Update app.py

app.py

@@ -17,688 +17,688 @@ from matplotlib.colors import ListedColormap
 import panel as pn
 import altair as alt
 
-def choices_to_df(choices, hue):
-    df = pd.DataFrame(choices, columns=['choices'])
-    df['hue'] = hue
-    df['hue'] = df['hue'].astype(str)
-    return df
-
-binrange = (0, 100)
-moves = []
-with open('dictator.csv', 'r') as f:
-    reader = csv.reader(f)
-    header = next(reader)
-    col2idx = {col: idx for idx, col in enumerate(header)}
-    for row in reader:
-        record = {col: row[idx] for col, idx in col2idx.items()}
-
-        if record['Role'] != 'first': continue
-        if int(record['Round']) > 1: continue
-        if int(record['Total']) != 100: continue
-        if record['move'] == 'None': continue
-        if record['gameType'] != 'dictator': continue
-
-        move = float(record['move'])
-        if move < binrange[0] or \
-            move > binrange[1]: continue
+# def choices_to_df(choices, hue):
+#     df = pd.DataFrame(choices, columns=['choices'])
+#     df['hue'] = hue
+#     df['hue'] = df['hue'].astype(str)
+#     return df
+
+# binrange = (0, 100)
+# moves = []
+# with open('dictator.csv', 'r') as f:
+#     reader = csv.reader(f)
+#     header = next(reader)
+#     col2idx = {col: idx for idx, col in enumerate(header)}
+#     for row in reader:
+#         record = {col: row[idx] for col, idx in col2idx.items()}
+
+#         if record['Role'] != 'first': continue
+#         if int(record['Round']) > 1: continue
+#         if int(record['Total']) != 100: continue
+#         if record['move'] == 'None': continue
+#         if record['gameType'] != 'dictator': continue
+
+#         move = float(record['move'])
+#         if move < binrange[0] or \
+#             move > binrange[1]: continue
         
-        moves.append(move)
-
-df_dictator_human = choices_to_df(moves, 'Human')
-
-choices = [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
-df_dictator_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-
-choices = [25, 35, 70, 30, 20, 25, 40, 80, 30, 30, 40, 30, 30, 30, 30, 30, 40, 40, 30, 30, 40, 30, 60, 20, 40, 25, 30, 30, 30]
-df_dictator_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-
-def extract_choices(recrods):
-    choices = [extract_amout(
-        messages[-1]['content'], 
-        prefix='$', 
-        print_except=True,
-        type=float) for messages in records['messages']
-    ]
-    choices = [x for x in choices if x is not None]
-    # print(choices)
-    return choices
-
-def extract_amout(
-    message, 
-    prefix='',
-    print_except=True,
-    type=float,
-    brackets='[]'
-):
-    try:
-        matches = extract_brackets(message, brackets=brackets)
-        matches = [s[len(prefix):] \
-            if s.startswith(prefix) \
-            else s for s in matches]
-        invalid = False
-        if len(matches) == 0:
-            invalid = True
-        for i in range(len(matches)):
-            if matches[i] != matches[0]:
-                invalid = True
-        if invalid:
-            raise ValueError('Invalid answer: %s' % message)
-        return type(matches[0])
-    except Exception as e: 
-        if print_except: print(e)
-        return None
-
-records = json.load(open('dictator_wo_ex_2023_03_13-11_24_07_PM.json', 'r'))
-choices = extract_choices(records)
-
-# Plot 1 - Dictator (altruism)
-def plot_facet(
-    df_list,
-    x='choices',
-    hue='hue',
-    palette=None,
-    binrange=None,
-    bins=10,
-    # binwidth=10,
-    stat='count',
-    x_label='',
-    sharex=True,
-    sharey=False,
-    subplot=sns.histplot,
-    xticks_locs=None,
-    # kde=False,
-    **kwargs
-):
-    data = pd.concat(df_list)
-    if binrange is None:
-        binrange = (data[x].min(), data[x].max())
-    g = sns.FacetGrid(
-        data, row=hue, hue=hue, 
-        palette=palette,
-        aspect=2, height=2, 
-        sharex=sharex, sharey=sharey,
-        despine=True,
-    )
-    g.map_dataframe(
-        subplot, 
-        x=x, 
-        # kde=kde, 
-        binrange=binrange, 
-        bins=bins,
-        stat=stat,
-        **kwargs
-    )
-    # g.add_legend(title='hue')
-    g.set_axis_labels(x_label, stat.title())
-    g.set_titles(row_template="{row_name}")
-    for ax in g.axes.flat:
-        ax.yaxis.set_major_formatter(
-            FuncFormatter(lambda y, pos: '{:.2f}'.format(y))
-        )
+#         moves.append(move)
+
+# df_dictator_human = choices_to_df(moves, 'Human')
+
+# choices = [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+# df_dictator_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+
+# choices = [25, 35, 70, 30, 20, 25, 40, 80, 30, 30, 40, 30, 30, 30, 30, 30, 40, 40, 30, 30, 40, 30, 60, 20, 40, 25, 30, 30, 30]
+# df_dictator_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+
+# def extract_choices(recrods):
+#     choices = [extract_amout(
+#         messages[-1]['content'], 
+#         prefix='$', 
+#         print_except=True,
+#         type=float) for messages in records['messages']
+#     ]
+#     choices = [x for x in choices if x is not None]
+#     # print(choices)
+#     return choices
+
+# def extract_amout(
+#     message, 
+#     prefix='',
+#     print_except=True,
+#     type=float,
+#     brackets='[]'
+# ):
+#     try:
+#         matches = extract_brackets(message, brackets=brackets)
+#         matches = [s[len(prefix):] \
+#             if s.startswith(prefix) \
+#             else s for s in matches]
+#         invalid = False
+#         if len(matches) == 0:
+#             invalid = True
+#         for i in range(len(matches)):
+#             if matches[i] != matches[0]:
+#                 invalid = True
+#         if invalid:
+#             raise ValueError('Invalid answer: %s' % message)
+#         return type(matches[0])
+#     except Exception as e: 
+#         if print_except: print(e)
+#         return None
+
+# records = json.load(open('dictator_wo_ex_2023_03_13-11_24_07_PM.json', 'r'))
+# choices = extract_choices(records)
+
+# # Plot 1 - Dictator (altruism)
+# def plot_facet(
+#     df_list,
+#     x='choices',
+#     hue='hue',
+#     palette=None,
+#     binrange=None,
+#     bins=10,
+#     # binwidth=10,
+#     stat='count',
+#     x_label='',
+#     sharex=True,
+#     sharey=False,
+#     subplot=sns.histplot,
+#     xticks_locs=None,
+#     # kde=False,
+#     **kwargs
+# ):
+#     data = pd.concat(df_list)
+#     if binrange is None:
+#         binrange = (data[x].min(), data[x].max())
+#     g = sns.FacetGrid(
+#         data, row=hue, hue=hue, 
+#         palette=palette,
+#         aspect=2, height=2, 
+#         sharex=sharex, sharey=sharey,
+#         despine=True,
+#     )
+#     g.map_dataframe(
+#         subplot, 
+#         x=x, 
+#         # kde=kde, 
+#         binrange=binrange, 
+#         bins=bins,
+#         stat=stat,
+#         **kwargs
+#     )
+#     # g.add_legend(title='hue')
+#     g.set_axis_labels(x_label, stat.title())
+#     g.set_titles(row_template="{row_name}")
+#     for ax in g.axes.flat:
+#         ax.yaxis.set_major_formatter(
+#             FuncFormatter(lambda y, pos: '{:.2f}'.format(y))
+#         )
     
-    binwidth = (binrange[1] - binrange[0]) / bins
-    if xticks_locs is None:
-        locs = np.linspace(binrange[0], binrange[1], bins//2+1)
-        locs = [loc + binwidth for loc in locs]
-    else: 
-        locs = xticks_locs
-    labels = [str(int(loc)) for loc in locs]
-    locs = [loc + 0.5*binwidth for loc in locs]
-    plt.xticks(locs, labels)
+#     binwidth = (binrange[1] - binrange[0]) / bins
+#     if xticks_locs is None:
+#         locs = np.linspace(binrange[0], binrange[1], bins//2+1)
+#         locs = [loc + binwidth for loc in locs]
+#     else: 
+#         locs = xticks_locs
+#     labels = [str(int(loc)) for loc in locs]
+#     locs = [loc + 0.5*binwidth for loc in locs]
+#     plt.xticks(locs, labels)
     
-    g.set(xlim=binrange)
-    return g
-
-df = df_dictator_human
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart1 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('steelblue'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
+#     g.set(xlim=binrange)
+#     return g
+
+# df = df_dictator_human
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart1 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('steelblue'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
 
-df = df_dictator_gpt4
-bin_ranges = [0, 10, 30, 50, 70]
+# df = df_dictator_gpt4
+# bin_ranges = [0, 10, 30, 50, 70]
 
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
 
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
 
-# Create the bar chart using Altair
-chart2 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('orange'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
+# # Create the bar chart using Altair
+# chart2 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('orange'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
 
-df = df_dictator_turbo
-
-bin_ranges = [0, 10, 30, 50, 70]
+# df = df_dictator_turbo
+
+# bin_ranges = [0, 10, 30, 50, 70]
 
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
 
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart3 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10)),
-    y='density:Q',
-    color=alt.value('green'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-final = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
-
-#Plot 2 - - Ultimatum (Fairness)
-df = pd.read_csv('ultimatum_strategy.csv')
-df = df[df['gameType'] == 'ultimatum_strategy']
-df = df[df['Role'] == 'player']
-df = df[df['Round'] == 1]
-df = df[df['Total'] == 100]
-df = df[df['move'] != 'None']
-df['propose'] = df['move'].apply(lambda x: eval(x)[0])
-df['accept'] = df['move'].apply(lambda x: eval(x)[1])
-df = df[(df['propose'] >= 0) & (df['propose'] <= 100)]
-df = df[(df['accept'] >= 0) & (df['accept'] <= 100)]
-
-df_ultimatum_1_human = choices_to_df(list(df['propose']), 'Human')
-df_ultimatum_2_human = choices_to_df(list(df['accept']), 'Human')
-
-choices = [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
-df_ultimatum_1_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-
-choices = [40, 40, 40, 30, 70, 70, 50, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 30, 30, 35, 50, 40, 70, 40, 60, 60, 70, 40, 50]
-df_ultimatum_1_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-
-choices = [50.0, 50.0, 50.0, 1.0, 1.0, 1.0, 50.0, 25.0, 50.0, 1.0, 1.0, 20.0, 50.0, 50.0, 50.0, 20.0, 50.0, 1.0, 1.0, 1.0, 50.0, 50.0, 50.0, 1.0, 1.0, 1.0, 20.0, 1.0] + [0, 1]
-df_ultimatum_2_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-
-choices = [None, 50, 50, 50, 50, 30, None, None, 30, 33.33, 40, None, 50, 40, None, 1, 30, None, 10, 50, 30, 10, 30, None, 30, None, 10, 30, 30, 30]
-df_ultimatum_2_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-
-choices = [50.0, 50.0, 10.0, 40.0, 20.0, 50.0, 1.0, 1.0, 50.0, 1.0, 50.0, 50.0, 20.0, 10.0, 50.0, 20.0, 1.0, 1.0, 50.0, 1.0, 20.0, 1.0, 50.0, 50.0, 20.0, 20.0, 50.0, 20.0, 1.0, 50.0]
-df_ultimatum_2_gpt4_female = choices_to_df(choices, hue='ChatGPT-4 Female')
-
-choices = [1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 1.0, 1.0, 50.0, 50.0, 50.0, 20.0, 20.0, 1.0, 50.0, 1.0, 1.0, 1.0, 50.0, 20.0, 1.0, 50.0, 20.0, 20.0, 10.0, 50.0, 1.0, 1.0, 1.0]
-df_ultimatum_2_gpt4_male = choices_to_df(choices, hue='ChatGPT-4 Male')
-
-choices = [40.0, 1.0, 1.0, 20.0, 1.0, 20.0, 50.0, 50.0, 1.0, 1.0, 1.0, 50.0, 1.0, 20.0, 50.0, 10.0, 50.0, 1.0, 1.0, 20.0, 1.0, 50.0, 20.0, 20.0, 20.0, 1.0, 1.0, 1.0, 1.0, 40.0]
-df_ultimatum_2_gpt4_US = choices_to_df(choices, hue='ChatGPT-4 US')
-
-choices = [1.0, 1.0, 20.0, 50.0, 1.0, 1.0, 1.0, 1.0, 20.0, 20.0, 50.0, 20.0, 20.0, 50.0, 20.0, 1.0, 40.0, 50.0, 1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 50.0, 1.0, 1.0, 1.0, 1.0]
-df_ultimatum_2_gpt4_Poland = choices_to_df(choices, hue='ChatGPT-4 Poland')
-
-choices = [50.0, 1.0, 20.0, 50.0, 50.0, 50.0, 50.0, 1.0, 1.0, 50.0, 1.0, 50.0, 1.0, 50.0, 1.0, 20.0, 1.0, 1.0, 20.0, 50.0, 0.0, 20.0, 1.0, 1.0, 1.0, 1.0, 20.0, 20.0, 50.0, 20.0]
-df_ultimatum_2_gpt4_China = choices_to_df(choices, hue='ChatGPT-4 China')
-
-choices = [1.0, 1.0, 1.0, 50.0, 1.0, 1.0, 50.0, 40.0, 1.0, 1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 1.0, 50.0, 1.0, 20.0, 1.0, 20.0, 1.0, 50.0, 1.0, 50.0, 20.0, 1.0, 1.0, 50.0]
-df_ultimatum_2_gpt4_UK = choices_to_df(choices, hue='ChatGPT-4 UK')
-
-choices = [50.0, 1.0, 20.0, 50.0, 50.0, 50.0, 50.0, 10.0, 1.0, 40.0, 50.0, 20.0, 1.0, 1.0, 1.0, 50.0, 50.0, 20.0, 20.0, 1.0, 1.0, 50.0, 20.0, 50.0, 50.0, 20.0, 1.0, 20.0, 50.0, 1]
-df_ultimatum_2_gpt4_Columbia = choices_to_df(choices, hue='ChatGPT-4 Columbia')
-
-choices = [50.0, 1.0, 50.0, 20.0, 20.0, 20.0, 50.0, 20.0, 20.0, 1.0, 1.0, 1.0, 1.0, 20.0, 1.0, 50.0, 1.0, 20.0, 20.0, 50.0, 1.0, 50.0, 1.0, 40.0, 1.0, 20.0, 1.0, 20.0, 1.0, 1.0]
-df_ultimatum_2_gpt4_under = choices_to_df(choices, hue='ChatGPT-4 Undergrad')
-
-choices = [1.0, 20.0, 1.0, 40.0, 50.0, 1.0, 1.0, 1.0, 25.0, 20.0, 50.0, 20.0, 50.0, 50.0, 1.0, 50.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 50.0, 20.0, 1.0, 1.0, 1.0, 50.0, 20.0, 20.0]
-df_ultimatum_2_gpt4_grad = choices_to_df(choices, hue='ChatGPT-4 Graduate')
-
-df = df_ultimatum_1_human
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart1 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('steelblue'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-df = df_ultimatum_1_gpt4
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart2 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('orange'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-df = df_ultimatum_1_turbo
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart3 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10)),
-    y='density:Q',
-    color=alt.value('green'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-final2 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
-
-#Plot 3 - - Ultimatum (Responder) (spite)
-df = df_ultimatum_2_human
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart1 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('steelblue'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-df = df_ultimatum_2_gpt4
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart2 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('orange'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-df = df_ultimatum_2_turbo
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart3 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('green'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-final3 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
-
-#Plot 4 - - Trust (as Investor) (trust)
-binrange = (0, 100)
-moves_1 = []
-moves_2 = defaultdict(list)
-with open('trust_investment.csv', 'r') as f:
-    reader = csv.reader(f)
-    header = next(reader)
-    col2idx = {col: idx for idx, col in enumerate(header)}
-    for row in reader:
-        record = {col: row[idx] for col, idx in col2idx.items()}
-
-        # if record['Role'] != 'first': continue
-        if int(record['Round']) > 1: continue
-        # if int(record['Total']) != 100: continue
-        if record['move'] == 'None': continue
-        if record['gameType'] != 'trust_investment': continue
-
-        if record['Role'] == 'first':
-            move = float(record['move'])
-            if move < binrange[0] or \
-                move > binrange[1]: continue
-            moves_1.append(move)
-        elif record['Role'] == 'second':
-            inv, ret = eval(record['roundResult'])
-            if ret < 0 or \
-                ret > inv * 3: continue
-            moves_2[inv].append(ret)
-        else: continue
-
-df_trust_1_human = choices_to_df(moves_1, 'Human')
-df_trust_2_human = choices_to_df(moves_2[10], 'Human')
-df_trust_3_human = choices_to_df(moves_2[50], 'Human')
-df_trust_4_human = choices_to_df(moves_2[100], 'Human')
-
-choices = [50.0, 50.0, 40.0, 30.0, 50.0, 50.0, 40.0, 50.0, 50.0, 50.0, 50.0, 50.0, 30.0, 30.0, 50.0, 50.0, 50.0, 40.0, 40.0, 50.0, 50.0, 50.0, 50.0, 40.0, 50.0, 50.0, 50.0, 50.0] 
-df_trust_1_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-
-choices = [50.0, 50.0, 30.0, 30.0, 30.0, 60.0, 50.0, 40.0, 20.0, 20.0, 50.0, 40.0, 30.0, 20.0, 30.0, 20.0, 30.0, 60.0, 50.0, 30.0, 50.0, 20.0, 20.0, 30.0, 50.0, 30.0, 30.0, 50.0, 40.0] + [30]
-df_trust_1_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-
-choices = [20.0, 20.0, 20.0, 20.0, 15.0, 15.0, 15.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 15.0, 20.0, 15.0, 15.0, 15.0, 15.0, 15.0, 20.0, 20.0, 15.0]
-df_trust_2_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-
-choices = [20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 25.0, 30.0, 30.0, 20.0, 25.0, 30.0, 20.0, 20.0, 18.0] + [20, 20, 20, 25, 25, 25, 30]
-df_trust_2_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-
-choices = [100.0, 75.0, 75.0, 75.0, 75.0, 75.0, 100.0, 75.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 75.0, 100.0, 75.0, 75.0, 75.0, 100.0, 100.0, 100.0, 75.0, 100.0, 100.0, 100.0, 100.0, 75.0, 100.0, 75.0]
-df_trust_3_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-
-choices = [150.0, 100.0, 150.0, 150.0, 50.0, 150.0, 100.0, 150.0, 100.0, 100.0, 100.0, 150.0] + [100, 100, 100, 100, 100, 100, 100, 100]
-df_trust_3_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-
-choices = [200.0, 200.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 200.0, 200.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0]
-df_trust_4_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-
-choices = [225.0, 225.0, 300.0, 300.0, 220.0, 300.0, 250.0] + [200, 200, 250, 200, 200]
-df_trust_4_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-
-df = df_trust_1_human
-
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart1 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('steelblue'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-
-
-df = df_trust_1_gpt4
-
-bin_ranges = [0, 10, 30, 50, 70]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart2 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('orange'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-
-df = df_trust_1_turbo
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart3 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10)),
+#     y='density:Q',
+#     color=alt.value('green'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+# final = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
+
+# #Plot 2 - - Ultimatum (Fairness)
+# df = pd.read_csv('ultimatum_strategy.csv')
+# df = df[df['gameType'] == 'ultimatum_strategy']
+# df = df[df['Role'] == 'player']
+# df = df[df['Round'] == 1]
+# df = df[df['Total'] == 100]
+# df = df[df['move'] != 'None']
+# df['propose'] = df['move'].apply(lambda x: eval(x)[0])
+# df['accept'] = df['move'].apply(lambda x: eval(x)[1])
+# df = df[(df['propose'] >= 0) & (df['propose'] <= 100)]
+# df = df[(df['accept'] >= 0) & (df['accept'] <= 100)]
+
+# df_ultimatum_1_human = choices_to_df(list(df['propose']), 'Human')
+# df_ultimatum_2_human = choices_to_df(list(df['accept']), 'Human')
+
+# choices = [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
+# df_ultimatum_1_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+
+# choices = [40, 40, 40, 30, 70, 70, 50, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 30, 30, 35, 50, 40, 70, 40, 60, 60, 70, 40, 50]
+# df_ultimatum_1_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+
+# choices = [50.0, 50.0, 50.0, 1.0, 1.0, 1.0, 50.0, 25.0, 50.0, 1.0, 1.0, 20.0, 50.0, 50.0, 50.0, 20.0, 50.0, 1.0, 1.0, 1.0, 50.0, 50.0, 50.0, 1.0, 1.0, 1.0, 20.0, 1.0] + [0, 1]
+# df_ultimatum_2_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+
+# choices = [None, 50, 50, 50, 50, 30, None, None, 30, 33.33, 40, None, 50, 40, None, 1, 30, None, 10, 50, 30, 10, 30, None, 30, None, 10, 30, 30, 30]
+# df_ultimatum_2_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+
+# choices = [50.0, 50.0, 10.0, 40.0, 20.0, 50.0, 1.0, 1.0, 50.0, 1.0, 50.0, 50.0, 20.0, 10.0, 50.0, 20.0, 1.0, 1.0, 50.0, 1.0, 20.0, 1.0, 50.0, 50.0, 20.0, 20.0, 50.0, 20.0, 1.0, 50.0]
+# df_ultimatum_2_gpt4_female = choices_to_df(choices, hue='ChatGPT-4 Female')
+
+# choices = [1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 1.0, 1.0, 50.0, 50.0, 50.0, 20.0, 20.0, 1.0, 50.0, 1.0, 1.0, 1.0, 50.0, 20.0, 1.0, 50.0, 20.0, 20.0, 10.0, 50.0, 1.0, 1.0, 1.0]
+# df_ultimatum_2_gpt4_male = choices_to_df(choices, hue='ChatGPT-4 Male')
+
+# choices = [40.0, 1.0, 1.0, 20.0, 1.0, 20.0, 50.0, 50.0, 1.0, 1.0, 1.0, 50.0, 1.0, 20.0, 50.0, 10.0, 50.0, 1.0, 1.0, 20.0, 1.0, 50.0, 20.0, 20.0, 20.0, 1.0, 1.0, 1.0, 1.0, 40.0]
+# df_ultimatum_2_gpt4_US = choices_to_df(choices, hue='ChatGPT-4 US')
+
+# choices = [1.0, 1.0, 20.0, 50.0, 1.0, 1.0, 1.0, 1.0, 20.0, 20.0, 50.0, 20.0, 20.0, 50.0, 20.0, 1.0, 40.0, 50.0, 1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 50.0, 1.0, 1.0, 1.0, 1.0]
+# df_ultimatum_2_gpt4_Poland = choices_to_df(choices, hue='ChatGPT-4 Poland')
+
+# choices = [50.0, 1.0, 20.0, 50.0, 50.0, 50.0, 50.0, 1.0, 1.0, 50.0, 1.0, 50.0, 1.0, 50.0, 1.0, 20.0, 1.0, 1.0, 20.0, 50.0, 0.0, 20.0, 1.0, 1.0, 1.0, 1.0, 20.0, 20.0, 50.0, 20.0]
+# df_ultimatum_2_gpt4_China = choices_to_df(choices, hue='ChatGPT-4 China')
+
+# choices = [1.0, 1.0, 1.0, 50.0, 1.0, 1.0, 50.0, 40.0, 1.0, 1.0, 1.0, 1.0, 20.0, 1.0, 1.0, 50.0, 1.0, 50.0, 1.0, 20.0, 1.0, 20.0, 1.0, 50.0, 1.0, 50.0, 20.0, 1.0, 1.0, 50.0]
+# df_ultimatum_2_gpt4_UK = choices_to_df(choices, hue='ChatGPT-4 UK')
+
+# choices = [50.0, 1.0, 20.0, 50.0, 50.0, 50.0, 50.0, 10.0, 1.0, 40.0, 50.0, 20.0, 1.0, 1.0, 1.0, 50.0, 50.0, 20.0, 20.0, 1.0, 1.0, 50.0, 20.0, 50.0, 50.0, 20.0, 1.0, 20.0, 50.0, 1]
+# df_ultimatum_2_gpt4_Columbia = choices_to_df(choices, hue='ChatGPT-4 Columbia')
+
+# choices = [50.0, 1.0, 50.0, 20.0, 20.0, 20.0, 50.0, 20.0, 20.0, 1.0, 1.0, 1.0, 1.0, 20.0, 1.0, 50.0, 1.0, 20.0, 20.0, 50.0, 1.0, 50.0, 1.0, 40.0, 1.0, 20.0, 1.0, 20.0, 1.0, 1.0]
+# df_ultimatum_2_gpt4_under = choices_to_df(choices, hue='ChatGPT-4 Undergrad')
+
+# choices = [1.0, 20.0, 1.0, 40.0, 50.0, 1.0, 1.0, 1.0, 25.0, 20.0, 50.0, 20.0, 50.0, 50.0, 1.0, 50.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 50.0, 20.0, 1.0, 1.0, 1.0, 50.0, 20.0, 20.0]
+# df_ultimatum_2_gpt4_grad = choices_to_df(choices, hue='ChatGPT-4 Graduate')
+
+# df = df_ultimatum_1_human
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart1 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('steelblue'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+# df = df_ultimatum_1_gpt4
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart2 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('orange'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+# df = df_ultimatum_1_turbo
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart3 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10)),
+#     y='density:Q',
+#     color=alt.value('green'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+# final2 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
+
+# #Plot 3 - - Ultimatum (Responder) (spite)
+# df = df_ultimatum_2_human
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart1 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('steelblue'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+# df = df_ultimatum_2_gpt4
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart2 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('orange'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+# df = df_ultimatum_2_turbo
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart3 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('green'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+# final3 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
+
+# #Plot 4 - - Trust (as Investor) (trust)
+# binrange = (0, 100)
+# moves_1 = []
+# moves_2 = defaultdict(list)
+# with open('trust_investment.csv', 'r') as f:
+#     reader = csv.reader(f)
+#     header = next(reader)
+#     col2idx = {col: idx for idx, col in enumerate(header)}
+#     for row in reader:
+#         record = {col: row[idx] for col, idx in col2idx.items()}
+
+#         # if record['Role'] != 'first': continue
+#         if int(record['Round']) > 1: continue
+#         # if int(record['Total']) != 100: continue
+#         if record['move'] == 'None': continue
+#         if record['gameType'] != 'trust_investment': continue
+
+#         if record['Role'] == 'first':
+#             move = float(record['move'])
+#             if move < binrange[0] or \
+#                 move > binrange[1]: continue
+#             moves_1.append(move)
+#         elif record['Role'] == 'second':
+#             inv, ret = eval(record['roundResult'])
+#             if ret < 0 or \
+#                 ret > inv * 3: continue
+#             moves_2[inv].append(ret)
+#         else: continue
+
+# df_trust_1_human = choices_to_df(moves_1, 'Human')
+# df_trust_2_human = choices_to_df(moves_2[10], 'Human')
+# df_trust_3_human = choices_to_df(moves_2[50], 'Human')
+# df_trust_4_human = choices_to_df(moves_2[100], 'Human')
+
+# choices = [50.0, 50.0, 40.0, 30.0, 50.0, 50.0, 40.0, 50.0, 50.0, 50.0, 50.0, 50.0, 30.0, 30.0, 50.0, 50.0, 50.0, 40.0, 40.0, 50.0, 50.0, 50.0, 50.0, 40.0, 50.0, 50.0, 50.0, 50.0] 
+# df_trust_1_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+
+# choices = [50.0, 50.0, 30.0, 30.0, 30.0, 60.0, 50.0, 40.0, 20.0, 20.0, 50.0, 40.0, 30.0, 20.0, 30.0, 20.0, 30.0, 60.0, 50.0, 30.0, 50.0, 20.0, 20.0, 30.0, 50.0, 30.0, 30.0, 50.0, 40.0] + [30]
+# df_trust_1_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+
+# choices = [20.0, 20.0, 20.0, 20.0, 15.0, 15.0, 15.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 15.0, 20.0, 15.0, 15.0, 15.0, 15.0, 15.0, 20.0, 20.0, 15.0]
+# df_trust_2_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+
+# choices = [20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0, 25.0, 30.0, 30.0, 20.0, 25.0, 30.0, 20.0, 20.0, 18.0] + [20, 20, 20, 25, 25, 25, 30]
+# df_trust_2_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+
+# choices = [100.0, 75.0, 75.0, 75.0, 75.0, 75.0, 100.0, 75.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 75.0, 100.0, 75.0, 75.0, 75.0, 100.0, 100.0, 100.0, 75.0, 100.0, 100.0, 100.0, 100.0, 75.0, 100.0, 75.0]
+# df_trust_3_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+
+# choices = [150.0, 100.0, 150.0, 150.0, 50.0, 150.0, 100.0, 150.0, 100.0, 100.0, 100.0, 150.0] + [100, 100, 100, 100, 100, 100, 100, 100]
+# df_trust_3_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+
+# choices = [200.0, 200.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 200.0, 200.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0, 150.0]
+# df_trust_4_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+
+# choices = [225.0, 225.0, 300.0, 300.0, 220.0, 300.0, 250.0] + [200, 200, 250, 200, 200]
+# df_trust_4_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+
+# df = df_trust_1_human
+
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart1 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('steelblue'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+
+
+# df = df_trust_1_gpt4
+
+# bin_ranges = [0, 10, 30, 50, 70]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart2 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('orange'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+
+# df = df_trust_1_turbo
 
-bin_ranges = [0, 10, 30, 50, 70]
+# bin_ranges = [0, 10, 30, 50, 70]
 
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
 
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
 
-# Create the bar chart using Altair
-chart3 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
-    y='density:Q',
-    color=alt.value('green'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
+# # Create the bar chart using Altair
+# chart3 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(extent=[0, 70], step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('green'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
     
     
-final4 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
-
-#Plot 5 - Trust (as Banker) (fairness, altruism, reciprocity)
-df = df_trust_3_human
-
-bin_ranges = [0, 25, 50, 75, 100, 125, 150]
-
-custom_ticks = [2, 6, 10, 14, 18]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+# final4 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
+
+# #Plot 5 - Trust (as Banker) (fairness, altruism, reciprocity)
+# df = df_trust_3_human
+
+# bin_ranges = [0, 25, 50, 75, 100, 125, 150]
+
+# custom_ticks = [2, 6, 10, 14, 18]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
 
-# Create the bar chart using Altair
-chart1 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(step=10), axis=None),
-    y='density:Q',
-    color=alt.value('steelblue')
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
+# # Create the bar chart using Altair
+# chart1 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('steelblue')
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
 
 
 
-df = df_trust_3_gpt4
-
-bin_ranges = [0, 25, 50, 75, 100, 125, 150]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
+# df = df_trust_3_gpt4
+
+# bin_ranges = [0, 25, 50, 75, 100, 125, 150]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
 
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
 
-# Create the bar chart using Altair
-chart2 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(step=10), axis=None),
-    y='density:Q',
-    color=alt.value('orange')
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
+# # Create the bar chart using Altair
+# chart2 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(step=10), axis=None),
+#     y='density:Q',
+#     color=alt.value('orange')
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
 
 
-df = df_trust_3_turbo
+# df = df_trust_3_turbo
 
-bin_ranges = [0, 25, 50, 75, 100, 125, 150]
+# bin_ranges = [0, 25, 50, 75, 100, 125, 150]
 
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
 
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
 
-# Create the bar chart using Altair
-chart3 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(step=10)),
-    y='density:Q',
-    color=alt.value('green')
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
+# # Create the bar chart using Altair
+# chart3 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(step=10)),
+#     y='density:Q',
+#     color=alt.value('green')
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
 
-# chart1
-final5 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
+# # chart1
+# final5 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
 
-#Plot 6 - Public Goods (Free-Riding, altruism, cooperation)
-df = pd.read_csv('public_goods_linear_water.csv')
-df = df[df['Role'] == 'contributor']
-df = df[df['Round'] <= 3]
-df = df[df['Total'] == 20]
-df = df[df['groupSize'] == 4]
-df = df[df['move'] != None]
-df = df[(df['move'] >= 0) & (df['move'] <= 20)]
-df = df[df['gameType'] == 'public_goods_linear_water']
+# #Plot 6 - Public Goods (Free-Riding, altruism, cooperation)
+# df = pd.read_csv('public_goods_linear_water.csv')
+# df = df[df['Role'] == 'contributor']
+# df = df[df['Round'] <= 3]
+# df = df[df['Total'] == 20]
+# df = df[df['groupSize'] == 4]
+# df = df[df['move'] != None]
+# df = df[(df['move'] >= 0) & (df['move'] <= 20)]
+# df = df[df['gameType'] == 'public_goods_linear_water']
 
-round_1 = df[df['Round'] == 1]['move']
-round_2 = df[df['Round'] == 2]['move']
-round_3 = df[df['Round'] == 3]['move']
-print(len(round_1), len(round_2), len(round_3))
-df_PG_human = pd.DataFrame({
-    'choices': list(round_1)
-})
-df_PG_human['hue'] = 'Human'
-# df_PG_human
+# round_1 = df[df['Round'] == 1]['move']
+# round_2 = df[df['Round'] == 2]['move']
+# round_3 = df[df['Round'] == 3]['move']
+# print(len(round_1), len(round_2), len(round_3))
+# df_PG_human = pd.DataFrame({
+#     'choices': list(round_1)
+# })
+# df_PG_human['hue'] = 'Human'
+# # df_PG_human
 
-file_names = [
-    'PG_basic_turbo_2023_05_09-02_49_09_AM.json',
-    'PG_basic_turbo_loss_2023_05_09-03_59_49_AM.json',
-    'PG_basic_gpt4_2023_05_09-11_15_42_PM.json',
-    'PG_basic_gpt4_loss_2023_05_09-10_44_38_PM.json',
-]
+# file_names = [
+#     'PG_basic_turbo_2023_05_09-02_49_09_AM.json',
+#     'PG_basic_turbo_loss_2023_05_09-03_59_49_AM.json',
+#     'PG_basic_gpt4_2023_05_09-11_15_42_PM.json',
+#     'PG_basic_gpt4_loss_2023_05_09-10_44_38_PM.json',
+# ]
 
-choices = []
-for file_name in file_names:
-    with open(file_name, 'r') as f:
-        choices += json.load(f)['choices']
-choices_baseline = choices
+# choices = []
+# for file_name in file_names:
+#     with open(file_name, 'r') as f:
+#         choices += json.load(f)['choices']
+# choices_baseline = choices
 
-choices = [tuple(x)[0] for x in choices]
-df_PG_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
-# df_PG_turbo.head()
-df_PG_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
-# df_PG_gpt4.head()
-
-df = df_PG_human
-
-bin_ranges = [0, 25, 50, 75, 100, 125, 150]
-
-custom_ticks = [2, 6, 10, 14, 18]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart1 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(step=2), axis=None),
-    y='density:Q',
-    color=alt.value('steelblue'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-
-
-df = df_PG_gpt4
-
-bin_ranges = [0, 25, 50, 75, 100, 125, 150]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart2 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(step=2), axis=None),
-    y='density:Q',
-    color=alt.value('orange'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
-
-
-df = df_PG_turbo
-
-bin_ranges = [0, 25, 50, 75, 100, 125, 150]
-
-# Calculate density as a percentage
-density_percentage = df['choices'].value_counts(normalize=True)
-
-# Create a DataFrame with the density percentages
-density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
-
-# Create the bar chart using Altair
-chart3 = alt.Chart(density_df).mark_bar().encode(
-    x=alt.X('choices:O', bin=alt.Bin(step=2)),
-    y='density:Q',
-    color=alt.value('green'),
-    tooltip=['density']
-).properties(
-    width=500,
-    title='Density of Choices'
-).interactive()
+# choices = [tuple(x)[0] for x in choices]
+# df_PG_turbo = choices_to_df(choices, hue=str('ChatGPT-3'))
+# # df_PG_turbo.head()
+# df_PG_gpt4 = choices_to_df(choices, hue=str('ChatGPT-4'))
+# # df_PG_gpt4.head()
+
+# df = df_PG_human
+
+# bin_ranges = [0, 25, 50, 75, 100, 125, 150]
+
+# custom_ticks = [2, 6, 10, 14, 18]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart1 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(step=2), axis=None),
+#     y='density:Q',
+#     color=alt.value('steelblue'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+
+
+# df = df_PG_gpt4
+
+# bin_ranges = [0, 25, 50, 75, 100, 125, 150]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart2 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(step=2), axis=None),
+#     y='density:Q',
+#     color=alt.value('orange'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
+
+
+# df = df_PG_turbo
+
+# bin_ranges = [0, 25, 50, 75, 100, 125, 150]
+
+# # Calculate density as a percentage
+# density_percentage = df['choices'].value_counts(normalize=True)
+
+# # Create a DataFrame with the density percentages
+# density_df = pd.DataFrame({'choices': density_percentage.index, 'density': density_percentage.values})
+
+# # Create the bar chart using Altair
+# chart3 = alt.Chart(density_df).mark_bar().encode(
+#     x=alt.X('choices:O', bin=alt.Bin(step=2)),
+#     y='density:Q',
+#     color=alt.value('green'),
+#     tooltip=['density']
+# ).properties(
+#     width=500,
+#     title='Density of Choices'
+# ).interactive()
     
-# chart1
-final6 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
+# # chart1
+# final6 = alt.vconcat(chart1, chart2, chart3).resolve_scale(x='shared')
 
-#Final_Final
-final_final = (final | final2 | final3 ) & (final4 | final5 | final6)
+# #Final_Final
+# final_final = (final | final2 | final3 ) & (final4 | final5 | final6)
 
 
 # # we want to use bootstrap/template, tell Panel to load up what we need