added helper library

explorer/lib/download.py

@@ -0,0 +1,35 @@
+import base64
+from io import BytesIO
+import pandas as pd
+
+
+def get_csv_download_link(df, name):
+    """Generates a link allowing the data in a given panda dataframe to be downloaded
+    in:  dataframe
+    out: href string
+    """
+    csv = df.to_csv(index=False)
+    b64 = base64.b64encode(
+        csv.encode()
+    ).decode()  # some strings <-> bytes conversions necessary here
+    href = f'<a href="data:file/csv;base64,{b64}" download="ioi_{name}.csv">Download as CSV file</a>'
+    return href
+
+
+def to_excel(df):
+    output = BytesIO()
+    writer = pd.ExcelWriter(output, engine="xlsxwriter")
+    df.to_excel(writer, sheet_name="data")
+    writer.save()
+    processed_data = output.getvalue()
+    return processed_data
+
+
+def get_excel_download_link(df, name):
+    """Generates a link allowing the data in a given panda dataframe to be downloaded
+    in:  dataframe
+    out: href string
+    """
+    val = to_excel(df)
+    b64 = base64.b64encode(val)
+    return f'<a href="data:application/octet-stream;base64,{b64.decode()}" download="ioi_{name}.xlsx">Download as Excel file</a>'

explorer/lib/figures.py

@@ -0,0 +1,78 @@
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+
+
+def generate_pie_chart(
+    df,
+    values,
+    names,
+    hover_name,
+    color,
+    color_discrete_sequence,
+    color_discrete_map,
+):
+    fig = px.pie(
+        df,
+        values=values,
+        names=names,
+        hover_name=hover_name,
+        color=color,
+        color_discrete_sequence=color_discrete_sequence,
+        color_discrete_map=color_discrete_map,
+        width=800,
+    )
+    fig.update_traces(textinfo="percent+value")
+    return fig
+
+
+def generate_histogram(df, x, y, nbins, color, color_discrete_map, labels):
+    fig = px.histogram(
+        df,
+        x=x,
+        y=y,
+        nbins=nbins,
+        color=color,
+        color_discrete_map=color_discrete_map,
+        labels=labels,
+    )
+    return fig
+
+
+def generate_stacked_bar_chart(data):
+    fig = go.Figure(data=data)
+    fig.update_layout(width=800, height=800, barmode="stack")
+    return fig
+
+
+def generate_ranking_plot(df, input_col, options, scoring):
+    input_col_score = pd.Series(index=options)
+    for i in range(1, 7):
+        input_col_counts = df[f"{input_col}[{i}]"].value_counts()
+        scores = input_col_counts.multiply(scoring[i])
+        input_col_score = input_col_score.add(scores, fill_value=0)
+        input_col_score = input_col_score.sort_values(ascending=False)
+        if i == 1:
+            ranked_first = df[f"{input_col}[1]"].value_counts()
+            ranked_first_clean = pd.DataFrame(
+                {"institution": ranked_first.index, "ranked_first": ranked_first.values}
+            )
+    input_col_df = pd.DataFrame(
+        {
+            "institution": input_col_score.index,
+            "score": input_col_score.values,
+        }
+    )
+    input_col_df = input_col_df.merge(
+        ranked_first_clean, on="institution", how="left"
+    ).fillna(0)
+    input_col_df = input_col_df.sort_values(["score", "ranked_first"])
+    fig = px.bar(
+        input_col_df.sort_values(by="score"),
+        y="institution",
+        x="score",
+        color="ranked_first",
+        color_continuous_scale="viridis",
+        orientation="h",
+    )
+    return fig