[feature] generic time series plot

Example and quick plot of variables from nyears_data dictionnaries

MODULES/DATA_MANAGEMENT/plots/generic_time_series.py

+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import numpy as np
+import datetime
+
+def plot_variables(nyears_data_list, variables, start_date, end_date):
+    """
+    This function is provided as an example and a quick way to plot time series data 
+    for mean multiple spatialized variables from multiple datasets within a specified date range.
+    
+    :param nyears_data_list: A list of dictionaries, each containing yearly data dictionaries.
+    :type nyears_Data_list: list
+    :param variables: A list of variable names to be plotted.
+    :type variables: list 
+    :param start_date: The plot start date in the format "YYYY-MM-DD"
+    :type start_date: str
+    :param start_date: The plot end date in the format "YYYY-MM-DD"
+    :type end_date: str
+    """
+    start_dt = datetime.datetime.strptime(start_date, "%Y-%m-%d")
+    end_dt = datetime.datetime.strptime(end_date, "%Y-%m-%d")
+    timedelta = end_dt - start_dt
+    
+    num_vars = len(variables)
+    fig, axes = plt.subplots(num_vars, 1, figsize=(12, 6 * num_vars), sharex=False)
+    
+    if num_vars == 1:
+        axes = [axes]
+        
+    
+    for ax, variable in zip(axes, variables):
+        dates = []
+        values = []
+        
+        for i, nyears_data in enumerate(nyears_data_list):
+            for year, year_dict in nyears_data.items():
+                if variable in year_dict:
+                    print(f'{variable} found in dict {i}, year {year}')
+                    var_dict = year_dict[variable]
+                    for date_str, value in var_dict.items():
+                        current_dt = datetime.datetime.strptime(str(date_str), "%Y-%m-%d %H:%M:%S")
+                        if start_dt <= current_dt <= end_dt:
+                            dates.append(current_dt)
+                            values.append(np.mean(value))
+        
+        if not dates:
+            ax.set_title(f"No data found for {variable}")
+            continue
+        
+        values = np.array(values)  # Convert to NumPy array in case of multiple dimensions
+    
+        
+        if values.ndim == 1:
+            ax.plot(dates, values, label=variable, linewidth='2')
+        else:
+            for i in range(values.shape[1]):
+                ax.plot(dates, values[:, i], label=f"{variable}_{i}")
+         
+        
+        ax.xaxis.set_major_locator(mdates.YearLocator())
+        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
+        if timedelta.total_seconds() <= 63072000:
+            ax.xaxis.set_minor_locator(mdates.MonthLocator())
+            ax.xaxis.set_minor_formatter(mdates.DateFormatter('%m'))
+        ax.set_ylabel("Value")
+        ax.set_title(f"{variable}")
+        ax.legend()
+        ax.grid(True)
+        ax.tick_params(axis='x', labelrotation=45)
+    
+    plt.xlabel("Date")
+    plt.xticks(rotation=45)
+    plt.show()
+
+# Example usage:
+# plot_variables([Soil_plot.nyears_data, Crop_plot[0].nyears_data, Crop_plot[1].nyears_data], ['Norg', 'exported_BM'], '2008-01-01', '2009-12-31')