Update vegetation analysis documentation

Vegetation impact analysis.py

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+import cfo
+import ipyplot
+from PIL import Image
+import IPython.display as dsp
+import numpy as np
+from scipy.interpolate import interp1d
+import pandas as pd
+import matplotlib.pyplot as plt
+
+forest = cfo.api()
+forest.authenticate()
+forest.search(geography="MarinCounty", metric="CanopyCover", just_assets=False)
+name = forest.search(geography="MarinCounty", metric="CanopyCover", just_assets=False)[0]['asset_id']
+print(name)
+forest.download(name,name)
+ipyplot.plot_images([name+".tif"], img_width=500)
+
+def get_values(specs,p0,p1):
+    img = Image.open(name+".tiff")
+    data = np.array(img)
+    x0 = int((p0["lon"]-box["W"])/(box["E"]-box["W"])*data.shape[0])
+    y0 = int((p0["lat"]-box["N"])/(box["S"]-box["N"])*data.shape[1])
+    x1 = int((p1["lon"]-box["W"])/(box["E"]-box["W"])*data.shape[0])
+    y1 = int((p1["lat"]-box["N"])/(box["S"]-box["N"])*data.shape[1])
+    dx = x1-x0
+    dy = y1-y0
+    s = 0.0
+    n = 0
+    zz = []
+    tt = []
+    if abs(dx) > abs(dy): # iterate over x
+        y = y0
+        r = dy/dx
+        for x in range(x0,x1+1,np.sign(dx)):
+            zz.append(data[int(x),int(y)])
+            tt.append(np.sqrt((x0-x)*(x0-x)+(y0-y)*(y0-y))*dd)
+            y += r
+    else: # iterate over y
+        x = x0
+        r = dx/dy
+        for y in range(y0,y1+1,np.sign(dy)):
+            zz.append(data[int(x),int(y)])
+            tt.append(np.sqrt((x0-x)*(x0-x)+(y0-y)*(y0-y))*dd)
+            x += r
+    d = round(np.sqrt(dx*dx+dy*dy)*dd) # distance in feet
+    zz = np.array(zz)
+    t = np.array(tt)
+    z = list(map(lambda x:float(x/res/res),list(map(interp1d(t,zz),np.arange(tt[0],tt[-1],1.0)))))
+    return {
+        "from" : p0,
+        "to" :p1,
+        "min":zz.min(),
+        "max":zz.max(),
+        "avg":np.round(zz.mean(),1),
+        "std":np.round(zz.std(),1),
+        "len":d,
+        "t" : np.arange(tt[0],tt[-1],1.0),
+        "z": np.array(z),
+    }
+
+res = 10 # TIFF resolution in meters
+dd = res*39/12 # image resolution in feet
+box = {"N":38.317511, "S":37.816952, "E":-122.372121, "W":-123.024393}
+#print('size:',data.shape)
+
+result = get_values(name+".tif",{"lat":38.131435, "lon":-122.740464},{"lat":38.110846, "lon":-122.724984})
+img = Image.open(name+".tif")
+data = np.array(img)
+print(result)
+
+plt.figure(1,figsize=(15,5))
+plt.plot(result["t"],result["z"]*100)
+plt.xlabel('Line location (ft)')
+plt.ylabel('Canopy cover (%)')
+plt.ylim([0,100])
+plt.grid()
+plt.title(f"Marin County Canopy Cover 2020 Summer from {result['from']} to {result['to']}")
+plt.show()
+
+df = pd.read_csv('TMY3-SF.csv')
+# Hourly wind speed
+wspd = df['Wspd (m/s)']
+wspd.head()
+# Vegetation contact
+
+# # V: wind speed - From TMY3
+# V = wspd
+# # d: line diameter in m
+# d = 0.01
+# # beta: line windage coefficient
+# beta = 4
+# F_w = d*beta*V**2
+# # w: line weight
+# w = 1000
+# # t: line location
+# # L: length of the span
+# L = 100
+# # T: tension in the conductor (should be in N) (depends on the sag - need to look for other ways to calculate)
+# # tension needs to be in this magnitude for the result to make sense
+# T = 450000
+# # y : observed line sag
+# y = w*L*L/(8*T)
+# D_l = y*np.sin((np.tan(w/F_w))**-1)
+
+
+# T1: temperature(degC); A: conductor area(m^2); H: horizontal tension(need to be given)(N); S: span length(m);
+# W: conductor unit weight(N/m); E: modulous of elasticity(Pa)
+
+T1 = 15
+H1 = 15450
+A = 0.0004029
+S = 300
+W1 = 10.89
+E = 58.9*10**9
+
+# Total conductor length
+L1 = S*(1+(S**2*W1**2/(24*H1**2)))
+# Initial sag
+D_init = H1/W1*(np.cosh(S/(2*H1/W1))-1)
+
+# With ice and wind
+# rho: density of ice(kg/m^3)
+rho = 915
+# t: thickness(m)
+t = 0.0125
+
+# d = diameter(m)
+d = 0.0261
+W_ice = rho*np.pi*t*(d+t)
+D_total = d+2*t
+# P_wind: wind pressure(pa)
+P_wind = 190
+W_wind = P_wind*D_total
+
+W_total = np.sqrt(W_wind**2+(W1+W_ice)**2)
+# Sag with wind and ice
+D_total = H1/W_total*(np.cosh(S/(2*H1/W_total))-1)
+
+# Blowout angle
+theta = np.tan(W_wind/(W_ice+W1))**(-1)
+D_l = D_total*np.sin(theta)
+
+# S: susceptibility
+S = 0.1
+D_v = S*V
+# D: right-of-way distance
+D = 30
+# R: vegetation growth rate (ft/yr)
+R = 3.28
+# dT: time since right-of-way was maintained
+dT = 5
+D_o = D - R*dT
+if D_o >= 0:
+    P_c = (D_l+D_v)/2
+else:
+    P_c = 0
+P_c.fillna(0)
+
+# Vegetation fall
+R_f = D_o/D
+# A : age of the tree
+A = 80
+V_c = (100-A)/5 + 10
+P_f = []
+for velocity in V:
+    if velocity >= V_c:
+        P_f.append(R_f*velocity/V_c)
+    else:
+        P_f.append(0)
+
+df = pd.DataFrame(data=result['t'], columns= ['t'])
+df['contact'] = pd.DataFrame(data=P_c)
+df['fall'] = pd.DataFrame(data=P_f)
+df.to_csv("output.csv",index=False)