deploy: ad39cf0

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 8e7163b5b3794a6ee80f01233ccf82a6
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/1079a3390f344e04cc17426a5c8b1192/get_3_tropomicoards.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Get COARDS formatted NetCDF TropOMI NO2\n\nShows how to get TropOMI as an xarray Dataset. This example downloads a NetCDF\nfile with COARDS compliant metadata, which is opened and returned.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# sphinx_gallery_thumbnail_path = '_static/tropomicoards.png'\n\nimport pyrsig\n\nrsigapi = pyrsig.RsigApi(bdate='2022-03-01', overwrite=True)\nds = rsigapi.to_netcdf('tropomi.offl.no2.nitrogendioxide_tropospheric_column')\nprint(ds.data_vars)\n# Data variables:\n#     longitude                            (points) float32 ...\n#     latitude                             (points) float32 ...\n#     nitrogendioxide_tropospheric_column  (points) float32 ...\n#     Longitude_SW                         (points) float32 ...\n#     Longitude_SE                         (points) float32 ...\n#     Longitude_NW                         (points) float32 ...\n#     Longitude_NE                         (points) float32 ...\n#     Latitude_SW                          (points) float32 ...\n#     Latitude_SE                          (points) float32 ...\n#     Latitude_NW                          (points) float32 ...\n#     Latitude_NE                          (points) float32 ...\n#     yyyyddd                              (points) int32 ...\n#     hhmmss                               (points) int32 ...\n#     time                                 (points) datetime64[ns] ...\nrsigapi = pyrsig.RsigApi(bdate='2022-03-02', overwrite=True)\nds = rsigapi.to_netcdf(\n    'tropomi.offl.no2.nitrogendioxide_tropospheric_column', grid=True\n)\nprint(ds.data_vars)\n# Data variables:\n#     column     (points) int32 ...\n#     row        (points) int32 ...\n#     count      (points) int32 ...\n#     longitude  (points) float32 ...\n#     latitude   (points) float32 ...\n#     no2        (points) float32 ...\n#     time       (points) datetime64[ns] ..."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/1722a1f3ace8b29cc668749d93d92a57/plot_nyc.py

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+"""
+NYC VIIRS AOD vs TropOMI NO2
+============================
+
+Timeseries comparison of VIIRS AOD and TropOMI in NYC.
+"""
+
+import matplotlib.pyplot as plt
+import pyrsig
+import os
+
+os.makedirs('nyc', exist_ok=True)
+
+# Create an RSIG api isntance
+# Define a Time and Space Scope: here end of February around Phoenix
+rsigapi = pyrsig.RsigApi(
+    bdate='2022-01-20', edate='2022-01-25',
+    bbox=(-74.8, 40.32, -71.43, 41.4), workdir='nyc'
+)
+
+# Get TropOMI NO2
+tomino2df = rsigapi.to_dataframe(
+    'tropomi.offl.no2.nitrogendioxide_tropospheric_column',
+    unit_keys=False, parse_dates=True
+)
+
+# Get VIIRS AOD
+viirsaoddf = rsigapi.to_dataframe(
+    'viirsnoaa.jrraod.AOD550', unit_keys=False, parse_dates=True
+)
+
+# Create spatial medians for TropOMI and AQS
+tomids = (
+    tomino2df.groupby('time').median(numeric_only=True)['nitrogendioxide_tropospheric_column']
+)
+viirsds = (
+    viirsaoddf.groupby('time').median(numeric_only=True)['AOD550']
+)
+
+# Create axes with shared x
+fig, ax = plt.subplots(figsize=(12, 4),
+                       gridspec_kw=dict(bottom=0.25, left=0.15, right=0.95))
+ax.tick_params(axis='x', labelrotation = 90)
+tax = ax.twinx()
+
+# Add VIIRS AOD
+ax.plot(viirsds.index.values, viirsds.values, marker='o', color='g')
+
+# Add TropOMI NO2
+tax.plot(tomids.index.values, tomids.values, marker='s', color='r')
+
+# Configure axes
+ax.set(ylabel='VIIRS AOD')
+tax.set(ylim=(0, 1.7e16), ylabel='TropOMI NO2 molecules/cm$^2$')
+
+plt.show()
+# Or save out figure
+# fig.savefig('nyc.png')

_downloads/1a06601ce4b68892b2b3938fb90fa85c/plot_nyc.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# NYC VIIRS AOD vs TropOMI NO2\n\nTimeseries comparison of VIIRS AOD and TropOMI in NYC.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pyrsig\nimport os\n\nos.makedirs('nyc', exist_ok=True)\n\n# Create an RSIG api isntance\n# Define a Time and Space Scope: here end of February around Phoenix\nrsigapi = pyrsig.RsigApi(\n    bdate='2022-01-20', edate='2022-01-25',\n    bbox=(-74.8, 40.32, -71.43, 41.4), workdir='nyc'\n)\n\n# Get TropOMI NO2\ntomino2df = rsigapi.to_dataframe(\n    'tropomi.offl.no2.nitrogendioxide_tropospheric_column',\n    unit_keys=False, parse_dates=True\n)\n\n# Get VIIRS AOD\nviirsaoddf = rsigapi.to_dataframe(\n    'viirsnoaa.jrraod.AOD550', unit_keys=False, parse_dates=True\n)\n\n# Create spatial medians for TropOMI and AQS\ntomids = (\n    tomino2df.groupby('time').median(numeric_only=True)['nitrogendioxide_tropospheric_column']\n)\nviirsds = (\n    viirsaoddf.groupby('time').median(numeric_only=True)['AOD550']\n)\n\n# Create axes with shared x\nfig, ax = plt.subplots(figsize=(12, 4),\n                       gridspec_kw=dict(bottom=0.25, left=0.15, right=0.95))\nax.tick_params(axis='x', labelrotation = 90)\ntax = ax.twinx()\n\n# Add VIIRS AOD\nax.plot(viirsds.index.values, viirsds.values, marker='o', color='g')\n\n# Add TropOMI NO2\ntax.plot(tomids.index.values, tomids.values, marker='s', color='r')\n\n# Configure axes\nax.set(ylabel='VIIRS AOD')\ntax.set(ylim=(0, 1.7e16), ylabel='TropOMI NO2 molecules/cm$^2$')\n\nplt.show()\n# Or save out figure\n# fig.savefig('nyc.png')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/23e0056b2581a790873eefe981e48542/get_5_purpleairpm.py

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+"""
+Get DataFrame for PurpleAir PM25
+================================
+
+Shows how to get PurpleAir measurements as a DataFrame. PurpleAir, unlike other
+coverages, requires an api_key that you must get from PurpleAir.
+"""
+
+# sphinx_gallery_thumbnail_path = '_static/purpleairpm.png'
+
+import pyrsig
+
+rsigapi = pyrsig.RsigApi(bdate='2022-03-01')
+rsigapi.purpleair_kw['api_key'] = '<put your api key here>'
+df = rsigapi.to_dataframe('purpleair.pm25_corrected')

_downloads/2ee99caa0f4848f38343be8075158f79/plot_cmaqpandora.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# CMAQ vs Pandora\n\nTimeseries comparison of NO2 columns from CMAQ and Pandora over a small window\nover New York/New Jersey for 2018-07-01.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pyrsig\n\n\nbbox = (-74.5, 40., -73.5, 41)\ncmaqkey = 'cmaq.equates.conus.integrated.NO2_COLUMN'\ndatakey = 'pandora.L2_rnvs3p1_8.nitrogen_dioxide_vertical_column_amount'\n# Or use TropOMI or any other NO2 columns data\n# datakey = 'tropomi.offl.no2.nitrogendioxide_tropospheric_column'\n\n# Get a CMAQ file from RSIG\napi = pyrsig.RsigApi(\n    bbox=bbox, bdate='2018-07-01T12', edate='2018-07-01T23:59:59'\n)\nds = api.to_ioapi(cmaqkey)\n\n# pair_rsigcmaq will match the CMAQ bbox, bdate, and edate\ndf = pyrsig.cmaq.pair_rsigcmaq(ds, 'NO2_COLUMN', datakey)\n\npdf = df.groupby(['time']).mean(numeric_only=True)\nz1 = pdf['nitrogen_dioxide_vertical_column_amount']\nz2 = (pdf['CMAQ_NO2_COLUMN'] * 1e15)\nax = z1.plot(marker='+', linestyle='none', label='Pandora')\nax = z2.plot(ax=ax, color='green', label='CMAQ')\nax.legend()\nax.set(ylim=(0, None), ylabel='NO2 [molecules/cm2]', xlabel='Time [UTC]')\nax.figure.savefig('cmaq_pandora.png')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/4935b96615792edd8dbdbf9c7eb4cffb/get_5_purpleairpm.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Get DataFrame for PurpleAir PM25\n\nShows how to get PurpleAir measurements as a DataFrame. PurpleAir, unlike other\ncoverages, requires an api_key that you must get from PurpleAir.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# sphinx_gallery_thumbnail_path = '_static/purpleairpm.png'\n\nimport pyrsig\n\nrsigapi = pyrsig.RsigApi(bdate='2022-03-01')\nrsigapi.purpleair_kw['api_key'] = '<put your api key here>'\ndf = rsigapi.to_dataframe('purpleair.pm25_corrected')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/4a24e9f33b8a80bdc0c720dad6cd2193/get_1_aqsno2.py

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+"""
+Get DataFrame for AQS ozone
+===========================
+
+Shows how to get AQS observations as a Pandas DataFrame. Replace aqs.ozone
+with airnow.ozone to get near-real-time data.
+"""
+
+import pyrsig
+
+# sphinx_gallery_thumbnail_path = '_static/aqsozone.png'
+
+rsigapi = pyrsig.RsigApi(bdate='2022-03-01')
+print([k for k in rsigapi.keys() if 'ozone' in k])
+# ['airnow.ozone', 'airnow2.ozone', 'aqs.ozone', 'aqs.ozone_8hour_average', 'aqs.ozone_daily_8hour_maximum', 'pandora.ozone']
+df = rsigapi.to_dataframe('aqs.ozone')
+print(df.shape, *df.columns)
+# (26760, 6) Timestamp(UTC) LONGITUDE(deg) LATITUDE(deg) STATION(-) ozone(ppb) SITE_NAME

_downloads/51a34c617be1f995d3fa70f4d9921c61/plot_shapefile.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# GIS TropOMI Processing\n\nGet TropOMI from RSIG and save as GeoJSON and shapefiles.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport geopandas as gpd\nfrom shapely import polygons\nimport pyrsig\nimport pycno\n\n\ncoordkeys = [\n    'Longitude_SW(deg)', 'Latitude_SW(deg)',\n    'Longitude_SE(deg)', 'Latitude_SE(deg)',\n    'Longitude_NE(deg)', 'Latitude_NE(deg)',\n    'Longitude_NW(deg)', 'Latitude_NW(deg)',\n    'Longitude_SW(deg)', 'Latitude_SW(deg)',\n]\n\ncno = pycno.cno()\n\n# Retrieve data from RSIG (or cache)\ndatakey = \"tropomi.offl.no2.nitrogendioxide_tropospheric_column\"\nbdate = \"2023-07-23\"\nbbox = (-75, 40, -69, 46)\napi = pyrsig.RsigApi(bbox=bbox)\n# Either ascii or xdr backend works, xdr is faster\ntropdf = api.to_dataframe(datakey, bdate=bdate, backend='xdr')\ngeom = polygons(tropdf[coordkeys].values.reshape(-1, 5, 2))\ngtropdf = gpd.GeoDataFrame(\n    tropdf.drop(columns=coordkeys), geometry=geom, crs=4326\n)\n\n# Make Plot\ncol = 'nitrogendioxide_tropospheric_column(molecules/cm2)'\nfig, ax = plt.subplots(figsize=(4, 4), dpi=300)\ngtropdf.plot(col, edgecolor=\"face\", linewidth=0.1, legend=True, ax=ax)\ncno.drawstates(ax=ax, resnum=1)\nfig.savefig(f'{datakey}_{bdate}.png')\n\n# Save as a GIS Format\ngtropdf.to_file(f'{datakey}_{bdate}.geojson')\n# Shapefiles prefer short names\ngtropdf.rename(columns={\n    'Timestamp(UTC)': 'time_utc',\n    'LATITUDE(deg)': 'lat_center',\n    'LONGITUDE(deg)': 'lon_center',\n    col: 'no2_trop',\n}).to_file(f'{datakey}_{bdate}.shp')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/55b848cb92e1312f56c1838bd20b5330/plot_hmssmoke.py

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+"""
+Plot Smoke Polygons
+===================
+
+Get HMS Smoke from RSIG and create daily plots.
+"""
+import matplotlib.pyplot as plt
+import matplotlib.colors as mc
+import pandas as pd
+import pycno
+import pyrsig
+
+
+cno = pycno.cno()
+api = pyrsig.RsigApi()
+
+# %%
+# Retrieve data from RSIG (or cache)
+# ----------------------------------
+hmsfs = []
+dates = pd.date_range('2023-06-12', '2023-06-15')
+for bdate in dates:
+    hmsf = api.to_dataframe("hms.smoke", bdate=bdate)
+    hmsfs.append(hmsf)
+
+hmsf = pd.concat(hmsfs)
+
+# %%
+# Make Plot
+# ---------
+
+# set up colormap and normalization
+levels = [0, 7.5, 20, 35]
+colors = ['green', 'yellow', 'red']
+cmap, norm = mc.from_levels_and_colors(levels, colors)
+
+# create multipanel figure
+gskw = dict(left=0.05, right=0.915, bottom=0.05, top=0.9)
+fig, axx = plt.subplots(
+    2, 2, figsize=(11, 8), gridspec_kw=gskw, sharex=True, sharey=True
+)
+# add axes for colorbar
+cax = fig.add_axes([0.925, 0.1, 0.025, 0.8])
+
+# add maps to each panel
+for di, date in enumerate(dates):
+    jdate = date.strftime('%Y%j')
+    ax = axx.ravel()[di]
+    plotf = hmsf.query(f'YYYYDDD1 == {jdate}').sort_values(['DENS_UGM3'])
+    plotf.plot('DENS_UGM3', cmap=cmap, norm=norm, ax=ax, aspect=None)
+    topts = dict(size=16, transform=ax.transAxes, bbox=dict(facecolor='white'))
+    ax.text(0.02, 0.04, date.strftime('%F'), **topts)
+    cno.drawstates(ax=ax)
+
+# set extent to data extent
+ax.set(xlim=api.bbox[::2], ylim=api.bbox[1::2])
+# add colorbar with categories
+cb = fig.colorbar(ax.collections[0], cax=cax, ticks=[3.25, 13.75, 27.5])
+cb.ax.set_yticklabels(
+    ['Light', 'Medium', 'Heavy'], rotation=90, verticalalignment='center',
+    size=16
+)
+fig.suptitle('HMS Smoke Qualitative Categories from GOES', size=20)
+fig.savefig('hms_smoke.png')