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Ceres 2026-02-16 16:05:07 +00:00
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Exercise 3/exercise3.py
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@ -1,8 +1,10 @@
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
from scipy import stats
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# from tqdm import tqdm #Import all needed modules
columns = ["Material", "Density", "Radius", "Mass", "Temperature", "Pressure", "Height", "Time"]
@ -49,74 +51,104 @@ df = getData('exercise3data.csv')
####Part 1
# for i in range(len(columns)):
# if columns[i] == "Material":
# continue
# else:
# columnStats(columns[i], units[i])
def part1():
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')
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()
plt.xlabel("Drop Height/m")
plt.ylabel("Fall Time/s")
plt.title(f'Material: {material}')
plt.legend()
plt.show()
####Part 2
# dfNoMaterial = df.drop("Material", axis=1)
# corrMatrix = dfNoMaterial.corr(method='pearson')
# print(corrMatrix)
def part2():
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)
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)
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")
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()
fig.colorbar(im)
fig.tight_layout()
plt.show()
####Part 3
features = df[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
targets = df["Time"]
def part3():
features = df[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
targets = df["Time"]
linearReg = LinearRegression()
linearFit = linearReg.fit(features, targets)
linearReg = LinearRegression()
linearFit = linearReg.fit(features, targets)
for i in range(len(linearFit.feature_names_in_)):
print(f'The coefficient of {linearFit.feature_names_in_[i]} is {linearFit.coef_[i]} {units[i+1]}')
for i in range(len(linearFit.feature_names_in_)):
print(f'The coefficient of {linearFit.feature_names_in_[i]} is {linearFit.coef_[i]} {units[i+1]}')
ironDf = df[df["Material"] == "iron"]
ironDf = df[df["Material"] == "iron"]
def fitByMeans(density, radius, mass, temp, pressure, height):
coefs = linearFit.coef_
time = linearFit.intercept_+(density*coefs[0])+(radius*coefs[1])+(mass*coefs[2])+(temp*coefs[3])+(pressure*coefs[4])+(height*coefs[5])
return time
def fitByMeans(density, radius, mass, temp, pressure, height):
coefs = linearFit.coef_
time = linearFit.intercept_+(density*coefs[0])+(radius*coefs[1])+(mass*coefs[2])+(temp*coefs[3])+(pressure*coefs[4])+(height*coefs[5])
return time
for radius in radii:
radiusDf = ironDf[ironDf["Radius"] == radius]
plt.scatter(radiusDf["Height"], radiusDf["Time"],label="Experimental data")
radiusFeatures = radiusDf[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
plt.scatter(radiusDf["Height"], linearReg.predict(radiusFeatures),label="Predicted data")
heightBounds = [radiusDf["Height"].min(),radiusDf["Height"].max()]
linearByMeans = [fitByMeans(radiusDf["Density"].mean(),radiusDf["Radius"].mean(),radiusDf["Mass"].mean(),radiusDf["Temperature"].mean(),radiusDf["Pressure"].mean(),radiusDf["Height"].min()),fitByMeans(radiusDf["Density"].mean(),radiusDf["Radius"].mean(),radiusDf["Mass"].mean(),radiusDf["Temperature"].mean(),radiusDf["Pressure"].mean(),radiusDf["Height"].max())]
plt.plot(heightBounds,linearByMeans,label="Fit Using Means")
plt.xlabel("Drop Height/m")
plt.ylabel("Fall Time/s")
plt.legend()
plt.title(f'Iron data and predictions for radius of {radius}m')
plt.show()
def predict():
for radius in radii:
radiusDf = ironDf[ironDf["Radius"] == radius]
plt.scatter(radiusDf["Height"], radiusDf["Time"],label="Experimental data")
radiusFeatures = radiusDf[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
plt.scatter(radiusDf["Height"], linearReg.predict(radiusFeatures),label="Predicted data")
heightBounds = [radiusDf["Height"].min(),radiusDf["Height"].max()]
linearByMeans = [fitByMeans(radiusDf["Density"].mean(),radiusDf["Radius"].mean(),radiusDf["Mass"].mean(),radiusDf["Temperature"].mean(),radiusDf["Pressure"].mean(),radiusDf["Height"].min()),fitByMeans(radiusDf["Density"].mean(),radiusDf["Radius"].mean(),radiusDf["Mass"].mean(),radiusDf["Temperature"].mean(),radiusDf["Pressure"].mean(),radiusDf["Height"].max())]
plt.plot(heightBounds,linearByMeans,label="Fit Using Means")
plt.xlabel("Drop Height/m")
plt.ylabel("Fall Time/s")
plt.legend()
plt.title(f'Iron data and predictions for radius of {radius}m')
plt.show()
trainData, testData = train_test_split(ironDf, test_size=0.1)
features = trainData[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
targets = trainData["Time"]
trainLinearReg = LinearRegression()
trainLinearFit = trainLinearReg.fit(features, targets)
def trueVpred():
for radius in radii:
radiusDf = testData[testData["Radius"] == radius]
plt.scatter(radiusDf["Height"], radiusDf["Time"])
trueR2 = (stats.linregress(radiusDf["Height"], radiusDf["Time"]).rvalue)**2
radiusFeatures = radiusDf[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
plt.scatter(radiusDf["Height"], trainLinearReg.predict(radiusFeatures),label="Predicted data")
predR2 = (stats.linregress(radiusDf["Height"], trainLinearReg.predict(radiusFeatures)).rvalue)**2
print(f'For radius of {radius}m, the true R^2 value is {trueR2} and the predicted R^2 value is {predR2}')
plt.show()
def calcResiduals():
residualsFeatures = testData[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
residuals = testData["Time"] - trainLinearReg.predict(residualsFeatures)
plt.scatter(testData["Radius"], residuals)
plt.show()