feat: add multiplot tabs to the streamlit dashboard

plots/barplot.py

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+# filepath: c:\Users\kamat\OneDrive\Desktop\Work\UIUC\Research\maidr\maidr_streamlit\plots\barplot.py
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from plots.utils import set_theme, color_palettes
+
+def create_barplot(input_barplot_color, theme):
+    """Create a bar plot based on input parameters"""
+    color = color_palettes[input_barplot_color]
+    categories = ["Category A", "Category B", "Category C", "Category D", "Category E"]
+    values = np.random.randint(10, 100, size=5)
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+    sns.barplot(x=categories, y=values, ax=ax, color=color)
+    ax.set_title("Plot of Categories")
+    ax.set_xlabel("Categories")
+    ax.set_ylabel("Values")
+
+    return ax
+
+def create_custom_barplot(df, var, color, theme):
+    """Create a bar plot from user data"""
+    if not var or df is None:
+        return None
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+    sns.countplot(data=df, x=var, color=color, ax=ax)
+    ax.set_title(f"{var}")
+    ax.set_xlabel(var.replace("_", " ").title())
+    ax.set_ylabel("Count")
+
+    return ax

plots/boxplot.py

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+# filepath: c:\Users\kamat\OneDrive\Desktop\Work\UIUC\Research\maidr\maidr_streamlit\plots\boxplot.py
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from plots.utils import set_theme, color_palettes
+
+def create_boxplot(input_boxplot_type, input_boxplot_color, theme):
+    """Create a box plot based on input parameters"""
+    boxplot_type = input_boxplot_type
+    color = color_palettes[input_boxplot_color]
+
+    # Generate data based on the selected box plot type
+    if boxplot_type == "Positively Skewed with Outliers":
+        data = np.random.lognormal(mean=0, sigma=0.5, size=1000)
+    elif boxplot_type == "Negatively Skewed with Outliers":
+        data = -np.random.lognormal(mean=0, sigma=0.5, size=1000)
+    elif boxplot_type == "Symmetric with Outliers":
+        data = np.random.normal(loc=0, scale=1, size=1000)
+    elif boxplot_type == "Symmetric without Outliers":
+        data = np.random.normal(loc=0, scale=1, size=1000)
+        data = data[(data > -1.5) & (data < 1.5)]  # Strict range to avoid outliers
+    else:
+        data = np.random.normal(loc=0, scale=1, size=1000)
+
+    # Create the plot using matplotlib
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+    sns.boxplot(x=data, ax=ax, color=color)  # Horizontal box plot
+    ax.set_title(f"{boxplot_type}")
+    ax.set_xlabel("Value")
+
+    return ax
+
+def create_custom_boxplot(df, var_x, var_y, color, theme):
+    """Create a box plot from user data"""
+    if df is None:
+        return None
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+
+    if var_x and var_y:
+        sns.boxplot(x=var_y, y=var_x, data=df, palette=[color], ax=ax)
+        ax.set_title(f"{var_x} grouped by {var_y}")
+        ax.set_xlabel(var_y.replace("_", " ").title())
+        ax.set_ylabel(var_x.replace("_", " ").title())
+    elif var_x:
+        sns.boxplot(y=df[var_x], color=color, ax=ax)
+        ax.set_title(f"{var_x}")
+        ax.set_ylabel(var_x.replace("_", " ").title())
+    else:
+        return None
+
+    return ax

plots/heatmap.py

@@ -0,0 +1,51 @@
+# filepath: c:\Users\kamat\OneDrive\Desktop\Work\UIUC\Research\maidr\maidr_streamlit\plots\heatmap.py
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+import pandas as pd
+from plots.utils import set_theme
+
+def create_heatmap(input_heatmap_type, theme):
+    """Create a heatmap based on input parameters"""
+    heatmap_type = input_heatmap_type
+
+    if heatmap_type == "Random":
+        data = np.random.rand(5, 5)  # Reduced size
+    elif heatmap_type == "Correlated":
+        data = np.random.multivariate_normal(
+            [0] * 5, np.eye(5), size=5
+        )  # Reduced size
+    elif heatmap_type == "Checkerboard":
+        data = np.indices((5, 5)).sum(axis=0) % 2  # Reduced size
+    else:
+        data = np.random.rand(5, 5)
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+    set_theme(fig, ax, theme)
+    sns.heatmap(data, ax=ax, cmap="YlGnBu", annot=True, fmt=".2f")
+    ax.set_title(f"{heatmap_type}")
+
+    return ax
+
+def create_custom_heatmap(df, var_x, var_y, var_value, colorscale, theme):
+    """Create a heatmap from user data"""
+    if not var_x or not var_y or df is None:
+        return None
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+    set_theme(fig, ax, theme)
+
+    # Check if both variables are categorical and a value variable is provided
+    if var_value:
+        # Create a pivot table
+        pivot_table = pd.pivot_table(df, values=var_value, index=var_y, columns=var_x, aggfunc='mean')
+    else:
+        # If no value variable, use crosstab for frequency counts
+        pivot_table = pd.crosstab(df[var_y], df[var_x], normalize='all')
+
+    sns.heatmap(pivot_table, ax=ax, cmap=colorscale, annot=True, fmt=".2f")
+    ax.set_title(f"Heatmap of {var_y} vs {var_x}")
+    ax.set_xlabel(var_x.replace("_", " ").title())
+    ax.set_ylabel(var_y.replace("_", " ").title())
+
+    return ax

plots/histogram.py

@@ -0,0 +1,61 @@
+# filepath: c:\Users\kamat\OneDrive\Desktop\Work\UIUC\Research\maidr\maidr_streamlit\plots\histogram.py
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from plots.utils import set_theme, color_palettes
+
+def create_histogram(input_distribution_type, input_hist_color, theme):
+    """Create a histogram based on input parameters"""
+    distribution_type = input_distribution_type
+    color = color_palettes[input_hist_color]
+
+    # Generate data based on the selected distribution
+    if distribution_type == "Normal Distribution":
+        data = np.random.normal(size=1000)
+    elif distribution_type == "Positively Skewed":
+        data = np.random.exponential(scale=3, size=1000)
+    elif distribution_type == "Negatively Skewed":
+        data = -np.random.exponential(scale=1.5, size=1000)
+    elif distribution_type == "Unimodal Distribution":
+        data = np.random.normal(loc=0, scale=2.5, size=1000)
+    elif distribution_type == "Bimodal Distribution":
+        data = np.concatenate(
+            [
+                np.random.normal(-2, 0.5, size=500),
+                np.random.normal(2, 0.5, size=500),
+            ]
+        )
+    elif distribution_type == "Multimodal Distribution":
+        data = np.concatenate(
+            [
+                np.random.normal(-2, 0.5, size=300),
+                np.random.normal(2, 0.5, size=300),
+                np.random.normal(5, 0.5, size=400),
+            ]
+        )
+    else:
+        data = np.random.normal(size=1000)
+
+    # Create the plot using matplotlib
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+    sns.histplot(data, kde=True, bins=20, color=color, edgecolor="white", ax=ax)
+    ax.set_title(f"{distribution_type}")
+    ax.set_xlabel("Value")
+    ax.set_ylabel("Frequency")
+
+    return ax
+
+def create_custom_histogram(df, var, color, theme):
+    """Create a histogram from user data"""
+    if not var or df is None:
+        return None
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+    sns.histplot(data=df, x=var, kde=True, color=color, ax=ax)
+    ax.set_title(f"{var}")
+    ax.set_xlabel(var.replace("_", " ").title())
+    ax.set_ylabel("Count")
+
+    return ax

plots/lineplot.py

@@ -0,0 +1,45 @@
+# filepath: c:\Users\kamat\OneDrive\Desktop\Work\UIUC\Research\maidr\maidr_streamlit\plots\lineplot.py
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from plots.utils import set_theme, color_palettes
+
+def create_lineplot(input_lineplot_type, input_lineplot_color, theme):
+    """Create a line plot based on input parameters"""
+    lineplot_type = input_lineplot_type
+    color = color_palettes[input_lineplot_color]
+
+    x = np.linspace(0, 10, 20)  # Reduced number of points
+    if lineplot_type == "Linear Trend":
+        y = 2 * x + 1 + np.random.normal(0, 1, 20)
+    elif lineplot_type == "Exponential Growth":
+        y = np.exp(0.5 * x) + np.random.normal(0, 1, 20)
+    elif lineplot_type == "Sinusoidal Pattern":
+        y = 5 * np.sin(x) + np.random.normal(0, 0.5, 20)
+    elif lineplot_type == "Random Walk":
+        y = np.cumsum(np.random.normal(0, 1, 20))
+    else:
+        y = x + np.random.normal(0, 1, 20)
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+    sns.lineplot(x=x, y=y, ax=ax, color=color)
+    ax.set_title(f"{lineplot_type}")
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+
+    return ax
+
+def create_custom_lineplot(df, var_x, var_y, color, theme):
+    """Create a line plot from user data"""
+    if not var_x or not var_y or df is None:
+        return None
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    set_theme(fig, ax, theme)
+    sns.lineplot(data=df, x=var_x, y=var_y, color=color, ax=ax)
+    ax.set_title(f"{var_y} vs {var_x}")
+    ax.set_xlabel(var_x.replace("_", " ").title())
+    ax.set_ylabel(var_y.replace("_", " ").title())
+
+    return ax