#%%
from os import replace
import plotly.graph_objects as go
import numpy as np
import random
import pandas as pd
from numpy.lib.function_base import average
import statistics
import pikepdf
from pikepdf import Pdf
import json
import random
import re
import xlsxwriter as xl

#%%
data_prel = [
    {
        "question_number": 1,
        "description": "£9.95 with 1% probability and £-0.10 otherwise.",
        "repetitions": 1,
        "prob_up": 0.01,
        "pay_up": 9.95,
        "pay_down": -0.1,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 2,
        "description": "£7.00 with 2% probability and £-0.14 otherwise.",
        "repetitions": 1,
        "prob_up": 0.02,
        "pay_up": 7,
        "pay_down": -0.14,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 3,
        "description": "£4.36 with 5% probability and £-0.23 otherwise.",
        "repetitions": 1,
        "prob_up": 0.05,
        "pay_up": 4.36,
        "pay_down": -0.23,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 4,
        "description": "£3.00 with 10% probability and £-0.33 otherwise.",
        "repetitions": 1,
        "prob_up": 0.1,
        "pay_up": 3,
        "pay_down": -0.33,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 5,
        "description": "£2.00 with 20% probability and £-0.50 otherwise.",
        "repetitions": 1,
        "prob_up": 0.2,
        "pay_up": 2,
        "pay_down": -0.5,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 6,
        "description": "£1.00 with 50% probability and £-1.00 otherwise.",
        "repetitions": 1,
        "prob_up": 0.5,
        "pay_up": 1,
        "pay_down": -1,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 7,
        "description": "£0.50 with 80% probability and £-2.00 otherwise.",
        "repetitions": 1,
        "prob_up": 0.8,
        "pay_up": 0.5,
        "pay_down": -2,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 8,
        "description": "£0.33 with 90% probability and £-3.00 otherwise.",
        "repetitions": 1,
        "prob_up": 0.9,
        "pay_up": 0.33,
        "pay_down": -3,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 9,
        "description": "£0.23 with 95% probability and £-4.36 otherwise.",
        "repetitions": 1,
        "prob_up": 0.95,
        "pay_up": 0.23,
        "pay_down": -4.36,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 10,
        "description": "£0.14 with 98% probability and £-7.00 otherwise.",
        "repetitions": 1,
        "prob_up": 0.98,
        "pay_up": 0.14,
        "pay_down": -7,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 11,
        "description": "£0.10 with 99% probability and £-9.95 otherwise.",
        "repetitions": 1,
        "prob_up": 0.99,
        "pay_up": 0.1,
        "pay_down": -9.95,
        "line_bool": False,
        "repetition_word": "(one)",
    },
    {
        "question_number": 12,
        "description": "£0.33 with 90% probability and £-3.00 otherwise; repeated up to five times.",
        "repetitions": 5,
        "prob_up": 0.9,
        "pay_up": 0.33,
        "pay_down": -3,
        "line_bool": False,
        "repetition_word": "(five)",
    },
    {
        "question_number": 13,
        "description": "£0.33 with 90% probability and £-3.00 otherwise; repeated up to ten times.",
        "repetitions": 10,
        "prob_up": 0.9,
        "pay_up": 0.33,
        "pay_down": -3,
        "line_bool": False,
        "repetition_word": "(ten)",
    },
    {
        "question_number": 14,
        "description": "£0.33 with 90% probability and £-3.00 otherwise; repeated up to 50 times.",
        "repetitions": 50,
        "prob_up": 0.9,
        "pay_up": 0.33,
        "pay_down": -3,
        "line_bool": False,
        "repetition_word": "(fifty)",
    },
    {
        "question_number": 15,
        "description": "£0.33 with 90% probability and £-3.00 otherwise; repeated up to 100 times.",
        "repetitions": 100,
        "prob_up": 0.9,
        "pay_up": 0.33,
        "pay_down": -3,
        "line_bool": False,
        "repetition_word": "(one hundred)",
    },
]

data = [
    [
        [0.5, 0.5],
        [3, -3],
        "202003ComprehensionLottery",
        "choices/202003ComprehensionLottery.png",
        False,
        1,
    ],
    [
        [0.5, 0.5],
        [3, -3],
        "202003ComprehensionLottery_line",
        "choices/202003ComprehensionLottery_line.png",
        True,
        1,
    ],
    [
        [0.2, 0.8],
        [6.0, -1.50],
        "gamble_risk_over_money_noline",
        "choices/gamble_risk_over_money_noline.png",
        False,
        1,
    ],
    [
        [0.2, 0.8],
        [6.0, -1.50],
        "gamble_risk_over_money",
        "choices/gamble_risk_over_money.png",
        True,
        1,
    ],
]

data_repeated = [
    [
        [0.9, 0.1],
        [0.33, -3.0],
        "repeated_gamble",
        "repeat_choices/repeated_gamble.png",
        False,
        2,
    ],
    [
        [0.9, 0.1],
        [0.3, -2.7],
        "explain_repeated_gamble",
        "repeat_choices/explain_repeated_gamble.png",
        False,
        2,
    ],
]

#%%

df_verify_final = pd.read_excel(
    r"C:\Users\bernh\Code\SkewPremExp\SkewPremExp\static_data\210527_BonnusFileList.xlsx",
    "Sheet1",
)
df_gambles = pd.DataFrame(data_prel)

#%%
df_verify_final["endowment"] = pd.to_numeric(df_verify_final["Endowment"].str[1:])
df_verify_final["pay_up"] = pd.to_numeric(
    df_verify_final["Option 1"].apply(
        lambda x: re.search("(£)(\d*[.,]?\d*)( )", x).group(2)
    )
)
df_verify_final["pay_down"] = pd.to_numeric(
    df_verify_final["Option 1"].apply(
        lambda x: re.search("(£)(-\d*[.,]?\d*)( )", x).group(2)
    )
)
df_verify_final["prob_up"] = (
    pd.to_numeric(
        df_verify_final["Option 1"].apply(
            lambda x: re.search("(with )(\d*)(%)", x).group(2)
        )
    )
    / 100
)
df_verify_final["prob_down"] = 1 - df_verify_final["prob_up"]
df_verify_final["repetitions"] = df_verify_final["Option 1"].apply(
    lambda x: re.search("(; repeated up to )(\w*)( times)", x).group(2)
    if re.search("(; repeated up to )(\w*)( times)", x) != None
    else 1
)
df_verify_final["repetitions"].replace("ten", 10, inplace=True)
df_verify_final["repetitions"].replace("five", 5, inplace=True)
df_verify_final["repetitions"] = pd.to_numeric(df_verify_final["repetitions"])
df_verify_final["option_2"] = pd.to_numeric(
    df_verify_final["Option 2"].apply(
        lambda x: re.search("(£)([-]?\d*[.,]?\d*)", x).group(2)
    )
)
df_verify_final["pictures"] = df_verify_final["Bonus file"].apply(
    lambda x: f"choices/choice_no_{x}.png"
)

#%%

df_test = pd.merge(
    left=df_verify_final,
    right=df_gambles,
    left_on="Option 1",
    right_on="description",
    how="left",
)

print(df_test["pay_up_x"].equals(df_test["pay_up_y"]))
print(df_test["pay_down_x"].equals(df_test["pay_down_y"]))
print(df_test["prob_up_x"].equals(df_test["prob_up_y"]))
print(df_test["repetitions_x"].equals(df_test["repetitions_y"]))

df_test.head(5)
#%%
otree_data = [
    [
        row["Bonus file"],
        row["question_number"],
        row["endowment"],
        row["Option 1"].replace("£", "_p_"),
        row["option_2"],
        [row["pay_up_x"], row["pay_down_x"]],
        [row["prob_up_x"], row["prob_down"]],
        row["pictures"],
        row["repetitions_x"],
        row["repetition_word"]
    ]
    for i, row in df_test.iterrows()
]
with open("otree_data.json", "w") as f:
    json.dump(otree_data, f)


#%% Seldomly used


def fixed_amount_panel(
    amount, line_bool,
):
    fig = go.Figure()
    fig.layout.font.family = "Arial"
    x_left = -0.2
    x_cent = 1.2
    x_right = 2.6
    y_top = 1.7
    y_mid = 1.5
    y_bot = -0.2
    fig.update_xaxes(
        showgrid=False, zeroline=False, visible=False, range=[x_left, x_cent],
    )
    fig.update_yaxes(
        showgrid=False,
        zeroline=False,
        visible=False,
        range=[y_bot, y_mid],
        scaleanchor="x",
        scaleratio=1,
    )
    fig.update_layout(
        showlegend=False,
        margin=dict(l=0, r=0, t=0, b=0),
        paper_bgcolor="rgba(255,255,255,1)",
        plot_bgcolor="rgba(255,255,255,1)",
        width=(904 * 0.75),
        height=((1095 * 0.75 + 200) * (y_mid - y_bot) / (1.7 + 0.2 - -0.2)),
        font=dict(
            # family="Courier New, monospace",
            size=26,
            color="rgba(0,0,0,1)",
        ),
    )
    fig.add_trace(
        go.Scatter(
            x=[
                x_left,
                x_cent,
                x_cent,
                # x_cent,
                # x_right,
                # x_right,
                # x_cent,
                x_left,
                x_left,
            ],
            y=[
                y_bot,
                y_bot,
                y_mid,
                # y_bot,
                # y_bot,
                # y_mid,
                # y_mid,
                y_mid,
                y_bot
                #
            ],
            mode="lines",
            marker=dict(
                color="rgba(0,0,0,0)" if line_bool == False else "rgba(0,0,0,1)"
            ),
        )
    )
    fig.add_annotation(
        x=statistics.mean([x_left, x_cent]),
        xanchor="center",
        y=statistics.mean([y_bot, y_mid]),
        yanchor="middle",
        text="£{:.2f}".format(amount),
        showarrow=False,
        font=dict(size=80,),
    )

    # fig.add_annotation(
    #     x=0.55, xanchor="left", y=0.45, text=f"If red", showarrow=False,
    # )
    return fig


fig = fixed_amount_panel(0.50, True)
fig.write_image(
    f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/choices/Screen3_fixed_amount.png"
)
print(f"Completed risk: Screen3_fixed_amount")


#%%


def urn_risk(
    pay_up, pay_down, prob_up, line_bool, repetitions,
):
    prob_up = int(prob_up * 100)
    prob_down = int(100 - prob_up)
    fig = go.Figure()
    fig.layout.font.family = "Arial"
    x_left = -0.2
    x_cent = 1.2
    x_right = 2.6
    y_top = 1.9 if repetitions > 1 else 1.7
    y_mid = 1.5
    y_bot = -0.2
    fig.update_xaxes(
        showgrid=False, zeroline=False, visible=False, range=[x_left, x_cent],
    )
    fig.update_yaxes(
        showgrid=False,
        zeroline=False,
        visible=False,
        range=[y_bot, y_mid],
        scaleanchor="x",
        scaleratio=1,
    )
    fig.update_layout(
        showlegend=False,
        margin=dict(l=0, r=0, t=0, b=0),
        paper_bgcolor="rgba(255,255,255,1)",
        plot_bgcolor="rgba(255,255,255,1)",
        width=(904 * 0.75),
        height=((1095 * 0.75 + 200) * (y_mid - y_bot) / (1.7 + 0.2 - -0.2)),
        font=dict(
            # family="Courier New, monospace",
            size=26,
            color="rgba(0,0,0,1)",
        ),
    )
    fig.add_trace(
        go.Scatter(
            x=[
                x_left,
                x_cent,
                x_cent,
                # x_cent,
                # x_right,
                # x_right,
                # x_cent,
                x_left,
                x_left,
            ],
            y=[
                y_bot,
                y_bot,
                y_mid,
                # y_bot,
                # y_bot,
                # y_mid,
                # y_mid,
                y_mid,
                y_bot
                #
            ],
            mode="lines",
            marker=dict(
                color="rgba(0,0,0,0)" if line_bool == False else "rgba(0,0,0,1)"
            ),
        )
    )

    fig.add_trace(
        go.Scatter(
            x=[-0.1, -0.1, 0.5, 0.5],
            y=[0.6, -0.1, -0.1, 0.6],
            mode="lines",
            marker=dict(color="rgba(0,0,0,1)"),
        )
    )
    fig.add_trace(
        go.Scatter(
            x=[
                elem
                for i in range(10)
                for elem in np.linspace(-0.1 + 0.05, 0.5 - 0.05, 10)
            ],
            y=[
                elem
                for elem in np.linspace(-0.1 + 0.05, 0.6 - 0.05, 10)
                for i in range(10)
            ],
            mode="markers",
            marker=dict(
                size=15,
                # color=random.sample(
                #     ["#1f77b4" for i in range(prob_down)]
                #     + ["#d62728" for i in range(prob_up)],
                #     100,
                # ),
                color=["#1f77b4" for i in range(prob_down)]
                + ["#d62728" for i in range(prob_up)],
            ),
        )
    )
    fig.add_trace(
        go.Scatter(
            x=[0.725, 0.74, 0.35, 0.35],
            y=[0.45, 0.05, 0.7, 0.85],
            mode="markers",
            marker=dict(size=15, color=["#d62728", "#1f77b4", "#1f77b4", "#d62728"]),
        )
    )
    if repetitions > 1:
        fig.add_annotation(
            x=statistics.mean([x_left, x_cent]),
            xanchor="center",
            y=1.3,
            text="<b>Single risk:</b> With {}% probability <br> the outcome is £{:.2f} and with {}% <br> probability the outcome is £{:.2f}".format(
                prob_up, pay_up, prob_down, pay_down
            ),
            showarrow=False,
        )
    else:
        fig.add_annotation(
            x=statistics.mean([x_left, x_cent]),
            xanchor="center",
            y=1.3,
            text="With {}% probability the <br> outcome is £{:.2f} and with {}% <br> probability the outcome is £{:.2f}".format(
                prob_up, pay_up, prob_down, pay_down
            ),
            showarrow=False,
        )
    fig.add_annotation(
        x=0.55, xanchor="left", y=0.45, text=f"If red", showarrow=False,
    )
    fig.add_annotation(
        x=0.55, xanchor="left", y=0.05, text=f"If blue", showarrow=False,
    )
    fig.add_annotation(
        x=0.775,
        xanchor="left",
        y=0.45,
        text="you get £{:.2f}".format(pay_up),
        showarrow=False,
    )
    fig.add_annotation(
        x=0.775,
        xanchor="left",
        y=0.05,
        text="you get £{:.2f}".format(pay_down),
        showarrow=False,
    )
    fig.add_annotation(
        x=0.0, xanchor="left", y=0.85, text=f"{prob_up}/100: red", showarrow=False,
    )
    fig.add_annotation(
        x=0.0, xanchor="left", y=0.7, text=f"{prob_down}/100: blue", showarrow=False,
    )
    return fig


# urn_risk(3, -3, 0.5, 0.5, 1)

#%%
for risk in data:
    fig = urn_risk(risk[1][0], risk[1][1], risk[0][0], risk[4], risk[5])
    fig.write_image(
        f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/choices/{risk[2]}.png"
    )
    print(f"Completed risk: {risk[2]}")

for risk in data_repeated:
    fig = urn_risk(risk[1][0], risk[1][1], risk[0][0], risk[4], risk[5])
    fig.write_image(
        f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/repeat_choices/{risk[2]}.png"
    )
    print(f"Completed risk: {risk[2]}")

for risk in data_prel:
    fig = urn_risk(
        risk["pay_up"],
        risk["pay_down"],
        risk["prob_up"],
        risk["line_bool"],
        risk["repetitions"],
    )
    fig.write_image(
        f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/choices/choice_no_{risk['question_number']}.png"
    )
    print(f"completed choice_number: {risk['question_number']}")

#%%


def gen_envelopes(
    pay_up, pay_down, prob_up, o2_offer, endowment, repetitions, repetitions_word
):
    prob_up = int(prob_up * 100)
    prob_down = int(100 - prob_up)
    fig = go.Figure()
    fig.layout.font.family = "Arial"
    x_left = -0.2
    x_cent = 1.2
    x_right = 2.6
    y_top = 1.9 if repetitions > 1 else 1.7
    y_mid = 1.5
    y_bot = -0.2
    fig.update_xaxes(
        showgrid=False,
        zeroline=False,
        visible=False,
        range=[x_left - 0.05, x_right + 0.05],
    )
    fig.update_yaxes(
        showgrid=False,
        zeroline=False,
        visible=False,
        range=[y_bot, y_top + 0.2],
        scaleanchor="x",
        scaleratio=1,
    )
    height_var = (1095 * 0.75 + 200) * (y_top + 0.2 - y_bot) / (1.7 + 0.2 - -0.2)
    width_var = 904 * 0.75 * 2
    # print(f"dimensions: {height_var}, {width_var}")
    fig.update_layout(
        showlegend=False,
        margin=dict(l=0, r=0, t=0, b=0),
        paper_bgcolor="rgba(255,255,255,1)",
        plot_bgcolor="rgba(255,255,255,1)",
        width=width_var,
        height=height_var,
        font=dict(
            # family="Courier New, monospace",
            size=26,
            color="rgba(0,0,0,1)",
        ),
    )
    fig.add_trace(
        go.Scatter(
            x=[
                x_left,
                x_cent,
                x_cent,
                x_cent,
                x_right,
                x_right,
                x_cent,
                x_left,
                x_left,
            ],
            y=[y_bot, y_bot, y_mid, y_bot, y_bot, y_mid, y_mid, y_mid, y_bot],
            mode="lines",
            marker=dict(color="rgba(0,0,0,1)"),
        )
    )
    fig.add_trace(
        go.Scatter(
            x=[
                x_left,
                x_cent,
                x_cent,
                x_cent,
                x_right,
                x_right,
                x_cent,
                x_left,
                x_left,
            ],
            y=[y_mid, y_mid, y_top, y_mid, y_mid, y_top, y_top, y_top, y_mid],
            mode="lines",
            marker=dict(color="rgba(0,0,0,1)"),
        )
    )

    fig.add_trace(
        go.Scatter(
            x=[-0.1, -0.1, 0.5, 0.5],
            y=[0.6, -0.1, -0.1, 0.6],
            mode="lines",
            marker=dict(color="rgba(0,0,0,1)"),
        )
    )

    fig.add_trace(
        go.Scatter(
            x=[
                elem
                for i in range(10)
                for elem in np.linspace(-0.1 + 0.05, 0.5 - 0.05, 10)
            ],
            y=[
                elem
                for elem in np.linspace(-0.1 + 0.05, 0.6 - 0.05, 10)
                for i in range(10)
            ],
            mode="markers",
            marker=dict(
                size=15,
                # color=random.sample(
                #     ["#1f77b4" for i in range(prob_down)]
                #     + ["#d62728" for i in range(prob_up)],
                #     100,
                # ),
                color=["#1f77b4" for i in range(prob_down)]
                + ["#d62728" for i in range(prob_up)],
            ),
        )
    )
    fig.add_trace(
        go.Scatter(
            x=[0.725, 0.74, 0.35, 0.35],
            y=[0.45, 0.05, 0.7, 0.85],
            mode="markers",
            marker=dict(size=15, color=["#d62728", "#1f77b4", "#1f77b4", "#d62728"]),
        )
    )
    if repetitions > 1:
        fig.add_annotation(
            x=statistics.mean([x_left, x_cent]),
            xanchor="center",
            y=1.3,
            text="<b>Single risk:</b> With {}% probability <br> the outcome is £{:.2f} and with {}% <br> probability the outcome is £{:.2f}".format(
                prob_up, pay_up, prob_down, pay_down
            ),
            showarrow=False,
        )
    else:
        fig.add_annotation(
            x=statistics.mean([x_left, x_cent]),
            xanchor="center",
            y=1.3,
            text="With {}% probability the <br> outcome is £{:.2f} and with {}% <br> probability the outcome is £{:.2f}".format(
                prob_up, pay_up, prob_down, pay_down
            ),
            showarrow=False,
        )
    fig.add_annotation(
        x=0.55, xanchor="left", y=0.45, text=f"If red", showarrow=False,
    )
    fig.add_annotation(
        x=0.55, xanchor="left", y=0.05, text=f"If blue", showarrow=False,
    )
    fig.add_annotation(
        x=0.775,
        xanchor="left",
        y=0.45,
        text="you get £{:.2f}".format(pay_up),
        showarrow=False,
    )
    fig.add_annotation(
        x=0.775,
        xanchor="left",
        y=0.05,
        text="you get £{:.2f}".format(pay_down),
        showarrow=False,
    )
    fig.add_annotation(
        x=0.0, xanchor="left", y=0.85, text=f"{prob_up}/100: red", showarrow=False,
    )
    fig.add_annotation(
        x=0.0, xanchor="left", y=0.7, text=f"{prob_down}/100: blue", showarrow=False,
    )

    if repetitions > 1:
        fig.add_annotation(
            x=statistics.mean([x_left, x_cent]),
            xanchor="center",
            y=statistics.mean([y_top, y_mid]),
            yanchor="middle",
            text=f"<b>Option 1: Take the risk {repetitions} {repetitions_word} times <br> or until losing the first time, <br> whichever happens first </b> (and add <br> the outcome to my endowment):",
            showarrow=False,
        )
    else:
        fig.add_annotation(
            x=statistics.mean([x_left, x_cent]),
            xanchor="center",
            y=statistics.mean([y_top, y_mid]),
            yanchor="middle",
            text=f"<b>Option 1: Take risk </b><br> (i.e., adjust  my endowment by the outcome of):",
            showarrow=False,
        )

    # Option 1: Take the risk 4 times or until losing the first time, whichever happens first (and add the outcome to my endowment):
    fig.add_annotation(
        x=statistics.mean([x_right, x_cent]),
        xanchor="center",
        y=statistics.mean([y_top, y_mid]),
        yanchor="middle",
        text=f"<b>Option 2: Take amount </b><br> (i.e., adjust my endowment by):",
        showarrow=False,
    )
    fig.add_annotation(
        x=statistics.mean([x_right, x_cent]),
        xanchor="center",
        y=statistics.mean([y_bot, y_mid]),
        yanchor="middle",
        text="£{:.2f}".format(o2_offer),
        showarrow=False,
        font=dict(size=80,),
    )
    fig.add_annotation(
        x=statistics.mean([x_right, x_left]),
        xanchor="center",
        y=y_top + 0.1,
        yanchor="middle",
        text="Your endowment is £{:.0f}.".format(endowment),
        showarrow=False,
    )
    return fig


def encrypt_pdf(password, filename):
    pdf = Pdf.open(filename, allow_overwriting_input=True)
    pdf.save(
        filename, encryption=pikepdf.Encryption(owner=password, user=password, R=4)
    )
    # you can change the R from 4 to 6 for 256 aes encryption
    pdf.close()
    print(f"--> encrypted file: {filename}")


fig = gen_envelopes(3.00, -3.00, 0.9, 3.00, 5.00, 6, "(six)")

fig


# %%
for i, row in df_test.iterrows():
    fig = gen_envelopes(
        row["pay_up_x"],
        row["pay_down_x"],
        row["prob_up_x"],
        row["option_2"],
        row["endowment"],
        row["repetitions_x"],
        row["repetition_word"],
    )
    fig.write_image(
        f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/rnd_vars/BonusFile{row['Bonus file']}.pdf"
    )
    print(f"--> generated file BonusFile{row['Bonus file']}.pdf")
    encrypt_pdf(
        "Tiger50",
        f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/rnd_vars/BonusFile{row['Bonus file']}.pdf",
    )

for i, row in df_test.iterrows():
    fig = gen_envelopes(
        row["pay_up_x"],
        row["pay_down_x"],
        row["prob_up_x"],
        row["option_2"],
        row["endowment"],
        row["repetitions_x"],
        row["repetition_word"],
    )
    fig.write_image(
        f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/rnd_vars/BonusFile{row['Bonus file']}_no_pwd.pdf"
    )
    print(f"--> generated file BonusFile{row['Bonus file']}.pdf")

fig = gen_envelopes(6.0, -1.5, 0.2, 0.5, 5, 1, "(one")
fig.write_image(
    f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/rnd_vars/BonusFile{999}.pdf"
)
print(f"--> generated file BonusFile{999}.pdf")
# encrypt_pdf(
#     "pwd",
#     f"C:/Users/bernh/Code/SkewPremExp/SkewPremExp/_static/rnd_vars/BonusFile{999}.pdf",
# )


#%%


def get_rnd_array(len):
    rnd_list = list(random.sample(range(1, 101, 1), len))
    rnd_list.sort()
    return rnd_list


def generate_rnd_tables(path, data):
    """generate random numbers for all entries in a df and save them to excel and json to be used in the verification file and loaded during the experiment

    Args:
        path (System path): where to save the data with suffixes for excel and json
        data (pd.DataFrame): a df containing at least the text description of the risk, the number of repetitions and the probability of winning a positive amount
    """
    # write df to dict to be used in this function
    verification_file_list = [
        {
            "description": decision["description"].replace("£", "_p_"),
            "index": decision["question_number"],
            "repetitions": decision["repetitions"],
            "rnd_numbers": [
                get_rnd_array(int(decision["prob_up"] * 100))
                for i in range(decision["repetitions"])
            ],
        }
        for decision in data
    ]
    for elem in verification_file_list:
        if elem["index"] >= 12 and elem["index"] <= 14:
            cur_len = len(elem["rnd_numbers"])
            elem["rnd_numbers"] = verification_file_list[-1]["rnd_numbers"][:cur_len]

    # verification_file_list = [
    #     {
    #         "description": row["Option 1_clear"],
    #         # "rnd_numbers": get_rnd_array(int(row["probs_x"][0] * 100)),
    #         "rnd_numbers": [
    #             get_rnd_array(int(row["probs_x"][0] * 100))
    #             for i in range(row["repetitions"])
    #         ],
    #         "index": i,
    #         "repetitions": row["repetitions"],
    #     }
    #     for i, row in data.iterrows()
    # ]
    # write to excel
    wb = xl.Workbook(f"{path}_excel.xlsx")
    ws = wb.add_worksheet()
    ws.hide_gridlines()
    row_init = 0
    col_init = 0
    for item in verification_file_list:
        for sublist in item["rnd_numbers"]:
            ws.write(row_init, col_init, item["index"] + 1)
            ws.write(
                row_init,
                col_init + 1,
                item["description"].replace("_p_", "£")
                # + f" This risk is repeated {item['repetitions']} times.",
            )
            ws.write(
                row_init,
                col_init + 2,
                "".join(
                    [
                        f"{no}, " if i < len(sublist) - 1 else f"{no}"
                        for i, no in enumerate(sublist)
                    ]
                ),
            )
            row_init += 1
    wb.close()
    # dump to json
    # print(verification_file_list)

    with open(f"{path}_json.json", "w") as f:
        json.dump(verification_file_list, f)


generate_rnd_tables("only_numbers_prel", data_prel)


#%%

############################################################################################

# Old stuff


# def risk_tree_figure(pay_up, pay_down, prob_up):
#     prob_up = int(prob_up * 100)
#     prob_down = int(100 - prob_up)
#     fig = go.Figure()
#     fig.layout.font.family = "Arial"
#     fig.update_xaxes(showgrid=False, zeroline=False, visible=False, range=[-0.2, 1.2])
#     fig.update_yaxes(
#         showgrid=False,
#         zeroline=False,
#         visible=False,
#         range=[-0.2, 1.5],
#         scaleanchor="x",
#         scaleratio=1,
#     )
#     fig.update_layout(
#         showlegend=False,
#         margin=dict(l=0, r=0, t=0, b=0),
#         paper_bgcolor="rgba(255,255,255,0)",
#         plot_bgcolor="rgba(255,255,255,0)",
#         width=904 * 0.75,
#         height=1095 * 0.75,
#         font=dict(
#             # family="Courier New, monospace",
#             size=26,
#             color="rgba(0,0,0,1)",
#         ),
#     )
#     fig.add_trace(
#         go.Scatter(
#             x=[0.2 - 0.1, 0.8 - 0.1],
#             y=[0.5, 0.8],
#             mode="lines",
#             marker=dict(color="rgba(0,0,0,1)"),
#         )
#     )
#     fig.add_trace(
#         go.Scatter(
#             x=[0.2 - 0.1, 0.8 - 0.1],
#             y=[0.5, 0.2],
#             mode="lines",
#             marker=dict(color="rgba(0,0,0,1)"),
#         )
#     )
#     fig.add_trace(
#         go.Scatter(
#             x=[-0.2, -0.2, 1.2, 1.2, -0.2],
#             y=[-0.2, 1.5, 1.5, -0.2, -0.2],
#             mode="lines",
#             marker=dict(color="rgba(0,0,0,1)"),
#         )
#     )
#     fig.add_annotation(
#         x=0.5,
#         y=1.3,
#         text=f"With {prob_up}% probability the <br> outcome is £{float(pay_up)} and with {prob_down}% <br> probability the outcome is £{float(pay_down)}",
#         showarrow=False,
#     )
#     fig.add_annotation(
#         x=0.5 - 0.1, y=0.7, text=f"{prob_up}%", showarrow=False, textangle=-30,
#     )
#     fig.add_annotation(
#         x=0.5 - 0.1, y=0.3, text=f"{prob_down}%", showarrow=False, textangle=30,
#     )
#     fig.add_annotation(
#         x=0.82 - 0.1, xanchor="left", y=0.8, text=f"£{float(pay_up)}", showarrow=False
#     )
#     fig.add_annotation(
#         x=0.82 - 0.1,
#         xanchor="left",
#         y=0.2,
#         text=f"£{float(pay_down)}",
#         showarrow=False,
#     )
#     return fig


# fig = risk_tree_figure(data[0][1][0], data[0][1][1], data[0][0][0])
# fig.show()