<reponame>alec-tschantz/action-oriented
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from core.config import *

plt.rc("text", usetex=True)


def create_heatmap(matrix, title, save_path, color_bar=True):
    f, (ax1, ax2) = plt.subplots(2, 1, figsize=(2.7, 4))

    x_labels = [r"$s_{t-1}^{neg}$", r"$s_{t-1}^{pos}$"]
    y_labels = [r"$s_{t}^{neg}$", r"$s_{t}^{pos}$"]
    g1 = sns.heatmap(
        matrix[0, :, :] * 100,
        cmap="OrRd",
        ax=ax1,
        vmin=0.0,
        vmax=70.0,
        linewidth=2.5,
        annot=True,
        xticklabels=x_labels,
        yticklabels=y_labels,
        cbar=color_bar,
    )
    g2 = sns.heatmap(
        matrix[1, :, :] * 100,
        cmap="OrRd",
        ax=ax2,
        vmin=0.0,
        vmax=70.0,
        linewidth=2.5,
        annot=True,
        xticklabels=x_labels,
        yticklabels=y_labels,
        cbar=color_bar,
    )
    g1.set_yticklabels(g1.get_yticklabels(), rotation=0, fontsize=14)
    g1.set_xticklabels(g1.get_xticklabels(), fontsize=14)
    g2.set_yticklabels(g2.get_yticklabels(), rotation=0, fontsize=14)
    g2.set_xticklabels(g2.get_xticklabels(), fontsize=14)

    f.savefig(save_path, dpi=600, bbox_inches="tight")
    plt.show()


if __name__ == "__main__":
    states = np.load(STATES_PATH)
    create_heatmap(states[FULL_ID, :, :, :], AGENT_NAMES[FULL_ID], FULL_STATES, color_bar=False)
    create_heatmap(states[INST_ID, :, :, :], AGENT_NAMES[INST_ID], INST_STATES, color_bar=False)
    create_heatmap(states[EPIS_ID, :, :, :], AGENT_NAMES[EPIS_ID], EPIS_STATES, color_bar=False)
    create_heatmap(states[RAND_ID, :, :, :], AGENT_NAMES[RAND_ID], RAND_STATES, color_bar=False)
    create_heatmap(states[RAND_ID, :, :, :], AGENT_NAMES[RAND_ID], COLOR_BAR, color_bar=True)

    reversed_states = np.load(REVERSED_STATES_PATH)
    create_heatmap(
        reversed_states[FULL_ID, :, :, :],
        AGENT_NAMES[FULL_ID] + " (Reversed prior)",
        FULL_STATES_REVERSED,
        color_bar=False,
    )
    create_heatmap(
        reversed_states[INST_ID, :, :, :],
        AGENT_NAMES[INST_ID] + " (Reversed prior)",
        INST_STATES_REVERSED,
        color_bar=False,
    )
    create_heatmap(
        reversed_states[EPIS_ID, :, :, :],
        AGENT_NAMES[EPIS_ID] + " (Reversed prior)",
        EPIS_STATES_REVERSED,
        color_bar=False,
    )
    create_heatmap(
        reversed_states[RAND_ID, :, :, :],
        AGENT_NAMES[RAND_ID] + " (Reversed prior)",
        RAND_STATES_REVERSED,
        color_bar=False,
    )

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from core.config import *

plt.rc("text", usetex=True)


def create_heatmap(matrix, title, save_path, color_bar=True):
    f, (ax1, ax2) = plt.subplots(2, 1, figsize=(2.7, 4))

    x_labels = [r"$s_{t-1}^{neg}$", r"$s_{t-1}^{pos}$"]
    y_labels = [r"$s_{t}^{neg}$", r"$s_{t}^{pos}$"]
    g1 = sns.heatmap(
        matrix[0, :, :] * 100,
        cmap="OrRd",
        ax=ax1,
        vmin=0.0,
        vmax=70.0,
        linewidth=2.5,
        annot=True,
        xticklabels=x_labels,
        yticklabels=y_labels,
        cbar=color_bar,
    )
    g2 = sns.heatmap(
        matrix[1, :, :] * 100,
        cmap="OrRd",
        ax=ax2,
        vmin=0.0,
        vmax=70.0,
        linewidth=2.5,
        annot=True,
        xticklabels=x_labels,
        yticklabels=y_labels,
        cbar=color_bar,
    )
    g1.set_yticklabels(g1.get_yticklabels(), rotation=0, fontsize=14)
    g1.set_xticklabels(g1.get_xticklabels(), fontsize=14)
    g2.set_yticklabels(g2.get_yticklabels(), rotation=0, fontsize=14)
    g2.set_xticklabels(g2.get_xticklabels(), fontsize=14)

    f.savefig(save_path, dpi=600, bbox_inches="tight")
    plt.show()


if __name__ == "__main__":
    states = np.load(STATES_PATH)
    create_heatmap(states[FULL_ID, :, :, :], AGENT_NAMES[FULL_ID], FULL_STATES, color_bar=False)
    create_heatmap(states[INST_ID, :, :, :], AGENT_NAMES[INST_ID], INST_STATES, color_bar=False)
    create_heatmap(states[EPIS_ID, :, :, :], AGENT_NAMES[EPIS_ID], EPIS_STATES, color_bar=False)
    create_heatmap(states[RAND_ID, :, :, :], AGENT_NAMES[RAND_ID], RAND_STATES, color_bar=False)
    create_heatmap(states[RAND_ID, :, :, :], AGENT_NAMES[RAND_ID], COLOR_BAR, color_bar=True)

    reversed_states = np.load(REVERSED_STATES_PATH)
    create_heatmap(
        reversed_states[FULL_ID, :, :, :],
        AGENT_NAMES[FULL_ID] + " (Reversed prior)",
        FULL_STATES_REVERSED,
        color_bar=False,
    )
    create_heatmap(
        reversed_states[INST_ID, :, :, :],
        AGENT_NAMES[INST_ID] + " (Reversed prior)",
        INST_STATES_REVERSED,
        color_bar=False,
    )
    create_heatmap(
        reversed_states[EPIS_ID, :, :, :],
        AGENT_NAMES[EPIS_ID] + " (Reversed prior)",
        EPIS_STATES_REVERSED,
        color_bar=False,
    )
    create_heatmap(
        reversed_states[RAND_ID, :, :, :],
        AGENT_NAMES[RAND_ID] + " (Reversed prior)",
        RAND_STATES_REVERSED,
        color_bar=False,
    )