done with convert data
This commit is contained in:
parent
9d143399ed
commit
33871fba77
75
read_data/convert_data.py
Normal file
75
read_data/convert_data.py
Normal file
@ -0,0 +1,75 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf-8
|
||||
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import csv
|
||||
|
||||
def generate_min_df(mins = 15):
|
||||
end = 60/mins * 24
|
||||
start_date = '2023-01-01'
|
||||
end_date = '2023-12-31'
|
||||
|
||||
all_dates = pd.date_range(start=start_date, end=end_date, freq='D')
|
||||
all_times = pd.timedelta_range(start='0 min', end=f'1435 min', freq=f'{mins} min')
|
||||
|
||||
date_times = [pd.Timestamp(date) + time for date in all_dates for time in all_times]
|
||||
|
||||
time_frame = pd.DataFrame({
|
||||
'Time': date_times
|
||||
})
|
||||
return time_frame
|
||||
|
||||
def save_csv(df, filename, columns):
|
||||
with open(filename, 'w', newline='') as file:
|
||||
writer = csv.writer(file)
|
||||
writer.writerow(['Time', 'PV yield[kW/kWp]'])
|
||||
for index, row in df.iterrows():
|
||||
time_formatted = index.strftime('%H:%M')
|
||||
writer.writerow([time_formatted, row[columns[1]]])
|
||||
|
||||
print(f'The file is written to {filename}')
|
||||
|
||||
def read_csv(filename):
|
||||
skip_rows = list(range(1, 17))
|
||||
data = pd.read_csv(filename, sep=';', skiprows=skip_rows)
|
||||
return data
|
||||
|
||||
def process(file_name):
|
||||
df = read_csv(file_name)
|
||||
city = file_name.split('_')[0]
|
||||
|
||||
remain_column = ['Time','PV energy (AC) minus standby use ']
|
||||
energy_row_name = remain_column[1]
|
||||
|
||||
df = df[remain_column]
|
||||
df[energy_row_name] = df[energy_row_name].str.replace(',','.').astype(float)
|
||||
|
||||
sum_energy = df[energy_row_name].sum()
|
||||
group_size = 15
|
||||
df['group_id'] = df.index // group_size
|
||||
|
||||
sums = df.groupby('group_id')[energy_row_name].sum()
|
||||
sums_df = sums.reset_index(drop=True).to_frame(name = 'Energy')
|
||||
|
||||
time_frame = generate_min_df(15)
|
||||
sums_df = pd.concat([time_frame, sums_df], axis=1)
|
||||
sums_df.set_index('Time', inplace=True)
|
||||
max_value = sums_df['Energy'].max()
|
||||
sums_df['Energy'] = sums_df['Energy'] / 390.
|
||||
sums_df['Energy'] = sums_df['Energy'].round(4)
|
||||
sums_df['Energy'].replace(0.0, -0.0)
|
||||
|
||||
save_csv(sums_df, f'{city}.csv', ['Time', 'Energy'])
|
||||
|
||||
if __name__ == '__main__':
|
||||
city_list = ['Riyahd', 'Cambodge', 'Berlin', 'Serbia']
|
||||
for city in city_list:
|
||||
print(f'Processing {city}')
|
||||
file_name = f'{city}_raw.csv'
|
||||
process(file_name)
|
||||
print(f'Processing {city} is done\n')
|
||||
|
Loading…
Reference in New Issue
Block a user