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ADD: Adding example for bringing BNF data sources together for plotti…
…ng (#881) * ADD: Adding example for bringing BNF data sources together for plotting * ENH: Updating plot a little * ENH: Updating plotting and error handling * ENH: Updating error * ENH: updating error handling * ENH: error handling * ENH: Error handling * ENH: adding additional catch * ENH: Removing error checking so we don't reveal secrets
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| """ | ||
| Consolidation of Data Sources | ||
| ----------------------------- | ||
| This example shows how to use ACT to combine multiple | ||
| datasets to support ARM's AMF3. | ||
| """ | ||
|
|
||
| import act | ||
| from datetime import datetime | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| import os | ||
|
|
||
| # Get Surface Meteorology data from the ASOS stations | ||
| station = '1M4' | ||
| time_window = [datetime(2024, 10, 19), datetime(2024, 10, 24)] | ||
| ds_asos = act.discovery.get_asos_data(time_window, station=station, regions='AL')[station] | ||
| ds_asos = ds_asos.where(~np.isnan(ds_asos.tmpf), drop=True) | ||
| ds_asos['tmpf'].attrs['units'] = 'degF' | ||
| ds_asos.utils.change_units(variables='tmpf', desired_unit='degC', verbose=True) | ||
|
|
||
| # Pull EPA data from AirNow | ||
| # You need an account and token from https://docs.airnowapi.org/ first | ||
| airnow_token = os.getenv('AIRNOW_API') | ||
| if airnow_token is not None and len(airnow_token) > 0: | ||
| latlon = '-87.453,34.179,-86.477,34.787' | ||
| ds_airnow = act.discovery.get_airnow_bounded_obs( | ||
| airnow_token, '2024-10-19T00', '2024-10-24T23', latlon, 'OZONE,PM25', data_type='B' | ||
| ) | ||
| ds_airnow = act.utils.convert_2d_to_1d(ds_airnow, parse='sites') | ||
| sites = ds_airnow['sites'].values | ||
| airnow = True | ||
|
|
||
| # Get NOAA PSL Data from Courtland | ||
| results = act.discovery.download_noaa_psl_data( | ||
| site='ctd', instrument='Temp/RH', startdate='20241019', enddate='20241024' | ||
| ) | ||
| ds_noaa = act.io.read_psl_surface_met(results) | ||
|
|
||
| # Place your username and token here | ||
| username = os.getenv('ARM_USERNAME') | ||
| token = os.getenv('ARM_PASSWORD') | ||
|
|
||
| # Download ARM data for the MET, OZONE, and SMPS | ||
| if username is not None and token is not None and len(username) > 1: | ||
| # Example to show how easy it is to download ARM data if a username/token are set | ||
| results = act.discovery.download_arm_data( | ||
| username, token, 'bnfmetM1.b1', '2024-10-19', '2024-10-24' | ||
| ) | ||
| ds_arm = act.io.arm.read_arm_netcdf(results) | ||
|
|
||
| results = act.discovery.download_arm_data( | ||
| username, token, 'bnfaoso3M1.b1', '2024-10-19', '2024-10-24' | ||
| ) | ||
| ds_o3 = act.io.arm.read_arm_netcdf(results, cleanup_qc=True) | ||
| ds_o3.qcfilter.datafilter('o3', rm_assessments=['Suspect', 'Bad'], del_qc_var=False) | ||
|
|
||
| results = act.discovery.download_arm_data( | ||
| username, token, 'bnfaossmpsM1.b1', '2024-10-19', '2024-10-24' | ||
| ) | ||
| ds_smps = act.io.arm.read_arm_netcdf(results) | ||
|
|
||
| # Set up display and plot all the data | ||
| display = act.plotting.TimeSeriesDisplay( | ||
| {'ASOS': ds_asos, 'ARM': ds_arm, 'EPA': ds_airnow, 'NOAA': ds_noaa, 'ARM_O3': ds_o3}, | ||
| figsize=(12, 10), | ||
| subplot_shape=(3,), | ||
| ) | ||
| # Plot surface temperature from ASOS, NOAA, and ARM sites | ||
| title = 'Comparison of ARM MET, NOAA Courtland, and Haleyville ASOS Station' | ||
| display.plot('tmpf', dsname='ASOS', label='ASOS', subplot_index=(0,)) | ||
| display.plot('Temperature', dsname='NOAA', label='NOAA', subplot_index=(0,)) | ||
| display.plot('temp_mean', dsname='ARM', label='ARM', subplot_index=(0,), set_title=title) | ||
| display.day_night_background(dsname='ARM', subplot_index=(0,)) | ||
|
|
||
| # Plot ARM and EPA Ozone data | ||
| title = 'Comparison of ARM and EPA Ozone Measurements' | ||
| display.plot('o3', dsname='ARM_O3', label='ARM', subplot_index=(1,)) | ||
| if airnow: | ||
| display.plot('OZONE_sites_1', dsname='EPA', label='EPA' + sites[1], subplot_index=(1,)) | ||
| display.plot( | ||
| 'OZONE_sites_2', | ||
| dsname='EPA', | ||
| label='EPA' + sites[2], | ||
| subplot_index=(1,), | ||
| set_title=title, | ||
| ) | ||
| display.set_yrng([0, 70], subplot_index=(1,)) | ||
| display.day_night_background(dsname='ARM', subplot_index=(1,)) | ||
|
|
||
| # Plot ARM SMPS Concentrations and EPA PM2.5 data on different axes | ||
| title = 'ARM SMPS Concentrations and EPA PM2.5' | ||
| if airnow: | ||
| display.plot('PM2.5_sites_0', dsname='EPA', label='EPA ' + sites[0], subplot_index=(2,)) | ||
| display.plot( | ||
| 'PM2.5_sites_2', | ||
| dsname='EPA', | ||
| label='EPA ' + sites[2], | ||
| subplot_index=(2,), | ||
| set_title=title, | ||
| ) | ||
| display.set_yrng([0, 25], subplot_index=(2,)) | ||
| ax2 = display.axes[2].twinx() | ||
| ax2.plot(ds_smps['time'], ds_smps['total_N_conc'], label='ARM SMPS', color='purple') | ||
| ax2.set_ylabel('ARM SMPS (' + ds_smps['total_N_conc'].attrs['units'] + ')') | ||
| ax2.set_ylim([0, 7000]) | ||
| ax2.legend(loc=1) | ||
| display.day_night_background(dsname='ARM', subplot_index=(2,)) | ||
|
|
||
| # Set legends | ||
| for ax in display.axes: | ||
| ax.legend(loc=2) | ||
|
|
||
| plt.show() | ||
| else: | ||
| pass |
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