Fixed nested repository issue and updated capstone project files

Dockerfile

@@ -0,0 +1,17 @@
+# Use the official Python image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents to the container
+COPY . /app
+
+# Install Python dependencies
+RUN pip install --no-cache-dir dash plotly pandas
+
+# Expose the port that the Dash app runs on
+EXPOSE 8080
+
+# Run the application
+CMD ["python", "fisher_dash_app.py"]

config.toml

@@ -0,0 +1,3 @@
+[server]
+enableCORS = false
+enableWebsocketCompression = false

fisher.html

@@ -18,38 +18,13 @@ <h1>Fisher Doubles Down</h1>
             <p>
                 After World War II, the world declared, “never again.” Racial pseudoscience and the atrocities it justified faced universal condemnation. Yet, categorization itself persisted. Statistical methods advanced, but their application to groups of people continued to legitimize harmful hierarchies.
             </p>
-            <p>
-                Ronald Fisher, a pioneer of modern statistics, carried the torch of eugenics from Francis Galton and Karl Pearson, both of whom laid the groundwork for applying statistical methods to human populations. Fisher’s contributions to statistics are immense: his work on analysis of variance (ANOVA), maximum likelihood estimation, and the design of experiments revolutionized mathematics, genetics, and scientific research. Yet, Fisher also believed deeply in eugenics, insisting that statistical tools could objectively determine which groups were "fit" to thrive. For Fisher, subjective judgments about "inferior" and "superior" populations became "scientific" through the application of mathematical methods.
-            </p>
-            <p>
-                Rather than abandoning eugenic thinking after the war, Fisher doubled down—arguing that the Earth’s finite resources required humanity to make hard choices. He claimed that statistical tools could help identify which populations contributed to sustainability and which were burdens. This rationalization gave his worldview a veneer of scientific legitimacy, even as it concealed its subjective roots.
-            </p>
-        </section>
-
-        <!-- Embed Fisher Visualization -->
-        <section id="ethnic-visualization">
-            <p>
-                Step into Fisher’s mindset: Adjust the weights assigned to variables like IQ, moral character, and crime statistics to explore how hierarchies might emerge.
-            </p>
-            <ul>
-                <li>Click on the variable names at the top of the visualization to include or exclude them from the analysis.</li>
-                <li>Adjust the sliders to assign weights to each variable. Observe how changing these weights shifts the bar chart and alters the perceived rankings of groups.</li>
-                <li>Reflect on the narratives created by your adjustments. Which groups appear to "succeed" under certain weightings? Which are marginalized? How does this depend on the values embedded in the variables?</li>
-            </ul>
-            <iframe 
-                src="https://colingeraghty.pythonanywhere.com/fisher_weight1" 
-                title="Fisher Visualization" 
-                width="100%" 
-                height="600px" 
-                style="border: none;">
-            </iframe>
         </section>
 
-        <!-- Conclusion -->
-        <section id="conclusion">
-            <p>
-                These exercises reveal how statistical tools, designed to understand the world, can easily divide it. From Fisher’s justification of resource allocation to today’s efforts to measure societal progress, the ability to assign weights and create hierarchies reflects subjective values rather than objective truths.
-            </p>
+        <!-- Fisher Visualization -->
+        <section id="fisher-visualization">
+            <p>Adjust the weights assigned to variables like IQ, moral character, and crime statistics to explore how hierarchies might emerge.</p>
+            <!-- Embed the Dash app -->
+            <iframe src="https://fisher-statistical-app-o6a2yfdseq-uc.a.run.app" title="Fisher Statistical Visualization" width="100%"height="600" style="border: none;"></iframe>
         </section>
     </div>
 

fisher_dash_app.py

@@ -0,0 +1,142 @@
+import dash
+from dash import dcc, html
+from dash.dependencies import Input, Output
+import plotly.graph_objects as go
+import pandas as pd
+
+# Initialize Dash app
+app = dash.Dash(__name__)
+app.title = "Fisher Statistical Analysis Visualization"
+
+# Data for visualization
+fisher_data = pd.DataFrame({
+    "Group": ["White", "Black", "Asian", "Hispanic", "Other"],
+    "IQ": [100, 108, 105, 102, 98],
+    "Moral Character": [95, 85, 90, 80, 85],
+    "Violent Crime": [30, 35, 25, 40, 45],
+    "Income": [75, 45, 70, 50, 55],
+    "Reproduction Rate": [48, 55, 47, 60, 52]
+})
+
+# App layout
+app.layout = html.Div(style={"font-family": "Avenir", "padding": "10px", "max-width": "1000px", "margin": "0 auto", "font-size": "0.95em"}, children=[
+    # Clickable variable titles for toggling visibility
+    html.Div(
+        style={"display": "flex", "justify-content": "center", "gap": "8px", "margin-bottom": "10px"},
+        children=[
+            html.Div("IQ", id="toggle-iq", style={"color": "#636EFA", "font-weight": "bold", "cursor": "pointer"}),
+            html.Div("Moral Character", id="toggle-moral", style={"color": "#EF553B", "font-weight": "bold", "cursor": "pointer"}),
+            html.Div("Violent Crime", id="toggle-crime", style={"color": "#00CC96", "font-weight": "bold", "cursor": "pointer"}),
+            html.Div("Income", id="toggle-income", style={"color": "#AB63FA", "font-weight": "bold", "cursor": "pointer"}),
+            html.Div("Reproduction Rate", id="toggle-reproduction", style={"color": "#FFA15A", "font-weight": "bold", "cursor": "pointer"})
+        ]
+    ),
+
+    # Bar Chart
+    dcc.Graph(id="fisher-chart", style={"width": "100%", "height": "400px", "border": "none"}),
+
+    # Sliders with corresponding value display
+    html.Div(
+        style={"display": "flex", "justify-content": "space-around", "align-items": "center", "margin-top": "10px"},
+        children=[
+            html.Div([
+                html.Label("IQ", style={"font-weight": "bold", "font-size": "0.85em", "text-align": "center"}),
+                dcc.Slider(
+                    id="weight-iq",
+                    min=0,
+                    max=5,
+                    step=0.1,
+                    value=1,
+                    marks={i: str(round(i, 1)) for i in range(6)}
+                ),
+                html.Div(id="weight-iq-value", style={"text-align": "center", "font-size": "0.85em", "margin-top": "-10px"})
+            ], style={"width": "18%"}),
+            html.Div([
+                html.Label("Moral Character", style={"font-weight": "bold", "font-size": "0.85em", "text-align": "center"}),
+                dcc.Slider(
+                    id="weight-moral",
+                    min=0,
+                    max=5,
+                    step=0.1,
+                    value=1,
+                    marks={i: str(round(i, 1)) for i in range(6)}
+                ),
+                html.Div(id="weight-moral-value", style={"text-align": "center", "font-size": "0.85em", "margin-top": "-10px"})
+            ], style={"width": "18%"}),
+            html.Div([
+                html.Label("Violent Crime", style={"font-weight": "bold", "font-size": "0.85em", "text-align": "center"}),
+                dcc.Slider(
+                    id="weight-crime",
+                    min=0,
+                    max=5,
+                    step=0.1,
+                    value=1,
+                    marks={i: str(round(i, 1)) for i in range(6)}
+                ),
+                html.Div(id="weight-crime-value", style={"text-align": "center", "font-size": "0.85em", "margin-top": "-10px"})
+            ], style={"width": "18%"}),
+            html.Div([
+                html.Label("Income", style={"font-weight": "bold", "font-size": "0.85em", "text-align": "center"}),
+                dcc.Slider(
+                    id="weight-income",
+                    min=0,
+                    max=5,
+                    step=0.1,
+                    value=1,
+                    marks={i: str(round(i, 1)) for i in range(6)}
+                ),
+                html.Div(id="weight-income-value", style={"text-align": "center", "font-size": "0.85em", "margin-top": "-10px"})
+            ], style={"width": "18%"}),
+            html.Div([
+                html.Label("Reproduction Rate", style={"font-weight": "bold", "font-size": "0.85em", "text-align": "center"}),
+                dcc.Slider(
+                    id="weight-reproduction",
+                    min=0,
+                    max=5,
+                    step=0.1,
+                    value=1,
+                    marks={i: str(round(i, 1)) for i in range(6)}
+                ),
+                html.Div(id="weight-reproduction-value", style={"text-align": "center", "font-size": "0.85em", "margin-top": "-10px"})
+            ], style={"width": "18%"})
+        ]
+    )
+])
+
+# Callback to update chart and toggle visibility
+@app.callback(
+    Output("fisher-chart", "figure"),
+    [Input("weight-iq", "value"),
+     Input("weight-moral", "value"),
+     Input("weight-crime", "value"),
+     Input("weight-income", "value"),
+     Input("weight-reproduction", "value")]
+)
+def update_fisher_chart(weight_iq, weight_moral, weight_crime, weight_income, weight_reproduction):
+    weighted_data = fisher_data.copy()
+    weighted_data["IQ"] *= weight_iq
+    weighted_data["Moral Character"] *= weight_moral
+    weighted_data["Violent Crime"] *= weight_crime
+    weighted_data["Income"] *= weight_income
+    weighted_data["Reproduction Rate"] *= weight_reproduction
+
+    fig = go.Figure()
+    for col, color in zip(
+        ["IQ", "Moral Character", "Violent Crime", "Income", "Reproduction Rate"],
+        ["#636EFA", "#EF553B", "#00CC96", "#AB63FA", "#FFA15A"]
+    ):
+        fig.add_trace(go.Bar(x=weighted_data["Group"], y=weighted_data[col], name=col, marker=dict(color=color), opacity=0.75))
+
+    fig.update_layout(
+        barmode="group",
+        plot_bgcolor="white",
+        font=dict(family="Avenir"),
+        xaxis=dict(showgrid=False, showline=True, zeroline=False, title="Group"),
+        yaxis=dict(showgrid=True, title="Weighted Value"),
+        margin=dict(l=10, r=10, t=20, b=10)
+    )
+    return fig
+if __name__ == "__main__":
+    app.run_server(debug=True, host="0.0.0.0", port=8080)
+
+

fisher_dash_app_1.py

@@ -0,0 +1,65 @@
+from dash import Dash, dcc, html
+from dash.dependencies import Input, Output
+import plotly.graph_objects as go
+import pandas as pd
+
+app = Dash(__name__)
+app.title = "Fisher Statistical Visualization"
+
+fisher_data = pd.DataFrame({
+    "Group": ["White", "Black", "Asian", "Hispanic", "Other"],
+    "IQ": [100, 108, 105, 102, 98],
+    "Moral Character": [95, 85, 90, 80, 85],
+    "Violent Crime": [30, 35, 25, 40, 45],
+    "Income": [75, 45, 70, 50, 55],
+    "Reproduction Rate": [48, 55, 47, 60, 52]
+})
+
+app.layout = html.Div([
+    html.H1("Fisher Statistical Visualization"),
+    html.Div([
+        html.Label("Select Variables:"),
+        dcc.Checklist(
+            id="variable-selector",
+            options=[{"label": col, "value": col} for col in fisher_data.columns[1:]],
+            value=fisher_data.columns[1:]
+        ),
+        html.Div([
+            html.Label("Adjust Weights for Each Variable:"),
+            *[
+                html.Div([
+                    html.Label(col),
+                    dcc.Slider(id=f"{col}-weight", min=0, max=5, step=0.1, value=1)
+                ]) for col in fisher_data.columns[1:]
+            ]
+        ]),
+    ]),
+    dcc.Graph(id="fisher-chart")
+])
+
+@app.callback(
+    Output("fisher-chart", "figure"),
+    [
+        Input("variable-selector", "value"),
+        *[Input(f"{col}-weight", "value") for col in fisher_data.columns[1:]]
+    ]
+)
+def update_chart(variables, *weights):
+    selected_data = fisher_data[["Group"] + variables].copy()
+    for i, col in enumerate(variables):
+        selected_data[col] *= weights[i]
+
+    fig = go.Figure()
+    for col in variables:
+        fig.add_trace(go.Bar(x=selected_data["Group"], y=selected_data[col], name=col))
+
+    fig.update_layout(
+        barmode="group",
+        plot_bgcolor="white",
+        xaxis=dict(title="Group"),
+        yaxis=dict(title="Weighted Values")
+    )
+    return fig
+
+if __name__ == "__main__":
+    app.run_server(debug=True, host="0.0.0.0", port=8080)

fisher_statistical_Dockerfile

@@ -0,0 +1,6 @@
+FROM python:3.9-slim
+WORKDIR /app
+COPY . /app
+RUN pip install --no-cache-dir dash pandas plotly
+EXPOSE 8080
+CMD ["python", "fisher_statistical_app.py"]

fisher_statistical_app.py

@@ -0,0 +1,58 @@
+import dash
+from dash import dcc, html
+from dash.dependencies import Input, Output
+import plotly.graph_objects as go
+import pandas as pd
+
+app = dash.Dash(__name__)
+app.title = "Fisher Statistical Visualization"
+
+fisher_data = pd.DataFrame({
+    "Group": ["White", "Black", "Asian", "Hispanic", "Other"],
+    "IQ": [100, 108, 105, 102, 98],
+    "Moral Character": [95, 85, 90, 80, 85],
+    "Violent Crime": [30, 35, 25, 40, 45],
+    "Income": [75, 45, 70, 50, 55],
+    "Reproduction Rate": [48, 55, 47, 60, 52]
+})
+
+app.layout = html.Div([
+    html.H1("Fisher Statistical Visualization"),
+    dcc.Graph(id="stat-chart"),
+    html.Div([
+        html.Label("Adjust Weights:"),
+        dcc.Slider(id="weight-iq", min=0, max=5, value=1, marks={i: str(i) for i in range(6)}),
+        dcc.Slider(id="weight-moral", min=0, max=5, value=1, marks={i: str(i) for i in range(6)}),
+        dcc.Slider(id="weight-crime", min=0, max=5, value=1, marks={i: str(i) for i in range(6)}),
+        dcc.Slider(id="weight-income", min=0, max=5, value=1, marks={i: str(i) for i in range(6)}),
+        dcc.Slider(id="weight-reproduction", min=0, max=5, value=1, marks={i: str(i) for i in range(6)})
+    ])
+])
+
+@app.callback(
+    Output("stat-chart", "figure"),
+    [Input("weight-iq", "value"),
+     Input("weight-moral", "value"),
+     Input("weight-crime", "value"),
+     Input("weight-income", "value"),
+     Input("weight-reproduction", "value")]
+)
+def update_chart(weight_iq, weight_moral, weight_crime, weight_income, weight_reproduction):
+    weighted_data = fisher_data.copy()
+    weighted_data["IQ"] *= weight_iq
+    weighted_data["Moral Character"] *= weight_moral
+    weighted_data["Violent Crime"] *= weight_crime
+    weighted_data["Income"] *= weight_income
+    weighted_data["Reproduction Rate"] *= weight_reproduction
+
+    fig = go.Figure()
+    for col, color in zip(
+        ["IQ", "Moral Character", "Violent Crime", "Income", "Reproduction Rate"],
+        ["#636EFA", "#EF553B", "#00CC96", "#AB63FA", "#FFA15A"]
+    ):
+        fig.add_trace(go.Bar(x=weighted_data["Group"], y=weighted_data[col], name=col, marker=dict(color=color)))
+    fig.update_layout(barmode="group", title="Weighted Statistical Values")
+    return fig
+
+if __name__ == "__main__":
+    app.run_server(debug=True, host="0.0.0.0", port=8080)