python_process_management.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "from glob import glob\n",
    "import os\n",
    "from multiprocessing import Process, Pipe\n",
    "import subprocess\n",
    "import multiprocessing\n",
    "import CarbonaraDataTools as cdt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Fix the PDB**\n",
    "If the PDB contains chain with missing residues or atoms, using OpenMM's PDBFixer to repair and infer full structures. Just remember this isnt a miracle performer and usually only suitable for small segments of missing structure!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try:\n",
    "#     from pdbfixer import PDBFixer\n",
    "# except ImportError:\n",
    "#     !conda install -c conda-forge pdbfixer\n",
    "\n",
    "# try:\n",
    "#     from openmm.app import PDBFile\n",
    "# except ImportError:\n",
    "#     !conda install -c omnia openmm\n",
    "\n",
    "\n",
    "# fixer = PDBFixer(filename='user_preprocessed_data/3v03_bsa.pdb')\n",
    "\n",
    "# fixer.findMissingResidues()\n",
    "# fixer.findMissingAtoms()\n",
    "# fixer.addMissingAtoms()\n",
    "# PDBFile.writeFile(fixer.topology, fixer.positions, open('user_preprocessed_data/3v03_bsa_fixed.pdb', 'w'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Setup Directories**\n",
    "This section sets up the master directory (contains all refinements) and the refinement directory (this is the name of the specific structure we want to refine!). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_master_name = 'newFitData' # default name\n",
    "fit_master_dir = cdt.setup_fit_master_dir(root_dir = os.getcwd(), fit_master_name = fit_master_name)\n",
    "\n",
    "refinement_directory_name = 'bsa_validation' # change to name of your molecule\n",
    "refine_dir = cdt.setup_refinement_dir(refinement_directory_name, fit_master_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### **Load User Data**\n",
    "This section loads the user's pre-processed data, which includes pdb files and a scatter profile file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_folder = 'user_preprocessed_data/' # containing pdb, saxs profile, (optionally) secondary structure, (optionally) varying sections\n",
    "user_raw_data_folder = os.path.join( os.getcwd(),my_data_folder)\n",
    "\n",
    "pdb_files = glob(user_raw_data_folder+'*.pdb') # currently selects ALL .pdb files in your data folder (is this what you want?)\n",
    "saxs_file = my_data_folder + '3v03_bsa_foxs_saxs.dat' # specific saxs file name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### **Extract Structure from PDB**\n",
    "For a single PDB (for now), for each chain in the PDB: pull out the xyz positions of alpha carbons, the sequence, infer the secondary structure (currently geometrically - DSSP or self defined possible) and any missing residues (maybe go back and fix your structure!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords_chains, sequence_chains, secondary_structure_chains, missing_residues_chains = cdt.pull_structure_from_pdb(pdb_files[0]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### **Check for Missing Residues**\n",
    "This section checks for segments in the chain(s) geometrically, if a distance greater than 10 Å between sequential alpha carbons is present\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for coords in coords_chains:\n",
    "    breaking_indices = cdt.missing_ca_check(coords, threshold_dist_Å=10)\n",
    "    if len(breaking_indices) > 0:\n",
    "        print(\"Missing segments of chain found: \", len(breaking_indices), breaking_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Write Fingerprint File**\n",
    "Write the sequence and secondary structure for each chain to the `fingerPrint1.dat` file (in the refinement directory). This is the format expected by our fitting algorithm!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_chains = len(coords_chains)\n",
    "fingerprint_file = cdt.write_fingerprint_file(number_chains = number_of_chains, sequence = sequence_chains,\n",
    "                           secondary_structure = secondary_structure_chains, working_path = refine_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Sanity Check**\n",
    "Lets perform a sanity check, the lengths should all be the same for: coords, sequence, secondary structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c, s, ss in zip(coords_chains, sequence_chains, secondary_structure_chains):\n",
    "    print(\"coords: \", len(c), \"-*- sequence: \", len(s), \"-*- secondary structure: \", len(ss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Write Coordinates to Refinement Directory**\n",
    "Write each chain to its own xyz file in the refinement directory, for 3 chains expect `coordinates1.dat`, `coordinates2.dat` &  `coordinates3.dat`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords_files = []\n",
    "for i, coords in enumerate( coords_chains ):\n",
    "    coords_files.append( cdt.write_coordinates_file(coords, working_path=refine_dir, carb_index=i+1) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Write Mixture File to Refinement Directory**\n",
    "Auto-Mixture file isnt fully implemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixture_file = cdt.write_mixture_file(working_path=refine_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Write SAXS File to Refinement Directory**\n",
    "Writes the SAXS file to the required format refinement directory as `Saxs.dat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scatter_file = cdt.write_saxs(saxs_file, working_path=refine_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PDB one multimer or series of monomers\n",
    "\n",
    "pdb_files = glob(user_raw_data_folder+'*.pdb') # currently selects ALL .pdb files in your data folder (is this what you want?)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_restraint(chainA, chainB, indexA, indexB, distance_constraint):\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coords:  583 -*- sequence:  583 -*- secondary structure:  583\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# >> Better distance constraints setup process - currently horrible\n",
    "# contactPreds = [[231,686],[228,683],[225,591],[136,680],[830,890],[375,435],[149,205],[604,660],[719,784],[477,551],[22,96],[264,329]]\n",
    "# fixedDistSet=[]\n",
    "#if I wanted to fix these distances as 6 a fixedDistSet = [6.0,6.0,6.0,6.0]\n",
    "# cdt.translate_distance_constraints(contactPreds,coords,splitList,working_path,fixedDistSet)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Find and write varying sections**\n",
    "Automatically find varying secondary sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 36/36 [00:01<00:00, 28.79it/s]\n"
     ]
    }
   ],
   "source": [
    "varying_linker_chains = []\n",
    "for coord_file in coords_files: \n",
    "    varying_linker_chains.append( cdt.auto_select_varying_linker(coord_file, fingerprint_file) )\n",
    "\n",
    "varying_section_files = []\n",
    "for varying_linkers in varying_linker_chains:\n",
    "    varying_section_files.append( cdt.write_varysections_file(varying_linkers, refine_dir) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualise_varying_sections(coords_chains, secondary_structure_chains, varying_linker_chains, chain_index=1):\n",
    "\n",
    "    x_lst, y_lst, z_lst, color_lst =  cdt.smooth_me_varying(coords_chains[chain_index], secondary_structure_chains[chain_index], varying_linker_chains[chain_index], oversample=5)\n",
    "    structure_fig = cdt.line_plotly(x_lst, y_lst, z_lst, color_lst, outline=True)\n",
    "    structure_fig.update_layout(height=600)\n",
    "    structure_fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
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   "source": [
    "visualise_varying_sections(coords_chains, secondary_structure_chains, varying_linker_chains, 0)"
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  {
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   "metadata": {},
   "outputs": [],
   "source": [
    "# need to now figure out how to set off carbonara from python\n",
    "runs_dirs = setup_runs(refine_dir, number_runs = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/run1',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/run2',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/run3']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs_dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def indv_carbonara_cmd(\n",
    "                        scatter_file, \n",
    "                        file_locs, \n",
    "                        resume_tag, \n",
    "                        paired_predictions, \n",
    "                        varying_sections, \n",
    "                        no_structures,\n",
    "                        within_monomer_hydro_cover, \n",
    "                        between_monomer_hydro_cover, \n",
    "                        kmin, \n",
    "                        kmax, \n",
    "                        max_no_fit_steps, \n",
    "                        prediction_file,\n",
    "                        scatter_out, \n",
    "                        mixture_file, \n",
    "                        prev_fit_str, \n",
    "                        log_loc, \n",
    "                        end_line_prev_log, \n",
    "                        affine_trans, \n",
    "                        number_runs=3, \n",
    "                        verbose=False,\n",
    "                        carbonara_executable=\"./predictStructureQvary\"\n",
    "                    ):\n",
    "    \"\"\"\n",
    "    Constructs a command to execute the Carbonara program with the provided arguments.\n",
    "\n",
    "    Parameters:\n",
    "    scatter_file (str): The file path containing scatter data.\n",
    "    file_locs (str): Refinement directory file path.\n",
    "    resume_tag (str): A tag to resume a previous operation.\n",
    "    paired_predictions (str): Add intra-chain distance constraints when predicting multiple chain structures. \n",
    "    varying_sections (str): The index of secondary sections of the protein structure that should be resampled in fitting.\n",
    "    no_structures (int): Number of chain structures in multimer (monomer = 1) (dimer = 2) (trimer = 3) ( ... ).\n",
    "    within_monomer_hydro_cover (str): hydrophobic cover within a monomer - not used.\n",
    "    between_monomer_hydro_cover (str): hydrophobic cover between monomers - not used.\n",
    "    kmin (float): Minimum angle of scattering (k or q) used to fit.\n",
    "    kmax (float): Maximum angle of scattering (k or q) used to fit.\n",
    "    max_no_fit_steps (int): Maximum number of steps before finishing fit attempt.\n",
    "    prediction_file (str): File path (with mol prefix) to store the xyz predictions (/path/to/prediction/molX).\n",
    "    scatter_out (str): Output file for simulated scatter fit (/path/to/simulated/scatter/scatterX).\n",
    "    mixture_file (str): File path to a mixture of multi- monomers/dimers/... \n",
    "    prev_fit_str (str): Previous fit string (not really used)\n",
    "    log_loc (str): Location of the log output file (/path/to/log/fitLogX).\n",
    "    end_line_prev_log (str): End line of the previous log (not really used)\n",
    "    affine_trans (str): Affine transformation (true or false) - used only for multimers.\n",
    "    number_runs (int, optional): Number of sequential runs. Defaults to 1.\n",
    "    verbose (bool, optional): Flag for verbose output. Defaults to False.\n",
    "    carbonara_executable (str, optional): Path to the carbonara executable. Defaults to \"./predictStructureQvary\".\n",
    "\n",
    "    Returns:\n",
    "    list: A list containing the command to be executed and its arguments.\n",
    "    \"\"\"\n",
    "\n",
    "    # Constructing the command with carbonara executable and arguments\n",
    "    cmd = [\n",
    "        carbonara_executable,\n",
    "        scatter_file,\n",
    "        file_locs,\n",
    "        resume_tag,\n",
    "        paired_predictions,\n",
    "        varying_sections,\n",
    "        str(no_structures),\n",
    "        within_monomer_hydro_cover,\n",
    "        between_monomer_hydro_cover,\n",
    "        str(kmin),\n",
    "        str(kmax),\n",
    "        str(max_no_fit_steps),\n",
    "        prediction_file,\n",
    "        scatter_out,\n",
    "        mixture_file,\n",
    "        prev_fit_str,\n",
    "        log_loc,\n",
    "        end_line_prev_log,\n",
    "        str(affine_trans)\n",
    "    ]\n",
    "\n",
    "    return cmd\n",
    "\n",
    "\n",
    "def run_command(cmd, conn):\n",
    "    result = subprocess.run(cmd, capture_output=True, text=True)\n",
    "    conn.send({'cmd': cmd, 'stdout': result.stdout, 'stderr': result.stderr, 'returncode': result.returncode})\n",
    "    conn.close()\n",
    "\n",
    "def track_commands(commands):\n",
    "    processes = []\n",
    "    parent_connections = []\n",
    "\n",
    "    # Start all processes\n",
    "    for cmd in commands:\n",
    "        parent_conn, child_conn = Pipe()\n",
    "        parent_connections.append(parent_conn)\n",
    "\n",
    "        process = Process(target=run_command, args=(cmd, child_conn,))\n",
    "        processes.append(process)\n",
    "        process.start()\n",
    "\n",
    "    # Wait for all processes to finish and get their results\n",
    "    results = [parent_conn.recv() for parent_conn in parent_connections]\n",
    "\n",
    "    for process in processes:\n",
    "        process.join()\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "### argv[ 3] restart tag (use to start from existing prediction)\n",
    "resume_tag = \"frompdb\"\n",
    "\n",
    "### argv[ 4] paired distances file (can be empty)\n",
    "pair_contact_restraint_bool = \"False\"\n",
    "\n",
    "### argv[ 5] varying sections file (again can be empty)\n",
    "varying_file = varying_section_files[0]\n",
    "\n",
    "### argv[ 7] request to apply hydrophobic covering WITHIN monomers will be a list of sections on which to apply it -- Currently not used\n",
    "withinMonomerHydroCover=\"none\"\n",
    "\n",
    "### argv[ 8] request to apply hydrophobic covering BETWEEN monomers will be a list of pairs to try to hydropobically pair -- currently not used\n",
    "betweenMonomerHydroCover=\"none\"\n",
    "\n",
    "### argv[ 9] kmin\n",
    "kmin=0.01\n",
    "\n",
    "### argv[10] kmax\n",
    "kmax=0.25\n",
    "\n",
    "### argv[11] Max number of fitting steps\n",
    "maxNoFitSteps=500\n",
    "\n",
    "seq_run_index = 1\n",
    "carb_index = 0\n",
    "prediction_file_out = os.path.join( runs_dirs[carb_index], \"mol\" + str(seq_run_index) )\n",
    "scatter_file_out = os.path.join( runs_dirs[carb_index], \"scatter\" + str(seq_run_index) )\n",
    "log_file_out = os.path.join( runs_dirs[carb_index], \"fitLog\" + str(seq_run_index) )\n",
    "\n",
    "run_cmd = indv_carbonara_cmd(scatter_file = scatter_file, resume_tag = resume_tag, file_locs = refine_dir+'/', paired_predictions = pair_contact_restraint_bool, \n",
    "                             varying_sections = varying_file, no_structures = number_of_chains, \n",
    "                             within_monomer_hydro_cover = withinMonomerHydroCover, between_monomer_hydro_cover = betweenMonomerHydroCover,\n",
    "                             kmin = kmin, kmax = kmax, max_no_fit_steps = maxNoFitSteps, prediction_file = prediction_file_out,\n",
    "                             scatter_out = scatter_file_out, mixture_file = mixture_file, prev_fit_str = \"not_needed\", log_loc = log_file_out,\n",
    "                             end_line_prev_log = \"null\", affine_trans = \"False\", number_runs=1, verbose=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./predictStructureQvary',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/Saxs.dat',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/',\n",
       " 'frompdb',\n",
       " 'False',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/varyingSectionSecondary1.dat',\n",
       " '1',\n",
       " 'none',\n",
       " 'none',\n",
       " '0.01',\n",
       " '0.25',\n",
       " '500',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/run1/mol1',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/run1/scatter1',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/mixtureFile.dat',\n",
       " 'not_needed',\n",
       " '/Users/josh/Documents/PhD/DevDungeon/carbonara/newFitData/bsa_validation/run1/fitLog1',\n",
       " 'null',\n",
       " 'False']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_runme(working_path, fit_name, fit_n_times, min_q, max_q, max_fit_steps, pairedQ=False, rotation=False):\n",
    "\n",
    "    curr = os.getcwd()\n",
    "    script_name = '/RunMe_bsa.sh'\n",
    "    run_file = curr + script_name\n",
    "\n",
    "    with open(run_file, 'w+') as fout:\n",
    "\n",
    "        saxs_file = 'Saxs.dat'\n",
    "        FP_file = \"fingerPrint1.dat\"\n",
    "        coords_file = 'coordinates1.dat'\n",
    "        varying_file = \"varyingSectionSecondary1.dat\"\n",
    "        mixture_file = \"mixtureFile.dat\"\n",
    "\n",
    "        # $1 is the molecule name $2 the output file $3 the restart file if true\n",
    "\n",
    "        fout.write('#!/bin/bash')\n",
    "        fout.write('\\n ROOT = '+working_path)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[ 1] scattering data file')\n",
    "        fout.write('\\n ScatterFile='+working_path+saxs_file)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[ 2] sequence file location')\n",
    "        fout.write('\\n fileLocs='+working_path)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[ 3] restart tag (use to start from existing prediction)')\n",
    "        fout.write('\\n initialCoordsFile=frompdb')\n",
    "        fout.write('\\n')\n",
    "        \n",
    "        fout.write('\\n ### argv[ 4] paired distances file (can be empty)')\n",
    "        if pairedQ==False:\n",
    "            fout.write('\\n pairedPredictions=False')\n",
    "        else:\n",
    "            fout.write('\\n pairedPredictions=True')\n",
    "        fout.write('\\n')\n",
    "        \n",
    "        fout.write('\\n ### argv[ 5] fixed sections file (again can be empty)')\n",
    "        fout.write('\\n fixedsections='+working_path+varying_file)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[ 6] number of structures')\n",
    "        fout.write('\\n noStructures=1')\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[ 7] request to apply hydrophobic covering WITHIN monomers will be a list of sections on which to apply it -- Currently not used')\n",
    "        fout.write('\\n withinMonomerHydroCover=none')\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[ 8] request to apply hydrophobic covering BETWEEN monomers will be a list of pairs to try to hydropobically pair -- currently not used')\n",
    "        fout.write('\\n betweenMonomerHydroCover=none')\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[ 9] kmin')\n",
    "        fout.write('\\n kmin='+str(min_q))\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[10] kmax')\n",
    "        fout.write('\\n kmax='+str(max_q))\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[11] Max number of fitting steps')\n",
    "        fout.write('\\n maxNoFitSteps='+str(max_fit_steps))\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[12] prediction file - mol[i] in the fitting folder')\n",
    "        fout.write('\\n predictionFile='+working_path+fit_name)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[13] scattering output file')\n",
    "        fout.write('\\n scatterOut='+working_path+fit_name)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[14] mixture list file, alist of sets of numbers indicatig the allowed set of mixture percentages of each species (e.g. dimer 20 monomer 80)')\n",
    "        fout.write('\\n mixtureFile='+working_path+mixture_file)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[15] previous fit string in form fitname/mol6Substep_10_1.dat+fitname/mol6Substep_10_2.dat')\n",
    "        fout.write('\\n prevFitStr='+working_path+'redundant')\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[16] log file location')\n",
    "        fout.write('\\n logLoc='+working_path+fit_name)\n",
    "        fout.write('\\n')\n",
    "\n",
    "        fout.write('\\n ### argv[17] last line of the previous fit log, this is only used for a restart if argv[3] = True')\n",
    "        fout.write('\\n endLinePrevLog=null')\n",
    "        fout.write('\\n')\n",
    "                   \n",
    "        fout.write('\\n ### argv[18] is true if we want to apply affine rotations,false if not.')\n",
    "        if rotation==False:\n",
    "            fout.write('\\n affineTrans=False')\n",
    "        else:\n",
    "            fout.write('\\n affineTrans=True')\n",
    "        fout.write('\\n')\n",
    "\n",
    "\n",
    "        fout.write('\\nfor i in {1..'+str(fit_n_times)+'}')\n",
    "\n",
    "        fout.write('\\n\\ndo')\n",
    "        fout.write('\\n\\n   echo \" Run number : $i \"')\n",
    "        fout.write('\\n\\n   ./predictStructureQvary $ScatterFile $fileLocs $initialCoordsFile $pairedPredictions $fixedsections $noStructures $withinMonomerHydroCover $betweenMonomerHydroCover $kmin $kmax $maxNoFitSteps $predictionFile/mol$i $scatterOut/scatter$i.dat $mixtureFile $prevFitStr $logLoc/fitLog$i.dat $endLinePrevLog $affineTrans')\n",
    "\n",
    "        fout.write('\\n\\ndone')\n",
    "\n",
    "    # return script_name[1:]\n",
    "    # print('Successfully written bash script to: ', run_file)\n",
    "        fout.close()\n",
    "        # now write the run folder if its empty\n",
    "\n",
    "        working = working_path+fit_name\n",
    "        if os.path.exists(working):\n",
    "            skip = True\n",
    "        else:\n",
    "            os.makedirs(working)\n",
    "\n",
    "    # os.mkdir(working_path+'/fitdata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write_runme(refine_dir+'/', 'bsa_validation', 2, 0.01, 0.25, 50, pairedQ=False, rotation=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dists = np.linalg.norm( np.diff(coords_chains[0],axis=0), axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dists[dists>4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Message sent!\n"
     ]
    }
   ],
   "source": [
    "import smtplib\n",
    "from email.mime.text import MIMEText\n",
    "\n",
    "subject = \"Carbonara Job Update\"\n",
    "body = \"Your carbonara is complete! Please check your fitting file: /where/it/at \"\n",
    "sender = \"carbonara.job.notification@gmail.com\"\n",
    "recipients = [\"josh.j.mckeown@durham.ac.uk\", \"christopher.prior@durham.ac.uk\", \"arron.n.bale@durham.ac.uk\"]\n",
    "password = \"zmod rgsw zkqn xugt\"\n",
    "\n",
    "\n",
    "def send_email(subject, body, sender, recipients, password):\n",
    "    msg = MIMEText(body)\n",
    "    msg['Subject'] = subject\n",
    "    msg['From'] = sender\n",
    "    msg['To'] = ', '.join(recipients)\n",
    "    with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp_server:\n",
    "       smtp_server.login(sender, password)\n",
    "       smtp_server.sendmail(sender, recipients, msg.as_string())\n",
    "    print(\"Message sent!\")\n",
    "\n",
    "\n",
    "send_email(subject, body, sender, recipients, password)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math \n",
    "import numpy as np\n",
    "def dotproduct(v1, v2):\n",
    "  return sum((a*b) for a, b in zip(v1, v2))\n",
    "\n",
    "\n",
    "def length(v):\n",
    "  return math.sqrt(dotproduct(v, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = [1,2,3]\n",
    "v2 = [1,2,3]\n",
    "\n",
    "dotproduct(v1, v2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(v1,v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7416573867739413"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "length(v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7416573867739413"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(v1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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