Added Power vs. Nitrogen Pressure Mapping Scripts

Added "Mapping Data - Median.py" for the generation of a test/mapping data set that is used to produce a PAMBE processing space of two axes: one of RF plasma power and the other of substrate temperature. The other script, "Random Forest - Median.py" is used to train  a random forest on the GaN Lattice Disorder S^2 training data CSV file and then makes predictions using the testing/mapping data previously produced by the "Mapping Data - Median.py" file.

Code/Python/Gallium Nitride/Lattice Disorder/Mapping Data - Median.py

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+# Andrew S. Messecar
+import csv
+
+# Median Substrate Temperature = 770
+N2_Pressure = 0.00001
+Gallium_Temperature = 960
+# Median RF Power = 150
+# Median Growth Time = 180
+
+header = ['Substrate Temperature', 'RF Power', 'Ga Temperature', 'N2 Pressure']
+
+with open('/home/garibasen/Documents/Data/WMU/Dropbox/S-squared/Mapping Data/Mapping_Data_Median.csv', 'w', encoding='UTF8', newline='') as f:
+   writer = csv.writer(f)
+   writer.writerow(header)
+
+   for i in range(550):
+
+      for j in range(1000):
+
+           data = [j, 550 - i, Gallium_Temperature, N2_Pressure]
+
+           writer.writerow(data)

Code/Python/Gallium Nitride/Lattice Disorder/Random Forest - Median.py

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+# Andrew Messecar
+
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import MinMaxScaler
+# from sklearn.preprocessing import StandardScaler
+from numpy import array, empty, reshape, arange
+import pandas as pd
+import seaborn as sb
+from pylab import title, show, xlabel, ylabel, savefig, figure, yticks, xticks
+
+Epitaxy = pd.read_csv(r'/home/garibasen/Documents/Data/WMU/Dropbox/S-squared/GaN_S2.csv')
+
+Test_Inputs = pd.read_csv(r'/home/garibasen/Documents/Data/WMU/Dropbox/S-squared/Mapping Data/Mapping_Data_Median.csv')
+
+Training_Data = pd.DataFrame(Epitaxy, columns=['Substrate Temperature', 'RF Power', 'Ga Temperature', 'N2 Pressure', 'S2'])
+
+Map_Space = pd.DataFrame(Test_Inputs, columns=['Substrate Temperature', 'RF Power', 'Ga Temperature', 'N2 Pressure'])
+
+
+Training_Inputs = Training_Data.loc[:, ['Substrate Temperature', 'RF Power', 'Ga Temperature', 'N2 Pressure']]
+
+Training_Outputs = Training_Data.loc[:, 'S2']
+
+scaler = MinMaxScaler() 
+scaler.fit(Training_Inputs)
+Training_Inputs = scaler.transform(Training_Inputs)
+Map_Space = scaler.transform(Map_Space)
+
+Predictor = RandomForestRegressor(max_features=5, random_state=42, n_estimators=245)
+
+Predictor.fit(Training_Inputs, Training_Outputs)
+
+# Map = empty([550 , 1000])
+
+# Parameter_Space = Map_Space.to_numpy()
+
+Map = Predictor.predict(Map_Space)
+
+figure(dpi=1200)
+sb.heatmap(Map.reshape(550, 1000))
+yticks(arange(-0.01, 550, step=50), ['550', '500', '450', '400', '350', '300', '250', '200', '150', '100', '50', '0'])
+xticks(arange(-0.01, 1000, step=100), ['0' , '100', '200', '300', '400', '500', '600', '700', '800', '900', '1000'],rotation=0)
+# title("Predicted S^2 in GaN Thin Film Crystal")
+xlabel("Substrate Temperature (Celsius)")
+ylabel("Plasma Source Power (Watts)")
+
+show()