AdaptableStreets/plot.py at main · rodydubey/AdaptableStreets · GitHub

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# import pandas as pd
# from matplotlib import pyplot as plt

# # Set the figure size
# plt.rcParams["figure.figsize"] = [7.00, 5.00]
# plt.rcParams["figure.autolayout"] = True
# plt.rcParams['axes.prop_cycle'] = plt.cycler(color=["r", "#e94cdc", "0.7"])
# # Make a list of columns
# columns = ['Adaptable', 'Static', 'Random']

# # Read a CSV file
# df = pd.read_csv("ForPeds_AllScenarios.csv", usecols=columns)

# # Plot the lines
# df.plot()

# plt.show()


# import pandas as pd
# from matplotlib import pyplot as plt
# import numpy as np
# # Set the figure size
# plt.rcParams["figure.figsize"] = [7.00, 5.00]
# plt.rcParams["figure.autolayout"] = True
# plt.rcParams['axes.prop_cycle'] = plt.cycler(color=["r", "#e94cdc", "0.7"])
# # Make a list of columns
# columns = ['Human_CognitiveAgent', 'Human_RandomAgent', 'Human_HeuristicAgent']
# # labels for x-asix
# labels = ['T0', 'T1', 'T2', 'T3', 'T4', 'T5','T6', 'T7', 'T8', 'T9', 'T10', 'T11','T12', 'T13', 'T14', 'T15', 'T16', 'T17']
# x = ['T0', 'T1', 'T2', 'T3', 'T4', 'T5','T6', 'T7', 'T8', 'T9', 'T10', 'T11','T12', 'T13', 'T14', 'T15', 'T16', 'T17']
# # setting x-axis values
# # plt.xticks(x,labels)
# # naming of x-axis and y-axis
# plt.xlabel("All Zones (Floot T)")
# plt.ylabel("DTW")

# # Read a CSV file
# df = pd.read_csv("DTW.csv", usecols=columns)

# # Plot the lines
# df.plot()

# plt.show()
# import pandas as pd
# import matplotlib.pyplot as plt
# # reading CSV file
# data = pd.read_csv("DTW_1.csv")
# # Define data values
# # x = ['T0', 'T1', 'T2', 'T3', 'T4', 'T5','T6', 'T7', 'T8', 'T9', 'T10', 'T11','T12', 'T13', 'T14', 'T15', 'T16', 'T17']
# # y = [5, 12, 19, 21, 31, 27, 35]
# # z = [3, 5, 11, 20, 15, 29, 31]

# # converting column data to list
# x = data['Zone'].tolist()
# y1 = data['Human_CognitiveAgent'].tolist()
# y2 = data['Human_RandomAgent'].tolist()
# y3 = data['Human_HeuristicAgent'].tolist()

# plt.plot(x, y1, "-b", label="Human vs CognitiveAgent")
# plt.plot(x, y2,  "-r", label="Human vs RandomAgent")
# plt.plot(x, y3,  "-y", label="Human vs HeuristicAgent")
# plt.legend(loc="upper left")
# plt.xlabel("All Zones (Floor 1)")
# plt.ylabel("DTW")

# plt.show()
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
def scatter_hist(x, y, ax, ax_histx, ax_histy):
    # no labels
    ax_histx.tick_params(axis="x", labelbottom=False)
    ax_histy.tick_params(axis="y", labelleft=False)

    # the scatter plot:
    ax.scatter(x, y)

    # now determine nice limits by hand:
    binwidth = 2
    xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
    lim = (int(xymax/binwidth) + 1) * binwidth

    bins = np.arange(-lim, lim + binwidth, binwidth)
    ax_histx.hist(x, bins=bins)
    ax_histy.hist(y, bins=bins, orientation='horizontal')


# reading CSV file
data = pd.read_csv("toPlot.csv")
# Define data values
# x = ['T0', 'T1', 'T2', 'T3', 'T4', 'T5','T6', 'T7', 'T8', 'T9', 'T10', 'T11','T12', 'T13', 'T14', 'T15', 'T16', 'T17']
# y = [5, 12, 19, 21, 31, 27, 35]
# z = [3, 5, 11, 20, 15, 29, 31]

# converting column data to list
x = data['Car_Lane_Width'].tolist()
y = data['Car_Flow_Rate'].tolist()


fig = plt.figure(figsize=(6, 6))
# Add a gridspec with two rows and two columns and a ratio of 1 to 4 between
# the size of the marginal axes and the main axes in both directions.
# Also adjust the subplot parameters for a square plot.
gs = fig.add_gridspec(2, 2,  width_ratios=(4, 1), height_ratios=(1, 4),
                      left=0.1, right=0.9, bottom=0.1, top=0.9,
                      wspace=0.05, hspace=0.05)
# Create the Axes.
ax = fig.add_subplot(gs[1, 0])
ax_histx = fig.add_subplot(gs[0, 0], sharex=ax)
ax_histy = fig.add_subplot(gs[1, 1], sharey=ax)
# Draw the scatter plot and marginals.
scatter_hist(x, y, ax, ax_histx, ax_histy)
plt.show()