLl9QYdGwMCAQD+y5dDmpkpcjoicmcOFTjV1dWYP38+Jk+ejEcffRQAcODAAXz33XdODedL6qaHc3sGovrqBh0fy67Aqp25EEpLEfKf/0C1eLHY0YjIjTlU4CxevBgBAQFYuHAhZDLr7ZPOnTtjz549Tg3nS+qmh8dHcno40YVsg44P5OO3QguKV66E5tVXxY5FRG7MoQLnyJEjuOeeexAWFmY7FhwcDI1G47RgvqRueni3diGcHk7UiLpBx19uyEJmaDwgk0GoqIDfjh1iRyMiN+RQgRMQEIDKysp6x4qLi+sVPNR8ddPDeXuKqHF1g44Dzw86rtIaEfzGGwi7915ISkrEjkdEbsahAmfEiBF49913kZ6eDovFghMnTuCjjz7C1Vdf7ex8PiE9UwOJACQncno40cWEBMrxwGjroOMdz30E3er1gFYL2eCRyPvwS7HjEZEbcWg+8pgxYyCXy/HZZ5/BZDJh0aJFGDlyJLdqaCUZmRq0j1UhQMnp4USXktRGhbvLf8e1K+dDaawFAKg1hQh6+z84XlIM9X+eFDkhEbkDwVK3VbgTGQwGLFmyBEeOHEFVVRViYmJw2223ITU11e7cbdu2YdGiRVAoFLZjzz77LLp163bJ1zl79myr5nYFTZUeT39yGGMHxeH6frFix6EGqNVqFBcXix2D/kWRkgq1ptDuuF6mQHE2p49T8/C97pliYxv+3elwl0FhYSFycnKg0+nqHR80aNAlP9dkMiEiIgKvvPIK1Go1Dh48iHnz5uGdd95BVFSU3fmdO3fGnDlzHI3m0dKz6nYPDxU3CJEHCdcUNXhcZtQ7/bVNJhOuv/56xMTE4KuvvsKcOXOwadMmKBQKtG3bFu+99x5CQuzH0z3xxBNIS0uDWq3Gli1bbMdff/11bN26FSkpKfjggw8AAMuXL0d5eTnuu+8+p389RN7KoTE4K1euxMyZM7F8+XJs3LjR9t+mTZscehGlUomJEyciKioKEokEvXv3RlRUFE6fPt2i8N4gI1ODkEBODydqitKQyAaPlwRHolanR+Bnnzltz6olS5agU6dOtsdDhgzBli1bkJaWhvbt2+PDDz9s8PMmTpyIb775pt6xiooKHDhwAGlpaTCbzTh27Bi0Wi2WLVuGKVOmOCU/ka9wqAdn3bp1eOuttxAfH98qL1peXo5z584hISGhwfasrCxMnToVKpUKgwcPxrhx4yCVSu3OS0tLQ1paGgBg7ty5UKvVrZLPVUwmM/7KqUS/btGIjGz4BzaJTyaTedy15e2Oz3wOqteetY3BAQCdzA9fDrwDpjdX4JklLyMwLg7mO+5o1dfNzc3Fjh078Oyzz2L+/PlQq9W4+eabbe1XXXUVfvzxxwavl1GjRiErKwtSqdTW7ufnB4vFgoiICJjNZkRGRuKrr77C448/jjZt2rRqdro0vte9i0MFjkqlarVfwEajEQsWLMDQoUMRFxdn156cnIx3330XarUaubm5mDdvHqRSKcaNG2d37siRIzFy5EjbY0+7d3oytxLVOiM6tlF6XHZfwvvy7idi2kQcqdUiaeH7CNcUoTQkEpkPPY4+Y8bh641ZeHzyO4iVdsXNefmturfbjBkz8Mwzz6CyshJ6vd7uuvj0008xevToRq+XsrIymEymeu3XXHMNevXqhUGDBsFkMmHPnj24//77ec2JgO91z9SiMTh33303PvnkE9x4441295abUu2azWZ8+OGHkMlkuPfeexs8Jzo62vZxYmIiJkyYgDVr1jRY4Hi69Kzz08O5ezhRk8U9MgX6R6Ygv+7x+f+/dFc3rNkThrTf83Huj+OYLjmJkFkPt/j1Nm3aBLVajcsvv7zBVdznz58PmUyG8ePHN+l5H3roITz00EMAgKeeegqzZs3C//73P2zfvh3Jycl4/PHHW5ydyBc5VOAYjUb8+eef2L17t13b999/79ALWSwWfPzxx9BoNHjuuedsWz5cijev7JvO6eFErU4hl2LC0AT07hyG6sdfQPz+X7A4vi9Gje0FlX/z32sHDhzAxo0bsWXLFtTW1qKyshIzZszAggULsGzZMqSlpWHZsmXN/pmVnp4OAGjfvj1efvll/Pjjj3jwwQdx+vRptG/fvtm5iXyVQ+/2JUuW4LbbbsPAgQPrTd9uisWLFyMvLw8vvfTSRZ/j4MGDSEpKQmhoKPLy8rBixQr079+/Wa/pzjTVBpwprMGYQfa36Yio5ZLaqGD48r9Y+9Pt2J4jwYH/O4LbhrdFr85hzSpCnnvuOTz33HMAgD179uDjjz/GggULsHXrVixcuBArVqyAv3/zJwu8/fbbePvtt2EwGGAymQAAEokEWicNlibydg4VOGazGcOGDYNE4tCkKztFRUVIS0uDXC7HtGnTbMenT5+O5ORkzJw5E/PmzYNarcaRI0ewcOFC6HQ6hISE2AYZe5u63cO7t+P2DETOIlcqcNXN/dChsAYnXv8Ye/8Kxm/Dh+L2EW0RomreH2sXevHFF1FbW4tbb70VANCrVy+89dZbyM/Px6xZs/D1118DsN6K+vXXX1FaWorevXvjqaeewm233QYA+OWXX9CzZ0/ExMQAAHr37o0RI0YgOTnZoTXAiMieQwv9rVmzBkajEePGjXPrW0aetNDfp2tP4u+8Krx9fw+3/p4SBx56Bb0e6uuuQ154PJ4Y9CgUMgluuSoRV3aL4PuPbPhe90wtGmS8fv16lJeXY+XKlVCpVPXaFi1a1PJ0PqZu9/AeHZvXVU5ETaRQoHTZMvgFB+OlKhO+2pCFLzdkYv/xEtx5dTuEB/uJnZCIWplDBc6MGTOcncOnZJ6rQk2tCd25eziRy5jPz/hso6zBawe/wIbrpuDb9Cq88n/pGD8kAUN6RELCPziIvIZDBU5KSoqzc/iU9EwNBE4PJxKF9PRpBKxejWFDhqDTlKuxdGMWvt2cjQPHSzEmdw+6fbag3to6cY9wRWEiT+TwnMmsrCwcO3YMlZWV+PewnUmTJjklmDfLyNSgfRsVAjk9nMjljN27o+DXX2EJD4cawGMTOmNPejFyF3yFfhs+qrdDueq/s3EEaLDIkUqlttlOROR+HJoWlZaWhpdeegnp6elYvXo1cnJysG7dOuTn51/6k6keTbUBOYU1vD1FJCJLeDgAQH7gAEKfeQYDU8Jx566v6239AABKYy2SFs2vd0wikUAul7O4IXJzDhU4q1evxvPPP49Zs2ZBoVBg1qxZeOKJJxrcH4ou7qht93AWOERiUxw6BL/duyEpKUG4puHZM6phA2xLZMhkMgiCAIPB4MqYRNQMDhU4FRUVSE5OBmBdWdhsNiM1NRW///67U8N5o/RMDYIDZIiPChA7CpHPq77vPhRt3AhzVFSDO5RbZDJUXzkIFovF1mvDnhsiz+BQgRMeHo7CwkIAQJs2bXDgwAEcO3bM4e0WyMpktuBotgbdkkI4W4PITVgCAwGLBYbkrjBI6v9Mq779DpR27AqpVAqDwQAHlg0jIjfhUIUyZswY5OXlISoqChMmTMB7770Ho9GIe+65x9n5vErWuSrU6EzoxtWLidyLyYSwABlKe/aB9NRp6yyqqHiYX52N0AB/WCwWyGQyWCwW9uAQeQiHVjK+kNFohNFohFKpdEamZnP3lYxX787F+n3n8O6DqQhswaZ/5Fpc3dRH1NYCCgUgCIDBAMjlEASBvTY+hO91z9TklYzNZnOjTyaRSKBQKGA2m5u9P5UvSq+bHs7ihshtDB8+HMePH7/keV26dMGWLVtckIiIWkOjv2nrNoG7lO+//77VwnizimoDcgpqMHogdw8ncicXFi2fvv8+Sn/4AS+//z70ffuKlIqIWqrRAufDDz90ZQ6vl8Hp4UQeweLvD93VV9uKm4Avv4QpPh61I0aInIyImqLRAicyMhLl5eUIDQ11YRzvlZGpQVCADAmcHk7kOYxGBHz3HUwJCSxwiDzMRQfQPPbYY/Uev/POO04N463MddPD23F6OJFHkclQvHIlyt99FwAgKSqCzIHxOkQkvosWOBfOHsjIyHBqGG+VmV+Nah13DyfySEolLEFBAIDg2bOhHj8eQnW1yKGI6FIuOp1HYG9Dq8g4v3t4SlsWOESerOLll6EdO9a6OCAA6HSAmy2XQURWFy1wTCYT0tPTbY/NZnO9xwDQvXt35yTzIumZ5UiKCeT0cCIPZ46KQu3IkQAAvx07EDpzJkq++QbGrl1FTkZEF7rob9yQkBAsWrTI9lilUtV7LAgCZ1tdQkWNAdkFNRg9gNPDibyJKSIC+l69YGrbVuwoRNSAixY4H330katyeK263cO7cfwNkVcxduuGssWLrQ8MBoQ8/zyqHngApg4dxA1GRAAc3GyTmi/9/PTwxGhODyfyVrKTJ+H/88+Q//WX2FGI6DwOCnEis9mCo1kaXNY+lNPDibyYMTkZBXv2wBJi7alV7NsHQ9eutsdE5HrswXGirPPTw7l7OJH3qytmhJoahE2ditBnnxU5EZFvYw+OE6XXTQ9ngUPkMywBASj9+muY1WrrAa0WkMsBGX/cErkSe3CcKCNLg3YxgVBxejiRTzGkpsKUkAAACHnpJURMnAgYjSKnIvIt/M3rJJU1BmTnV2PUgFixoxCRiPSDBsGUmMgeHCIX4zvOSTKyNLCAu4cT+Trt2LG2j2UZGQj8v/9DxX/+A4tKJV4oIh/AW1ROkpGpQZC/DInRgWJHISI3oThwAMotWyDU1oodhcjrscBxArPZgoysCqRw93Ai+peaKVNQuGMHzBERgMUC5dq1gNksdiwir8QCxwmyC6pRrTPy9hQR2anbqNNv61aEP/AAlOvWiZyIyDuxwHGC9EwNBAApbYPFjkJEbqp22DCUfPUVdDfdBAAQysvFDUTkZVjgOEF6pgbt2gRCFSAXOwoRuStBQO2IEYAgQCgrQ9TIkVDNny92KiKvwQKnldVND+fqxUTkKEtAALRjx6J22DCxoxB5DU4Tb2VHsys4PZyImsbPDxUvvmh7qFqwABZ/f1RPnQpwogJRs7DAaWXpmeVQ+cvQNobTw4moGcxmyA8dsg5GZnFD1GwscFqR2WLB0awKpLQL5vRwImoeiQRlS5YA59fKkeTlQf7336i96ipxcxF5GI7BaUXZ+dWo0hrRPSlU7ChE5MkEAVAqAQBB8+cj7P77IZSViRyKyLOwwGlFddPDu3F6OBG1Es3s2Sj55htYwsIAAJLiYpETEXkGFjitKCNLg7YxnB5ORK3I3x+GPn0AAH4bNyKqf3/IDxwQORSR+2OB00qqagzIOlfN2VNE5DSGHj1Qc9ttMFx2mdhRiNweC5xWwunhRORs5uhoVMyZA/j5ATodwu+8E4q9e8WOReSWWOC0kvRMDQKVMrTl7uFE5ALSwkJIs7MhVFWJHYXILXGaeCuwTg/XoFu7YEgknB5ORM5nSkxE0ebNgNw65s9v40YYO3eGqV07cYMRuQn24LSCnIJqVGqN6MbbU0TkSueLG9TWIvT55xE8e7a4eYjcCHtwWoFtejj3nyIiMfj5oXj1alikUgCAUFkJWCywBHPJCvJd7MFpBemZ1unhQZweTkQiMcXFwRwTAwAIef55RN5wA6DTiZyKSDzswWmhKq0RWeeqcUP/WLGjEBEBAKqnTIG+Tx/bashEvog9OC10NEvD6eFE5FYMffqgZsoUAID84EFETJwISX6+yKmIXIsFTgtlZFmnh7fj7uFE5Iak+fmQFBfDwt4c8jEscFrAbLEgI1Nj3T2c08OJyA3prr8eRZs2wRIaCpjNCPz0UwjV1WLHInI6FjgtkFNQg0qtkbeniMi9nZ9dpThwAMGvvgrl+vUiByJyPg4yboGMTA0AIIXTw4nIA+ivuAJFGzfCmJwMAJBmZ8OUkABI+LcueR9e1S2QnlmOttEBCOb0cCLyEMaUFEAQIJSXQz1mDEJeeknsSEROwR6cZqrWGpGZX40b+nF6OBF5HktICCpnzYK+V6/zByyAwLGE5D1Y4DTT0WwNLBZwewYi8kyCgJrJk20Pg958E5LqamjmzOEtK/IKvIqbybp7uBRJnB5ORJ7OYoFgMgFGI4sb8hrswWkGs8WCjCwNUtqGcHo4EXk+QUDFSy9Zb1PBOvjYb+tW62KBvG1FHoqlejOcKaxBZQ13DyciL3O+mAn45hsEv/UWJEVFIgciaj4WOM2Qfn56OHcPJyJvVPnccyj66SeYo6IAALLjx0VORNR0LHCaISNTg8ToAAQHcno4EXkhQYCpfXsAgN/GjYgaPhx+W7eKHIqoaVjgNFG11ojT56q4ejER+QT94MGoeOEF1A4aZD1gNosbiMhBLHCaqG56eHfeniIiH2Dx90fVQw8BcjmEmhqoR42CcvVqsWMRXRILnCZKz9QgQClFUhuV2FGIiFxK0GphCQ6GOTxc7ChEl8Rp4k3A6eFE5MvMEREo+fZb22wr/+XLYUpIgL5fP5GTEdljgdMEddPDOf6GiHxW3bo4RiNUixbBlJCAUhY45IZ4i6oJOD2ciOg8mQzFq1ej7L33AADSvDxEjhwJ+e+/ixyMyIo9OE2QkalBYhSnhxMRAYBF9c9YRElJCSxKJczR0QAA6alTELRaGLt3Fyse+TiXFDgGgwFLlizBkSNHUFVVhZiYGNx2221ITU1t8Px169Zh9erV0Ov16NevH6ZNmwa5XNyiom56+HVXtBE1BxGROzJcfjmK162zPVYtXAj/tWtRcOgQLAEBIiYjX+WSW1QmkwkRERF45ZVX8H//93+YNGkS5s2bh8LCQrtzDx06hNWrV+Pll1/GRx99hMLCQixbtswVMS/KNj2c42+IiC6p4qWXUPr557biJmz6dKgWLhQ5FfkSlxQ4SqUSEydORFRUFCQSCXr37o2oqCicPn3a7tzt27dj2LBhSEhIgEqlws0334xt27a5IuZFZWRpEODH6eFERI6whIZCX7c4oMEASKWw1O1UbjbDb9Mm63EiJxFlDE55eTnOnTuHhIQEu7bc3Fz07dvX9rht27bQaDSorKxEUFBQvXPT0tKQlpYGAJg7dy7UarVT8prNFhzLPoyenSMRHRXplNcg9yWTyZx2bZH7CQwMhL+/P//NW9sPP8AfgD8AYetWyO++G8avvoJ50iSxk9nwve5dXF7gGI1GLFiwAEOHDkVcXJxdu06nQ8C/7tfWfazVau0KnJEjR2LkyJG2x8XFxU7JnFNQjfIqPTrF+jvtNch9qdVq/rv7kOrqami1Wv6bO1NKCvy++AK1AwcCxcXw/+EHKH/5BeXvvw/LBT/nXYnvdc8UGxvb4HGXThM3m8348MMPIZPJcO+99zZ4jlKpRE1Nje2xVqsFAPj7+7skY0Ns08M5/oaIqOWkUtRecw2gVAKwrpAs0Whss7LkBw5AUloqZkLyAi4rcCwWCz7++GNoNBo8+eSTkMka7jyKj49Hdna27XF2djZCQkLsem9c4X9pmXjwvf1YvTsPAPDTr7kuz0BE5O1q7roLJcuXWxcRNJkQ/sADCH38cbFjkYdzWYGzePFi5OXl4ZlnnoFCoWj0vKFDh2LLli3Izc1FVVUVVqxYgauuuspVMW3+l5aJ7YeLYbb8c2z74WL8Ly3T5VmIiHyGVIqSpUtR8fTTAABBo4F69Ggo9uwRORh5GpeMwSkqKkJaWhrkcjmmTZtmOz59+nQkJydj5syZmDdvHtRqNXr27IkxY8Zg9uzZtnVwJk6c6IqY9ez8s+H7sDv/LMbtI5NcnIaIyHcYu3a1fSw9dw6CTmcbmyM5dw6S8nIYk5PFikcewiUFTmRk5EXXsvn666/rPR41ahRGjRrl7FgX9e+eG0eOExFR6zN27YqiDRtse2CpPvsMgYsXo+DgQe5qThfFrRoaIREaLma4iTgRkYsJ//zgrXz4Yej79rUVNyEvvABTdDSqHn1UrHTkprjZZiMGX97wWgiNHSciIuezhIVBd+215x9YIJSVQaLR2NoVe/YARqNI6cidsMBpxO0jkzC0h9rWYyMRgKE91Bx/Q0TkLgQB5QsXouLFFwEAsqNHob7lFgQsXSpyMHIHvEV1EbePTGJBQ0Tk7s7fwjJ27ozSJUtQ278/AMBv61YEfPstNG+8ATNXKPY57MEhIiLvIJNBd/31sISFAQAkhYWQnTwJc4h1kVbZ0aMQystFDEiuxAKHiIi8knbSJBRt3gzI5YDFgrBHH0XEXXfZ2oXqahHTkbPxFhUREXmvuhlYgoCy99+HpKrK+thgQHTfvqiaNg1VM2daj5nN4mQkp2APDhER+QRj9+7Qnx+fI+j1qLr/fuj79QMASM6ehTwxEX6bNokZkVoRCxwiIvI5lsBAVD32GPQDBgAAhNpamK++GqZ27QAAil9/hXr0aEhPnxYxJbUECxwiIvJ5pqQkmL74AsZOnQBYe3gAwBwdDQDwX7kSoU88AWi1omWkpuEYHCIiogvUDh2K2qFDbY8lBQWQHT0KKJUAgIAvv4RgMKD6vvvEikiXwB4cIiKiS6h+4AEUr19vG7Tst3Mn/LZts7UHfv45FLt3i5SOGsICh4iIyBH/2hOrbMkSlH72mfWB0QjV/PlQbthgfWyxIGDpUkjPnBEhJNVhgUNERNQcfn7W/8tkKNi/H5VPPAEAkObkIPSZZ+C3eTMAQKipgd+WLRy/42IscIiIiFpKoYAlNBQAYGrbFgV790I7Zoy1adcuRNx5JxQHDgAAJHl5UOzcCeh0YqX1CSxwiIiIWpkpIcG2ZUTt4MEoWboU+iuuAAD4//QT1LfeatsFXf777/D/4Qfg/Mwtah0scIiIiJzJ3x+1w4bZbmnV3Horir///p8p6KtWIeTllwGZdWKz/4oVCFy0SLS43oIFDhERkQtZgoOhHzTI9rhi9mwUbdoESKy/kv127oT/2rW29qA334Rq3jyX5/R0XAeHiIhITBIJTPHxtofl778P1NbaHkvPnoXF39/2OGLSJNT27//PHloGg3VDUaqHPThERETupm6GFoDyBQugeftt6wOzGaa4OJjDw62PjUbEXH45VAsXWh9bLJCePAmYTC4O7H5Y4BAREXkKiQTl772HmilTAACCTofqu+6C/vLLAQDSvDxEDx2KgG++sbaXlyPw888hycsTLbJYWOAQERF5KItKhcrnnrON6TEHBaHsvfds20zI09MR8tJLkJ3fNFSWno7wu++29vIA1plbXtrbwwKHiIjIS1hCQqCdNAmmtm0BAPqBA5H/++/Q9+kDAJCUlkKanW3bU8t/zRrEdOkCaU4OAECalQXFvn3WcT0ejgUOERGRtxIEmGNigPODlPVDhqBo61bboGZjx46omTwZpthYAEDA8uWImDABMBoBAMqff0bQm28CFov1+Tyo8GGBQ0RE5KMMPXuiYvZs2xo81VOmoOS772wFkeL33+G/bp1tH66QZ59F5PDhts/327EDfjt2uD64A7xqmrh8//56j80xMTAlJAAmE+R//GF3vik2Fua4OECvh/zwYfv2hARr5avVQp6ebt/erh3MkZEQqqshO3rUvr19e5gjIiBUVkL211927caOHWEJC4NQVgZZ3f3Qf7d37QpLUBAkJSWQnr9/Wq89JQWWwEBIioogzcqyazd07w74+0OSn9/gpm+GHj0AhQKSvDxIz561b+/VC5BKIT1zBpL8fPv2vn0BWLs0JUVF9RulUuvnA5CeOgVJaWn9doXC+voAZH//DaG8vH67UgnDZZdZ248fh1BRUa/ZEhAAY7du1vaMDAg1NfXbg4Nh7NIFACA/csRuSXRLaCiMnTpZ2w8ftltB1BweDlOHDgAA4bffIC8pqd8eGQlTu3bWz7/gugN47XnytSc9d872sdjXnvyPP+zGR/Dac961J4SGAu3b+/zPPX1kpO3a0153HbTXXgv5/v0wR0aidsgQmDp0sF17Qa+/DsFgQPlbb8EcEwPVggWwqFTQXn+99fuQlwdzeDgs/v7Ou/bOb4lxIa8qcOwuJqkUFrkcMJns2wBrm1QKGAwNt5+/RwmdruH2gADAZIJQU9Ngu1mlgsRggFBV1WC7pLAQltpaCBpNw+0FBbBUV0MoK2u8PSAAkpKSRtuhVEJSVNR4u1zeeHt+PiCVXrwdaLhdIvmnvbgYkgveyBa53NYuFBdDcuEbWaGo9/lCVVX9dq32n/aSEggXbGJn0eshCQmxPb9w4RvZaIQkKMjaXlIC4cJuV7MZlsBA68dFRXb5gX+uj4a+N7z2PPfaE6qrIQgCJPn5ol97kpISwGy2+/p57Tnp2jMaIQkI4M89NH7t6fv1gx6wFShV998Pobra+r2QSiHodLBIJLbvTfDs2dDdeCNqBw92+rV3IcFiqbux5vnyG/hrhailwsPDUerAm4m8w8dLl+JcYSFmn98ZmnwH3+utT7FrF4ydOtm2pXCGmPO9Zhfyqh4cIiIich//3pLC1TjImIiIiLwOCxwiIiLyOixwiIiIyOuwwCEiIiKvwwKHiIiIvA4LHCIiIvI6LHCIiIjI67DAISIiIq/DAoeIiIi8DgscIiIi8joscIiIiMjrsMAhIiIir+NVm22GT59e77F25EhoJ04EtFqEP/aY3fnaUaOgHT0aQlkZwp55xq69ZsIE6K65BpL8fIS+/LJde/Udd6B2yBBIs7IQ8sYbdu1VU6dC368fZMePI/jdd+3aKx9+GIYePSA/fBhBH31k117x5JMwdukCxb59UH32mV275vnnYWrXDn47diBw6VK79vJXX4U5JgbKjRsRsHy5XXvZW2/BEhYG/zVr4L9unV176fz5gL8//Jctg39amn37p58CAAK++grKXbvqtVn8/FC2YAEAIHDxYvjt31+v3RwSgvL//hcAoFqwAIojR+q1m6KioHntNQBA0DvvQH7iRL12Y2IiKl58EQAQ/NprkOXk1Gs3dO6MyqeeAgCEvPgipIWF9dr1l12GqhkzAAChs2ZBotHUa6/t2xfV06YBAGR3343wqqp67bpBg1Bz110A7K87gNeeJ197/tXVQO/eAMS/9sJmzIBQW1uvndee8649mUwGvPuu0649Y3Aw/nzsMbRLSHD7n3sede0dOGB3LuBlBQ4RUVOlZmTgqE5X/+Bff+HTb76pdyhFqcTBbt1cmIw8XYPX1tatdufx2nIOwWKxWMQO0Vry//hD7AjkhcLDw1FaWip2DCJyMme/1w8fO4ZZr72GjRcUz9QyMb16NXicY3CIiIjI67DAISIiIq/DAoeIiIi8DgscIiIi8joscIiIiMjrsMAhIiIir8MCh4iIiLwOCxwiIiLyOixwiIiIyOuwwCEiIiKvwwKHiIiIvA4LHCIiIvI6LHCIiIjI68jEDtCa5IcP13tsjoyEKTYWMJkgT0+3O98UHQ1zTAxgMEB+9Kh9e2wszJGRgE4H+fHj9u3x8TBHRECoqYHs77/t2o2JibCEhUGoqoLs1Cn79nbtYAkJgaDRQJaVZd/eoQMsKhWEsjLIcnLs2zt1giUgAJKSEkhzc+3aDV26AEolJEVFkJ49a9+ekgLI5ZDk50NaUGDf3r07IJVCevYsJEVF9u09egAApGfOQHLhDrwSCQyXXWZtz86GpLy8XrNFLocxJcXanpkJSUVF/XaFAsbkZACA7NQpCFVV9dv9/WHs3NnafuIEBK22frtKBWOHDtb2Y8cg6PX12s3BwTAlJVnbjx6FYDDUbw8NhaltWwCAcPAg5BfkN4eHw5SQAMD+ugN47fHaa51rT37kCGA212/ntee0a08IDgYSE5127cn+lZfXXitee43sJu5VBY45PLz+48hI6zfTZIK5gQvd1q7Xw5yf33i7VgtzAxe6OSoK5shICNXVMJeU2LVboqKs/xiVlTCXlTX4+ZawMAh+fjBfcKEDgDk6GpagIEjkcpgvuNBt7YGBgFQKoaamwXb4+wMABJ2u4XaFAjCZ7C50ANavXSq1tplMDbc38tyQSv9pr64GJBd0FioUtnZJZSXMsgsuRaXS1m7WaCAoFPWaLQEB/7SXlNh9/ZbgYFu7pagIlgsyWkJD/2kvKIDlwh8E4eG2dtTUwHxBftu1AcB85ozdl89rj9ce0PJrz3z2rN3Xz2vPidde3b9NK117Qx5/HH81UKS16d273uOuiYnY8cEHvPZaeO1dSLBYLJZLnuUhzjbwDSVqKbVajeLiYrFjEJGTufK9LpfLYTKZYL6gl4SaLjY2tsHjXtWDQ0RE5AkEQYBUKoXkfA+jxWKBqYEeI2o+FjhEREQuZjQaIQgCJBIJDA3cpqWW4ywqIiIiFzObzRAEAQaDAXK5XOw4XokFDhERkQgsFoutyJHJZBAEQexIXoUFDhERkQhMJhPq5vkYjUZIpVIWOa2IY3CIiIjcgNFoFDuCV2EPDhEREXkdl/Xg/PLLL9i2bRtycnIwcOBAPPzwww2et23bNixatAiKfy1w9Oyzz6Jbt26uikpEREQezmUFTlhYGMaPH4/Dhw9Df8HqiRfq3Lkz5syZ46JkRERE5G1cdouqX79+uOKKKxAUFOSqlyQiIvIYGo0G06ZNw5AhQzB06FAcOHCgXvvJkydx0003ISkpCR9//LHteElJCcaOHYvhw4fjl19+sR2/5557kN/Algi+wi0HGWdlZWHq1KlQqVQYPHgwxo0bB6lUandeWloa0tLSAABz585tdLlmopbitUXkG8R8rz/33HMYN24c7rvvPuj1etTU1CA0NNTWLpPJ8PHHH2PVqlUIDg62ZV2+fDmmTZuGW2+9Fddddx3uvfderF27FgMGDECvRjai9AVuN8g4OTkZ7777LhYvXownn3wSu3fvxpo1axo8d+TIkZg7dy7mzp3b6jk++eSTVn9Od3rt1nyN1niuljxHcz63KZ/z7LPPNvn5fZmY753m4Hvd9c/X3Ofw5vd6RUUFduzYgalTpwIAFApFveIGAKKiotC3b1+7hQHlcjm0Wi1qa2shkUhgNBrx/vvvY9asWU7N7O7vdbcrcKKjoxEVFQWJRILExERMmDABe/fudXmO3hfs9uptr92ar9Eaz9WS52jO54r57+vtPO17y/e665+vuc/hze/106dPIzIyEvfccw9SU1Nx3333obq62qHPvf3227FhwwZcd911eOWVV7Bw4ULcddddCAgIcGpmd//eul2BcyGxFj3q06ePKK/rqtduzddojedqyXM053PF/Pf1dp72veV73fXP19zn8Ob3utFoxB9//IEHH3wQBw8eRGBgoMN3J0JCQvDTTz/hwIED6NWrF9atW4ebb74Z06ZNw4QJE/Drr786JbO7f29dVuCYTCbo9XqYzWaYzWbo9foGd049ePAgysvLAQB5eXlYsWKF238TybuNHDlS7AhE5AJivtfj4+MRHx+Pfv36AQAmTJiAP/74o8nP8+qrr+KFF17At99+i969e+Pzzz/H888/39pxPYLLBhmvWLECy5cvtz3euXMnJkyYgOHDh2PmzJmYN28e1Go1jhw5goULF0Kn0yEkJMQ2yJhILCxwiHyDmO/1mJgYJCQk4Pjx4+jSpQs2b96MlJSUJj3H33//jbNnz2Lo0KE4dOgQ/P39IQgCdDqdk1K7N8FStxEGERERiebQoUO2GVTt27fHF198ge+//x4A8MADDyA/Px99+vRBRUUFJBIJVCoVjh49iuDgYADAxIkT8frrr6NTp04oLCzE2LFjodFo8Oqrr+Lmm28W80sTBQscIiIi8jpuuQ4Okbs7ceIEvvzyS8hkMoSFheGRRx6BTMa3E5G3KS8vxzvvvAOpVAqJRIJHH30UYWFhYsciB7AHh6gZSktLoVKpoFAo8O233yIpKQn9+/cXOxYRtTKz2QwAkEgk2LZtG0pKSnzydo8n4p+cRM0QHh5u+1gqlYq2nAEROZdE8s9kY61Wi4SEBBHTUFOwwCGfdrFd7quqqrBo0SL8+eefCAoKwu23345BgwbV+/zCwkIcPHgQ48ePd3V0ImqClrzXs7Ky8Omnn6K6uhovvviiGPGpGVjgkE+72C73S5YsgUwmw+LFi5GVlYU333wTbdu2tf0FV1NTg48++ggzZszg+BsiN9eS93q7du3wxhtvYM+ePVi5ciWmT58uxpdATeT2KxkTOVNju9zrdDrs27cPkyZNglKpRNeuXdGnTx/s2LEDgHXhyvnz5+OWW27hRpxEHqC573WDwWA7NyAgAH5+fi7NTc3HPzuJGnDu3DlIJJJ6xUvbtm1x9OhRAMDu3btx8uRJLF++HMuXL8c111yDAQMGiBWXiJrpUu/1zMxMfPPNN5BIJJDL5XjwwQfFikpNxAKHqAE6nc5uo7qAgADbiqBDhgzBkCFDxIhGRK3oUu/1zp07Y/bs2WJEoxbiLSqiBiiVSmi12nrHtFotlEqlSImIyBn4XvdeLHCIGtCmTRuYTCacO3fOdiw7O5tTRIm8DN/r3osFDvm0xna5VyqV6NevH77//nvodDr89ddf2L9/P29LEXkovtd9D1cyJp+2bNmyervcA8CECRMwceJEVFVVYeHChThy5AhUKhUmT55stw4OEXkGvtd9DwscIiIi8jq8RUVERERehwUOEREReR0WOEREROR1WOAQERGR12GBQ0RERF6HBQ4RERF5HRY4RERE5HVY4BAREZHXYYFDRG7p008/tVt59t8mTpyI/Pz8Jj3nzp078dprr7U0GhF5AK5kTEROt3v3bvz00084c+YM/Pz8EBUVhaFDh+Kaa66BIAjNes6JEyfigw8+QExMTCunJSJvIBM7ABF5t7Vr12LNmjWYOnUqevToAaVSiaysLKxduxbDhw+HXC63+xyz2QyJhB3MRNR8LHCIyGlqamqwbNkyPPzww+jfv7/teFJSEh599FHb448++ggKhQLFxcU4evQoZs2ahZ07dyIiIgK33norAGDNmjVYt24dBEHApEmTLvq627Ztw/Lly1FRUYGgoCDceuutGDx4MLZt24bNmzdjzpw5WL16db1bYEajEYMGDcLDDz+MmpoafPnllzh48CAEQcCwYcMwceJEFl1EHoQFDhE5zYkTJ2AwGNC3b99Lnrtr1y4899xzeOaZZ2A0GrFz505b26FDh7B27Vq89NJLiIqKwieffNLo8+h0OnzxxRd48803ERsbi7KyMlRVVdmdN2bMGIwZMwYAUFxcjBdeeAFXXnklAODDDz9EaGgoPvjgA9TW1mLu3LmIiIjA1Vdf3dRvARGJhH+OEJHT1PWgSKVS27EXX3wRd999NyZPnoyjR4/ajvft2xddu3aFRCKBQqGo9zx79uzBVVddhcTERCiVStxyyy0XfV1BEJCTkwO9Xo+wsDAkJCQ0eq5er8d///tfXH/99ejVqxfKy8tx6NAh3H333VAqlQgJCcGNN96IPXv2NPO7QERiYA8OETlNUFAQKisrYTKZbEVO3SymBx54AP+e4xAREdHo85SVlaF9+/a2x5GRkY2eq1Qq8fjjj2Pt2rX4+OOP0aVLF9x1112Ii4tr8PxFixYhNjYWY8eOBWDtzTGZTJg+fbrtHIvFctF8ROR+WOAQkdN07twZcrkc+/fvrzcGpyEXm00VFhaGkpIS2+Pi4uKLPlfPnj3Rs2dP6PV6fPfdd/jkk0/w6quv2p23atUqnD17FnPmzLEdi4iIgEwmw2effVav54mIPAtvURGR0wQGBmLChAn47LPPsHfvXuh0OpjNZmRlZaG2ttbh57nyyiuxbds25Obmora2Fj/88EOj55aXl+PAgQPQ6XSQyWRQKpUNDg4+ePAg1q9fj1mzZtW7JRYWFoYePXrgq6++Qk1NDcxmM/Lz8+vdTiMi98ceHCJyqjFjxiA8PByrV6/Ghx9+CD8/P0RHR2Py5Mno0qWLQ8+RmpqKG2+8EbNnz4ZEIsGkSZOwa9euBs+1WCxYu3YtFixYAEEQ0K5dO9x333125+3ZswcVFRWYOXOm7djgwYMxffp0PPLII/jmm2/wxBNPQKvVIjo62jYgmYg8Axf6IyIiIq/DW1RERETkdVjgEBERkddhgUNERERehwUOEREReR0WOEREROR1WOAQERGR12GBQ0RERF6HBQ4RERF5nf8Hat3Ggl5slCMAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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lF4NfKKX2wQxWk4FBF6AkF9Tg4N1rL8pfeAF68K6bKyeF0cMyWLtBApQQvRVxgFJKpQN70WpIOIDW+vfRrlR/IbmgBhHDgOZmsu6+G/cpp+CN4D2pqePz+Mdb37OzupH8XBlII0RPRfTUVyl1LlAKrMGcvLXl68nYVS3xSS6owcVWW0v6U0+R+tprEW0/ZbwTgLUbq2JZLSEGrEjvrL8DTtVavxrLyvQ3kgtqcPEPGcLONWvwO50Rbd+6m++4H0g3nxA9FemLus3AGzGsR78U6uKTADVotAQn+5YtJH38cbfbT5vg5LsdDeysbox11YQYcCINUEuApUopyRLWirtRBkkMSoEAeQsXknvtteb7UV2YMj4PgLUbpJtPiJ6KtItvI/Br4JJWE70aQEBrPWjvzpILapAyDKqXLiWQnt7ty7tDslMYMzyDjzdWctx06eYToicibUE9CjyCOZP5+ODXuOC/g5bkghq8vOPHh/JF2UtKutx22gQnW3c0UFYl3XxC9ESkH/2HADdqrQOxrEx/I7mgRMb995N1113sfO01fKNGhd1myrg8nn5jK2s3VnL89PCprYUQHUX60f9vwDmxrEh/JLmghPukk6hbuBBfYeeBx5mdwp7DzdF8QojIRXp3/QFwqVLqV5hJBEO01j+Meq36CckFJfyFhdRdeSUARnW1+VwqObnDdlMnOHn6ja2UVroZ5kyzuJZC9E+RBqgHg1+iFckFJVoYbjeuOXNonj6dmt/9rkP51PHOYDdfFSccLAFKiEhEFKC01itjXZH+SHJBiRaBtDQafvITPFOmhC3Py0pmbGEmn2ys5ISD5TmUEJHo9BmUUurkSA4Q6XYDkeSCEq3VX3wxzdOnA2Cr7Pi8adoEJ9/vdFNa0bMMvUIMVl21oH6ilPot8DjwJuZs5bVAFubw8h8BZwOfAs/HtpqJSXJBiXBSXn2VvEsuoeLvf8czbVpo/YHj8tCvf2d28x0i3XxCdKfTFpTW+qfAmUAR5ntQOwE3sANYCQwDztBan21BPROO5IISnWmeNg33qafinTChzfq8rGTGFmXy8UYZzSdEJLq8u2qt1wGXQijdRi5QrbVuiH3VEpvkghKdCeTlUXPHHeaC14tRX08gJwcwB0s89fp3bKtwUzhEWlFCdCXij//BoDToA1MLyQUlIpF38cXYd+yg/B//AIeDKeOD3XwbKik8tCje1RMiockcPb0kuaBEJNxz5tCgFDjM6yQ3M5mTvnuXUy44gWFFI0iedCAlf5JBskKEI3fXXpJcUCISjSeeGPreqKri+8ef55xV95LqbQLAVbODzN/fwjqg6NLz4lRLIRKTBKheklxQoifs33yDa+5cMhu9oeDUItXbxJhlf6BZApQQbUgXXy9JLijRE77Ro2k86SQy3LVhy501Oy2ukRCJr9MWlFJqK9Dt7OVa65FRrVE/IbmgRI84HNTcfjvu517BVbOjQ3FlTn4cKiVEYuuqBXU25gzm5wD3AjXAb4CfBf+tAv4Y6womKskFJXpj8yVX4rG1bXU3OlLYfMmV8amQEAms04//Wus3W75XSv0ZOFZrXdJq3cvAK8DdMa1hgpJcUKI3ii49jw1l2xj72ApSmt2UZ+fzxA/P4fjzfxrvqgmRcCL9+F8I1LVbV4c5y8SgJLmgRG85f3M9VZu/obSkhJJVL/BF4SSe+M+WeFdLiIQT6R32eeB5pdStwPfAHsD1DNI5+EByQYkoCASY9Kur+P3WUual38XH451Mm+CMd62ESBiRBqiLgJuBv2C2prYBTwO3xKZaiU9yQYk+Mwx23XgjgYpKRm7L5onXtjB+RBbZGUnxrpkQCSHSfFCNwC+DXz2mlEoBlgGzACfwDbBYa/1yN/utAWYASVprb2/OHSuSC0pEg2e//QCYV+HmlZsfZs2KEuZcdoI82xSCHrwHpZQ6Wim1Qin1QnB5mlJqZoS7O4CtmCk6coAlgFZKje7ifGeRwC8SSy4oEU3Dc5K49MNHOeTJZXz4v4p4V0eIhBBRAFBKXQZcATwEnBZc7cYcfn5od/trresxuwhbvKiU2gxMBYrDnC8HuAk4F3g/kjpaTXJBiahyOGj459M8/e/v+WbNd0zYI5u8rOR410qIuIq0BXUlMEtrfQfgD677CpjQ6R5dUEoVYCY9/LKTTX4LLAdKe3P8WJNcUCIWAiOKUD/en4DHi/u8i0l55ZV4V0mIuIr0DpuF2UUHu2eXSAKae3pCpVQSZpbelVrrr8KUTwMOw2yxjejmWBcCFwJorXG5XD2tTq/sqjd/7Pwh2ZadM9E5HA75XUSBywXnHVlD1mPfsvOjr9jz7EGZD7RLcq0NHpEGqLcwB0jc1mrd5cDrPTmZUsqGmZ23mWAixDDly4ArtNZepVSXx9NaPwA8EFwMlJeX96Q6vbajqhEAv7cRq86Z6Fwul/wuouSAA4Zy3zX3sqnCw42bSnAZzQSysuJdrYQh19rAU1hYGHZ9pF18lwGnKKWKgSyl1AbgdODqSCuglDKAFUABcKrW2hNms2xgGvCUUqoU+Ci4/nul1BGRnivWJBeUiCWbYXDWCRMIBGDVU/9H/pFHkv7II/GulhCWiyhAaa23AwcBCvgpcB4wXWvdk2dEy4G9gZO01u5OtqnBfM/qgODX8cH1U4EPe3CumJJcUCLWXDkpnH7kSD6pslO83yE0H3RQvKskhOV60gRwACmATWv9gVIqQynVMkKvS0qpUcACoAkobdV1twB4G1gPTNJaf0ergRFKqZYXjcoS6T0oyQUlrHD4ZBf//bqK61Lms2TYGIYCyR98QPP06SDvSYlBINJh5pMxpzVqwhy48BTmO03nAWd0t7/WegvQ1f+ozE72K+5mv7iQXFDCCoZhcM4xo7ll5Res/Pdmrh9Ti+vUU6m+6y4azjwz3tUTIuYifQa1HLhRaz0RaHl29CZweExqleAkF5SwSl5WMmfMGMk3JXW8bNuDqqVLafjxj83CQLfp2oTo1yINUPsAjwW/D0Do5du0WFQq0UkuKGGlgycNYf+xuex84HG8t9zOsD3Hkrz3AWQdfBhpq1bFu3pCxEykd9hizIEKIUqpH2DOqTfoSC4oYSXDMDhu8ztcunoZ+TU7sRHAtWsn6SXfsfONhBk7JETURRqglgD/UkrdAiQrpa7HnM38hpjVLIFJLihhtYkP3kuqt6nNOnsggHO1OdtEyquvYt8S35xSjY2NnHDCCcyaNYsZM2Zw1113hd3uvffe4+ijj2bGjBmceuqpAFRUVDB37lxmzpzJK61m0Jg/fz6lpQk5oYywQKTDzF8EZgP5mM+eRgE/1lqvjmHdEpbkghJWc9bs7Hx9UxO5119P9s03W1updlJSUtBa89prr7F69WreeOMN1q5d22abmpoaFi9ezMMPP8zrr7/O/fffD8CqVas4/fTTef7551m+fDkAq1evZvLkyQwbNszyn0UkhoibAVrrT4BLYliXfkNyQQmrVebk46rZ0WF9ebaLne4A9n/+k0Cq+VaG4XYTSEoCh7WtfMMwyMjIAMDr9eLxeDp0gz/77LPMnj2boiIzGXfLlEUOh4PGxkaam5ux2Wx4vV4eeughVq5caenPIBJLpMPMkzG7885kd8LCJ4HbgrmiBhXJBSWstvmSK8n8/S1tuvkaHSk8fsQ5vL3yS045oogZB+ZjCwTIWbQI+44dVDz5JNit/SDl8/k47rjjKC4uZt68eUyZMqVN+aZNm/B6vZx22mnU1dVxwQUXcPrpp3PKKaewcOFCnnnmGRYvXszKlSs57bTTSEsblOOwRFCkH7GWY85cfjmwBbOL73qgCDg/NlVLXJILSlit6NLzWAeMWfYHnDU7qczJZ/MlV3LMeWey49Vi9Otb+WRjFeceO4a0mTOxlZdbHpwA7HY7r776KjU1NVxwwQV89dVXTJw4MVTu8/n4/PPP0VrT2NjISSedxJQpUxg7diyPPvooANXV1SxbtoyHHnqIa6+9lurqahYsWMC0adMs/3lEfEUaoOYCY7XW1cHl9UqpDzFH8Q26ACW5oEQ8FF16Hs2XnheaaqUo+O+lp4zjg/UV6Ne/4zePfMGcww7jqFMKsAFJn35KyrvvUnfxxWCz7rWInJwcDj30UN544402AWr48OE4nU7S09NJT0/n4IMPZv369YwdOza0zT333MPll1/OqlWrmDx5Mqeccgrz58/nmWeesaz+IjFEesWWAunt1qUB26NbncQnuaBEojEMg0P2cXHTvH3Ze2QOz7y5ld8/+T9KK92kPfcc6StXYtTWxrweFRUV1NTUAOB2u3n77bfbBB6AY489lg8//BCv14vb7ea///0v48aNC5Vv2rSJsrIyDjnkENxuNzabDcMwaGpqO4JRDA6R3mUfBV5RSt0HfA/sASwEHmmd9l1rvSb6VUws7iaZ5kgkptzMZC6Zuxf/91UlT67Zwm8e+ZKTZ/+cYxdcBDk54Pfj+PprvBN6lWe0W2VlZVx55ZX4/X78fj8nnXQSRx99NI8EZ2I/99xzGTduHDNmzGDWrFnYbDbOPPPMNi2sO++8k+uuuw6AuXPncv7557NixQoWLVoUkzqLxGYEIpguJZievTsBrfWefa9SrwW2bdsW85PsqGpkyV/XMX/2GA6eJEnTWkiOnsRSU+/hideK+fSbasYMz+DHJR8w7S93ktFYR3VmHt9cdi1Fl54X72r2ilxrA08wH1SHmQ8iakFprcdEu0L9leSCEv1BTkYSF528Fx9vqGTTPQ9z8Ct/Do0AzKurYvLvb2FjRTmuJVdZ+mxKiJ7o1ZWplJqRSAkErSS5oER/YRgGB00cwjlvP9phFopUbxP7PngPOTcMyslgRD8RUYBSSr2plDos+P11mO9APamUWhzLyiUiyQUl+htnTSfdYYEA9xcczmtrS9mxdSdUVlpbMSG6EWkLal/gg+D3PweOBA4GLopBnRKa5IIS/U1lTn7Y9RXZ+XyVswdPv7GVzQtvIvPgw3n2hS/4srgGj9ffq3MZhoE9Du9fiYEp0gcpNiCglBoLGFrr/wEopfJiVrMEJbmgRH/T2SwUxQuv4uZ5kymvaeL7PX/KW/8p4j+bGnll40bym2oYvvcoJu+Zw75jcnBmpwBQ8qeVbV4W3nLlLxhx8dn4fD5sNltomiIhoiHSu+w7wJ+A4cCzAMFgNeiG0kguKNHfdDYLRcsoPldOCq5TDodTDmdfj5+t763j4HkX8vDxC3l8zx+ax3ClMeOrN5nzxN2hQOeq2UHq9m/4+m9PUzj/dAAJTiKqIg1Q84BrgJ3A74LrJgJ/jEGdEprkghL9UWezULSXnGRjrwPH0HT+fE78+VlMScrhqy++59PtHmY8+2CbVljAMLAVDWdkcw2eQAC/v3fdgkJ0JtJh5hXA4nbr/hWTGiU4yQUlBrpAdja7liwBzC6TSY/eyWm7dpFU2zblh+ekk/Dttx9pb76JT7r3RAxIP1UPSS4oMagEAjTOno37+OOpzBnapsg3bhxJL71Ew3/exuv1SnASUSdNgR6SXFBiUDEM3MGstyV1Blm/u4l/+zwcA6TdfTeNjhQ2X3tTp12GQvSFBKgeklxQYrCZOXMmGzZsCF/obYLbF8Pti5kwYQJr1gz46TiFhXrUxaeUsimlhseqMv2B5IISg82aNWsoKSkJfU2cOJHXXnuNkpIS6ufMoXnsWEq+/541a9Zg274dIpjfU4hIRJpRNxdYBpwGeIAMpdTJwA+01oNqrhTJBSXEbtV//jO2sjIwDPD5cJ18Mk0/+hE1d90V76qJASDSFtRfgBrMTLrNwXXvA2fEolKJSnJBCdGOYeAfNsz83u+ndtEi3HPnmkW1teSdfz5Jn30Wv/qJfi3SAHUUcLnWejsQANBa7wSGdrnXACO5oIToQlIS7jPOoPnwwwFwbNpE8uefQ/D9KNv27SR9/rl0AYqIRRqgaoA2yY+UUiMZZBl1QxPFyig+Ibrl2X9/yj78EM8BBwCQsXIlrhNOwNaSy0kClehGpH1VDwH/UEr9CrAppQ4BfovZ9TdoSC4oIXqo1cSxdRddRPPUqfjzzclrc6+8koDDQc3dd8erdiLBRdqCuhPQwJ+BJOCvwHMMsqmOJBeUEL0XyM2l6eijQ8u+oiL8ZiZVADIeegj7N9/Eo2oiQUU61VEA+EPwa9CSXFBCRE/tL34R+t5WWkr2rbeC10v9XnuBz4dRW0sgNzd+FRRxF3FflVJqNLAfkNl6vdb6iSjXKWFJLighYsM/bBhla9cSSEoCIOXNN3H+7GeUP/MMnilT4lw7ES+Rvgd1PXAj8CXgblUUAAZNgJJcUELEjn/IkND33lGjqJ83D8+++wKQ9uyzODZsoPaaa+JVPREHkd5prwGmaq3Xx7IyiU5yQQlhDd/Ysey68cbQctK6dSS/9x61111nLn/6Kd699iKQmdnZIcQAEGmAqgCKe3sSpVQK5kwUswAn8A2wWGv9cphtfwLcAgwDmoCXgcu01rt6e/5okVxQQsTHrhtvhOZmc8YKrxfnvHk0T59O1f33x7tqIoYibQpcCTyglJqmlBrZ+ivC/R3AVuBHQA6wBNDB51rtvQscprXOAfYM7ntrhOeJKckFJUQcJSeb/9rtVD74IHWXXgqAUVlJ/pFHkvLmm3GsnIiFSO+2ycAxwE/brQ8A3Y4Y0FrXAze3WvWiUmozMJV2LTOt9dZ2u/uAvSKsZ0xJLighEoBh4DnooNCivbIS/9Ch+ILvV9m//Zbkzz7DffzxkCqZB/qzSAPUMsyMuk/SdpBEryilCoDxmIMuwpUfDvwLyAYagFM62e5C4EIArTUulyvcZlHj8X1DTlZazM/THzkcDvm9DBJ2u528vLy4/b07XGsuF6xZQ25w0b58ObY77yTjxz82y3btgqwss3tQ9CuRBigH8Detta+vJ1RKJQGPAyu11l+F20Zr/Q6Qo5QqAn5OJ8+/tNYPAA8EFwPlLVOoxMiuukYKnKnE+jz9kcvlkt/LIOHz+aiqqorb37vba23BAhwzZ+IFKC8n7/zzsdXWUvH001ZVUfRQYasXtluL9BnUXcAvlVJ9+giilLIBj2LOiH5pd9trrUuAVzBbbnEnuaCE6AdsNrwTJoQWG487DvfJJ4eWh5xxBukPP7x7e1+fP3eLGIm0BXU55qi6xUqpitYFWuuIBkoEg9sKoAA4Xmvt6UEdx0a4bUxJLigh+h+3UrsXmprwp6eHBlwYbjcF++/PriVLaDjnHPD5sJeW4isslC7BBBBpgDo7CudaDuwNzNJad/ocSyl1FvA25qi/kcBtwH+icP4+kVxQQgwAKSlU/e1vu5fdbhrOOgvvxIkAOL7+mqFHHUXlsmU0zpmDUV1N0hdf4Jk6lUBaWpwqPXhFOhdfn8ZvKqVGAQsw32sqVbs/0SzADEbrgUla6++ASZiT0+YBVcBLwPV9OX80SC4oIQaegNPJrptuCi37XS6qb7uN5uAowZT33sP585+z84UX8EyZgmPjRpI/+gj3yScTyMqKV7UHjZ7MxXcAcARmXqhQ21drfWNn+7TaZkvrfcLIbLXtr4BfRVovq0guKCEGPr/LRcO8eaHlpiOOoOKxx/Dssw8AKWvWkPOb3+CePdtcfvVVkj/6iNqrr5Yh7TEQ0SCJ4HDud4GZwHXAZMzpjxLi/SQrSC4oIQafQFYWTTNmQEoKAPULFlD2wQcEnE7AnIIp/R//CJVn3ncfuZdfvvsAnkgftYtwIh3F9wvgOK31KYA7+O9pwKD57UsuKCEEhoFvjz1Ci3VXX03Zhx/uHlDh8WC0CkrO+fMZcsYZoeXUl18m+f33LatufxdpgBqqtX47+L1fKWULzqN3UozqlXAkF5QQIizH7l6Vuquvpmr58tBy4/HH4z7xxNBy1u23k/HXv4aW8+bPJ/OPu/O+Or74AqOqKsYV7j8i7a/6Xik1WmtdDGwE5iilyjHfZ0oYSR991GbZP2yY+WnH5yPpk086bO8rLMRfVATNzSR99lnH8j32wD9sGLjdpH+ylonbSnGu95CUbuas8Y0ejT8/H6O+Hsf6jhO9+/bcE/+QIRi1tTi+6vhOsnevvQjk5WFUVeEIk0nUO3EigawsbBUV2Ddt6lg+aRKBjAxsO3diLy7uUO7Zd19IS8NWWop9a/sZpMCz//6QnIytpAT7tm0dy6dMAbsd+9at2EpLO5YHHyTbi4sxvv6apOrq3YV2eyiPj/3bb7FVVrbdOTnZPD/myCmj9b4Aqal4Jk82yzdswNjVdq7gQHo63uBzAceXX2I0NLQtz84OvQuTtG4dNDa2Lc/NxTtunFn+2WfmRKSt+J1OfGPNtxuSPvmkw7sy/vx8fKNHm+XtrjuI7rWX9MUXHcvjeO0ZDQ0YbnMgbjyuPSM3F4J/O3txMbadO9vunEDXnmfyZGhsDF0jNbfcQqBlTkHAaG7GVl4eKh9yxhm4TzqJmj/+EQIB8i64gOaDD6b5wAPN7Rsa8I0aNfCuvTlzOmwHkQeo32EOES8Gfg08gzk/3+Vd7GO5Dhei3W4mQPP5OpaBWWa3g8cTvrzloWdjI03VtQCk19VgazQbnoH0dDPzZ0ND2P39mZnYPB6Murqw5bYdOwg0NWHU1IQvLysjUF+PUVXVeXl6OraKik7LSU3FtnNn5+VJSZ2Xl5aC3d51OeZNCq8XW+v/yDbb7vLycmztbgKBpKRQuVFe3nZfIJCc3GZ/o66ubbnbvbu8oiJ0wwyVNzdjy8kJHd9oH4C8XmzBUVhGRUWbbhlzAz+BjIzQ8fH7O/z8LddHuN9NNK+9sOXxvPZ8PvOaKy2Nz7XX6m8XtjzBr71Aampo/7oLLsBouQb8fuouvhjvyJHYSksxamtJ/vBD/EOG4B01CqOuDufll1P7s59Rf/HFGLW1ZP3hDzQdfjjePfcErxejpgYCgX597bVmBAKBLjcIRymVDCRrreu63dg6gdIwnxai5dlPa3h5/S7uP3OEpNsIw+l0UtnNxSYGhhlKsey229g72Iqx2qC71gIBMAyM2lrSVq2i+Qc/wDthAvYtWxgyfz41v/oVTUcdhWPjRlxnnknVnXfSNGsW9q1byVy+nPr58/GOG4dRU4OjuBjvuHFmkEkgw8wWb4cba6ctqOC0RJ3xAt7gs6iOHy0HILfHT3qSTYKTEMJawXtOICvLnO0iyDdqFDvWrDEDGOYQ+ZobbghlIbZVVZlddMHegeTPPiPvqquoWLkSz777kvzee2TfdRdVS5fiGz0ax9dfk/LeezTMnUsgJwejthbD7cbvcoEtPklauwtCni6+WsoHhYZmP+nJEpyEEAkmGMD8TifuU04xnx8Bnv32o/z55/FOmgRA8+TJVN53H94xYwAIZGTgGTcu9MJx0rp1ZN17b6hLMvXllxk6eza24KCN1H/9C+d554W6PJP++1/Sn3gipj9aV8+gxsT0zP1MQ7OftGRJ9S6E6J8CeXk0H3poaNmz//7UBAeLALh//GMajzsu9Ayq+aCDqFm8GH/weVogJYVARkZoyqeU998n7cUXafhp+zSB0dNpgNJab1FKDdNadxy+NQi1dPEJIcRA1frZlG/MGNxjdrdTmmbNomnWrNBy3cUXUzd/fkzr090dd2PrBaXUP2NYl4RmdvFJgBJCCMDsWozxBLrd3XHbP3Q5Mkb1SHgNzQHp4hNCCAt19x5Uz8egx5HzwgvbLLtnzTJzwbjdOK+4osP27hNPxH3yyRhVVeRdd12H8obTTqPxmGOwlZbyy0euJyfNhvOvSaHy+rPPpumHP8ReXEzOb3/bYf+6Cy6gefp0HBs2kH333R3KaxcuxLP//iR99hlZf/5zh/Jd11yDd8IEkj/8kMwVKzqU1yxejG/0aFLeeouMxx7rUF7961/jHzaM1NWrSX/mmQ7lVXfeSSAvj7TnnyftxRc7lFf+8Y+Qlkaa1qS99lrH8gfMZMbpjzyC44MPcHq9obJASgpV990HQMaDD5LS/iXqnByqf/97wJy/LHndujblvqFDqbn1VgCy7rqLpI1tGvN4R45k1w03AJB96604vvuuTbln/HhqFy0CIOeGG7Dv2NGmvHnyZOouuwyA3GuvxVZT06a86aCDqP/5zwHIu+wyjKamNuWNhx9Ow7nnAh2vO4jutZd7Y8f5mON57dlLSrBt2wbjxsXl2nM4HLBsGWBee6nvvNOmfCBee/5AAG8gQLLNNjCvvY8/7rAddB+gHEqpGexuSbVfRmu9pptj9Hs+fwB/IIBdhpiLQejAL79kfbuZOI68+uoO201KTeW/wRkWRN8cqRQbvv02fOHatRCcHmmg/867fFFXKVVM162ogNZ6z2hXqpdi9qJubaOPq/+xjZ9My+WoCZIDJpxB9/KkiJvBeK09t3o1L61Zw/133BHvqsREj1/UBdBaj45RffoVt8d8F1lG8QkhhHXkjhuBhmazESmj+IQQwjpyx41AQ7PZgpJRfEIIYR2540ZgdxefDJIQQgirSICKQEsLSrr4hBDCOnLHjYAEKCGEsJ7ccSPQ0BzAMCDFIV18QghhlUgz6vYL7dMX+/Pz8RUWmqmPw6UuLigwp6b3eEgKl7q4sBB/fj6eejcHlG0g+fO27174RowwUxs3NOD4+usO+3tHjjRTG9fV4Qjz0p139Ggz70owkViH8rFjCWRmmqmR272tDoQSj9kqKrB//32Hcs+ECaGspmHTak+aZGY1LS3FXlbWsXzffc2029u2dUyrDaG02fatWzE2byapXUbdlrTZ9i1bwmY1bUkDYN+8OWxWU+/eewPg+PbbjllN09Lwjh9vlm/c2DGraWYm3mDKdsf//tcxo252Nr7gRJiO9es7ZNT15+biGzUKCKaMb5dR1+90mmm16XjdQfSuPRobSdqwoWP5IL72jOxsCP7t7Fu3hs2oO9CuvZStW0mqrSXps88G5rVnvgfVwYAKUH6ns+1yfr75h/D58If5TxIqb27GX9px0vaW8np7JSkpjo7HHzoUf34+Rn09/oqKDvsHhg41/5C1tfiDOVXa7x/Iy8NIScHf7j8JgL+ggEBWFrakJPx1HZMX+wsKzLTkdjtGQ0PY8pbJHI12MwGEypOTzfTN7VOeg/m7sdvNMp8vfHnLsT0e/I5Wl5Pdvru8vr5jwrPk5FC5rba27b4Aqamhcn9NDUZycpviQHr67vKKig4/fyA7O1Qe2LmTQLufP5Cbu7u8rIxA+5uI07n7+Nu2dfj5Q9cO4N+6tf2vJmrXHm43/jAfDgb1tdfqbxfu2APh2jvypz9lQ5jg8I8PPmizPHHkSN669962+/fza6+1XqV8T1CBbWH+GNHwp2c3UlPn4VfnDNwpRfrK5XJRXl4e72qIQWCwX2spKSn4fD78fj9+/8BIaF5YWAhhZpKQZ1ARaGj0kZZqj3c1hBACv9+PEZwX1OFwYItTOnYrDNyfLIrcTT7SUwZUb6gQop/y+Xz4fD7sdnuoJTVQSYCKQEOTl/QUaUEJIeLP7/djs9nweDzY7XZpQQ127iYfaRKghBAJoqXV5PV6MQwDu31g3p8kQHXD5/PT5PFLF58QImG07tbz+XwEAoHQc6mBRO663XA3mUNcpQUlhEhUA/U5lLSgutEQDFDpMopPCCEsJQGqGw2hFpQ0NoUQwkqW3HWVUinAMmAW4AS+ARZrrV8Os+15wOXAOGAX8ERwW68VdW3P3WSeVkbxCSGEtaxqQTmArcCPgBxgCaCVUqPDbJsOXAm4gOnAUcAiS2oZRqiLTwKUECIBXX311ey3337MnDkzbHkgEGDJkiUcdthhzJo1i3Xr1gFQUVHB3LlzmTlzJq+88kpo+/nz51MaZgqkeLCkBaW1rgdubrXqRaXUZmAqUNxu2+WtFkuUUo8DM2Jdx864G2WQhBAicSmlmD9/PldccUXY8jVr1rB582beeecdPvnkE66//npefPFFVq1axemnn86cOXM466yzOO6441i9ejWTJ09mWHCuwHiLy4MVpVQBMB74MoLNf9jZdkqpC4ELAbTWuFyuqNWxheEwJzPco6hAnkN1weFwxOT3L0R7cq21deKJJ1JcXIzdbg/7e3nzzTeZP38++fn5HHvssVxzzTV4PB5yc3PxeDxkZmaSkpJCbm4uK1eu5NlnnyU9PT0OP0lHlt9xlVJJwOPASq31V91sOx+YBvwsXLnW+gHggeBiIBYTSO6s3IVhQN2uKuoH4HsG0TLYJ/AU1pFrraOqqip8Pl/Y30txcTEnnnhiqGzo0KGsX7+eo48+moULF7Jy5UoWL17M3XffzZw5c2hoaKAhzAz1sRScLLYDSwOUUsoGPAo0A5d2s+1c4A5gltY6blejOQ+ffUC+BCeEGPjCZawwDIPs7GweffRRAKqrq1m2bBkPPfQQ1157LdXV1SxYsIBp06ZZXd02LBtmrpQygBVAAXCq1rpjEpjd2x4HPAicpLVeZ1EVw2po8krXnhCi3xo+fDitUxFt376dgoKCNtvcc889XH755axatYrJkyezdOlS7rjjDqur2oGV70EtB/bGDDruzjZSSs3E7AI8VWv9f1ZVrjMtLSghhOiPjjnmGJ555hkCgQBr164lOzu7TYDatGkTZWVlHHLIIbjdbmw2G4Zh0NTUFMdam6x6D2oUsABoAkqVUi1FC4C3gfXAJK31d5hD0HOAl1pt97bWerYVdW1PckEJIRLZJZdcwvvvv09lZSVTp05l0aJFeIJZis8991yOOuoo1qxZw2GHHUZaWhpLly5ts/+dd97JddddB8DcuXM5//zzWbFiBYsWxe3tnhDJqNuNWx7+ggJnKhedvFfUjz2QyINrYRW51gYeyajbS5ILSggh4kMCVDckF5QQQsSHBKguSC4oIYSIHwlQXZBcUEIIET8SoLoguaCEECJ+JEB1QXJBCSFE/EiA6oLkghJCiPiRANUFyQUlhBDxIwGqC5ILSggh4kcCVBcaWrr4UuUZlBBCWE0CVBcamnwYBqQkya9JCCGsJnfeLkguKCGEiB8JUF2QXFBCCBE/EqC6ILmghBAifiRAdUFyQQkhRPxIgOqC2YKSLj4hhIgHCVBdkFxQQggRPxKguiC5oIQQIn4kQHVCckEJIUR8SYDqhOSCEkKI+JIA1QnJBSWEEPElAaoTkgtKCCHiSwJUJyQXlBBCxJcEqE5ILighhIgvCVCdkFxQQggRXxKgOiG5oIQQIr4kQHVCckEJIUR8yd23Ey2zSEguKCGEiA8jEAjEuw7RMmB+ECGEGIQ6tAYGUgvK6O2XUurBvuwfrWP1dN+ebB/Jtt1t01W5UmpttH6HVn9F8+9v5bmsvNZ6so9ca9b8/a0+lwXXWwcDKUD1xQsJcqye7tuT7SPZtrttovl7SiRW/lz99VrryT5yrXWuv15rfT1er/YdSF18Io6UUh9rrafFux5i4JNrbfCQFpSIlgfiXQExaMi1NkhIC0oIIURCkhaUEEKIhCQBSgghREKSeXxEzCilDgGWAs3ANuBcrbUnvrUSA5FSqgB4FvAAPuAsrfX2+NZK9JW0oEQsbQFmaq1/BGwC5sS5PmLgKgcOD15rjwAXxLk+IgqkBSViRmu9rdWiF/DHqy5iYNNa+1otZgFfxqsuInokQIluKaUuBeYBk4G/a63ntSpzAiuAYzA/xV6vtX6i3f5jgNnAbRZVWfRTfbnWlFIHAPcDucFtRD8nXXwiEtuAW4G/hin7M+YzpgLgLGC5UmqflkKlVDawEjhHa91sQV1F/9bra01r/anWejqwBLjegrqKGJMAJbqltf6n1noVUNF6vVIqAzgVWKK1rtNavwM8D5wTLHcAfwdu1lpvsLbWoj/qw7WW0mrzGqDBmhqLWJIuPtEX4wGf1npjq3WfAT8Kfn8mMB24USl1I7Bca/2UxXUUA0N319oUpdSdmCP4GoHzLa6fiAEJUKIvMjE/rbZWg/mQGq31o8CjVldKDEjdXWvvAz+0ulIitqSLT/RFHZDdbl02UBuHuoiBTa61QUgClOiLjYBDKTWu1br9kSG+IvrkWhuEZLJY0a3gYAcHcBMwAvg54NVae5VST2JmM/4ZcADwEnCo1lpuHKLH5FoTrUkLSkTiBsAN/BI4O/j9DcGyS4A0YAfmiL2L5YYh+kCuNREiLSghhBAJSVpQQgghEpIEKCGEEAlJApQQQoiEJAFKCCFEQpIAJYQQIiFJgBJCCJGQJEAJIYRISBKghBBCJCQJUELEiVLqL0qpJV2UB5RSe/XwmGcppVb3vXZCxJ/MJCFEFCilfgJcBewL1AObMTMJL9da9+o/mVIqAIzTWn8TtYoK0Y9IC0qIPlJKXQP8Efg9MAwzJflFwGFAcif72C2roBD9lLSghOgDpVQOsA04V2v9jy62exhz4tNRmFlg52BOhvq91vqG4DbXAldjzth9A7CCTlpQSql5wI1APlAO3KC1fjy4/mda68OVUr8IbtMiBXhcaz0vWO+lwPGAH/gbcJPW2tfLX4UQUSctKCH65hDMG/9zEWz7U+A2zCyw77QuUEodBywCjgbGAbM6O4hSKgO4F5ittc4CDgU+bb+d1vp3WutMrXUmsDewE9DB4pWAF9gLOBA4BjONhRAJQ1K+C9E3LqBca+1tWaGUeg+YhBm4jtVavxUsek5r/W7w+0alVOvjKOBvWusvgse4GTizi/P6gX2VUt9prbcD2zvbUCmVBqwC/qi1fkkpVQDMBnK11m6gXil1D3AhcH9kP7YQsSctKCH6pgJwBRPtAaC1PlRrnRssa/1/bGsXxylsV76lsw211vXAGZjPubYrpf6llJrYxbFXABu01ncGl0cBScF9q5VS1ZiBaWgXxxDCctKCEqJv3geaMJ8pdfoMKqirB77bgT1aLY/s6kBa638D/w62jm4FHgSOaL+dUuqXwATg8Fartwbr7Grd8hMi0UiAEqIPtNbVSqlbgGVKKQN4BWgA9gMyenIo4G9KqUeAYsyU52EFu+imA//BHHhRB3QY3KCUmg1cDkwPduW11Hl78F2pu4PvYdUBY4ARWus3e1BnIWJKuviE6COt9e8wR9/9AjMdeRlml9l1wHsRHuNl4A/AGuCb4L+dsQHXYI4erMQcFXhJmO3OwBzl9z+lVF3w6y/BsnMxh8CvB6qAZ4DhkdRVCKvIMHMhhBAJSVpQQgghEpIEKCGEEAlJApQQQoiEJAFKCCFEQpIAJYQQIiFJgBJCCJGQJEAJIYRISBKghBBCJKT/B/abqHTypeIZAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -3255,12 +3165,12 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" + "print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -3274,14 +3184,12 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -3294,7 +3202,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -3389,14 +3297,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -3573,7 +3479,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -3582,7 +3488,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": { "scrolled": true }, @@ -3705,14 +3611,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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5XykIkBQKSE2YdVypVKB/Vz1+P10Ko5lTNxARUdP5yKciNUWV0YbsgmqPPx5+WQoFyt9+G5XPPQcAECorr+rlA5MiYbVJOHiy1B3VERFRC8GA48dOZldAAtDNy/1v6qkZ4ViRl4foW29F0EcfufzSdnHBiIkI4G0qIiK6Jgw4fiwt0wCNSoF2rYK9XUqDxKgomIcOhbV/f5dfIwgCBnaPwsmsCpQYzG6sjoiI5IwBx4+dzKpAp9YhUCl99H+jSoXyt96CtWdPAIDy9GmXXnZ9dz0kAL+e4AzjRETUND76yUhXYqiyIrfY6PXxb1wV8MMPiLn5ZgT88MMVt40O16JjfAh+PlHEqRuIiKhJGHD8VHqWY6yYbm18qIPxZZhvugkVzz0H8003ubT9wKRInC82Iaug2s2VERGRHDHg+Kn0rApoNUokxvhm/5t6tFpUzpgBaLWA0QjNTz9ddvN+XfRQKQX8zM7GRETUBAw4fio904AuCaFQKgRvl3LVQhcuROT990OZk9PoNsGBKvRor8P+E8Wwi7xNRUREV4cBxw+VGMwoKDOjq5/cnrpU5TPPoGTVKthbt77sdgOTomCotiEtk1M3EBHR1WHA8UPpWRUAvD//VFNJwcEwDx8OAFAfPtxox+Me7XUI0irx8/EiT5ZHREQyoPJ2AXT10rMMCNaq0Do60NulXLOwN96AIi8PhUOHAqq6p6NapUD/Lnr8fLwYJosdWo3SS1USEZG/4RUcPyNJEtIyK9C1TSgUgv/1v7lUyfLlKP7ii3rhplZKUiQsNhGHT3HqBiIich0Djp8pKjejtMLie9MzNJEUEQExPh6QJIT861/Q/Pe/ddo7xocgSheAn4/zaSoiInIdA46fScus7X/jnx2MGyNUVSFw82Zot22ru14QkNI9EmmZBpRVWrxUHRER+RsGHD+TnmmALliNWL3W26U0KykkBEUbN8IwZ07NiguPhqckRUKSgP1pnLqBiIhcw4DjRyRJQnqWAV3bhEKQQf+bS0nh4YBCAUVRESLHj4f6998BALERWrRvFcynqYiIyGUMOH7kfIkJhmqb3z4e7jKzGYqSEgiGC+PfpHSPRHahETmFnLqBiIiujAHHj6Rn1s4/Je+AI7ZujcLUVFgGD3asMJkwoKseCgWnbiAiItcw4PiRtEwDIsM0iNIFeLsU91M6xrwJSE1F7KBB0OWeQ492Ovx6ohgip24gIqIrYMDxE6Ik4WR2hfxvT13C1r49LL16QYyORkpSJMoqrc6RnImIiBrDgOMnsguqUW2y++38U01l79gRpR9+CEmnQ6+2oWhlLsMvvE1FRERXwIDjJ/x9/qnmED33VSz44m9IO5IFi9Xu7XKIiMiHcS4qP5GeZUBshBYRoRpvl+I1VZMmoVwfj1JFIA6fKsP13SO9XRIREfkoXsHxA3a7iJNZFS3u9tSlbD16IPCFadCHanDqx/9Bcf68t0siIiIfxYDjB87lV8NsFWUz/9S1UAgCUrqGY+LiWQh77Ik6Ix4TERHV4i0qP5Ce5Rj/povM5p9qqut7xGDRbVMxeHAH9JPhiM5ERHTteAXHD6RnVqB1VCBCg9TeLsUnxEcGor21GCkvTUNc6wSEdeyKiude8XZZRETkQxhwfJzVJuJUbgW6ynz04quRs/gTPLR5EWIMhVBAQoipEp3WfYKcxZ94uzQiIvIRDDg+7mxeFaw2Cd14e8qp/dJ3obWZ66xTSiLaL30XQkUFIIpeqoyIiHyFx/rgvP/++zh69CjMZjPCw8Nx55134tZbb21w261bt2LTpk2wWCxISUnBlClToFa3zNsz6ZkGCALQOYEBp5a+vLCR9QWwTp4Me2IiyhYt8nBVRETkSzwWcMaOHYsnn3wSarUaOTk5mDNnDtq3b48OHTrU2e7w4cPYtGkTXn31VURERODtt9/GunXrMGnSJE+V6lPSMg1oExOEIC37g9cq0UUjqryg/vqwaKhHjoS9VSsvVEVERL7EY7eoEhMTnVdhBEGAIAjIy8urt93u3bsxbNgwJCYmIiQkBOPGjcOuXbs8VaZPsVjtOHu+iv1vLnH2qWdhUtWdcNSkCsDJx59F1eOPw3TnnQAAzY8/QnXypDdKJCIiL/PoZYFVq1Zh165dsFgsaN++Pfr27Vtvm+zsbAwYMMC53LZtW5SXl6OiogKhoXVv06SmpiI1NRUAMH/+fERFRbn3DXjY738UwS5KuP66BNm9t2sRNed5pAcEotXCN6AvL0SJLhof3zQJho434yV9JBQKAbDZoJ49G1JCAmzff+/R+srKyvDEE0/g2LFjEAQBH3zwARYtWoSTNWGrvLwcOp0O+/fvb/D1drsdN9xwA+Lj47Fx40YAwEsvvYTvv/8evXv3xocffggA+Oyzz1BSUoLp06d75H25m0ql4nlORM3GowHn0UcfxcMPP4yTJ0/i2LFjUKnqH95kMiEoKMi5XPu10WisF3CGDx+O4cOHO5eLiorcVLl3/Ho0CwqFgOhQUXbv7VpFTpkAy5QJqL0G2PpwAXb/cA5rvz2CUTfEAwCUn38OSamEWFTkGBDQQ2PmPPPMM7jxxhuxePFiWCwWGI1GvPfee872uXPnIiwsrNH/pytWrED79u1RUVGBoqIiGAwG7NmzB99//z2mTZuGPXv2oF27dli9ejU+++wz2ZwbUVFRsnkvROQ58fHxDa73+FNUCoUC3bp1Q3FxMbZv316vXavVorq62rlsNBoBAIGBgR6r0VekZVagfVwwtBqlt0vxeUN6RyOleyS27MvB8YxyAIC9dWuIcXGAJEH3t78h9I033D7ycUVFBX755Rfce++9AACNRgOdTudslyQJW7ZswZgxYxp8fW5uLn744Qfn6wHHz4zVaoUkSTCZTFCr1Vi+fDkeeeSRFtv5nojoSrz2mLgoisjPz6+3PiEhAefOnXMunzt3Djqdrt7VG7kzmm04l1/V4uefcpUgCJh0W1u0igrEqm/PoMRw0WPkoui4eqNUuv0qzrlz5xAZGYkZM2ZgxIgReOGFF+oE9l9++QXR0dH1OtfXeu211/DKK69AobjwoxkSEoLbb78dI0aMQGJiIkJDQ3H48GH86U9/cut7ISLyZx4JOOXl5di7dy9MJhNEUcThw4exd+9e9OjRo962Q4cOxc6dO5GdnY3Kykps2LABN998syfK9Cl/ZFdCksD5p65CgFqJJ+7oBLsoYsWW07DaasbDUSpR/uabqJg507F45gwUbroVYrfbceTIETzwwAPYvn07goKCsHjxYmf7xo0bG716s2PHDkRFRaFXr1712p566ins2LEDr732GhYsWICZM2fi888/x+OPP453333XLe+FiMifeSTgCIKA7du344knnsBDDz2EtWvX4sEHH8SAAQNQVFSEyZMnO++99+nTB2PGjMHcuXMxdepUREdHY8KECZ4o06ekZRmgUgroEB/i7VL8Sqxeiwf/3B4ZeVVYvzvrQoMgOP6JIvSPPgr9Qw+55XZVq1at0KpVK2cH+lGjRuHIkSMAAJvNhm3btuHOmqe8LnXgwAFs374dKSkpeOqpp7B37956HYiPHj0KAOjQoQPWr1+PFStWID09HWfOnGn290JE5M880sk4LCwMc+fObbAtKioKa9eurbNu9OjRGD16tCdK81knMyvQMT4EahUHm75afTvrcVu/Suz4LR8d4kOQ0j3yQqNCgbJ33nF87YbbVTExMYiPj8epU6fQqVMn/PTTT+jSpQsAYM+ePejUqVOjHeJefPFFvPjiiwCAffv2Yfny5Vh0yYCFb731Ft566y1YrVbY7faat6Rw9lUjIiIHfnr6oEqjDVmF1Rz/5hqMHZyATq1D8On2DOQW1f3wtyYnw5qcDAAI+vhjBH71VbMee968eZg+fTqGDx+OY8eOOa/CbNq0qd7tqby8PEyePNml/X733Xfo06cP4uLioNPp0K9fP9x6660QBAHXXXdds74HIiJ/J0iSmx8r8aDc3Fxvl9AsDp4swYotp/G3e7qhY2t2Mm6q8koL/rH2GAIDVHhxUhICAy55Gk0UoX/gAUgaDUpXr/bYY+TUMD4mTkRN4TOPidOVpWdVIECtQLu4YG+X4td0IRpMuaMTCstMWLP9LOpleYUCJR99hLIlSwBBgGAwACaTd4olIqJmxYDjg9IzDejUOhRKJf/3XKsuCaEYOzgBB0+W4oeD9YclgFoNKTAQkCTop0xB5P33u32sHCIicj/O4OhjyistOF9iwg09OGR9c7mtfxxO51Ziw+4stI0NbnhmdkFA1eTJEMxm3qoiIpIBXiLwMelZFQCAbuxg3GwEQcBf/9weUboArNx6GuVV1ga3M40eDeO4cQAAzd690Ozbd03HvXiwPiIi8iz+BvYx6VkGBAUokRgddOWNyWWBASo8fmcnVJvtWPXtadjFy9yGkiSEvfUWwubOdYyC3AQNzbNGRESew4DjY9IyK9A5IdQxIzY1q4ToINx/W1uczKrApp+yG99QEFC8Zg1KPvoIUCgAu/2K/XJqA40gCFCr1bDZbBCbGI6IiOjaMeD4kGKDGUXlZt6ecqOBSVEY0jsa3+/Pw+E/ShvdTtLpINY8ehj22msInz7dEXQaoFKpYLPZoFQqoVQqYbU2fAuMiIg8hwHHh6RnOvrfdE3k2DfuNOHmNmgbG4SPvjuLgtIrPBYuSRDj4hyzkisbn9VdrVZDFEXYbLZmrpaIiJqCAceHpGcZEBqoQquoQG+XImtqlQKP3dEJSgWwYsspWKwNX5kBAAgCKqdNg+GVVwAAyowMKE+fvrAvtRqCIECSJCiVSva9ISLyEQw4PkKSJKRlGtAlMRQKPqbsdlG6ADx8e0fkFBrx+Q/n6g8C2BBJQvhzzyHywQeBmis1tVdtLv5HRETex4DjIwrKzCirtHL+KQ/q0V6HUTfE47/HivHTERemCBAElC1ciOOzZ+OFWbMAux1SYaFr4YiIiDyK19N9RHqmAQDQLZEBx5NGDYzHmfOV+HLnOSTGBDU4PcYtt9yC9PT0euu/+OKLOstdu3bFzp073VYrERG5jldwfERapgHhIWrERAR4u5QWRaEQ8MjIDggNUuODLadQZax/i2nnzp3Iyclx/vv++++RlJSE/F27YHjqKeRkZyMnJwe7167lNA9ERD6CAccHSJKEk1kV6JoYBoH9bzwuJEiNx+/oiLJKKz7cdgaiiyHF1rkzKl5+2TFRZ2Ulom6/3TE4IBEReR0Djg/ILTaiwmhD1zZ8PNxb2rcKwcRhbXD0bDm2/Xz+ql8vabWoeP55GMeOBQAIlZVQ5OU1d5lEROQiBhwfUDv+DQf4864hvaOR0j0SW/bl4HhG+dW9WKVC9QMPwNq7NwAgZPFixAwZAkWRC52XiYio2bkUcGw2GzIzM5GWlobMzEw+CtvM0rMMiNIFIDKM/W+8SRAETLqtLVpFBWLVt2dQYjA3eV/V99wDw4svQoxyzAqvOnq0yfNaERHR1bvsU1QHDx7E9u3bcfToUSiVSgQGBsJoNMJut6NHjx647bbb0K9fP0/VKkui6Oh/k9w5wtulEIAAtRJP3NEJ//zsGFZsOY0XJnaDWnX1Fzrt7dqh+qGHAACK3FxE33knKp98EhUzZzZ3yURE1IBGA87s2bMRHByMQYMG4bHHHoNer3e2lZaW4tixY9ixYwc2btyIefPmeaRYOcoqrEa12c7bUz4kVq/Fg39qjxVbTmP97izce2vba9qfGBeHsjffhPnGGwEAivPnIVitsLdp0xzlEhFRAxoNOFOmTEGbRn4BR0REYNCgQRg0aBAyMzPdVlxLUDv+Deef8i19u+hxW79K7PgtHx3jQ3B998im70yhgPHuu52LYfPnQ7tjB/IPHIAUFNQM1RIR0aUavfbeWLhp6nbUsPSsCsTptdCFaLxdCl1i7OAEdGodgrXbM5BbZGy2/RpmzULZO+84w03Ajz86p34gIqLm4VLngq1btyIjIwMAcPLkSTz55JOYNm1ag6O7kuvsdhF/ZDvGvyHfo1Qq8NjojtBqFFi++RSM5stMynkVxFatYBo5EgCgOn4ckffei+CPPmqWfRMRkYNLAefbb79FTEwMAMfw9KNHj8Zdd92FTz75xK3FyV1GXhXMVhHdOP6Nz9KFaDBldEcUlpmwZvvZZp93yta9O0pWr0b1ffcBAFQnTkD1xx/NegwiopbIpYBTXV2NoKAgGI1GZGRkYOTIkbjllluQm5vr7vpkLT3LMf5NlwQGHF/WJTEMfxmcgIMnS/HDwfzm3bkgwPTnP0MKdsyBFTZvHvT33cdbVkRE18ilgBMZGYn09HTs3bsX3bt3h0KhQHV1NRQKjhN4LdKyDEiIDkRIkNrbpdAVjOgfhz6dwlGw7FOoxt0D9fHj0CQlI2dx817FLFu0CKVLlwIqFSBJCFy/HrBYmvUYREQtgUuzid9///3417/+BZVKheeffx6AY4ycTp06ubU4ObPaRJzOqcTQPjHeLoVcIAgCbj+7F8k7liLN5hgAMKq8ACEL5uIIgNbTHmyW44iRkRAjHU9saX76CRHPPAOoVDD+5S/Nsn8iopZCkC7TqSAvLw9xcXENttWOZqxSuZSRPMKfbpmlZxnwr3XpeOovndG7Y7i3yyEXaJKSEVVegMMA/grgcM36Il0MLMcPueeY+/bBMnAgoFBAs2cPpPBwWHv2dMuxvC0qKgpFnNqCiK5SfHx8g+svm07eeOMNAEBycjL69u2LpKQkZ6DxpWDjj9IzKyAIQJeEEG+XQi7Slxc2ut5d02paagYHhCQh7I03AFFE0bZtAGedJyK6rMumlPfeew/5+fk4ePAgtm7divfeew9du3ZF3759kZycjMjIaxj8rIVLyzSgbWwwAgMYFP1FiS4aUeUFDa53O0FA8RdfQFFY6Ag3RiOCP/kE1Q8+CCkw0P3HJyLyM1f8dI2NjcXIkSMxcuRIWCwWHDlyBIcOHcI333yDwMBAJCcnY9iwYY1eIqL6zFY7zuZVYUT/WG+XQlfh7FPPImTBXMB2YRJOkyoAZ596Fq09cHxJp4NdpwMAaHfuhG7ePFh794blhhs8cHQiIv9yVZcPNBoN+vXr55xgMysrC4cOHUJmZiYDzlU4lVMJUZQ4wJ+faT3tQRwBULloAVBZiiJdjCPcNFMH46thGjUKBTt3wta1KwAgcMMG2Nu0gWXAAI/XQkTki1wOOGazGXl5eTCZTHXW33nnnc1elNylZRqgVAjo2Jr9b/xN62kP4ujN/WCdMQOWHTs8cuWmMbXhBnY7Qt5/H7aOHRlwiIhquBRwdu/ejQ8//BAqlQoaTd05k5YtW+aWwuTsZFYF2rcKRoBa6e1SSA6UShRt2wahshIAoCgoQPBHH6HyqacghXIQSSJqmVwKOJ9++imef/559OrVy931yF61yYZz+VW4PYW39Kj5SEFBFybv/L//Q8jy5agePx52BhwiaqFcGopYpVIhKSnJ3bW0CH9kV0CSwPmnyG2MEyci/7//hb1jRwBAyKJF0Pz4o5erIiLyLJcCzsSJE7FmzRoYDAZ31yN76VkVUKsEtG/F/jfkPmLtAJ1GI4K++gra1FTvFkRE5GEu3aKKj4/HunXr8P3339dr++qrr5q9KDlLzzKgY3wo1CrO40UeEBiIgh9+gFA78vjx4wj6/HNUvPACpPBw79ZGRORGLgWcRYsWYciQIbjxxhvrdTL2Jer9++ssi3FxsCcmAnY71AcP1tveHh8PsXVrwGKB+vff67cnJjr+EjYaoT56tH57u3YQo6MhVFVBdfx4/fYOHSBGRkKoqIAqLQ3VZjtCfv8Dg3tFQb3fAFunTpAiIiCUlkJ16lS919u6dYMUGgpFcTGUZ87Ub09KghQcDEVhIZQZGfXarT16AIGBUOTlQZmVVb+9d29Ao4EiJwfKBqa5sPbtCyiVUGZlQZFXf6xea80TO8qMDMcAdBdTKh2vB6A8fRqKkpK67RqN4/gAVH/8AaGsrG67VuuckkCVng7hkquHUlAQbNdd52g/dgxCdXXd9rAw51NG6iNHgEue/pPCw2Hr3NnR/vvv9Sa0FPV65y0e9cGDgN3ubFOdOQPhou0vPe8A3zv3amn/8x8Efv01Kh99FFJ4uE+de0J4ONQ15wHPvYbPPQAQo6Nhb9fO0e5H514t/t7judfs596YMfW2BVwMOJWVlZg4cSIEHx8evt7JpFRCUqsBu71+G+BoUyoBq7Xhdq3W8YXJ1HB7UBBgt0Oorm6wXQwJgcJqhVBZCUVJCbLzHCdbO62jHkVBASSzGUJ5eYOvV+TnQ6qqglBa2nh7UBAUxcWNtkOrhaKwsPF2tbrx9rw8QKm8fDvQcLtCcaG9qAiKS36QJbXa2S4UFUFx6Q+yRlPn9bVPCDnbjcYL7cXFEIzGuu0WCxQ1g+IJRUV1AgkAiDYbFDUdcIXiYghWa936RRFScLBz/xDFC2/NYABstgvHb+B742vnXi3LwIGw9u4NwWyG4vx5hL32GmydO8N6yQMEXjn3bDbnecBzr+Fzz3mMmvPDn869Wvy9x3PPXefepS472WatTz75BO3atcPQoUOvuENvymsgMfqSz34twX/PVuPdu1tDpfDtsEiNO5qejmfnzEHqF194u5RrIpSVIfLRR1E9bhyq773X2+VAr9ejxIVfWkREF4uruWp2KZeu4Jw6dQrfffcdvv76a4Rfct9+7ty511xcS5GWb0aXmACGG/IJUng4ir74wjlxZ8CePQjYuRMVzz3H8XOIyO+5FHBuvfVW3Hrrre6uRdbKqu3IM9gwuFOwt0shukCtdn6pzMqC+sSJC5eJiYj8mEsB5+abb3ZzGfKXlu/of9M1lh8e5Juq77sP1Xff7Qg9VisinnkG1ffcA/OQId4ujYjoqjX6rPKBAwdc2oGr27V0aflmBGkUSAxXX3ljIm+puaKjKCmBorz8wvord9UjIvIpjV7B2bt3L7744gsMGjQISUlJiI+PR2BgIIxGI86fP4/jx49jz549aNu2Lfr37+/Jmv1SWp4JXWMDoGD/G/IDYmwsiteudfbPCVq3DurDh1H+6qtAYKCXqyMiurJGA84zzzyDzMxM7NixA4sXL0ZBQYGzLS4uDsnJyXj22WeRmJjokUL9WWGlDcVVdozoHuDtUohcp7joAq/FAsFkAmr750iSM/wQEfmiy/bBadOmDR555BEAgNlsRlVVFYKDgxEQcHUf1FarFatWrcKRI0dQWVmJuLg43HvvvUhOTq637a5du7Bs2bI6AwrOmjUL19UMbuSP0tn/hvxc9eTJqL7/fkAQIBgM0D/yCCqeeQaWQYO8XRoRUYNc6mQMAAEBAVcdbGrZ7XZERkZizpw5iIqKwqFDh7Bw4UK8/fbbiImJqbd9ly5dMG/evCYdyxel5ZkRqlUgXufyt5vI99RcsVEYDJDCwyFGRzvW22yAiuc2EfkWj/xW0mq1mDBhgnO5X79+iImJwZkzZxoMOHIiSRLS883oFqv1+ZGgiVxhT0hAycqVzuXQ99+HMjsbZQsWOEYpJSLyAV75s6usrAznz59vtP9ORkYGHnnkEYSEhGDw4MEYO3YslH76izO/woYyox3dYtn/huTJHhvr+KL2Z9RiAXx4zjoiahk8HnBsNhsWLVqEoUOHonXr1vXau3fvjnfeeQdRUVHIzs7GwoULoVQqMXbs2HrbpqamIjU1FQAwf/586PV6t9d/tX7NKQIApHSNhT6cIUcOdDodlEqlT55vXjF9OgBADwBnz0J9992wLVwIafDgq9qNSqXi95SImo1HA44oili8eDFUKhUefvjhBreJrf1rEI5OzuPHj8fmzZsbDDjDhw/H8OHDncu+OI/NwTMl0AcpobFXoqSkytvlUDMoLy+H3W73yfPN25QGA0J79YIhNhZiSQkEo9ExMrILt2c5FxURNUVczQznl2o04Dz55JMu7XjZsmUubSdJEpYvX47y8nK8+OKLULnYKdGf+62INf1vesaz/w21DPbERJS9/bZzWffKK4Akoeydd/hYORF5VKMpY3rNZWfAMdnm7t27MXLkSERHR6OwsBDff/89hlzFEO4rV65ETk4OZs+eXecR8EsdOnQI7du3R3h4OHJycrBhwwYMHDjQ5eP4ktwyKyrNIrrF8fFwaoEkCeaUFAgXjZkjVFRwIk8i8ohGA05SUpLz69WrV+Pll1+uc388OTkZ//znP3HHHXdc8SCFhYVITU2FWq3GlClTnOsfe+wxdO/eHTNmzMDChQsRFRWFI0eOYOnSpTCZTNDpdM5Oxv4oLd8MAOjKDsbUEgkCjBc9Pak5cADhM2agdNEiWPv08V5dRNQiuHSfqKSkBNpLZhjWarUu3y+Pjo7GunXrGm1fu3at8+sHHngADzzwgEv79XVp+SZEh6gQGcwxQojscXEw/fnPsHbrBsAx35UYEcFbV0TkFi598vbv3x9vvvkmxo0bB71ej+LiYmzcuBH9+vVzd31+yy5KOJlvRv+2Qd4uhcgn2BMSYHj55ZoFOyKmTYM9IQFlb73l3cKISJZcCjhTpkzBv//9b6xcuRIlJSXQ6/UYOHAg7r77bnfX57cySy0wWiV04/QMRPUJAqrvuQdibX8cUQRycy/MdUVEdI1cCjgajQaTJk3CpEmT3F2PbKTnsf8NUaMUChjvvNO5GLh1K9RvvAHVmjWwde7sxcKISC5c7hzyv//9D3v37kV5eTlmzZqF06dPw2g0okePHu6sz2+l5ZvRSqeCLtA/R2Am8iTz9ddDfPJJ2Dp1AgAoMzNhb92aUz8QUZMpXNlo27ZtWLlyJVq1aoUTJ04AcFzV+fLLL91anL+y2SX8UWDm7SkiF4lxcbA/+6xjtnKjEfopUxAmowl3icjzXLqC85///AezZ89GTEwMNm3aBABo3bo1cnNz3Vqcv8ootsBil9AtjreniK6WpNWi4vnnYY+PBwAIRiMU+fmwNzJaKRFRQ1y6gmM0GhEVFVVnnc1mc3k04pYmLd8EAUDXGAYcoqsmCDCNGAFrze3voLVrETVxIhTnz3u5MCLyJy4FnO7du2Pjxo111m3btg3XXXedO2rye2n5ZiRGqBEcwP4DRNfKOH48DLNmQWzVCgCgPnIEsFq9XBUR+TqXAs7DDz+MX3/9FVOnToXJZMIzzzyDn3/+GQ8++KC76/M7FpuI04VmTs9A1ExEvR7GmtHMFSUl0D/+OELfe8/LVRGRr3PpHlNERATeeOMNnD59GoWFhYiMjESnTp2gULiUj1qU00UW2EQ+Hk7kDmJEBErfegv2Dh0AAIrCQihKS2Hr0sXLlRGRr3E5odjtdlitVkiShC5dusBiscBkMrmzNr+UlmeGQgA6s/8NUfMTBFgGDXJ2QA5ZuRL6hx6CYDB4uTAi8jUuXcHJzMzEm2++CbVajeLiYtx44404fvw4du/ejRkzZri7Rr+Snm9Cu0gNAtW8ukXkbhXTpsE8eDCksDAAQMDu3TCnpHBEZCJy7QrOypUrMXHiRLz77rvOJ6eSkpKQlpbm1uL8jckqIqPYwttTRB4ihYXBPHgwAMfggBHPPYfgTz/1clVE5AtcuoKTnZ2NwTW/RGpptVpYLBa3FOWv/igwwy6BA/wReYG9TRuUrFjhnK1c9ccfEIxGWHv18nJlROQNLl3BiY6OxpkzZ+qsO3XqFOLi4txSlL9KyzdDpQA6Rmu8XQpRi2Tp3x9SSAgAIOSDDxD+wguA2ezlqojIG1y6gjNx4kTMnz8ft912G2w2G7755hvs2LEDjz/+uLvr8yvp+SZ0iApAgIr9b4i8rXzuXCjPngUCAgBJQuD69TDdfjuk4GBvl0ZEHuDSJ3G/fv3w4osvwmAwICkpCYWFhXjhhRfQu3dvd9fnN6rMIjJLrOx/Q+QjpKAg2GoGI1UfPQrd/PnQpqZ6uSoi8hSX51ro0KEDOtSMPUH1nSwwQQI4wB+RD7L27ImiTz91jpej+eUXxyPn11/v5cqIyF1cCjg2mw0bNmzA3r17UVpaioiICNx444246667oNGwvwkApOeboVEK6BDJ7weRL7J17+78Ovjjj6EoLkbxl18CHLCUSJZcCjgrV65Ebm4uHnroIURHR6OwsBAbN27EqlWr8NRTT7m7Rr+QlmdGp2gNVErB26UQ0RWUvvsulAUFjnBjsSDko49Qdc89kHQ6b5dGRM3EpYCzf/9+LFq0CME1nfMSEhLQuXNnTJ8+3a3F+QuDyY6ccitS2vOXI5FfCAiAPTERAKD57TcEr1oFS69esNxwg5cLI6Lm4tK12fDwcJgvedTSYrEgIiLCLUX5m/R8x/emGzsYE/kdyw03oOibb5zhJmTFCoT/7W+AKHq5MiK6Fi5dwRkyZAj++c9/4s9//jMiIyNRXFyM77//HkOGDMHRo0ed2/Xo0cNthfqytDwTtCoBbfTsf0Pkj+wJCc6vJa0WYnCws29O8KpVsCcmwvSnP3mrPCJqApcCzo4dOwAA33zzTb31tW2CIGDx4sXNXJ5/SMs3o0tsAJQK9r8h8ndVDz54YUEUof3hB1iSk50BJ3DzZphTUiDGxnqpQiJyhUsBZ8mSJe6uw2+VVNtQUGHDzZ1DvF0KETU3hQLFn38O1ExLo8zJgW7uXBheeAHV994LWCxQnj8Pe9u2Xi6UiC7VpOcjjx49ihMnTjR3LX4pPc/R/4YD/BHJlCA4RkMGYG/dGoXffAPjyJEAgIBffkH0XXdBs3+/Y1urFZAkb1VKRBdxKeC89tprzpnDN27ciPfeew/vvvsuvv76a7cW5w/S8k0I1iiQEKH2dilE5AH2Nm0ghYcDAKzdu8Pwt7/BUjOqe9C//42ov/wFQkWFFyskIsDFgJOVlYUuNSOA/vDDD3jttdfw//7f/3P2v2mpJElCWp4ZXWMDoBDY/4aopRGjolA9cSJQM+CpPSEBloEDIYWGAgCCP/gAIe+/780SiVosl/rgSDWXXPPy8gA4xsEBgKqqKjeV5R+KKu0oqbbjz0m8PUVEgHnIEJiHDHEuK4uKIBiNzuWgTz+F9brrYE1O9kZ5RC2KSwGna9eu+PDDD1FaWooBAwYAcISd0Jq/UlqqtHwTAM4/RUQNM7z00oU+OSYTQlavRvW4cY6AI0kI2LMH5gEDgMBA7xZKJEMu3aKaOnUqgoKC0LZtW0yYMAEAkJubi9tvv92txfm6tDwzdFoF4sJcnrOUiFqa2tvXWi0KvvvO+Ri6Ki0NETNmIHD7dke72QyhstJLRRLJj0ufzKGhobjvvvvqrOvbt69bCvIXkiQhPd+ErrFaCOx/Q0SuCAiAVPNElq1TJ5QsWwZrt24AAO2uXdC99hqKP/sMto4dHVd++LuFqMk4jW4T5RlsKDeJ6BbH/jdE1ARqNSzXXw8pLAwAYO3cGVWTJ8PWrh0AIPijjxDx5JOOR8+J6Kox4DRRWl5N/5tY9r8homtn79ABlVOnAkolAEAMC4MYHQ2oHUNQBK9ejcANG7xZIpFfYcBporR8MyKDlYgKUXq7FCKSIeP48Sh//XXHgiRB8+uvUB854mwP3LgRyqwsL1VH5PuuKuCIoojS0lJ31eI3RElCer5j/Bv2vyEitxMElK5YAcPLLwMAFCUlCPt//w/a2rHIbDao0tI4ijLRRVwKOFVVVXjvvfcwadIkPP300wCAAwcO4Msvv3Rrcb4qu9SKKovI21NE5Fk1t6tEvR6F//kPqseOBQBoDh9G1KRJCNi927Gd1QqIoreqJPIJLgWclStXIigoCEuXLoVK5XjwqkuXLti3b59bi/NV6fk180+xgzEReYkYHQ0pIgIAYO3SBeWvvgpLSgoAIHDLFkSPHAlFYaE3SyTyKpcCzpEjR/DQQw8houaHCQDCwsJQXl7utsJ8WVq+CTGhKuiDOP4NEXmfFBYG45gxkGoGDLS3aQPzzTdDjIoC4OigHPbPf/IWFrUoLgWcoKAgVFwyeVxRUVGdwNNS2EUJJ/PN6MbZw4nIR1n694fhxRed4+gIVVWOQQRrloM+/RSaFnoFnloOlwLOrbfeinfeeQdHjx6FJEk4efIklixZgttuu83d9fmczBILTDaJ0zMQkd+ofPpplP/zn44Fmw3Bn3+OgL17ne0BO3dyBnSSHZfusYwZMwZqtRqrV6+G3W7HsmXLMHz48BY5VUNabf8bXsEhIn+kUqFw82bnJKDKjAxEzJwJw9/+5pgZ3WKBorISol7v5UKJro1LAUcQBIwaNQqjRo1ydz0+Ly3PhNY6NcK0HP+GiPyUSgWpZrJke5s2KP74Y9gSEgAAAf/9L8JfeAElH34Ia8+enDKC/JbLvWQLCgqQmZkJk8lUZ/2gQYOavShfZbNLOFVoweBOwd4uhYioeSgUjiBTw9axIyqnTHHOkRX02WcI3LQJxZ9+CgQEQKiudnRmZughH+dSwPnmm2+wfv16JCYmQqPRONcLgtCiAs6ZYgssdom3p4hItuwJCah67DHnshgdDWvv3kDNJKGhCxZA87//oahm2ghlTg7sej1Q8wQXka9wKeBs3boVb775JhJqLmG2VGl5JggAusSwgzERtQymP/0Jpj/9yblsHjwYti5dnMu62bMBQUDJ6tUAAPWhQ7C3bg0xJsbjtRJdzKWAExISgujoaHfX4vPS881oo1cjOIBTeBFRy2S+5ZY6y5WPPnphfB1JQsQLL8A0eDAMc+YAAAI3b4ald2/Y27b1cKXU0rn0Sf3Xv/4VK1aswOnTp1FUVFTnX0thtok4U2RGV07PQETkZLnxRlhuusm5XLJ4Marvvx8AIJSXQzd3LrQ7d9ZsbEHIkiVQnT7tjVKphXHpCo7NZsP//vc/7L1o3IRaX331VbMX5YtOF1pgE8EB/oiIGiMIsHXv7lyUdDoUfPstUNN3U5WRgeBPPoG1e3fYOnaEMicHwatXo+qBB2Bv185LRZNcuRRwVq1ahXvvvRc33XRTnU7GrrJarVi1ahWOHDmCyspKxMXF4d5770VycnKD22/duhWbNm2CxWJBSkoKpkyZAnXNJHPekpZvglIAOscw4BARuUqMi3N+bevSBQW7d0NSOG4eKLOzod21C9WTJgEANL/8gqDPP4fhpZcgxsZ6pV6SD5duUYmiiGHDhkGr1UKhUNT55wq73Y7IyEjMmTMHH3/8MSZOnIiFCxeioKCg3raHDx/Gpk2b8Oqrr2LJkiUoKCjAunXrru5duUF6vhntIjXQqtn/hoioqaTAQOcTWZaUFBT88ANsHToAcEwpoczLgxgWBgAI+vxzRN5/P1AzKCEsFs6nRS5z6dP6jjvuwMaNGyE18cTSarWYMGECYmJioFAo0K9fP8TExODMmTP1tt29ezeGDRuGxMREhISEYNy4cdi1a1eTjttcjFYRGcUW9r8hImpuguAcU8d8yy0o/uor5yPnol4PW5s2zuWwBQsQOXGiM+QoCgsBq9U7dZPPc+kW1bZt21BWVoZvvvkGISEhddqWLVt21QctKyvD+fPnkZiYWK8tOzsbAwYMcC63bdsW5eXlqKioQGjNyJu1UlNTkZqaCgCYP38+9G4aWvy3swaIEnB95yjo9aFXfgHJmk6ng1KpdNv51lKpVCp+T6mu++4D7rsPtWeFYtgwoFMn6CMjAQCqp54CbDbY1q8HAAj/+x+kxESgBU4ETfW5FHCmT5/ebAe02WxYtGgRhg4ditatW9drN5lMCAoKci7Xfm00GusFnOHDh2P48OHO5ZKSkmar82IHTpdBpQCiA8woKeFfCy1deXk57Ha72863lkqv1/N7Spd3442O/9acJwETJgCiCHNJCSBJiH74YViuvx7l//iHo33HDtiSkmBv4LOG5COukQ7qLgWcpKSkZilCFEUsXrwYKpUKDz/8cIPbaLVaVFdXO5eNNfdeA70wSuanv5Zgz6kqiDV35tYdLMP91/MvTCIiX2C++eY6y2Xz5zv6+AAQKioQ/uKLqHz8cVRNmQJYLAhdtAjGkSNha6bPNPJtLs9FlZGRgRMnTqCioqJOX5yJEye69HpJkrB8+XKUl5fjxRdfhErV8KETEhJw7tw53FiT1M+dOwedTlfv6o27ffprCXb/UVVnXe0yQw4RkY8RBFj79nUuSiEhKNqwwRl4lHl5CNqwAdYePWBLSoIyMxMR06fD8PLLsFx/PYTqaijy82FPTAQa+Xwi/+JSJ+PU1FTMnj0bR48exaZNm5CZmYmtW7ciLy/P5QOtXLkSOTk5+Pvf/37ZR82HDh2KnTt3Ijs7G5WVldiwYQNuviSle8KeU1VXtZ6IiHyIIMDetq1zygh7mzbI37MHptqRmO122Lp2hVjT70t96BCix4+H+sgRAIDyzBkErVkDoazMG9VTM3Ap4GzatAkvvfQSZs6cCY1Gg5kzZ+K5556DUql06SCFhYVITU1FRkYGpkyZgsmTJ2Py5MnYs2cPioqKMHnyZOeoyH369MGYMWMwd+5cTJ06FdHR0ZgwYULT32ETiY08MNbYeiIi8nFKJVAzppq9fXuUvfUWbJ06AXCM0VM2Zw5snTsDADSHDyPsvfcg1Dylpf3uO+gfewxCeTkAQCgrg1DFP3h9mUvX4QwGA7rXjE4pCAJEUURycjLef/99lw4SHR192bFs1q5dW2d59OjRGD16tEv7dheF0HCYUQier4WIiNxLjI6G6Y47nMvGu+6CadgwSOHhjhU1475JNd0lgtesQfDnnyN/zx5ArYbm11+hKCuDacQIT5dOjXAp4Oj1ehQUFCAmJgatWrXCgQMHEBoa2mg/GjkY3Cm4Xh+c2vVERCR/0kWPm5tGjKgTXszDhjmezqq5IhS4YQPU6enObUKWLoVgNKLi+ecBODo9SyEhzjF/yP1cSihjxoxBTk4OYmJiMH78ePzrX/+CzWbDQw895O76vKa2I3HtU1QKwRFu2MGYiIisPXvC2rOnc7l83jwoioudy0JVVZ1bWBFPPw0pJASlixYBAAJ27YK9VSvYunb1XNEtjCA1YXhim80Gm80Grda3RvbNO3jQ2yVQC3A0PR3PzpmD1C++8HYpssJxcEjOAjdvhqTVOq/wRA8fDvOQITC8+ioAIPyZZ2AeMgTGceMAAKoTJ2BPSHDeEqPGxV309NzFGu1kLIpio/8UCgU0Gg1EUXRbwURERHJhvPPOC7e4JAnFn36KqkcfdSzb7Y7OzLWfqWYzIidPRlDtH1FWK3SvvALNr786lkXR2dmZGtfoLap7773XpR189dVXzVYMERGR7AlCnVnWoVSidOnSC8sKBcr+9S/YaqYzUpSWQnP4MMw33ODYPDcX0WPGoGzOHJjuuAOKkhIE/fvfMI4cCXubNo65utjXp/GAs3jxYk/WQURERACgVsM8ZIhzUYyJQeHWrReWg4NhePZZWHv1AgAoz51D8MqVsCQnw96mDdSHDiHihRdQ+t57sPbsCcX5846ANGhQi7rl1WjAiY6ORllZGcJrH5EjIiIir5MiIlA9ebJz2ZqcjPy9ey88yh4WBtPw4bDHxgIANAcOIHzOHBR+/TXsoaEI2LkTwWvWoOyddyBGRkKZmQlldjYsAwY4nwqTg8sO9PfMM8/UWX777bfdWgwRERE1QUCAM5zYOnWC4aWXnKM4m0aMQNG6dRcmHVWpIAUEQAx2DHui3bED+unTAbsdABD05ZfQP/AAUDPIoer4cQT83/85bn35kcs+Jn7pA1bHjh1zazFERETUzAICYOvY0bloHjKkzi2w6rvucly9qXkyWgwLg71VK2dgCvrmG2h37kTBsGEAgNCFC6E+dgwlq1YBADQ//wzBZKo3+am3XTbgCOykREREJGtSRASsFw9qePvtMN1+u3O5Yto0VF304JGtbdsLT3wBCPr8cyiLipwBJ3zmTMBmQ9nChQAc01pIXujuctmAY7fbcfToUeeyKIp1lgGgR48e7qmMiIiIvE7S6WDX6ZzLxrvuqtNe/sYbEAwG57IlORmw2ZzLiooK2H0t4Oh0Oixbtsy5HBISUmdZEAQ+bUVERNSCScHBkIIvTGNUfd99ddrtNY+7e9plA86SJUs8VQcRERFRs7nsU1RERERE/ogBh4iIiGSHAYeIiIhkhwGHiIiIZIcBh4iIiGSHAYeIiIhkhwGHiIiIZIcBh4iIiGSHAYeIiIhkhwGHiIiIZIcBh4iIiGSHAYeIiIhkhwGHiIiIZIcBh4iIiGSHAYeIiIhkR+XtApqT/rHH6iwbhw+HccIEwGiE/pln6m1vHD0axjvvhFBaioi//71ee/X48TCNGAFFXh7CX321XnvV/ffDPGQIlBkZ0P3zn/XaKx95BJaUFKjS0xH2zjv12iumToW1d2+of/8doUuW1Gs3PP88bF27QvPLLwhZvbpee/lLL8Herh0CfvwRwZ9+Wq+97PXXIcbFQbt9O4LWr6/XXvrmm5AiIhC4eTMCt26t117y3ntAYCAC161DYGpq/fYPPgAABK1ZA+1PP9VpkwICULpoEQAgeOVKBOzfX6dd1OlQtmABACBk0SJojhyp026PiUH5P/4BAAh9+22oT56s025r0waGV14BAIT94x9QZWbWabd26YKKF14AAOheeQXKgoI67ZaePVE5fToAIHzmTCjKy+u0mwcMQNWUKQCAiOnTIZjNzjZddTUUJSXO5UvPO4DnXlPOPZVKBb3NBoDnXmPnHgCYBg1C9QMPAOC55+q5ZxRFaAQBSkHg7z05nnsHDtTbFpBZwCFyh76HDuG40Vhvfat+/eosJ2m1OHTddZ4qi4gacfOECUg/fbrhxuHDnV/yZ1beBEmSJG8X0VzyDh70dglE1ER6vR4lF10ZI2ou02bPxtCBA3H3qFHeLoXcIK5v3wbXsw8OERERyQ4DDhEREckOAw4RERHJDgMOERERyQ4DDhEREckOAw4RERHJDgMOERERyQ4DDhEREckOAw4RERHJDgMOERERyQ4DDhEREckOAw4RERHJDgMOERERyQ4DDhEREckOAw4RERHJDgMOERERyQ4DDhEREckOAw4RERHJjspTB/ruu++wa9cuZGZm4qabbsLUqVMb3G7Xrl1YtmwZNBqNc92sWbNw3XXXeapUIiIi8nMeCzgRERG466678Pvvv8NisVx22y5dumDevHkeqoyIiIjkxmMBJyUlBQBw5swZFBcXe+qwRERE1AJ5LOBcjYyMDDzyyCMICQnB4MGDMXbsWCiVynrbpaamIjU1FQAwf/586PV6T5dKRM1EpVLxZ5jcIkCjQUhwMM+vFsbnAk737t3xzjvvICoqCtnZ2Vi4cCGUSiXGjh1bb9vhw4dj+PDhzuWSkhJPlkpEzUiv1/NnmNzCbLGgsqqK55dMxbVr1+B6n3uKKjY2FjExMVAoFGjTpg3Gjx+Pn3/+2dtlERERkR/xuYBzKUEQvF0CERER+RmPBRy73Q6LxQJRFCGKIiwWC+x2e73tDh06hLKyMgBATk4ONmzYgP79+3uqTCIiIpIBj/XB2bBhA9avX+9c3rNnD8aPH49bbrkFM2bMwMKFCxEVFYUjR45g6dKlMJlM0Ol0zk7GRERERK4SJEmSvF1Ec8k7eNDbJRBRE7GTMbnLtNmzMXTgQNw9apS3SyE3iOvbt8H1Pt8Hh4iIiOhqMeAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeww4BAREZHsMOAQERGR7DDgEBERkeyoPHWg7777Drt27UJmZiZuuukmTJ06tdFtt27dik2bNsFisSAlJQVTpkyBWq32VKlERETk5zx2BSciIgJ33XUXhg0bdtntDh8+jE2bNuHVV1/FkiVLUFBQgHXr1nmoSiIiIpIDjwWclJQUXH/99QgNDb3sdrt378awYcOQmJiIkJAQjBs3Drt27fJMkURERCQLPtcHJzs7G+3atXMut23bFuXl5aioqPBeUURERORXPNYHx1UmkwlBQUHO5dqvjUZjvas/qampSE1NBQDMnz8fer3ec4USUbNSqVT8GSa3CNBoEBIczPOrhfG5gKPValFdXe1cNhqNAIDAwMB62w4fPhzDhw93LpeUlLi/QCJyC71ez59hcguzxYLKqiqeXzIVd9Fdn4v53C2qhIQEnDt3zrl87tw56HS6K/bdISIiIqrlsYBjt9thsVggiiJEUYTFYoHdbq+33dChQ7Fz505kZ2ejsrISGzZswM033+ypMomIiEgGPHaLasOGDVi/fr1zec+ePRg/fjxuueUWzJgxAwsXLkRUVBT69OmDMWPGYO7cuc5xcCZMmOCpMomIiEgGBEmSJG8X0VzyDh70dglE1ETsg0PuMm32bAwdOBB3jxrl7VLIDeL69m1wvc/1wSEiIiK6Vgw4REREJDsMOERERCQ7DDhEREQkOww4REREJDsMOERERCQ7DDhEREQkOww4REREJDsMOERERCQ7DDhEREQkOww4REREJDsMOERERCQ7DDhEREQkOww4REREJDsMOERERCQ7DDhEREQkOww4REREJDsMOERERCQ7Km8X0JzUv/9eZ1mMjoY9Ph6w26E+erTe9vbYWIhxcYDVCvXx4/Xb4+MhRkcDJhPU6en12xMSIEZGQqiuhuqPP+q129q0gRQRAaGyEqrTp+u3t2sHSaeDUF4OVUZG/faOHSGFhEAoLYUqM7N+e+fOkIKCoCguhjI7u167tWtXQKuForAQytzc+u1JSYBaDUVeHpT5+fXbe/QAlEooc3OhKCys3967NwBAmZUFRUlJ3UaFAtaePR3t585BUVZWp1lSq2FLSnK0nz0LhcFQt12jga17dwCA6vRpCJWVddsDA2Hr0sXRfvIkBKOxbntICGwdOzraT5yAYLHUaRfDwmBv397Rfvw4BKu1bnt4OOxt2wIA1EeOAKJYt12vhz0x0dF+yXkH8NxryrknhIVBXXMe8Nzjudec515odTV0+flQ//47f+/J8dzr27fetoDMAo6o19ddjo52fDPtdogN/JJ1tlssEPPyGm83GiE2cKKLMTEQo6MhVFVBLC6u1y7FxDj+Z1RUQCwtbfD1UkQEhIAAiJec6AAgxsZCCg2FQq2GeMmJ7mwPDgaUSgjV1Q22IzAQACCYTA23azSA3V7vRAfgeO9KpaPNbm+4vZF9Q6m80F5VBSguuVio0TjbFRUVEFWXnIparbNdLC+HoNHUaZaCgi60FxfXe/9SWJizXSoshHRJjVJ4+IX2/HxIl/4i0Osv7D83t977d54bAMSsrHpvn+deE8698HDnecBzj+fetZx7t4wahfRLPiA/3rULoaGhCAkJwfnz5wEA3dq0wY/vv1/39Tz3HF/74bl3KUGSJOmKW/mJ3Aa+oUTkH6KiolBUVOTtMkjGBEGARqOBKIqo/egTRRHiJVcqyL/Ex8c3uF5WV3CIiIgaI0kS7DVXJS7+muSJnYyJiKjFsNlsABwBR61We7kacidZ3aIiIiK6EqPRiICAAACAyWSCWq1m2JEhXsFxgxUrVni7hGvmq+/Bm3V58tjuPFZz77u59jdr1qxm2Q/5Fl/8XaLVaiEIAhQKBQIDAyGKIkwmEzz1974vfk+ulj+8BwYcN+jXr5+3S7hmvvoevFmXJ4/tzmM197599Vwh3+CL54cgCBAEwfl1QEAA1Gq18/aVu/ni9+Rq+cN74C0qIvIJs2bNwvz5871dBhHJBK/gEJFPGD58uLdLICIZ4RUcIiIikh2Og0NERC1au3btEBoaCqVSCZVKhQMHDmD27NnYtGkTFAoFYmJi8PHHHzc4oFxDrwWAv//979i2bRv69OmDNWvWAADWrl2LkpISPPPMMx59fy0Vr+AQEVGL1q5dOxw4cABRUVHOdQaDAWFhYQCA999/H8ePH8fy5ctdem15eTlGjx6NPXv2YNKkSZg1axY6deqE0aNH47vvvuMj6R7CKzhE5LNOnjyJTz75BCqVChEREZg2bRpUl87fQ+QGteEGAKqqqpxPXblCoVDAYrFAkiQYjUao1WosWLAATz/9NMONB7GTMRH5rKioKLz22muYO3cuYmNjnZf/iZqTIAgYMWIE+vXrhw8++MC5/uWXX0ZiYiI+++wzvP766y6/NjQ0FOPGjUNycjLat28PnU6H/fv3Y8yYMR55P+TAW1RE5BfWrVuHtm3bIiUlxdulkMzk5uYiPj4eBQUFuO2227Bo0SIMGTLE2f7GG2/AZDJh7ty5V/1aAHj00UcxdepU/Pbbb9i+fTt69eqFV155xe3vq6XjFRwicrvvvvsOs2bNwn333YclS5bUaausrMSCBQswefJkPPXUU/jpp5/qvb6goACHDh3yi8HFyP/Udh6OiYnB2LFj8euvv9Zpv++++7Bhw4YmvfbQoUMAgC5dumDNmjVYt24djh49ij/++KO53wZdggGHiNwuIiICd911F4YNG1avbdWqVVCpVFi5ciWefvpprFy5EllZWc726upqLFmyBNOnT2f/G2p2VVVVqKiocH69fft29OjRo04A2bx5M7p16+byay82e/ZsvP7667Barc7ZyxUKBaqrq931lqgGf1sQkdvV3lY6c+YMiouLnetNJhN++eUXvPPOO9BqtejWrRv69++PH3/8EZMmTYLdbsd7772Hu+++u8FHdImuVX5+PsaOHQvAMdP4fffdhz//+c8YN24c0tPToVAo0LZtW+cTVLm5uXj00Ufxn//8p9HX1tq4cSMGDBjgPHdvuOEG9OzZE7169ULv3r09/E5bHvbBISKP+fLLL1FcXIypU6cCAM6ePYtXXnkFn332mXObzZs34/jx45g1axZ+/PFHfPLJJ0hMTAQAjBgxAjfeeKNXaici/8IrOETkNSaTCUFBQXXWBQUFwWQyAQCGDBlSr8MmEZEr2AeHiLxGq9XCaDTWWWc0GqHVar1UERHJBQMOEXlNq1atYLfbcf78eee6c+fOOW9JERE1FQMOEbmd3W6HxWKBKIoQRREWiwV2ux1arRYpKSn46quvYDKZkJaWhv379/O2FBFdM3YyJiK3W7duHdavX19n3fjx4zFhwgRUVlZi6dKlOHLkCEJCQjBp0iQMGjTIS5USkVww4BAREZHs8BYVERERyQ4DDhEREckOAw4RERHJDgMOERERyQ4DDhEREckOAw4RERHJDgMOERERyQ4DDhEREckOAw4R+aQPPvig3ujHF5swYQLy8vKuap979uzBP/7xj2stjYj8AEcyJiK327t3L7799ltkZWUhICAAMTExGDp0KEaMGAFBEJq0zwkTJuD9999HXFxcM1dLRHKg8nYBRCRvW7ZswebNm/HII4+gd+/e0Gq1yMjIwJYtW3DLLbdArVbXe40oilAoeIGZiJqOAYeI3Ka6uhrr1q3D1KlTMXDgQOf69u3b4+mnn3YuL1myBBqNBkVFRTh+/DhmzpyJPXv2IDIyEvfccw8AYPPmzdi6dSsEQcDEiRMve9xdu3Zh/fr1MBgMCA0NxT333IPBgwdj165d+OGHHzBv3jxs2rSpzi0wm82GQYMGYerUqaiursYnn3yCQ4cOQRAEDBs2DBMmTGDoIvIjDDhE5DYnT56E1WrFgAEDrrjtTz/9hBdffBF///vfYbPZsGfPHmfb4cOHsWXLFsyePRsxMTFYsWJFo/sxmUz46KOP8MYbbyA+Ph6lpaWorKyst92YMWMwZswYAEBRURFefvll3HDDDQCAxYsXIzw8HO+//z7MZjPmz5+PyMhI3HbbbVf7LSAiL+GfI0TkNrVXUJRKpXPdK6+8gr/+9a+YNGkSjh8/7lw/YMAAdOvWDQqFAhqNps5+9u3bh5tvvhlt2rSBVqvF3XfffdnjCoKAzMxMWCwWREREIDExsdFtLRYLFixYgJEjR6Jv374oKyvD4cOH8de//hVarRY6nQ6jRo3Cvn37mvhdICJv4BUcInKb0NBQVFRUwG63O0NO7VNMTzzxBC5+xiEyMrLR/ZSWlqJDhw7O5ejo6Ea31Wq1ePbZZ7FlyxYsX74cXbt2xQMPPIDWrVs3uP2yZcsQHx+Pv/zlLwAcV3Psdjsee+wx5zaSJF22PiLyPQw4ROQ2Xbp0gVqtxv79++v0wWnI5Z6mioiIQHFxsXO5qKjosvvq06cP+vTpA4vFgi+//BIrVqzA66+/Xm+7jRs3Ijc3F/PmzXOui4yMhEqlwurVq+tceSIi/8JbVETkNsHBwRg/fjxWr16Nn3/+GSaTCaIoIiMjA2az2eX93HDDDdi1axeys7NhNpvx73//u9Fty8rKcODAAZhMJqhUKmi12gY7Bx86dAjbtm3DzJkz69wSi4iIQO/evbFmzRpUV1dDFEXk5eXVuZ1GRL6PV3CIyK3GjBkDvV6PTZs2YfHixQgICEBsbCwmTZqErl27urSP5ORkjBo1CnPnzoVCocDEiRPx008/NbitJEnYsmULFi1aBEEQ0K5dOzz66KP1ttu3bx8MBgNmzJjhXDd48GA89thjmDZtGj777DM899xzMBqNiI2NdXZIJiL/wIH+iIiISHZ4i4qIiIhkhwGHiIiIZIcBh4iIiGSHAYeIiIhkhwGHiIiIZIcBh4iIiGSHAYeIiIhkhwGHiIiIZOf/A1ceFUrJs0pVAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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Jubm5AG1u1HeqWqjWug7rUt8bAEqpcVjJagIQ6QQVdQqKXIwalBrpMMA06XPPPdjKy6l88EHwD9hwOmwcNqQP67ZVYppDwjeQQwghghB0glJKJQEjgBZPnGqt7+/uoGJBZU0jlTXuQ5vivbsYBmZCAmZSktWLaja67/D8dL7eUklheR0Ds0Jb7UIIIToj2PmgLgX+CDQCdc2aTGBICOLq8QIP6Ebo/lNr1fPmBXpOzY3PT+PkjR+Qd+KV9N1XRkVaP7ZdeyMD514WgSiFEGK/YHtQi4CfaK1XhDKYWFJQ5MJmwODs6EhQTcnJvnMn6b/+NZX33493yBBcT7/E3BWLSfA0AJBVVULK/XexFiRJCSEiKtixxY3A+yGMI+YUFLkYmJUUfcO33W7sO3Zg370bgGGLHwokpyYJngaGLX4oAsEJIcR+wfagfgM8qJS6S2vdpaFMSqnnsUYAJgNFwCKt9ZMdbPtL4BYgEXgFuEZr3dDettHINE22F7mYGIXzLXnz8yn58ENwWqWXMqtK290us6qUonAGJoQQrQT75/1m4GygWCnl9X/5lFKdqcV3L5Cnte7j39dCpdTE1hsppU4H5mMlszwgH7irE8eJuJLKBmobvFFz/6kNf3JKXLaMuoT2Y6xI6xfOiIQQoo1ge1DPAc8Cf6XlIImgaa3XN1s0/V/DgTWtNr0MWNq0vVLqt1jTy8/vynEjITBAIhpG8HXENIn/979x5/SnfmcjCd79RWPrHfHWQIkIhieEEMEmqL7AAq118E/1tkMptRi4HOvS3ZdYD/m2Ng54rdny10B/pVRfrXV5q/3NAeYAaK3Jyso6lPC6TXFlCXFOGxNGDcJuj7J7UM09/TQOh4NNv3+CAX+4l8yqUspSs/jishuYcue8SEcXVRwOR9ScX0L0FsEmqKeAS7B6UV2mtb5WKXU9cBxwCtDefaUUoKrZctPrVKBFgtJaLwGW+BfNaHnSf+O2MoZkJ7F3b0WkQwlK1qwfkbB7A3tuvJFFbxVSUtlA/o49pCSFaYqQHkAqSQgROv5KEm0Em6COAeYqpf4XKG7eoLU+qTOBaK29wMdKqdnANcAjrTapAfo0W256Xd2Z40SK1+tjZ2ltj6oQ7ti4keRnn6XxuOOYPf1EFj63gWUf7uLyM4ZFOjQhRC8WbIJ6wv/V3cce3s769Viz9mr/8hFAcevLe9Fqd1kdbo/JsAhVMO8K96RJFK9ahS8nh4HA9Ek5vPPfPfxgbF8OG9LnoO8XQohQCCpBaa2fOZSDKKWygSlYNfzqgGnATGBWO5s/CzytlHoB2APcDjx9KMcPp4IIT7HRVb6cHACcX37JZS8s4eujruCFdwtYcOl4nI4ovo8mhIhZHX7yKKXODmYHQW5nYl3O2wXsBX4P3Ki1fk0pNUQpVaOUGgKgtX4Hq3LFv4Ht/q87goklGhTscZGcYCcrre0kgT2B47vvSFj7DZdO7EPJ3gbeWh1dFeKFEL1Hh9NtKKVeBA7HGuL9AVa18mqswQqjgJOB2cBXWuvZYYn2wKJiuo27n1lHWoqTG34yOtKhdF1dHSQm8tRbW/j823L+97IJ5PZNjHRUESWDJIQInY6m2+iwB6W1noV1GW4g1nNQpViX50qAZ4Ac4MIoSU5RocHtpbC8jmE5KQffOJolJoJpcvWal7lx+aO88M5WfJ2YN0wIIbrDAe9Baa3XAnMhMN1GOlCpta4NfWg9z47iWkwThvaw+0/tMgycKUkMGpzJlj01fLy2lJMO7zkjE4UQPV/Q80H5k5IkpgMIVJCIhQQF1Nx4Iwmmyahlm3nz3e84Ylgaaak9896aEKLnkeFZ3aigyEVGahxpyTHygKthYNhsXHJsX377zE0Uz+tRJRGFED2cJKhuVFDk6lHPPwWr35Asqo89nhWJ+azdWhnpcIQQvYQkqG5SU+umrKohNu4/tWYYpP1pEaVHHctL723Hu7Ug0hEJIXoBSVDdpKDYuj0Xiz0oAIfdxuxpeWR9+w25p5xMwmuvHfxNQghxCDocJKGU2on1gO0Baa2HdGtEPVRBUQ0GMCRa54DqBiMGpZIz7TiWbTuP/HHHyHQcQoiQOtAovubPN03GmqfpEazKDkOxhp8fUnXzWFJQ5CInM4HEeHukQwmpc07N486Cy0j/tIL5Q7JI+Por3JMnRzosIUQM6jBBaa0/aHqtlHoMOF1rvbvZureBd4AHQhphD2CaJgVFLsYPS4t0KCGXnOBAnTqEJ9/cStlvFnHkS3+mZOVKvCNGRDo0IUSMCfYeVC7WNBjN1YBc5QHYW91Ida2HoTF8ea+5SaMzGT8sjQeyTmLXvfdLchJChESwD+q+DryulFqIVfB1MHCrf32vt83/gO6waJ7ivRsZhsHMqUO5a2c1jycdzbWmiWPbNuxFRTQef3ykwxNCxIhge1BXA58AjwNfAH8CVvvX93oFRS7sNoOBWUmRDiVsstLiOfuHA/lmayVffreXtNtvJ/2Xv4TGxkiHJoSIEcHOB1UPzPd/iVYKilwMzk7qdfMmTTm6P6s3lvPyyh2MX/QASXU1EBcX6bCEEDEi6E9UpdRpSqmlSql/+pcnKaWmhC60nsHnM9lR7Oo195+as9sMZp+Wx75aN8u+bcQzciQASS+8gPOLLyIcnRCipwsqQSmlrse6rPcdcJJ/dR2wMERx9RhFe+upb/T1mvtPreXlJHPqUf358OsSthTWYLhcpDz2GMnPHNIkzEIIEXQP6kZgmtb6PsDnX/ct0INn5ese2/0DJGKyxFGQzvnhQNJTnDy/ogBPQiJl//wnlffdB4BRVwcyl5QQoguCTVCpwE7/66ZPGyfQ6++Ib9vjIiHORk5GQqRDiZiEODszpw6lsKyO5Z8X4evb15r0sKGBzJkz6XOXVEEXQnResAnqQ9oOkPgF8O/uDafn2V7sYkj/ZGy2NrMV9ypHjMjg6JEZVD75Is6xR5IzcBBxRx5LhT2RxokTIx2eEKIHCjZBXQ+cp5QqAFKVUpuAnwK/ClVgPYHb42NnSW3MTFB4qE77/iOuW76YflWl2DDJ2ldKxuer2bK9AoC4VauwFRZGOEohRE8RVILSWu/BqsengFlYdfmO1VoXhTC2qLertBavz5QE5XfYk4+Q4GlosS7B08CwxQ9BfT0Z119P+nx5UkEIEZzOPLjjAOIBm9b6UyBRKdWrP5mbpniP1Sk2OiuzqrTj9QkJlD//PJWLFoU5KiFETxXsMPMJwGbgCWCpf/XJwF9CFFePUFDkIjXJQUaqPJwKUJHWr9315WlZAHjGjMGXkwOmSdott5D09NNhjE4I0dME24P6E7BAa30Y4Pav+wA4ISRR9RDbi1zk5SRjGL17gESTbdfeSL0jvsW6ekc8zxw/m399tgezabh5YyP24mLsJSURiFII0VMEm6DGAc/7X5sAWmsXkBiKoHqCugYvRRX1cv+pmYFzL2PtTXdQlpaND4OytGy+/NUCqs85l79/uIsl/9xCfaMX4uOpWLqU6ptuAsC+Y4f1vJQQQjQTbDXzAmAi8HnTCqXUMcD3IYipR9hR7MIE8nJSIh1KVBk49zIa515G0+iZocAc02TF50X8/aNdFL5Qx9Vnj2BAX//fNo2N9J05E8+IEVRI9QkhRDPBJqjfAG8qpR4H4pRSt2JVMr8yZJFFuaYpNqQHdXCGYTB98gCG9k/miTe2cO8LG7js9GFMHJ0JcXHsW7AAb3Z2pMMUQkSZYIeZvwGcCfTDuvc0FDhfa708hLFFte1FLrLS4klJDDbHi9FD+vC/l4wjNyuRJW9sYdkHO/H6TOpPPx33UUcBkPT888R99FGEIxVCRIOgP1211l8A14Ywlh5lW5GLEblyea+zMlLj+PWFh/G393ey4vMithe7uPJHw+mT7AS3m+RnnsEzbBiNJ54Y6VCFEBEWVIJSSsUBtwMzsaZ/LwReBu7xzxXVq1S53OytbuzVBWIPhcNuY+bUoeTlJPPCuwXc8/x65vx4BMNzUyh75RVwWKelUVuLmZgIMkpSiF6pM8PMp2DV35vs//dkYHGI4opq8oBu9zhuXBa3zByLw27jgb9+y/tfleBLTcVMSgKPh8zLLrNm6Y0CXq+X6dOnc+mllwLwwAMPMHHiRE477TROO+003nvvvTbv2b17NxdccAEnn3wyp556Kk8++WSg7Z577mHatGn84he/CKxbtmxZi22E6O2CvcR3LjBca13pX96glFqNNYrvf0IQV1TbXuTCMGBw/94zxXuoDM5O4rbZY3nq7a289N52tu2p4eJpQ4lz2Gk45RS8/ftHOkQAHn30UUaOHEl1dXVg3ZVXXsnVV1/d4XscDgd33HEHEyZMoKamhjPOOIOTTjqJnJwcPv/8c959913mzp3Lxo0bycvLQ2vNCy+8EI5vR4geIdgeVBHQ+tM4EdjTveH0DNuKahiYlUi80x7pUGJCcoKDa88dyY+Pz2X1hnJ+99JGSqsaqLnuOuouuACA+H//m7jVqyMSX2FhIW+//TYzZ87s1Pv69+/PhAkTAEhJSWHkyJEUFRVhs9lwu92Ypkl9fT1Op5PHH3+cK664AqfTGYpvQYgeKdge1HPAO0qpR4FdwGDgOuDZ5tO+a61Xdn+I0cU0TbYXuThyREakQ4kpNsPgrOMGkpeTzNK3tnLP8xv4nzPzOXx4OpgmqQ8+CF4vZW++GfZ7UnfccQf33nsvu3btarH+qaeeYtmyZRx++OEsWLCA9PT0Dvexc+dO1q1bx1FHHUVKSgozZsxg+vTpnHDCCaSmpvLVV1/xyyi5nClEtAi2B3UV1qSFt2Hdd7oV6IP1LNRS/1evuHheVtWAq95LXi+d4j3Uxg9L539njyMrLZ7HXv2O/9y2mLhxR+P44gtqtu5i92PPQn09ju/D84z4ihUryMrK4uijj26x/tJLL2XVqlUsX76c7Oxs7r777g734XK5uPLKK7nrrrtITU0F4Nprr2XFihXccccd3H///dx00028+OKLXHXVVTz00EOh/JaE6DGC6kFprYcdykGUUvFYiW0akIl17+o2rfXb7Wx7OVbCa1775iyt9fuHEkN3aRogkddfElSoZKXFc/NFY/jotsX8WD8YmMIjs7qcpPvvouKD5WR98SklH32ELzc3pLF8/vnnLF++nFGjRlFbW0t1dTXXX389jz76aGCbiy++mMsuu6zd97vdbq688krOO+88ZsyY0aZ93bp1AOTn57NgwQL+/ve/c80117B161by8/ND800J0UN06SlTpdSpgEdrHewTlQ6sKeNPBnYAMwCtlJqgtS5oZ/tPtNZRWYh2W5ELp8NgYFavLUMYFnFOG2e+ubTd+aXi165j3913709OjY0QF5qK8rfeeiu33norWVlZvP766zz++OM8+uijFBcX098/gOPtt99m9OjRbd5rmibz5s1jxIgRXHXVVe3uf9GiRSxatAi3243X6wXAZrNRJ7UJhQh6uo0PlFI/9L++BesZqJeVUrcF836ttUtrfafWukBr7fNXptiGVd+vR9le5GJwvyTs9s5MpSW6oqP5pTKqy1nc9zjWbq2E776n//HHE7dqVVhjW7hwIVOnTmXatGmsWrWKO++8E4CioiIuueQSAD777DNeeeUVVq1a1e5w9HfeeYcjjzySnJwc0tLSmDhxIlOnTsUwDMaNGxfW70eIaBRsD2o88Kn/9ZXAKUAN8B/g/zp7UKVUf2AUsL6DTY5SSpUBFVgDNO7VWnva2c8cYA6A1pqsrKzOhtIpXq+PHSW1nDZ5cMiPJaz5pbKq2k7JUdYni6+3VPHJ+nLy68u4MXsYlZnDGJ2RiSNEfzg4HA7OPvtszj77bABeeumldrfLysri7betK9czZsygoaGh3e0AZs+e3WL5kUce6aZohYgNwSYoG2AqpYYDhtZ6I4BSqtND2ZRSTuAF4Bmt9bftbPIhVkLcjjXNx18BD3Bv6w211kuAJf5Fs6ysrLPhdMqu0loa3T76p9sJ9bEE7L72RlLuv6vFZb56Rzzbr/sli646gg3b97FmU19u7jOf+jd3kfxeEfPWPI/t7DPJPnsqdlv3jfbLysqS/3MhQiS3g3vJwSaoj4E/AgOAfwD4k1WnfmOVUjasHlEjMLe9bbTWW5strlVK3Q3cRDsJKtwK9kgF83AaOPcy1gLDFj9EZlUpFWn92HbtjQycaw1IOGJ4OkcMT8ft8bF+WxXrvixg0H8/4N0aOw/s6cvRIzOYODqTkQNTsXVjshJChEewCepyYB5QCizyrzsMeDjYAymlDKzRef2BGVpr90He0sQEouLTpaDYRVK8nez0+INvLLpF6/mlBrazjdNh48iRGRw5MoPG0/9NZmEdo77fR9F7/+WVdw325o3k6JEZTBqdyfCBKdiktp8QPUKww8zLsZ6Bar7uzU4e60/AGGCa1rrDIUpKqTOBL7TWxUqpw7DmovpbJ48VEgV7XAyVKd6jmjMtlaPSUjlqTDaZT87DXVzGA/P/wn/WlfL+VyWkpzg5elQmk0ZlMCw3hT2PPdthD601wzD2T1svhAi5sExmpJQaivWwbwNQpJRqaroK+AjYAIzVWu8ApgJPK6VSgGKsqeY7PRCjuzW6vewuq+X0YwZEOhQRpMqlS7EVFTFn/Gjq691s+nwL/ymz8+HXJaz8opjTvv+IOW/9MXCPK6uqhJT772IttElSjY2N2Gy2wFBwIUToGTH0F6FZWFgYsp1v2V3Nope/5ZpzRkiZox4oeckSUh96iNJ33qGmfy5fb6nkhJ9MI3tf26HsZbnD8H35KR6PB8MwcDgcpKamUlFREYHIhYh9/kESbS5NyXSwQZIp3nu2+tNOw1ZZiXfwYBINgx+M6UvWvvbH+CSf/yMqvV7sdjuGYeB2u7HZ5Lk3IcKtU791SimbUqpXXuPaXuQiPcVJekpoKhaI0PIOG0b1zTeDYWArKyNrxgz2Jae32c6XkUFtziDsdjumaeLxtHn8TggRJsFWkkhXSr0I1GPV0UMpdbZSamEog4smBUUu6T3FCFtFBYbbza6Zl1DvaDkis/re31F3xumYponNZsPhkIsMQkRKsD2ox4EqYCjWM0wAnwAXhiKoaOOq81BS2SAJKkZ4Ro2idMUKMu+6hbU33UFNQgo+oCwtm+LMHBwDc/B6vXg8HulBCRFBwSaoqcAvtNZ7sJ5LQmtdCmSHKrBosr246f5TSoQjEd3G/6jAoMvOZ19qIj8fO5bGDV+S8sOJ+Hy+CAcnhIDgB0lUAVk0m0FXKTWEXjKjbtMUG0NliveYMGXKFDZt2tRyZWkpTw1s+Rjw6NGjWbky5ufgFCJqBduDehJ4xT/Nhk0pdRzwDNalv5hXUOSif0YCSQlyPyIWrFy5kt27dwe+3nvvPUaPHs3u3bspe+45GseMofDbb1m5ciX2nTsxZOoLISIi2E/c32ENkHgMcAJ/Af5MJ0od9VSmabKtyMWYIX0iHYoIg4YpUyg99dTAJcD0X/0KW1UV5po1EY5MiN4n2FJHJvCQ/6tXqaxxs8/llgESvUmzUlbVN92Ebe9eUgwDTJPU++6j7qc/xTNiRAQDFKJ3CPqalVIqDzgcaDFSQGv9YjfHFFUK5AHdXq3xmGMA66R3fP89yU8+iWfkSCtBeb1gs7VIaEKI7hNUglJK3QoswJpgsPkFeROI8QRVg81mMDhbBkj0dp6RIyn+7DPMFOtvtMRly0h54gnKX3oJX79+EY5OiNgTbA9qHjBRa70hlMFEo4KiWgZlJeJ0SKkbAWZmZuC1LyMDz4gR+PyzKzs//xzP8OGYGVKrUYjuEOynbjlQEMI4opLPNK0KEgPk8p5oq2H6dPY+/rh1ic/jIfOqq8i44YZIhyVEzAi2B3UjsEQp9RBQ0rzBP0VGTCrZW099o5e8/pKgxEE4HJQ//7x1XwowqqpIu/VWam68Ec+oUREOToieKdgeVBwwHfgvVk+q6WtbKIKKFoEBEtKDEkHwjBmDZ/x4AJybNpHwwQfQaFUGM1wukLJJQnRKsAlqMdaMun2wnoNq+orp0t4FRS7inTYGZCZGOhTRwzQecwxFa9YEElbqAw+QffLJ0NAQ4ciE6DmCvcTnAJ7SWveq6UQLilwM6Z+MzSbDiEUXJCQEXjb88If40tIg3qqenvjqqzRMnoyvVXklIcR+wfagfg/MV0r1mk9qj9fHzpJa8qT+nugGDVOnUuMfQGFUVZE+bx4pS5ZEOCoholuwPahfADnAbUqp8uYNWush3R5VFNhdVofHa5I3QCqYi+5lpqVR8v77mP7elGP9etLuvJPK3/0Ob35+hKMTInoEm6BmhzSKKFSwRypIiNDxDh4ceG0vLsZWWorP/4yVbfdu63Wi3PsUvVuwtfg+CHUg0aagyEVKooO+fWJ6HIiIAm0K1N50E/aSEkpXrJAySqJX60wtviOBE7HmhQr81mitF3R/WJHXNMW7IR8QIhyanWc1c+diq6iw1pkmqQ8+SO055+CVArWilwm2Ft8c4A/AcuBM4G2s56JeC11okVPf6GVPeR1Hj5KSNSL8Go8/PvDavnUrKYsX483NpXbECPD5rMQlfziJXiDYUXw3A2dorc8D6vz/XgC4QxZZBO0odmEi959E5HmHD6f4s8+oPf98wBqe3u/007EVF0c4MiFCL9gEla21/sj/2qeUsmmt3wZ+HKK4Ikqm2BDRxJeZGXh+ypeSgmfw4ED1dOdXX2FUVkYwOiFCJ9gEtcs/HxTAZuAcpdSJQGNIooqwgiIXffvEkZrkjHQoQrTQMH06e5cuteah8nrJmDOHjLlzIx2WECER7CCJRcAYrPp7dwPLsMoc/SI0YUVW0wAJIaKa3U7FU09h+Gv8GTU1pN12GzVz50qBWhETgh1m/nSz128rpTKAOK11TagCi5TqWjfl+xo55cjsSIcixEF5xo0LvHZu3EjCu+/i+tnPADBqa62Hge32SIUnxCHpMEEppQ50+c8DePz3onzdH1bkyP0n0VM1Tp5M8Zo1mP4HfFMefpjEN9+kZMUKeehX9EgH6kF5sKZ074jhb4+pP88KilwYBgyROaBED2Q2S0SNkyeD0xlITglvvEHjpEn4cnIiFZ4QnXKgBDUsbFFEkYIiFwMyE0mIi6m8K3qhhmnTaJg2DQCjupr0G26g7qKLqLrnnghHJkRwOkxQWuvtSqkcrXVROAOKJNM/xfvh+emRDkWIbmWmplK6ciVmnFW6y7F5M2kLFlB5zz14hw+PcHRCtO9gw8w3N19QSv09hLFEXPm+RmrqPHL/ScQk79Ch+AYMAMC+ezf2nTsxM6xqKbaiIqivj2R4QrRxsATVup7KKSGKIyrIFO+it2g49VRKPv44UEE9/ZZb6PejH4F5oNvOQoTXwYaZd8vZqpSKx5o2fhqQCXwP3OavRtHe9r8EbgESgVeAa7TWIZ8ru6DIhcNuMDBLRjyJXqB5gdorr8ReVhYoUJvyyCPU/fjHMj+ViKiDJSiHUupU9vekWi+jtV4Z5HF2AicDO4AZgFZKTdBaFzTfUCl1OjAfmAIUAv8A7vKvC6mCIheDs5Nw2IMtsCFEbGg84YTAa3tBAakPP4wvI4Pa/Pz9vSopUCvC7GAJqgT4S7Pl8lbLJnDQP7G01i7gzmar3lBKbQMmYlWnaO4yYKnWej2AUuq3wAuEOEH5fCY7il0cNy4rlIcRIup5hw2jePVqfCnWbNIJb7xByuLFVPzlL4F7WEKEwwETlNY6LxQHVUr1B0YB69tpHkfLaTy+Bvorpfpqrcvb2b5b7Kmoo8HtY5jcfxIiUIwWwIyPx5eVhS/bqq6S+PLL2FwuXFdcEanwRC8R9ISF3UUp5cTqET2jtf62nU1SgKpmy02vU7F6cM33NQeYA6C1Jiura72fJa+uY/l/dwLwzDvbKKzwMOfc8V3al+h5MjIysNvtBzx/HA5Hl8+vHm/WLJg1i6bv3vHxx1BRQeIttwBgu+8+GDoU38yZkYtRxKSwJih/+aTnsKqgd1SCuQbo02y56XV16w211kuAJf5Fs6ysrNMxvfjuNj74ev/7fCb8a/VO6uvrmDWtVz6r3Ovs3bsXr9fLgc6frKysA7b3Kn/8ozUkvawMTJOsZctwH3EEVaedBkCf22+nYcoUGqZMiXCgoqfIzc1td33YRgMopQxgKdAf+InWuqPJDtcDRzRbPgIoDtXlvY++af9Dp6P1QgggIcH61zAoe+cdqn77W2uxuprEd97B8f33VntdHem/+AXOL76IUKCiJwtnD+pPWFN2TNNa1x1gu2eBp5VSLwB7gNuBp0MVlK+DgfQdrRdCtMNfocJMTaX4s8/APwWIY8cO4j/4gLpzzgGsKexTH32U6uuvlyHs4qDCkqCUUkOBq4AGoEgp1dR0FfARsAEYq7XeobV+Rym1CPg3+5+DuiNUsdmM9pORTUbUCtE1hmEVqQU8o0dT/NVX4LMmPXAUFJCwfDnVN9wAQNzHH5P4+uvsmz8f0//QsBBNwpKgtNbbaVuVormUVts/CDwY0qD8Tjw8q8U9qObrhRDdwDACc1I1TJlC0dq1gWeqHAUFJLz7LlULFwKQ+Ne/4ly3jn133inzWInw3YOKVrOmDePkI7ICPSabAScfkSUDJIQIFZstkKBqZ8+meM2awCVCx7ZtxH35ZSA5pfzhD6T84Q8RC1VEVtiHmUejWdOGSUISIlKaVaionj+f6mb1AB1bt7aoD5h28824J0yg9pJLwhqiiAxJUEKI6NIsYVU++uj+BOXz4di6NfDAMD4fmZdfTu3MmdSfeWYEAhWh1usv8QkholxTwrLZKF+2jOpf/9parKjAVl6OUVtrLZeX01cp4j77LFKRim4mCUoI0SP5srIoe/NN6n7yE8Ca08pWWYlpsz7W4j79lH7TpuHYuBEAo6rKmvdKphTpMSRBCSFigmfcOEqXL8c9cSIApt2Od8AAfP4SVYlvvknOxInYd+wAwPn11yT+9a8yUWMUkwQlhIhJ7smTqXjuuUDh24Zjj6Vy4UK8gwcDkPDWW6TPnx8YMZj07LOkX3tt4Jkto7ZWelsRJglKCNEreIcPp/ZnP7OGuQPVN99MyUcfBR4qtlVXYy8vD7Sn3XIL/ZrVE4xbvRrn2rXhD7wXk1F8QojeyW7HO2hQYLHmuuuoue66wHL96afTePTRgeU+CxdixsdTvmwZAKkPPIBn4EDqLrrI2sA0ZVLHbiYJSggh2lF/1lktlvcuXoxRUxNYjv/wQ2yjRwcSVL+pU6mfMSMwyjD+3//GM2oU3oEDwxd0jImpBOVsNbzUl5NjXW/2etutpuzNzcU3cCA0NuL8+uu27YMH48vJgbo6nOvWtW3Py8PXrx+Gy4Vjw4a27fn5+Pr2xaiuxvFt26mvPCNGYGZkYOzdu7/6c/P2ww7DTE3FVl6OfevWtu1jx2ImJ2MrLcVeUNCm3T1+PCQmYisqwr5zZ9v2I46AuDhsu3djLyxs23700WC3Y9+50xr91Lp98mTAmiLcVlrastFut94P2LdswVZR0bI9Ls46PuD47juMysqW7QkJuCdMsNo3bcLYt69Fs5mUhGfcOKt9/frAUONAe58+eEaPBrAuy7S6EW6mp+MZOXL//uvqWpw/vsxMvMOHW+//4guM1FSczWL09euHNy/Pam9nWLOce7F77jnWr8czbhxlr72GY/166//f58M9YgSmzYZj0yY8eXlkXnIJrosvpu6CC8DrJe2OO6g/6yzqzzgDT34+zg0bMGprA6MOm7Q+9/B6W7bH4rnnLybcWkwlqDYnot2O6XSC19u2Daw2ux3c7vbbm6YUqK9vvz0pCbxejNradtt9KSnY3G6Mmpp2220lJZgNDdbw1/bai4sxXS6MvXs7bk9KwlZe3mE7CQnYSks7bnc6O24vKgK7/cDt0H67zba/vawMW6sPAdPpDLQbZWXYWieguLgW72/+lyuAWVe3v728HKOuZYF8s7ERW1paYP9GY2OLdp/Hgy011WqvrGx7jvh8mMnJgf3T0NA2Rv/50d7PRs693nfuBYa7l5VhS06m/NlnsZeVYauowKiuxmhogKoqjLIynPv20e/ss6m54goafvhDjMpKkrWmbvp03EceacVYU2Ode/5BGy1iiOFzrznDjJ1RKmaRzDkjuuDb77/n6ttu432tO9wmMzOTioP8MgkRLMPlIm71atxjxuAbMADHpk1kzJtH1R130Dh5Ms41a+g7Zw4Vjz1G4w9+gH3bNhLffJPan/4UX//+0NBg7Sg+PrLfSDfJsXq8bW7gySg+IYQIMzM5mYYpU/ANGABY05KUvvEGjf5Ll97cXPbNm7f/MvTWrSQ/+yyG/1J1wrvvknP88fuf6VqzhtRFizCqrYnHjb17se3Z027vqyeRBCWEEFHGN2AAtbNm4evbF4CGqVMpXrUq8AyXZ9Qoqq+5Bq+/LqFj+3YS33gDHNZdm6R//IPss84K9LQS3nqLtPnzA/ez7Nu3t3v/KNrE1D0oIYSIWY79H9eekSMDvSuAuvPPp+788wPLDSedZFXQSEwEwFZZaQ1G8T+UnPziiySsWEHJypUApDz2GM7169m7eDEA8e+/j23fPurOPhuw7tOaCQnQdH8qTCRBCSFEjPGMGIFnxIjAcu2sWdTOmhVYds2eTf20aYFlX79+gd4ZQOLrr2MvLAwkqLQ778ReXEz5Sy8BkPLHP4LNRs2114b0+5AEJYQQvYx38OAWCalWqRbtlfff3+LRjbrzz28xUtZWURGWh5IlQQkhhGjJbsf0P4YB1iXD5vYtWBCWMGSQhBBCiKgkCUoIIURUkgQlhBAiKkmCEkIIEZUkQQkhhIhKkqCEEEJEJUlQQggholJMVTNvnDSpxYq6adOoUwrq6si84YY2b6g76yzqzj4bY+9eMm65pU177QUXUD99OraiItLbGffvmj2bhpNOwl5QQNr//V+b9porrqDx2GNxbNpEnwceaNNefd11uI84AufXX5P62GNt2vfNm4dn9GjiVq8mZenSNu1Vt92GNy+P+A8/JPn559u0V959N76cHBKWLyfJPwtoc3t/9zvMjAwSX3/dquPVSsXDD0NiIolak/juu23blywBIOnZZ0n4+OMWbWZ8PHsffRSA5CeeIL71XF1paVTefz8AKY8+SlyrqbS92dlULVwIQOrvf49z8+YW7Z4hQ9h3++2ANdOpw180s4l71KjAxHFpt9+OvaSkRXvjhAnUXH89AGXXXcel33zDp2PGBNobJk/GdeWVAGRcfz1OrxePxxNorz/hBGovvRSAzDlz2vxs5NzrneeeaZpcUl7OI3//O3Dwcy/9ppuwVVW1aG997hlNlcv9YvHci/v8c2inmrk8qCt6naPWr2fDmjXw9NMt1sevWbN/Yc0aRq9YccApOISY9O67bPRXEG/ubxMntlgem5DAl/4JNkXwYqoHJfNBiVCR+aBEMHw+H4OOOYZCq0cggiTzQQkhhOhRJEEJIYSISpKghBBCRCVJUEIIIaKSJCghhBBRSRKUEEKIqBS256CUUnOBy4EJwEta68s72O5yYClQ12z1WVrr90MboRBCiGgSzgd1C4GFwOlA4kG2/URrfULoQxJCCBGtwpagtNZ/B1BKTQIGheu4QggheqZoLXV0lFKqDKgAngPu1Vp7Wm+klJoDzAHQWpOZmRneKEWv4XA45PwSB+Xz+QDkXOkm0ZigPgTGA9uBccBfAQ9wb+sNtdZLgCX+RVNK0YhQkVJHIhhNCUrOlc7Jyctrd33UJSit9dZmi2uVUncDN9FOghJCCBG7esIwc5N2iggKIYSIbeEcZu7wH88O2JVSCYCn9b0lpdSZwBda62Kl1GHAb4C/BXMM59dft1j29euHNzcXvF6c69a12d7bvz++nBxwu3Fu2NC2PTcXX79+UF+Pc9Omtu2DBuHr2xejthbHd9+1afcMGYKZkYFRU4Njy5a27Xl5mGlpGFVVOAoK2rYPH46ZkoKxd2+b+Y4APCNHYiYlYSsvx75rV5t29+jRkJCArbQUe2Fh2/axY8HpxFZUhL24uG37+PFgt2MvLMRWWtq2/YgjALDv3Imt9SUNmw33hAlW+/bt2CorWzSbTieesWOt9m3bsO3b17I9Lg6Pf34mx5YtGDU1LdsTE/GMGmW1b96MUVfXsj0lBc/w4Vb7xo0YjY0t2n19+uAdNsxq37ABw+1u2Z6ejnfoUACca9dipKTgbBajLzMT7+DBVnur8w7k3Out557P5yM5ISGw3B3nHv7LhoH2WDz3rGrmbYTzEt/twB3NlmcDdyml/gJsAMZqrXcAU4GnlVIpQDHwPNB2Vqx2+FrdmPT162f9R3i9+Nr5JQm0NzbiKyrquL2uDl87vyS+7Gx8/fphuFz4ysvbtJvZ2dZ/ZHU1vr17232/mZGBER+Pr9UvCYCvf3/M1FRsTie+Vr8kgfbkZLDbMWpr220n0RrRb9TXt98eFwdeb5tfEsD63u12q83rbb+9g31jt+9vd7nA1qqzHhcXaLdVV+NztDoVExIC7b6qKoy4uBbNZlLS/vby8jbfv9mnT6DdLC3FbBWjmZ6+v724GLP1h0hm5v79FxZCamqLGAPnBuDbubPNty/nXu849044/3w2bd/eJoQBEydit9vx+mM/bMgQPnzkEev9nT33Wn3/sX7uNRdT80EVtvOfIUR3yMrKoqysLNJhiB7E6XRiGEZg4IRpmoGEJVrKzc0FmQ9KCCHCw+124/P5MAwDj8cjyakLJEEJIUQIud1uHA4HttaXGsVByU9MCCFCxOPx4HA48HissWCO1ve7xAFJghJCiBBqurTn8/nwer2Be1Pi4GJqkESkAxBCiIMxTZOGhgbi4uLksl9LMT1Iwgjll1LqiVAfIxLH7u59H+r+uvr+rryvM+9RSq2J1P9/T/qK5O9JT4n1wgsvfCIhIcGw2WxR/311x746sY82YilBhdo/Y/TY3b3vQ91fV9/flfdF8v80VvWkn2mkYg31cbtz/92xry7vI5Yu8QkRMkqpz7XWkyIdhxC9ifSghAjOkoNvIoToTpKghAiCf2oXIQ6JYRh/MQyjxDCMdc3W/dQwjPWGYfgMw+iwl24YxhmGYWwyDON7wzDmN1v/O8MwvjEM49lm6y4xDOOG0H0n4SEJSgghwudp4IxW69YB52PNhdcuwzDswGPAmcBYYKZhGGMNw0gDjjdN83DAbhjGBMMwEoHLgcXdH354SYISQogwMU3zQ6yZwpuv22iaZtuy4S0dA3xvmuZW0zQbgZeBcwAfEGdYD1YlAm6s+fMeMU2zbRXeHkYeaxaii5RSxwEPAo1AIXCp1rrHfyiIqDQQaF66fBdwrGma1YZhvAJ8CbwHVAGTTdO8OwIxdjvpQQnRdduBKVrrk4GtWH/RChEK7T0nZAKYprnINM0jTdOcB/wWWGAYxs8Nw9CGYdwe1ii7mfSghOgirXXz+V08WJdbhAiFXcDgZsuDsHrtAYZhHOV/uRl42DTNkwzDeNkwjJGmabadWbAHkAQlej2l1Fysm8oTgJe01pc3a8sElgLTgTLgVq31i63ePwzr5vU9YQpZ9D6fASMNwxgG7AYuAma12ua3wBzAiTVzOVh/NCWFK8juJpf4hLD+El0I/KWdtsew7jH1By4G/qSUGtfUqJTqAzwDXKK1bmzn/UIEGIbxEvAJMNowjF2GYVxhGMZ5hmHsAo4D3jQM41/+bXMNw3gLwDRNDzAX+BewEdCmaa5vtt9zgc9M0yw0TbMS+MQwjLXWW82vw/gtdiupJCGEn1JqITCoqQellEoG9gLjtdab/eueA3ZrrecrpRzAa8ADWuuVEQpbiJglPSghOjYK8DYlJ7+vgaYe1EzgWGCBUup9pdSF4Q5QiFgm96CE6FgK1rDd5qqAVACt9XPAc+EOSojeQnpQQnSsBujTal0foDoCsQjR60iCEqJjmwGHUmpks3VHAOs72F4I0Y1kkITo9fyDHRzAHVjPl1wJeLTWHqXUy1gPRP4cOBJ4Czheay1JSogQkx6UEHA7UAfMB2b7Xzc9gX8tVo2zEuAl4BpJTkKEh/SghBBCRCXpQQkhhIhKkqCEEEJEJUlQQgghopIkKCGEEFFJEpQQQoioJAlKCCFEVJIEJYQQIipJghJCCBGVJEEJESFKqceVUr85QLuplBrRyX1erJRafujRCRF5UklCiG6glLoI+CUwHnAB27Bm2v2T1rpLv2RKKRMYqbX+vtsCFaIHkR6UEIdIKTUPeBi4H8jBmh7+auCHQFwH77GHLUAheijpQQlxCJRSaUAhcKnW+pUDbPc0VhHaocDJwDlYhWl3aa1v929zE/ArrOrptwNL6aAHpZS6HFgA9APKgNu11i/41/9ca32CUupm/zZN4oEXtNaX++N+EJgB+ICngDu01t4u/iiE6HbSgxLi0ByH9cH/WhDbzgLuwZqR9+PmDUqpM4BfA6cBI4FpHe1EKZUMPAKcqbVOBY4Hvmq9ndZ6kdY6RWudAowBSgHtb34G8AAjgKOA6VhTiggRNWTKdyEOTRZQprX2NK1QSq0CxmIlrtO11h/6m17TWv/H/7peKdV8Pwp4Smu9zr+PO4GZBziuDxivlNqhtd4D7OloQ6VUIvAq8LDW+i2lVH/gTCBda10HuJRSfwDmAH8O7tsWIvSkByXEoSkHsvyTHgKgtT5ea53ub2v+O7bzAPvJbdW+vaMNtdYu4EKs+1x7lFJvKqUOO8C+lwKbtNa/8y8PBZz+91YqpSqxElP2AfYhRNhJD0qIQ/MJ0IB1T6nDe1B+B7rhuwcY3Gx5yIF2pLX+F/Avf+9oIfAEcGLr7ZRS84HRwAnNVu/0x5zVvOcnRLSRBCXEIdBaVyql7gIWK6UM4B2gFjgcSO7MroCnlFLPAgVY08+3y3+J7ljgPayBFzVAm8ENSqkzgV8Ax/ov5TXFvMf/rNQD/uewaoBhwCCt9QediFmIkJJLfEIcIq31IqzRdzdjTQ1fjHXJ7BZgVZD7eBt4CFgJfO//tyM2YB7W6MEKrFGB17az3YVYo/w2KqVq/F+P+9suxRoCvwHYCywDBgQTqxDhIsPMhRBCRCXpQQkhhIhKkqCEEEJEJUlQQgghopIkKCGEEFFJEpQQQoioJAlKCCFEVJIEJYQQIipJghJCCBGV/h8E8xn5jbFLogAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -3956,12 +3848,12 @@ "flame.solve(loglevel=loglevel, auto=True)\n", "\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))" + "print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -4028,14 +3920,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { @@ -4086,26 +3976,26 @@ " \n", " 43\n", " 60.433331\n", - " 215.110670\n", + " 215.110671\n", " 54.440126\n", " \n", " \n", " 51\n", " 30.817708\n", - " 122.606520\n", - " 58.417575\n", + " 122.606521\n", + " 58.417576\n", " \n", " \n", " 61\n", " 16.255720\n", " 26.903686\n", - " 120.895428\n", + " 120.895427\n", " \n", " \n", " 74\n", " 8.520372\n", - " 67.690362\n", - " 273.037266\n", + " 67.690361\n", + " 273.037255\n", " \n", " \n", " 93\n", @@ -4140,10 +4030,10 @@ "28 NaN NaN \n", "28 NaN NaN \n", "36 NaN NaN \n", - "43 60.433331 215.110670 \n", - "51 30.817708 122.606520 \n", + "43 60.433331 215.110671 \n", + "51 30.817708 122.606521 \n", "61 16.255720 26.903686 \n", - "74 8.520372 67.690362 \n", + "74 8.520372 67.690361 \n", "93 4.571941 29.579963 \n", "114 2.438634 14.245514 \n", "129 1.422479 8.344867 \n", @@ -4154,9 +4044,9 @@ "28 NaN \n", "36 NaN \n", "43 54.440126 \n", - "51 58.417575 \n", - "61 120.895428 \n", - "74 273.037266 \n", + "51 58.417576 \n", + "61 120.895427 \n", + "74 273.037255 \n", "93 56.196084 \n", "114 22.226241 \n", "129 11.259605 \n", diff --git a/flames/flame_speed_with_sensitivity_analysis.ipynb b/flames/flame_speed_with_sensitivity_analysis.ipynb index 043c3aa..7278179 100644 --- a/flames/flame_speed_with_sensitivity_analysis.ipynb +++ b/flames/flame_speed_with_sensitivity_analysis.ipynb @@ -42,7 +42,7 @@ "import numpy as np\n", "import pandas as pd\n", "\n", - "print(\"Running Cantera Version: \" + str(ct.__version__))" + "print(f\"Running Cantera Version: {ct.__version__}\")" ] }, { @@ -52,7 +52,7 @@ "outputs": [], "source": [ "# Import plotting modules and define plotting preference\n", - "%matplotlib notebook\n", + "%matplotlib inline\n", "import matplotlib.pylab as plt\n", "\n", "plt.rcParams[\"axes.labelsize\"] = 14\n", @@ -131,7 +131,10 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "scrolled": true, + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -142,23 +145,23 @@ "\n", "..............................................................................\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 2.136e-05 5.452\n", + "Take 10 timesteps 2.136e-05 5.453\n", "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 0.0003649 4.427\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 2.435e-05 6.061\n", + "Take 10 timesteps 1.624e-05 6.029\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 3.468e-05 5.63\n", + "Take 10 timesteps 3.468e-05 5.674\n", "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 0.001333 4.122\n", "Attempt Newton solution of steady-state problem... success.\n", "\n", "Problem solved on [9] point grid(s).\n", - "Expanding domain to accomodate flame thickness. New width: 0.028 m\n", + "Expanding domain to accommodate flame thickness. New width: 0.028 m\n", "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 0 1 2 3 4 5 6 \n", - " to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NO NO2 O O2 OH T u \n", + " to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NO NO2 O O2 OH T velocity \n", "##############################################################################\n", "\n", "*********** Solving on 16 point grid with energy equation enabled ************\n", @@ -167,23 +170,25 @@ "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 2.136e-05 5.751\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 4.055e-05 5.58\n", + "Take 10 timesteps 4.055e-05 5.579\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 2.887e-05 5.947\n", + "Take 10 timesteps 2.887e-05 5.946\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 2.055e-05 6.11\n", + "Take 10 timesteps 2.055e-05 6.111\n", "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 0.0001756 5.504\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 1.172e-05 6.764\n", + "Take 10 timesteps 1.172e-05 6.773\n", + "Attempt Newton solution of steady-state problem... failure. \n", + "Take 10 timesteps 0.0003003 4.727\n", "Attempt Newton solution of steady-state problem... success.\n", "\n", "Problem solved on [16] point grid(s).\n", - "Expanding domain to accomodate flame thickness. New width: 0.056 m\n", + "Expanding domain to accommodate flame thickness. New width: 0.056 m\n", "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 3 4 5 6 7 8 9 \n", - " to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NO NO2 O O2 OH T u \n", + " to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NO NO2 O O2 OH T velocity \n", "##############################################################################\n", "\n", "*********** Solving on 23 point grid with energy equation enabled ************\n", @@ -194,25 +199,25 @@ "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 3.604e-05 5.689\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 1.283e-05 6.137\n", + "Take 10 timesteps 1.283e-05 6.138\n", "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 0.0001096 5.738\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 2.601e-05 6.05\n", + "Take 10 timesteps 2.601e-05 6.051\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 6.943e-06 6.869\n", + "Take 10 timesteps 6.943e-06 6.87\n", "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 7.909e-05 5.845\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 6.256e-06 6.571\n", + "Take 10 timesteps 6.256e-06 6.574\n", "Attempt Newton solution of steady-state problem... failure. \n", "Take 10 timesteps 0.0001069 5.566\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 7.61e-05 5.798\n", + "Take 10 timesteps 7.61e-05 5.773\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 2.709e-05 5.646\n", + "Take 10 timesteps 5.417e-05 5.296\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 0.0006942 4.499\n", + "Take 10 timesteps 0.001388 4.168\n", "Attempt Newton solution of steady-state problem... success.\n", "\n", "Problem solved on [23] point grid(s).\n", @@ -231,7 +236,7 @@ "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 6 7 8 9 10 11 12 13 \n", - " to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NO NO2 O O2 OH T u \n", + " to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NO NO2 O O2 OH T velocity \n", "##############################################################################\n", "\n", "..............................................................................\n", @@ -245,12 +250,12 @@ "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 8 9 10 11 12 13 14 15 16 27 28 \n", - " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T u \n", + " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T velocity \n", "##############################################################################\n", "\n", "..............................................................................\n", "Attempt Newton solution of steady-state problem... failure. \n", - "Take 10 timesteps 7.594e-05 5.228\n", + "Take 10 timesteps 7.594e-05 5.229\n", "Attempt Newton solution of steady-state problem... success.\n", "\n", "Problem solved on [42] point grid(s).\n", @@ -259,7 +264,7 @@ "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 12 13 14 15 16 17 18 19 20 21 40 \n", - " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T point 40 u \n", + " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T point 40 velocity \n", "##############################################################################\n", "\n", "..............................................................................\n", @@ -271,7 +276,7 @@ "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 15 16 17 18 19 20 21 22 23 24 25 26 27 50 \n", - " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NO NO2 O O2 OH T u \n", + " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 N2O NCO NO NO2 O O2 OH T velocity \n", "##############################################################################\n", "\n", "..............................................................................\n", @@ -283,7 +288,7 @@ "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 \n", - " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 NO NO2 O O2 OH T u \n", + " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 NO NO2 O O2 OH T velocity \n", "##############################################################################\n", "\n", "..............................................................................\n", @@ -295,7 +300,7 @@ "##############################################################################\n", "Refining grid in flame.\n", " New points inserted after grid points 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 \n", - " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO H H2 H2O H2O2 HCCO HCCOH HCN HCO HNCO HO2 N2 NO2 O O2 OH T u \n", + " to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO H H2 H2O H2O2 HCCO HCCOH HCN HCO HNCO HO2 N2 NO2 O O2 OH T velocity \n", "##############################################################################\n", "\n", "..............................................................................\n", @@ -317,14 +322,14 @@ "\n", "..............................................................................\n", "no new points needed in flame\n", - "Flame Speed is: 38.22 cm/s\n" + "Flame Speed is: 38.23 cm/s\n" ] } ], "source": [ "flame.solve(loglevel=loglevel, auto=True)\n", "Su0 = flame.velocity[0]\n", - "print(\"Flame Speed is: {:.2f} cm/s\".format(Su0 * 100))\n", + "print(f\"Flame Speed is: {Su0 * 100:.2f} cm/s\")\n", "\n", "# Note that the variable Su0 will also be used downstream in the sensitivity analysis" ] @@ -347,791 +352,9 @@ "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " if (mpl.ratio != 1) {\n", - " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", - " }\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " fig.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
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IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], + "image/png": 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" ] }, "metadata": {}, @@ -1970,9 +413,9 @@ "\n", "plt.figure()\n", "\n", - "plt.plot(flame.grid * 100, X_CH4, \"-o\", label=r\"$CH_{4}$\")\n", - "plt.plot(flame.grid * 100, X_CO2, \"-s\", label=r\"$CO_{2}$\")\n", - "plt.plot(flame.grid * 100, X_H2O, \"-<\", label=r\"$H_{2}O$\")\n", + "plt.plot(flame.grid * 100, X_CH4, \"-o\", label=\"$CH_{4}$\")\n", + "plt.plot(flame.grid * 100, X_CO2, \"-s\", label=\"$CO_{2}$\")\n", + "plt.plot(flame.grid * 100, X_H2O, \"-<\", label=\"$H_{2}O$\")\n", "\n", "plt.legend(loc=2)\n", "plt.xlabel(\"Distance (cm)\")\n", @@ -1995,9 +438,7 @@ "outputs": [], "source": [ "# Create a dataframe to store sensitivity-analysis data\n", - "sensitivities = pd.DataFrame(\n", - " data=[], index=gas.reaction_equations(range(gas.n_reactions))\n", - ")" + "sensitivities = pd.DataFrame(index=gas.reaction_equations(), columns=[\"base_case\"])" ] }, { @@ -2014,10 +455,7 @@ "outputs": [], "source": [ "# Set the value of the perturbation\n", - "dk = 1e-2\n", - "\n", - "# Create an empty column to store the sensitivities data\n", - "sensitivities[\"baseCase\"] = \"\"" + "dk = 1e-2" ] }, { @@ -2033,12 +471,13 @@ " # Always force loglevel=0 for this\n", " # Make sure the grid is not refined, otherwise it won't strictly\n", " # be a small perturbation analysis\n", - " flame.solve(loglevel=0, refine_grid=False)\n", + " # Turn auto-mode off since the flame has already been solved\n", + " flame.solve(loglevel=0, refine_grid=False, auto=False)\n", "\n", " # The new flame speed\n", " Su = flame.velocity[0]\n", "\n", - " sensitivities[\"baseCase\"][m] = (Su - Su0) / (Su0 * dk)\n", + " sensitivities.iloc[m, 0] = (Su - Su0) / (Su0 * dk)\n", "\n", "# This step is essential, otherwise the mechanism will have been altered\n", "gas.set_multiplier(1.0)" @@ -2070,41 +509,41 @@ " \n", " \n", " \n", - " baseCase\n", + " base_case\n", " \n", " \n", " \n", " \n", " 2 O + M <=> O2 + M\n", - " 0.00156775\n", + " 0.00157\n", " \n", " \n", " H + O + M <=> OH + M\n", - " 0.00110824\n", + " 0.001112\n", " \n", " \n", " H2 + O <=> H + OH\n", - " 0.0252473\n", + " 0.025264\n", " \n", " \n", " HO2 + O <=> O2 + OH\n", - " 0.00313172\n", + " 0.003139\n", " \n", " \n", " H2O2 + O <=> HO2 + OH\n", - " 0.000752237\n", + " 0.000755\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " baseCase\n", - "2 O + M <=> O2 + M 0.00156775\n", - "H + O + M <=> OH + M 0.00110824\n", - "H2 + O <=> H + OH 0.0252473\n", - "HO2 + O <=> O2 + OH 0.00313172\n", - "H2O2 + O <=> HO2 + OH 0.000752237" + " base_case\n", + "2 O + M <=> O2 + M 0.00157\n", + "H + O + M <=> OH + M 0.001112\n", + "H2 + O <=> H + OH 0.025264\n", + "HO2 + O <=> O2 + OH 0.003139\n", + "H2O2 + O <=> HO2 + OH 0.000755" ] }, "execution_count": 11, @@ -2130,791 +569,9 @@ "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
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IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], + "image/png": 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1499,16 +719,23 @@ "source": [ "plt.figure()\n", "\n", - "plt.plot(oppFlame.grid * 100, oppFlame.velocity, \"r-o\", lw=2)\n", - "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1] * 100)\n", + "plt.plot(opposed_flame.grid * 100, opposed_flame.velocity, \"r-o\", lw=2)\n", + "plt.xlim(opposed_flame.grid[0] * 100, opposed_flame.grid[-1] * 100)\n", "plt.xlabel(\"Distance (cm)\")\n", "plt.ylabel(\"Axial Velocity (m/s)\")\n", "\n", "# Identify the point where the strain rate is calculated\n", - "plt.plot(oppFlame.grid[strainRatePoint] * 100, oppFlame.velocity[strainRatePoint], \"gs\")\n", + "plt.plot(\n", + " opposed_flame.grid[strain_rate_point] * 100,\n", + " opposed_flame.velocity[strain_rate_point],\n", + " \"gs\",\n", + ")\n", "plt.annotate(\n", " \"Strain-Rate point\",\n", - " xy=(oppFlame.grid[strainRatePoint] * 100, oppFlame.velocity[strainRatePoint]),\n", + " xy=(\n", + " opposed_flame.grid[strain_rate_point] * 100,\n", + " opposed_flame.velocity[strain_rate_point],\n", + " ),\n", " xytext=(0.001, 0.1),\n", " arrowprops={\"arrowstyle\": \"->\"},\n", ");" @@ -1528,791 +755,9 @@ "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " if (mpl.ratio != 1) {\n", - " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", - " }\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " fig.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var backingStore = this.context.backingStorePixelRatio ||\n", - "\tthis.context.webkitBackingStorePixelRatio ||\n", - "\tthis.context.mozBackingStorePixelRatio ||\n", - "\tthis.context.msBackingStorePixelRatio ||\n", - "\tthis.context.oBackingStorePixelRatio ||\n", - "\tthis.context.backingStorePixelRatio || 1;\n", - "\n", - " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width * mpl.ratio);\n", - " canvas.attr('height', height * mpl.ratio);\n", - " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], + "image/png": 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9hvLycrz88st4+OGH4e/vD5VKZVD/8OHDqKiowJAhQ5ocBxERNY9cKceifYtQXlPu7FBaPfbgWEGXmRc8V4CZUTPh4+4DT7Fns6/722+/oVevXgZlubm56NOnj/79kiVLMG/ePADAmDFj8MUXX+Dy5csAgPz8fMycORMbNmwwOWaHiIgcIzY9FuuOrnN2GAQmOE1ya6LT3IFjtyY4ZWVlEAQBwcHBEAQB8+fPxyOPPIJ+/eqnHUZHR2PBggUYPnw4IiMjMXPmTHzyySfsvSEicrK0iWl4vNfjzg6DwEdUzaJLdJorNTXV4H379u1x6dIlAMCKFSuwe/duXLt2DWfOnMGcOXMAAPHx8ZwtRUTkYqQSKWb2m4kv8r5AJ0knlNWUoVZTa3JWFdkXExwX9+yzz+LZZ591dhhERGShy9frhw/4ePhAqBZgZlNxsjMmOERERDYiV8qx6sgqAMC5q+fYc+NEHINDRERkI7HpsTh0/hAAMLlxMiY4RERENpI2MQ33BN0DABCLOKvVmZjgABCElv2AtKXfHxGRq5BKpPqZtUNkQ2y2nAhZr9UnON7e3igtLW2xSYAgCCgtLYW3t7ezQyEiahV0i/xN7TMVkyImwc/Tj/tQOUGrH2TcpUsXXLhwQb9oXkvk7e2NLl26ODsMIqJWQVGpAADM2V6/rEetphZXXr3izJBapVaf4Hh4eKBbt27ODoOIiG5zcqUcKftTkHkxEwAMtvMhx2v1CQ4REZEtxKbH4mDxQaNdxMk5Wv0YHCIiIltIm5iGOf3nODsMuoEJDhERkQ1IJVKsGLVC/54zqJyLCQ4REZGNVNZWAgAknhIcTjiMHu17wNuds1idwaEJTllZGcaNGwdfX1/IZDJs3rzZZL2cnByMGDECHTp0gEgkMjimUqmQkJAAmUwGPz8/REVFYceOHY4In4iIqEEVqgoA9asY37f+PpwuO40adY2To2qdHJrgzJs3D56enlAoFEhNTcXcuXORm5trVM/DwwOTJk3C+vXrjY6p1Wp07doV+/btw7Vr15CSkoJJkyahsLDQAXdARERkmlwpx8u7XgYAXK+7jhp1DWdSOZFIcNAKd1VVVWjXrh1ycnIQHh4OAIiLi0Pnzp2xbNkyk+ecOXMGPXr0aHQRvj59+iA5ORkTJkxosF50dDQyMzObdgNEREQNGLJxCA4UHTA5i0pI5swqazX3N9thPTj5+fkQi8X65AYAIiMjTfbgWEOhUCA/Px8REREmj69duxbR0dGIjo5u0Yv5ERGRc6VNTMOY8DEAABFEjdQme3NYglNZWQl/f3+DMn9/fyiVyiZfs66uDlOmTMH06dNx1113mayTmJiIzMxMZGZmIjAwsMltERERNUQqkSK+bzwAQNZWxllUTuawBEcikaCiosKgrKKiAn5+fk26nlarRVxcHDw9PfHxxx/bIkQiIqJm0c2iGhwyGAXPFWBm1EzuQ+UkDlvJODw8HGq1GqdPn0aPHj0AANnZ2WYfLTVEEAQkJCRAoVDg+++/h4eHh63DJSIisppSVf9Uws/TD1KJFCtHr3RyRK2Xw3pwfH19MX78eCxcuBBVVVXIyMjAtm3bEBcXZ1RXEATU1NSgtrZ+9HlNTQ1UKpX++Ny5c3Hy5El899138PHxcdQtEBERNUjXgwMASduTELUmyonRtG4OnSa+atUqVFdXIygoCJMnT8bq1asRERGB4uJiSCQSFBcXAwCKiorg4+Oj793x8fFBz5499cfWrFmDrKwsSKVSSCQSSCQSpKamOvJWiIiIjFysvAgAWHdsHdYfX4+skiznBtSKOXSzzfbt2+Obb74xKg8JCUFl5Z9Zb2hoqNmp4TKZrNFp40RERI6k20l87dG1AAC1Vu3kiIi7iRMRETWTbidxraB1dih0A/eiIiIiaibdTuJikdjZodANTHCIiIiaSTdj6q/d/goA8BR7cg0cJ+MjKiIiIhvRjb35bNxn2Fe4D4cuHHJyRK0XExwiIiIbqaqrAgB0vaMr18BxMj6iIiIispGq2voEx9fT18mREBMcIiIiG9H14FTVVnGhPyfjIyoiIiIbqVDV77n4101/hRZa1GpqnRxR68UeHCIiomaSK+VI2p6EsuoyAECNpobJjZOxB4eIiKiZdAv9ketgDw4REVEzpU1Mw8yomc4Og27CBIeIiKiZpBIp3hr+FgDAS+wFH3cfLvTnZExwiIiIbOB63XUAQKBvIAqeK8DMqJnoK+3r3KBaMY7BISIisgFdgtPGo41+6wZyHvbgEBER2cDNCQ45HxMcIiIiG2CC41qY4BAREdnAzQmObl0crmTsPByDQ0REZAO6BOdM6RmEfRQGrcCVjJ2JCQ4REVEzyZVyfPzrxwCA4mvF0ELr5IiICQ4REVEzxabH4kDRAQBgcuMiOAaHiIiomdImpmFQ10EAADcRf1pdAT8FIiKiZpJKpHi056MAgN5BvbmSsQtggkNERGQD1XXVAICYnjFcydgFcAwOERGRDehmUfl4+HAlYxfAHhwiIiIbqFbX9+BwoT/XwASHiIjIBvQ9OO4+To6EACY4RERENqHrwfHxYILjCpjgEBER2YCuB0elVnGbBhfAQcZEREQ2cLX6KgAg6fskAOA2DU7GHhwiIqJm0G2sub9oP4D6xIbJjfOxB4eIiKgZYtNjcbD4ILdocDHswSEiImqGtIlpmNN/DkQQOTsUugkTHCIiombQLerX1b8rAMBL7MVtGlwAExwiIiIbqNPUAQAOJRziNg0ugGNwiIiIbEC3Do7MX8ZtGlwAe3CIiIhsQLfZJhf6cw1McIiIiJpJK2ih0qgAAN7u3k6OhgAHJzhlZWUYN24cfH19IZPJsHnzZpP1cnJyMGLECHTo0AEikfGodEuvQ0RE5Ag16hoA9QOM3UTsO3AFDv0U5s2bB09PTygUCqSmpmLu3LnIzc01qufh4YFJkyZh/fr1zboOERGRI9z8eEq38B+3anAukSAIgiMaqqqqQrt27ZCTk4Pw8HAAQFxcHDp37oxly5aZPOfMmTPo0aMHbg6xKdfRiY6ORmZmpo3uiIiIqN6Figvourwr2ri3gRZaaAUtajW1EJId8hPbIjX3N9thPTj5+fkQi8X6pAQAIiMjre55sfY6a9euRXR0NKKjo3H58uWmBU9ERGSGXCnHqz++CgC4rr6OGnUNt2pwAQ5LcCorK+Hv729Q5u/vD6VSadfrJCYmIjMzE5mZmQgMDLQuaCIiokbEpsdiS84WZ4dBt3BYgiORSFBRUWFQVlFRAT8/P6dch4iIyBbSJqZh/N3jAYDbNbgQhyU44eHhUKvVOH36tL4sOzsbERERTrkOERGRLUglUjxz7zP61z7uPtyqwQU4LMHx9fXF+PHjsXDhQlRVVSEjIwPbtm1DXFycUV1BEFBTU4Pa2vpnmDU1NVCpVFZfh4iIyBF008TvCboHBc8VcKsGF+DQaeKrVq1CdXU1goKCMHnyZKxevRoREREoLi6GRCJBcXExAKCoqAg+Pj76XhkfHx/07Nmz0esQERE5g26bBh8PH/3mm8dnH3dyVK2bQ/eiat++Pb755huj8pCQEFRWVurfh4aGoqHZ6+auQ0RE5Ay6Hhwfd27T4Cq43CIREVEz6Rb64zYNroMJDhERUTPpH1GxB8dlMMEhIiJqJt0jKo2g4TYNLsKhY3CIiIhaokuVlwAAG7I2wE3kxpWMXQATHCIioiaSK+VI2Z+CdcfWAQDUWrWTIyIdJjhERERNFJsei4PFB6EVtM4OhW7BMThERERNlDYxDXP6z4FYJHZ2KHQLJjhERERNpFvU7/FejwMAPNw8uE2Di+AjKiIiomYSieo32fxw5IfIvZSLQxcOOTkiYoJDRETUTLp1cKQSKZIGJDk5GgKa+Iiquroau3fvRlFRka3jISIiuu1wqwbXY1GCEx8fj1WrVgEAamtrce+99+Lhhx9Gz549sWPHDrsGSERE5Oq4VYPrsSjB2blzJ+677z4AwLfffgulUomSkhIsWrQIixYtsmd8RERELk/fg+PBHhxXYVGCU15ejqCgIADADz/8gAkTJiAoKAixsbHIy8uza4BERESuTjcGR6lScqsGF2HRIGOpVIqcnBx07NgRO3fuxNq1awEAlZWV8PDwsGuARERErk6pUgIAHv38UQgQuFWDC7AowZkxYwaeeOIJdOrUCWKxGMOHDwcA/O9//8Ndd91l1wCJiIhclW6rhnNXzwEAVBqVkyMiHYsSnIULFyIiIgLFxcV4/PHH4elZv4iRu7s75s+fb9cAiYiIXJVuqwZyPRavgzNhwgSjsunTp9s0GCIiottJ2sQ0pOxLwarMVc4OhW5hcYJz/vx5HDhwAJcuXYJWa7ip2IsvvmjzwIiIiFydbquGTzI/gRZa+Lj7QCNoOAbHBViU4KSmpmLGjBlwd3dHYGCgfklqoH55aiY4RETUWqm1amihhZvIDWefPYsl+5dwqwYXYPEYnJdeegkpKSkQi7ljKhERkY5ukT8fdx909OuIlaNXOjkiAixcB0ehUGDmzJlMboiIiG6hW+SPqxi7FosSnFGjRuF///ufvWMhIiK67egW+eMqxq7FokdUDz30EObPn4/c3Fz07t3baHG/8ePH2yU4IiIiV8ceHNdkUYIze/ZsAMBbb71ldEwkEkGj0dg2KiIiotvEzWNwyHVY9IhKq9Wa/WNyQ0RErZnuEZW3uzfkSjn3onIRFq+DQ0RERMZ0j6guVFxA2Edh0AparoPjAizqwQGA7du348EHH0SHDh0QGBiIIUOG4Pvvv7dnbERERC5NrpTjvYz3AAAllSWoUdcwuXERFiU4n376KcaNG4c777wT77zzDpYtW4Zu3bph3Lhx+Ne//mXvGImIiFxSbHosdpzZAQAQIDg5GrqZSBCERj+RHj164LnnnsPTTz9tUL5ixQqsWLEC+fn5dgvQlqKjo5GZmensMIiIqIUoqSxB3NY47D63G25wgxZ/bmUkJDPhaY7m/mZb1INTXFyMkSNHGpU/8sgjKCoqanLjREREtzOpRIrJvScDAHoE9ICPuw88xZ5OjooACxOckJAQ/Pjjj0blu3btgkwms3lQREREtwvdIONhocNwOOEwerTvwTVxXIBFs6hefvllPPPMMzh27BgeeOABiEQiHDx4EJ999hlWrFhh7xiJiIhcli7BOXT+EDZmb+QsKhdh8UJ/QUFBeP/997F161YAwN13340vvvgCMTExdg2QiIjIVcmVcqTlpAEAci/nQiNwbThXYfE6OOPGjcO4cePsGQsREdFtJTY9FkcuHgEAJjcuxuJ1cIiIiMhQ2sQ09AnuAwAQi8ROjoZuZjbBueOOO3DlyhUAgJ+fH+644w6zf0RERK2RVCLF4JDBAID7u9zPWVQuxOwjqhUrVsDPz0//WiQSNbuxsrIyJCQkYNeuXejQoQPefvttPPnkkybrLl++HO+88w6qq6sxYcIErF69Gl5eXgCAwsJCJCUl4fDhw/Dy8sLEiRPx4Ycfwt2dO08QEZFj6QYZx9wVgzvb34n/5v8XXf27OjkqMpsRTJ8+Xf86Pj7eJo3NmzcPnp6eUCgUyMrKwujRoxEZGYmIiAiDejt37sSyZcvw008/oVOnThg3bhySk5OxbNkyAEBSUhKCgoIgl8tx9epVPPTQQ1i1ahWeffZZm8RJRERkqbLqMgDA3/f8HSKRCLWaWlx59YqToyKLxuCEhYWhtLTUqPzq1asICwuzqKGqqiqkp6cjJSUFEokEgwcPxtixY/HZZ58Z1d20aRMSEhIQERGBdu3aYcGCBdi4caP++Llz5zBp0iR4e3tDKpVi5MiRyM3NtSgOIiIiW9DtHL7t1DYAQJ22jtPDXYhFCU5hYSE0GuPR4SqVChcuXLCoofz8fIjFYoSHh+vLIiMjTSYmubm5iIyMNKinUCj0SdZzzz2HLVu24Pr16/jjjz+wY8cOkystA8DatWsRHR2N6OhoXL582aJYiYiIGhObHos1R9dAK2gbr0wO1+CgFd2aN0D9buL+/v769xqNBnv27EG3bt0saqiystLgfADw9/eHUqlstK7utVKpREBAAIYMGYJ169bhjjvugEajwfTp0/HYY4+ZbDcxMRGJiYkA6ve1ICIisoW0iWlI2ZeCT45+wiTHBTWY4EycOBEAIBKJkJCQYHDMw8MDoaGheP/99y1qSCKRoKKiwqCsoqJCP5C5obq6135+ftBqtRgxYgRmz56NQ4cOobKyEjNmzMD8+fPx7rvvWhQLERFRc0klUqwcvRK//PELjsmPwUvsBQECH1O5iAYfUWm1Wmi1WoSEhODSpUv691qtFiqVCqdOncKYMWMsaig8PBxqtRqnT5/Wl2VnZxsNMAaAiIgIZGdnG9QLDg5GQEAAysrKcP78eTz99NPw8vJCQEAAnnrqKXz//feW3jMREZHNaLT1Qzg2PraR+1C5EIvG4Jw7dw4dOnRoVkO+vr4YP348Fi5ciKqqKmRkZGDbtm2Ii4szqjtt2jSsX78eeXl5KC8vx5IlS/QzuTp06IBu3bph9erVUKvVuHr1KjZt2mQwZoeIiMhRKmsrAQDx38TjdNlp/bRxci6LEpwZM2aYfBT1wQcfYObMmRY3tmrVKlRXVyMoKAiTJ0/G6tWrERERgeLiYkgkEhQXFwMARo4ciVdffRXDhg2DTCaDTCbD4sWL9dfZunUrfvjhBwQGBqJ79+5wd3fH8uXLLY6DiIiouXSzqM6WnwUAqDQqPp5yISJBEITGKkmlUuzYsQNRUVEG5VlZWRg1ahQuXrxotwBtKTo6GpmZmc4Og4iIWoAhG4fgYPFBkwOMheRGf1qpEc39zbaoB+fq1auQSCRG5b6+vigrK2ty40RERLertIlpmNN/jrPDIDMsSnDCw8NNDuLdvn07unfvbvOgiIiIXJ1uFpW7qH5CsrfYG24i7mHtKizavOmll17CnDlzcOnSJfz1r38FAOzZswcffvghVq5cadcAiYiIXJVGq4FaUAMABAgAn0y5DIsSnOnTp6OmpgZLlizB22+/DQDo3LkzPvjgAzz11FN2DZCIiMgVyZVyLPh5gf69SqNyYjR0K4u33549ezZmz56Ny5cvQxAEBAUF2TMuIiIilxabHouDRQedHQaZYfXDwsDAQCY3RETU6qVNTMOUPlOcHQaZYXEPzoYNG/D555+juLgYtbWG8/wLCgpsHhgREZErk0qkeOPBN/DZic9wh+cdqNPWQSNouBaOi7AowXnvvffw9ttvY/bs2di/fz+SkpJw5swZ7N+/Hy+//LK9Y7SZo0olQg8fxtKwMEwJDnZ2OFZJVSjwekEBilUqhHh58R5acBuueA9NicnebdgyJnuXO7Pt2yUmAPqy9mIxIBKhTK02+9rUeda2UaPVouqmpeDcAGgByKz43lXXVQMAQtqG4Me4H5GyLwWHLhxq9LyWJkOagTpFnfEB3b/UJlDCeDNua1i00F94eDjeeustTJw4EX5+fsjOzkZYWBhSUlJQXFyMdevWNSsIRxH17AmsWQMPkQiPBwaiv4mNPl3RUaUSX16+jLqbPireQ8tswxXvoSkx2bsNW8YULZEgs7LSbuWPBwYCgFPavl1icgMgAqCBdUyd5yES4V6JBL/e0ob4xj8tbcMDwIa77240yfnfhf/hvvX3YUCnAfh11q+WB38bMpfEeAR7mE5ummk2ZuOUcKrJ51uU4LRp0wa///47QkJCEBQUhF27dqFv3744c+YM7r333ttmsT9dgkNERNQYNwD/biTJ2Vu4F8M2DcODsgexL36f44KzgYYSlkElgwzKtGot9nvsd1RoAJqf4Fj0iEoqleLKlSsICQmBTCbD4cOH9QmOSCRqcuPO9kKXLs4OwSLLL1wwe4z30LLacMV7aEpM9m7D1jERmaIFkHiq/gfWXJKje0Tl4+7jqLBsxlyvS52iDqefOQ3VHyqoLqig+kOF2pLbb1yRRQnOX//6V3z77bfo168fEhIS8MILL+CLL77AsWPHMGnSJHvHaBcyLy98cJuswrz18mUUqYzXV+A9tLw2XPEemhKTvduwZUximH5sYatymZcXADil7dspJluyZRvXtVpMP3kSgOkkp1pdn+B4u3vbqMWms6RHplZRi8rsSlRmVzZ4rT8+/sOw4Dbsy7BomvjatWvxxhtvAADmzJmDjRs3onfv3li6dClWrVpl1wDtoY2bm37A2e1gaVgY2rgZflS8h5bZhiveQ1NisncbtowpsVMnu5YvDQtzWtu3S0weADyb8DTA1Hm2bgOoT5YST51CqkJhdEzfg+Ph/B6chnpksh/ORkZwBg5JD+HEiBMoeLXh2c93Lr8Tvb7ohaiMKNxXdB8eVD1oj5DtqtEEp66uDq+99hr++OPPbO6JJ57ARx99hKeffhoeHh52DdDWZF5eWNuz5201A2lKcDDW9uwJmZcXROA9tOQ2XPEemhKTvduwZUyrwsPtWj4lONhpbd8uMW24+27866679GUBYjEC3N0bfG3qPGvb8LUi4bmu1eJ1E0ui1KhrANQ/opIr5UjanoSoNVEWX9dRyn8sR92lOojvEMN/sD86zevUYP2uz3dF0ONB8H/AH94h3nDzaDhd8Ag2kws4cWsuiwYZSyQS5OTkIDQ01AEh2U9zt14nIqKWKVWhwHP5+SjVNPxwSxg61OD9x79+jGd2PIOIwAicLT8LraBFraYWQrJjNqWqKarBpS8v4XLaZSgzzU+rvuebe+Ab6Qtvmbd+7Oxe0V6z9YcKQ43KrBmUbAvN/c22aAzOiBEj8NNPP2HGjBlNboiIiMhV6Xq1UhUKTD950uQYHhHqEyFdT6FcKUfqiVQAwO9XfodGsN3oooaSif6Z/XH5q8u4nHYZFb9UWHS9DjEdTF7LXBum2COJsSeLEpzhw4fj73//O06cOIH+/fvD19fX4Pj48ePtEhwREZEj6ZKXuJMnjTYGF1C/UKCuTmx6LP73x/8AwKbJDdDweJpfuv6if+/Wxg0BYwIQ9EQQcifkWtXG7ZawWMuiBOfpp58GAHz00UdGx0QiETSNdOkRERHdLqYEB2PqjZlTtyq+aeZZ2sQ0jEodheMlxyEWiW2e5Jjj5u2G9qPaI+iJIASMDoDYt34ZQ2t7ZFo6ixIcrbaJ6ywTERHdhmReXian0YfcmGIP1O9F9aDsQRwvOY6BnQfieMlxh+xF9cClB+DuZ/zz3dJ7ZKzV4Pjm9u3b48qVK/r3y5Ytw9WrV+0dExERkVOZmkYPAJUajcF0cd008al9pqLguQLMjJqJvtK+zWq79nLDCZKp5IaMNfhv6erVqwa9N2+99RYmTZqEtm3b2jsuIiIip9GNs3k2Px9lNw3DKFWrDVY3rtHcmCbu4QOpRIqVo1da1Y7ZTSqp2ayaoW7BjHIiIqIWYUpwMPzcjfsBbl4Tp7lbNVib3LTW8TRNwX4uIiIiM4pNjMO5uVy3VYM9VjI2tRYNWa7RBOeTTz6BRCIBAKjVaqxfvx4BAQEGdV588UX7REdEROREIY0MNr6dN9ts6RpMcEJCQrBhwwb9e6lUis2bNxvUEYlETHCIiKhFGhUQgNUXL5osB/7swamqq0LS9iQcvnAYx2cfb/CaHHfjGA0mOIWFhQ4Kg4iIyPV8X1raYHlFTf1KwrFfxUKAYNEUcSY3jsExOERERGaYG4NTVHERSduTkHu5fvVglcZ0vabiYOLmY4JDRERkhrkxOF6nlmDN1RMQjDZ0aB4OLLYdJ25kTkRE5NpMLfgnAhA75GPM6T/H6utlSDNsFBk1hgkOERGRGVOCgzFdKoXopjIBwJfXNHggeiF8Peo3n/Z294an2LPR63H8jeMwwSEiImrA96WlRg+idIv96cbe5D+d3+xtGjjuxrYsHoNTU1OD//73vzh79ixmz56Ntm3b4uzZs2jXrh3at29vzxiJiIicxuxA4+oqQKuGWCRGV/+uVm/TcCtulmlbFiU4Z86cwUMPPQSlUomrV6/i8ccfR9u2bbF69WpcvXoVn376qb3jJCIicgpzA427egDnYZ9VjKn5LHpE9fzzz+Ohhx6CQqGAj8+fH+TYsWPx888/2y04IiIiZzM10LiNmxte6yIFYNkqxhnSDOwV7bVHeGSGRT04hw4dwi+//AKxWGxQHhISgosmVngkIiJqKXQ7i8/+/XdU3dh02sfNDbU37UMlV8qRsj/F7ErGjQ0u5vgb27N4DE5dnfGHU1xcDH9/f5sGRERE5IrUN70uVavxtzP1O4pfq7mGsI/CoBW0Fq1kfCuufWMfFj2ievjhh/HBBx/o34tEIlRUVCA5ORmjR4+2W3BERESu4PWCAqiEm+ZSqUpRU7AeAHBNdQ016hqzyQ3XvnEOixKcDz74AAcPHkTPnj1RU1ODJ554AqGhoSgpKcGyZcssbqysrAzjxo2Dr68vZDKZ0cadN1u+fDmkUin8/f0xY8YMqG4Z4LVlyxbcfffd8PX1xZ133okDBw5YHAcREZE1jGZSnXwTuGLZ7w7XvnEOixKcTp06ISsrC/Pnz8fs2bMRHR2Nd999F8eOHUNgYKDFjc2bNw+enp5QKBRITU3F3LlzkZuba1Rv586dWLZsGfbs2YPCwkIUFBQgOTlZf/zHH3/E/PnzsWHDBiiVSuzfvx9hYWEWx0FERGSN9reMQUWvZKD9fQAAkcEygOQqRIIg2HYjDTOqqqrQrl075OTkIDw8HAAQFxeHzp07G/UCPfnkkwgNDcVbb70FANizZw+mTJmCkpISAMADDzyAhIQEJCQkWBVDdHQ0MjMzbXA3RETUmnQ4eBClarVh4ZWDQO4CyPxluFR1CRpBg1pNLYRkw5/VhmZPeQR7cP0bM5r7m212kPHWrVstvsj48eMbrZOfnw+xWKxPbgAgMjIS+/btM6qbm5uLmJgYg3oKhQKlpaVo27YtMjMzMXbsWHTv3h01NTV47LHH8N577xlMYddZu3Yt1q5dCwC4fPmyxfdERESkU3ZrcgMA2voxNwO7DMQ/R/4TKftScOjCIauuy+TGfswmOBMnTrToAiKRCBqNptF6lZWVRjOu/P39oVQqG62re61UKqFSqVBXV4evvvoKBw4cgIeHB2JiYrBkyRIsXbrU6FqJiYlITEwEUJ8NEhERWcvkYn+aGgD16+BIJdJmr2RMtmV2DI5Wq7Xoz5LkBgAkEgkqKioMyioqKuDn59doXd1rPz8/fS/NM888g44dO6JDhw548cUX8f3331sUBxERkbWWhoXBaKUabX3Cc4FjiF2SwzbbDA8Ph1qtxunTp/Vl2dnZiIiIMKobERGB7Oxsg3rBwcEICAhAu3bt0KVLF4hEHNRFRESOMSU4GHe43/LQ48Yjql+rTO9VRc5lcYJz4sQJTJs2DdHR0RgwYACmT5+O3377zeKGfH19MX78eCxcuBBVVVXIyMjAtm3bEBcXZ1R32rRpWL9+PfLy8lBeXo4lS5YgPj5ef/ypp57CihUrcOnSJZSXl+PDDz/EmDFjLI6FiIjIWkbjcG48olIK5lch5ho4zmNRgvPtt9+iX79+OH/+PB555BGMHDkSxcXF6NevH7777juLG1u1ahWqq6sRFBSEyZMnY/Xq1YiIiEBxcTEkEgmKi4sBACNHjsSrr76KYcOGQSaTQSaTYfHixfrrLFiwAAMGDEB4eDjuvvtuREVF4fXXX7fy1omIiCwX4uVlWHCjB+cONwFJ25MQtSbK6ByugeM8Fk0T79OnD8aNG2eQZADAwoULsW3bNoPHSa6M08SJiKipkvLzsfrm/RdPvQuU7IBI5A4PNzdOEbex5v5mW9SDk5+fb/JRUlxcHE6dOtXkxomIiG4X35eW1r9QlQL5y4GSnQAAQVA3aQ8qJjf2ZVGCExQUhKNHjxqVHz16FME3dlklIiJqyfTbNZx8E5B/B0Dr1HioYRbtJj5r1izMnj0bZ86cwQMPPACRSISDBw/iH//4B1555RV7x0hEROR0+rVweiUDRf8GLjac5HCAsXNZlOC88cYbkEgkeP/997FgwQIA9ftTLV68GM8++6xdAyQiInIFowIC6sfgeLYHejwPXC8Grh6Hm8gDgAZawTDZaWiAsUew+ZlXZBsWJTgikQgvvPACXnjhBf3Kw6YW6CMiImqp9GNwdDT1j6y0gmD1onIcf2N/FiU4N2NiQ0RErZF+DI6qtP4RlfLkjSNqjsZxQQ0mOGPHjrXoIt9++61NgiEiInJV+jE4J98Erv0GoNFVVsiJGkxw/vvf/0Imk2Ho0KEOCoeIiMg1LQ0Lw1MnT6JOP8h4m7NDogY0mOC8/PLL+M9//oP9+/fjqaeeQnx8PLp06eKo2IiIiFzGlOBgPHf6NEpxY5Dx5f1AXTkg8gAEDW6dUeUR7GFyoDEHGDtGg+Oi3n33XZw/fx7Lly9HZmYmevTogUceeQRfffUV6uq4/DQREbUuBvtR3diLqv5JleHjqgxphtnkhgOMHaPRgd9isRhjx47FN998g3PnzmHYsGF444030LlzZ1RWVjoiRiIiIpfQXiz+cyVjbfWN0jrcmuCYmyLOvakcx6pZVFVVVbh69SoqKyshkUggEonsFRcREZHrEYluGmRMrqzRHpzq6mps2rQJDz74IHr37o2ioiJs2rQJBQUF8PX1dUSMRERELqFUra5fyVg6ytmhUCMa7MFJTExEWloaevTogYSEBHz77bdo27atg0IjIiJyLWIAGs/2QLcZQMl2QORZf0AwfkxFztVggvPpp58iJCQEHTt2xI4dO7Bjxw6T9bgODhERtQYa/Ysbi/6JxICgNlednKjBBGfatGkcZ0NERHRDgFiM0uuXgHPr6gv0A40NcYq48zWY4GzcuNFBYRAREd0GLBhkzCnirsHa/cGIiIharTLdIOOAB8zW4RRx18AEh4iIyEIhXl6AZ3ug46P1BZ6BNwYacziHq2GCQ0REZKGlYWH1qYz2xirGIndw9pRrYoJDRERkoSnBwRBUpX9utKkq4RRxF2XVSsZERESt3sk3gWsnbrxhYuOq2INDRERkjV7JwB0RN97wZ9RV8ZMhIiKyQoBPIND+vvo3bWTgT6lr4qdCRERkDZEIqCuvf11dDN0MKrGku/NiIiMcg0NERGQhuVKO0rz3APl/6wsE/eYN0PZf56SoyBQmOERERBaKTY8F5AdganBxe7HY8QGRWXxERUREZKG0iWnw6hwDkz+f3LvRpTDBISIispBUIkVt9+eAdtH1BSJ36MbglKm5q7grYYJDRERkhfZi8Z8rGQuCQXmGNMNJUdGtOAaHiIjIQnKlHJWnPrhpN/E/BxlDJOKGmi6ECQ4REZGFYtNjofrD9CDjxh5ReQR72CkqMoWPqIiIiCyUNjENki7jTB4L8fJq8NxBJYPsERKZwQSHiIjIQlKJFJ+MWQV4tDU6tjQszPEBkVl8REVERGQhuVKOjMwUoO6qs0OhRjDBISIislBseiwOFh80eSzx1Clsd3A8ZB4fUREREVkobWIaZvWbZfLYda3WwdFQQxya4JSVlWHcuHHw9fWFTCbD5s2bzdZdvnw5pFIp/P39MWPGDKhUKqM6p0+fhre3N6ZOnWrPsImIiADUj8FZ9n/LbrwTAyJP6Bb6I9fi0ARn3rx58PT0hEKhQGpqKubOnYvc3Fyjejt37sSyZcuwZ88eFBYWoqCgAMnJySavN2DAAEeETkREBACoqq2qf+HmDlPTxck1OCzBqaqqQnp6OlJSUiCRSDB48GCMHTsWn332mVHdTZs2ISEhAREREWjXrh0WLFiAjRs3GtTZsmUL2rZti+HDhzvoDoiIqLWTK+V45cdX6t9oVYBQB12S08aNoz5cicM+jfz8fIjFYoSHh+vLIiMjTfbg5ObmIjIy0qCeQqFAaWkpAKCiogILFy7E+++/32i7a9euRXR0NKKjo3H58mUb3AkREbVWsemx2JKzxeSx6VKpg6OhhjgswamsrIS/v79Bmb+/P5RKZaN1da91dRcsWICEhAR07dq10XYTExORmZmJzMxMBAYGNucWiIiolUubmIaYnjEmj20qKXFwNNQQhyU4EokEFRUVBmUVFRXw8/NrtK7utZ+fH7KysrB792688MIL9g2YiIjoFlKJFLOjZ9e/8QwE3Lxu7CjOWVSuxmHr4ISHh0OtVuP06dPo0aMHACA7OxsRERFGdSMiIpCdnY1Jkybp6wUHByMgIACfffYZCgsLERISAqC+t0ej0SAvLw/Hjh1z1O0QEVErVVlbWf/ijruAHs8DRf8GrhkPtyDnclgPjq+vL8aPH4+FCxeiqqoKGRkZ2LZtG+Li4ozqTps2DevXr0deXh7Ky8uxZMkSxMfHA6h/5HT27FlkZWUhKysLc+bMwejRo7Fz505H3QoREbVi+llUYm/As319khO9DunjnRoW3cKhQ75XrVqF6upqBAUFYfLkyVi9ejUiIiJQXFwMiUSC4uJiAMDIkSPx6quvYtiwYZDJZJDJZFi8eDEAoE2bNpBKpfo/iUQCb29vjq8hIiKH0PXgiCEC8pcDmfUL/7UvN38OdxJ3PJEgCK1mEn90dDQyMzOdHQYREd3G3vjpDSw9sBRuIjG0cAOEOgQMP4Cv/qI2e85QYajjAmwhmvubzb2oiIiILCBXypGyPwXrjq0DAGgFDQANAKBUbT65IedggkNERGQB3UabWoGzpW4HXHaRiIjIAmkT0zCn/xyIRWJnh0IWYIJDRERkAalEipWjV+LR8EfrC0Tu+jVwAEBrbs9N/tI6BR9RERERWUEj1I+7Ceg1H6XlOfo1cNzMTdnhEy2nYIJDRERkBd008Vnd+mJ54ENQtZ7JyLcVdpwRERFZQZfgDPbzQcTFdfp1cMi1sAeHiIjICuU19Sv6TfhiAtSCFtDWOTkiMoU9OERERBaQK+VI2p6EM2VnAAAqjQqaG8kNt2lwPezBISIisoBuHRxTuE2D62EPDhERkQXSJqZhdv/ZVp83qGSQHaKhxjDBISIisoBUIsX7D78PAHCDG+DmZbAODrkWJjhEREQWUtYqAQDtfNqh3QNpQMfRgG93J0dFpjD1JCIispBuiriflx+KxP5Aj+edGxCZxR4cIiIiCylV9T04fp5+6AQlkL+c6+C4KPbgEBERWUj3iOpS1SVcOfg4oNUAgtrJUZEpTHCIiIgsIFfK8faBtwHUJzgCuEWDK2OCQ0REZIHY9FgcKDoAAExubgMcg0NERGSBtIlpGCIbAuDGNHFyafyEiIiILCCVSPFoz0cBAPcE3QMPsTfXwXFhTHCIiIgsVKGqAADE3BWD4ufPcR0cF8YEh4iIyEK6aeIA8Oa+N+GhzAOi1zkxIjKHfWtEREQWKqksAQAsO7gMIpEIdZpaJ0dE5rAHh4iIqBFypRxJ25OwJXcLAKBOW4faG8lN+nhnRkbmsAeHiIioEbHpsThYfBBaQWt0rH25+fM8gj3sGBU1hD04REREjUibmIY5/edYPT18UMkgO0VEjWGCQ0RE1AipRIqVo1eie0D9jCkvsRc8xZ5OjooawgSHiIjIQtfrrgMADjx1ADOjZqKvtK9zAyKzmOAQERFZ6FrNNQCAn5cft2twcRxkTEREZAGNVqPfTTxqTRS0glY/k4pcD3twiIiIGiFXypH4XaL+fY26hsmNi2MPDhERUSNu3kmcbg/swSEiImpE2sQ0TOw10dlhkBWY4BARETVCKpFibvRcAEAnSSf4uPtwmriLY4JDRERkgfKa+iWLB3QegILnCjAzaia6y83vJM5VjJ2LY3CIiIgsUF5dn+C082mnX/hv75i9ZutzFWPnYg8OERGRBXQ9OO282zk5ErKEQxOcsrIyjBs3Dr6+vpDJZNi8ebPZusuXL4dUKoW/vz9mzJgBlUoFAFCpVEhISIBMJoOfnx+ioqKwY8cOR90CERG1UvoeHCY4twWHJjjz5s2Dp6cnFAoFUlNTMXfuXOTm5hrV27lzJ5YtW4Y9e/agsLAQBQUFSE5OBgCo1Wp07doV+/btw7Vr15CSkoJJkyahsLDQkbdCREStjL4Hx4cJzu3AYQlOVVUV0tPTkZKSAolEgsGDB2Ps2LH47LPPjOpu2rQJCQkJiIiIQLt27bBgwQJs3LgRAODr64tFixYhNDQUbm5uGDNmDLp164ajR4866laIiKgV4iOq24vDEpz8/HyIxWKEh4fryyIjI0324OTm5iIyMtKgnkKhQGlpqVFdhUKB/Px8REREmGx37dq1iI6ORnR0NC5fvmyDOyEiotZI94hKgICk7UmIWhPl5IioIQ6bRVVZWQl/f3+DMn9/fyiVykbr6l4rlUoEBAToy+vq6jBlyhRMnz4dd911l8l2ExMTkZhYv7x2dHR0s++DiIhaJ0WVAgCQ8G0CAHCrBhfnsB4ciUSCiooKg7KKigr4+fk1Wlf3+ua6Wq0WcXFx8PT0xMcff2ynqImIqLWTK+VI2p6E7JJsAPWJDZMb1+ewBCc8PBxqtRqnT5/Wl2VnZ5t8tBQREYHs7GyDesHBwfreG0EQkJCQAIVCgfT0dHh4cDElIiKyj9j0WKw5ugYCBKvOS1Uo7BQRWcJhCY6vry/Gjx+PhQsXoqqqChkZGdi2bRvi4uKM6k6bNg3r169HXl4eysvLsWTJEsTHx+uPz507FydPnsR3330HHx8fR90CERG1QmkT05DYL7Hxird4vaDADtGQpRw6TXzVqlWorq5GUFAQJk+ejNWrVyMiIgLFxcWQSCQoLi4GAIwcORKvvvoqhg0bBplMBplMhsWLFwMAioqKsGbNGmRlZUEqlUIikUAikSA1NdWRt0JERK2EVCLFm8PeBAB4ib0s3oeq+Mb6beQcDt2qoX379vjmm2+MykNCQlBZWWlQ9uKLL+LFF180qiuTySAI1nUTEhERNceV61cAAKFtQ7E3fi9S9qXgxx0/NnhOiJeXI0IjM7gXFRERUSN0CU6HNh0s2ocKAJaGhTkgMjKHe1ERERE14uYEx1JTgoPtFQ5ZgAkOERFRI3QJjo+7Dxf5u03wERUREVEjCsrrZ0R9lfcV3NzcGl0Hp7xd/TRx9uI4D3twiIiIzNAt8vfeofcAAGpBbdEif+O3cpq4s7EHh4iIyIzY9FgcLD4IraC1+lxOE3cu9uAQERGZkTYxDXP6z4GbyPqfS04Tdy4mOERERGbopoSHtw8HUL/QnyWL/LVxc+M0cSdjgkNERNSIK9X1s6h+nfUrZkbNRF9p3wbr+7jx59XZOAaHiIioAXWaOly5fgVuIjdEBEZg5eiVyJBmoA51Zs8pVauReOoUAK6H4yxMMYmIiBpwqeoSACDINwhiNzEAoE5hPrnRua7VciaVEzHBISIiaoC8Ug6gfjyObtr4rNmzLDqXM6mch4+oiIiIGlBSWQIAKL1eirCPwqAVtKjtaH4tnLJ2f77mTCrnYYJDRERkhlwp1y/y90fFH9Ci8fVwJmyt/ydnUjkXH1ERERGZEZsei/1F+wHAouRGRwxgulTKAcZOxASHiIjIjLSJabiz3Z0AAHc3yx96aABsKilBqkJhp8ioMUxwiIiIzJBKpPD39gcAjA0fCx93H7irLUt0OIvKuTgGh4iIyAxBEHC27CwAYPWY1dAKWsx5Yg5yu+ZadD5nUTkPExwiIiIzymvKcU11DRJPCQLbBEIkEuH57583W//mGVQAZ1E5Ex9RERERmaHrvQlrFwaRSNRofd0MKgDwADiLyomY4BAREZlxtrw+wdENNLaGJQkR2Q8THCIiIjMKyusHCesSnAxphsXn1goCBxk7ERMcIiIiM3SPqAJ8ApC0PQnxj8VbdT4HGTsPBxkTERGZcfLKSQBA8r5kAGhwiwZT2ovFNo+JLMMeHCIiolvoNtU8fOEwAKBWU4tajXXJDQAotVou9uckTHCIiIhuEZseizWZa5p9HY7DcR4mOERERLdIm5iGkd1HAgBEsGw21K1r4OhwHI5zMMEhIiK6hVQiRVTHKABAZHCkRVs0vLjd9KJ+XOzPOZjgEBERmfDLhV8AAMlDk1HwXAFGHxuN7vLuZuuPCgiwqpzsiwkOERHRLTRaDX7941cAQKh/KN7c9yZyu+Zi3Zp1Zs/5vrTUqnKyL04TJyIiusXJKyehrFVC4inB/f+6H5oaDeo61pmt7xHsYXasTRHH4DgFe3CIiIhuIlfKMe/7eQCA67XXUaOuQZ27+eQGAAaVDDI71kYEcKq4EzDBISIiuklseiz2F+0HAGihtfi8pWFhJudbCQCnijsBExwiIqKbLBu+TP/azYqfySnBwRDMHONjKsdjgkNERHSTz3M+B1Cf3Aja+pSlsSniOuY2ZuCGDY7HBIeIiAj1Y28StiVgxa8rANQ/nhLc6hOchqaIewR76F9rzFzbXDnZD2dRERFRqydXytFndR9cqb5i8vjz3z9v9txBJYP0r8Uwn8ykKhSYEhzc9CDJKg5NcMrKypCQkIBdu3ahQ4cOePvtt/Hkk0+arLt8+XK88847qK6uxoQJE7B69Wp43Rihbs11bqY8qkS6Xzr+PeTfOBl6Evkf59v0/uwlQ5qBOsWfI/hLJaW8hxbahiveQ1Nisncbtowpr0uewdomti4/GXoSG77a4JS2b6eYbi6/uWxZ6rJGX5s6z9I2ToScgLRMil/uql/Uz8JdGfRu7r0BGu6pmXryJKaePGlU7isSwVssRplajRAvLywNC8OU4GCkKhR4vaAAxSqVQXlDrD3HlvXNHbu5vL1YDIhEKFOrG31dqlQ2eK+NcWiCM2/ePHh6ekKhUCArKwujR49GZGQkIiIiDOrt3LkTy5Ytw08//YROnTph3LhxSE5OxrJly6y6zq3kbeWY/PRkCBCgdlfj5Anj/6G5omJVMdAWKG9Tjq8Hfo19Eft4Dy20DVe8h6bEZO82bB2TvK3cruXObPt2iimvU56+TAstNO4aTH5+stnXuvNyO+fi63u/xv6I/fqyP9r/Ud/GvV/jQK8D+vOOhB3BtwO+xeEeh6Fx0wBuQGFQodnExl3tDrW72vRBGPbeAIDMy8vqAcVVgoAqdX0bRSoVZp06hR2lpdh65QqqtVqD8su1tYjp0MHkdbZduYK/nztn8Tm2rA/A5LFb76NU82cKaMnr5hAJgmBu0LdNVVVVoV27dsjJyUF4eDgAIC4uDp07d9YnLjpPPvkkQkND8dZbbwEA9uzZgylTpqCkpMSq69xK1EkEzLbDzRERUYsU82uM2RWMPYI9jBKcVIXCZC8NNcHs2RBOnWry6Q7rwcnPz4dYLNYnJQAQGRmJffv2GdXNzc1FTEyMQT2FQoHS0lIUFxdbfB0AWLt2LdauXWvyWEdlx6bejkNd8b5Sv8iUif/C4D20rDZc8R6aEpO927B1TBBg13KPOg+ntX07xWRTNmrD0rE3OlOCgxF38qTZ6eK2EOrtbbK8sKbGqnNsWd8VOSzBqayshL+/v0GZv78/lCaesd1aV/daqVRadR0ASExMRGJiIoAbPTg3ufiPi9bfiBNs9duKfz/4b/wQ9QM0Io1BdynvoWW14Yr30JSY7N2GrWOKORJj1/It/9zitLZvp5i86rygFqmhcbfuEYVXnZfN2xBpRfoZVKbcOvbmZnM6dcLqi837vpobrCzz8sK5++4zeU7o4cMmH4+ZO8eW9QHTa/00NOja3hw2TVwikaCiosKgrKKiAn5+fo3W1b328/Oz6jq38q/yh1edl8XrGbiK9pXt8fz3z2PzPzdj9LHRvIcW3IYr3kNTYrJ3G7aOyd7lzmz7dopp8z83Y8yxMfCq84JYXb9yTEOvbz7PVm3cUXUHQhWhGJs51uS0cI9gDwwVhprsvdFZFR6O4W3bmj3emDZubkjs1Alt3NyMypeGhZk9b2lYmFXn2LK+uWOm7sNRHD4GJzc3Fz169AAATJs2DZ06dTI5Bqdbt25YunQpAOCnn37Ck08+aTAGx5Lr3KqnqCfelryNfz/4b+R1y7ttZyCVScp4Dy20DVe8h6bEZO82bBnTreMrbF2e1y3PaMaSo9q+nWK6ufzmsndS32n0tanzmtqGpWNtLJGqUOC5/PxGB81yFpWZWVQJCc0ag+OwBAcAYmNjIRKJ8OmnnyIrKwujRo3CoUOHjGY//fDDD4iPj8dPP/2Ejh07YsKECbj33nv1CYyl17lVdHQ0MjMz7XZ/REREZBvN/c12aL/RqlWrUF1djaCgIEyePBmrV69GREQEiouLIZFIUFxcDAAYOXIkXn31VQwbNgwymQwymQyLFy9u9DpEREREgIN7cJyNPThERES3h9uqB4eIiIjIEZjgEBERUYvDBIeIiIhaHCY4RERE1OIwwSEiIqIWhwkOERERtThMcIiIiKjFYYJDRERELQ4THCIiImpxmOAQERFRi9OqtmqQSCS46667nB1Gq3f58mUEBgY6OwwCPwtXws/CNfBzcB2///47Kisrm3y+uw1jcXl33XUX96JyAdwTzHXws3Ad/CxcAz8H1xEdHd2s8/mIioiIiFocJjhERETU4rSqBCcxMdHZIRD4ObgSfhaug5+Fa+Dn4Dqa+1m0qkHGRERE1Dq0qh4cIiIiah2Y4BAREVGLwwSHiIiIWpwWleCUlZVh3Lhx8PX1hUwmw+bNm83WXb58OaRSKfz9/TFjxgyoVCoHRtryWfpZbNy4EWKxGBKJRP+3d+9exwbbgn388ceIjo6Gl5cX4uPjG6zL74R9WfpZ8DthXyqVCgkJCZDJZPDz80NUVBR27Nhhtj6/F/ZhzefQ1O9Ei0pw5s2bB09PTygUCqSmpmLu3LnIzc01qrdz504sW7YMe/bsQWFhIQoKCpCcnOyEiFsuSz8LALj//vtRWVmp/xs6dKhjg23BOnXqhDfeeAMzZsxosB6/E/Zn6WcB8DthT2q1Gl27dsW+fftw7do1pKSkYNKkSSgsLDSqy++F/VjzOQBN/E4ILURlZaXg4eEhnDp1Sl82depUYf78+UZ1J0+eLPztb3/Tv9+9e7cQHBzskDhbA2s+iw0bNgiDBg1yZHit0uuvvy5Mnz7d7HF+Jxynsc+C3wnH6927t/DVV18ZlfN74VjmPoemfidaTA9Ofn4+xGIxwsPD9WWRkZEmew1yc3MRGRlpUE+hUKC0tNQhsbZ01nwWAHD8+HF06NAB4eHhSElJgVqtdlSodAO/E66F3wnHUSgUyM/PR0REhNExfi8cp6HPAWjad6LF7EVVWVkJf39/gzJ/f38olcpG6+peK5VKBAQE2DfQVsCaz+LBBx9ETk4OZDIZcnNz8cQTT8Dd3R1/+9vfHBUugd8JV8LvhOPU1dVhypQpmD59usmNmPm9cIzGPoemfidaTA+ORCJBRUWFQVlFRQX8/Pwarat7baouWc+azyIsLAzdunWDm5sbevfujYULF+Krr75yVKh0A78TroPfCcfQarWIi4uDp6cnPv74Y5N1+L2wP0s+h6Z+J1pMghMeHg61Wo3Tp0/ry7Kzs012d0VERCA7O9ugXnBwMDNyG7Hms7iVSCSCwMW1HY7fCdfF74TtCYKAhIQEKBQKpKenw8PDw2Q9fi/sy9LP4VaWfidaTILj6+uL8ePHY+HChaiqqkJGRga2bduGuLg4o7rTpk3D+vXrkZeXh/LycixZsqTRKbRkOWs+ix07dkChUAAAfv/9d6SkpCAmJsbRIbdYarUaNTU10Gg00Gg0qKmpMfnsmt8J+7P0s+B3wv7mzp2LkydP4rvvvoOPj4/Zevxe2Jeln0OTvxPNHPTsUkpLS4WYmBihTZs2QteuXYXU1FRBEAShqKhI8PX1FYqKivR133//fSEoKEjw8/MT4uPjhZqaGmeF3SJZ+lm89NJLQlBQkNCmTRuhW7duwoIFC4Ta2lpnht6iJCcnCwAM/pKTk/mdcAJLPwt+J+yrsLBQACB4eXkJvr6++r///Oc//F44kDWfQ1O/E9xsk4iIiFqcFvOIioiIiEiHCQ4RERG1OExwiIiIqMVhgkNEREQtDhMcIiIianGY4BAREVGLwwSHiCw2dOhQPP30084Ow27Ky8sRHByMs2fP2q0NlUqFkJAQZGZm2q0NImKCQ9TqxcfHQyQSQSQSwcPDA0FBQRg2bBhWrlyJuro6g7pbt27F22+/bdF1Fy1ahHvuucceIdvNW2+9hVGjRuHOO++0WxteXl545ZVXMH/+fLu1QURMcIgIwP/93/9BLpejsLAQu3btwqOPPork5GT85S9/QVVVlb5e+/btW+xGg9evX8enn36KhIQEu7c1ZcoUHDx4ELm5uXZvi6i1YoJDRPDy8oJUKkXnzp3Rt29fvPjii9i7dy+OHTuGd999V1/v1kdUW7duRZ8+feDj44P27dtjyJAhUCgU2LhxIxYvXozc3Fx979DGjRsBAB988AH69OkDX19fdO7cGTNnzsTVq1f119y4cSMkEgn27NmDe+65B76+vhg2bBjOnTtnEPP27dsxcOBA+Pj4ICAgAI8++ihqamoAALW1tZg/fz66dOkCX19fDBgwADt37mzw38H3338PNzc3DBo0yKD8999/x9ixY+Hv7w+JRIL7778fv/32G4D63q8xY8bgnXfegVQqhb+/P1577TVotVosWrQIQUFBkEqleOeddwyu2b59ewwaNAiff/65ZR8QEVmNCQ4RmXTPPfdg5MiRSE9PN3m8pKQEsbGxmD59Ok6ePIn9+/frN1R94okn8NJLL6Fnz56Qy+WQy+V44oknAABubm748MMPkZubi82bN+PXX3/FM888Y3BtlUqFt99+G//6179w+PBhXL16FXPmzNEf/+GHHxATE4OHHnoIR48exc8//4whQ4ZAq9UCAJ566ins27cPmzdvxm+//Ybp06fj0UcfNdgZ+lYHDhxA//79IRKJ9GUXL17E4MGDIRKJ8OOPP+LYsWOYN28eNBqNvs7+/ftx7tw57N27F5988gneffddjBo1CiqVCgcPHsSiRYvw2muv4ejRowbt3Xvvvdi3b58lHwURNYV9t9MiIlc3ffp0YfTo0SaPzZ8/X/Dx8dG/HzJkiDBv3jxBEATh6NGjAgChsLDQ5LnJyclCREREo+3v2LFD8PT0FDQajSAIgrBhwwYBgPD777/r6/znP/8RPDw89HUeeOAB4YknnjB5vTNnzggikchgw0RBEISYmBhh7ty5ZuOIiYkRpk2bZlD297//XQgJCRFUKpXJc6ZPny506dJFUKvV+rL+/fsLvXv3Nqgnk8mE9957z6Dsn//8p9ClSxez8RBR87AHh4jMEgTBoEfjZpGRkfi///s/3HPPPZgwYQJWr16Ny5cvN3rNn376CQ899BC6dOkCPz8/jB8/HrW1tSgpKdHX8fLyQs+ePfXvO3XqhLq6Ov2jrOPHj2P48OEmr3/s2DEIgoBevXpBIpHo/7Zv397g7Kjq6mp4e3sblB0/fhyDBw+Gp6en2fN69eoFsVisfx8cHIzevXsb1AkODsalS5cMynx8fFBdXW32ukTUPO7ODoCIXFdeXh7CwsJMHhOLxdi1axd++eUX7Nq1C+vXr8ff/vY37Nu3D5GRkSbPKSoqwujRozFr1iy8+eabCAgIwLFjxzB58mTU1tbq67m7G/5fky7J0j2CaohWq4VIJMKRI0fg4eFhcMzHx8fseR06dEB5eblBmSAIjbZ3axu62Wi3lt0ae1lZGQIDAxu9PhE1DXtwiMiknJwc/PDDD5g4caLZOiKRCPfffz+Sk5Nx5MgRdOrUCWlpaQAAT09Pg7EqAJCZmYna2losX74c999/P8LDw3Hx4kWrY4uKisKePXvMHhMEASUlJejevbvBX+fOnRu8Zl5enkFZv379cPDgQYPky1ZycnLQr18/m1+XiOoxwSEiqFQqlJSU4OLFi8jOzsYHH3yAoUOHon///nj55ZdNnvPLL79gyZIlOHLkCIqLi/Htt9/i/Pnz6NWrFwAgNDQURUVFOHbsGK5cuQKVSoUePXpAq9Xiww8/xLlz5/D555/jww8/tDre119/HV9++SXeeOMN5OXlITc3F8uXL8f169cRHh6OKVOmID4+Hl999RUKCgqQmZmJf/zjH9i6davZa44YMQInT55EaWmpviwpKQmVlZWYNGkSjhw5gjNnzuDzzz9HVlaW1THf6sCBAxg5cmSzr0NEpjHBISLs3r0bHTt2REhICIYPH45vv/0WycnJ2L9/P3x9fU2e4+/vj4yMDIwZMwY9evTASy+9hAULFmDq1KkAgAkTJmDUqFEYPnw4AgMD8fnnn6NPnz745z//iQ8++AC9evXCp59+in/84x9Wxztq1Ch8/fXX2LFjB6KiojBkyBD8/PPPcHOr/7+0DRs24KmnnsKrr76Ku+66C2PGjMH+/fshk8nMXrN379649957sWXLFn1Z586dsX//ftTW1mLYsGGIiorCihUrjB6hWevw4cO4du1ag71jRNQ8IsGSh8xERK3ADz/8gOeeew55eXkGA4dt7fHHH0dUVBT+/ve/260NotaOPThERDeMHDkS8+bNw4ULF+zWhkqlQmRkJF544QW7tUFE7MEhIiKiFog9OERERNTiMMEhIiKiFocJDhEREbU4THCIiIioxWGCQ0RERC0OExwiIiJqcf4fcIODJIf16i8AAAAASUVORK5CYII=", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -3134,25 +810,18 @@ } ], "source": [ - "\"\"\"\n", - "# To plot species, we first have to identify the index of the species in the array\n", - "# For this, cut & paste the following lines and run in a new cell to get the index\n", - "for i, specie in enumerate(gas.species()):\n", - " print(str(i) + '. ' + str(specie))\n", - "\"\"\"\n", - "\n", "# Extract concentration data\n", - "X_CH4 = oppFlame.X[13]\n", - "X_CO2 = oppFlame.X[15]\n", - "X_H2O = oppFlame.X[5]\n", + "X_CH4 = opposed_flame.X[13]\n", + "X_CO2 = opposed_flame.X[15]\n", + "X_H2O = opposed_flame.X[5]\n", "\n", "plt.figure()\n", "\n", - "plt.plot(oppFlame.grid * 100, X_CH4, \"c-o\", lw=2, label=r\"$CH_{4}$\")\n", - "plt.plot(oppFlame.grid * 100, X_CO2, \"m-s\", lw=2, label=r\"$CO_{2}$\")\n", - "plt.plot(oppFlame.grid * 100, X_H2O, \"g-<\", lw=2, label=r\"$H_{2}O$\")\n", + "plt.plot(opposed_flame.grid * 100, X_CH4, \"c-o\", lw=2, label=\"$CH_{4}$\")\n", + "plt.plot(opposed_flame.grid * 100, X_CO2, \"m-s\", lw=2, label=\"$CO_{2}$\")\n", + "plt.plot(opposed_flame.grid * 100, X_H2O, \"g-<\", lw=2, label=\"$H_{2}O$\")\n", "\n", - "plt.xlim(oppFlame.grid[0], oppFlame.grid[-1] * 100)\n", + "plt.xlim(opposed_flame.grid[0] * 100, opposed_flame.grid[-1] * 100)\n", "plt.xlabel(\"Distance (cm)\")\n", "plt.ylabel(\"Mole Fractions\")\n", "\n", diff --git a/python_tutorial.ipynb b/python_tutorial.ipynb index d74fc4a..22ba5b5 100644 --- a/python_tutorial.ipynb +++ b/python_tutorial.ipynb @@ -27,10 +27,20 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Cantera version: 2.6.0a4\n" + ] + } + ], "source": [ "import cantera as ct\n", - "import numpy as np" + "import numpy as np\n", + "\n", + "print(f\"Using Cantera version: {ct.__version__}\")" ] }, { @@ -68,24 +78,25 @@ "\n", " gri30:\n", "\n", - " temperature 300 K\n", - " pressure 101325 Pa\n", - " density 0.0818891 kg/m^3\n", - " mean mol. weight 2.01588 amu\n", + " temperature 300 K\n", + " pressure 1.0133e+05 Pa\n", + " density 0.081894 kg/m^3\n", + " mean mol. weight 2.016 kg/kmol\n", + " phase of matter gas\n", "\n", - " 1 kg 1 kmol\n", - " ----------- ------------\n", - " enthalpy 26470 5.336e+04 J\n", - " internal energy -1.2109e+06 -2.441e+06 J\n", - " entropy 64914 1.309e+05 J/K\n", - " Gibbs function -1.9448e+07 -3.92e+07 J\n", - " heat capacity c_p 14312 2.885e+04 J/K\n", - " heat capacity c_v 10187 2.054e+04 J/K\n", + " 1 kg 1 kmol \n", + " --------------- ---------------\n", + " enthalpy 26469 53361 J\n", + " internal energy -1.2108e+06 -2.441e+06 J\n", + " entropy 64910 1.3086e+05 J/K\n", + " Gibbs function -1.9447e+07 -3.9204e+07 J\n", + " heat capacity c_p 14311 28851 J/K\n", + " heat capacity c_v 10187 20536 J/K\n", "\n", - " X Y Chem. Pot. / RT\n", - " ------------- ------------ ------------\n", - " H2 1 1 -15.7173\n", - " [ +52 minor] 0 0\n", + " mass frac. Y mole frac. X chem. pot. / RT\n", + " --------------- --------------- ---------------\n", + " H2 1 1 -15.717\n", + " [ +52 minor] 0 0 \n", "\n" ] } @@ -147,24 +158,25 @@ "\n", " gri30:\n", "\n", - " temperature 1200 K\n", - " pressure 101325 Pa\n", - " density 0.0204723 kg/m^3\n", - " mean mol. weight 2.01588 amu\n", + " temperature 1200 K\n", + " pressure 1.0133e+05 Pa\n", + " density 0.020473 kg/m^3\n", + " mean mol. weight 2.016 kg/kmol\n", + " phase of matter gas\n", "\n", - " 1 kg 1 kmol\n", - " ----------- ------------\n", - " enthalpy 1.3296e+07 2.68e+07 J\n", - " internal energy 8.3462e+06 1.682e+07 J\n", - " entropy 85228 1.718e+05 J/K\n", - " Gibbs function -8.8978e+07 -1.794e+08 J\n", - " heat capacity c_p 15378 3.1e+04 J/K\n", - " heat capacity c_v 11253 2.269e+04 J/K\n", + " 1 kg 1 kmol \n", + " --------------- ---------------\n", + " enthalpy 1.3295e+07 2.6802e+07 J\n", + " internal energy 8.3457e+06 1.6825e+07 J\n", + " entropy 85222 1.7181e+05 J/K\n", + " Gibbs function -8.8972e+07 -1.7937e+08 J\n", + " heat capacity c_p 15377 31000 J/K\n", + " heat capacity c_v 11253 22686 J/K\n", "\n", - " X Y Chem. Pot. / RT\n", - " ------------- ------------ ------------\n", - " H2 1 1 -17.9775\n", - " [ +52 minor] 0 0\n", + " mass frac. Y mole frac. X chem. pot. / RT\n", + " --------------- --------------- ---------------\n", + " H2 1 1 -17.978\n", + " [ +52 minor] 0 0 \n", "\n" ] } @@ -213,7 +225,7 @@ { "data": { "text/plain": [ - "1200.0044548350836" + "1200.5188172713504" ] }, "execution_count": 7, @@ -240,7 +252,7 @@ { "data": { "text/plain": [ - "13295636.190310445" + "13302755.250164837" ] }, "execution_count": 8, @@ -267,7 +279,7 @@ { "data": { "text/plain": [ - "(8346238.627182945, 48.84649013545132)" + "(8351530.632807602, 48.84649013545132)" ] }, "execution_count": 9, @@ -331,26 +343,27 @@ "\n", " gri30:\n", "\n", - " temperature 1200 K\n", - " pressure 101325 Pa\n", - " density 0.280629 kg/m^3\n", - " mean mol. weight 27.6332 amu\n", + " temperature 1200 K\n", + " pressure 1.0133e+05 Pa\n", + " density 0.28063 kg/m^3\n", + " mean mol. weight 27.633 kg/kmol\n", + " phase of matter gas\n", "\n", - " 1 kg 1 kmol\n", - " ----------- ------------\n", - " enthalpy 8.6194e+05 2.382e+07 J\n", - " internal energy 5.0088e+05 1.384e+07 J\n", - " entropy 8914.3 2.463e+05 J/K\n", - " Gibbs function -9.8352e+06 -2.718e+08 J\n", - " heat capacity c_p 1397.3 3.861e+04 J/K\n", - " heat capacity c_v 1096.4 3.03e+04 J/K\n", + " 1 kg 1 kmol \n", + " --------------- ---------------\n", + " enthalpy 8.6193e+05 2.3818e+07 J\n", + " internal energy 5.0087e+05 1.3841e+07 J\n", + " entropy 8914.2 2.4633e+05 J/K\n", + " Gibbs function -9.8351e+06 -2.7178e+08 J\n", + " heat capacity c_p 1397.3 38611 J/K\n", + " heat capacity c_v 1096.4 30296 J/K\n", "\n", - " X Y Chem. Pot. / RT\n", - " ------------- ------------ ------------\n", - " O2 0.190114 0.220149 -28.7472\n", - " CH4 0.095057 0.0551863 -35.961\n", - " N2 0.714829 0.724665 -25.6789\n", - " [ +50 minor] 0 0\n", + " mass frac. Y mole frac. X chem. pot. / RT\n", + " --------------- --------------- ---------------\n", + " O2 0.22014 0.19011 -28.747\n", + " CH4 0.055187 0.095057 -35.961\n", + " N2 0.72467 0.71483 -25.679\n", + " [ +50 minor] 0 0 \n", "\n" ] } @@ -576,7 +589,96 @@ "cell_type": "code", "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31mCall signature:\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mType:\u001b[0m Solution\n", + "\u001b[0;31mString form:\u001b[0m \n", + "\u001b[0;31mFile:\u001b[0m /opt/conda/lib/python3.9/site-packages/cantera/composite.py\n", + "\u001b[0;31mDocstring:\u001b[0m \n", + "A class for chemically-reacting solutions. Instances can be created to\n", + "represent any type of solution -- a mixture of gases, a liquid solution, or\n", + "a solid solution, for example.\n", + "\n", + "Class `Solution` derives from classes `ThermoPhase`, `Kinetics`, and\n", + "`Transport`. It defines no methods of its own, and is provided so that a\n", + "single object can be used to compute thermodynamic, kinetic, and transport\n", + "properties of a solution.\n", + "\n", + "To skip initialization of the Transport object, pass the keyword argument\n", + "``transport_model=None`` to the `Solution` constructor.\n", + "\n", + "The most common way to instantiate `Solution` objects is by using a phase\n", + "definition, species and reactions defined in an input file::\n", + "\n", + " gas = ct.Solution('gri30.yaml')\n", + "\n", + "If an input file defines multiple phases, the corresponding key in the\n", + "*phases* map (in YAML), *name* (in CTI), or *id* (in XML) can be used\n", + "to specify the desired phase via the ``name`` keyword argument of\n", + "the constructor::\n", + "\n", + " gas = ct.Solution('diamond.yaml', name='gas')\n", + " diamond = ct.Solution('diamond.yaml', name='diamond')\n", + "\n", + "The name of the `Solution` object defaults to the *phase* identifier\n", + "specified in the input file. Upon initialization of a 'Solution' object,\n", + "a custom name can assigned via::\n", + "\n", + " gas.name = 'my_custom_name'\n", + "\n", + "`Solution` objects can also be constructed using `Species` and `Reaction`\n", + "objects which can themselves either be imported from input files or defined\n", + "directly in Python::\n", + "\n", + " spec = ct.Species.listFromFile('gri30.yaml')\n", + " spec_gas = ct.Solution(thermo='IdealGas', species=spec)\n", + " rxns = ct.Reaction.listFromFile('gri30.yaml', spec_gas)\n", + " gas = ct.Solution(thermo='IdealGas', kinetics='GasKinetics',\n", + " species=spec, reactions=rxns, name='my_custom_name')\n", + "\n", + "where the ``thermo`` and ``kinetics`` keyword arguments are strings\n", + "specifying the thermodynamic and kinetics model, respectively, and\n", + "``species`` and ``reactions`` keyword arguments are lists of `Species` and\n", + "`Reaction` objects, respectively. Note that importing the reactions from a\n", + "YAML input file requires a `Kinetics` object containing the species, as\n", + "shown.\n", + "\n", + "Types of underlying models that form the composite `Solution` object are\n", + "queried using the ``thermo_model``, ``kinetics_model`` and\n", + "``transport_model`` attributes; further, the ``composite`` attribute is a\n", + "shorthand returning a tuple containing the types of the three constitutive\n", + "models.\n", + "\n", + "For non-trivial uses cases of this functionality, see the examples\n", + "`extract_submechanism.py `_\n", + "and `mechanism_reduction.py `_.\n", + "\n", + "In addition, `Solution` objects can be constructed by passing the text of\n", + "the YAML phase definition in directly, using the ``yaml`` keyword\n", + "argument::\n", + "\n", + " yaml_def = '''\n", + " phases:\n", + " - name: gas\n", + " thermo: ideal-gas\n", + " kinetics: gas\n", + " elements: [O, H, Ar]\n", + " species:\n", + " - gri30.yaml/species: all\n", + " reactions:\n", + " - gri30.yaml/reactions: declared-species\n", + " skip-undeclared-elements: true\n", + " skip-undeclared-third-bodies: true\n", + " state: {T: 300, P: 1 atm}\n", + " '''\n", + " gas = ct.Solution(yaml=yaml_def)\n" + ] + } + ], "source": [ "?g" ] @@ -598,13 +700,13 @@ { "data": { "text/plain": [ - "['DP',\n", + "['CK_mode',\n", + " 'DP',\n", " 'DPX',\n", " 'DPY',\n", " 'HP',\n", " 'HPX',\n", " 'HPY',\n", - " 'ID',\n", " 'P',\n", " 'P_sat',\n", " 'SP',\n", @@ -621,6 +723,7 @@ " 'TPX',\n", " 'TPY',\n", " 'T_sat',\n", + " 'Te',\n", " 'UV',\n", " 'UVX',\n", " 'UVY',\n", @@ -628,6 +731,7 @@ " 'Y',\n", " '__call__',\n", " '__class__',\n", + " '__composition_to_array',\n", " '__copy__',\n", " '__delattr__',\n", " '__dir__',\n", @@ -637,6 +741,7 @@ " '__ge__',\n", " '__getattribute__',\n", " '__getitem__',\n", + " '__getstate__',\n", " '__gt__',\n", " '__hash__',\n", " '__init__',\n", @@ -648,9 +753,12 @@ " '__new__',\n", " '__pyx_vtable__',\n", " '__reduce__',\n", + " '__reduce_cython__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__setattr__',\n", + " '__setstate__',\n", + " '__setstate_cython__',\n", " '__sizeof__',\n", " '__slots__',\n", " '__str__',\n", @@ -658,19 +766,29 @@ " '_check_kinetics_species_index',\n", " '_check_phase_index',\n", " '_check_reaction_index',\n", + " '_cinit',\n", " '_full_states',\n", " '_init_cti_xml',\n", " '_init_parts',\n", + " '_init_yaml',\n", + " '_native_state',\n", + " '_partial_states',\n", " '_references',\n", " 'activities',\n", " 'activity_coefficients',\n", " 'add_reaction',\n", " 'add_species',\n", + " 'add_species_alias',\n", " 'atomic_weight',\n", " 'atomic_weights',\n", " 'basis',\n", " 'binary_diff_coeffs',\n", + " 'case_sensitive_species_names',\n", + " 'charges',\n", " 'chemical_potentials',\n", + " 'clear_user_data',\n", + " 'clear_user_header',\n", + " 'composite',\n", " 'concentrations',\n", " 'cp',\n", " 'cp_mass',\n", @@ -706,17 +824,30 @@ " 'entropy_mole',\n", " 'equilibrate',\n", " 'equilibrium_constants',\n", + " 'equivalence_ratio',\n", + " 'find_isomers',\n", " 'forward_rate_constants',\n", " 'forward_rates_of_progress',\n", " 'g',\n", - " 'get_equivalence_ratio',\n", + " 'get_binary_diff_coeffs_polynomial',\n", + " 'get_collision_integral_polynomials',\n", + " 'get_thermal_conductivity_polynomial',\n", + " 'get_viscosity_polynomial',\n", " 'gibbs_mass',\n", " 'gibbs_mole',\n", " 'h',\n", + " 'has_phase_transition',\n", + " 'heat_production_rates',\n", + " 'heat_release_rate',\n", + " 'input_data',\n", + " 'input_header',\n", " 'int_energy_mass',\n", " 'int_energy_mole',\n", + " 'is_compressible',\n", + " 'is_pure',\n", " 'is_reversible',\n", " 'isothermal_compressibility',\n", + " 'kinetics_model',\n", " 'kinetics_species_index',\n", " 'kinetics_species_name',\n", " 'kinetics_species_names',\n", @@ -727,6 +858,7 @@ " 'mix_diff_coeffs',\n", " 'mix_diff_coeffs_mass',\n", " 'mix_diff_coeffs_mole',\n", + " 'mixture_fraction',\n", " 'mobilities',\n", " 'modify_reaction',\n", " 'modify_species',\n", @@ -749,17 +881,22 @@ " 'partial_molar_entropies',\n", " 'partial_molar_int_energies',\n", " 'partial_molar_volumes',\n", + " 'phase_of_matter',\n", " 'product_stoich_coeff',\n", " 'product_stoich_coeffs',\n", + " 'product_stoich_coeffs3',\n", + " 'product_stoich_coeffs_reversible',\n", " 'products',\n", " 'reactant_stoich_coeff',\n", " 'reactant_stoich_coeffs',\n", + " 'reactant_stoich_coeffs3',\n", " 'reactants',\n", " 'reaction',\n", " 'reaction_equation',\n", " 'reaction_equations',\n", " 'reaction_phase_index',\n", " 'reaction_type',\n", + " 'reaction_type_str',\n", " 'reactions',\n", " 'reference_pressure',\n", " 'report',\n", @@ -767,30 +904,44 @@ " 'reverse_rates_of_progress',\n", " 's',\n", " 'selected_species',\n", + " 'set_binary_diff_coeffs_polynomial',\n", + " 'set_collision_integral_polynomial',\n", " 'set_equivalence_ratio',\n", + " 'set_mixture_fraction',\n", " 'set_multiplier',\n", + " 'set_thermal_conductivity_polynomial',\n", " 'set_unnormalized_mass_fractions',\n", " 'set_unnormalized_mole_fractions',\n", + " 'set_viscosity_polynomial',\n", + " 'source',\n", " 'species',\n", " 'species_index',\n", " 'species_name',\n", " 'species_names',\n", " 'species_viscosities',\n", + " 'standard_concentration_units',\n", " 'standard_cp_R',\n", " 'standard_enthalpies_RT',\n", " 'standard_entropies_R',\n", " 'standard_gibbs_RT',\n", " 'standard_int_energies_RT',\n", " 'state',\n", + " 'state_size',\n", + " 'stoich_air_fuel_ratio',\n", " 'thermal_conductivity',\n", " 'thermal_diff_coeffs',\n", " 'thermal_expansion_coeff',\n", + " 'thermo_model',\n", + " 'third_body_concentrations',\n", " 'transport_model',\n", " 'u',\n", + " 'update_user_data',\n", + " 'update_user_header',\n", " 'v',\n", " 'viscosity',\n", " 'volume_mass',\n", - " 'volume_mole']" + " 'volume_mole',\n", + " 'write_yaml']" ] }, "execution_count": 23, @@ -813,7 +964,21 @@ "cell_type": "code", "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31mDocstring:\u001b[0m\n", + "ThermoPhase.species_index(self, species) -> int\n", + "\n", + "The index of species *species*, which may be specified as a string or\n", + "an integer. In the latter case, the index is checked for validity and\n", + "returned. If no such species is present, an exception is thrown.\n", + "\u001b[0;31mType:\u001b[0m builtin_function_or_method\n" + ] + } + ], "source": [ "?g.species_index" ] @@ -831,7 +996,17 @@ "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31mType:\u001b[0m float\n", + "\u001b[0;31mString form:\u001b[0m 300.0\n", + "\u001b[0;31mDocstring:\u001b[0m Convert a string or number to a floating point number, if possible.\n" + ] + } + ], "source": [ "?g.T" ] @@ -847,7 +1022,17 @@ "cell_type": "code", "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31mType:\u001b[0m getset_descriptor\n", + "\u001b[0;31mString form:\u001b[0m \n", + "\u001b[0;31mDocstring:\u001b[0m Temperature [K].\n" + ] + } + ], "source": [ "?g.__class__.T" ] @@ -959,313 +1144,313 @@ "name": "stdout", "output_type": "stream", "text": [ - " 0 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7.73e-15 \n", + " 265 6.52e-15 \n", + " 266 2.526e-15 \n", + " 267 1.263e-15 \n", + " 268 1.209e-14 \n", + " 269 1.793e-15 \n", + " 270 5.49e-15 \n", + " 271 8.644e-15 \n", + " 272 9.561e-16 \n", + " 273 1.244e-14 \n", + " 274 1.956e-15 \n", + " 275 7.73e-15 \n", + " 276 -2.879e-15 \n", + " 277 -2.499e-16 \n", + " 278 -2.247e-15 \n", + " 279 -7.943e-15 \n", + " 280 -3.345e-15 \n", + " 281 7.111e-15 \n", + " 282 1.079e-14 \n", + " 284 1.086e-14 \n", + " 285 1.239e-14 \n", + " 286 -3.341e-15 \n", + " 288 0 \n", + " 290 -5.374e-15 \n", + " 293 1.725e-14 \n", + " 294 1.27e-14 \n", + " 295 -7.339e-16 \n", + " 298 1.128e-14 \n", + " 303 5.522e-15 \n", + " 307 -7.913e-15 \n", + " 308 -1.25e-14 \n", + " 309 -3.853e-15 \n", + " 310 3.072e-15 \n", + " 311 9.317e-15 \n", + " 312 1.506e-14 \n", + " 313 1.429e-14 \n", + " 314 1.745e-14 \n", + " 315 -2.027e-14 \n", + " 316 2.277e-15 \n", + " 317 9.991e-15 \n", + " 318 -3.385e-14 \n", + " 319 -1.377e-14 \n", + " 320 -2.41e-14 \n", + " 321 -2.613e-14 \n", + " 322 -2.009e-14 \n", + " 324 -3.706e-14 \n" ] } ], @@ -1374,7 +1559,7 @@ { "data": { "text/plain": [ - "Arrhenius(A=38.7, b=2.7, E=2.61918e+07)" + "" ] }, "execution_count": 35, @@ -1452,7 +1637,7 @@ { "data": { "text/plain": [ - "array([-8.80914265e-20, -1.03761536e-20, -1.77635684e-15, -4.99749439e-20])" + "array([-2.64274280e-19, -6.56450534e-21, 0.00000000e+00, -9.82558219e-20])" ] }, "execution_count": 38, @@ -1479,7 +1664,7 @@ { "data": { "text/plain": [ - "array([3.18644948e-05, 5.00489577e-08, 1.05964923e-01, 2.89502609e-06])" + "array([3.18644907e-05, 5.00489883e-08, 1.05964910e-01, 2.89502678e-06])" ] }, "execution_count": 39, @@ -1507,7 +1692,7 @@ { "data": { "text/plain": [ - "array([-5.33034657e+08, -2.23248515e+07, -8.76650086e+07, -2.49169628e+08])" + "array([-5.33034690e+08, -2.23248529e+07, -8.76650141e+07, -2.49169644e+08])" ] }, "execution_count": 40, @@ -1534,7 +1719,7 @@ { "data": { "text/plain": [ - "-58013370.72088288" + "-58013369.59815948" ] }, "execution_count": 41, @@ -1561,7 +1746,7 @@ { "data": { "text/plain": [ - "-58013370.72088274" + "-58013369.598159574" ] }, "execution_count": 42, @@ -1588,7 +1773,7 @@ { "data": { "text/plain": [ - "-9307123.262565034" + "-9307122.692794016" ] }, "execution_count": 43, @@ -1624,7 +1809,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/reactors/1D_pfr_surfchem.ipynb b/reactors/1D_pfr_surfchem.ipynb index ad69b7a..2e2467c 100644 --- a/reactors/1D_pfr_surfchem.ipynb +++ b/reactors/1D_pfr_surfchem.ipynb @@ -31,7 +31,7 @@ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", - "print(\"Runnning Cantera version: \" + ct.__version__)" + "print(f\"Runnning Cantera version: {ct.__version__}\")" ] }, { @@ -244,10 +244,8 @@ "outputs": [], "source": [ "######## Solve linear system for the initial vecp ###########\n", - "\"\"\"\n", - " a = coefficient of [u', rho', Yk', P']\n", - " b = RHS constant of each conservation equations\n", - "\"\"\"\n", + "# a = coefficient of [u', rho', Yk', P']\n", + "# b = RHS constant of each conservation equations\n", "rho0 = gas.density # initial density of the flow\n", "u0 = 11.53 # m/s initial velocity of the flow\n", "W = gas.molecular_weights\n", @@ -369,7 +367,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -390,7 +388,7 @@ "# plot gas density along the flow direction\n", "ax[0, 1].plot(times, solution.values.y[:, 1], color=\"C1\")\n", "ax[0, 1].set_xlabel(\"Distance (m)\")\n", - "ax[0, 1].set_ylabel(\"Density ($\\mathregular{kg/m^3}$)\")\n", + "ax[0, 1].set_ylabel(r\"Density ($\\mathregular{kg/m^3}$)\")\n", "ax[0, 1].ticklabel_format(\n", " axis=\"y\", style=\"sci\", scilimits=(-2, 2)\n", ") # scientific notation\n", @@ -679,7 +677,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -701,7 +699,7 @@ "# plot gas density along the flow direction\n", "ax[0, 1].plot(time, solution[:, 1], color=\"C1\")\n", "ax[0, 1].set_xlabel(\"Distance (m)\")\n", - "ax[0, 1].set_ylabel(\"Density ($\\mathregular{kg/m^3}$)\")\n", + "ax[0, 1].set_ylabel(r\"Density ($\\mathregular{kg/m^3}$)\")\n", "ax[0, 1].ticklabel_format(\n", " axis=\"y\", style=\"sci\", scilimits=(-2, 2)\n", ") # scientific notation\n", diff --git a/reactors/NonIdealShockTube.ipynb b/reactors/NonIdealShockTube.ipynb deleted file mode 100644 index b009a4f..0000000 --- a/reactors/NonIdealShockTube.ipynb +++ /dev/null @@ -1,2026 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Non-Ideal Shock Tube Example\n", - "In this example we will illustrate how to setup and use a constant volume, adiabatic reactor to simulate reflected shock tube experiments. This reactor will then be used to compute the ignition delay of a gas at any temperature and pressure.\n", - "\n", - "The example very explicitly follows the form set in `batch_reactor_ignition_delay_NTC.pynb`, which does very similar calculations, but with an IdealGasReactor. All credit is due to the developer of that example. This example generalizes that work to use a Reactor with no pre-assumed EoS. One can also run ideal gas phases through this simulation, simply by specifying a cti file with that thermodynamic EoS.\n", - "\n", - "Other than the typical Cantera dependencies, plotting functions require that you have matplotlib installed, and data storing and analysis requires pandas. See https://matplotlib.org/ and http://pandas.pydata.org/index.html, respectively, for additional info.\n", - " \n", - "The example here demonstrates the calculations carried out by G. Kogekar, et al., \"Impact of non-ideal behavior on ignition delay and chemical kinetics in high-pressure shock tube reactors,\" Combust. Flame., 2018, https://doi.org/10.1016/j.combustflame.2017.10.014\n", - "\n", - "The reflected shock tube reactor is modeled as a constant-volume, adiabatic reactor. The heat transfer and the work rates are therefore both zero. With no mass inlets or exits, the 1st law energy balance reduces to:\n", - "\n", - "\\begin{equation*}\n", - "\\frac{dU}{dt} = \\dot{Q} - \\dot{W} = 0.\n", - "\\end{equation*}\n", - " \n", - "Because of the constant-mass and constant-volume assumptions, the density is also therefore constant:\n", - "\n", - "\\begin{equation*}\n", - "\\frac{d\\rho}{dt} = 0.\n", - "\\end{equation*}\n", - "\n", - "Along with the evolving gas composition, then, the thermodynamic state of the gas is defined by the initial total internal energy $U = mu = m\\sum_k\\left(Y_ku_k\\right)$, where $u_k$ and $Y_k$ are the specific internal energy (kJ/kg) and mass fraction of species $k$, respectively. \n", - "\n", - "The species mass fractions evolve according to the nety chemical production rates due to homogeneous gas-phase reactions:\n", - "\n", - "\\begin{equation*}\n", - "\\frac{dY_k}{dt} = \\frac{W_k}{\\rho}\\dot{\\omega}_k,\n", - "\\end{equation*}\n", - "\n", - "where $W_k$ is the molecular weight of species $k$ $\\left({\\rm kg}\\,{\\rm kmol}^{-3}\\right)$, $\\rho$ is the (constant) gas-phase density $\\left({\\rm kg}\\,{\\rm m^{-3}}\\right)$, and $\\dot{\\omega}_k$ is the net production rate of species $k$ $\\left({\\rm kmol}\\,{\\rm m^{-3}}\\,{\\rm s^{-1}}\\right)$." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Runnning Cantera version: 2.5.0a2\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import time\n", - "\n", - "import cantera as ct\n", - "\n", - "print(\"Runnning Cantera version: \" + ct.__version__)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib notebook\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.rcParams[\"axes.labelsize\"] = 16\n", - "plt.rcParams[\"xtick.labelsize\"] = 12\n", - "plt.rcParams[\"ytick.labelsize\"] = 12\n", - "plt.rcParams[\"figure.autolayout\"] = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define the gas\n", - "\n", - "In this example we will choose a stoichiometric mixture of n-dodecane and air as the gas. For a representative kinetic model, we use that developed by Wang, Ra, Jia, and Reitz (https://www.erc.wisc.edu/chem_mech/nC12-PAH_mech.zip) by [H.Wang, Y.Ra, M.Jia, R.Reitz, Development of a reduced n-dodecane-PAH mechanism and its application for n-dodecane soot predictions, $Fuel$ 136 (2014) 25–36].\n", - "\n", - "To fun a different model or use a different EoS, simply replace this cti file with a different mechanism file." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "gas = ct.Solution(\"../data/WangMechanismRK.yaml\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define reactor conditions : temperature, pressure, fuel, stoichiometry" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the reactor temperature and pressure:\n", - "reactorTemperature = 1000 # Kelvin\n", - "reactorPressure = 40.0 * 101325.0 # Pascal\n", - "\n", - "# Set the state of the gas object:\n", - "gas.TP = reactorTemperature, reactorPressure\n", - "\n", - "# Define the fuel, oxidizer and set the stoichiometry:\n", - "gas.set_equivalence_ratio(phi=1.0, fuel=\"c12h26\", oxidizer={\"o2\": 1.0, \"n2\": 3.76})\n", - "\n", - "# Create a reactor object and add it to a reactor network\n", - "# In this example, this will be the only reactor in the network\n", - "r = ct.Reactor(contents=gas)\n", - "reactorNetwork = ct.ReactorNet([r])\n", - "\n", - "# Now compile a list of all variables for which we will store data\n", - "stateVariableNames = [r.component_name(item) for item in range(r.n_vars)]\n", - "\n", - "# Use the above list to create a DataFrame\n", - "timeHistory = pd.DataFrame(columns=stateVariableNames)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define useful functions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def ignitionDelay(df, species):\n", - " \"\"\"\n", - " This function computes the ignition delay from the occurence of the\n", - " peak in species' concentration.\n", - " \"\"\"\n", - " return df[species].idxmax()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computed Ignition Delay: 4.093e-04 seconds. Took 3.98s to compute\n" - ] - } - ], - "source": [ - "# Tic\n", - "t0 = time.time()\n", - "\n", - "# This is a starting estimate. If you do not get an ignition within this time, increase it\n", - "estimatedIgnitionDelayTime = 0.005\n", - "t = 0\n", - "\n", - "counter = 1\n", - "while t < estimatedIgnitionDelayTime:\n", - " t = reactorNetwork.step()\n", - " if counter % 20 == 0:\n", - " # We will save only every 20th value. Otherwise, this takes too long\n", - " # Note that the species concentrations are mass fractions\n", - " timeHistory.loc[t] = reactorNetwork.get_state()\n", - " counter += 1\n", - "\n", - "# We will use the 'oh' species to compute the ignition delay\n", - "tau = ignitionDelay(timeHistory, \"oh\")\n", - "\n", - "# Toc\n", - "t1 = time.time()\n", - "\n", - "print(\n", - " \"Computed Ignition Delay: {:.3e} seconds. Took {:3.2f}s to compute\".format(\n", - " tau, t1 - t0\n", - " )\n", - ")\n", - "\n", - "# If you want to save all the data - molefractions, temperature, pressure, etc\n", - "# uncomment the next line\n", - "# timeHistory.to_csv(\"time_history.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Figure illustrating the definition of ignition delay" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
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');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var backingStore = this.context.backingStorePixelRatio ||\n", - "\tthis.context.webkitBackingStorePixelRatio ||\n", - "\tthis.context.mozBackingStorePixelRatio ||\n", - "\tthis.context.msBackingStorePixelRatio ||\n", - "\tthis.context.oBackingStorePixelRatio ||\n", - "\tthis.context.backingStorePixelRatio || 1;\n", - "\n", - " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width * mpl.ratio);\n", - " canvas.attr('height', height * mpl.ratio);\n", - " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "ax = fig.add_subplot(111)\n", - "ax.semilogy(1000 / ignitionDelays[\"T\"], ignitionDelays[\"ignDelay\"], \"o-\", color=\"b\")\n", - "ax.set_ylabel(\"Ignition Delay (s)\", fontname=\"Times New Roman\", fontsize=16)\n", - "ax.set_xlabel(\n", - " r\"$\\mathdefault{1000/T\\, (K^{-1})}$\", fontsize=16, fontname=\"Times New Roman\"\n", - ")\n", - "\n", - "# Add a second axis on top to plot the temperature for better readability\n", - "ax2 = ax.twiny()\n", - "ticks = ax.get_xticks()\n", - "ax2.set_xticks(ticks)\n", - "ax2.set_xticklabels((1000 / ticks).round(1))\n", - "ax2.set_xlim(ax.get_xlim())\n", - "ax2.set_xlabel(\"Temperature (K)\", fontname=\"Times New Roman\", fontsize=16)\n", - "\n", - "for tick in ax.xaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname(\"Times New Roman\")\n", - "for tick in ax.yaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname(\"Times New Roman\")\n", - "for tick in ax2.xaxis.get_major_ticks():\n", - " tick.label1.set_fontsize(12)\n", - " tick.label1.set_fontname(\"Times New Roman\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/reactors/batch_reactor_ignition_delay_NTC.ipynb b/reactors/batch_reactor_ignition_delay_NTC.ipynb index 2fc6b53..d97dd0c 100644 --- a/reactors/batch_reactor_ignition_delay_NTC.ipynb +++ b/reactors/batch_reactor_ignition_delay_NTC.ipynb @@ -31,7 +31,7 @@ "\n", "import cantera as ct\n", "\n", - "print(\"Runnning Cantera version: \" + ct.__version__)" + "print(f\"Runnning Cantera version: {ct.__version__}\")" ] }, { @@ -47,7 +47,7 @@ "metadata": {}, "outputs": [], "source": [ - "%matplotlib notebook\n", + "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams[\"axes.labelsize\"] = 18\n", @@ -184,7 +184,7 @@ "\n", "# If you want to save all the data - molefractions, temperature, pressure, etc\n", "# uncomment the next line\n", - "# timeHistory.to_csv(\"time_history.csv\")" + "# time_history.to_csv(\"time_history.csv\")" ] }, { @@ -208,980 +208,7 @@ "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] @@ -1341,9 +368,7 @@ " t1 = time.time()\n", "\n", " print(\n", - " \"Computed Ignition Delay: {:.3e} seconds for T={}K. Took {:3.2f}s to compute\".format(\n", - " tau, state.T, t1 - t0\n", - " )\n", + " f\"Computed Ignition Delay: {tau:.3e} seconds for T={state.T}K. Took {t1 - t0:3.2f}s to compute\"\n", " )\n", "\n", " ignition_delays.tau[i] = tau" @@ -1365,980 +390,7 @@ "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. 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// override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] @@ -2372,7 +424,7 @@ "ax2.set_xticks(ticks)\n", "ax2.set_xticklabels((1000 / ticks).round(1))\n", "ax2.set_xlim(ax.get_xlim())\n", - "ax2.set_xlabel(r\"Temperature: $T(K)$\");" + "ax2.set_xlabel(\"Temperature: $T(K)$\");" ] } ], diff --git a/reactors/interactive_path_diagram.ipynb b/reactors/interactive_path_diagram.ipynb index 0420364..60eb2a2 100644 --- a/reactors/interactive_path_diagram.ipynb +++ b/reactors/interactive_path_diagram.ipynb @@ -23,10 +23,11 @@ "import numpy as np\n", "\n", "%matplotlib inline\n", - "import matplotlib\n", "from matplotlib import pyplot as plt\n", "from collections import defaultdict\n", - "import subprocess" + "import subprocess\n", + "\n", + "print(f\"Using Cantera version: {ct.__version__}\")" ] }, { @@ -516,9 +517,9 @@ "description": "annotation_cutoff", "layout": "IPY_MODEL_b31e0f2f2acb403b83a084fb18495310", "max": 0.0001, - "min": 1e-06, + "min": 0.000001, "step": 0.1, - "value": 1e-06 + "value": 0.000001 } }, "11d4490fa73f470fa12134bdbe95172f": { @@ -779,9 +780,9 @@ "description": "annotation_cutoff", "layout": "IPY_MODEL_cebcfa314a17448385e00d23be4e1132", "max": 0.0001, - "min": 1e-06, + "min": 0.000001, "step": 0.1, - "value": 1e-06 + "value": 0.000001 } }, "549874ce98c54398bdb34b108c543913": { @@ -929,9 +930,9 @@ "description": "annotation_cutoff", "layout": "IPY_MODEL_410cc700779545278ab29d6bdb515078", "max": 0.0001, - "min": 1e-06, + "min": 0.000001, "step": 0.1, - "value": 1e-06 + "value": 0.000001 } }, "6c48772d2156460e98f2d2e53d19ce7d": { @@ -944,9 +945,9 @@ "description": "annotation_cutoff", "layout": "IPY_MODEL_d46402fffd6243a58b1eab788b948adc", "max": 0.1, - "min": 1e-05, + "min": 0.00001, "step": 0.1, - "value": 1e-05 + "value": 0.00001 } }, "6db5fd30b2f7461ca1cc5547b1330a98": { @@ -1515,9 +1516,9 @@ "description": "annotation_cutoff", "layout": "IPY_MODEL_466f3c1486704068ac57d20e554de8cf", "max": 0.1, - "min": 1e-05, + "min": 0.00001, "step": 0.1, - "value": 1e-05 + "value": 0.00001 } }, "ce95d34031c1420abab17b1f372f6edc": { diff --git a/reactors/nonideal_shock_tube.ipynb b/reactors/nonideal_shock_tube.ipynb new file mode 100644 index 0000000..b15eaec --- /dev/null +++ b/reactors/nonideal_shock_tube.ipynb @@ -0,0 +1,360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Non-Ideal Shock Tube Example\n", + "In this example we will illustrate how to setup and use a constant volume, adiabatic reactor to simulate reflected shock tube experiments. This reactor will then be used to compute the ignition delay of a gas at any temperature and pressure.\n", + "\n", + "The example very explicitly follows the form set in `batch_reactor_ignition_delay_NTC.pynb`, which does very similar calculations, but with an `IdealGasReactor`. This example generalizes that work to use a `Reactor` with no pre-assumed EoS. One can also run ideal gas phases through this simulation, simply by specifying an input file with that thermodynamic EoS.\n", + "\n", + "Other than the typical Cantera dependencies, plotting functions require that you have matplotlib installed, and data storing and analysis requires pandas. See https://matplotlib.org/ and https://pandas.pydata.org/index.html, respectively, for additional info.\n", + " \n", + "The example here demonstrates the calculations carried out by G. Kogekar, et al., \"Impact of non-ideal behavior on ignition delay and chemical kinetics in high-pressure shock tube reactors,\" Combust. Flame., 2018, https://doi.org/10.1016/j.combustflame.2017.10.014\n", + "\n", + "The reflected shock tube reactor is modeled as a closed, constant-volume, adiabatic reactor. The heat transfer and the work rates are therefore both zero. With no mass inlets or exits, the 1st law energy balance reduces to:\n", + "\n", + "\\begin{equation*}\n", + "\\frac{dU}{dt} = \\dot{Q} - \\dot{W} = 0.\n", + "\\end{equation*}\n", + " \n", + "Because of the constant-mass and constant-volume assumptions, the density is also therefore constant:\n", + "\n", + "\\begin{equation*}\n", + "\\frac{d\\rho}{dt} = 0.\n", + "\\end{equation*}\n", + "\n", + "Along with the evolving gas composition, the thermodynamic state of the gas is defined by the initial total internal energy $U = mu = m\\sum_k\\left(Y_k u_k\\right)$, where $u_k$ and $Y_k$ are the specific internal energy (kJ/kg) and mass fraction of species $k$, respectively. \n", + "\n", + "The species mass fractions evolve according to the net chemical production rates due to homogeneous gas-phase reactions:\n", + "\n", + "\\begin{equation*}\n", + "\\frac{dY_k}{dt} = \\frac{W_k}{\\rho}\\dot{\\omega}_k,\n", + "\\end{equation*}\n", + "\n", + "where $W_k$ is the molecular weight of species $k$ $\\left({\\rm kg}\\,{\\rm kmol}^{-3}\\right)$, $\\rho$ is the (constant) gas-phase density $\\left({\\rm kg}\\,{\\rm m^{-3}}\\right)$, and $\\dot{\\omega}_k$ is the net production rate of species $k$ $\\left({\\rm kmol}\\,{\\rm m^{-3}}\\,{\\rm s^{-1}}\\right)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "import time\n", + "\n", + "import cantera as ct\n", + "\n", + "print(f\"Runnning Cantera version: {ct.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams[\"axes.labelsize\"] = 16\n", + "plt.rcParams[\"xtick.labelsize\"] = 12\n", + "plt.rcParams[\"ytick.labelsize\"] = 12\n", + "plt.rcParams[\"figure.autolayout\"] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the gas\n", + "\n", + "In this example we will choose a stoichiometric mixture of n-dodecane and air as the gas. For a representative kinetic model, we use that developed by Wang, Ra, Jia, and Reitz (https://www.erc.wisc.edu/chem_mech/nC12-PAH_mech.zip) by [H.Wang, Y.Ra, M.Jia, R.Reitz, Development of a reduced n-dodecane-PAH mechanism and its application for n-dodecane soot predictions, $Fuel$ 136 (2014) 25–36].\n", + "\n", + "To fun a different model or use a different EoS, simply replace this cti file with a different mechanism file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gas = ct.Solution(\"../data/WangMechanismRK.yaml\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define reactor conditions : temperature, pressure, fuel, stoichiometry" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the reactor temperature and pressure:\n", + "reactor_temperature = 1000 # Kelvin\n", + "reactor_pressure = 40.0 * 101325.0 # Pascal\n", + "\n", + "# Set the state of the gas object:\n", + "gas.TP = reactor_temperature, reactor_pressure\n", + "\n", + "# Define the fuel, oxidizer and set the stoichiometry:\n", + "gas.set_equivalence_ratio(phi=1.0, fuel=\"c12h26\", oxidizer={\"o2\": 1.0, \"n2\": 3.76})\n", + "\n", + "# Create a reactor object and add it to a reactor network\n", + "# In this example, this will be the only reactor in the network\n", + "r = ct.Reactor(contents=gas)\n", + "reactor_network = ct.ReactorNet([r])\n", + "# Create a SolutionArray to store the solution data\n", + "time_history = ct.SolutionArray(gas, extra=\"time\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define useful functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def ignition_delay(df, species):\n", + " \"\"\"\n", + " This function computes the ignition delay from the occurence of the\n", + " peak in species' concentration.\n", + " \"\"\"\n", + " return df[species].idxmax()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Tic\n", + "t0 = time.time()\n", + "\n", + "# This is a starting estimate. If you do not get an ignition within this time, increase it\n", + "estimated_ignition_delay_time = 0.005\n", + "t = 0\n", + "\n", + "counter = 1\n", + "while t < estimated_ignition_delay_time:\n", + " t = reactor_network.step()\n", + " if counter % 20 == 0:\n", + " # We will save only every 20th value. Otherwise, this takes too long\n", + " # Note that the species concentrations are mass fractions\n", + " time_history.append(state=r.thermo.state, time=t)\n", + " counter += 1\n", + "\n", + "# We will use the 'oh' species to compute the ignition delay\n", + "# Since 'ignition_delay' operates on a pandas DataFrame, we must\n", + "# convert the SolutionArray to a DataFrame and set the 'time'\n", + "# column as the DataFrame index\n", + "time_history_df = time_history.to_pandas().set_index(\"time\")\n", + "tau = ignition_delay(time_history_df, \"Y_oh\")\n", + "# Toc\n", + "t1 = time.time()\n", + "\n", + "print(f\"Computed Ignition Delay: {tau:.3e} seconds. Took {t1 - t0:3.2f}s to compute\")\n", + "\n", + "# If you want to save all the data - molefractions, temperature, pressure, etc\n", + "# uncomment the next line\n", + "# time_history.to_csv(\"time_history.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Figure illustrating the definition of ignition delay" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "ax.plot(time_history_df.index, time_history_df[\"Y_oh\"], \"-o\", color=\"b\", markersize=4)\n", + "ax.set_xlabel(\"Time (s)\")\n", + "ax.set_ylabel(r\"$\\mathdefault{OH\\, mass\\, fraction,}\\, Y_{OH}}$\")\n", + "\n", + "# Figure formatting:\n", + "ax.set_xlim([0, 0.00075])\n", + "ax.annotate(\n", + " \"\",\n", + " xy=(tau, 0.005),\n", + " xytext=(0, 0.005),\n", + " arrowprops=dict(arrowstyle=\"<|-|>\", color=\"r\", linewidth=2.0),\n", + " fontsize=14,\n", + ")\n", + "ax.annotate(\n", + " \"Ignition Delay Time (IDT)\",\n", + " xy=(0, 0),\n", + " xytext=(0.00004, 0.00525),\n", + " fontsize=16,\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Illustration : NTC behavior\n", + "In the paper by Kogekar, et al., the reactor model is used to demonstrate the impacts of non-ideal behavior on IDTs in the **N**egative **T**emperature **C**oefficient region, where observed IDTs, counter to intuition, increase with increasing temperature." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the temperatures for which we will run the simulations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make a list of all the temperatures we would like to run simulations at\n", + "inverse_T = np.hstack(\n", + " (\n", + " np.linspace(0.6, 0.8, num=3),\n", + " np.linspace(0.9, 1.1, num=9),\n", + " np.linspace(1.15, 1.4, num=6),\n", + " )\n", + ")\n", + "T = 1000.0 / inverse_T\n", + "\n", + "# Set the initial guesses to a common value. We could probably speed up simulations\n", + "# by tuning this guess, but as seen in the figure above, the 'extra' time after igntion\n", + "# does not add many data points or simulation steps. The time savings would be small.\n", + "estimated_ignition_delay_times = np.ones_like(T) * 0.005\n", + "\n", + "# Now create a SolutionArray\n", + "ignition_delays = ct.SolutionArray(\n", + " gas, shape=T.shape, extra={\"tau\": estimated_ignition_delay_times}\n", + ")\n", + "ignition_delays.TP = T, 40.0 * 101325.0\n", + "ignition_delays.set_equivalence_ratio(\n", + " phi=1.0, fuel=\"c12h26\", oxidizer={\"o2\": 1.0, \"n2\": 3.76}\n", + ")\n", + "ignition_delays._extra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the code above for each temperature, and save the IDT for each." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i, state in enumerate(ignition_delays):\n", + " # Set up the gas and reactor\n", + " gas.TPX = state.TPX\n", + " r = ct.Reactor(contents=gas)\n", + " reactor_network = ct.ReactorNet([r])\n", + " time_history = []\n", + " Y_oh_history = []\n", + "\n", + " t0 = time.time()\n", + " t = 0\n", + " counter = 0\n", + "\n", + " while t < ignition_delays._extra[\"tau\"][i]:\n", + " t = reactor_network.step()\n", + " if not counter % 20:\n", + " time_history.append(t)\n", + " Y_oh_history.append(r.thermo.Y[gas.species_index(\"oh\")])\n", + " counter += 1\n", + "\n", + " tau = time_history[np.array(Y_oh_history).argmax()]\n", + " t1 = time.time()\n", + "\n", + " print(\n", + " f\"Computed Ignition Delay: {tau:.3e} seconds for T={state.T}K. Took {t1 - t0:3.2f}s to compute\"\n", + " )\n", + "\n", + " ignition_delays._extra[\"tau\"][i] = tau" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Figure: ignition delay ($\\tau$) vs. the inverse of temperature ($\\frac{1000}{T}$). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "ax.semilogy(1000 / ignition_delays.T, ignition_delays.tau, \"o-\", color=\"b\")\n", + "ax.set_ylabel(\"Ignition Delay (s)\", fontsize=16)\n", + "ax.set_xlabel(r\"$\\mathdefault{1000/T\\, (K^{-1})}$\", fontsize=16)\n", + "\n", + "# Add a second axis on top to plot the temperature for better readability\n", + "ax2 = ax.twiny()\n", + "ticks = ax.get_xticks()\n", + "ax2.set_xticks(ticks)\n", + "ax2.set_xticklabels((1000 / ticks).round(1))\n", + "ax2.set_xlim(ax.get_xlim())\n", + "ax2.set_xlabel(\"Temperature (K)\", fontsize=16);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/reactors/stirred_reactor.ipynb b/reactors/stirred_reactor.ipynb index bfb328e..38c7d9d 100644 --- a/reactors/stirred_reactor.ipynb +++ b/reactors/stirred_reactor.ipynb @@ -36,7 +36,6 @@ ], "source": [ "import pandas as pd\n", - "import numpy as np\n", "import time\n", "import cantera as ct\n", "\n", @@ -56,7 +55,7 @@ "metadata": {}, "outputs": [], "source": [ - "%matplotlib widget\n", + "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "plt.style.use(\"ggplot\")\n", @@ -73,8 +72,7 @@ "metadata": {}, "source": [ "### Define the gas\n", - "In this example, we will work with $nC\n", - "_{7}H_{16}$/$O_{2}$/$He$ mixtures, for which experimental data can be found in the paper by [Zhang et al.](http://dx.doi.org/10.1016/j.combustflame.2015.08.001). We will use the same mechanism reported in the paper. It consists of 1268 species and 5336 reactions" + "In this example, we will work with $nC_{7}H_{16}$/$O_{2}$/$He$ mixtures, for which experimental data can be found in the paper by [Zhang et al.](http://dx.doi.org/10.1016/j.combustflame.2015.08.001). We will use the same mechanism reported in the paper. It consists of 1268 species and 5336 reactions" ] }, { @@ -113,29 +111,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Inlet gas conditions\n", - "reactorTemperature = 925 # Kelvin\n", - "reactorPressure = 1.046138 * ct.one_atm # in atm. This equals 1.06 bars\n", + "reactor_temperature = 925 # Kelvin\n", + "reactor_pressure = 1.046138 * ct.one_atm # in atm. This equals 1.06 bars\n", "inlet_concentrations = {\"NC7H16\": 0.005, \"O2\": 0.0275, \"HE\": 0.9675}\n", - "gas.TPX = reactorTemperature, reactorPressure, inlet_concentrations\n", + "gas.TPX = reactor_temperature, reactor_pressure, inlet_concentrations\n", "\n", "# Reactor parameters\n", - "residenceTime = 2 # s\n", - "reactorVolume = 30.5 * (1e-2) ** 3 # m3\n", - "\n", - "# Instrument parameters\n", - "\n", - "# This is the \"conductance\" of the pressure valve and will determine its efficiency in\n", - "# holding the reactor pressure to the desired conditions.\n", - "pressureValveCoefficient = 0.01\n", - "\n", - "# This parameter will allow you to decide if the valve's conductance is acceptable. If there\n", - "# is a pressure rise in the reactor beyond this tolerance, you will get a warning\n", - "maxPressureRiseAllowed = 0.01" + "residence_time = 2 # s\n", + "reactor_volume = 30.5 * (1e-2) ** 3 # m3" ] }, { @@ -152,7 +140,7 @@ "outputs": [], "source": [ "# Simulation termination criterion\n", - "maxSimulationTime = 50 # seconds" + "max_simulation_time = 50 # seconds" ] }, { @@ -175,43 +163,54 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "fuelAirMixtureTank = ct.Reservoir(gas)\n", + "fuel_air_mixture_tank = ct.Reservoir(gas)\n", "exhaust = ct.Reservoir(gas)\n", "\n", - "stirredReactor = ct.IdealGasReactor(gas, energy=\"off\", volume=reactorVolume)\n", + "stirred_reactor = ct.IdealGasReactor(gas, energy=\"off\", volume=reactor_volume)\n", "\n", - "massFlowController = ct.MassFlowController(\n", - " upstream=fuelAirMixtureTank,\n", - " downstream=stirredReactor,\n", - " mdot=stirredReactor.mass / residenceTime,\n", + "mass_flow_controller = ct.MassFlowController(\n", + " upstream=fuel_air_mixture_tank,\n", + " downstream=stirred_reactor,\n", + " mdot=stirred_reactor.mass / residence_time,\n", ")\n", "\n", - "pressureRegulator = ct.PressureController(\n", - " upstream=stirredReactor, downstream=exhaust, master=massFlowController\n", + "pressure_regulator = ct.PressureController(\n", + " upstream=stirred_reactor, downstream=exhaust, master=mass_flow_controller\n", ")\n", "\n", - "reactorNetwork = ct.ReactorNet([stirredReactor])" + "reactor_network = ct.ReactorNet([stirred_reactor])" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# Use the above list to create a DataFrame\n", - "timeHistory = ct.SolutionArray(gas, extra=[\"t\"])" + "# Create a SolutionArray to store the data\n", + "time_history = ct.SolutionArray(gas, extra=[\"t\"])\n", + "\n", + "# Set the maximum simulation time\n", + "max_simulation_time = 50 # seconds" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation Took 43.34s to compute, with 1245 steps\n" + ] + } + ], "source": [ "# Start the stopwatch\n", "tic = time.time()\n", @@ -219,21 +218,21 @@ "# Set simulation start time to zero\n", "t = 0\n", "counter = 1\n", - "while t < maxSimulationTime:\n", - " t = reactorNetwork.step()\n", + "while t < max_simulation_time:\n", + " t = reactor_network.step()\n", "\n", " # We will store only every 10th value. Remember, we have 1200+ species, so there will be\n", - " # 1200 columns for us to work with\n", + " # 1200+ columns for us to work with\n", " if counter % 10 == 0:\n", " # Extract the state of the reactor\n", - " timeHistory.append(stirredReactor.thermo.state, t=t)\n", + " time_history.append(stirred_reactor.thermo.state, t=t)\n", "\n", " counter += 1\n", "\n", "# Stop the stopwatch\n", "toc = time.time()\n", "\n", - "print(\"Simulation Took {toc-tic:3.2f}s to compute, with {counter} steps\")" + "print(f\"Simulation Took {toc-tic:3.2f}s to compute, with {counter} steps\")" ] }, { @@ -252,29 +251,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6b7f4233317c443aa917bee86eaa5e4b", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "
" ] }, "metadata": {}, @@ -283,9 +267,9 @@ ], "source": [ "plt.figure()\n", - "plt.semilogx(timeHistory.t, timeHistory(\"CO\").X, \"-o\")\n", + "plt.semilogx(time_history.t, time_history(\"CO\").X, \"-o\")\n", "plt.xlabel(\"Time (s)\")\n", - "plt.ylabel(r\"Mole Fraction : $X_{CO}$\");" + "plt.ylabel(\"Mole Fraction : $X_{CO}$\");" ] }, { @@ -300,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -385,19 +369,19 @@ "4 600 0.00355 0.0233 0.000968 0.000251" ] }, - "execution_count": 18, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "expData = pd.read_csv(\"../data/zhangExpData.csv\")\n", - "expData.head()" + "experimental_data = pd.read_csv(\"../data/zhangExpData.csv\")\n", + "experimental_data.head()" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -406,7 +390,7 @@ "T = [650, 700, 750, 775, 825, 850, 875, 925, 950, 1075, 1100]\n", "\n", "# Create a SolutionArray to store values for the above points\n", - "tempDependence = ct.SolutionArray(gas)" + "temp_dependence = ct.SolutionArray(gas)" ] }, { @@ -418,79 +402,57 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Simulation at T=650K took 49.59s to compute\n", - "Simulation at T=700K took 55.48s to compute\n", - "Simulation at T=750K took 18.47s to compute" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOStream.flush timed out\n", - "IOStream.flush timed out\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Simulation at T=775K took 24.76s to compute\n", - "Simulation at T=825K took 27.71s to compute\n", - "Simulation at T=850K took 27.48s to compute\n", - "Simulation at T=875K took 24.66s to compute\n", - "Simulation at T=925K took 27.04s to compute\n", - "Simulation at T=950K took 17.73s to compute\n", - "Simulation at T=1075K took 33.45s to compute\n", - "Simulation at T=1100K took 20.27s to compute\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IOStream.flush timed out\n", - "IOStream.flush timed out\n" + "Simulation at T=650K took 49.27s to compute\n", + "Simulation at T=700K took 32.18s to compute\n", + "Simulation at T=750K took 19.53s to compute\n", + "Simulation at T=775K took 19.49s to compute\n", + "Simulation at T=825K took 26.45s to compute\n", + "Simulation at T=850K took 29.19s to compute\n", + "Simulation at T=875K took 25.29s to compute\n", + "Simulation at T=925K took 24.33s to compute\n", + "Simulation at T=950K took 23.85s to compute\n", + "Simulation at T=1075K took 26.68s to compute\n", + "Simulation at T=1100K took 24.61s to compute\n" ] } ], "source": [ "concentrations = inlet_concentrations\n", "\n", - "for reactorTemperature in T:\n", + "for reactor_temperature in T:\n", "\n", " # We will use concentrations from the previous iteration to speed up convergence\n", - " gas.TPX = reactorTemperature, reactorPressure, concentrations\n", - "\n", - " stirredReactor = ct.IdealGasReactor(gas, energy=\"off\", volume=reactorVolume)\n", - " massFlowController = ct.MassFlowController(\n", - " upstream=fuelAirMixtureTank,\n", - " downstream=stirredReactor,\n", - " mdot=stirredReactor.mass / residenceTime,\n", + " gas.TPX = reactor_temperature, reactor_pressure, concentrations\n", + "\n", + " stirred_reactor = ct.IdealGasReactor(gas, energy=\"off\", volume=reactor_volume)\n", + " fuel_air_mixture_tank = ct.Reservoir(gas)\n", + " mass_flow_controller = ct.MassFlowController(\n", + " upstream=fuel_air_mixture_tank,\n", + " downstream=stirred_reactor,\n", + " mdot=stirred_reactor.mass / residence_time,\n", " )\n", - " pressureRegulator = ct.PressureController(\n", - " upstream=stirredReactor, downstream=exhaust, master=massFlowController\n", + " pressure_regulator = ct.PressureController(\n", + " upstream=stirred_reactor, downstream=exhaust, master=mass_flow_controller\n", " )\n", - " reactorNetwork = ct.ReactorNet([stirredReactor])\n", + " reactor_network = ct.ReactorNet([stirred_reactor])\n", "\n", " # Re-run the isothermal simulations\n", " tic = time.time()\n", - " reactorNetwork.advance(maxSimulationTime)\n", + " reactor_network.advance(max_simulation_time)\n", "\n", " toc = time.time()\n", - " print(f\"Simulation at T={reactorTemperature}K took {toc-tic:3.2f}s to compute\")\n", + " print(f\"Simulation at T={reactor_temperature}K took {toc-tic:3.2f}s to compute\")\n", "\n", - " concentrations = stirredReactor.thermo.X\n", + " concentrations = stirred_reactor.thermo.X\n", "\n", - " tempDependence.append(stirredReactor.thermo.state)" + " temp_dependence.append(stirred_reactor.thermo.state)" ] }, { @@ -502,29 +464,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ab83a2a36df44f9f9a5ffdb7ee315733", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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" ] }, "metadata": {}, @@ -533,13 +480,18 @@ ], "source": [ "plt.figure()\n", - "plt.plot(tempDependence.T, tempDependence(\"NC7H16\").X, \"r-\", label=\"$nC_{7}H_{16}$\")\n", - "plt.plot(tempDependence.T, tempDependence(\"CO\").X, \"b-\", label=\"CO\")\n", - "plt.plot(tempDependence.T, tempDependence(\"O2\").X, \"k-\", label=\"O$_{2}$\")\n", - "\n", - "plt.plot(expData[\"T\"], expData[\"NC7H16\"], \"ro\", label=r\"$nC_{7}H_{16} (exp)$\")\n", - "plt.plot(expData[\"T\"], expData[\"CO\"], \"b^\", label=\"CO (exp)\")\n", - "plt.plot(expData[\"T\"], expData[\"O2\"], \"ks\", label=\"O$_{2}$ (exp)\")\n", + "plt.plot(temp_dependence.T, temp_dependence(\"NC7H16\").X, \"r-\", label=\"$nC_{7}H_{16}$\")\n", + "plt.plot(temp_dependence.T, temp_dependence(\"CO\").X, \"b-\", label=\"CO\")\n", + "plt.plot(temp_dependence.T, temp_dependence(\"O2\").X, \"k-\", label=\"O$_{2}$\")\n", + "\n", + "plt.plot(\n", + " experimental_data[\"T\"],\n", + " experimental_data[\"NC7H16\"],\n", + " \"ro\",\n", + " label=\"$nC_{7}H_{16} (exp)$\",\n", + ")\n", + "plt.plot(experimental_data[\"T\"], experimental_data[\"CO\"], \"b^\", label=\"CO (exp)\")\n", + "plt.plot(experimental_data[\"T\"], experimental_data[\"O2\"], \"ks\", label=\"O$_{2}$ (exp)\")\n", "\n", "plt.xlabel(\"Temperature (K)\")\n", "plt.ylabel(r\"Mole Fractions\")\n", diff --git a/thermo/equations_of_state.ipynb b/thermo/equations_of_state.ipynb index 4ab041f..c165390 100644 --- a/thermo/equations_of_state.ipynb +++ b/thermo/equations_of_state.ipynb @@ -48,7 +48,7 @@ "import numpy as np\n", "from CoolProp.CoolProp import PropsSI\n", "\n", - "%matplotlib notebook\n", + "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "print(f\"Running Cantera version: {ct.__version__}\")" @@ -128,7 +128,7 @@ " return h, u, s, cp, cv\n", "\n", "\n", - "def plot(T, p, thermo_Ideal, thermo_RK, thermo_CoolProp, name):\n", + "def plot(p, thermo_Ideal, thermo_RK, thermo_CoolProp, name):\n", "\n", " line_width = 3\n", "\n", @@ -180,2946 +180,51 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; 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" fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " fig.rubberband_canvas.style.cursor = msg['cursor'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "image/png": 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", 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' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " fig.rubberband_canvas.style.cursor = msg['cursor'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "image/png": 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' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " fig.rubberband_canvas.style.cursor = msg['cursor'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "image/png": 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", "text/plain": [ - "" + "
" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the results\n", - "plot(T, p, u_ideal, u_RK, u_CoolProp, \"Relative Internal Energy [kJ/kg]\")\n", - "plot(T, p, h_ideal, h_RK, h_CoolProp, \"Relative Enthalpy [kJ/kg]\")\n", - "plot(T, p, s_ideal, s_RK, s_CoolProp, \"Relative Entropy [kJ/kg-K]\")" + "plot(p, u_ideal, u_RK, u_CoolProp, \"Relative Internal Energy [kJ/kg]\")\n", + "plot(p, h_ideal, h_RK, h_CoolProp, \"Relative Enthalpy [kJ/kg]\")\n", + "plot(p, s_ideal, s_RK, s_CoolProp, \"Relative Entropy [kJ/kg-K]\")" ] }, { @@ -3133,1969 +238,39 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " fig.rubberband_canvas.style.cursor = msg['cursor'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "image/png": 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' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " fig.rubberband_canvas.style.cursor = msg['cursor'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "image/png": 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", "text/plain": [ - "" + "
" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], "source": [ "# Specific heat at constant pressure\n", - "plot(T, p, cp_ideal, cp_RK, cp_CoolProp, \"Cp [kJ/kg-K]\")\n", + "plot(p, cp_ideal, cp_RK, cp_CoolProp, \"$C_p$ [kJ/kg-K]\")\n", "# Specific heat at constant volume\n", - "plot(T, p, cv_ideal, cv_RK, cv_CoolProp, \"Cv [kJ/kg-K]\")" + "plot(p, cv_ideal, cv_RK, cv_CoolProp, \"$C_v$ [kJ/kg-K]\")" ] }, { @@ -5123,986 +298,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "tags": [] }, "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_device_pixel_ratio', {\n", - " device_pixel_ratio: fig.ratio,\n", - " });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " fig.rubberband_canvas.style.cursor = msg['cursor'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * https://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], + "image/png": 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", "text/plain": [ - "" + "
" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], diff --git a/thermo/flame_temperature.ipynb b/thermo/flame_temperature.ipynb index 4b04c4e..94f4c01 100644 --- a/thermo/flame_temperature.ipynb +++ b/thermo/flame_temperature.ipynb @@ -12,12 +12,22 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Cantera version: 2.6.0a4\n" + ] + } + ], "source": [ - "%matplotlib notebook\n", + "%matplotlib inline\n", "import cantera as ct\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "\n", + "print(f\"Using Cantera version: {ct.__version__}\")" ] }, { @@ -95,794 +105,14 @@ "outputs": [ { "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", - "\n", - " $(parent_element).append(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " if (mpl.ratio != 1) {\n", - " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", - " }\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", - "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function() {\n", - " fig.ws.close();\n", - " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var backingStore = this.context.backingStorePixelRatio ||\n", - "\tthis.context.webkitBackingStorePixelRatio ||\n", - "\tthis.context.mozBackingStorePixelRatio ||\n", - "\tthis.context.msBackingStorePixelRatio ||\n", - "\tthis.context.oBackingStorePixelRatio ||\n", - "\tthis.context.backingStorePixelRatio || 1;\n", - "\n", - " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width * mpl.ratio);\n", - " canvas.attr('height', height * mpl.ratio);\n", - " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('