Add exercise 3

Exercise 3/exercise3.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import integrate
+import pandas as pd
+# from tqdm import tqdm #Import all needed modules
+
+columns = ["Material", "Density", "Radius", "Mass", "Temperature", "Pressure", "Height", "Time"]
+columnsNoMaterial = ["Density", "Radius", "Mass", "Temperature", "Pressure", "Height", "Time"]
+units = ["", "kg/m^3", "m", "kg", "K", "Pa", "m", "s"]
+materials = ["magnesium", "polycarbonate", "silica", "zinc_oxide", "silicon_carbide", "titanium", "iron"]
+radii = [0.005, 0.01, 0.015, 0.02, 0.025]
+
+def getData(file):
+    columns = ["Material", "Density", "Radius", "Mass", "Temperature", "Pressure", "Height", "Time"]
+    data = pd.read_csv(file, sep=',', names=columns, skiprows=9, on_bad_lines='skip')
+
+    data["Density"] = pd.to_numeric(data["Density"], errors='coerce')
+    data["Temperature"] = pd.to_numeric(data["Temperature"], errors='coerce')
+    data["Time"] = pd.to_numeric(data["Time"], errors='coerce')
+    
+    for column in columns:
+        if column == "Material":
+            for i in data.index:
+                if data[column][i] not in materials:
+                    data.drop(i, inplace=True)
+        else:
+            for i in data.index:
+                    if data[column][i] < 0:
+                        data.loc[i, column] = -data.loc[i, column]
+
+    data.dropna(inplace=True)
+    return data
+
+def columnStats(column, units):
+    min = df[column].min()
+    max = df[column].max()
+    mean = df[column].mean()
+    stdDev = df[column].std()
+
+    print(f'Statistics for {column}')
+    print(f'Minimum: {min}{units}')
+    print(f'Maximum: {max}{units}')
+    print(f'Mean: {mean}{units}')
+    print(f'Standard Deviation: {stdDev}{units}')
+    print()
+
+df = getData('exercise3data.csv')
+
+# for i in range(len(columns)):
+#     if columns[i] == "Material":
+#         continue
+#     else:
+#         columnStats(columns[i], units[i])
+
+# for material in materials:
+#     materialDf = df[df["Material"] == material]
+#     for radius in radii:
+#         radiusDf = materialDf[materialDf["Radius"] == radius]
+#         print(radiusDf)
+#         plt.scatter(radiusDf["Height"], radiusDf["Time"], label=f'Radius {radius}m')
+
+#     plt.xlabel("Drop Height/m")
+#     plt.ylabel("Fall Time/s")
+#     plt.title(f'Material: {material}')
+#     plt.legend()
+#     plt.show()
+
+dfNoMaterial = df.drop("Material", axis=1)
+corrMatrix = dfNoMaterial.corr(method='pearson')
+print(corrMatrix)
+
+fig, ax = plt.subplots()
+im = ax.imshow(corrMatrix, cmap="gnuplot", vmin=-1, vmax=1)
+
+ax.set_xticks(range(len(columnsNoMaterial)), labels=columnsNoMaterial)
+ax.set_yticks(range(len(columnsNoMaterial)), labels=columnsNoMaterial)
+
+for i in range(len(columnsNoMaterial)):
+    for j in range(len(columnsNoMaterial)):
+        text = ax.text(j, i, round(corrMatrix[columnsNoMaterial[i]][columnsNoMaterial[j]], 2),
+                       ha="center", va="center", color="w")
+
+fig.colorbar(im)
+fig.tight_layout()
+plt.show()