upload the project

hairloss/visuals.py

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+from tinydb import TinyDB
+from datetime import datetime
+import matplotlib.pyplot as plt
+from os import path
+from scipy.stats import spearmanr
+import numpy as np
+from datetime import date
+from logging import getLogger
+logger = getLogger("hairloss")
+
+def averageWithDates(dates, values): # average points with same date
+    dates = np.array(dates)
+    values = np.array(values)
+    datesUnique = np.unique(dates)
+    valuesUnique = np.array([values[dates == datesCopy].mean() for datesCopy in datesUnique])
+    return (datesUnique, valuesUnique)
+
+def plotHairline(models: [str], withClean: bool, withNormalized: bool, withRaw: bool, image_path: str, filepath: str, doSave: bool, doShow: bool, doRegression: bool, doCorrelation: bool, start: date, end: date, angles: [str], doAverage: bool, doPlot: bool):
+    db = TinyDB(path.abspath(path.join(image_path, "../hairlineResults.json")))
+
+    if doPlot and (doRegression or doCorrelation):
+        raise ValueError("You can't use regression or correlation wit doPlot set to true.")
+
+    if doPlot and not doAverage:
+        raise ValueError("doPlot needs doAverage set to True")
+
+    databaseEntry = db.all()
+    filteredDatabaseEntry = []
+    for entry in databaseEntry:
+        # we filter by date
+        if start <= date.fromisoformat(entry["filename"][:10]) <= end:
+            # and by angles
+            for angleType in angles:
+                if angleType in entry["filename"]:
+                    filteredDatabaseEntry.append(entry)
+
+    dates = [datetime.strptime(d["filename"][:10], "%Y-%m-%d") for d in filteredDatabaseEntry]
+
+    plt.figure(figsize=(12, 6))
+
+
+    if (doRegression or doCorrelation) and withClean + withNormalized + withRaw != 1:
+        raise ValueError(f"With doRegression or doCorrelation exaclty one of withClean, withNormalized or withRaw must be set to true")
+    
+    values = []
+
+    # plot raw values
+    if withRaw:
+        for model in models:
+            values = [d[model] for d in filteredDatabaseEntry]
+            if doAverage:
+                dates, values = averageWithDates(dates, values)
+            if doPlot:
+                plt.plot(dates, values, label=model)
+            else:
+                plt.scatter(dates, values, label=model)
+
+    # plot without uncertain
+    if withClean:
+        for model in models:
+            key = model + "WithoutUnsure"
+            values = [d[key] for d in filteredDatabaseEntry]
+            if doAverage:
+                dates, values = averageWithDates(dates, values)
+            if doPlot:
+                plt.plot(dates, values, label=key)
+            else:
+                plt.scatter(dates, values, label=key)
+    if withNormalized:
+        for model in models:
+            key = model + "Normalized"
+            values = [d[key] for d in filteredDatabaseEntry]
+            if doAverage:
+                dates, values = averageWithDates(dates, values)
+            if doPlot:
+                plt.plot(dates, values, label=key)
+            else:
+                plt.scatter(dates, values, label=key)
+
+    if doCorrelation:
+        rho, p = spearmanr([d.toordinal() for d in dates], values)
+        plt.plot([], [], ' ', label=f"Correlation factor of {rho:-3f} (with p-value of {p:.3g})")
+
+    if doRegression:
+        x = np.array([d.toordinal() for d in dates])
+        m, b = np.polyfit(x, values, 1)
+        plt.plot(dates, m*x + b, label=f"Regression line y={m}*x + {b}")
+
+
+    plt.xlabel("Dates")
+    plt.ylabel("Baldness score")
+    plt.title(filepath)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    plt.tight_layout()
+    if doShow:
+        plt.show()
+    if doSave:
+        plt.savefig(filepath, bbox_inches="tight")
+    plt.close()