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extras/reports/datacake_report_pv.py

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+###################################################################################################
+# datacake_report_pv.py
+#
+# This script generates a PDF report from CSV files containing PV inverter data
+# which are provided by Datacake.
+#
+# created: 09/2024
+#
+#
+# MIT License
+#
+# Copyright (c) 2024 Matthias Prinke
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+#
+# History:
+#
+# 20240902 Created
+#
+# ToDo:
+# -
+###################################################################################################
+
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from matplotlib.backends.backend_pdf import PdfPages
+
+COLUMNS = {
+    'power': 0,
+    'energytoday': 2,
+    'energytotal': 3,
+
+}
+
+COLORS = {
+    'power': 'r',
+    'energytoday': 'lime',
+    'energytotal': 'limegreen',
+}
+
+WIDTH = 18  / 2.54
+HEIGHT = 27 / 2.54
+
+# Directory containing the CSV files
+src_dir = "/home/mp/datacake/PV-Inverter/2024"
+pdf_file = "/home/mp/datacake/PV-Inverter/pv_inverter_2024.pdf"
+last_column = 4
+
+# List all CSV files in the directory
+csv_files = [f for f in os.listdir(src_dir) if f.endswith('.csv')]
+
+# Initialize an empty list to store DataFrames
+dataframes = []
+
+# Read each CSV file
+for file in csv_files:
+    file_path = os.path.join(src_dir, file)
+    df = pd.read_csv(file_path, skiprows=0, parse_dates=[0], date_parser=lambda x: pd.to_datetime(x, format='%a, %d %b %Y %H:%M:%S'))
+
+    for col in range(1, last_column):
+        # Replace comma with dot and convert to float
+        df.iloc[:, col] = df.iloc[:, col].astype(float)
+
+    dataframes.append(df)
+
+# Combine all DataFrames into a single DataFrame
+combined_df = pd.concat(dataframes, ignore_index=True)
+
+# Set the first column as the index (time)
+combined_df.set_index(combined_df.columns[0], inplace=True)
+
+# Sort the DataFrame by the index (time)
+combined_df.sort_index(inplace=True)
+
+# Print the combined DataFrame
+print(combined_df)
+
+# Remove invalid rows from the combined DataFrame
+combined_df.dropna(inplace=True)
+
+def title_page(plt, pdf, title, font_size=24):
+    """Create a title page with the given title."""
+    fig, ax = plt.subplots(figsize=(WIDTH, HEIGHT))
+    ax.text(0.5, 0.5, title, transform=ax.transAxes, fontsize=font_size, ha='center', va='center')
+    ax.axis('off')
+    pdf.savefig(fig)
+    plt.close(fig)
+
+def plot_data(plt, pdf, df, columns, title, xlabel, ylabels, colors, avg_label, avg_colors):
+    """Plot the data from the DataFrame."""
+    fig, axes = plt.subplots(len(columns), 1, figsize=(WIDTH, HEIGHT), layout="tight")
+    for i in range(len(columns)):
+        if len(columns) == 1:
+            ax = axes
+        else:
+            ax = axes[i]
+        ax.plot(df.index, df.iloc[:, columns[i]], label=ylabels[i], color=colors[i])
+        if xlabel:
+            ax.set_xlabel(xlabel)
+        ax.set_ylabel(ylabels[i])
+        ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%y %H:%M'))
+        ax.xaxis.set_major_locator(mdates.AutoDateLocator())
+        if i < len(columns) - 1:
+            # Remove x-axis labels for all but the last subplot
+            ax.set_xticklabels([])
+        if avg_colors[i]:
+            # Calculate daily average and plot as dashed line
+            daily_avg = df.iloc[:, columns[i]].resample('D').mean()
+            ax.plot(daily_avg.index, daily_avg, label=f'{avg_label} {ylabels[i]}', color=avg_colors[i], linestyle='--', linewidth=2)
+
+        ax.legend()
+        ax.grid(True)
+
+        # Rotate x-axis labels for better readability
+        plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
+
+    fig.suptitle(title, fontsize=16)
+
+    pdf.savefig(fig)
+    plt.close(fig)
+
+
+# Create a PdfPages object to save the figures
+with PdfPages(pdf_file) as pdf:
+    title_page(plt, pdf, 'PV-Inverter 2024')
+
+    title_page(plt, pdf, 'Yearly Overview', 18)
+
+    plot_data(plt, pdf, combined_df, 
+              [COLUMNS['power'], COLUMNS['energytoday'], COLUMNS['energytotal']], 
+              'PV-Inverter', None, ['Power [W]', 'Energy today [Wh]', 'Energy total [Wh]'],
+              [COLORS['power'], COLORS['energytoday'], COLORS['energytotal']], 
+              'Daily average', [None, None, None])
+
+    title_page(plt, pdf, 'Monthly Reports', 18)
+
+    # Group the data by month
+    combined_df['Month'] = combined_df.index.to_period('M')
+
+    # Create a separate figure for each month
+    for month, month_df in combined_df.groupby('Month'):
+        title_page(plt, pdf, month, 16)
+
+        plot_data(plt, pdf, month_df,
+                  [COLUMNS['power'], COLUMNS['energytoday'], COLUMNS['energytotal']],
+                  f'PV-Inverter {month}', None, ['Power [W]', 'Energy today [Wh]', 'Energy total [Wh]'],
+                  [COLORS['power'], COLORS['energytoday'], COLORS['energytotal']],
+                  'Daily average', [None, None, None])