-
Notifications
You must be signed in to change notification settings - Fork 7
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #156 from nismod/p3_weather_final
P3 weather final
- Loading branch information
Showing
10 changed files
with
399 additions
and
347 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,191 @@ | ||
| import os | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
|
|
||
| from energy_demand.read_write import read_data, write_data, data_loader | ||
| from energy_demand.plotting import basic_plot_functions | ||
|
|
||
| print("START") | ||
| def plotting_weather_data(path): | ||
| """ | ||
| Things to plot | ||
| - annual t_min of all realizations | ||
| - annual t_max of all realizations | ||
| - annual maximum t_max | ||
| - annual minimum t_min | ||
| """ | ||
| sim_yrs = range(2015, 2051, 5) | ||
| weather_reationzations = ["NF{}".format(i) for i in range(1, 101,1)] | ||
|
|
||
| container_weather_stations = {} | ||
| container_temp_data = {} | ||
|
|
||
| # All used weather stations from model run | ||
| used_stations = ['station_id_253', 'station_id_252', 'station_id_253', 'station_id_252', 'station_id_252', 'station_id_328', 'station_id_329', 'station_id_305', 'station_id_282', 'station_id_335', 'station_id_335', 'station_id_359', 'station_id_358', 'station_id_309', 'station_id_388', 'station_id_418', 'station_id_420', 'station_id_389', 'station_id_433', 'station_id_385', 'station_id_374', 'station_id_481', 'station_id_481', 'station_id_480', 'station_id_466', 'station_id_531', 'station_id_532', 'station_id_535', 'station_id_535', 'station_id_484', 'station_id_421', 'station_id_472', 'station_id_526', 'station_id_525', 'station_id_526', 'station_id_504', 'station_id_503', 'station_id_504', 'station_id_505', 'station_id_504', 'station_id_504', 'station_id_455', 'station_id_548', 'station_id_546', 'station_id_537', 'station_id_545', 'station_id_236', 'station_id_353', 'station_id_352', 'station_id_384', 'station_id_510', 'station_id_527', 'station_id_550', 'station_id_501', 'station_id_456', 'station_id_472', 'station_id_201', 'station_id_470', 'station_id_487', 'station_id_505', 'station_id_486', 'station_id_457', 'station_id_533', 'station_id_458', 'station_id_441', 'station_id_440', 'station_id_473', 'station_id_217', 'station_id_247', 'station_id_199', 'station_id_232', 'station_id_234', 'station_id_478', 'station_id_248', 'station_id_388', 'station_id_377', 'station_id_376', 'station_id_376', 'station_id_388', 'station_id_354', 'station_id_376', 'station_id_388', 'station_id_515', 'station_id_514', 'station_id_514', 'station_id_531', 'station_id_532', 'station_id_494', 'station_id_512', 'station_id_535', 'station_id_535', 'station_id_517', 'station_id_534', 'station_id_533', 'station_id_549', 'station_id_549', 'station_id_550', 'station_id_549', 'station_id_508', 'station_id_490', 'station_id_507', 'station_id_526', 'station_id_508', 'station_id_491', 'station_id_507', 'station_id_489', 'station_id_509', 'station_id_526', 'station_id_545', 'station_id_492', 'station_id_490', 'station_id_451', 'station_id_467', 'station_id_450', 'station_id_451', 'station_id_466', 'station_id_451', 'station_id_521', 'station_id_538', 'station_id_537', 'station_id_537', 'station_id_522', 'station_id_546', 'station_id_536', 'station_id_522', 'station_id_520', 'station_id_537', 'station_id_488', 'station_id_487', 'station_id_488', 'station_id_472', 'station_id_487', 'station_id_487', 'station_id_551', 'station_id_544', 'station_id_419', 'station_id_525', 'station_id_552', 'station_id_525', 'station_id_543', 'station_id_542', 'station_id_552', 'station_id_543', 'station_id_544', 'station_id_542', 'station_id_542', 'station_id_306', 'station_id_305', 'station_id_282', 'station_id_306', 'station_id_406', 'station_id_264', 'station_id_306', 'station_id_283', 'station_id_284', 'station_id_306', 'station_id_305', 'station_id_304', 'station_id_283', 'station_id_418', 'station_id_403', 'station_id_419', 'station_id_418', 'station_id_403', 'station_id_402', 'station_id_392', 'station_id_379', 'station_id_391', 'station_id_422', 'station_id_404', 'station_id_379', 'station_id_443', 'station_id_444', 'station_id_445', 'station_id_423', 'station_id_469', 'station_id_425', 'station_id_444', 'station_id_461', 'station_id_438', 'station_id_437', 'station_id_439', 'station_id_438', 'station_id_455', 'station_id_454', 'station_id_438', 'station_id_284', 'station_id_268', 'station_id_286', 'station_id_266', 'station_id_288', 'station_id_270', 'station_id_333', 'station_id_389', 'station_id_378', 'station_id_389', 'station_id_389', 'station_id_377', 'station_id_390', 'station_id_403', 'station_id_469', 'station_id_485', 'station_id_484', 'station_id_469', 'station_id_499', 'station_id_498', 'station_id_516', 'station_id_497', 'station_id_479', 'station_id_400', 'station_id_387', 'station_id_401', 'station_id_374', 'station_id_400', 'station_id_386', 'station_id_375', 'station_id_401', 'station_id_491', 'station_id_459', 'station_id_492', 'station_id_476', 'station_id_475', 'station_id_477', 'station_id_462', 'station_id_523', 'station_id_523', 'station_id_522', 'station_id_523', 'station_id_523', 'station_id_523', 'station_id_505', 'station_id_522', 'station_id_541', 'station_id_539', 'station_id_523', 'station_id_417', 'station_id_417', 'station_id_437', 'station_id_436', 'station_id_436', 'station_id_548', 'station_id_547', 'station_id_488', 'station_id_539', 'station_id_540', 'station_id_540', 'station_id_540', 'station_id_547', 'station_id_416', 'station_id_434', 'station_id_435', 'station_id_434', 'station_id_434', 'station_id_415', 'station_id_488', 'station_id_488', 'station_id_489', 'station_id_329', 'station_id_330', 'station_id_330', 'station_id_330', 'station_id_330', 'station_id_329', 'station_id_354', 'station_id_330', 'station_id_329', 'station_id_329', 'station_id_328', 'station_id_328', 'station_id_328', 'station_id_304', 'station_id_327', 'station_id_331', 'station_id_357', 'station_id_356', 'station_id_355', 'station_id_221', 'station_id_221', 'station_id_221', 'station_id_237', 'station_id_416', 'station_id_417', 'station_id_416', 'station_id_416', 'station_id_417', 'station_id_400', 'station_id_400', 'station_id_307', 'station_id_307', 'station_id_331', 'station_id_308', 'station_id_332', 'station_id_221', 'station_id_506', 'station_id_507', 'station_id_506', 'station_id_525', 'station_id_506', 'station_id_524', 'station_id_506', 'station_id_524', 'station_id_505', 'station_id_506', 'station_id_524', 'station_id_506', 'station_id_506', 'station_id_506', 'station_id_505', 'station_id_507', 'station_id_505', 'station_id_505', 'station_id_506', 'station_id_506', 'station_id_523', 'station_id_524', 'station_id_524', 'station_id_524', 'station_id_506', 'station_id_507', 'station_id_505', 'station_id_524', 'station_id_524', 'station_id_506', 'station_id_506', 'station_id_524', 'station_id_506', 'station_id_172', 'station_id_193', 'station_id_173', 'station_id_133', 'station_id_130', 'station_id_168', 'station_id_194', 'station_id_152', 'station_id_170', 'station_id_214', 'station_id_195', 'station_id_103', 'station_id_124', 'station_id_110', 'station_id_176', 'station_id_136', 'station_id_125', 'station_id_121', 'station_id_9', 'station_id_111', 'station_id_105', 'station_id_36', 'station_id_108', 'station_id_52', 'station_id_119', 'station_id_4', 'station_id_94', 'station_id_159', 'station_id_137', 'station_id_102', 'station_id_77', 'station_id_303', 'station_id_2', 'station_id_154', 'station_id_64', 'station_id_89', 'station_id_124', 'station_id_109', 'station_id_109', 'station_id_123', 'station_id_86', 'station_id_105', 'station_id_110', 'station_id_110', 'station_id_448', 'station_id_348', 'station_id_349', 'station_id_350', 'station_id_351', 'station_id_372', 'station_id_394', 'station_id_449', 'station_id_464', 'station_id_407', 'station_id_428', 'station_id_446', 'station_id_446', 'station_id_463', 'station_id_463', 'station_id_464', 'station_id_447', 'station_id_448', 'station_id_448', 'station_id_396', 'station_id_447'] | ||
|
|
||
| # Load full data | ||
| for weather_realisation in weather_reationzations: | ||
| print("weather_realisation: " + str(weather_realisation)) | ||
| path_weather_data = path | ||
|
|
||
| weather_stations, temp_data = data_loader.load_temp_data( | ||
| {}, | ||
| sim_yrs=sim_yrs, | ||
| weather_realisation=weather_realisation, | ||
| path_weather_data=path_weather_data, | ||
| same_base_year_weather=False, | ||
| crit_temp_min_max=True, | ||
| load_np=False, | ||
| load_parquet=False, | ||
| load_csv=True) | ||
|
|
||
| # Load only data from selected weather stations | ||
| temp_data_used = {} | ||
| for year in sim_yrs: | ||
| temp_data_used[year] = {} | ||
| all_station_data = temp_data[year].keys() | ||
| for station in all_station_data: | ||
| if station in used_stations: | ||
| temp_data_used[year][station] = temp_data[year][station] | ||
|
|
||
| container_weather_stations[weather_realisation] = weather_stations | ||
| container_temp_data[weather_realisation] = temp_data_used | ||
|
|
||
| # Create plot with daily min | ||
| print("... creating min max plot") | ||
| t_min_average_every_day = [] | ||
| t_max_average_every_day = [] | ||
| t_min_min_every_day = [] | ||
| t_max_max_every_day = [] | ||
| std_dev_t_min = [] | ||
| std_dev_t_max = [] | ||
| std_dev_t_min_min = [] | ||
| std_dev_t_max_max = [] | ||
|
|
||
| for year in sim_yrs: | ||
|
|
||
| for realization in container_weather_stations.keys(): | ||
| t_min_average_stations = [] | ||
| t_max_average_stations = [] | ||
| t_min_min_average_stations = [] | ||
| t_max_max_average_stations = [] | ||
| stations_data = container_temp_data[realization][year] | ||
| for station in stations_data.keys(): | ||
| t_min_annual_average = np.average(stations_data[station]['t_min']) | ||
| t_max_annual_average = np.average(stations_data[station]['t_max']) | ||
| t_min_min_stations = np.min(stations_data[station]['t_min']) | ||
| t_max_max_stations = np.max(stations_data[station]['t_max']) | ||
|
|
||
| t_min_average_stations.append(t_min_annual_average) #average cross all stations | ||
| t_max_average_stations.append(t_max_annual_average) #average cross all stations | ||
| t_min_min_average_stations.append(t_min_min_stations) | ||
| t_max_max_average_stations.append(t_max_max_stations) | ||
|
|
||
| av_t_min = np.average(t_min_average_stations) #average across all realizations | ||
| av_t_max = np.average(t_max_average_stations) #average across all realizations | ||
| av_min_t_min = np.average(t_min_min_average_stations) #average across all realizations | ||
| av_max_t_max = np.average(t_max_max_average_stations) #average across all realizations | ||
|
|
||
| std_t_min = np.std(t_min_average_stations) | ||
| std_t_max = np.std(t_max_average_stations) | ||
| std_t_min_min = np.std(t_min_min_average_stations) | ||
| std_t_max_max = np.std(t_max_max_average_stations) | ||
|
|
||
| t_min_average_every_day.append(av_t_min) | ||
| t_max_average_every_day.append(av_t_max) | ||
| t_min_min_every_day.append(av_min_t_min) | ||
| t_max_max_every_day.append(av_max_t_max) | ||
|
|
||
| std_dev_t_min.append(std_t_min) | ||
| std_dev_t_max.append(std_t_max) | ||
| std_dev_t_min_min.append(std_t_min_min) | ||
| std_dev_t_max_max.append(std_t_max_max) | ||
|
|
||
| # Plot variability | ||
| fig = plt.figure(figsize=basic_plot_functions.cm2inch(9, 6)) #width, height | ||
|
|
||
| colors = { | ||
| 't_min': 'steelblue', | ||
| 't_max': 'tomato', | ||
| 't_min_min': 'peru', | ||
| 't_max_max': 'r'} | ||
|
|
||
| # plot | ||
| plt.plot(sim_yrs, t_min_average_every_day, color=colors['t_min'], label="t_min") | ||
| plt.plot(sim_yrs, t_max_average_every_day, color=colors['t_max'], label="t_max") | ||
| plt.plot(sim_yrs, t_min_min_every_day, color=colors['t_min_min'], label="t_min_min") | ||
| plt.plot(sim_yrs, t_max_max_every_day, color=colors['t_max_max'], label="t_max_max") | ||
|
|
||
| # Variations | ||
| plt.fill_between( | ||
| sim_yrs, | ||
| list(np.array(t_min_average_every_day) - (2 * np.array(std_dev_t_min))), | ||
| list(np.array(t_min_average_every_day) + (2 * np.array(std_dev_t_min))), | ||
| color=colors['t_min'], | ||
| alpha=0.25) | ||
|
|
||
| plt.fill_between( | ||
| sim_yrs, | ||
| list(np.array(t_max_average_every_day) - (2 * np.array(std_dev_t_max))), | ||
| list(np.array(t_max_average_every_day) + (2 * np.array(std_dev_t_max))), | ||
| color=colors['t_max'], | ||
| alpha=0.25) | ||
|
|
||
| plt.fill_between( | ||
| sim_yrs, | ||
| list(np.array(t_min_min_every_day) - (2 * np.array(std_dev_t_min_min))), | ||
| list(np.array(t_min_min_every_day) + (2 * np.array(std_dev_t_min_min))), | ||
| color=colors['t_min_min'], | ||
| alpha=0.25) | ||
|
|
||
| plt.fill_between( | ||
| sim_yrs, | ||
| list(np.array(t_max_max_every_day) - (2 * np.array(std_dev_t_max_max))), | ||
| list(np.array(t_max_max_every_day) + (2 * np.array(std_dev_t_max_max))), | ||
| color=colors['t_max_max'], | ||
| alpha=0.25) | ||
|
|
||
| print("AAAAAAAA") | ||
| print(list(std_dev_t_max_max)) | ||
| print(list(np.array(t_max_max_every_day))) | ||
| print(list(np.array(t_max_max_every_day) - (2 * np.array(std_dev_t_max_max)))) | ||
| print("--") | ||
| print(list(std_dev_t_min_min)) | ||
| print(list(np.array(t_min_min_every_day))) | ||
| print(list(np.array(t_min_min_every_day) - (2 * np.array(std_dev_t_min_min)))) | ||
|
|
||
|
|
||
| # Legend | ||
| legend = plt.legend( | ||
| ncol=2, | ||
| prop={'size': 10}, | ||
| loc='upper center', | ||
| bbox_to_anchor=(0.5, -0.1), | ||
| frameon=False) | ||
| legend.get_title().set_fontsize(8) | ||
|
|
||
| result_path = "C:/_scrap/" | ||
| seperate_legend = True | ||
| if seperate_legend: | ||
| basic_plot_functions.export_legend( | ||
| legend, | ||
| os.path.join(result_path, "{}__legend.pdf".format(result_path))) | ||
| legend.remove() | ||
|
|
||
| plt.legend(ncol=2) | ||
| plt.xlabel("Year") | ||
| plt.ylabel("Temperature (°C)") | ||
|
|
||
| plt.tight_layout() | ||
| plt.margins(x=0) | ||
|
|
||
| fig.savefig(os.path.join(result_path, "test.pdf")) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| """ | ||
| """ | ||
| plotting_weather_data("//linux-filestore.ouce.ox.ac.uk/mistral/nismod/data/energy_demand/J-MARIUS_data/_weather_realisation") |
Oops, something went wrong.