From b25daa20e1d6205c87e0b86191f660ae20508d74 Mon Sep 17 00:00:00 2001 From: zbuster05 Date: Wed, 12 Jan 2022 11:58:33 -0800 Subject: [PATCH] Fixed deps --- flaskapi.py | 17 + randomcrap/Untitled.ipynb | 33 + randomcrap/Untitled1.ipynb | 1100 + randomcrap/Untitled2.ipynb | 141 + randomcrap/Untitled3.ipynb | 53 + randomcrap/Untitled4.ipynb | 78 + randomcrap/cartopy.ipynb | 127 + randomcrap/chelsa.ipynb | 519 + randomcrap/frost_copy.py | 0 randomcrap/frost_test.py | 182 + randomcrap/gfs.ipynb | 1067 + randomcrap/matplot_lib.ipynb | 1334 ++ randomcrap/metpy.ipynb | 172 + randomcrap/operational.ipynb | 39727 +++++++++++++++++++++++++++++++++ randomcrap/operational.py | 188 + randomcrap/plotly.ipynb | 71 + randomcrap/pygrib.ipynb | 65 + randomcrap/test.ipynb | 300 + randomcrap/untitled.py | 33 + randomcrap/untitled1.py | 182 + randomcrap/untitled2.py | 0 randomcrap/working.ipynb | 700 + requirements.txt | 41 + 23 files changed, 46130 insertions(+) create mode 100644 flaskapi.py create mode 100644 randomcrap/Untitled.ipynb create mode 100644 randomcrap/Untitled1.ipynb create mode 100644 randomcrap/Untitled2.ipynb create mode 100644 randomcrap/Untitled3.ipynb create mode 100644 randomcrap/Untitled4.ipynb create mode 100644 randomcrap/cartopy.ipynb create mode 100644 randomcrap/chelsa.ipynb create mode 100644 randomcrap/frost_copy.py create mode 100644 randomcrap/frost_test.py create mode 100644 randomcrap/gfs.ipynb create mode 100644 randomcrap/matplot_lib.ipynb create mode 100644 randomcrap/metpy.ipynb create mode 100644 randomcrap/operational.ipynb create mode 100644 randomcrap/operational.py create mode 100644 randomcrap/plotly.ipynb create mode 100644 randomcrap/pygrib.ipynb create mode 100644 randomcrap/test.ipynb create mode 100644 randomcrap/untitled.py create mode 100644 randomcrap/untitled1.py create mode 100644 randomcrap/untitled2.py create mode 100644 randomcrap/working.ipynb create mode 100644 requirements.txt diff --git a/flaskapi.py b/flaskapi.py new file mode 100644 index 0000000..75fd425 --- /dev/null +++ b/flaskapi.py @@ -0,0 +1,17 @@ +from flask import Flask, request +from flask_restful import Resource, Api + +import xarray as xr +import matplotlib.pyplot as plt +import os +import csv + +from datetime import datetime, timedelta +import io + +from metpy.units import units +from metpy.plots import ImagePlot, MapPanel, PanelContainer +from siphon.catalog import TDSCatalog +import xarray as xr + +import cartopy.crs diff --git a/randomcrap/Untitled.ipynb b/randomcrap/Untitled.ipynb new file mode 100644 index 0000000..e48e6f4 --- /dev/null +++ b/randomcrap/Untitled.ipynb @@ -0,0 +1,33 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "9285279c-4df8-47e4-98d7-4ec4ae65cdab", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/Untitled1.ipynb b/randomcrap/Untitled1.ipynb new file mode 100644 index 0000000..1ae15f7 --- /dev/null +++ b/randomcrap/Untitled1.ipynb @@ -0,0 +1,1100 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 42, + "id": "4ba57d6c-d140-45be-a87a-45a6c35d1959", + "metadata": {}, + "outputs": [], + "source": [ + "from siphon.catalog import TDSCatalog\n", + "import xarray as xr\n", + "import metpy" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "055ed603-d09c-431a-b4be-f66171959447", + "metadata": {}, + "outputs": [], + "source": [ + "best_gfs = TDSCatalog('https://thredds-jumbo.unidata.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p25deg/Best')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4ce5da44-5547-43c8-8322-0889c85fa225", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best GFS Quarter Degree Forecast Time Series\n" + ] + } + ], + "source": [ + "best_ds = best_gfs.datasets[0]\n", + "print(best_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "1a8855d8-0f37-4afd-935c-13c7edc2a66b", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.open_dataset(best_ds.access_urls['OPENDAP'])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "2bc69754-b948-4fc9-9e74-6262b543263d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'Ice_cover_surface' (time: 121, lat: 721, lon: 1440)>\n",
+       "[125627040 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n",
+       "  * lon        (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 2021-11-26 ... 2021-12-15T12:00:00\n",
+       "    reftime    (time) datetime64[ns] 2021-11-26 ... 2021-11-29T12:00:00\n",
+       "    metpy_crs  object Projection: latitude_longitude\n",
+       "Attributes:\n",
+       "    long_name:                      Ice cover @ Ground or water surface\n",
+       "    units:                          \n",
+       "    abbreviation:                   ICEC\n",
+       "    grid_mapping:                   LatLon_Projection\n",
+       "    Grib_Variable_Id:               VAR_10-2-0_L1\n",
+       "    Grib2_Parameter:                [10  2  0]\n",
+       "    Grib2_Parameter_Discipline:     Oceanographic products\n",
+       "    Grib2_Parameter_Category:       Ice\n",
+       "    Grib2_Parameter_Name:           Ice cover\n",
+       "    Grib2_Level_Type:               1\n",
+       "    Grib2_Level_Desc:               Ground or water surface\n",
+       "    Grib2_Generating_Process_Type:  Forecast
" + ], + "text/plain": [ + "\n", + "[125627040 values with dtype=float32]\n", + "Coordinates:\n", + " * lat (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n", + " * lon (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 2021-11-26 ... 2021-12-15T12:00:00\n", + " reftime (time) datetime64[ns] ...\n", + " metpy_crs object Projection: latitude_longitude\n", + "Attributes:\n", + " long_name: Ice cover @ Ground or water surface\n", + " units: \n", + " abbreviation: ICEC\n", + " grid_mapping: LatLon_Projection\n", + " Grib_Variable_Id: VAR_10-2-0_L1\n", + " Grib2_Parameter: [10 2 0]\n", + " Grib2_Parameter_Discipline: Oceanographic products\n", + " Grib2_Parameter_Category: Ice\n", + " Grib2_Parameter_Name: Ice cover\n", + " Grib2_Level_Type: 1\n", + " Grib2_Level_Desc: Ground or water surface\n", + " Grib2_Generating_Process_Type: Forecast" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ice = ds.metpy.parse_cf('Ice_cover_surface')\n", + "ice" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "7743acb0-61cb-4a3a-b282-fa8836588e3a", + "metadata": {}, + "outputs": [], + "source": [ + "first_ice = ice.isel(time=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "7f0a948b-5e17-468e-980f-3ac0be47ad49", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'Ice_cover_surface' (lat: 721, lon: 1440)>\n",
+       "[1038240 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lat        (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n",
+       "  * lon        (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "    time       datetime64[ns] 2021-11-26\n",
+       "    reftime    datetime64[ns] 2021-11-26\n",
+       "    metpy_crs  object Projection: latitude_longitude\n",
+       "Attributes:\n",
+       "    long_name:                      Ice cover @ Ground or water surface\n",
+       "    units:                          \n",
+       "    abbreviation:                   ICEC\n",
+       "    grid_mapping:                   LatLon_Projection\n",
+       "    Grib_Variable_Id:               VAR_10-2-0_L1\n",
+       "    Grib2_Parameter:                [10  2  0]\n",
+       "    Grib2_Parameter_Discipline:     Oceanographic products\n",
+       "    Grib2_Parameter_Category:       Ice\n",
+       "    Grib2_Parameter_Name:           Ice cover\n",
+       "    Grib2_Level_Type:               1\n",
+       "    Grib2_Level_Desc:               Ground or water surface\n",
+       "    Grib2_Generating_Process_Type:  Forecast
" + ], + "text/plain": [ + "\n", + "[1038240 values with dtype=float32]\n", + "Coordinates:\n", + " * lat (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n", + " * lon (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n", + " time datetime64[ns] 2021-11-26\n", + " reftime datetime64[ns] 2021-11-26\n", + " metpy_crs object Projection: latitude_longitude\n", + "Attributes:\n", + " long_name: Ice cover @ Ground or water surface\n", + " units: \n", + " abbreviation: ICEC\n", + " grid_mapping: LatLon_Projection\n", + " Grib_Variable_Id: VAR_10-2-0_L1\n", + " Grib2_Parameter: [10 2 0]\n", + " Grib2_Parameter_Discipline: Oceanographic products\n", + " Grib2_Parameter_Category: Ice\n", + " Grib2_Parameter_Name: Ice cover\n", + " Grib2_Level_Type: 1\n", + " Grib2_Level_Desc: Ground or water surface\n", + " Grib2_Generating_Process_Type: Forecast" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "first_ice" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9bd62c42-e649-4f4b-8d3e-319c175fe4de", + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "5ee8a655-e637-4429-906b-4b746671abac", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'GeoAxesSubplot' object has no attribute 'countourf'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_2839985/2770955780.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_feature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSTATES\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_scale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'50m'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountourf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst_ice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_ice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_ice\u001b[0m\u001b[0;34m,\u001b[0m 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(15, 12))\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "ax.add_feature(cfeature.STATES.with_scale('50m'), linewidth=0.5)\n", + "\n", + "cf = ax.countourf(first_ice.lon, first_ice.lat, first_ice, transform=ccrs.PlateCarree(), zorder=0, cmap='coolwarm')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d11c98cc-ed90-44ea-87d8-beec14af6b37", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/Untitled2.ipynb b/randomcrap/Untitled2.ipynb new file mode 100644 index 0000000..356d1da --- /dev/null +++ b/randomcrap/Untitled2.ipynb @@ -0,0 +1,141 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b961e722-8293-46a6-b139-7412ceeb73ab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hliu/Projects/modap/env/lib/python3.9/site-packages/numpy/ma/core.py:1015: RuntimeWarning: overflow encountered in multiply\n", + " result = self.f(da, db, *args, **kwargs)\n", + "/home/hliu/Projects/modap/env/lib/python3.9/site-packages/cartopy/crs.py:825: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", + " if len(multi_line_string) > 1:\n", + "/home/hliu/Projects/modap/env/lib/python3.9/site-packages/cartopy/crs.py:877: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n", + " for line in multi_line_string:\n", + "/home/hliu/Projects/modap/env/lib/python3.9/site-packages/cartopy/crs.py:944: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.\n", + " if len(p_mline) > 0:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from datetime import datetime, timedelta\n", + "from urllib.request import urlopen\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.pyplot as plt\n", + "from metpy.units import masked_array, units\n", + "from netCDF4 import Dataset\n", + "\n", + "dt = datetime.utcnow() - timedelta(days=1) # This should always be available\n", + "url = 'http://water.weather.gov/precip/downloads/{dt:%Y/%m/%d}/nws_precip_1day_'\\\n", + " '{dt:%Y%m%d}_conus.nc'.format(dt=dt)\n", + "data = urlopen(url).read()\n", + "nc = Dataset('data', memory=data)\n", + "\n", + "prcpvar = nc.variables['observation']\n", + "data = masked_array(prcpvar[:], units(prcpvar.units.lower())).to('mm')\n", + "x = nc.variables['x'][:]\n", + "y = nc.variables['y'][:]\n", + "proj_var = nc.variables[prcpvar.grid_mapping]\n", + "\n", + "globe = ccrs.Globe(semimajor_axis=proj_var.earth_radius)\n", + "proj = ccrs.Stereographic(central_latitude=90.0,\n", + " central_longitude=proj_var.straight_vertical_longitude_from_pole,\n", + " true_scale_latitude=proj_var.standard_parallel, globe=globe)\n", + "\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot(1, 1, 1, projection=proj)\n", + "\n", + "# draw coastlines, state and country boundaries, edge of map.\n", + "ax.coastlines()\n", + "ax.add_feature(cfeature.BORDERS)\n", + "ax.add_feature(cfeature.STATES)\n", + "\n", + "# draw filled contours.\n", + "clevs = [0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40,\n", + " 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 750]\n", + "# In future MetPy\n", + "# norm, cmap = ctables.registry.get_with_boundaries('precipitation', clevs)\n", + "cmap_data = [(1.0, 1.0, 1.0),\n", + " (0.3137255012989044, 0.8156862854957581, 0.8156862854957581),\n", + " (0.0, 1.0, 1.0),\n", + " (0.0, 0.8784313797950745, 0.501960813999176),\n", + " (0.0, 0.7529411911964417, 0.0),\n", + " (0.501960813999176, 0.8784313797950745, 0.0),\n", + " (1.0, 1.0, 0.0),\n", + " (1.0, 0.6274510025978088, 0.0),\n", + " (1.0, 0.0, 0.0),\n", + " (1.0, 0.125490203499794, 0.501960813999176),\n", + " (0.9411764740943909, 0.250980406999588, 1.0),\n", + " (0.501960813999176, 0.125490203499794, 1.0),\n", + " (0.250980406999588, 0.250980406999588, 1.0),\n", + " (0.125490203499794, 0.125490203499794, 0.501960813999176),\n", + " (0.125490203499794, 0.125490203499794, 0.125490203499794),\n", + " (0.501960813999176, 0.501960813999176, 0.501960813999176),\n", + " (0.8784313797950745, 0.8784313797950745, 0.8784313797950745),\n", + " (0.9333333373069763, 0.8313725590705872, 0.7372549176216125),\n", + " (0.8549019694328308, 0.6509804129600525, 0.47058823704719543),\n", + " (0.6274510025978088, 0.42352941632270813, 0.23529411852359772),\n", + " (0.4000000059604645, 0.20000000298023224, 0.0)]\n", + "cmap = mcolors.ListedColormap(cmap_data, 'precipitation')\n", + "norm = mcolors.BoundaryNorm(clevs, cmap.N)\n", + "\n", + "cs = ax.contourf(x, y, data, clevs, cmap=cmap, norm=norm)\n", + "\n", + "# add colorbar.\n", + "cbar = plt.colorbar(cs, orientation='horizontal')\n", + "cbar.set_label(data.units)\n", + "\n", + "ax.set_title(prcpvar.long_name + ' for period ending ' + nc.creation_time)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a2c8f2f-9b84-44ef-9faf-afab853f5544", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/Untitled3.ipynb b/randomcrap/Untitled3.ipynb new file mode 100644 index 0000000..1742911 --- /dev/null +++ b/randomcrap/Untitled3.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0b4bce0a-a8a7-40b5-ab18-e08a83201020", + "metadata": {}, + "outputs": [], + "source": [ + "import pygrib" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "df5f0f57-9513-4893-bb9f-78a02895c91e", + "metadata": {}, + "outputs": [], + "source": [ + "bruh = pygrib.open('gfs.grib2')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5afcac2f-8d14-4cba-a0fa-8eecf2bd960a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/Untitled4.ipynb b/randomcrap/Untitled4.ipynb new file mode 100644 index 0000000..813632b --- /dev/null +++ b/randomcrap/Untitled4.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "e7efe1cb-c3d1-49fe-b0c8-ca45f5eea040", + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from metpy.plots import USCOUNTIES" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b203114e-0a91-41d6-84a4-1f5986474ce1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "proj = ccrs.LambertConformal(central_longitude=-85.0, central_latitude=45.0)\n", + "\n", + "fig = plt.figure(figsize=(12, 9))\n", + "ax1 = fig.add_subplot(1, 3, 1, projection=proj)\n", + "ax2 = fig.add_subplot(1, 3, 2, projection=proj)\n", + "ax3 = fig.add_subplot(1, 3, 3, projection=proj)\n", + "\n", + "for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):\n", + " axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())\n", + " axis.add_feature(USCOUNTIES.with_scale('500k'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef5c03a5-5f74-4bf8-baea-49737555ecfd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/cartopy.ipynb b/randomcrap/cartopy.ipynb new file mode 100644 index 0000000..a0258a3 --- /dev/null +++ b/randomcrap/cartopy.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "b961e722-8293-46a6-b139-7412ceeb73ab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from datetime import datetime, timedelta\n", + "from urllib.request import urlopen\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.pyplot as plt\n", + "from metpy.units import masked_array, units\n", + "from netCDF4 import Dataset\n", + "\n", + "dt = datetime.utcnow() - timedelta(days=1) # This should always be available\n", + "url = 'http://water.weather.gov/precip/downloads/{dt:%Y/%m/%d}/nws_precip_1day_'\\\n", + " '{dt:%Y%m%d}_conus.nc'.format(dt=dt)\n", + "data = urlopen(url).read()\n", + "nc = Dataset('data', memory=data)\n", + "\n", + "prcpvar = nc.variables['observation']\n", + "data = masked_array(prcpvar[:], units(prcpvar.units.lower())).to('mm')\n", + "x = nc.variables['x'][:]\n", + "y = nc.variables['y'][:]\n", + "proj_var = nc.variables[prcpvar.grid_mapping]\n", + "\n", + "globe = ccrs.Globe(semimajor_axis=proj_var.earth_radius)\n", + "proj = ccrs.Stereographic(central_latitude=90.0,\n", + " central_longitude=proj_var.straight_vertical_longitude_from_pole,\n", + " true_scale_latitude=proj_var.standard_parallel, globe=globe)\n", + "\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = fig.add_subplot(1, 1, 1, projection=proj)\n", + "\n", + "# draw coastlines, state and country boundaries, edge of map.\n", + "ax.coastlines()\n", + "ax.add_feature(cfeature.BORDERS)\n", + "ax.add_feature(cfeature.STATES)\n", + "\n", + "# draw filled contours.\n", + "clevs = [0, 1, 2.5, 5, 7.5, 10, 15, 20, 30, 40,\n", + " 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 750]\n", + "# In future MetPy\n", + "# norm, cmap = ctables.registry.get_with_boundaries('precipitation', clevs)\n", + "cmap_data = [(1.0, 1.0, 1.0),\n", + " (0.3137255012989044, 0.8156862854957581, 0.8156862854957581),\n", + " (0.0, 1.0, 1.0),\n", + " (0.0, 0.8784313797950745, 0.501960813999176),\n", + " (0.0, 0.7529411911964417, 0.0),\n", + " (0.501960813999176, 0.8784313797950745, 0.0),\n", + " (1.0, 1.0, 0.0),\n", + " (1.0, 0.6274510025978088, 0.0),\n", + " (1.0, 0.0, 0.0),\n", + " (1.0, 0.125490203499794, 0.501960813999176),\n", + " (0.9411764740943909, 0.250980406999588, 1.0),\n", + " (0.501960813999176, 0.125490203499794, 1.0),\n", + " (0.250980406999588, 0.250980406999588, 1.0),\n", + " (0.125490203499794, 0.125490203499794, 0.501960813999176),\n", + " (0.125490203499794, 0.125490203499794, 0.125490203499794),\n", + " (0.501960813999176, 0.501960813999176, 0.501960813999176),\n", + " (0.8784313797950745, 0.8784313797950745, 0.8784313797950745),\n", + " (0.9333333373069763, 0.8313725590705872, 0.7372549176216125),\n", + " (0.8549019694328308, 0.6509804129600525, 0.47058823704719543),\n", + " (0.6274510025978088, 0.42352941632270813, 0.23529411852359772),\n", + " (0.4000000059604645, 0.20000000298023224, 0.0)]\n", + "cmap = mcolors.ListedColormap(cmap_data, 'precipitation')\n", + "norm = mcolors.BoundaryNorm(clevs, cmap.N)\n", + "\n", + "cs = ax.contourf(x, y, data, clevs, cmap=cmap, norm=norm)\n", + "\n", + "# add colorbar.\n", + "cbar = plt.colorbar(cs, orientation='horizontal')\n", + "cbar.set_label(data.units)\n", + "\n", + "ax.set_title(prcpvar.long_name + ' for period ending ' + nc.creation_time)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a2c8f2f-9b84-44ef-9faf-afab853f5544", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/chelsa.ipynb b/randomcrap/chelsa.ipynb new file mode 100644 index 0000000..d45ad5f --- /dev/null +++ b/randomcrap/chelsa.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 32, + "id": "22a56d6f-3fd9-4d24-995a-fe026e2c4997", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import netCDF4 as nc\n", + "ds = 'chelsa.nc'\n", + "ds = xr.open_dataset(\"chelsa.nc\")\n", + "#for var in ds.variables.values():\n", + " #print(var)\n", + " \n", + "from datetime import datetime, timedelta\n", + "import io\n", + "\n", + "from metpy.units import units\n", + "from metpy.plots import ImagePlot, MapPanel, PanelContainer\n", + "from siphon.catalog import TDSCatalog\n", + "import xarray as xr\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "cc99db8e-81d7-470d-8660-9dffb58737de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'pr' (time: 12, lat: 4176, lon: 8640)>\n",
+       "[432967680 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float64 -180.0 -180.0 -179.9 -179.9 ... 179.9 179.9 180.0\n",
+       "  * lat      (lat) float64 83.96 83.92 83.88 83.84 ... -89.91 -89.95 -90.0\n",
+       "Dimensions without coordinates: time\n",
+       "Attributes:\n",
+       "    units:         kg*m⁻2\n",
+       "    long_name:     precipitation rate\n",
+       "    grid_mapping:  crs\n",
+       "    proj4:         +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=...\n",
+       "    min:           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+       "    max:           [65.53500366 65.53500366 65.53500366 65.53500366 65.535003...
" + ], + "text/plain": [ + "\n", + "[432967680 values with dtype=float32]\n", + "Coordinates:\n", + " * lon (lon) float64 -180.0 -180.0 -179.9 -179.9 ... 179.9 179.9 180.0\n", + " * lat (lat) float64 83.96 83.92 83.88 83.84 ... -89.91 -89.95 -90.0\n", + "Dimensions without coordinates: time\n", + "Attributes:\n", + " units: kg*m⁻2\n", + " long_name: precipitation rate\n", + " grid_mapping: crs\n", + " proj4: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=...\n", + " min: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " max: [65.53500366 65.53500366 65.53500366 65.53500366 65.535003..." + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.pr" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d23ece4d-df23-4db1-b5da-0321d99a932b", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Must provide a combination of subsetting values to give 2D data subset for plotting", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_1822120/2421113249.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mpc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mpc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpanels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpanel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m 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combination of subsetting values to give 2D data subset for plotting" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img = ImagePlot()\n", + "img.data = ds\n", + "#gfs.Temperature_surface[0][10][10] = 400.0\n", + "img.field = 'pr'\n", + "img.colormap = 'coolwarm'\n", + "\n", + "panel = MapPanel()\n", + "panel.area = 'wa'\n", + "panel.layers = ['states']\n", + "panel.title = 'GFS precip forecast'\n", + "panel.plots = [img]\n", + "\n", + "pc = PanelContainer()\n", + "pc.size = (10, 8)\n", + "pc.panels = [panel]\n", + "pc.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4305d680-54b5-4b6a-b946-2fed2fe49d9a", + "metadata": {}, + "outputs": [], + "source": [ + "#rewrite 'time (month)' variable to 'time' variable\n", + "#from netCDF4 import Dataset\n", + "#path=\"chelsa.nc\"\n", + "#f=Dataset(path,'r+')\n", + "#f.renameDimension(u'time (month)',u'time')\n", + "#f.close()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/frost_copy.py b/randomcrap/frost_copy.py new file mode 100644 index 0000000..e69de29 diff --git a/randomcrap/frost_test.py b/randomcrap/frost_test.py new file mode 100644 index 0000000..e4b5a6d --- /dev/null +++ b/randomcrap/frost_test.py @@ -0,0 +1,182 @@ +# Resolve the latest GFS dataset +import metpy +from siphon.catalog import TDSCatalog +import sys +import numpy as np +import matplotlib +matplotlib.use('Agg') #Avoid the need for X11 forwarding + +#------------------------ +# Set up access via NCSS. +#------------------------ +# We can plot just about anything from https://thredds.ucar.edu/thredds/catalog.html +# Use this to plot GFS output +#catalog = ('http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/' +# 'Global_0p5deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p5deg/Best') +# +# Here, let's plot the latest HRRR model analysis +catalog = ('https://thredds.ucar.edu/thredds/catalog/grib/NCEP/HRRR/CONUS_2p5km_ANA/catalog.xml') + +# This works, but... +#cat = TDSCatalog(catalog) +#ncss = cat.datasets[0].subset() + +# ...this is faster +from siphon.catalog import get_latest_access_url +from siphon.ncss import NCSS +latest = get_latest_access_url(catalog,"NetcdfSubset") +ncss = NCSS(latest) + +# We can see what variables are available from ncss +print(ncss.variables) + +# From here, we can build a query to ask for the data we want from the server +from datetime import datetime, timedelta + +# Create a new NCSS query +query = ncss.query() + +# Request data in netCDF format +query.accept('netcdf') + +# Ask for a specific variable (or variables with a comma-delimited set of strings) +query.variables('Temperature_isobaric', 'Geopotential_height_isobaric') + +# Ask for the 850 hPa surface. Native units are pascals. +#query.vertical_level(85000) #This works for a single variable, but not for two or more because + #the number and values of levels might be different. Handle this later. + +# Set the time range of the data we want +now = datetime.utcnow() #This grabs the current system time and saves it as a datetime object +#query.time_range(now, now + timedelta(days=1)) #This sets a range from now to a day from now (use this for forecasts) +query.time_range(now - timedelta(hours=3), now) #For the latest HRRR data, we want to find data within the last 3 hours. THREDDS can be slow (adjust as needed). + +# Set the spatial limits. If this is left out, we plot the whole domain. +#query.lonlat_box(west=-110, east=-45, north=50, south=10) + +# Get the data +data = ncss.get_data(query) + +######################## +# Making sense of netCDF +######################## + +#View the properties of our data +print(type(data)) + +#We can use a library called XArray to make working with this a little simpler +from xarray.backends import NetCDF4DataStore +import xarray as xr + +# Use NetCDFDataStore to open and read the existing netCDF dataset we downloaded +ds = xr.open_dataset(NetCDF4DataStore(data)) + +# Grab a specific dataset +temp_var = ds.metpy.parse_cf('Temperature_isobaric') +print(temp_var) +z_var = ds.metpy.parse_cf('Geopotential_height_isobaric') +print(z_var) + +# XArray handles parsing things like dates, times, latitude, and longitude for us. This is so much easier than it used to be! +latitude = temp_var.metpy.y +longitude = temp_var.metpy.x + +#------- +# Levels +#------- +# Select a level to plot. We can even choose different levels for each variable! +level = 85000 +temp_var = temp_var.metpy.sel(vertical=level) +z_var = z_var.metpy.sel(vertical=level) + +# Explore the available levels here if you want +#temp_levels = temp_var['isobaric'] +#print("Temperature levels:",temp_levels) +#z_levels = z_var['isobaric1'] +#print("Height levels:",z_levels) +#------- + +#################### +# Visualize the grid +#################### + +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import matplotlib.pyplot as plt + +# The GFS uses a lon/lat grid +#data_projection = ccrs.PlateCarree() + +# The HRRR uses a specific Lambert Conformal grid called the AWIPS 184 grid. See the parameters here: https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID184 +# You can specify the parameters of your projection: https://scitools.org.uk/cartopy/docs/latest/crs/projections.html +# Set the map projection with the specific grid parameters (note that the grid parameters are also embedded within the data) +data_projection = ccrs.LambertConformal(central_longitude=-95.000,central_latitude=25.000) + +# Make it easy to change what time step we look at +t_step = 0 + +#------------- +# Color tables +#------------- +# Look at the documentation for metpy.plots.colortables (https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ctables.ColortableRegistry.html) +# or choose one from here: https://matplotlib.org/examples/color/colormaps_reference.html +# Here are the custom color tables that MetPy supports: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ctables.html +# Put '_r' at the end of any colormap name to reverse it. + +# Import for colortables +from metpy.plots import colortables +colormap=colortables.get_colortable('ir_rgbv') #Use this only for the MetPy-specific color tables. Otherwise, comment out this line and put your Matplotlib color selection below +#colormap='rainbow' +#------------- + +#------------- +# Create a figure and plot the data +#------------- +fig = plt.figure(figsize=(12, 8)) +ax = fig.add_subplot(1, 1, 1, projection=ccrs.LambertConformal()) +#ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) #You can use a different projection if you like +mesh = ax.pcolormesh(longitude, latitude, temp_var[t_step].squeeze(), + transform=data_projection, zorder=0, cmap=colormap) + +# Add contours of 850-mb geopotential height +contours = np.arange(1140, 1620, 30) +ax.contour(longitude, latitude, z_var[t_step].squeeze(), contours, colors='k', + transform=data_projection, linewidths=0.75, zorder=1) + +# Add some common geographic features +ax.add_feature(cfeature.COASTLINE.with_scale('50m')) +ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='black') +ax.add_feature(cfeature.BORDERS.with_scale('50m')) + +# Add some lat/lon gridlines +ax.gridlines() + +# Add a colorbar +cax = fig.colorbar(mesh) +cax.set_label(temp_var.attrs['units']) + +# Get a sensible datetime format +vtime = ds.time.data[0].astype('datetime64[ms]').astype('O') + +# Add a title +ax.set_title(temp_var.metpy.time[t_step].values) + +#plt.show() #View the plot via a GUI window. Remove matplotlib.use('Agg') above. I suggest saving it instead (see the next line). +plt.savefig("outofthebox.png") #Just reload this image in your browser for fast viewing +plt.close() + +#------------------------------------------------------------------------------------------------- +################################################################################################## +################################################################################################## +# Create the following plots: +# 1) 850-mb temperature as a colored contour, along with geopotential heights and associated wind barbs +# 2) 500-mb divergence as a colored contour, along with geopotential heights and associated wind barbs +# 3) 500-mb vorticity as a colored contour, along with geopotential heights and associated wind barbs +# 4) 500-mb isotachs (colored, starting at 20 knots), along with geopotential heights and associated wind barbs + +# NOTE: You are welcome to zoom in on a region of interest in each plot. + +# Add a title to each plot. Post your code and all four maps on your password-protected webpage. +################################################################################################## +################################################################################################## +#------------------------------------------------------------------------------------------------- \ No newline at end of file diff --git a/randomcrap/gfs.ipynb b/randomcrap/gfs.ipynb new file mode 100644 index 0000000..f9cd71b --- /dev/null +++ b/randomcrap/gfs.ipynb @@ -0,0 +1,1067 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 36, + "id": "7e0c5939-3495-46e4-b486-bb9834c90975", + "metadata": {}, + "outputs": [], + "source": [ + "data_url = ('https://thredds-jumbo.unidata.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p25deg/Best')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "22a56d6f-3fd9-4d24-995a-fe026e2c4997", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime, timedelta\n", + "import io\n", + "\n", + "from metpy.units import units\n", + "from metpy.plots import ImagePlot, MapPanel, PanelContainer\n", + "from siphon.catalog import TDSCatalog\n", + "import xarray as xr\n", + "\n", + "%matplotlib inline\n", + "\n", + "from PIL import Image\n", + "import glob\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "012bf6b8-f52b-4fa8-90db-5aeac404c575", + "metadata": {}, + "outputs": [], + "source": [ + "best_gfs = TDSCatalog(data_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e4c9c118-ac4f-40f1-b3bd-00fa16b91bb5", + "metadata": {}, + "outputs": [], + "source": [ + "best_ds = best_gfs.datasets[0]\n", + "ncss = best_ds.subset()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "318f0e28-1dd0-43d9-a328-77e6be0718d4", + "metadata": {}, + "outputs": [], + "source": [ + "query = ncss.query()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "b6ebf567-edd7-4954-979f-33a38c996003", + "metadata": {}, + "outputs": [], + "source": [ + "frame = 40" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ca16666a-c23c-443d-9dc5-1f8ae1eeca21", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "query.lonlat_box(north=50, south=25, east=-60, west=-130).time(datetime.utcnow() + timedelta(hours=frame))\n", + "query.accept('netcdf4')\n", + "query.variables('Precipitation_rate_surface')\n", + "\n", + "data = ncss.get_data_raw(query)\n", + "\n", + "gfs = xr.open_dataset(io.BytesIO(data))\n", + "\n", + "img = ImagePlot()\n", + "img.data = gfs\n", + "#gfs.Temperature_surface[0][10][10] = 400.0\n", + "img.field = 'Precipitation_rate_surface'\n", + "img.colormap = 'Greens'\n", + "\n", + "panel = MapPanel()\n", + "panel.area = 'co'\n", + "panel.layers = ['states']\n", + "panel.title = 'GFS precip forecast'\n", + "panel.plots = [img]\n", + "\n", + "pc = PanelContainer()\n", + "pc.size = (10, 8)\n", + "pc.panels = [panel]\n", + "pc.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "b8a313ba-1ec8-4f80-b39d-0e308b4a931b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:                     (time1: 1, lat: 101, lon: 281)\n",
+       "Coordinates:\n",
+       "    reftime1                    (time1) datetime64[ns] 2021-12-21T18:00:00\n",
+       "  * time1                       (time1) datetime64[ns] 2021-12-23T18:00:00\n",
+       "  * lat                         (lat) float32 50.0 49.75 49.5 ... 25.25 25.0\n",
+       "  * lon                         (lon) float32 230.0 230.2 230.5 ... 299.8 300.0\n",
+       "Data variables:\n",
+       "    Precipitation_rate_surface  (time1, lat, lon) float32 7.2e-06 ... 0.0\n",
+       "    LatLon_Projection           int32 0\n",
+       "Attributes: (12/13)\n",
+       "    Originating_or_generating_Center:                                        ...\n",
+       "    Originating_or_generating_Subcenter:                                     ...\n",
+       "    GRIB_table_version:                                                      ...\n",
+       "    Type_of_generating_process:                                              ...\n",
+       "    Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n",
+       "    Conventions:                                                             ...\n",
+       "    ...                                                                                ...\n",
+       "    featureType:                                                             ...\n",
+       "    History:                                                                 ...\n",
+       "    geospatial_lat_min:                                                      ...\n",
+       "    geospatial_lat_max:                                                      ...\n",
+       "    geospatial_lon_min:                                                      ...\n",
+       "    geospatial_lon_max:                                                      ...
" + ], + "text/plain": [ + "\n", + "Dimensions: (time1: 1, lat: 101, lon: 281)\n", + "Coordinates:\n", + " reftime1 (time1) datetime64[ns] ...\n", + " * time1 (time1) datetime64[ns] 2021-12-23T18:00:00\n", + " * lat (lat) float32 50.0 49.75 49.5 ... 25.25 25.0\n", + " * lon (lon) float32 230.0 230.2 230.5 ... 299.8 300.0\n", + "Data variables:\n", + " Precipitation_rate_surface (time1, lat, lon) float32 ...\n", + " LatLon_Projection int32 ...\n", + "Attributes: (12/13)\n", + " Originating_or_generating_Center: ...\n", + " Originating_or_generating_Subcenter: ...\n", + " GRIB_table_version: ...\n", + " Type_of_generating_process: ...\n", + " Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n", + " Conventions: ...\n", + " ... ...\n", + " featureType: ...\n", + " History: ...\n", + " geospatial_lat_min: ...\n", + " geospatial_lat_max: ...\n", + " geospatial_lon_min: ...\n", + " geospatial_lon_max: ..." + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gfs" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "d1c7e214-f91e-4538-972d-bff21895e6d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:                     (time1: 1, lat: 101, lon: 281)\n",
+       "Coordinates:\n",
+       "    reftime1                    (time1) datetime64[ns] 2021-12-21T18:00:00\n",
+       "  * time1                       (time1) datetime64[ns] 2021-12-23T03:00:00\n",
+       "  * lat                         (lat) float32 50.0 49.75 49.5 ... 25.25 25.0\n",
+       "  * lon                         (lon) float32 230.0 230.2 230.5 ... 299.8 300.0\n",
+       "Data variables:\n",
+       "    Precipitation_rate_surface  (time1, lat, lon) float32 6.8e-06 ... 0.0\n",
+       "    LatLon_Projection           int32 0\n",
+       "Attributes: (12/13)\n",
+       "    Originating_or_generating_Center:                                        ...\n",
+       "    Originating_or_generating_Subcenter:                                     ...\n",
+       "    GRIB_table_version:                                                      ...\n",
+       "    Type_of_generating_process:                                              ...\n",
+       "    Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n",
+       "    Conventions:                                                             ...\n",
+       "    ...                                                                                ...\n",
+       "    featureType:                                                             ...\n",
+       "    History:                                                                 ...\n",
+       "    geospatial_lat_min:                                                      ...\n",
+       "    geospatial_lat_max:                                                      ...\n",
+       "    geospatial_lon_min:                                                      ...\n",
+       "    geospatial_lon_max:                                                      ...
" + ], + "text/plain": [ + "\n", + "Dimensions: (time1: 1, lat: 101, lon: 281)\n", + "Coordinates:\n", + " reftime1 (time1) datetime64[ns] 2021-12-21T18:00:00\n", + " * time1 (time1) datetime64[ns] 2021-12-23T03:00:00\n", + " * lat (lat) float32 50.0 49.75 49.5 ... 25.25 25.0\n", + " * lon (lon) float32 230.0 230.2 230.5 ... 299.8 300.0\n", + "Data variables:\n", + " Precipitation_rate_surface (time1, lat, lon) float32 6.8e-06 ... 0.0\n", + " LatLon_Projection int32 0\n", + "Attributes: (12/13)\n", + " Originating_or_generating_Center: ...\n", + " Originating_or_generating_Subcenter: ...\n", + " GRIB_table_version: ...\n", + " Type_of_generating_process: ...\n", + " Analysis_or_forecast_generating_process_identifier_defined_by_originating...\n", + " Conventions: ...\n", + " ... ...\n", + " featureType: ...\n", + " History: ...\n", + " geospatial_lat_min: ...\n", + " geospatial_lat_max: ...\n", + " geospatial_lon_min: ...\n", + " geospatial_lon_max: ..." + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gfs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab35812d-837a-4e2f-b005-0af8c662ac05", + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "for i in range(0,360,12):\n", + " query.lonlat_box(north=50, south=25, east=-60, west=-130).time(datetime.utcnow() + timedelta(hours=i))\n", + " query.accept('netcdf4')\n", + " query.variables('Precipitation_rate_surface')\n", + "\n", + " data = ncss.get_data_raw(query)\n", + "\n", + " gfs = xr.open_dataset(io.BytesIO(data))\n", + "\n", + " img = ImagePlot()\n", + " img.data = gfs\n", + " #gfs.Temperature_surface[0][10][10] = 400.0\n", + " img.field = 'Precipitation_rate_surface'\n", + " img.colormap = 'Greens'\n", + "\n", + " panel = MapPanel()\n", + " panel.area = 'wa'\n", + " panel.layers = ['states']\n", + " panel.title = 'GFS precip forecast'\n", + " panel.plots = [img]\n", + "\n", + " pc = PanelContainer()\n", + " pc.size = (10, 8)\n", + " pc.panels = [panel]\n", + " #pc.show()\n", + " pc.save(str(i)+\"gfs_img.jpg\")\n", + "\n", + "# Create the frames\n", + "frames = []\n", + "imgs = list(glob.glob(\"*gfs_img.jpg\"))\n", + "imgs.sort()\n", + "for i in imgs:\n", + " new_frame = Image.open(i)\n", + " frames.append(new_frame)\n", + " os.remove(i)\n", + " \n", + "# Save into a GIF file that loops forever\n", + "frames[0].save('gfs.gif', format='GIF',\n", + " append_images=frames[1:],\n", + " save_all=True,\n", + " duration=400, loop=0)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/matplot_lib.ipynb b/randomcrap/matplot_lib.ipynb new file mode 100644 index 0000000..56b09c9 --- /dev/null +++ b/randomcrap/matplot_lib.ipynb @@ -0,0 +1,1334 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 98, + "id": "ad51f956-5d21-4b66-8a46-e705e5d856d6", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import requests\n", + "import time\n", + "import subprocess\n", + "\n", + "datestr = time.strftime('%Y%m%d')\n", + "\n", + "\n", + "gfs_url = 'https://nomads.ncep.noaa.gov/cgi-bin/filter_gfs_0p25_1hr.pl?file=gfs.t00z.pgrb2.0p25.f000&lev_surface=on&var_PRATE=on&leftlon=0&rightlon=360&toplat=90&bottomlat=-90&dir=%2Fgfs.20220109%2F00%2Fatmos'\n", + "r = requests.get(gfs_url, allow_redirects=True)\n", + "open('gfs1.grib2', 'wb').write(r.content)\n", + "\n", + "gfs_ds = xr.load_dataset(\"gfs1.grib2\", engine=\"cfgrib\",filter_by_keys={'typeOfLevel': 'surface'})" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "60469952-5b42-425c-a28b-ce2e625db9a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "23.352114583444983\n", + "21.13812300000003\n" + ] + } + ], + "source": [ + "print(float(hrrr_ds.latitude[0][500]))\n", + "print(float(hrrr_ds.latitude[0][0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "b36366ce-c987-41ef-b0ae-a2af418c5d0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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"latitude_length = len(hrrr_ds.latitude)-1\n", + "longitude_length = len(hrrr_ds.latitude[0])-1\n", + "\n", + "coordinates_list = []\n", + "coord_strs = []\n", + "gfs_value_list = []\n", + "nam_value_list = []\n", + "hrrr_value_list = []\n", + " \n", + "file = open('coord_strs.txt')\n", + "contents = file.read()\n", + "coord_strs = contents.splitlines()\n", + " \n", + "for y in range(len(hrrr_ds.t['y'])):\n", + " coord_str = coord_strs[y]\n", + "\n", + " gfs_tmp = \"/usr/bin/wgrib2 %s -match '%s' %s\" % ('gfs.grib2', 'TMP', coord_str)\n", + " gfs_value = (str(subprocess.check_output(gfs_tmp, shell=True))[:-3])\n", + " gfs_value = gfs_value.split(':')\n", + " for z in range (len(gfs_value)):\n", + " current_value = gfs_value[z]\n", + " if 'val' in current_value:\n", + " current_value = float((current_value.split(',')[2]).split('=')[1])\n", + " gfs_value_list.append(current_value)\n", + "\n", + " #nam_tmp = \"/usr/bin/wgrib2 %s -match '%s' %s\" % ('nam.grib2', 'TMP', coord_str)\n", + " #nam_value = (str(subprocess.check_output(nam_tmp, shell=True))[:-3])\n", + " #nam_value = nam_value.split(':')\n", + " #for z in range (len(nam_value)):\n", + " #current_value = nam_value[z]\n", + " #if 'val' in current_value:\n", + " #current_value = float((current_value.split(',')[2]).split('=')[1])\n", + " #nam_value_list.append(current_value)\n", + " \n", + " hrrr_tmp = \"/usr/bin/wgrib2 %s -match '%s' %s\" % ('hrrr.grib2', 'TMP', coord_str)\n", + " hrrr_value = (str(subprocess.check_output(hrrr_tmp, shell=True))[:-3])\n", + " hrrr_value = hrrr_value.split(':')\n", + " for z in range (len(hrrr_value)):\n", + " current_value = hrrr_value[z]\n", + " if 'val' in current_value:\n", + " current_value = float((current_value.split(',')[2]).split('=')[1])\n", + " hrrr_value_list.append(current_value)\n", + " \n", + " for x in range(len(hrrr_ds.t['x'])):\n", + " coord_str = coord_strs[y]\n", + " coord_str = coord_str.split('-')\n", + " value = (hrrr_ds['t'][y][x])\n", + " gfs_value = gfs_value_list[x]\n", + " #nam_value = nam_value_list[x]\n", + " hrrr_value = hrrr_value_list[x]\n", + " hrrr_ds.t[y][x] = float((gfs_value+hrrr_value)/2)\n", + " print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "c6e840b1-8dc1-4676-ac98-b16d2a7a1b27", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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c/QGS9pB0m6TFkr6b29dC0rg8vTgvnzFch+mAYWbWJJGffSkxlLAWODQi9iF1S3ykpINIzeZ/PSL2IvUXf3JOfzKpE7G9SB2afbniw1tv2AKGpG9KelrSPYV5O0iaJ+nB/Lp9ni9JF+QIebek/QvrnJjTPyjpxOHKr5nZZhPpmZcSQyORrMyT9X58gtQ/zRV5/qXAO/P4MXmavPywQhe/lRrOK4xLSJ2VFJ1F6tB+b1Lb9vVuFI8C9s7DKcA3IAUY4LPAa0l9Hn+2HmTMzNqH1j982WgotTWpU9J84GlgHvAQ8FxE9OQkS0gdiZFfHwfIy58Hdqzu2DYYtoARETeTOokpKkbCvhHyWzmy3gpMkbQL8BZgXkQ8ExHPkk7cqHyk3sy2XBJ0ju0sNQBTJd1RGE7pu72I6I2IfUk9Nh4IDNTn/Yga6bukdo6IpXn8D6SuOqEQIbN69Bxo/ibyST8FYPrUKdXl2MysEVH66oHUk+TsMgkj4jlJNwB/Tvoj3ZWvInYDnsjJniB1S7tEUhewHal30cq1rNI7ot9W35rZ3pyImB0Rs6dN3raqzZqZlVCu/qJMHYakaZKm5PEJwJtJ3RLfALwnJzuR1IUtwNV5mrz8FzFM/VaM9BXGU5J2iYilucjp6Ty/HiHr6tHzCVKfu8X5N45APs3MSlO9peBq7AJcKqmT9Kf+exHxE0n3ApdLOhf4LTA3p58LfFvSYlI1wHFVZaSvkQ4Y9Uj4JTaNkB+VdDmpgvv5HFSuA/6pUNF9BPCpEc6zmVlD6qimwCYi7qaf7nkj4mFSfUbf+WuA91ay8waGLWBI+i/S1cFUSUtIdzt9CfiepJOBR4Fjc/JrgKOBxcBq4CSAiHgmd1l5e073hYjoW5FuZtZaEp1ufHDzRcT7Blh0WD9pAzh1gO18E/hmhVkzM6uUhlbpPWq5LSkzswpUVSTVzhwwzMya5SsMMzMrp9wts6OdA4aZWZNch2FmZuUIOro6W52LYeeAYWbWtPINC45mDhhmZhVwwDAzs4ZSHYZvqzUzsxJ8l5SZmTUm12GYmVkJ8l1SZmZWlq8wzMysBLnS28zMSthKnvTe8kOimdmwS1cYZYaGW5KmS7pB0r2SFko6Lc/fR9JvJC2Q9GNJk/P8MZIuzfMXSRq2TuYcMMzMmpS6aO0oNZTQA5wZEbOAg4BTJc0CLgLOiohXAz8APp7TvxcYl+cfAPxvSTOqPkZwwDAzq0RVVxgRsTQi7srjK4BFwK7ATODmnGwe8O76KsC2krqACcA6YHm1R5c4YJiZNSs/h1FmIHVbfUdhOGXgzWoGqX/v24CFwDF50XuB6Xn8CmAVsBR4DPjacHVl7UpvM7MKDOEuqWURMbvh9qSJwJXA6RGxXNKHgAsknQNcTbqSADgQ6AVeDGwP/FLSzyPi4aEeQyMOGGZmFajyLilJY0jB4rKIuAogIu4DjsjLZwJvzcnfD/w0IrqBpyX9CpgNVB4wXCRlZtakegdKJYukGmxLAuYCiyLivML8nfJrB/AZ4MK86DHg0LxsW1JF+X2VHmDmKwwzs6YJdVbWNMjBwPHAAknz87yzgb0lnZqnrwIuzuP/ClwsaSEg4OKIuLuqzBQ5YJiZNavC5s0j4pa0xX6d30/6laRK8GHngGFm1jS3VmtmZmUIcFtSZmZWhq8wzMysISHSzUtbNgcMM7NmCeQOlMzMrAwXSZmZWWPpyb1W52LYOWCYmVXAVxhmZlbOVnBbbUuOUNLHck9S90j6L0njJe0h6TZJiyV9V9LYnHZcnl6cl89oRZ7NzAYipaZBygyj2YgHDEm7Av8XmB0RrwI6geOALwNfj4i9gGeBk/MqJwPP5vlfz+nMzNpKVY0PtrNWXUN1ARNyD1HbkDr+OJTUEQjApcA78/gxeZq8/LDcmqOZWXuoV3qXGUaxEc99RDwBfI3UJO9S4HngTuC5iOjJyZaQuiQkvz6e1+3J6Xfsu11Jp9R7sPrj8lXDexBmZn11qNwwirWiSGp70lXDHqQeorYFjmx2uxExJyJmR8TsaZO3bXZzZmZDInWUGkazVuT+cOD3EfHH3EPUVaT236fkIiqA3YAn8vgT5L5r8/LtgD+NbJbNzAYhfIUxTB4DDpK0Ta6LOAy4F7gBeE9OcyLwozx+dZ4mL/9FRMQI5tfMrIHq7pKSNF3SDZLuzXeTnpbn7yPpN5IWSPqxpMmFdf4sL1uYl48fjqNsRR3GbaTK67uABTkPc4BPAmdIWkyqo5ibV5kL7JjnnwGcNdJ5NjMbVL158zJDYz3AmRExi9Td6qmSZgEXAWdFxKuBHwAfh/UlL98BPhIRrwQOAborP0Za9OBeRHwW+Gyf2Q8DB/aTdg0j1JuUmdnmUbpTqgIRsZR0QxARsULSItLNPzOBm3OyecB1wDnAEcDdEfG7vM6wFdmP7hoYM7M2oY6OUgMwtX5HZx5OGXCb6UHl/YDbgIWkG4Yg/YmensdnAiHpOkl3SfrEcB2jmwYxM2uWGMozFssiYnbDTUoTgSuB0yNiuaQPARdIOodUt7suJ+0CXg+8BlgNXC/pzoi4fohH0ZADhplZ06q9A0rSGFKwuCwirgKIiPtIxU9Imgm8NSdfAtwcEcvysmuA/YHKA4aLpMzMmiRR5V1SIt3ssygizivM3ym/dgCfAS7Mi64DXp3vPO0C3kS687RyvsIwM2tapf1hHAwcDyyQND/POxvYW9Kpefoq4GKAiHhW0nnA7UAA10TEf1eVmSIHDDOzKlR3l9QtpFqR/pw/wDrfId1aO6wcMMzMqrAV9IfhgGFm1ix30WpmZqWN8s6RynDAMDOrwlbQTY8DhplZsyTXYZiZWUm+wjAzs1LauNJb0gUlki2PiM8MlsABw8ysWe1fJHUM8PcN0pxFeoJ8QA4YZmZV6Gjru6S+HhGXDpYgd589qLYOiWZmo0PuD6PM0BpXDrRA0tsAIuKfG23EAcPMrFnV9rg3HOblvjU2kptM77e5kf44YJiZNSmAkEoNLXIG8DNJe9dnSPoU8DFS67aluA7DzKxp7d00SERcI2ktcK2kdwJ/TeoS+40R8WzZ7ThgmJlVoY0DBkBEXC/pJOBG4NfAoRGxZijbcMAwM2uWRLTxXVKSVpBKzgSMAw4Dns6dNUVETC6znfYOiWZmo0VFd0lJmi7pBkn3Sloo6bQ8fx9Jv5G0QNKPJU3us95LJK2U9Hd9txkRkyJicn4dGxHbFqZLBQtwwDAzq0Z1d0n1AGdGxCzgIOBUSbOAi4CzIuLVwA+Aj/dZ7zzg2gqPaBMOGGZmTSt3h1SZu6QiYmlE3JXHVwCLgF2BmcDNOdk84N3r954qsn8PLOw3d9JdDY+gRBrXYZiZNUsMpdJ7qqQ7CtNzImJOv5tNz07sB9xGCgbHAD8E3gtMz2kmAp8E3gxsUhyVvULS3Q2OYLtGGXfAMDNr2pAqvZdFxOyGW0yB4Erg9IhYnh+yu0DSOcDVwLqc9HOkpj9WauArmJeXyFdvowQOGGZmFYgKb6uVNIYULC6LiKsAIuI+4Ii8fCbw1pz8tcB7JH0FmALUJK2JiH9Zn7eIR6vIlwOGmVkVKnqKO9/qOhdYFBHnFebvFBFPS+ogtSp7IUBEvKGQ5nPAymKwqJIDhplZs1Tpk94HA8cDCyTNz/POBvaWdGqevgq4uKodluWAYWbWpHpbUpVsK+IWUiV0fwZtKDAiPtdo+5J2B/aOiJ9LmgB05buxGvJttWZmVVBHuaGVWZQ+DFwB/HuetRvprqtSfIVhZtY0UVP7Ng1ScCqp0cHbACLiQUk7lV25JeFO0hRJV0i6T9IiSX8uaQdJ8yQ9mF+3z2kl6QJJiyXdLWn/VuTZzGxQo+AKA1gbEfXbcZHURSpRK6VVuT8f+GlEvBzYh/Qk41nA9RGxN3B9ngY4Ctg7D6cA3xj57JqZDUJt3x9G3U2SzgYmSHoz8H3gx2VXHvGAIWk74I2k28aIiHUR8RzpCcZ6n7OXAu/M48cA34rkVmCKpF1GNNNmZoMIRKij1NBinwT+CCwA/jdwDekW3VJaUYexBynDF0vaB7gTOA3YOSKW5jR/AHbO47sCjxfWX5LnLcXMrF20/uphUJI6gYW5ZOc/NmcbrQh3XcD+wDciYj9gFRuKn4DUODtDKFcDkHSKpDsk3fHH5asqy6yZWWOp0rvM0CoR0QvcL+klm7uNVlxhLAGWRMRtefoKUsB4StIuEbE0Fzk9nZc/QW5kK9stz9tIbrxrDsABe+42pGBjZtasNihuKmN7YKGk/yH9WQcgIt5RZuURDxgR8QdJj0t6WUTcT+r56d48nAh8Kb/+KK9yNfBRSZeT2kx5vlB0ZWbWeqLti6Syc5pZuVXPYfwtcJmkscDDwEmk4rHvSToZeBQ4Nqe9BjgaWAyszmnNzNqIiFHwHHRE3NTM+i0JGBExH+ived/D+kkbpIdNzMzaUpVNgwynQt/eAGOBMcCqst20+klvM7MKjIY6jIiYVB/PreIeQ+oGtpT2P0Izs7bX/ndJ9ZWfbfsh8Jay6/gKw8ysAqOkSOpdhckOUtXAmrLrO2CYmTUpPTjW/gEDeHthvAd4hFQsVYoDhplZs6TK6jAkTQe+RWrtIoA5EXF+bhnjQmAi6Yf+A7mv7zeTHkcYS+rn++MR8YsBNn9RRPyqz/4OZsNzb4NyHYaZWQUClRpK6AHOjIhZpArpUyXNAi4CzoqIVwM/AD6e0y8D3p7nnwh8e5Bt/7+S8/rlKwwzswpUdYWRH0xemsdXSFpEaj9vJnBzTjYPuA44JyJ+W1h9Iakl2nERsbY+U9KfA68Dpkk6o5B+MlC6Jt4Bw8ysSTG0DpSmSrqjMD0nN220CUkzgP1IHR4tJNU3/BB4Lxs3mVT3buCuYrDIxpKKsrqASYX5y4H3lM14w4Ah6W+B70TEs2U3ama2tRlCpfeyiOjvweWNSJoIXAmcnusqPgRcIOkcUpNJ6/qkfyXwZeCITfKWnvC+SdIlEfFo2Yz2VeYKY2fgdkl3Ad8ErstPX5uZWVblbbWSxpCCxWURcRVARNxHDgaSZgJvLaTfjVSvcUJEPDTIpldL+irwSmD8+rxHHFomXw0L3SLiM6Te7uYCHwQelPRPkvYsswMzs61BhEoNjeQnsOcCiyLivML8nfJrB6nTowvz9BTgv0kV4r/aZIMbuwy4j9Qv0edJd1vdXvYYS9XS5CuKP+Shh9RE7hWSvlJ2R2ZmW67U+GCZoYSDgeOBQyXNz8PRwPskPUD6wX8SuDin/yiwF/D3hfQ7DbDtHSNiLtAdETdFxIeAUlcXUK4O4zTgBNKtWxeR7vHtzlHuQeATZXdmZrYlCqBW0VMKEXELDFghcn4/6c8Fzi25+e78ulTSW0mBZ4eyeStTh7ED8K6+FSURUZP0trI7MjPbko2SJ73PlbQdcCbp+YvJwMfKrtwwYETEZwdZtqjsjszMtlylH8prmdyn994R8RPgeeAvhroNP+ltZlaBqiq9hy9/0Qu8r5lt+ME9M7MmjaLGB38l6V+A77Jxn953lVnZAcPMrAKjJGDsm1+/UJgXlLxTygHDzKxpohbtX8IfEUOutyhq/yM0M2tz6bZalRpaSdLOkuZKujZPz5J0ctn1HTDMzCpQYfPmw+kSUiu3L87TDwCnl13ZAcPMrFnR/ndJZVMj4ntADSAieoDesiu7DsPMrAJtcPVQxipJO5JK0ZB0EOmZjFIcMMzMmtYWVw9lnEFqGn1PSb8CplFlfxhmZja4gNFyl9Rdkt4EvIzUXtX9EdHdYLX1HDDMzCpQa3UGSpA0Hvgb4PWkOPdLSRdGxJoy6ztgmJlVYJQUSX0LWEFqeBDg/cC3SV2+NuSAYWbWpDa5ZbaMV0XErML0DZLuLbty+xe6mZmNAhX2uDdd0g2S7pW0MPdJhKR9JP1G0gJJP5Y0ubDOpyQtlnS/pLcMsvm78p1R9fVeC9xR9hh9hWFm1qyA3uqKpHqAM3MF9STgTknzSB3Y/V1E3CTpQ8DHgXMkzQKOI/XT/WLg55Jm5tZp+zoA+LWkx/L0S4D7JS0gda76Z4NlzAHDzKxJVbZWGxFLgaV5fIWkRcCuwEzg5pxsHumJ7XOAY4DLI2It8HtJi4EDgd/0s/kjm8mbi6TMzCowhCKpqZLuKAynDLRNSTOA/YDbgIWk4ACpknp6Ht8VeLyw2pI8r588xqPAcmA7YMf6EBGP9u1VtT8tu8LIvT/dATwREW+TtAdwOekA7gSOj4h1ksaRavYPAP4E/FVEPNKibJuZ9SuidNJlETG7USJJE4ErgdMjYnkuhrpA0jmkh+/WDTWPkv4B+CDwEPlpb0ZJ8+anAYtIfcoCfBn4ekRcLulC4GTgG/n12YjYS9JxOd1ftSLDZmb9q7YlWkljSMHisoi4CiAi7gOOyMtnAm/NyZ9gw9UGwG55Xn+OBfaMiCEHG2hRkZSk3UgHe1GeFinCXZGTXAq8M48fk6fJyw/L6c3M2kJQ6V1SAuYCiyLivML8nfJrB/AZ4MK86GrgOEnjcknN3sD/DLD5e4Apm3mYLbvC+GfgE8CkPL0j8FxuORE2LoNbXz4XET2Sns/pl41Ybs3MGqhVd5fUwcDxwAJJ8/O8s4G9JZ2ap68CLgaIiIWSvgfcS7rD6tQB7pAC+CLwW0n3AGvrMyPiHWUyNuIBQ9LbgKcj4k5Jh1S43VOAUwCmT51S1WbNzBoLqJWvwxh8UxG3wIDlW+cPsM4/Av9YYvOXkor1F7AZrZm04grjYOAdko4GxpPqMM4HpkjqylcZxTK4evncEkldpNr9P/XdaETMAeYAHLDnbhW9dWZmjdWLpEaB1RFxweauPOJ1GBHxqYjYLSJmkB42+UVEfAC4gQ3N7J4I/CiPX52nyct/ETGE+xHMzEZARLmhxX4p6YuS/lzS/vWh7Mrt9ODeJ4HLJZ0L/JZU6UN+/XZ+GOUZUpAxM2srre6vu6T98utBhXmj4rZaIuJG4MY8/jDp6cS+adZQsiVFM7NWaYOrh4Yi4i+aWd9PepuZNSlC9NbKDa0kaWdJcyVdm6dnSTq57PoOGGZmFRgldRiXkNqgenGefgA4vezKDhhmZhWo94nRaGiFfIcpwNSI+B75ltp8V+pAz2xswgHDzKxJqU/vckOL1J/8XiVpx5xlct8Yz5fdSDvdJWVmNmq1QXHTYOqXNmeQHlXYU9KvgGlseJyhIQcMM7MmRdDyCu0Gpkk6I4//ALiGFETWAocDd5fZiAOGmVkF2vwKoxOYyKZNjmwzlI04YJiZVaDNA8bSiPhCsxtxwDAzq0ALK7TLqKS8zAHDzKxJo6DxwcOq2IgDhplZs9rjobwBRcQzVWzHz2GYmTUpgN5auaERSdMl3SDpXkkLJZ2W5+8r6VZJ8yXdIenAPH87ST+W9Luc/qThOk5fYZiZVaDCK4we4MyIuEvSJOBOSfOArwCfj4hrc39CXwEOAU4F7o2It0uaBtwv6bLN7bd7MA4YZmYVqLDHvaXA0jy+QtIiUlfVQepwDlJHck/WVwEm5b7AJ5K6gehhGDhgmJk1a2h1GFMl3VGYnpN7DN2EpBmkPixuIzUSeJ2kr5GqE16Xk/0L6entJ4FJwF9FxJC7Xy3DAcPMrEkB1Mr/RC+LiNmNEkmaCFwJnB4Ry3Pnch+LiCslHUvqXO5w4C3AfFInSHsC8yT9MiKWD/lAGnClt5lZBWq1ckMZksaQgsVlEXFVnn0iUB//Phs6nDsJuCqSxcDvgZdXdVxFDhhmZk2Kki3VlqnnyHURc4FFEXFeYdGTwJvy+KHAg3n8MfJzFpJ2Bl4GPFzNkW3MRVJmZhWI6m6TOhg4HlggaX6edzbwYeD83LfFGuCUvOwfgEskLSA90f3JiFhWVWaKHDDMzCpQVbyIiFsYuCmPA/pJ/yRwRDV7H5wDhplZBYZQ6T1qOWCYmTWpTfrrHnYOGGZmFSjT7Mdo54BhZlaBaPP2zavggGFm1qQoecvsaOeAYWZWAddhmJlZKbWt4BLDAcPMrEmpx71W52L4OWCYmTUrgl5fYZiZWRnD06B4e3HAMDNrUiqS8hWGmZk1EltH0yAj3rz5IB2c7yBpnqQH8+v2eb4kXSBpsaS7Je0/0nk2M2skIkoNo1kr+sOod3A+CzgIOFXSLOAs4PqI2Bu4Pk8DHAXsnYdTgG+MfJbNzAYWAb29UWoYzUY8YETE0oi4K4+vAOodnB8DXJqTXQq8M48fA3wr9yZ1KzBF0i4jm2szs8HVGyBsNIxmLe1xr08H5ztHxNK86A/Aznl8V+DxwmpL8jwzs7ZRq0WpoZFBiu33lXSrpPmS7pB0YGGdQ/L8hZJuGq5jbFmldz8dnK9fFhEhaUixWNIp5B6opk+dUmFOzcwGV3H9RL3Y/i5Jk4A7Jc0DvgJ8PiKulXR0nj5E0hTg34AjI+IxSTtVlZG+WnKFMUAH50/Vi5ry69N5/hPA9MLqu+V5G4mIORExOyJmT5u87fBl3sysH1ErNzTczsDF9gFMzsm2I/XxDfB+4KqIeCyv8zTDpBV3SQ3UwfnVwIl5/ETgR4X5J+S7pQ4Cni8UXZmZtYVaRKlhKPoU258OfFXS48DXgE/lZDOB7SXdKOlOSSdUdlB9tKJIaqAOzr8EfE/SycCjwLF52TXA0cBiYDVw0ojm1sysgXSXVOkHMaZKuqMwPSci5vRN1E+x/bnAxyLiSknHkv54H076HT8AOAyYAPxG0q0R8UATh9SvEQ8YDTo4P6yf9AGcOqyZMjNr0hAuHpZFxOzBEgxQbH8icFoe/z5wUR5fAvwpIlYBqyTdDOwDVB4wWnqXlJnZliJqUWpoZJBi+yeBN+XxQ4EH8/iPgNdL6pK0DfBaUr1H5dw0iJlZk2Iz6icGMVCx/YeB8yV1AWvId4VGxCJJPwXuBmrARRFxT1WZKXLAMDOrQFV9ejcotj9ggHW+Cny1kgwMwgHDzKwCVQWMduaAYWbWpHpbUls6Bwwzs6aN/pZoy3DAMDNrVlCqnajRzgHDzKwCvsIwM7OGAld6m5lZGRFDaRpk1HLAMDOrgK8wzMysocB1GGZmVobvkjIzs7JcJGVmZiX4wT0zMyshAnp7eludjWHngGFmVgFfYZiZWWNRrnOk0c4Bw8ysSVvLk97uotXMrAK1qJUaGpE0XdINku6VtFDSaXn+vpJulTRf0h2SDuyz3msk9Uh6zzAdoq8wzMyaFpVeYfQAZ0bEXZImAXdKmgd8Bfh8RFwr6eg8fQiApE7gy8DPqspEfxwwzMyaFAS1itqSioilwNI8vkLSImBXUsnX5JxsO+DJwmp/C1wJvKaSTAzAAcPMrFkBtVrpgDFV0h2F6TkRMae/hJJmAPsBtwGnA9dJ+hqpOuF1Oc2uwF8Cf4EDhplZ+xtCkdSyiJjdKJGkiaSrhtMjYrmkc4GPRcSVko4F5gKHA/8MfDIiapI2L/MlOWCYmTUpCKJEhXZZksaQgsVlEXFVnn0icFoe/z5wUR6fDVyeg8VU4GhJPRHxw8oylDlgmJk1q8JKb6Vf/rnAoog4r7DoSeBNwI3AocCDABGxR2HdS4CfDEewAAcMM7MKBL29lTUNcjBwPLBA0vw872zgw8D5krqANcApVe2wLAcMM7MmRYVXGBFxCzBQZcQBDdb9YCWZGIADhplZBaL8XVKjlgOGmVmzqn1wr205YJiZNa3au6TalQOGmVmTAnfRamZmZURQ2wo6UBo1rdVKOlLS/ZIWSzqr1fkxMyuKqJUaRrNRcYWRW2L8V+DNwBLgdklXR8S9rc2ZmRmu9G4zBwKLI+JhAEmXA8cADhhm1nJBbBW31Wo09EObOwQ5MiL+Ok8fD7w2Ij5aSHMKG558fBVwz4hntLGpwLJWZ6IP56mcdswTtGe+Rluedo+Iac1sXNJP8z7KWBYRRzazv1YZLVcYDeXmgecASLqjTGuQI60d8+U8ldOOeYL2zNfWmKfRGgCGarRUej8BTC9M75bnmZnZCBktAeN2YG9Je0gaCxwHXN3iPJmZbVVGRZFURPRI+ihwHdAJfDMiFg6ySr+9V7WBdsyX81ROO+YJ2jNfztMWalRUepuZWeuNliIpMzNrMQcMMzMrZYsLGK1qQkTSdEk3SLpX0kJJp+X5n5P0hKT5eTi6sM6ncj7vl/SWYcrXI5IW5H3fkeftIGmepAfz6/Z5viRdkPN0t6T9hyE/Lyuci/mSlks6vRXnSdI3JT0t6Z7CvCGfG0kn5vQPSjpxGPL0VUn35f3+QNKUPH+GpBcK5+zCwjoH5Pd9cc73QB3ybG6ehvx+VfndHCBP3y3k5xHl3upG6jxtFSJiixlIFeIPAS8FxgK/A2aN0L53AfbP45OAB4BZwOeAv+sn/aycv3HAHjnfncOQr0eAqX3mfQU4K4+fBXw5jx8NXEvq7esg4LYReL/+AOzeivMEvBHYH7hnc88NsAPwcH7dPo9vX3GejgC68viXC3maUUzXZzv/k/OpnO+jKs7TkN6vqr+b/eWpz/L/D/j7kTxPW8OwpV1hrG9CJCLWAfUmRIZdRCyNiLvy+ApgEbDrIKscA1weEWsj4vfAYlL+R8IxwKV5/FLgnYX534rkVmCKpF2GMR+HAQ9FxKODpBm28xQRNwPP9LO/oZybtwDzIuKZiHgWmAds9kNc/eUpIn4WET158lbSc0gDyvmaHBG3RvpV/FbhOCrJ0yAGer8q/W4Olqd8lXAs8F+DbaPq87Q12NICxq7A44XpJQz+oz0sJM0A9gNuy7M+mosTvlkv4mDk8hrAzyTdqdR8CsDOEbE0j/8B2HmE81R3HBt/qVt5nuqGem5GOn8fIv0TrttD0m8l3STpDYW8LhmBPA3l/RrJ8/QG4KmIeLAwr5XnaYuxpQWMlpM0EbgSOD0ilgPfAPYE9gWWki6VR9LrI2J/4CjgVElvLC7M/6xG/N5qpQcw3wF8P89q9XnaRKvOzUAkfRroAS7Ls5YCL4mI/YAzgP+UNHmEstN271fB+9j4j0grz9MWZUsLGC1tQkTSGFKwuCwirgKIiKciojdSQ/j/wYbilBHJa0Q8kV+fBn6Q9/9Uvagpvz49knnKjgLuioincv5aep4KhnpuRiR/kj4IvA34QA5k5GKfP+XxO0l1BDPz/ovFVpXnaTPer5E6T13Au4DvFvLasvO0pdnSAkbLmhDJ5aZzgUURcV5hfrEO4C/Z0Iru1cBxksZJ2gPYm1QBV2WetpU0qT5Oqjy9J++7fjfPicCPCnk6Id8RdBDwfKF4pmob/Qts5XnqY6jn5jrgCEnb52KZI/K8ykg6EvgE8I6IWF2YP02prxgkvZR0bh7O+Vou6aD8uTyhcBxV5Wmo79dIfTcPB+6LiPVFTa08T1ucVte6Vz2Q7mZ5gPQv4tMjuN/Xk4ov7gbm5+Fo4NvAgjz/amCXwjqfzvm8n2G4O4N0R8rv8rCwfj6AHYHrgQeBnwM75PkidVT1UM7z7GE6V9sCfwK2K8wb8fNEClhLgW5S+fXJm3NuSPUKi/Nw0jDkaTGp/L/+ubowp313fl/nA3cBby9sZzbpR/wh4F/IrTpUmKchv19Vfjf7y1OefwnwkT5pR+Q8bQ2DmwYxM7NStrQiKTMzGyYOGGZmVooDhpmZleKAYWZmpThgmJlZKQ4YZmZWigOGmZmV4oBhWzxJr8mN5I3PT78vlPSqVufLbLTxg3u2VZB0LjAemAAsiYgvtjhLZqOOA4ZtFXL7RbcDa4DXRURvi7NkNuq4SMq2FjsCE0m9IY5vcV7MRiVfYdhWQdLVpF7e9iA1lPfRFmfJbNTpanUGzIabpBOA7oj4z9zM9a8lHRoRv2h13sxGE19hmJlZKa7DMDOzUhwwzMysFAcMMzMrxQHDzMxKccAwM7NSHDDMzKwUBwwzMyvl/wcmPHK0fhVjJgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hrrr_ds.t.plot(cmap=plt.cm.coolwarm)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "44274766-4c9d-4224-934f-70a874b968d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1059\n" + ] + } + ], + "source": [ + "print(len(hrrr_ds.t['y']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "057337aa-889d-4442-ad03-d92c77283206", + "metadata": {}, + "outputs": [], + "source": [ + "hrrr_ds.t.plot(cmap=plt.cm.coolwarm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b203a02-d28b-4d5e-962c-d4b4fe79ad73", + "metadata": {}, + "outputs": [], + "source": [ + "print((hrrr_ds['t']['x']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e270a5c-8de9-4224-91f7-bb8cc882963b", + "metadata": {}, + "outputs": [], + "source": [ + "for y in range(0, (len(hrrr_ds['t']['y'])), 1):\n", + " coord_str = \"\"\n", + " for x in range(len(hrrr_ds['t']['x'])):\n", + " lat = float(hrrr_ds['t'][y][x].latitude)\n", + " lon = float(hrrr_ds['t'][y][x].longitude)\n", + " coordinates = str(str(lon)+', '+str(lat))\n", + " coordinates_list.append(coordinates)\n", + " value = float(hrrr_ds['t'][y][x])\n", + " \n", + " coord_str += \"-lon %f %f \" % (float(lon), float(lat))\n", + " \n", + " #hrrr_ds['t'][y][x] = (value+gfs_value)/2 \n", + " #print(float(hrrr_ds['t'][y][x]))\n", + " coord_strs.append(coord_str) \n", + " file = open('coord_strs.txt', 'a')\n", + " file.write(str(coord_str))\n", + " file.write('\\n')\n", + " print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9df1d6ea-29b6-4fe0-bf67-2f6e1fbc8100", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/metpy.ipynb b/randomcrap/metpy.ipynb new file mode 100644 index 0000000..22c9003 --- /dev/null +++ b/randomcrap/metpy.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 38, + "id": "c44ddede-702f-4c88-8f3f-660126df0224", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Ignoring index file 'hrrr.grib2.923a8.idx' older than GRIB file\n", + "Ignoring index file 'gfs.grib2.923a8.idx' older than GRIB file\n", + "Ignoring index file 'gfs.grib2.923a8.idx' older than GRIB file\n" + ] + } + ], + "source": [ + "import requests\n", + "import xarray as xr\n", + "import cartopy.crs\n", + "from metpy.units import units\n", + "from metpy.plots import ImagePlot, MapPanel, PanelContainer\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import requests\n", + "import time\n", + "import subprocess\n", + "\n", + "datestr = time.strftime('%Y%m%d')\n", + "\n", + "\n", + "gfs_url = 'https://nomads.ncep.noaa.gov/cgi-bin/filter_gfs_0p25_1hr.pl?file=gfs.t00z.pgrb2.0p25.f001&lev_surface=on&var_TMP=on&leftlon=0&rightlon=360&toplat=90&bottomlat=-90&dir=%2Fgfs.'+datestr+'%2F00%2Fatmos'\n", + "r = requests.get(gfs_url, allow_redirects=True)\n", + "open('gfs.grib2', 'wb').write(r.content)\n", + "\n", + "nam_url = 'https://nomads.ncep.noaa.gov/cgi-bin/filter_nam_conusnest.pl?file=nam.t00z.conusnest.hiresf00.tm00.grib2&lev_surface=on&var_TMP=on&leftlon=0&rightlon=360&toplat=90&bottomlat=-90&dir=%2Fnam.'+datestr\n", + "r = requests.get(nam_url, allow_redirects=True)\n", + "open('nam.grib2', 'wb').write(r.content)\n", + "\n", + "hrrr_url = 'https://nomads.ncep.noaa.gov/cgi-bin/filter_hrrr_2d.pl?file=hrrr.t00z.wrfsfcf01.grib2&lev_surface=on&var_TMP=on&leftlon=0&rightlon=360&toplat=90&bottomlat=-90&dir=%2Fhrrr.'+datestr+'%2Fconus'\n", + "r = requests.get(hrrr_url, allow_redirects=True)\n", + "open('hrrr.grib2', 'wb').write(r.content)\n", + "\n", + "ds = xr.load_dataset(\"hrrr.grib2\", engine=\"cfgrib\",filter_by_keys={'typeOfLevel': 'surface'})\n", + "gfs_ds = xr.load_dataset(\"gfs.grib2\", engine=\"cfgrib\",filter_by_keys={'typeOfLevel': 'surface'})\n", + "nam_ds = xr.load_dataset(\"gfs.grib2\", engine=\"cfgrib\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc2298bf-87ed-4649-8439-2ab4fb6ebc45", + "metadata": {}, + "outputs": [], + "source": [ + "img = ImagePlot()\n", + "img.data = ds\n", + "img.field = 't'\n", + "img.colormap = 'coolwarm'\n", + "\n", + "panel = MapPanel()\n", + "panel.area = 'ca'\n", + "panel.layers = ['states']\n", + "panel.title = 'GFS temp forecast'\n", + "panel.plots = [img]\n", + "\n", + "pc = PanelContainer()\n", + "pc.size = (10, 8)\n", + "pc.panels = [panel]\n", + "pc.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2cc58871-e10a-47b9-a461-c9be68d54d17", + "metadata": {}, + "outputs": [], + "source": [ + "ds = ds.expand_dims(\"time\", 0)\n", + "ds = ds.assign_coords(longitude=ds.longitude - 360)\n", + "\n", + "# Set units on longitude\n", + "ds.longitude.attrs['units'] = 'degree'\n", + "#ds.t" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c9fed0c0-578d-4cc2-922b-8f7cd6f08efa", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "crs attribute is not available.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_1242234/936088086.py\u001b[0m in 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\u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: crs attribute is not available." + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img = ImagePlot()\n", + "img.data = ds\n", + "img.field = 't'\n", + "img.colormap = 'coolwarm'\n", + "\n", + "panel = MapPanel()\n", + "panel.area = 'ca'\n", + "panel.layers = ['states']\n", + "panel.title = 'GFS temp forecast'\n", + "panel.plots = [img]\n", + "\n", + "pc = PanelContainer()\n", + "pc.size = (10, 8)\n", + "pc.panels = [panel]\n", + "pc.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98864362-3806-49e1-80bb-aaba60ddba3b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/operational.ipynb b/randomcrap/operational.ipynb new file mode 100644 index 0000000..10f6fcb --- /dev/null +++ b/randomcrap/operational.ipynb @@ -0,0 +1,39727 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "231a07ea-cadc-495c-ad75-22825c2ca8af", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d14d7e6-90c8-4eaf-9edf-ba952ac01c03", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.load_dataset(\"hrrr.t00z.wrfsfcf00_1.grib2\", engine=\"cfgrib\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9193bbdc-e08a-443e-b745-08a133e6b2da", + "metadata": {}, + "outputs": [], + "source": [ + "len(ds['latitude'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a3bc8ec-06e1-4223-b7c0-436e67dceeaf", + 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range(len(ds['latitude'][0])):\n", + " current_lat = float(ds['latitude'][i][x])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25030f1a-25c6-42a3-aa2f-a8cf9e7af5bc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/operational.py b/randomcrap/operational.py new file mode 100644 index 0000000..d797df2 --- /dev/null +++ b/randomcrap/operational.py @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 409, + "id": "7e0c5939-3495-46e4-b486-bb9834c90975", + "metadata": {}, + "outputs": [], + "source": [ + "data_url = ('http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p25deg/Best')" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "id": "22a56d6f-3fd9-4d24-995a-fe026e2c4997", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime, timedelta\n", + "import io\n", + "\n", + "from metpy.units import units\n", + "from metpy.plots import ImagePlot, MapPanel, PanelContainer\n", + "from siphon.catalog import TDSCatalog\n", + "import xarray as xr\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "id": "012bf6b8-f52b-4fa8-90db-5aeac404c575", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Best GFS Quarter Degree Forecast Time Series']\n" + ] + } + ], + "source": [ + "best_gfs = TDSCatalog(data_url)\n", + "print(list(best_gfs.datasets))" + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "id": "e4c9c118-ac4f-40f1-b3bd-00fa16b91bb5", + "metadata": {}, + "outputs": [], + "source": [ + "best_ds = best_gfs.datasets[0]\n", + "ncss = best_ds.subset()" + ] + }, + { + "cell_type": "code", + "execution_count": 413, + "id": "318f0e28-1dd0-43d9-a328-77e6be0718d4", + "metadata": {}, + "outputs": [], + "source": [ + "query = ncss.query()" + ] + }, + { + "cell_type": "code", + "execution_count": 418, + "id": "ca16666a-c23c-443d-9dc5-1f8ae1eeca21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time=2021-11-30T08%3A46%3A14.731567&west=-130&east=-60&south=25&north=50&accept=netcdf4\n" + ] + } + ], + "source": [ + "query.lonlat_box(north=50, south=25, east=-60, west=-130).time(datetime.utcnow() + timedelta(hours=12))\n", + "query.accept('netcdf4')\n", + "#query.variables('Temperature_surface')\n", + "print(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14fb8091-6596-4f3a-8014-bfcabfa96ddb", + "metadata": {}, + "outputs": [], + "source": [ + "data = ncss.get_data_raw(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8616545c-e95e-4b78-92c4-4313910a7f67", + "metadata": {}, + "outputs": [], + "source": [ + "gfs = xr.open_dataset(io.BytesIO(data))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d2bdf30-762b-46db-a402-841b8e6875d9", + "metadata": {}, + "outputs": [], + "source": [ + "gfs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b00a4c1-2137-4b98-8553-7a06fab423ec", + "metadata": {}, + "outputs": [], + "source": [ + "img = ImagePlot()\n", + "img.data = gfs\n", + "#gfs.Temperature_surface[0][0][0] = float(gfs.Temperature_surface[0][0][0])\n", + "#gfs.Temperature_surface[0][1][1] = 250.0\n", + "#print(float(gfs.Temperature_surface[0][1][1]))\n", + "#print(float(gfs.Temperature_surface[0][0][0]))\n", + "img.field = 'Temperature_surface'\n", + "img.colormap = 'coolwarm'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0c2433a-c1db-44e4-a09b-a722dde0e761", + "metadata": {}, + "outputs": [], + "source": [ + "panel = MapPanel()\n", + "panel.area = 'co'\n", + "panel.layers = ['states']\n", + "panel.title = 'GFS temperature forecast'\n", + "panel.plots = [img]\n", + "\n", + "pc = PanelContainer()\n", + "pc.size = (10, 8)\n", + "pc.panels = [panel]\n", + "pc.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6f20b6c-2e30-4d49-96c1-c9af144576a8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/plotly.ipynb b/randomcrap/plotly.ipynb new file mode 100644 index 0000000..c849415 --- /dev/null +++ b/randomcrap/plotly.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "e364af09-f20f-43ab-b59c-aaba5d425896", + "metadata": {}, + "outputs": [], + "source": [ + "from urllib.request import urlopen\n", + "import json\n", + "with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:\n", + " counties = json.load(response)\n", + "\n", + "import pandas as pd\n", + "df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv\",\n", + " dtype={\"fips\": str})\n", + "\n", + "import plotly.express as px\n", + "\n", + "fig = px.choropleth_mapbox(df, geojson=counties, locations='fips', color='unemp',\n", + " color_continuous_scale=\"Viridis\",\n", + " range_color=(0, 12),\n", + " mapbox_style=\"carto-positron\",\n", + " zoom=3, center = {\"lat\": 37.0902, \"lon\": -95.7129},\n", + " opacity=0.5,\n", + " labels={'unemp':'unemployment rate'}\n", + " )\n", + "fig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "200414a9-0626-4a34-a78c-00e80b653e92", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a3f30d8-ba8f-4fb4-b112-96170d6fb688", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/pygrib.ipynb b/randomcrap/pygrib.ipynb new file mode 100644 index 0000000..c61f251 --- /dev/null +++ b/randomcrap/pygrib.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "e8da5b45-b7b4-4815-9c1b-2868021c8884", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "179c7ddb-4108-4148-804d-4bad77e2d003", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'mpl_toolkits.basemap'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_3965228/3341290477.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasemap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBasemap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mpl_toolkits.basemap'" + ] + } + ], + "source": [ + "from mpl_toolkits.basemap import Basemap" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a476d76c-d5ab-48dc-8df0-6f959643c642", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/test.ipynb b/randomcrap/test.ipynb new file mode 100644 index 0000000..1a8cebb --- /dev/null +++ b/randomcrap/test.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "c88b8d36-6e22-42a7-817d-be098d33fee8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Ignoring index file 'hrrr.grib2.923a8.idx' older than GRIB file\n", + "Ignoring index file 'gfs.grib2.923a8.idx' older than GRIB file\n", + "Ignoring index file 'nam.grib2.923a8.idx' older than GRIB file\n" + ] + } + ], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import requests\n", + "import time\n", + "import subprocess\n", + "\n", + "datestr = time.strftime('%Y%m%d')\n", + "\n", + "\n", + "gfs_url = 'https://nomads.ncep.noaa.gov/cgi-bin/filter_gfs_0p25_1hr.pl?file=gfs.t00z.pgrb2.0p25.f001&lev_surface=on&var_TMP=on&leftlon=0&rightlon=360&toplat=90&bottomlat=-90&dir=%2Fgfs.'+datestr+'%2F00%2Fatmos'\n", + "r = requests.get(gfs_url, allow_redirects=True)\n", + "open('gfs.grib2', 'wb').write(r.content)\n", + "\n", + "hrrr_url = 'https://nomads.ncep.noaa.gov/cgi-bin/filter_hrrr_2d.pl?file=hrrr.t00z.wrfsfcf01.grib2&lev_surface=on&var_TMP=on&leftlon=0&rightlon=360&toplat=90&bottomlat=-90&dir=%2Fhrrr.'+datestr+'%2Fconus'\n", + "r = requests.get(hrrr_url, allow_redirects=True)\n", + "open('hrrr.grib2', 'wb').write(r.content)\n", + "\n", + "hrrr_ds = xr.load_dataset(\"hrrr.grib2\", engine=\"cfgrib\",filter_by_keys={'typeOfLevel': 'surface'})\n", + "gfs_ds = xr.load_dataset(\"gfs.grib2\", engine=\"cfgrib\",filter_by_keys={'typeOfLevel': 'surface'})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a1e82601-9163-4688-b5ce-f4527efa0971", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\"/usr/bin/wgrib2 %s -match '%s' %s\" % ('gfs.grib2', 'TMP', coord_str)\n", + " gfs_value = (str(subprocess.check_output(gfs_tmp, shell=True))[:-3])\n", + " gfs_value = gfs_value.split(':')\n", + " for z in range (len(gfs_value)):\n", + " current_value = gfs_value[z]\n", + " if 'val' in current_value:\n", + " current_value = float((current_value.split(',')[2]).split('=')[1])\n", + " gfs_value_list.append(current_value)\n", + " \n", + " #delete\n", + " print('HRRR')\n", + " \n", + " coord_str = coord_str.split('-')\n", + " for e in range(1,len(coord_str)):\n", + " print(coord_str[e])\n", + " hrrr_tmp = \"/usr/bin/wgrib2 %s -match '%s' -%s\" % ('hrrr.grib2', 'TMP', coord_str[e])\n", + " hrrr_value = (str(subprocess.check_output(hrrr_tmp, shell=True))[:-3])\n", + "\n", + " #end delete\n", + " \n", + " hrrr_tmp = \"/usr/bin/wgrib2 %s -match '%s' %s\" % ('hrrr.grib2', 'TMP', coord_str)\n", + " hrrr_value = (str(subprocess.check_output(hrrr_tmp, shell=True))[:-3])\n", + " hrrr_value = hrrr_value.split(':')\n", + " for z in range (len(hrrr_value)):\n", + " current_value = hrrr_value[z]\n", + " print(current_value)\n", + " if 'val' in current_value:\n", + " current_value = float((current_value.split(',')[2]).split('=')[1])\n", + " hrrr_value_list.append(current_value)\n", + " \n", + "#GFS_VALUE_LIST IS VALUES FOR EACH LONGITUDE FOR ONE LATITUDE\n", + " print(len(hrrr_value_list))\n", + " \n", + " # THEORY: HRRR domain is not a rectangle" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2a0e9b33-8b7c-4453-911e-fb97095a0980", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1799\n" + ] + } + ], + "source": [ + "gfs_value_list = []\n", + "hrrr_value_list = []\n", + "\n", + "gfs_tmp = \"/usr/bin/wgrib2 %s -match '%s' %s\" % ('gfs.grib2', 'TMP', coord_str)\n", + "gfs_value = (str(subprocess.check_output(gfs_tmp, shell=True))[:-3])\n", + "gfs_value = gfs_value.split(':')\n", + "for z in range (len(gfs_value)):\n", + " current_value = gfs_value[z]\n", + " if 'val' in current_value:\n", + " current_value = float((current_value.split(',')[2]).split('=')[1])\n", + " gfs_value_list.append(current_value)\n", + " \n", + "#GFS_VALUE_LIST IS VALUES FOR EACH LONGITUDE FOR ONE LATITUDE\n", + "print(len(gfs_value_list))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25f3e932-273d-4940-b0db-ccc5f5c8da35", + "metadata": {}, + "outputs": [], + "source": [ + "#find all lowest coordinates for NETCDF files\n", + "#store coordinates\n", + "\n", + "#cycle through coordinates and find values from each model. weight accordingly\n", + "#plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84ea6088-2010-4d7c-acdc-e747c74ce193", + "metadata": {}, + "outputs": [], + "source": [ + "for y in range(len(hrrr_ds['y'])):\n", + " coord_str = \"\"\n", + " latitude = float(hrrr_ds['latitude'][y][0])\n", + " for x in range(len(hrrr_ds['x'])):\n", + " longitude = float(hrrr_ds['longitude'][0][x])\n", + " coord_str += \"-lon %f %f \" % (float(longitude), float(latitude))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/randomcrap/untitled.py b/randomcrap/untitled.py new file mode 100644 index 0000000..2bb01ff --- /dev/null +++ b/randomcrap/untitled.py @@ -0,0 +1,33 @@ +#import modules +import os +import csv +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +#open and clear csv file +filename = "urma_temp.csv" +f = open(filename, "w+") + +f = open('coordinates.txt') #open coordinates document +contents = f.read() #read coordinates +coordinates = contents.splitlines() #turn coordinates into list + +for x in range (len(coordinates)): #for each coordinate + cmd_tmp = "/usr/bin/wgrib2 %s -match '%s' -lon %s >> urma_temp.csv" % ('hrrr.t00z.wrfsfcf00_1.grib2', ':TMP:2 m above ground:anl:', coordinates[x]) + os.system(cmd_tmp) #perform command to put lat, lon, and value into csv + +with open('urma_temp.csv', 'r') as f: + csv_reader = csv.reader(f) + csv_list = list(csv_reader) + +for location in csv_list: + location[0] = float((location[0]).split('=')[1]) + location[1] = float((location[1]).split('=')[1]) + location[2] = float((location[2]).split('=')[1]) + location = str(location) + +df = pd.DataFrame(columns=['latitude', 'longitude', 'value']) +for i in range(len(csv_list)): + df.loc[i] = csv_list[i][0],csv_list[i][1],csv_list[i][2] +print(df) \ No newline at end of file diff --git a/randomcrap/untitled1.py b/randomcrap/untitled1.py new file mode 100644 index 0000000..6f53228 --- /dev/null +++ b/randomcrap/untitled1.py @@ -0,0 +1,182 @@ +# Resolve the latest GFS dataset +import metpy +from siphon.catalog import TDSCatalog +import sys +import numpy as np +import matplotlib +matplotlib.use('Agg') #Avoid the need for X11 forwarding + +#------------------------ +# Set up access via NCSS. +#------------------------ +# We can plot just about anything from https://thredds.ucar.edu/thredds/catalog.html +# Use this to plot GFS output +#catalog = ('http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/' +# 'Global_0p5deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p5deg/Best') +# +# Here, let's plot the latest HRRR model analysis +catalog = ('https://thredds.ucar.edu/thredds/catalog/grib/NCEP/HRRR/CONUS_2p5km_ANA/catalog.xml') + +# This works, but... +#cat = TDSCatalog(catalog) +#ncss = cat.datasets[0].subset() + +# ...this is faster +from siphon.catalog import get_latest_access_url +from siphon.ncss import NCSS +latest = get_latest_access_url(catalog,"NetcdfSubset") +ncss = NCSS(latest) + +# We can see what variables are available from ncss +print(ncss.variables) + +# From here, we can build a query to ask for the data we want from the server +from datetime import datetime, timedelta + +# Create a new NCSS query +query = ncss.query() + +# Request data in netCDF format +query.accept('netcdf') + +# Ask for a specific variable (or variables with a comma-delimited set of strings) +query.variables('Temperature_isobaric', 'Geopotential_height_isobaric') + +# Ask for the 850 hPa surface. Native units are pascals. +#query.vertical_level(85000) #This works for a single variable, but not for two or more because + #the number and values of levels might be different. Handle this later. + +# Set the time range of the data we want +now = datetime.utcnow() #This grabs the current system time and saves it as a datetime object +#query.time_range(now, now + timedelta(days=1)) #This sets a range from now to a day from now (use this for forecasts) +query.time_range(now - timedelta(hours=3), now) #For the latest HRRR data, we want to find data within the last 3 hours. THREDDS can be slow (adjust as needed). + +# Set the spatial limits. If this is left out, we plot the whole domain. +#query.lonlat_box(west=-110, east=-45, north=50, south=10) + +# Get the data +data = ncss.get_data(query) + +######################## +# Making sense of netCDF +######################## + +#View the properties of our data +print(data) + +#We can use a library called XArray to make working with this a little simpler +from xarray.backends import NetCDF4DataStore +import xarray as xr + +# Use NetCDFDataStore to open and read the existing netCDF dataset we downloaded +ds = xr.open_dataset(NetCDF4DataStore(data)) + +# Grab a specific dataset +temp_var = ds.metpy.parse_cf('Temperature_isobaric') +print(temp_var) +z_var = ds.metpy.parse_cf('Geopotential_height_isobaric') +print(z_var) + +# XArray handles parsing things like dates, times, latitude, and longitude for us. This is so much easier than it used to be! +latitude = temp_var.metpy.y +longitude = temp_var.metpy.x + +#------- +# Levels +#------- +# Select a level to plot. We can even choose different levels for each variable! +level = 85000 +temp_var = temp_var.metpy.sel(vertical=level) +z_var = z_var.metpy.sel(vertical=level) + +# Explore the available levels here if you want +#temp_levels = temp_var['isobaric'] +#print("Temperature levels:",temp_levels) +#z_levels = z_var['isobaric1'] +#print("Height levels:",z_levels) +#------- + +#################### +# Visualize the grid +#################### + +import cartopy.crs as ccrs +import cartopy.feature as cfeature +import matplotlib.pyplot as plt + +# The GFS uses a lon/lat grid +#data_projection = ccrs.PlateCarree() + +# The HRRR uses a specific Lambert Conformal grid called the AWIPS 184 grid. See the parameters here: https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID184 +# You can specify the parameters of your projection: https://scitools.org.uk/cartopy/docs/latest/crs/projections.html +# Set the map projection with the specific grid parameters (note that the grid parameters are also embedded within the data) +data_projection = ccrs.LambertConformal(central_longitude=-95.000,central_latitude=25.000) + +# Make it easy to change what time step we look at +t_step = 0 + +#------------- +# Color tables +#------------- +# Look at the documentation for metpy.plots.colortables (https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ctables.ColortableRegistry.html) +# or choose one from here: https://matplotlib.org/examples/color/colormaps_reference.html +# Here are the custom color tables that MetPy supports: https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.ctables.html +# Put '_r' at the end of any colormap name to reverse it. + +# Import for colortables +from metpy.plots import colortables +colormap=colortables.get_colortable('ir_rgbv') #Use this only for the MetPy-specific color tables. Otherwise, comment out this line and put your Matplotlib color selection below +#colormap='rainbow' +#------------- + +#------------- +# Create a figure and plot the data +#------------- +fig = plt.figure(figsize=(12, 8)) +ax = fig.add_subplot(1, 1, 1, projection=ccrs.LambertConformal()) +#ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree()) #You can use a different projection if you like +mesh = ax.pcolormesh(longitude, latitude, temp_var[t_step].squeeze(), + transform=data_projection, zorder=0, cmap=colormap) + +# Add contours of 850-mb geopotential height +contours = np.arange(1140, 1620, 30) +ax.contour(longitude, latitude, z_var[t_step].squeeze(), contours, colors='k', + transform=data_projection, linewidths=0.75, zorder=1) + +# Add some common geographic features +ax.add_feature(cfeature.COASTLINE.with_scale('50m')) +ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='black') +ax.add_feature(cfeature.BORDERS.with_scale('50m')) + +# Add some lat/lon gridlines +ax.gridlines() + +# Add a colorbar +cax = fig.colorbar(mesh) +cax.set_label(temp_var.attrs['units']) + +# Get a sensible datetime format +vtime = ds.time.data[0].astype('datetime64[ms]').astype('O') + +# Add a title +ax.set_title(temp_var.metpy.time[t_step].values) + +#plt.show() #View the plot via a GUI window. Remove matplotlib.use('Agg') above. I suggest saving it instead (see the next line). +plt.savefig("outofthebox.png") #Just reload this image in your browser for fast viewing +plt.close() + +#------------------------------------------------------------------------------------------------- +################################################################################################## +################################################################################################## +# Create the following plots: +# 1) 850-mb temperature as a colored contour, along with geopotential heights and associated wind barbs +# 2) 500-mb divergence as a colored contour, along with geopotential heights and associated wind barbs +# 3) 500-mb vorticity as a colored contour, along with geopotential heights and associated wind barbs +# 4) 500-mb isotachs (colored, starting at 20 knots), along with geopotential heights and associated wind barbs + +# NOTE: You are welcome to zoom in on a region of interest in each plot. + +# Add a title to each plot. Post your code and all four maps on your password-protected webpage. +################################################################################################## +################################################################################################## +#------------------------------------------------------------------------------------------------- \ No newline at end of file diff --git a/randomcrap/untitled2.py b/randomcrap/untitled2.py new file mode 100644 index 0000000..e69de29 diff --git a/randomcrap/working.ipynb b/randomcrap/working.ipynb new file mode 100644 index 0000000..4b895b0 --- /dev/null +++ b/randomcrap/working.ipynb @@ -0,0 +1,700 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 95, + "id": "c02a9949-a0ce-4e48-8254-65f08637acf6", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import csv\n", + "\n", + "from datetime import datetime, timedelta\n", + "import io\n", + "\n", + "from metpy.units import units\n", + "from metpy.plots import ImagePlot, MapPanel, PanelContainer\n", + "from siphon.catalog import TDSCatalog\n", + "import xarray as xr\n", + "\n", + "import cartopy.crs" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "1938e6cb-3222-477f-9f05-e2e86e6e6a3c", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.load_dataset(\"hrrr.t00z.wrfsfcf00_1.grib2\", engine=\"cfgrib\")" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "87948229-628c-4a38-a269-b3b6371de71c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:            (y: 1059, x: 1799)\n",
+       "Coordinates:\n",
+       "    time               datetime64[ns] 2021-11-07\n",
+       "    step               timedelta64[ns] 00:00:00\n",
+       "    heightAboveGround  float64 2.0\n",
+       "    latitude           (y, x) float64 21.14 21.15 21.15 ... 47.86 47.85 47.84\n",
+       "    longitude          (y, x) float64 237.3 237.3 237.3 ... 299.0 299.0 299.1\n",
+       "    valid_time         datetime64[ns] 2021-11-07\n",
+       "Dimensions without coordinates: y, x\n",
+       "Data variables:\n",
+       "    t2m                (y, x) float32 295.0 295.0 295.0 ... 281.7 281.7 281.7\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             kwbc\n",
+       "    GRIB_centreDescription:  US National Weather Service - NCEP \n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             US National Weather Service - NCEP \n",
+       "    history:                 2021-12-07T14:42 GRIB to CDM+CF via cfgrib-0.9.9...
" + ], + "text/plain": [ + "\n", + "Dimensions: (y: 1059, x: 1799)\n", + "Coordinates:\n", + " time datetime64[ns] 2021-11-07\n", + " step timedelta64[ns] 00:00:00\n", + " heightAboveGround float64 2.0\n", + " latitude (y, x) float64 21.14 21.15 21.15 ... 47.86 47.85 47.84\n", + " longitude (y, x) float64 237.3 237.3 237.3 ... 299.0 299.0 299.1\n", + " valid_time datetime64[ns] 2021-11-07\n", + "Dimensions without coordinates: y, x\n", + "Data variables:\n", + " t2m (y, x) float32 295.0 295.0 295.0 ... 281.7 281.7 281.7\n", + "Attributes:\n", + " GRIB_edition: 2\n", + " GRIB_centre: kwbc\n", + " GRIB_centreDescription: US National Weather Service - NCEP \n", + " GRIB_subCentre: 0\n", + " Conventions: CF-1.7\n", + " institution: US National Weather Service - NCEP \n", + " history: 2021-12-07T14:42 GRIB to CDM+CF via cfgrib-0.9.9..." + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bc169bc-5b8b-4d44-87ec-789d1d0ce007", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c486a0a-3af3-44aa-adf2-c36e5f6129ba", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98c3a71e-dfda-4852-ac68-054554d6c3d5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bb76369-67fe-48ff-886a-991395fbddc8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9026311f-bf67-44ec-83b5-7c1597fca42d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20de3313-75a8-4049-a581-0ba86799b96e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84f79a91-0de8-417e-b498-b780006b9b96", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1ca115e-3120-4e45-920e-7a4712f7dacf", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "640f1eef-91ed-4025-b16a-78723e60c8a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "272.599\n" + ] + } + ], + "source": [ + "latitude = float(ds['latitude'][0][0]['latitude'])\n", + "longitude = float(ds['latitude'][0][0]['longitude'])\n", + "value = float(ds['t2m'][0][0])\n", + "coordinates = str(str(latitude)+', '+str(longitude))\n", + "\n", + "\n", + "cmd_tmp = \"/usr/bin/wgrib2 %s -match '%s' -lon %s >> urma_temp.csv\" % ('hrrr.t00z.wrfsfcf00_1.grib2', ':TMP:2 m above ground:anl:', coordinates)\n", + " \n", + "os.system(cmd_tmp)\n", + " \n", + "with open('urma_temp.csv', 'r') as f:\n", + " csv_reader = csv.reader(f)\n", + " csv_list = list(csv_reader)\n", + " \n", + "current_value = float((csv_list[0][2]).split('=')[1])\n", + "print(current_value)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "a3746b18-46f6-47ba-b470-91a5c6de7477", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hliu/Projects/modap/env/lib/python3.9/site-packages/metpy/xarray.py:349: UserWarning: More than one time coordinate present for variable \"t2m\".\n", + " warnings.warn('More than one ' + axis + ' coordinate present for variable'\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "crs attribute is not available.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_1575262/3620631283.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_extent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marea\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mccrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[0;31m# Draw all of the plots.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Projects/modap/env/lib/python3.9/site-packages/metpy/plots/declarative.py\u001b[0m in \u001b[0;36max\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[0;31m# map projection.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 803\u001b[0m \u001b[0;32mif\u001b[0m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img = ImagePlot()\n", + "img.data = ds\n", + "img.field = 't2m'\n", + "img.colormap = 'coolwarm'\n", + "\n", + "panel = MapPanel()\n", + "panel.area = 'us'\n", + "panel.layers = ['states']\n", + "panel.title = 'GFS precip forecast'\n", + "panel.plots = [img]\n", + "\n", + "pc = PanelContainer()\n", + "pc.size = (10, 8)\n", + "pc.panels = [panel]\n", + "pc.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a38eaa5-637b-4f57-af94-1bbe2f697595", + "metadata": {}, + "outputs": [], + "source": [ + "for x in range(len(ds['latitude'])):\n", + " for y in range(len(ds['latitude'][0])):\n", + " latitude = float(ds['latitude'][x][y]['latitude'])\n", + " longitude = float(ds['latitude'][x][y]['longitude'])\n", + " value = float(ds.t2m[x][y])\n", + " coordinates = str(str(latitude)+', '+str(longitude))\n", + " \n", + " cmd_tmp = \"/usr/bin/wgrib2 %s -match '%s' -lon %s >> urma_temp.csv\" % \n", + " ('GFS DATA HERE', ':TMP:2 m above ground:anl:', coordinates)\n", + " \n", + " os.system(cmd_tmp)\n", + " \n", + " with open('urma_temp.csv', 'r') as f:\n", + " csv_reader = csv.reader(f)\n", + " csv_list = list(csv_reader)\n", + " \n", + " gfs_value = float((csv_list[0][2]).split('=')[1])\n", + " \n", + " ds.t2m[x][y] = (value+gfs_value)/2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "069dfe27-ab40-4042-b80e-35bcefa69fae", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..b876b76 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,41 @@ +aniso8601==9.0.1 +appdirs==1.4.4 +beautifulsoup4==4.10.0 +certifi==2021.10.8 +charset-normalizer==2.0.10 +click==8.0.3 +cycler==0.11.0 +Flask==2.0.2 +Flask-RESTful==0.3.9 +fonttools==4.28.5 +idna==3.3 +importlib-metadata==4.10.0 +importlib-resources==5.4.0 +itsdangerous==2.0.1 +Jinja2==3.0.3 +kiwisolver==1.3.2 +MarkupSafe==2.0.1 +matplotlib==3.5.1 +MetPy==1.1.0 +numpy==1.21.5 +packaging==21.3 +pandas==1.3.5 +Pillow==9.0.0 +Pint==0.18 +pooch==1.5.2 +protobuf==4.0.0rc2 +pyparsing==3.0.6 +pyproj==3.2.1 +python-dateutil==2.8.2 +pytz==2021.3 +requests==2.27.1 +scipy==1.7.3 +siphon==0.9 +six==1.16.0 +soupsieve==2.3.1 +traitlets==5.1.1 +typing-extensions==4.0.1 +urllib3==1.26.8 +Werkzeug==2.0.2 +xarray==0.20.2 +zipp==3.7.0