started implementing goes download script

spaceml-org · Dec 4, 2023 · b6b32b6 · b6b32b6
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diff --git a/notebooks/0.1.1-GOES2GO.ipynb b/notebooks/0.1.1-GOES2GO.ipynb
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,20 +124,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "📦 Finished downloading [15] files to [data/noaa-goes16/ABI-L1b-RadF].\n"
+      "📦 Finished downloading [16] files to [/Users/lillifreischem/data/noaa-goes16/ABI-L1b-RadF].\n"
      ]
     }
    ],
    "source": [
     "# download the data nearest a specific time\n",
-    "files = G.nearesttime('2018-10-01 17:00', return_as='filelist', save_dir='./data/')"
+    "files = G.nearesttime('2018-10-02 17:00', return_as='filelist')#, save_dir=None)"
    ]
   },
   {

diff --git a/notebooks/0.3-GOES-downsampling.ipynb b/notebooks/0.3-GOES-downsampling.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator\n",
+    "import numpy as np\n",
+    "# from xy_to_latlon import calc_latlon\n",
+    "\n",
+    "def downsample(ds, target_points):\n",
+    "    \"\"\"\n",
+    "    target_points\n",
+    "    \"\"\"\n",
+    "    # attach latitude & longitude coordinates to xr.Datasets\n",
+    "    ds = calc_latlon(ds)\n",
+    "    \n",
+    "    # extract 1d arrays of latitudes and longitudes\n",
+    "    lat = ds.lat.to_numpy().flatten()\n",
+    "    lon = ds.lon.to_numpy().flatten()\n",
+    "    \n",
+    "    # turn into 2d array of latitudes and longitudes\n",
+    "    points = np.vstack((lon, lat)).T\n",
+    "    \n",
+    "    # initialise interpolation\n",
+    "    nn_interpolation = NearestNDInterpolator(points, ds.Rad)\n",
+    "    interpolated_nn = nn_interpolation(target_points)\n",
+    "    \n",
+    "    # create new xr.Dataset with lowres data \n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.load_dataset('../data/noaa-goes16/ABI-L1b-RadF/2018/274/17/OR_ABI-L1b-RadF-M3C01_G16_s20182741700337_e20182741711104_c20182741711147.nc')\n",
+    "ds_target = xr.load_dataset('../data/noaa-goes16/ABI-L1b-RadF/2018/274/17/OR_ABI-L1b-RadF-M3C04_G16_s20182741700337_e20182741711104_c20182741711130.nc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10848,)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.x.to_numpy().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = ds.x.to_numpy()\n",
+    "y = ds.y.to_numpy()\n",
+    "\n",
+    "x_target = ds_target.x.to_numpy()\n",
+    "y_target = ds_target.y.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# points = np.vstack((x, y)).T\n",
+    "# points_target = np.vstack((x_target, y_target)).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xx, yy = np.meshgrid(x, y)\n",
+    "points = np.vstack((xx.flatten(), yy.flatten())).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nn_interpolation = NearestNDInterpolator(points, ds.Rad.to_numpy().flatten())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(117679104,)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.Rad.to_numpy().flatten().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10848, 10848)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xx.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10848, 10848)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds.Rad.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gcmodel",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/goes-scripts/goes_downsample.py b/notebooks/goes-scripts/goes_downsample.py
@@ -0,0 +1,24 @@
+from scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator
+import numpy as np
+from xy_to_latlon import calc_latlon
+
+def downsample(ds, target_points):
+    """
+    target_points
+    """
+    # attach latitude & longitude coordinates to xr.Datasets
+    ds = calc_latlon(ds)
+
+    # extract 1d arrays of latitudes and longitudes
+    lat = ds.lat.to_numpy().flatten()
+    lon = ds.lon.to_numpy().flatten()
+
+    # turn into 2d array of latitudes and longitudes
+    points = np.vstack((lon, lat)).T
+
+    # initialise interpolation
+    nn_interpolation = NearestNDInterpolator(points, ds.Rad)
+    interpolated_nn = nn_interpolation(target_points)
+
+    # create new xr.Dataset with lowres data 
+    ...