first commit

charts/__init__.py

@@ -0,0 +1 @@
+# This file can be empty but is required to make the charts directory a package

charts/charts.py

@@ -0,0 +1,104 @@
+import seaborn as sns
+import matplotlib.pyplot as plt
+import io
+import base64
+import pandas as pd
+
+def bar_chart(data, title):
+    data = data.str.split(';').explode().value_counts()
+    fig, ax = plt.subplots(figsize=(12,6))
+    palette = sns.color_palette("husl", len(data))
+    sns.barplot(x=data.index, y=data.values, ax=ax, hue=data.index, palette=palette, legend=False)
+    ax.set_xlabel('Answer')
+    ax.set_ylabel('Count')
+
+    # Rotate the x-axis labels and set their horizontal alignment
+    for tick in ax.get_xticklabels():
+        tick.set_rotation(45)
+        tick.set_horizontalalignment('right')
+        tick.set_fontsize(12)
+
+    fig.tight_layout()
+
+    img = io.BytesIO()
+    fig.savefig(img, format='png', dpi=300, bbox_inches='tight', pad_inches=0.5)
+    img.seek(0)
+    plot_url = base64.b64encode(img.getvalue()).decode('utf8')
+    plt.close()
+    return plot_url
+
+def bar_chart_with_special_legend(data, title, scale='recommendation'):
+    if scale == 'recommendation':
+        data = data.map({1:'not likely', 2:'less likely', 3:'not sure', 4:'more likely', 5:'definitely'})
+
+    # create a value counts series
+    data_counts = data.value_counts()
+
+    fig, ax = plt.subplots(figsize=(12,6))
+    palette = sns.color_palette("husl", 5)
+    sns.barplot(x=data_counts.index, y=data_counts.values, ax=ax, hue=data_counts.index, palette=palette)
+    ax.set_xlabel('Answer')
+    ax.set_ylabel('Count')
+
+    # set y-axis to only have integer values
+    ax.yaxis.set_major_locator(plt.MultipleLocator(1.0))
+
+    # Rotate the x-axis labels and set their horizontal alignment
+    for tick in ax.get_xticklabels():
+        tick.set_rotation(45)
+        tick.set_horizontalalignment('right')
+        tick.set_fontsize(12)
+
+    fig.tight_layout()
+
+    img = io.BytesIO()
+    fig.savefig(img, format='png', dpi=300, bbox_inches='tight', pad_inches=0.5)
+    img.seek(0)
+    plot_url = base64.b64encode(img.getvalue()).decode('utf8')
+    plt.close()
+    return plot_url
+
+def pie_chart(data, title):
+    data = data.str.split(';').explode().value_counts()
+    fig, ax = plt.subplots(figsize=(12,6))
+    ax.pie(data.values, labels=data.index, autopct='%1.1f%%')
+    fig.tight_layout()
+    img = io.BytesIO()
+    fig.savefig(img, format='png', dpi=300, bbox_inches='tight', pad_inches=0.5)
+    img.seek(0)
+    plot_url = base64.b64encode(img.getvalue()).decode('utf8')
+    plt.close()
+    return plot_url
+
+def point_chart(data, title):
+    # Count the occurrences of each value
+
+    # Map the x-axis values to the appropriate string
+    data = data.map({1:'not important', 2:'less important', 3:'important', 4:'more important', 5:'mandatory'})
+
+    # Create a figure and axis
+    fig, ax = plt.subplots()
+
+    # Plot your data
+    for answer, value in data.items():
+        ax.plot([value], [answer], marker='o')
+
+    # Set the x-axis ticks and labels
+    ax.set_xticks(list(set(data.values)))
+    ax.set_xticklabels(list(set(data.values)), rotation=45)
+
+    # Set the y-axis limits to remove the axis
+    ax.set_yticks(list(data.keys()))
+
+    # Set the font size of the y-axis
+    for tick in ax.get_yticklabels():
+        tick.set_fontsize(9)
+
+    fig.tight_layout()
+    img = io.BytesIO()
+    fig.savefig(img, format='png', dpi=300, bbox_inches='tight', pad_inches=0.5)
+    img.seek(0)
+    plot_url = base64.b64encode(img.getvalue()).decode('utf8')
+    plt.close()
+    return plot_url
+