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@matthias-bs
matthias-bs committed Sep 4, 2024
1 parent 98ce23a commit 29ec3ef
Showing 1 changed file with 129 additions and 73 deletions.
202 changes: 129 additions & 73 deletions extras/reports/datacake_report.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
###################################################################################################
# datacake_report.py
# datacake_report-2.py
#
# This script generates a PDF report from CSV files containing sensor data
# which are provided by Datacake.
Expand Down Expand Up @@ -45,22 +45,84 @@
import matplotlib.dates as mdates
from matplotlib.backends.backend_pdf import PdfPages

LOCATION = '<Location>'

# Set the desired language
LANG = 'en'

# Directory containing the CSV files
src_dir = "datacake_csv"

# Output PDF file
pdf_file = "weather_report.pdf"

# Define the last column in the CSV files
last_column = 11

COLUMNS = {
'ws_temp_c': 0,
'ws_humidity': 1,
'soil_temp_c': 10,
'soil_moisture': 9,
'rain_mm': 6,
'hot_water_temp_c': 11,
'wind_avg': 12,
'wind_gust': 14,
'wind_direction': 13
'ws_humidity': 6,
'rain_mm': 8,
'wind_avg': 5,
'wind_gust': 9,
'wind_direction': 10,
'battery_voltage': 11,
}

# Directory containing the CSV files
src_dir = "/home/mp/pCloudDrive/datacake/Garten"
pdf_file = "/home/mp/pCloudDrive/datacake/Garten/garten.pdf"
last_column = 16
COLORS = {
'ws_temp_c': 'r',
'ws_humidity': 'skyblue',
'rain_mm': 'b',
'wind_avg': 'g',
'wind_gust': 'lime',
'wind_direction': 'gray',
'battery_voltage': 'orange',
}


# Define translations
translations = {
'en': {
'Weather Report': 'Weather Report',
'Annual Overview': 'Annual Overview',
'Air Temperature and Humidity': 'Air Temperature and Humidity',
'Temperature [°C]': 'Temperature [°C]',
'Relative Humidity [%]': 'Relative Humidity [%]',
'Daily Avg.': 'Daily Avg.',
'Rain': 'Rain',
'Rain [mm]': 'Rain [mm]',
'Rain [mm] (Rain Gauge)': 'Rain [mm] (Rain Gauge)',
'Wind': 'Wind',
'Average [m/s]': 'Average [m/s]',
'Gusts [m/s]': 'Gusts [m/s]',
'Direction [°]': 'Direction [°]',
'Battery Voltage': 'Battery Voltage',
'Voltage [mV]': 'Voltage [mV]',
'Monthly Reports': 'Monthly Reports'
},
'de': {
'Weather Report': 'Wetterbericht',
'Annual Overview': 'Jahresübersicht',
'Air Temperature and Humidity': 'Luftemperatur und -feuchte',
'Temperature [°C]': 'Temperatur [°C]',
'Relative Humidity [%]': 'rel. Feuchte [%]',
'Daily Avg.': 'tägl. Durchschnitt',
'Rain': 'Regen',
'Rain [mm]': 'Regen [mm]',
'Rain [mm] (Rain Gauge)': 'Regen [mm] (Regenmesser)',
'Wind': 'Wind',
'Average [m/s]': 'Durchschnitt [m/s]',
'Gusts [m/s]': 'Böen [m/s]',
'Direction [°]': 'Richtung [°]',
'Battery Voltage': 'Batteriespannung',
'Voltage [mV]': 'Spannung [mV]',
'Monthly Reports': 'Monatsberichte'
}
}

def tr(text):
return translations.get(LANG, {}).get(text, text)


# List all CSV files in the directory
csv_files = [f for f in os.listdir(src_dir) if f.endswith('.csv')]
Expand All @@ -74,7 +136,7 @@
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 in the second column and convert to float
# Replace comma with dot and convert to float
df.iloc[:, col] = df.iloc[:, col].astype(float)

dataframes.append(df)
Expand Down Expand Up @@ -119,6 +181,9 @@ def plot_data(plt, pdf, df, columns, title, xlabel, ylabels, colors, avg_label,
#ax.tick_params(axis='y', labelcolor=colors[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]:
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)
Expand All @@ -130,7 +195,6 @@ def plot_data(plt, pdf, df, columns, title, xlabel, ylabels, colors, avg_label,
plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)

fig.suptitle(title, fontsize=16)
#plt.tight_layout()

pdf.savefig(fig)
plt.close(fig)
Expand Down Expand Up @@ -167,9 +231,6 @@ def plot_rain(plt, pdf, df_diff, df, column, title, xlabel, ylabel, color):
plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)
plt.setp(ax2.xaxis.get_majorticklabels(), rotation=45)

# Adjust layout to prevent clipping of tick-labels
#plt.tight_layout()

# Set the title of the figure
fig.suptitle(title, fontsize=16)

Expand All @@ -179,19 +240,20 @@ def plot_rain(plt, pdf, df_diff, df, column, title, xlabel, ylabel, color):
# Close the figure to free up memory
plt.close(fig)

# Create a new figure for Rain Gauge
# Create a new figure for Wind
def plot_wind(plt, pdf, df, columns, title, xlabel, ylabel, colors):
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), layout="tight")

# Plot the Rain Gauge differences as a bar graph
ax1.plot(df.index, df.iloc[:, columns[1]], label=ylabel[2], color=colors[1], linewidth=2)
ax1.plot(df.index, df.iloc[:, columns[0]], label=ylabel[1], color=colors[0], linewidth=2)
# Plot average and gust wind speed on the first subplot
ax1.plot(df.index, df.iloc[:, columns[1]], label=ylabel[1], color=colors[1], linewidth=2)
ax1.plot(df.index, df.iloc[:, columns[0]], label=ylabel[0], color=colors[0], linewidth=2)
if xlabel:
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel[0])
#ax.tick_params(axis='y', labelcolor='b')
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%y %H:%M'))
ax1.xaxis.set_major_locator(mdates.AutoDateLocator())
ax1.set_xticklabels([])
#ax1.set_xticklabels([])
ax1.legend()
ax1.grid(True)
Expand All @@ -202,12 +264,13 @@ def plot_wind(plt, pdf, df, columns, title, xlabel, ylabel, colors):
#v = columns[0] * np.sin(df.iloc[:, columns[2]]/180*np.pi)
#ax2.quiver(df.index, [0] * len(df.index), u, v, color=colors[2])
hourly_avg = df.iloc[:, columns[2]].resample('H').mean()
ax2.plot(hourly_avg.index, hourly_avg, label=ylabel[3], color=colors[2])
ax2.plot(hourly_avg.index, hourly_avg, label=ylabel[2], color=colors[2])
if xlabel:
ax2.set_xlabel(xlabel)
ax2.set_ylabel(ylabel[3])
ax2.set_ylabel(ylabel[2])
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%y %H:%M'))
ax2.xaxis.set_major_locator(mdates.AutoDateLocator())


ax2.legend()
ax2.grid(True)
Expand All @@ -227,42 +290,38 @@ def plot_wind(plt, pdf, df, columns, title, xlabel, ylabel, colors):

# Create a PdfPages object to save the figures
with PdfPages(pdf_file) as pdf:
title_page(plt, pdf, 'Wetterbericht Lehmanger, Garten 95')
title_page(plt, pdf, tr('Weather Report') + f' {LOCATION}')

title_page(plt, pdf, 'Jahresübersicht', 18)
title_page(plt, pdf, tr('Annual Overview'), 18)

plot_data(plt, pdf, combined_df,
[COLUMNS['ws_temp_c'], COLUMNS['ws_humidity']],
'Luftemperatur und -feuchte', None, ['Temperatur [°C]', 'rel. Feuchte [%]'],
['r', 'b'],
'tägl. Durchschnitt', ['k', 'k'])

# Filter out rows where soil moisture is zero
soil_df = combined_df[combined_df.iloc[:, COLUMNS['soil_moisture']] != 0]
plot_data(plt, pdf, soil_df,
[COLUMNS['soil_temp_c'], COLUMNS['ws_humidity']],
'Bodentemperatur und -feuchte', None, ['Temperatur [°C]', 'Feuchte [%]'],
['m', 'g'],
'tägl. Durchschnitt', ['k', None])
tr('Air Temperature and Humidity'), None,
[tr('Temperature [°C]'), tr('Relative Humidity [%]')],
[COLORS['ws_temp_c'], COLORS['ws_humidity']],
tr('Daily Avg.'), ['k', 'k'])

# Calculate the difference between consecutive values in column "Rain Gauge"
rain_df = combined_df.iloc[:, COLUMNS['rain_mm']].diff().fillna(0)
rain_df = rain_df[rain_df >= 0]
plot_rain(plt, pdf, rain_df, combined_df, COLUMNS['rain_mm'],
'Niederschlag', None, ['Niederschlag [mm]', 'Niederschlag [mm] (Regenmesser)'],
'b')

plot_data(plt, pdf, combined_df, [COLUMNS['hot_water_temp_c']],
'Warmwasser', None, ['Temperatur [°C]'],
['orange'], 'tägl. Durchschnitt',
['k'])
rain_df = combined_df[combined_df.iloc[:, COLUMNS['rain_mm']] != 0]
rain_diff_df = rain_df.iloc[:, COLUMNS['rain_mm']].diff().fillna(0)
#rain_diff_df = rain_df[rain_df >= 0]
plot_rain(plt, pdf, rain_diff_df, rain_df, COLUMNS['rain_mm'],
tr('Rain'), None,
[tr('Rain [mm]'), tr('Rain [mm] (Rain Gauge)')],
COLORS['rain_mm'])

plot_wind(plt, pdf, combined_df,
[COLUMNS['wind_avg'], COLUMNS['wind_gust'], COLUMNS['wind_direction']],
'Wind', None, ['Wind [m/s]', 'Durchschnitt [m/s]', 'Böen [m/s]', 'Richtung [°]'],
['g', 'b', 'k'])
tr('Wind'), None,
[tr('Average [m/s]'), tr('Gusts [m/s]'), tr('Direction [°]')],
[COLORS['wind_avg'], COLORS['wind_gust'], COLORS['wind_direction']])

plot_data(plt, pdf, combined_df,
[COLUMNS['battery_voltage']],
tr('Battery Voltage'), None, [tr('Voltage [mV]')],
[COLORS['battery_voltage']], tr('Daily Avg.'), [None])

title_page(plt, pdf, 'Monatsberichte', 18)
title_page(plt, pdf, tr('Monthly Reports'), 18)

# Group the data by month
combined_df['Month'] = combined_df.index.to_period('M')
Expand All @@ -273,32 +332,29 @@ def plot_wind(plt, pdf, df, columns, title, xlabel, ylabel, colors):

plot_data(plt, pdf, month_df,
[COLUMNS['ws_temp_c'], COLUMNS['ws_humidity']],
f'Luftemperatur und -feuchte {month}', None, ['Temperatur [°C]', 'rel. Feuchte [%]'],
['r', 'b'],
'tägl. Durchschnitt', ['k', 'k'])

# Filter out rows where soil moisture (column 9) is zero
soil_df = month_df[month_df.iloc[:, COLUMNS['soil_moisture']] != 0]
plot_data(plt, pdf, soil_df,
[COLUMNS['soil_temp_c'], COLUMNS['soil_moisture']], f'Bodentemperatur und -feuchte {month}', None, ['Temperatur [°C]', 'Feuchte [%]'],
['m', 'g'],
'tägl. Durchschnitt', ['k', None])
tr('Air Temperature and Humidity') + f' {month}', None,
[tr('Temperature [°C]'), tr('Relative Humidity [%]')],
[COLORS['ws_temp_c'], COLORS['ws_humidity']],
tr('Daily Avg.'), ['k', 'k'])

# Calculate the difference between consecutive values in column "Rain Gauge"
rain_df = month_df.iloc[:, COLUMNS['rain_mm']].diff().fillna(0)
rain_df = rain_df[rain_df >= 0]
plot_rain(plt, pdf, rain_df, month_df, COLUMNS['rain_mm'],
f'Niederschlag {month}', None, ['Niederschlag [mm]', 'Niederschlag [mm] (Regenmesser)'],
'b')

plot_data(plt, pdf, month_df,
[COLUMNS['hot_water_temp_c']], f'Warmwasser {month}', None, ['Temperatur [°C]'],
['orange'],
'tägl. Durchschnitt',
['k'])

#rain_df = month_df.iloc[:, COLUMNS['rain_mm']].diff().fillna(0)
#rain_df = rain_df[rain_df > 0]
rain_df = month_df[month_df.iloc[:, COLUMNS['rain_mm']] != 0]
rain_diff_df = rain_df.iloc[:, COLUMNS['rain_mm']].diff().fillna(0)
plot_rain(plt, pdf, rain_diff_df, rain_df, COLUMNS['rain_mm'],
tr('Rain') + f' {month}', None,
[tr('Rain [mm]'), tr('Rain [mm] (Rain Gauge)')],
COLORS['rain_mm'])

plot_wind(plt, pdf, month_df,
[COLUMNS['wind_avg'], COLUMNS['wind_gust'], COLUMNS['wind_direction']],
f'Wind {month}', None, ['Wind [m/s]', 'Durchschnitt [m/s]', 'Böen [m/s]', 'Richtung [°]'],
['g', 'b', 'k'])
tr('Wind') +f' {month}', None,
[tr('Average [m/s]'), tr('Gusts [m/s]'), tr('Direction [°]')],
[COLORS['wind_avg'], COLORS['wind_gust'], COLORS['wind_direction']])

plot_data(plt, pdf, month_df,
[COLUMNS['battery_voltage']],
tr('Battery Voltage'), None, [tr('Voltage [mV]')],
[COLORS['battery_voltage']], tr('Daily Avg.'), [None])

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