From c3b4399cf37e85fd1c448261e4a8385a0fdb91b0 Mon Sep 17 00:00:00 2001 From: Bingqing Liu Date: Sat, 24 Aug 2024 22:35:27 -0500 Subject: [PATCH 1/3] add pace cyano --- docs/examples/multispectral.ipynb | 233 +- docs/examples/pace.ipynb | 66 +- docs/examples/pace_cyano.ipynb | 18989 ++++++++++++++++++++++++++++ docs/examples/pace_oci_l1.ipynb | 264 +- docs/examples/pace_oci_l2.ipynb | 2 +- 5 files changed, 19523 insertions(+), 31 deletions(-) create mode 100644 docs/examples/pace_cyano.ipynb diff --git a/docs/examples/multispectral.ipynb b/docs/examples/multispectral.ipynb index 16c1a67..f9a8b63 100644 --- a/docs/examples/multispectral.ipynb +++ b/docs/examples/multispectral.ipynb @@ -11,16 +11,197 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: hypercoast[extra] in 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asttokens>=2.1.0 in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (from stack-data->ipython>=6.1.0->ipywidgets->leafmap->hypercoast[extra]) (2.4.1)\n", + "Requirement already satisfied: pure-eval in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (from stack-data->ipython>=6.1.0->ipywidgets->leafmap->hypercoast[extra]) (0.2.2)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "# %pip install \"hypercoast[extra]\"" + "#%pip install \"hypercoast[extra]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -29,9 +210,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data/cog.tif already exists. Skip downloading. Set overwrite=True to overwrite.\n" + ] + }, + { + "data": { + "text/plain": [ + "'c:\\\\Users\\\\C00553090\\\\DATA\\\\Hyperspectral\\\\HyperCoast\\\\docs\\\\examples\\\\data\\\\cog.tif'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "url = \"https://github.com/opengeos/datasets/releases/download/raster/cog.tif\"\n", "filepath = \"data/cog.tif\"\n", @@ -40,9 +239,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5a688895726343c993d6f1bc6fe05846", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[53.4070895, 6.8754805], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title'…" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "m = hypercoast.Map()\n", "filepath = \"data/cog.tif\" # replace it with your own raster data\n", @@ -77,7 +292,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/examples/pace.ipynb b/docs/examples/pace.ipynb index 2de4659..17469c8 100644 --- a/docs/examples/pace.ipynb +++ b/docs/examples/pace.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -47,10 +47,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data/PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n" + ] + }, + { + "data": { + "text/plain": [ + "'c:\\\\Users\\\\C00553090\\\\DATA\\\\Hyperspectral\\\\HyperCoast\\\\docs\\\\examples\\\\data\\\\PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "#filepath = \"data/PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc\"\n", "filepath = \"data/PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc\"\n", "hypercoast.download_file(url, filepath)" ] @@ -64,9 +83,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot = hypercoast.view_pace_pixel_locations(filepath, step=20)" ] @@ -80,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -193,9 +223,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "df13805906dd46d5ae8af98481d8da4d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[25.194741, -92.490864], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title'…" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "m = hypercoast.Map()\n", "m.add_basemap(\"Hybrid\")\n", @@ -231,7 +277,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/examples/pace_cyano.ipynb b/docs/examples/pace_cyano.ipynb new file mode 100644 index 0000000..276b386 --- /dev/null +++ b/docs/examples/pace_cyano.ipynb @@ -0,0 +1,18989 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "yk1OuuFc-_I4" + }, + "source": [ + "## What is HyperCoast?\n", + "HyperCoast streamlines the processing of hyperspectral data from existing spaceborne and airborne missions (e.g., PACE, EMIT, AVIRIS, NEON, and DESIS) and upcoming hyperspectral missions, such as, SBG and GLIMR.\n", + "\n", + "https://hypercoast.org\n", + "\n", + "\"HyperCoast" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AvpjFXjN-_I_" + }, + "source": [ + "Uncomment the following cell to install the HyperCoast package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ASucI-Sc-_JD" + }, + "source": [ + "Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3Tb_-nbK-_JE" + }, + "outputs": [], + "source": [ + "import hypercoast\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bsEz1U-e-_JE" + }, + "source": [ + "Download a sample dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oY5qbVGL-_JF" + }, + "outputs": [], + "source": [ + "filepath = \"./PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n", + "dataset = hypercoast.read_pace(filepath)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oIUM0vLh-_JG" + }, + "source": [ + "Load the HYpoxia field data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 377 + }, + "id": "4QV5JjIZ-_JG", + "outputId": "82773c91-a525-4c7b-9b4d-4736fac2a743" + }, + "outputs": [], + "source": [ + "kml_path = \"./GOM_Hypoxia.kml\"\n", + "filepath = \"./Hypoxia_Data_Sheet.xlsx\"\n", + "df = pd.read_excel(filepath)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 377 + }, + "id": "hq6TIsGs-_JH", + "outputId": "fe56aa5f-483c-473d-c2d6-f4837a825dda" + }, + "outputs": [], + "source": [ + "df_filtered = df.dropna(subset=['Lon', 'Lat']).reset_index(drop=True)\n", + "df_filtered.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "csMuk4GW-_JH" + }, + "source": [ + "Visualize PACE image and field data on an interactive map." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 621, + "referenced_widgets": [ + "be80ca51182d478e99a1660335dcbeca", + "6db2efdcce5c4e388751ac0673451e4b", + "4e890464e94e475583af3f962fa2c09c", + "af2f16ec6a4540fb9fda5f40aca124a3", + "f6cf3b1f6b61471dab82efe1d86599e1", + 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"eed5934aeefe447b8d024a982cb72659", + "c3c2cdd7412d4290acdd3fd0ea8c0732", + "8c4ac42cdcd24291a948bbcb827c029c", + "f370a8cb3f29435794ad774fffdc86de", + "18bfb1396330461ebb5ae3deba83005c", + "0e93210d5abe4a2db3a1781fd73fea33", + "7a80d98e86d548a8abf10620cd2220ae", + "a9e4dc95bb854d2bbf51a8d280c4218e", + "404144853dfe4ce3ade5f0efc37befaf", + "6d10dea71e714f94bb5958d813f649cc", + "10bdbdfd2d7447289efe884cee4b1af8", + "7f0290b0c09d4c6cbe865e4e2f609ffd", + "6c2810005ade4f76a63e4f68cc1b5766", + "49c5f6712e6648428d2152cd18210c23", + "bd56db73d83e488b9d47394e7f8250bc", + "e33ebc3aba34474aba698db97b0744d9", + "3befae3deb6f486e95d7be0ead544323", + "69d99eb2a9d448979d06f1e51fc32c19", + "40d09355e35d4b34926054a5d8aa5919", + "f1711e1cc20343f98863e9b8afda5866", + "7f8f098cbb3d4c35a82e8129488efe14", + "0c4811beb5b94b258e660474f4863a4e", + "9b3d817ffbf843c8864e1221f53bc93a", + "a77747b70d4e46d78546c2d973f9e655", + "c538071256084246aafbd2a8438c854e", + "cb34e63faca84745892eb27926a94aeb", + "817ac325411942598e38570e44a1cd78", + "fb7d2df2338d4e30a759ac37949ae14c", + "397428e309ea4d05ab2f4ce9ed51187d", + "4e4e7d320a014ea0b97fb403c2e12b0d", + "55b34edaa5d247cd9731cacec3d78cc0", + "739248a24c334db6931c7102398c65de", + "f6f213069cfc4349a604183dc315c015", + "6a3e6e3bd9614f2293febcdd4aaa6c2b", + "28b80d3c0af94f4ab4374dda65c9fabf", + "688d7fed51c34b86aaa9e0ba4e877249", + "79a76a13392846448e24ecff76664abe", + "0196ea2cebcd4cc88c698ea2bd6d59a0", + "18e00e793dd247ccac363d800cf97666", + "8c63d714ef8d407d86c7b8e026445b2e", + "c8a2be9cd4774bb7a08fbb4056792603", + "ded6fa414ad9445f9260b4894cb29a3e", + "085f457e019b4e6994f1cbd274c27f81", + "118792e005ac4e6ab6f1886dbaf0300b", + "426e531c95d846b3b58e47b6d2ecfac5", + "b128cccc1ee2488c8cc07659a7c293a2", + "452fc8ae38944c1f8429ac67a8dc78e2", + "b3c71d44d1e047feac2f1203d99903f4", + "dd937f989eb24a6b870a00152af8fcda", + "899636029d47449b9d8f7e0dfe478aa3", + "5f454dbaf8694be889760d87a7abc0b9", + "619ccc9bb0ed4f7886e1c2edd622b24c", + "b59050f64b07415fb42e80dd9fcc2959", + "13074abdbcf34ff7a0bf0e38d2e66b86", + "376fb75d58e74233ba50671bd8bca557", + "417be0799afd44f78166b6ee7e7e35f8", + "e2cf4737e83f43a997230d6604770bd1", + "918ca0d532eb4865991054a7338a27e6", + "a3049ec66aea42fbb7b6338140ea967b", + "c9db744e88e54d45bb992aca6a6e597e", + "b7421fb32b6e47e9b2ffc61819a09683", + "c33ce28e496e4b50965c628f09d06e3e", + "dc5c870a1ec045b49d592fb57aa3cc3b", + "ed48954a1ac4493d804e10939517fb12", + "73479fd0479f40e5b41c5afd34972e48" + ] + }, + "id": "IMtsAbVj-_JH", + "outputId": "179c7bd8-2ea9-499d-a628-26bd77cf9f89" + }, + "outputs": [], + "source": [ + "m = hypercoast.Map()\n", + "m.add_basemap(\"Hybrid\")\n", + "wavelengths = [450, 550, 650]\n", + "m.add_pace(\n", + " dataset, wavelengths, indexes=[3, 2, 1], vmin=0, vmax=0.02, layer_name=\"PACE\"\n", + ")\n", + "m.add(\"spectral\")\n", + "style = {\"weight\": 2, \"color\": \"red\"}\n", + "m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\n", + "m.add_points_from_xy(df_filtered, x=\"Lon\", y=\"Lat\", max_cluster_radius=50, layer_name=\"Hypoxia Data Points\")\n", + "m.set_center(-91.46118, 28.89758, zoom=8)\n", + "m\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3EK3XYY2-_JI" + }, + "source": [ + "![](https://i.imgur.com/JdgotIT.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NYo6cw13-_JI" + }, + "source": [ + "Compute band ratios." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZQ7TxZZa-_JI" + }, + "outputs": [], + "source": [ + "da = dataset[\"Rrs\"]\n", + "data = (da.sel(wavelength=650) > da.sel(wavelength=620)) & (da.sel(wavelength=701) > da.sel(wavelength=681)) & (da.sel(wavelength=701) > da.sel(wavelength=450))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KRF5ow0x-_JJ" + }, + "source": [ + "## The spectra of cyanobacteria bloom:\n", + "\n", + "![](https://i.imgur.com/pQP50bz.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EuKeNoNp-_JJ" + }, + "source": [ + "Visualize the selected region based on band ratios." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 540 + }, + "id": "be6r9fkf-_JJ", + "outputId": "a1964c49-6587-475a-87bd-a95db40bb35e" + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "# Create a plot\n", + "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "#ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())\n", + "\n", + "# Plot the data\n", + "data.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', cbar_kwargs={'label': 'Cyano'},)\n", + "\n", + "# Add coastlines\n", + "ax.coastlines()\n", + "\n", + "# Add state boundaries\n", + "states_provinces = cfeature.NaturalEarthFeature(\n", + " category='cultural',\n", + " name='admin_1_states_provinces_lines',\n", + " scale='50m',\n", + " facecolor='none')\n", + "\n", + "ax.add_feature(states_provinces, edgecolor='gray')\n", + "\n", + "# Optionally, add gridlines, labels, etc.\n", + "ax.gridlines(draw_labels=True)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7WHdt4El-_JJ" + }, + "source": [ + "## What you can do next about Cyanobacteria using PACE???\n", + "\n", + "Spectral Angle Mapper: Spectral similarity\n", + "Input: library of Cyanobacteria bloom Rrs spectra with Chla at different levels\n", + "\n", + "Spectral Mixture Analysis: unmix different cyanobacteria species based on spectral difference.\n", + "\n", + "![](https://i.imgur.com/xLaLMA4.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-means applied to whole image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8SYB8MKyZc0-", + "outputId": "02c93296-323b-41e9-e175-eaa18e0d19eb", + "scrolled": true + }, + "outputs": [], + "source": [ + "# Get the shape of the DataArray\n", + "print(\"Shape of da:\", da.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eCH5c_uBZc28", + "outputId": "1301bab1-74d6-4507-ce27-b1f0ae81d943" + }, + "outputs": [], + "source": [ + "# Get the dimension names of the DataArray\n", + "print(\"Dimension names:\", da.dims)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0nzjK69bZc6d", + "outputId": "fd909e7d-dcc2-451c-f8aa-d67545ee7673" + }, + "outputs": [], + "source": [ + "# Get the size of each dimension\n", + "print(\"Size of each dimension:\", da.sizes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jDrdL13RcLFm" + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.cluster import KMeans" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_O_obmMXZc9Z" + }, + "outputs": [], + "source": [ + "reshaped_data = da.values.reshape(-1, da.shape[-1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1V22cNgbZdAz" + }, + "outputs": [], + "source": [ + "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + }, + "id": "70hwmWwZelJn", + "outputId": "b0daf9fc-3b4a-400e-e512-c40de1ae885a" + }, + "outputs": [], + "source": [ + "# Apply K-means clustering to classify into 5-6 water types.\n", + "n_clusters = 6\n", + "kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n", + "kmeans.fit(reshaped_data_no_nan)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yF1Y40bhelMn" + }, + "outputs": [], + "source": [ + "# Initialize an array for cluster labels with NaN\n", + "labels = np.full(reshaped_data.shape[0], np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nIrI-WyGelPj" + }, + "outputs": [], + "source": [ + "# Assign the computed cluster labels to the non-NaN positions\n", + "labels[~np.isnan(reshaped_data).any(axis=1)] = kmeans.labels_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6u86WCEnfnUU" + }, + "outputs": [], + "source": [ + "# Reshape the labels back to the original spatial dimensions\n", + "cluster_labels = labels.reshape(da.shape[:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704 + }, + "id": "K7mEYr4iny5f", + "outputId": "394a976d-d9d7-496c-9dec-0764dc4f1bf5" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "\n", + "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n", + "# Also assume that 'da' has the original latitude and longitude data\n", + "latitudes = da.coords['latitude'].values\n", + "longitudes = da.coords['longitude'].values\n", + "\n", + "# Create a custom discrete color map for K-means clusters\n", + "cmap = mcolors.ListedColormap(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "bounds = np.arange(-0.5, n_clusters, 1)\n", + "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "# Create a figure and axis with the correct map projection\n", + "fig, ax = plt.subplots(figsize=(12, 10), dpi=400, subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "# Plot the K-means classification results on the map\n", + "im = ax.pcolormesh(longitudes, latitudes, cluster_labels, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "\n", + "# Add geographic features for context\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "\n", + "# Add gridlines\n", + "ax.gridlines(draw_labels=True)\n", + "\n", + "# Set the extent to zoom in to the specified region\n", + "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", + "\n", + "# Add color bar with labels\n", + "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", + "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", + "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "\n", + "# Add title\n", + "ax.set_title('Water Type Classification using K-means', fontsize=16)\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Keans applied to selected pixels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qqtpdP4qelVT" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import numpy as np\n", + "\n", + "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n", + "# Also assume that 'da' has the original latitude and longitude data\n", + "latitudes = da.coords['latitude'].values\n", + "longitudes = da.coords['longitude'].values\n", + "\n", + "# Filtering condition based on wavelength values\n", + "filter_condition = (\n", + " (da.sel(wavelength=650) > da.sel(wavelength=620)) & \n", + " (da.sel(wavelength=701) > da.sel(wavelength=681)) & \n", + " (da.sel(wavelength=701) > da.sel(wavelength=450))\n", + ")\n", + "\n", + "# Apply the filtering condition to the K-means classification results\n", + "filtered_cluster_labels = np.where(filter_condition, cluster_labels, np.nan)\n", + "\n", + "# Create a custom discrete color map for K-means clusters\n", + "cmap = mcolors.ListedColormap(['#e41a1c', '#377eb8', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "bounds = np.arange(-0.5, n_clusters, 1)\n", + "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "# Create a figure and axis with the correct map projection\n", + "fig, ax = plt.subplots(figsize=(12, 10), dpi=400, subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "# Plot the filtered K-means classification results on the map\n", + "im = ax.pcolormesh(longitudes, latitudes, filtered_cluster_labels, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "\n", + "# Add geographic features for context\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "\n", + "# Add gridlines\n", + "ax.gridlines(draw_labels=True)\n", + "\n", + "# Set the extent to zoom in to the specified region\n", + "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", + "\n", + "# Add color bar with labels\n", + "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", + "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", + "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "\n", + "# Add title\n", + "ax.set_title('Water Type Classification using K-means', fontsize=16)\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PCA + SAM applied to whole image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "svg0vrnxh6vp", + "outputId": "f7d2d17f-5035-4349-cd2d-32801f7feb17" + }, + "outputs": [], + "source": [ + "# Check the dimensions and coordinates\n", + "print(da.dims)\n", + "print(da.coords) # This will show the available spectral bands" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dksBIw9KjUfS" + }, + "outputs": [], + "source": [ + "# Reshape data to (n_pixels, n_bands)\n", + "reshaped_data = da.values.reshape(-1, da.shape[-1])\n", + "\n", + "# Handle NaNs by removing them\n", + "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 718 + }, + "id": "f_R6a2nDjUjR", + "outputId": "2b8e4151-e9d3-492c-edcf-1db12e505322" + }, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "# Apply PCA to reduce dimensionality\n", + "pca = PCA(n_components=3)\n", + "pca_data = pca.fit_transform(reshaped_data_no_nan)\n", + "\n", + "# Visualize PCA components to manually identify endmembers\n", + "plt.figure(figsize=(10, 8))\n", + "plt.scatter(pca_data[:, 0], pca_data[:, 1], c='blue', s=1)\n", + "plt.title('PCA of Spectral Data')\n", + "plt.xlabel('Principal Component 1')\n", + "plt.ylabel('Principal Component 2')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sLCiBgBajpGC" + }, + "outputs": [], + "source": [ + "# Apply K-means to find clusters representing endmembers\n", + "n_clusters = 6 # Number of endmembers you want to find\n", + "kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n", + "kmeans.fit(pca_data)\n", + "\n", + "# The cluster centers in the original spectral space are your endmembers\n", + "endmembers = pca.inverse_transform(kmeans.cluster_centers_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iPkI2yuRjpR9" + }, + "outputs": [], + "source": [ + "def spectral_angle_mapper(pixel, reference):\n", + " norm_pixel = np.linalg.norm(pixel)\n", + " norm_reference = np.linalg.norm(reference)\n", + " cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n", + " angle = np.arccos(np.clip(cos_theta, -1, 1))\n", + " return angle\n", + "\n", + "# Apply SAM for each pixel and each endmember\n", + "angles = np.zeros((reshaped_data_no_nan.shape[0], endmembers.shape[0]))\n", + "\n", + "for i in range(reshaped_data_no_nan.shape[0]):\n", + " for j in range(endmembers.shape[0]):\n", + " angles[i, j] = spectral_angle_mapper(reshaped_data_no_nan[i, :], endmembers[j, :])\n", + "\n", + "# Find the minimum angle (best match) for each pixel\n", + "best_match = np.argmin(angles, axis=1)\n", + "\n", + "# Reshape best_match back to the original spatial dimensions\n", + "original_shape = da.shape[:-1] # Get the spatial dimensions\n", + "best_match_full = np.full(reshaped_data.shape[0], np.nan)\n", + "best_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match\n", + "best_match_full = best_match_full.reshape(original_shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 704 + }, + "id": "3EuvKbPHjUq7", + "outputId": "41372b92-48e2-4c47-e732-5e597e4086a0" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "\n", + "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n", + "# Also assume that 'da' has the original latitude and longitude data\n", + "latitudes = da.coords['latitude'].values\n", + "longitudes = da.coords['longitude'].values\n", + "\n", + "# Create a custom discrete color map\n", + "cmap = mcolors.ListedColormap(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "bounds = np.arange(-0.5, n_clusters, 1)\n", + "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "# Create a figure and axis with the correct map projection\n", + "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "# Plot the SAM classification results\n", + "im = ax.pcolormesh(longitudes, latitudes, best_match_full, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "\n", + "# Add geographic features for context\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "\n", + "# Add gridlines\n", + "ax.gridlines(draw_labels=True)\n", + "\n", + "# Add color bar with labels\n", + "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", + "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", + "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "\n", + "# Add title\n", + "ax.set_title('Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 653 + }, + "id": "jnJ48tcxjUve", + "outputId": "70585cfb-d9e4-49e7-80a7-8393365a3094" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "import numpy as np\n", + "\n", + "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n", + "# Also assume that 'da' has the original latitude and longitude data\n", + "latitudes = da.coords['latitude'].values\n", + "longitudes = da.coords['longitude'].values\n", + "\n", + "# Create a custom discrete color map\n", + "cmap = mcolors.ListedColormap(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "bounds = np.arange(-0.5, n_clusters, 1)\n", + "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "# Create a figure and axis with the correct map projection\n", + "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "# Plot the SAM classification results\n", + "im = ax.pcolormesh(longitudes, latitudes, best_match_full, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "\n", + "# Add geographic features for context\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "\n", + "# Add gridlines\n", + "ax.gridlines(draw_labels=True)\n", + "\n", + "# Set the extent to zoom in to the specified region\n", + "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", + "\n", + "# Add color bar with labels\n", + "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", + "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", + "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "\n", + "# Add title\n", + "ax.set_title('Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HflML7NnjUym" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "import numpy as np\n", + "\n", + "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n", + "# Also assume that 'da' has the original latitude and longitude data\n", + "latitudes = da.coords['latitude'].values\n", + "longitudes = da.coords['longitude'].values\n", + "\n", + "# Filtering condition based on wavelength values\n", + "filter_condition = (\n", + " (da.sel(wavelength=650) > da.sel(wavelength=620)) & \n", + " (da.sel(wavelength=701) > da.sel(wavelength=681)) & \n", + " (da.sel(wavelength=701) > da.sel(wavelength=450))\n", + ")\n", + "\n", + "# Apply the filtering condition to the SAM classification results\n", + "filtered_best_match_full = np.where(filter_condition, best_match_full, np.nan)\n", + "\n", + "# Create a custom discrete color map\n", + "cmap = mcolors.ListedColormap(['#e41a1c', '#377eb8', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "bounds = np.arange(-0.5, n_clusters, 1)\n", + "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "# Create a figure and axis with the correct map projection\n", + "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "# Plot the filtered SAM classification results\n", + "im = ax.pcolormesh(longitudes, latitudes, filtered_best_match_full, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "\n", + "# Add geographic features for context\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "\n", + "# Add gridlines\n", + "ax.gridlines(draw_labels=True)\n", + "\n", + "# Set the extent to zoom in to the specified region\n", + "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", + "\n", + "# Add color bar with labels\n", + "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", + "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", + "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "\n", + "# Add title\n", + "ax.set_title('PCA + Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SAM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as mcolors\n", + "from scipy.interpolate import interp1d\n", + "import hypercoast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the netCDF data\n", + "file_path = './PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'\n", + "dataset = hypercoast.read_pace(file_path)\n", + "da = dataset['Rrs'] # Assuming 'Rrs' contains the reflectance data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract PACE wavelengths\n", + "pace_wavelengths = da['wavelength'].values\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(pace_wavelengths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to load and resample a single CSV spectral library file\n", + "def load_and_resample_spectral_library(csv_path, target_wavelengths):\n", + " df = pd.read_csv(csv_path)\n", + " original_wavelengths = df.iloc[:, 0].values # First column is wavelength\n", + " spectra_values = df.iloc[:, 1].values # Second column is spectral values\n", + " \n", + " # Interpolation function\n", + " interp_func = interp1d(original_wavelengths, spectra_values, kind='linear', fill_value='extrapolate')\n", + " \n", + " # Resample to the target (PACE) wavelengths\n", + " resampled_spectra = interp_func(target_wavelengths)\n", + " \n", + " return resampled_spectra" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Load and resample all 6 endmembers\n", + "endmember_paths = [\n", + " './SAM_spectral_library/Dataset_1.csv',\n", + " './SAM_spectral_library/Dataset_2.csv',\n", + " './SAM_spectral_library/Dataset_3.csv',\n", + " './SAM_spectral_library/Dataset_4.csv',\n", + " './SAM_spectral_library/Dataset_5.csv',\n", + " './SAM_spectral_library/Dataset_6.csv'\n", + "]\n", + "\n", + "endmembers = np.array([load_and_resample_spectral_library(path, pace_wavelengths) for path in endmember_paths])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(endmembers)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot sample spectra from the CSV files and their resampled versions\n", + "def plot_sample_spectra(csv_paths, pace_wavelengths):\n", + " plt.figure(figsize=(14, 8))\n", + " \n", + " for i, csv_path in enumerate(csv_paths):\n", + " df = pd.read_csv(csv_path)\n", + " original_wavelengths = df.iloc[:, 0].values\n", + " spectra_values = df.iloc[:, 1].values\n", + " resampled_spectra = load_and_resample_spectral_library(csv_path, pace_wavelengths)\n", + " \n", + " plt.plot(original_wavelengths, spectra_values, label=f'Original Spectra {i+1}', linestyle='--')\n", + " plt.plot(pace_wavelengths, resampled_spectra, label=f'Resampled Spectra {i+1}')\n", + " \n", + " plt.xlabel('Wavelength (nm)')\n", + " plt.ylabel('Spectral Reflectance')\n", + " plt.title('Comparison of Original and Resampled Spectra')\n", + " plt.legend()\n", + " plt.grid(True)\n", + " plt.show()\n", + "\n", + "plot_sample_spectra(endmember_paths, pace_wavelengths)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to calculate spectral angle\n", + "def spectral_angle_mapper(pixel, reference):\n", + " norm_pixel = np.linalg.norm(pixel)\n", + " norm_reference = np.linalg.norm(reference)\n", + " cos_theta = np.dot(pixel, reference) / (norm_pixel * norm_reference)\n", + " angle = np.arccos(np.clip(cos_theta, -1, 1))\n", + " return angle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Reshape data to (n_pixels, n_bands)\n", + "reshaped_data = da.values.reshape(-1, da.shape[-1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Apply SAM for each pixel and each endmember\n", + "angles = np.zeros((reshaped_data.shape[0], endmembers.shape[0]))\n", + "\n", + "for i in range(reshaped_data.shape[0]):\n", + " for j in range(endmembers.shape[0]):\n", + " angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Find the minimum angle (best match) for each pixel\n", + "best_match = np.argmin(angles, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reshape best_match back to the original spatial dimensions\n", + "best_match = best_match.reshape(da.shape[:-1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "import numpy as np\n", + "\n", + "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n", + "# Also assume that 'da' has the original latitude and longitude data\n", + "latitudes = da.coords['latitude'].values\n", + "longitudes = da.coords['longitude'].values\n", + "\n", + "# Create a custom discrete color map\n", + "cmap = mcolors.ListedColormap(['#377eb8', '#e41a1c', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "bounds = np.arange(-0.5, len(endmembers), 1)\n", + "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "# Create a figure and axis with the correct map projection\n", + "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "# Plot the SAM classification results\n", + "im = ax.pcolormesh(longitudes, latitudes, best_match, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "\n", + "# Add geographic features for context\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "\n", + "# Adding axis labels\n", + "ax.set_xlabel('Longitude')\n", + "ax.set_ylabel('Latitude')\n", + "\n", + "# Adding a title\n", + "ax.set_title('Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "\n", + "# Adding a color bar with discrete values\n", + "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(len(endmembers)))\n", + "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(len(endmembers))])\n", + "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "\n", + "# Adding gridlines\n", + "ax.gridlines(draw_labels=True, linestyle='--', linewidth=0.5)\n", + "\n", + "# Set the extent to zoom in to the specified region (adjust as needed)\n", + "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "import numpy as np\n", + "\n", + "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n", + "# Also assume that 'da' has the original latitude and longitude data\n", + "latitudes = da.coords['latitude'].values\n", + "longitudes = da.coords['longitude'].values\n", + "\n", + "# Extract specific wavelengths for the conditions\n", + "wavelength_450 = da.sel(wavelength=450).values\n", + "wavelength_620 = da.sel(wavelength=620).values\n", + "wavelength_650 = da.sel(wavelength=650).values\n", + "wavelength_681 = da.sel(wavelength=681).values\n", + "wavelength_701 = da.sel(wavelength=701).values\n", + "\n", + "# Apply the condition to filter the pixels\n", + "condition = (wavelength_650 > wavelength_620) & (wavelength_701 > wavelength_681) & (wavelength_701 > wavelength_450)\n", + "\n", + "# Filter the best_match data based on the condition\n", + "filtered_best_match = np.where(condition, best_match, np.nan)\n", + "\n", + "# Create a custom discrete color map\n", + "cmap = mcolors.ListedColormap(['#377eb8', '#e41a1c', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "bounds = np.arange(-0.5, len(endmembers), 1)\n", + "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "# Create a figure and axis with the correct map projection\n", + "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "# Plot the SAM classification results for the filtered pixels\n", + "im = ax.pcolormesh(longitudes, latitudes, filtered_best_match, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "\n", + "# Add geographic features for context\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", + "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "\n", + "# Adding axis labels\n", + "ax.set_xlabel('Longitude')\n", + 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Microscopy: nan
Absorption: 250ml
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"Granules found: 1\n" + ] + } + ], "source": [ "hypercoast.nasa_earth_login()\n", "\n", @@ -65,9 +73,195 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gdf.explore()" ] @@ -97,9 +291,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ChunkedEncodingError", + "evalue": "('Connection broken: IncompleteRead(398458880 bytes read, 1477995760 more expected)', IncompleteRead(398458880 bytes read, 1477995760 more expected))", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIncompleteRead\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:710\u001b[0m, in \u001b[0;36mHTTPResponse._error_catcher\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 709\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 710\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[0;32m 712\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 713\u001b[0m \u001b[38;5;66;03m# FIXME: Ideally we'd like to include the url in the ReadTimeoutError but\u001b[39;00m\n\u001b[0;32m 714\u001b[0m \u001b[38;5;66;03m# there is yet no clean way to get at it from this context.\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:835\u001b[0m, in \u001b[0;36mHTTPResponse._raw_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 825\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 826\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menforce_content_length\n\u001b[0;32m 827\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength_remaining \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 833\u001b[0m \u001b[38;5;66;03m# raised during streaming, so all calls with incorrect\u001b[39;00m\n\u001b[0;32m 834\u001b[0m \u001b[38;5;66;03m# Content-Length are caught.\u001b[39;00m\n\u001b[1;32m--> 835\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IncompleteRead(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp_bytes_read, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength_remaining)\n\u001b[0;32m 837\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n", + "\u001b[1;31mIncompleteRead\u001b[0m: IncompleteRead(398458880 bytes read, 1477995760 more expected)", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mProtocolError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\requests\\models.py:816\u001b[0m, in \u001b[0;36mResponse.iter_content..generate\u001b[1;34m()\u001b[0m\n\u001b[0;32m 815\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 816\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 817\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:936\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[1;34m(self, amt, decode_content)\u001b[0m\n\u001b[0;32m 935\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 936\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 938\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:879\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[0;32m 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer\u001b[38;5;241m.\u001b[39mget(amt)\n\u001b[1;32m--> 879\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 881\u001b[0m flush_decoder \u001b[38;5;241m=\u001b[39m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (amt \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data)\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:813\u001b[0m, in \u001b[0;36mHTTPResponse._raw_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 811\u001b[0m fp_closed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m--> 813\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_error_catcher\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 814\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fp_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfp_closed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\contextlib.py:158\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen\u001b[38;5;241m.\u001b[39mthrow(typ, value, traceback)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 162\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:727\u001b[0m, in \u001b[0;36mHTTPResponse._error_catcher\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (HTTPException, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 726\u001b[0m \u001b[38;5;66;03m# This includes IncompleteRead.\u001b[39;00m\n\u001b[1;32m--> 727\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ProtocolError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection broken: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 729\u001b[0m \u001b[38;5;66;03m# If no exception is thrown, we should avoid cleaning up\u001b[39;00m\n\u001b[0;32m 730\u001b[0m \u001b[38;5;66;03m# unnecessarily.\u001b[39;00m\n", + "\u001b[1;31mProtocolError\u001b[0m: ('Connection broken: IncompleteRead(398458880 bytes read, 1477995760 more expected)', IncompleteRead(398458880 bytes read, 1477995760 more expected))", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mChunkedEncodingError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2\u001b[0m filepath \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/PACE_OCI.20240615T182549.L1B.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[43mhypercoast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\DATA\\Hyperspectral\\HyperCoast\\hypercoast\\common.py:89\u001b[0m, in \u001b[0;36mdownload_file\u001b[1;34m(url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume, unzip, overwrite, subfolder)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://drive.google.com/file/d/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m url:\n\u001b[0;32m 87\u001b[0m fuzzy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 89\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mgdown\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 90\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquiet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproxy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspeed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_cookies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mid\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfuzzy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresume\u001b[49m\n\u001b[0;32m 91\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unzip:\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.zip\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\gdown\\download.py:344\u001b[0m, in \u001b[0;36mdownload\u001b[1;34m(url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume, format, user_agent, log_messages)\u001b[0m\n\u001b[0;32m 342\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm\u001b[38;5;241m.\u001b[39mtqdm(total\u001b[38;5;241m=\u001b[39mtotal, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mB\u001b[39m\u001b[38;5;124m\"\u001b[39m, unit_scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 343\u001b[0m t_start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m--> 344\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mres\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter_content\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCHUNK_SIZE\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 345\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 346\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mquiet\u001b[49m\u001b[43m:\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\requests\\models.py:818\u001b[0m, in \u001b[0;36mResponse.iter_content..generate\u001b[1;34m()\u001b[0m\n\u001b[0;32m 816\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 817\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m--> 818\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n\u001b[0;32m 819\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m DecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 820\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ContentDecodingError(e)\n", + "\u001b[1;31mChunkedEncodingError\u001b[0m: ('Connection broken: IncompleteRead(398458880 bytes read, 1477995760 more expected)', IncompleteRead(398458880 bytes read, 1477995760 more expected))" + ] + } + ], "source": [ "url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\"\n", "filepath = \"data/PACE_OCI.20240615T182549.L1B.nc\"\n", @@ -115,9 +338,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] Unable to synchronously open file (unable to open file: name = 'data/PACE_OCI.20240615T182549.L1B.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mhypercoast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnetcdf_groups\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\DATA\\Hyperspectral\\HyperCoast\\hypercoast\\common.py:144\u001b[0m, in \u001b[0;36mnetcdf_groups\u001b[1;34m(filepath)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;124;03mGet the list of groups in a NetCDF file.\u001b[39;00m\n\u001b[0;32m 131\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[38;5;124;03m ['group1', 'group2', 'group3']\u001b[39;00m\n\u001b[0;32m 141\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mh5netcdf\u001b[39;00m\n\u001b[1;32m--> 144\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mh5netcdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[0;32m 145\u001b[0m groups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(file)\n\u001b[0;32m 146\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m groups\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\h5netcdf\\core.py:1054\u001b[0m, in \u001b[0;36mFile.__init__\u001b[1;34m(self, path, mode, invalid_netcdf, phony_dims, **kwargs)\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preexisting_file \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(path) \u001b[38;5;129;01mand\u001b[39;00m mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1053\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h5py \u001b[38;5;241m=\u001b[39m h5py\n\u001b[1;32m-> 1054\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h5file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_h5py\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1055\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrack_order\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrack_order\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m 1056\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1057\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# file-like object\u001b[39;00m\n\u001b[0;32m 1058\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preexisting_file \u001b[38;5;241m=\u001b[39m mode \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\h5py\\_hl\\files.py:562\u001b[0m, in \u001b[0;36mFile.__init__\u001b[1;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[0m\n\u001b[0;32m 553\u001b[0m fapl \u001b[38;5;241m=\u001b[39m make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[0;32m 554\u001b[0m locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[0;32m 555\u001b[0m alignment_threshold\u001b[38;5;241m=\u001b[39malignment_threshold,\n\u001b[0;32m 556\u001b[0m alignment_interval\u001b[38;5;241m=\u001b[39malignment_interval,\n\u001b[0;32m 557\u001b[0m meta_block_size\u001b[38;5;241m=\u001b[39mmeta_block_size,\n\u001b[0;32m 558\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 559\u001b[0m fcpl \u001b[38;5;241m=\u001b[39m make_fcpl(track_order\u001b[38;5;241m=\u001b[39mtrack_order, fs_strategy\u001b[38;5;241m=\u001b[39mfs_strategy,\n\u001b[0;32m 560\u001b[0m fs_persist\u001b[38;5;241m=\u001b[39mfs_persist, fs_threshold\u001b[38;5;241m=\u001b[39mfs_threshold,\n\u001b[0;32m 561\u001b[0m fs_page_size\u001b[38;5;241m=\u001b[39mfs_page_size)\n\u001b[1;32m--> 562\u001b[0m fid \u001b[38;5;241m=\u001b[39m \u001b[43mmake_fid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muserblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswmr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mswmr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(libver, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m 565\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_libver \u001b[38;5;241m=\u001b[39m libver\n", + "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\h5py\\_hl\\files.py:235\u001b[0m, in \u001b[0;36mmake_fid\u001b[1;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[0;32m 233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swmr \u001b[38;5;129;01mand\u001b[39;00m swmr_support:\n\u001b[0;32m 234\u001b[0m flags \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mACC_SWMR_READ\n\u001b[1;32m--> 235\u001b[0m fid \u001b[38;5;241m=\u001b[39m \u001b[43mh5f\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 236\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m 237\u001b[0m fid \u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mopen(name, h5f\u001b[38;5;241m.\u001b[39mACC_RDWR, fapl\u001b[38;5;241m=\u001b[39mfapl)\n", + "File \u001b[1;32mh5py\\_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mh5py\\_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mh5py\\h5f.pyx:102\u001b[0m, in \u001b[0;36mh5py.h5f.open\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] Unable to synchronously open file (unable to open file: name = 'data/PACE_OCI.20240615T182549.L1B.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)" + ] + } + ], "source": [ "hypercoast.netcdf_groups(filepath)" ] @@ -243,7 +485,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/examples/pace_oci_l2.ipynb b/docs/examples/pace_oci_l2.ipynb index e087542..11b7b04 100644 --- a/docs/examples/pace_oci_l2.ipynb +++ b/docs/examples/pace_oci_l2.ipynb @@ -225,7 +225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.1.-1" + "version": "3.11.9" } }, "nbformat": 4, From 9a844820790599f74bd7401e698b54ddbed8c952 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 25 Aug 2024 03:36:05 +0000 Subject: [PATCH 2/3] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- docs/examples/multispectral.ipynb | 231 +- docs/examples/pace.ipynb | 65 +- docs/examples/pace_cyano.ipynb | 17771 +--------------------------- docs/examples/pace_oci_l1.ipynb | 262 +- 4 files changed, 329 insertions(+), 18000 deletions(-) diff --git a/docs/examples/multispectral.ipynb b/docs/examples/multispectral.ipynb index f9a8b63..8aaf9d7 100644 --- a/docs/examples/multispectral.ipynb +++ b/docs/examples/multispectral.ipynb @@ -11,197 +11,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: hypercoast[extra] in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (0.7.0)\n", - "Requirement already satisfied: dask in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (from hypercoast[extra]) (2024.7.0)\n", - "Requirement already satisfied: geopandas in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (from hypercoast[extra]) (0.14.4)\n", - "Requirement already satisfied: h5netcdf in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (from hypercoast[extra]) (1.3.0)\n", - "Requirement already satisfied: hvplot in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (from hypercoast[extra]) (0.10.0)\n", - "Requirement already satisfied: leafmap in c:\\users\\c00553090\\appdata\\local\\miniconda3\\envs\\hyper\\lib\\site-packages (from hypercoast[extra]) (0.35.9)\n", - "Requirement already satisfied: 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1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -210,27 +29,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data/cog.tif already exists. Skip downloading. Set overwrite=True to overwrite.\n" - ] - }, - { - "data": { - "text/plain": [ - "'c:\\\\Users\\\\C00553090\\\\DATA\\\\Hyperspectral\\\\HyperCoast\\\\docs\\\\examples\\\\data\\\\cog.tif'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "url = \"https://github.com/opengeos/datasets/releases/download/raster/cog.tif\"\n", "filepath = \"data/cog.tif\"\n", @@ -239,25 +40,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5a688895726343c993d6f1bc6fe05846", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map(center=[53.4070895, 6.8754805], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title'…" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m = hypercoast.Map()\n", "filepath = \"data/cog.tif\" # replace it with your own raster data\n", diff --git a/docs/examples/pace.ipynb b/docs/examples/pace.ipynb index 17469c8..70acc38 100644 --- a/docs/examples/pace.ipynb +++ b/docs/examples/pace.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -47,29 +47,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data/PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc already exists. Skip downloading. Set overwrite=True to overwrite.\n" - ] - }, - { - "data": { - "text/plain": [ - "'c:\\\\Users\\\\C00553090\\\\DATA\\\\Hyperspectral\\\\HyperCoast\\\\docs\\\\examples\\\\data\\\\PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "#filepath = \"data/PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc\"\n", + "# filepath = \"data/PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc\"\n", "filepath = \"data/PACE_OCI.20240423T184658.L2.OC_AOP.V1_0_0.NRT.nc\"\n", "hypercoast.download_file(url, filepath)" ] @@ -83,20 +65,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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NqVQKl1xySfpznudh8uTJ6Nq1K1q3bo1hw4bhgw8+sHeRRERERGSViOB1yZIluO+++7DvvvtmfP7mm2/GjBkzcPfdd2PJkiUoKyvDiBEjsHnzZktXSkREREQ2WQ9et2zZgtNOOw33338/OnTokP6853m4/fbbcc0112D06NGoqKjA7Nmz8e233+Kxxx7L+fO2bduG6urqjA8iIiIiigfrwev555+PY489FkcccUTG51etWoV169bhyCOPTH+uoKAAQ4cOxZtvvpnz502bNg0lJSXpD5bJIiIiIooPq8Hr3Llz8e6772LatGkN/m7dunUAgNLS0ozPl5aWpv8um6uuugqbNm1Kf6xZs0btRRMRERGRNdZKZa1ZswYXX3wxXn75ZRQWFub8uvolEzzPa7SMQkFBAQoKCpRdJxERERHJYS3z+s4772D9+vU44IAD0KJFC7Ro0QILFizAnXfeiRYtWqQzrvWzrOvXr2+QjSUiIiKiZLAWvB5++OFYvnw5li1blv4YMGAATjvtNCxbtgy77747ysrK8Morr6S/54cffsCCBQtw8MEH27psIiIiIrLI2rKBoqIiVFRUZHyubdu26NSpU/rzl1xyCaZOnYo+ffqgT58+mDp1Ktq0aYNTTz3VxiUTERERkWWij4e94oor8N133+G8887DN998g4MOOggvv/xyXufeEhEREVH8pDzP82xfhE7V1dUoKSnBpk2bUFxcbPtyiIiIiKieIPGa9TqvRERERET5Er1sgIiIiEiqmloP/1z1b6zf/D26FBViYK+OaN4sdzlPUoPBKxEREVFAlSuqMOXZD1G16fv058pLCjHp+L44uqLc4pXFH5cNEBEREQVQuaIK5z7ybkbgCgDrNn2Pcx95F5UrqixdWTIweCUiIiLKU02thynPfohsu939z0159kPU1MZ6P7xVDF6JiIiI8vTPVf9ukHGtywNQtel7/HPVv81dVMIweCUiIiLK0/rNuQPXMF9HwTF4JSIiIspTl6JCpV9HwbHaABEREYWWtHJRA3t1RHlJIdZt+j7rutcUgLKSHe1AejB4JSIiolCSWC6qebMUJh3fF+c+8i5SQEYA64fsk47vG+sA3jYuGyAiIqLAklwu6uiKcsz82U9QVpK5NKCspBAzf/aT2AbuUjDzSkRERIE0VS4qhR3lokb0LYttBvLoinKM6FuWqCUTUjB4JSIiokCClIsa3LuTuQszrHmzVKz//6Ri8EpERCSU1M1QLBdFNjF4JSIiEkjyZiiWi9pJ6gAjzhi8EhERCeNvhqq/ptTfDGV7UxDLRe0geYARZ6w2QEREJEhTm6GAHZuhamqzfYUZfrkoYGd5KF9SykUludqCbQxeiYiImlBT62HRyg14etlaLFq5QWvgGGQzlE1JLhflwgAjzrhsgIiIqBGmp4Zd2gyV1HJRrLZgF4NXIiKiHGysPXVtM1QSy0W5NMCIIy4bICIip+ma0rc1NexvhsqVu0xhR+Y37puhJHNtgBE3zLwSEZGzdE7p25oa9jdDnfvIu0gBGcGz9M1QSSkbxWoLdjHzSkRETtK929vm1LCLm6EqV1RhyPR5GHv/Ylw8dxnG3r8YQ6bPi+Wue1ZbsIuZVyIiMkpFdq6pKf0Udkzpj+hbFjqAsD017NJmKOl1aXXwBxj1M/9lrPOqHYNXIiIyRtU0v4kpfQlTwy5shjIxkJDKpQFGnHDZABERGaFymt/ElD6nhvPjSl1aXfwBxqj+u2Jw706Jvx9MYPBKRER5ibKrX/XOfVNT+pLWnpo8KCEIlo0i07hsgIiImhR1ul/1NL/JKX0JU8OmD0oIwvbaYCmSUmlBAgavREQJEbZzVbEZR3V2znQ5KZtrT6VvhpKwNtg2yYOLOOKyASKiBAhbxkjVdL+O7JykKX1dbB2UEETS1wbrLtlGDTHzSkQUc1Eyd6qm+3Vl52xM6ZucHrZ1UEJQSS0bleRKCzYxeCUickjQwClq56pqul/nNL/JKX3T08MubYaSsDbYNFcGF3HD4JWIyBFhAqeonavK6X7Xs3M21p66thnKhbq0Krk0uIgTBq9ERBYEzaCGDZyidq6qp/tdzc7Zmh7mZijZXBtcxAWDVyIiw4JmUKMETlE7Vx3T/bqzczrWpNqaHjZdVYGC4eDCDlYbICKKIGjh+DA7k6OcYOR3rrlCmxR2BM6Nda4u7eoPW1WhKTanh11qf5/UAxVUS3qlBVuYeSUiCslUBjVK4KQqc+fCdL/ONam2p4ddaH9f0mqeur6W20UMXomIYGYNatip56iBk6rOVdd0v4ppft1rUiVMD7uwGUr6gQq6uDS4iAMGr0SUeNIzqCoCJ6mdq6osne41qVx72rSk1zx1YXARF1zzSkSJZnINatgMqqp1dX7nOqr/rhjcu1PkACLqukaVJxOZWJPq4tpTk6KszSYKgplXIooNUwX8bWRQpa2ri5oxVZ2lM7UmVUoG2+QpX/lizVMyhcErEcWCyQL+UTOoYaeepQROKtY1qp7mN7km1fb0sNQNUbY3tUkhcWARN1w2QESihJmKDjv9HDWDGqb8VNSpZ5VT/2HauqmMKbAjY9rUz1KdpUtKySKVSy1UU1GWzXW6SrVRJmZeiUiMMBklGwX845BBDZu9U5Ux1ZGlk7a0QjXpG6KSvqktqZUWbGDwSkQihH3xRwmmbK5BVTX1HGaKMkonqypjqmua3/TAwOQUsa1TvoKI+wAiF+kDi7hh8EpESoXpzKO8+G0W8LedQTWdqQbUZUx1ZulMrUk1vfbUlQ1Rtp8LG1wYWMQJg1ciUsbGVLTtAv62Mqg2MtWA2oypy1k6G1PELm2Isr2pzTRXBhZxweCViJSwNRUdhwL+pg5JAKJ3sqozprrbXse0vq0pYgmnfFF2Lg0s4oDBKxGlhe3obU5FqwqmXMqg2sxUA+ozprqydLqm9W1NEbu4ISopZaM4sDCLwSsRAYjW0dueipYy/Sz9mFlAXSdrO1vdFJ3T+janiKXc6/mQWo9WBxcHFi5j8EpEkTt6CVPRtoMpkxlUCZlq/2epzpiqyNTpnta3PUVs+17PRxLLRrk0sHAdg1eihFPR0UuZilYZTAUJolw6ZhaQ28mqytTpntaXMEUseUNUkstGuTCwiAMGr0SOUrWWTEVHH7ep6KBBlGvHzAJy2tqnMlOne1qfU8SNS3rZKMkDi7hg8ErkIJVryVR09JKnok1soLKRQZWWqQbsbfirz8S0vqTstbRNUSwbRboxeCVyjOq1ZKo6ekmduc/UBqokHzPrs7nhrz5T0/oS2l/ipijba4KlkDaoiBMGr0QO0bGWTHXReduduc/kBqo4HDMLhO9sbW/4q8/ktL7NKWKpm6IkrAm2TeKgIk4YvBJpomPUrWMtmeqOXsIOdNMbqOKQQQ3b2UrZ8FefxJkAlSRvikr6mmCpg4o4YfBKpIGuUbeutWSSO/owbWmjBJWkDGpQUTpbSRv+6jM9KDA5TSx9U5Tkd4pOkgcVccLglUgxnaNunWvJJB7TGbYtbZagsp1BNZWl9knb8JftZ5sI3kxPE7uwKUrC82Ca9EFFXDB4JVJI96hb91oyScd0RmlLmyWobGdQTWWpfXHe8JcvG9PErmyKSlrZKBcGFXHQzPYFEMVJkEAgDD+4AnYGUz6pa8n8jr1+u/gde+WKqqzfF6Ut/SA/VyuksCOoa2wDVVlJZqdfVlJodK1aTa2HRSs34Olla7Fo5QbU1GYL43cK285RO9sobV3f0RXlWDhxOOacPQh3jOmPOWcPwsKJw0UHrk0NsoAdg6ymfn9BqWx3UseVQYXrmHklUsjEqNt0hirKOr4o2dMoben6BipTJb6A6J2tCxv+6lK9LtXWNHHSN0VJxUoLZjB4pdgzuYnC1KjbVHAVdR1flI49altK2kAV5B40WeILUNPZujLlr2Ndqs1pYlfaHUhOzVMOKsywGrzOnDkTM2fOxOrVqwEA/fr1w3XXXYeRI0cCAMaPH4/Zs2dnfM9BBx2ExYsXm75UcpTpTRQmR926M1Qq1vFF6dhVBVW2N4wEuQdNl/gC1HW2Etq6MbrWpdqeJpbe7kDyap66NKhwldXgtVu3brjpppuwxx57AABmz56NUaNGYenSpejXrx8A4Oijj8asWbPS39OqVSsr10rusbGJIi6jblUbz6J07Kra0mYR/6D3oI0SX4C6zlZCjd9cP0PXRkoJ08SSN0UlteapC4MKl1kNXo8//viMP994442YOXMmFi9enA5eCwoKUFZWZuPyyGE2a+3ZHnWr6OxVreNTUXpKSgbDxDpUWyW+AJmdraqMnc51qXEZsOqQ9JqnkgcVrhOz5rWmpgZPPPEEtm7disGDB6c/P3/+fHTp0gXt27fH0KFDceONN6JLly45f862bduwbdu29J+rq6u1XjfJZLvWnq1AQFVnr2odn4qOXUJQZWodqs0SX/7PUfk8RBlIqczY6V6XKmmQJYnt9zDFl/Xgdfny5Rg8eDC+//57tGvXDk899RT69t1RCmjkyJE4+eST0aNHD6xatQrXXnsthg8fjnfeeQcFBQVZf960adMwZcoUk/8LJJCEWnumR90qO3uV6/hUdOw2gyqT61CjZFClBVBRBlKqM3Ym1qVKGGQBsjZGSXgP2ybp9xEn1oPXvfbaC8uWLcPGjRvx5JNPYty4cViwYAH69u2LU045Jf11FRUVGDBgAHr06IHnn38eo0ePzvrzrrrqKkyYMCH95+rqanTv3l37/wfJYnsThWmqO3vV6/ikdOxA8KDK5DpU10t8+aIOpFRn7EytS7U9TSxtY1TS3sP1Sft9xIn1QwpatWqFPfbYAwMGDMC0adOw33774Y477sj6teXl5ejRowc+/fTTnD+voKAAxcXFGR+UPJILeActPp8P1Ycj6DgMwe/YR/XfFYN7d1ISUJko5B91HWrQezDqIQkq2znMvaqiaL/qjJ2Lh3sEFfaQCp0kv4d1k/j7iBPrmdf6PM/LWLNa14YNG7BmzRqUl3PEIonEaRGpmyh0jcR1TM9Jm4auz1QhfxvrUCVkUMPeqyqypjoydtLv5yikboyS+h7WTervI06sBq9XX301Ro4cie7du2Pz5s2YO3cu5s+fj8rKSmzZsgWTJ0/GSSedhPLycqxevRpXX301OnfujBNPPNHmZVMdkqdFpHVWOkvG6JqekxBEZWOykL+tdagulfiqS8VAStc0v+n72dTAXvLGKGnvYRMk/z7iwmrw+tVXX+H0009HVVUVSkpKsO+++6KyshIjRozAd999h+XLl+Phhx/Gxo0bUV5ejsMOOwyPP/44ioqKbF42/R8X6vdJCb50j8R1runTuY4vTOduupC/6+tQTR41C6gZSOnM2Jlal2pyYC99Y5TtZ8A06b+POLAavD7wwAM5/65169Z46aWXDF4NBeHStIjtTRSA/pG4i9NzpqelowRVUo6aNZFBjXqvqhpIuZyxMz2wd2FjlIT3sCku/D5cJ27NK7mB0yLBmBiJu9TZ25iWVnFgQhIyqFHvVZUDKd1trmNa38bAXsIpX7QTfx/6MXilUOIyLWJqTZqpkbipACtKu9mallYRVCUhg6riXlU5kNKVsdM1rW9jYO/azIvETb4qufb7cBGDVwolDtMiJtekmRyJ656ei9puNqelJWSnpWdQVU77S13nqHNa39bAXsK9nQ/Jm3xVcuX34SoGrxSK69MiptekxWUkrqLdbE9L2wyqXMigqrxXdQykombtdE/r2xzYSx4wAG5s8lVJ+u/DZdYPKSA3uVz0W0UR9TCiFp+PKurhCKraTeW0tEuF/MO2n+lDEgD792oulSuqMGT6PIy9fzEunrsMY+9fjCHT5wUq+K76QI/6bBfm13EYiAq23ru2Sf19uI6ZVwrN1WkRm5vNbI3EVUzVqWq3OE1LB2lX1zKoEtq3LlVZO93T+nGZZVGNm3xJJQavFIm0Di4ftjebmS4ZI63TlzotrXsTlY01qFJKfPnCTvmrnOo3Ma0vYWAvbVOU7fcuxQuDV4rMtfp9cdhsli+pnb6Ezr0uE5uokp5BjZL9V5m1M7Ve3/baammbopL03s1G2mDCdQxeFeLN6QbXN5sFIbnT19G5h3kGTW2iikMGNew7Lmr2X2XWzuS0vo2BvdRNUUl679YncTDhOgavivDmzE5iQC91TZqOtpLe6avs3MM8gybLULmeQQ37jlOR/VedtZOW+VdF8smHUt+7ukkdTLiOwasCvDmzkxzQS+u8dLVVUjr9sM+g6U1UUjKoQUV5x6nI/uvI2pkeDJgYyEvfFCX1/aGL5MGE6xi8RsSbMzsXAnrbmSyfzrZysdMP2slHeQZtbaKyfd8FaeOo7zgV2X9dWTtTgwFTA3kXNkVJuP9NkT6YcBmD14h4czbkUkBve7OZ7rZyrdMP08lHeQZtbaKyed8FbeOo7zhV2X9Xs3YmB/KubIqy/d41xYXBhKt4SEFEvDkb0l0EPE5MtJXUgvP1+Z18/fbwO/lcheijPIOuF/IPevBEmDaO+o5TWbT/6IpyLJw4HHPOHoQ7xvTHnLMHYeHE4WLu4fpMF+a3fUACZXJlMOEiZl4j4s3ZUBwCelMbzUy1lampOht1PKM8gy5vojJR3guI/o5Tnf3XmbVT/dybnplzcVOUxE29qiS5woJuDF4j4s3ZkOsBvcmNZibbSvdUna06nlGfQSmbqIJ04qbKewFq3nEuTPnreO5tDORdaGuf5E29Krg4mHAFg9eIeHM25HJAb3qjmcttVZfNOp4qnkHbm0iCdOImy3sB6t5xttu4Mbqee1sDeclt7XNhU68KLg0mXMI1rwpIWPsmid/ZAWiw9kpyQG96fRrgblvVpaLdonbyKp5BP4M6qv+uGNy7U+g2170ONew66ShtrOodp6qNfUHbOtfP0PXc21yDqrqtVbLxrrXJtbXaLmDmVREXRromuTjatFU5wmZbqVhvJqWOp4Rn0MQ6VBvlvQAZ7VuXqilnnc89Z+ayS2KVnqRUWDCFwatCvDkzSevsmmJzo5mNtlLV+Uuq46n6GZS4DtVWeS//Z9hq37pUTjnrfu5dHMjrFodNvWQXg1fSyqWA3vZGM5NtpbLzj2sdT6nrUKMekCCljW0eN1uXiefe9kBe2o5+2+9am6T9LlzF4NUQ3rDyxWXzVFNUd/4q2812J+8LGtybPGbW5fJePtvHzdZl6rm3ebSvtB39SXnX1ifxd+EqbtgyoHJFFYZMn4ex9y/GxXOXYez9izFk+rycRdeTQMVGC9Wkbp5S3VaqD0ZQ3W62N/WE2UwSdR1q0A09UTdQ2dqc5n9PlM06qqecpT73KoQ9+EO3OLd5LlJ/F65i5lWzpJQDCULy6FPStCrgTu1Jae3mM3XcrI11qLYzqGHvTSnHzdYl9f6NQvox3XFs81yk/y5cxOBVI96wDbkQzNsOCnyu1Z6U0m6+sO3n0jpUGwckANHuTVXHzaqecjZ5/5pYRubCjn5p7wxdXPhduIbBq0a8YTO5FMzb3mims610rjfT1W5BO3vTx826vA7V1DGzPmnHzdb/2bqfe1MzT67s6Lf9rjXBld+FS7jmVSPesJlUr7WMM51t5dp6szBrxqO0n8vrUHUfkABEvzdVFO539WAYk+sek7yjXxr+LtRj5lUj3rCZ4hDMm6oaEafak1HazOTUv8/VdaimMqhR701XjptV/aybnnlK6o5+ifi7UI/Bq0a8YTO5Hsyb3GgWl9qTUdrM9NR/Xa6tQzV1QAKg5t5UNXjSNeWs41k3vYzMtdO94lxO0rXfhQsYvGrEGzaTy8G86Y1mcag9GbXNonT2rh83K/WABEDdvSl1s46uZ93GzJMrO/olV6BRxZXfhSsYvGrGG3YnV4N5GxvNXG0rn4o2szX1X//nmN5MIvmABEDtvSnluNm636/rWbc18yR1kOBzoQKNKtJ/Fy5h8GoAb9idXAzmbVWNsNVWKqbvVLSZzal/1fJt0zDBk40MqqS29anI3ul81m3OPEnd0e9SBRpVpP4uXMPg1RDesDu5Fszb3Ghmuq1UTd+paDPXp/59QdrUlQMSABlt61OVvdP5rLs+m6IDy0lSWAxeyQqXgnnbG81MtZXK6TsVbSZx6l93MX+XDkgA7LZt3e9Tlb3T/axLyFhL2hgVhwo0YUn6PbiIwasAvIllc3mjWb5UT9+p3NRju7P3mShFlbQDEnxRMv4qs3cmnnVXNgKaYDsxYIu034OLGLxaxps4k8RAXuJ0n+p2Uj19p7LNdHT2Jo5DDdOmYYMnKRnUMPdl1Iy/yuydqWfdhY2AJiQhMVCfxN+Dixi8WsSbOJPkQN7lDGA+dEzfqWwzlZ295GL+rh6QAIS7L1Vk/FVn7yQ966pI3RglMTGgk9Tfg4sYvFrCmziTC4G87eAA0NdOuqbvJLRZXS4U85dwQEJQYe9LFRl/Hdk7k/etidkmyRuj4jhYyEXy78E1DF4t4U28k0uBvM2NZjrbSef0na42C9rpu1TM33bQH6Rto9yXKjL+urJ3Jp51U7NN0jdG2b7fTZH+e3AJg1dLeBPvxEA+PzrbybXpuzCdvmvF/G1mUYO0bZT7UlXG38XsncnZJhc2RrlUgSYsF34Prmhm+wKSijfxTnEI5GtqPSxauQFPL1uLRSs3oKY2Wx4qGt3t5AcAZSWZ91xZSaGIZRs+v9OvHzD5nX7liqqs3xc1g5orbE9hR3DXWCkqm20a5N4M07ZR7ssobVvf0RXlWDhxOOacPQh3jOmPOWcPwsKJw8Xct3U1la0GdmSrVb1HVLYzhcffgzrMvFqSxF2WubgeyJua+jPRTqam72zU9ExiMf8g92bYto1yX6rO+OvM3qlcm2p6tsm1mRWJVWdUcO33IBmDV0t4E+/kciBvcurPVDvpnr6zVdPT9WL+ust7hW3bqPelC1P+qgeoNmabXGhnQHbVGRVc+T1Ix+DVIt7EO7gayJveaOZqO9Vls6ZnUjKoQLh7M2zbqrgvJW/Y0TFAtTXbJLmdATeqzqgg/ffgAgavlvEm3sHFQN7GRjOb7RR1Kk9CTU8JGVQgWFuaKu8VpW1V3Jc6Mv4S7tlsbM42Sd0Y5VLVGRWk/h5cweBVAN7EO7gWyNvaaGajnVRM5Ump6Wn7PjOxDjXMvali+l/S8yvlns0mDrMoqrHqDAXBagMkih/Ij+q/Kwb37iT65W1zo5nJdgq7u78+lTU9gZ2dvC9Ip6+y/XTu5g/SodcV5t5U0ba22rU+SfdsLhIqUUgSh6ozZA4zr8LFdddlHLi80SxfKqfy4ljTU3cW1fQBCVLaNkrWVOI9m4vtbLWk/sX1qjNhSfoduITBq2Bx33UZlLSHXOLUn+o2UjmVpzLYt93pA2Z289so72W7baNu2pF6z+biymEUuiUhGVCftN+BS7hsQChV015xUbmiCkOmz8PY+xfj4rnLMPb+xRgyfZ71dpA09aejjVRO5ama8q/781QunQgyTR2myHyUdaimD0hQ1bZBp/5VFO+XfM9KIbF/iWtb5yLxd+ASZl4FStquy6ZIL59iO1MF6Gsj1VN5Uqal6zNxJGqUdahJKO8FqMmaJuWeDUty/xK3ts5F8u/AFQxeBeKuy51cechtVozQ2UY6pvJ0BlRhlk2ECfxN7uaXUN5L9wEJPhVZU9fu2bpMLI2S3r9ISAboJv134AIGrwJx1+VOfMibprONdK3r1RHsh8n0mTwS1dV1qCYOSPCpyJq6dM/WZWr9owv9S9zLR7rwO5COa14FSuquy2z4kDdNdxtJWtebS9j1Y2FLUYVdi+raOtQw7Rq2TYFoa3zrcuGercvk+kf2L/bxdxAdM68CJXHXZS4uP+SmqiOYaCMTmb+w7RUl02fjSFRXsqgmD0jwqcyaSlue0tjPMrk0iv2LffwdRMfgVSCJJZhscfUhN1kCxVQb6ZzKi9JeUZZN2DoS1YVjZsO2q+0jfOuSsjylMaaXRrnUv0grj6iKS78DqRi8CpWUXZdNcfEhN10dwcU2qitqe0XJ9Ll8JGrcDkioS+qmHR3Pto2lUS70L3GvgerC70AyBq+CSX2Bm+bSQ26rOoJLbVSXivaKkulTEfjb2FwS1wMS6v8cle0aNYun69m2tTRKcv8ivTyiKpJ/B9IxeBUu7rsu8+XKQ26zOoKNNooaEKhoLxXZUwmBf75taSqLGqVdpbSpT0UWT9ezbXNplMT+xZXyiKpI/B24gMErOcOFh9x2dQSTbaQiIFDRXioyfbYHR0HakgckBKMqi6fr2XZ92Y9qLI9I+WCpLEcFPXaRzHC5OkIQqkr7qGovFaWRbB2JGrQtTR4zG7VdVR7hG+adp+K4WZ/OZ1tCaS8pfYrtBIAtUtrfFcy8OijuC9mDkrQj1dXqCEGonNZT2V4SMn0mCvon6YAEX9h3nsosnu5n25XNf7olJQFQl6T2dwUzr44xWczaBZUrqjBk+jyMvX8xLp67DGPvX4wh0+dZawc/SADQIMtlawpQ9Yg+ShH6+lS3l81Mn6mC/kk5IMEX5Z2nMotn4tlWef/mS1qfouqgCldIa39XMPPqkKQtZG+K1B2pkjar6BjRq57Wk9RePlNHooZpS1ezqCaP7/WpzuJJvFejkNinJGkNsMT2d4XV4HXmzJmYOXMmVq9eDQDo168frrvuOowcORIA4HkepkyZgvvuuw/ffPMNDjroIPz+979Hv379LF61PVzIvpP0h17KVKuO4F7HtJ6E9vKFaTfTBf0lHJAQRNh7Meo7T8dUv6l71cRyKKl9StwGCblIbX8XWA1eu3Xrhptuugl77LEHAGD27NkYNWoUli5din79+uHmm2/GjBkz8NBDD2HPPffEDTfcgBEjRuCTTz5BUVGRzUu3IqkL2bNx4aG3WR1BZ3Cva+2fjvYKGgCYPhI1ajkqWwF/kHa1cXyvT1cWT/ezbWoNpOQ+RdKAVhfJ7S+d1eD1+OOPz/jzjTfeiJkzZ2Lx4sXo27cvbr/9dlxzzTUYPXo0gB3BbWlpKR577DGcc845Ni7ZqiQuZM+FD33jdAb3rkzrhQkATGdQo7alrSxqkHa1dXyvz7UsnsnlUNL7FBfKI0Yhvf0lE7Nhq6amBnPnzsXWrVsxePBgrFq1CuvWrcORRx6Z/pqCggIMHToUb775Zs6fs23bNlRXV2d8xEXSFrI3xuWH3kRJFN3BvYTSPo0JuwkiagY1zLNpuy2D3I9h2lXF8b1R33lHV5Rj4cThmHP2INwxpj/mnD0ICycOt36f1qeytFc+2KfYxfYPz/qGreXLl2Pw4MH4/vvv0a5dOzz11FPo27dvOkAtLS3N+PrS0lJ8/vnnOX/etGnTMGXKFK3XbIsrGS8TXC1JZWo60ERwb2JaL8y6vyjT1LYyqLamSIPcj2Hb1fbxvXV/lo4snsq1qaaXQ7nUp0gqiaiKS+0vjfXgda+99sKyZcuwceNGPPnkkxg3bhwWLFiQ/vtUKvOX5nleg8/VddVVV2HChAnpP1dXV6N79+7qL9wS16bAdHHxoTc5HWgquNc5rWejtqfNI1FVtGWQDj7o/Ri2XeNyfG82qgejNpZDSW5fX5zroLrQ/hJZD15btWqV3rA1YMAALFmyBHfccQcmTpwIAFi3bh3Ky3f+8tavX98gG1tXQUEBCgoK9F60ZUlYyJ4Plx5609URXAzu64oS6EcJAFzNoAL6s6hh2zUOx/dmo2Mwams5lMT29UktiaiS5PaXynrwWp/nedi2bRt69eqFsrIyvPLKK9h///0BAD/88AMWLFiA6dOnW75K++K+kD1frjz0Nqoj2Aruo07v2a7tKSGDCsjLokZpVxX3oup3XpT7VNdg1OZyKIl9ivSSiCpJbH/JrAavV199NUaOHInu3btj8+bNmDt3LubPn4/KykqkUilccsklmDp1Kvr06YM+ffpg6tSpaNOmDU499VSbl03CuPDQ26qOYDq4VzG9J6G2p+1BkcQsqorpfykDzaj3qa7BqOszJqq5UBKR7LAavH711Vc4/fTTUVVVhZKSEuy7776orKzEiBEjAABXXHEFvvvuO5x33nnpQwpefvnlRNZ4DSKOC9tdZ7M6gqngXtX0npTanirbLQ5ZVBXtKmGgqeI+1TkYdWk5lG5JLInI/js/VoPXBx54oNG/T6VSmDx5MiZPnmzmgmIgzgvbg5D2AnC1OkK+VE7vxa22Z5yyqFLaNezzreo+1T0YtZ2llvL+dLkkYhjsv/Mnbs0rhZeEhe35kPgCkDYdqLpzUjm9pyrQtx0AAPHMotpu1yjPt6r71MRg1FaWWtL7M+6D/rrYfwcj5pACisZ0cWupwhanN8F2MXpf5YoqDJk+D2PvX4yL5y7D2PsXY8j0eZHaRuX0nh9YATsDKV/Y2p6j+u+Kwb07RQ6wghT0D/NMRsmiBi10HuV+VNWuQQ/siPp8q7pPVd6jkkh7f8a1netj/x0cM68xwYXtbuxMlZC10jG6Vz29J2V6ui4Tx6ImKYsatD1VPN8q71OJ92gUUt+fcWvnbNh/B8fgNSaSuLC9PldeALamA3V2Tjqm93QFVmGWTIQJ+l1Zi2r6cAQgXHuqeL5V36emgn8Ta1Alvz9tD/p1Y/8dHIPXmEjawvZs+AJonM7OSdeaXtWBfpj1fCaPRXUxi2oqg6ri+dZxn+oejJpagyr9/SmhUoUu7L+D45rXmAi77i1OXH4BBF37F4buzknKmt5cwq7nCxL01+XqWtQg92KYNg3bnqqeb+n3aV0m16C6/P50Hfvv4Jh5jQlpu9ltcHVnqqnMionOyUT2L8wUapQlEzaORXUhi2o6g6ry+dbZvqqm+E2vQXX1/RkH7L+DY+Y1RlzKKOjg4s5Uk5kVU6N71Tv86wpbKSFstg9QcyxqHLOopjOoqp9vHfepykoeUe7ZMFx5f5qYpbIh6f13UMy8xkzcF7Y3xaWdqaYzK66P7qNUSoiyZMLVY1ElHo4ARGtPyc+36koeNtagSm5fQFYNWh2S3n8HweA1huK8sD0frrwAbOzutdU5RZ1KjRroR8meungsqtTDEYDo7anj+bZ9f2Zjaw2q1PdnUor4J73/zheDV4olF14Atnb3mu6cVGRLogb6KrKntjNS+QZY0o+YBaK3p8rnW8L9mY3NNajS3p9Sa9CSPQxeE0bKmdVkd3evqc5JVbYkaqCvInvqSkF/Fw5HAGRk+KTcn9m4vsxHJck1aMkOBq8JEvf1QvmQFLzHfXevymyJikBfRfZUVdAf5D4MGmC5cjgCYKc9636PpPszG5cy/jpJr0Grg4R2l4zBa0IkZb1QY6QF7xIzKypfmCqzJaoCfSnZPp0bqZJyOIIv7HMt8f7MxpWMv05Jq0Erpd0lY6msBGiqAwR2dIBxKTmSjcmSVEFIKo+isswPoDZborKMj8oSSUHL9pgoR5WEwxF8UZ5rqfdnrp+vq/xcLpLemUkq4i+p3SVj5jUBkr5eSPpif9uZK0BPZl51tkTCFGpdJo5FDRNguZhFNXlsry/u92cU0t6ZEmepdJDW7pIxeE2AJK4XqsuF4N3m7l5dL0wdU6kSAn0gXLBvshxVlEBKelkvn+0KFNlIuT+jkvjOjNPgIBeJ7S4Vg9cESNp6ofqSHrw3RdcLU1e2REdwFWStr8ljUaOWo7IVSOks6+WTUIEi18/VHVjo3swj9Z0Zl8FBLlLbXSIGrwkQ913tTUl68N4UnS9MF7IlQaeswwb7NspR2cjo6y7r5ZNSgcI0E5t5JL8zpdWgVUlyu0vD4DUBkrJeKBeXg3cT5VJ0vzAlZ0vCTFmbPhbVdoAlrayXL04VKPJlqmqMy+9Ml7Hd88fgNSFsd4A2uRq8myqXYuKFqTNbEjbADztlbeNYVBc2Upkq6+VT+Vzruj9VDj5NbuZx5Z0Zt1qorrS7BCnP8+JbHwlAdXU1SkpKsGnTJhQXF9u+HOvi9rAH4VLtvFwZFv83pbqMlv/vAdlfmFLrAEf5nS5auQFj71/c5L8x5+xBGYFNTa2HIdPnNRnsL5w4POuzZfM+VJFFzXVPhGnPqG3pX6fE51r1dYW9X6OQ2raA7GuLKs7/b40JEq8xeKVEcSF49zv0XGsB8+nQw3DthRk1wH962VpcPHdZk//OHWP6Y1T/XbP+20C4YN/GfRg0ixr0HgzbnioGTtKeax2Dzyj3axTS2hYwP7i3QWK76xYkXuOyAUoUFxb72yqXYnpqOsrLWcUUapQpawnHoupci+pKWS+f6ufa9r2Zja3NPNLemUmphSqt3aVh8EqJHOFJZrNciqkXZtQsr4oAP+paX1eO7TR1OIKrZb3qk3BvZsPNPDuwFioBPB428VQfCUrRxb1ciorjD1UE+CqO9FR1bGeQo1FNHDEbpawXEK49bRyBWp+UezMb3UfQuiJptVDDHJucBMy8JpipsisukJR9jnOGRdWUn6oAX0IVjjhlUSW0JxDueZZ2b2Zjs32lvCPjPrivy7V9CCYxeE2opKwbyoe0F4S0cikqOy1VU34qA3zb0/8S16K6WNbLF/Z5lnhvZmOjfSW9I+M8uK+LyaXGcdlAQoWZSowjFdOEOvgZlrKSzMCjrKTQ6EtL9bISVVN+qqdQVU5Z5zvN19QAEtgxgKz7/VGyqLn+j1LYEYjkyqKGuQdVtGeY6dIoz7PUezPXv2FqiYW0d2QSlk+EeTckDTOvCZW0dUPZSM8+S8hgqR75q5zykzJFXZfuo1GTkkUNk+mL+jzH/d4MQ+o7Mi7tmws3pTWNwWtCJWndUC4uvCBslUvR1WmpnvLTFVyFWSph4mhUG2tRpZf18kV9nl25N+vTuRZV8jvS9uBeJyaXmsbgNaGSsm6oMXxB5Kar09Kxnld1gG8q68csqpoNab6oz7ML92Z9uteiSn9HxrUWKpNLTeOa14RKwrqhprj6gjBROkVnpyVlPW82Ydf3hVlDnoS1qCbKevlUPM+S7836TKxFdfUd6bqw74YkYeY1weK+bqgpLmafTe361d1p6c4Cmi6VFCbYZxZVTVkvn6rn2YXpaFNrUV18R8aBtIozEjF4TTgXXtS6uPaCMFk6xUSnpWvKz0apJBtHo5qeMpVa1sun8nnW0bYSS841xZV3pJQatColPbnUFAavFNt1Q/lw5QVhetevK51WfVECfFtZP5sDyHw7femHI/ikPs+qZ0xMrkWV2qY+STVoVUtycqkpDF4p8Vx4QdjY9Wuj04qSQbFZKilqsG9jABm3sl4+ac+z9JJz+ZDWpr4kFPJPcnKpMQxeqVFxnI7JRvoLwtauX5OdVtQMiu1SSRIyVPk+r3Et6+VT+TzbHFDlYmMtqrR3pNQatGQGg1fKKc7TMa6xuevXRKelIoMioVSS7aNm83leWdYrf7YHVLm4uqxHJck1aHVISiIpXyyVRVlJOxLQBhMlqfIV59Ipqo5ClFIqSdXRnbpKUiWprBcQ/jlW8Q6Ma8k5Ce9G6TVoVVJ9THccMPNKDXA6Rl7WWVqmReLO6TiVStJZkiopZb2A8M+xqneg6yXnspHybkxKDdokrOsNg5lXaiBKofA4kJp1llJAXXUWQFUGReXBG6qyfkDwLJXuwv5Ry3rZzKIGEeU5VvUONDFjYrJtJb0b4zwb5VM1KxVHoTOvK1euxKxZs7By5Urccccd6NKlCyorK9G9e3f069dP5TWSYUmajqlPetbZdlZQ+s5pCZum6gqapTJRkiruZb38r43yHKseUEmZMYlC2rsxTm2bS9LW9QYRKvO6YMEC/L//9//w1ltv4a9//Su2bNkCAHj//fcxadIkpRdI5iVlOiYbF7LONrJYgL4sgOoMytEV5Vg4cTjmnD0Id4zpjzlnD8LCicOtBK5Bs1Rh7r+gz2vUDLWtLGqQbH/U51jHgMr2jElUEt+NcWnbXJKcSGpKqMzrlVdeiRtuuAETJkxAUVFR+vOHHXYY7rjjDmUXR3Yk+UhAvixyc2nntO0TksJmqUyVpLKZoQ66XjpMtj/qc6z6HWgqY61zR7rUd6Pt2SidkpxIakqo4HX58uV47LHHGnx+l112wYYNGyJfFNmVhOmYXFx9WZgoo2Ji57SU6f76gk7/hw30TZakcmGzT9hBQNTn2JUBVV26N1JJfjdKq0GrSpITSU0JFby2b98eVVVV6NWrV8bnly5dil133VXJhZFd0oMJXVx8WZja/ev6zumwAb7JzJ/pwv4qOn1dByMA4QcBKp5jl96BJnaku/hudF2SE0lNCRW8nnrqqZg4cSKeeOIJpFIp1NbW4h//+Acuv/xynHHGGaqvkSyJ83RMLq69LEyWUTHReenKoJgumRQ20HetJJXOgxGA8IMAVc+xrjZVOVNiaiOVK+/GuBXzd2kQZVLK87zANRa2b9+O8ePHY+7cufA8Dy1atEBNTQ1OPfVUPPTQQ2jevLmOaw2luroaJSUl2LRpE4qLi21fDjlCSi3DxtTUehgyfV7OzJQfTC6cOFzZy9sPloHsnZfETRK5Avx8rnnRyg0Ye//iJv+NOWcPygi6/d9NU4F+rt+NrfsvSMcfpF3DtmPY76t7jdKeY9XXFLWNgpLYpj7J1xZV3ILybILEa6Eyry1btsSjjz6K66+/HkuXLkVtbS32339/9OnTJ9QFE0njQtbZRhkV17IAtkomRc1SSc6iAmYORgCiZ/ulPcc6ZkpMb6SS1qa+uBfzj+u63rAinbDVu3dv9O7dW9W1kEOSMAqU/rKwtfvXZOcV9T6LGuBHWecbNdCXvB41aLvaWEZR92eoeo6j3I+6pvdtbKSS9m6UVoOW9Ms7eJ0wYULeP3TGjBmhLobcEOepGZfY3P1rovNScZ/ZLpnkwtGoLhyMICHbH/V+1DVTwo1ULOafRHkHr0uXLs348zvvvIOamhrstddeAID//u//RvPmzXHAAQeovUISJe5TM/mQknWOc6el6j6TUDJJVaCvYj1qtvYL0/GHPRjBpWUUdam4H3XNlLiykUonqTVodZDS/9iWd/D62muvpf97xowZKCoqwuzZs9GhQwcAwDfffIMzzzwTP/3pT9VfJYnAqRlZWee4dloq77O4lEySth7VxsEINgYB/teruB91zpS4dOiEDpJr0Kokqf+xLdSa19tuuw0vv/xyOnAFgA4dOuCGG27AkUceicsuu0zZBZIcSZ+akZh1lhBYAWo7MJX3mfSSSfmQuB7VpYMR6grT+au6H3XPlEjf5KdTnGehfBL7H5tCBa/V1dX46quv0K9fv4zPr1+/Hps3b1ZyYSRPkqZm6pOcdXYxIGiM6vtMVYBvI/MneT2qCwcj1BW281d1P5qYKTG5kUpSMBXXWSif5P7HllDB64knnogzzzwTt912GwYNGgQAWLx4MX79619j9OjRSi+Q5EjK1Ew20rPOtnb/6ujAdNxntgN8n4ljZk2uR3Ul2xel81d5P0qZKYlKYjAVl7bNRnr/Y0Oo4PXee+/F5Zdfjp/97GfYvn37jh/UogV+/vOf45ZbblF6gSRHEqZmckly1jkXXR2YrvtMdYAfNPtn6phZ0+tRo7arro1odUXp/FXfj6YCfp1rUaUGU1IGqaqx/2koVPDapk0b3HPPPbjllluwcuVKeJ6HPfbYA23btlV9fSRI3KdmGuNi1ln3RgpdHZgL91nQ7J/JY2ZdWo+qcyNaXVE6fx33o+6ZEt1rUSUHU9Jq0KrgYv+jW7Mo39y2bVvsu+++2G+//Ri4JoSfoSkryXxIykoKY71g3M++5OqeUtjROUjJOleuqMKQ6fMw9v7FuHjuMoy9fzGGTJ+HyhVVyv4NnR2Y5PvMz/7VD9z97F+2Ng4S6NcV9r4L235+xz+q/64Y3LtT4MC1ptbDopUb8PSytVi0cgNqanOfPh60HcO2IRC985d8P9YX5v4MisGUWa71PyaEyrwedthhSKVyv9TmzZsX+oJIvrhOzTTGhWygz9RGCt0dmO77LExmOmz2z8Yxs6afU91Z1CiDJVUl06S/90ytRXVhCZmEEl6quNT/mBIqeO3fv3/Gn7dv345ly5ZhxYoVGDdunIrrIuHiODXTFBc2BJjcSGGiA9N1n4WdVg27VMLWMbOmnlPd5byAaG2oqvPX1Z6qAi1Ta1GlB1NSSnip5EL/Y1Ko4PV3v/td1s9PnjwZW7ZsiXRBRJJJz76Y3EghvQPLJUpmOmz2z9VjZvMJqkxlUVW0ocTOX2WgZXItquT2lFLCSzXp/Y9JoYLXXH72s59h4MCBuPXWW1X+WHJInKZqcpGcdTa9kcJGBxblHouamQ6b/ZN0zGy+8g2qTGVRVbShtM5fdaBlei2qtPaUWMJLNcn9j0lKg9dFixahsDD/h2LatGn461//io8//hitW7fGwQcfjOnTp2OvvfZKf8348eMxe/bsjO876KCDsHjxYmXXTWrEcarGNTY2UpjswKLeY1Ez01GyfxIyVfkG/kGCKpNZVBVtqLLztzmQysbGWlRJwZTUEl6kXqjgtf5BBJ7noaqqCm+//TauvfbavH/OggULcP755+PAAw/Ejz/+iGuuuQZHHnkkPvzww4zqBUcffTRmzZqV/nOrVq3CXDZpFOepmnxIyTjb2khhogNTcY9FzUxHzf7ZPmY2n8A/aFBlOosqJdtneyCVjatLeVSRXMJLNSl9ji2hgtfi4uKMagPNmjXDXnvtheuvvx5HHnlk3j+nsrIy48+zZs1Cly5d8M477+DQQw9Nf76goABlZWVhLpUMSMJUTWMkZZzj2nmpusdUZKajZv9sHDMbJPAPGlTZyKLaaMO6JAykcrGZ4bcdUCWlhJekPseWUMHrQw89pPgydti0aRMAoGPHzJfc/Pnz0aVLF7Rv3x5Dhw7FjTfeiC5dumT9Gdu2bcO2bdvSf66urtZyrbRTkqdqJGacJUxPA2o7MlX3mKrMtO3sn86yVEGDKlezqGEDAEkDqVykHzihiwslvKKS2OfYECp43X333bFkyRJ06pTZSWzcuBE/+clP8D//8z+Bf6bneZgwYQKGDBmCioqK9OdHjhyJk08+GT169MCqVatw7bXXYvjw4XjnnXdQUFDQ4OdMmzYNU6ZMCf4/RaElaaqmLskZZ5eCq3yousdUZqZtrZ3UXZYqTFAlIYsaRJQAQNpAKheT7SoloIrrzJNPcp9jWqjgdfXq1aipqWnw+W3btmHt2rWhLuSCCy7A+++/j4ULF2Z8/pRTTkn/d0VFBQYMGIAePXrg+eefb7D2FgCuuuoqTJgwIf3n6upqdO/ePdQ1UX6SMlVTn/SMs62NFDo6MpX3mJTMtE9acf8oywBsDZiCBP9RAwCJAymbpAVU0p5vlaT3OSYFCl6feeaZ9H+/9NJLKCkpSf+5pqYGf//739GzZ8/AF3HhhRfimWeeweuvv45u3bo1+rXl5eXo0aMHPv3006x/X1BQkDUjS/okYaomm6RmnBujqyNTfY/Zzkz7JBb3jxJU2cqiBsnyRw0A4jyQCkNiQCXl+VaNfc5OgYLXE044AQCQSqUanKTVsmVL9OzZE7fddlveP8/zPFx44YV46qmnMH/+fPTq1avJ79mwYQPWrFmD8nL5D3VSxCWDEFRSM86N0dWR6bjHVAdaQdf4Si7u78qmnzBZ/qgBgIsDKZ0bqaQGVJJKeKnCPmenQMFrbW0tAKBXr15YsmQJOnfuHOkfP//88/HYY4/h6aefRlFREdatWwcAKCkpQevWrbFlyxZMnjwZJ510EsrLy7F69WpcffXV6Ny5M0488cRI/zapFYcMQlAuZpx17wbW2ZFJvsfCrPGVXtxf+qafsFn+qAGACwOpunRvpGJAZY6LfY4uoda8rlq1Ssk/PnPmTADAsGHDMj4/a9YsjB8/Hs2bN8fy5cvx8MMPY+PGjSgvL8dhhx2Gxx9/HEVFRUqugdSJ61RNLq5lnE3sBtbdkem+x8IE92HX+LpQ3D9qUCVpI5pPRQAgeSBVl4mNVAyozHGtz9Ep7+D1zjvvxC9/+UsUFhbizjvvbPRrL7roorx+pudlu9V3at26NV566aV8L5EEiONUTWPYiWUy0ZHpusfCBPdR1vjGvbi/tI1oPlUBgK72VDU7YmojlQsBle36syq50ufolvKaiiD/T69evfD222+jU6dOja5NTaVSoUpl6VJdXY2SkhJs2rQJxcXFti+HYkryy7Gm1sOQ6fNyZqn8gHLhxOFKrtkPlIHsHZnEOoS5gvumrnnRyg0Ye3/TR1XPOXtQg4Db/700Fehn+73YqqkZ9XjZXO0Zph2jtL1/jbbrkuq8pqjtE5TE9pR8XVFJ7nPCChKv5Z15rbtUQNWyAUqGOD5k9UnOOJveDexaZiBKhirKGl+XsqiAvuNlAbNLKHzSljmpnh0xvZFKWnsCcurP6iC5zzEh1JrX66+/HpdffjnatGmT8fnvvvsOt9xyC6677jolF0fui+uo1yU2dgOb7MiiDo6iBPdR1/jaLu4fNZOq4nhZwPwSiro/w8ZBE9m+V/UUv42NVJICKmn1Z0mtUMHrlClT8Ktf/apB8Prtt99iypQpDF4JQLxHvfmSkHW2tRvYREemYnAUJbhXtfnHRsZKVybVhY1oqkW9D3XMjiR9I5XE+rO6SOhnTAsVvHqeh1SqYcO899576Ngxng8CBcNRr5ysc1w7MVWDoyjBvarNKiazqIDeTGrcN6LVp+I+1DE74sJGKp2k1p9VTUo/Y1qzIF/coUMHdOzYEalUCnvuuSc6duyY/igpKcGIESPwX//1X7qulRwSpMOLI79Dq98GfodWuaLK2LX4nRiws9Pyme7Eamo9LFq5AU8vW4tFKzegpjav/aJZf05jgyNgx+Aon5/vB/e5/u9T2NEZNLZ2cubPfoKyksygrayk0NjsQuWKKgyZPg9j71+Mi+cuw9j7F2PI9HlZ77OgbRf2eNmg7RmlHf3gf1T/XTG4d6fQ93LQ+1PVfahrdsTmvanqWQ8rCfVnJfUzpgXKvN5+++3wPA9nnXUWpkyZknE8bKtWrdCzZ08MHjxY+UWSe5Iy6s1GYtZZwvSqygyByilBFRkqm9k/3fVRTR4va7sdTRw0kY3O2RHJm/t0iuuMk09iP2NSoODVPxK2V69eOPjgg9GyZUstF0XuS8KoNxepa61sBwaSd1KrCO5tbP4xsbPf9PGyppdQAGYPmshG9xS/yY1UUvY6xH3ZhNR+xpRQa16HDh2a/u/vvvsO27dvz/h71lOluI96GyM562xjN7ArO6ltr530BclamdjZ79LxskDwrJ/pgyZykTA7EpW0bGAc2jQXyf2MCaGC12+//RZXXHEF/vznP2PDhg0N/r6mpibyhZHb4j7qbUySs87ZuLSTWnVwrzsDaGpnv63jZQG9R8wC0e5P1fehiYBf5850idlAKYNS1ZLez4QKXn/961/jtddewz333IMzzjgDv//977F27Vr84Q9/wE033aT6GslRcR71Nsa1rLPuMitJ3UltIgNocme/9LWTYbN+tg6ayEXn7IjutahSs4GS6s+q4lo/o1qgagO+Z599Fvfccw/+8z//Ey1atMBPf/pT/OY3v8HUqVPx6KOPqr5GctjRFeVYOHE45pw9CHeM6Y85Zw/CwonDYxu4ArJ29zclyC71sOK4k7opYXYBh6nQYXpnv6pd/fkI2oZhK5yoOmhC4n1Yl4md6UnPBprkUj+jQ6jM67///W/06tULwI71rf/+946XwZAhQ3DuueequzqKhTiOepviQtbZ1MYKl3dSh8lKm8wAurazX9JGNJ/LB03ky9RaVBeygXEq6O9CP6NLqOB19913x+rVq9GjRw/07dsXf/7znzFw4EA8++yzGeWziJJMcodmcmOFqzupw06xhl33FzZrZXtnf76kbUTzSTpooj5VgZaptajSl/NIKOGlmuR+RqdQweuZZ56J9957D0OHDsVVV12FY489FnfddRd+/PFHzJgxQ/U1UkzFaQSci9Sss+mNFa5lCKJkpW1kAG11YPk+w1I3ovkk3p8qAy2Ta1EltiUgp4SXDlL7GZ1CBa+XXnpp+r8PO+wwfPzxx3j77bexyy67YNasWcoujuIrjiNgl9jYWGEywIoyMIqalbaVATTdgeX7DEvfiOaTlMFSHWiZXosqqS0BeSW8KLpQG7bq22233TB69GgUFxdj9uzZKn4kxViSj7STwtbGChMbfqJuQot6tHGU42Ztb/7J90jPIM+wCxvRfCrvz7DHo6o8+tgX9QjkMExu7mtK0o8rj6NQmVeisDgClrFcwoWNFWGoyFhFzUq7mgHUlUlNwka0+qLMLOlY0iN9LapuUkt4UXhKMq9E+Ur6CNhEaap8xLHMiqqMlYqstIQMYJDMn85MatSNaGHa0HT71RV1ZklXoGUzqx+2LVVJQgkv221sGjOvZFSSR8DSNgxI2FihMgutKmOlKittMwOos7h/0GfYxY1oYTOnKmaWdAZa0g+a0CWuM00+CW1sWqDgdfTo0Y3+/caNG6NcCyVAEkbA2UhdLuFKgJUPVQMjlVOsqjZR6TwiNWjQH/QZdnEjWthBpooBlO5Ay3R5NAkD9jgvm5DSxqYFWjZQUlLS6EePHj1wxhln6LpWigEbGwckkLxcwsbGCh2b9lQOjGxvnKoryFKTMEsnwmZSgzzDrkxZR116omIAFZclPTo2nkUh6ZlWRVobmxQo88oyWBRVnEfAjUnycon6dGWhVWespGz80ZlFBYIH/WGfYRemrKNmTlUNoCQs6YnKdC3pfEh4plWS2MamcM0rGReHF3NQri2X0FkRQdcLV8fASPUUa5B2NXVEapigP+wzHLU9dS6fAKIPMlUOoEwEWjqfc6kD9jgV9JfaxiYweCUr4jYCbopLGwZ0L/7X+cKVPDAykQU0Wdzf9DOscxOaL+ogU/UASmegpfs5d23A7qIktzFLZZE1kopY6+bKOjYTB0jofuEeXVGOhROHY87Zg3DHmP6Yc/YgLJw4XEmHbLJ8UpQsqqni/lGfYR2HIgDh15irWJPvwtpKE895Uvc3mJTkNmbmlcgQyVlBwFxFBBNZaB0ZK9Plk0wfkSo1k2pq+QSgLnOqoy1VTfGbes6Tur/BpCS3MYNXEkXC6VM6SV4uYWrxv4svXBvlk8IG+VEGSabWpAZpT1PLJ3yqBpkqB1Aqp/hNbvKRPGCPS18juY11YvBKYiSl0LLUDQMmF/+bfuFG6aiiZqpsZAEl7+w3cbxs1Oy+pEGm6jqepjf5SGpLX9z6GoltrBuDVxIhqYWWJTG9+N/UCzdqR2WzfJLNLCogI5NqevlE3Z+hapAZdvCkY4rfxiYfSQP2uPY1ktrYBAavZJ3U06dMkjCFZaMigu4XroqOynb5JOlHpEo9XlbKdGqUwZOOKX6XKp+oxr4mPhi8knVJLrQMyJnCcnEtamNUdVQSyieZzKICsjKpri2fqCvq4EnHFH/cnvMgkt7XxAlLZZF1SS60bKJkTRASyvyELUdVn6ojeeNQPknnEbPSj5dVVZIv6H2p4uhOXVP8tu9HW5Lc18QNM69kXVILLUudwrKZrVKZhVbVUUkun5QP3UfMxvl4WV+Y+1JFlk/nFL+N9rS9PCqpfU0cMXgl65K6BkvyFJaNxf+qN1Ko7KiklU/KNwgwUSPVpeNlgeABVNj7UsXgSfcUv8nnXMLyqKT2NXHE4JWsS+oaLE5h7aQjC626o7K9ftIXJAgwUSPVpUxq0AAqyn2pavAkZeNZFFJ2+Ce1r4kjrnklEZK4BotTWDupWp9al44jeVUfaRx0HWXQNdKmjpi1dbxsEGHWl0e5L1Ue3anzyGPdVKz9VSmJfU0cMfNKYkjJbJni0hSW7rVqurLQkrNWJrKAJmukSl5DGTaDGuW+VJ3lc7WOp8TlUUnra+KIwSuJ4uoLOgxXprBMrFXTmYWW2FGFmUYNEwSYrpEqdQ1l2AAq6n0pefBUn64BqtTlUUnqa+KIwSuRRdI7N1Nr1XRnoXV1VGE6fJNZQNdqpOo40QsIH0CpuC8lDp7q0zlA5fIo0oHBK4lnu7yKblI7N5OlvFzJQtcVtsM3nQW0fcRsvnSd6AWEbztV96XkLJ/uAapLy6PIHQxeSTQJ5VVMkNi5mV6rJj0LXVeUDt9GFlByFhXQe6IXEL3tJN6XKgb1JgaoLg5MST4GrySWlPIqSWVjrZrJICts5x+1w7eVBZSYRQWCt6fp5ROAvNkRVYN6UwNUqQMAcheDVxJJ6ulTJtleLmFrrZqJICtK5x+1w3c1C6hrParuE718UdtOyuyIykG9yQGqtAEAuY3BK4kksbyKSRKWS8R1rVrUzj9qh+9iFlDnelQTJ3r5JARQUQalqgf1pgeoUgYA5D4eUkAiSS2vYkKYYuo66Cjyb5uKgukqOvyohdJVFPfP94CEIPdjmKL+YU/0AsLdlyYPRqivckUVhkyfh7H3L8bFc5dh7P2LMWT6vLyfadWHeag8SIHIJGZeSaSklleRtlxCylo1VUsoVGT0VWWkbWYBdWVSo5zoFaQ9pdyXQaiY7lc9qOdmKnIVg1cSKa5T1k2RuFzC9lSryiUUKjp/lR2+qmlUKTv7436ily/oYErVoFTHoN7FgQARg1cSKakZAanLJWytVVNdcUJV5y+pw5e0sz/OJ3r5wgymVA1KdQ3qbQ9QiYJi8EpiSQoQTEnqcolsdCyhUNn5S+jwpe3sj+uJXr6wgylVg1Kdg3pupiKXMHgl0SQECCa5tFxCdykvHUsoVHf+qjv8IG0qdWd/3E708kUZTKkclCZxUE9UH4NXEi9JGQFXlkuYKOWlawmF1M4/aJuGCe5NZVJtDTp11aIFog2mVA9KkzaoJ6qPwSuRMFKDK5+pk890LqHQ2fmHyUiHaVPpO/tNDzp11qIFog2mdAxKkzSoJ6qPwSs5yfbpU7pJzayYLOWlewmFjs4/TEY6bJsmZWd/PnRWUPBFHUxJH5QSuYTBKzlHwulTJkjMrJgs5eXKEgpf2Ix02DZNws7+fAapJmrRAmoGU9IHAUSuYPBKTjE1ZU3ZmS7lZTJbZevYzrBt6trO/qDyHaSaqEULqBtMSRyUErmGwSs5Q9rpUzbYXi5ho5SXiUArajY/SkY6Spu6srPfp2NDlalatACn/omkYPBKzpB4+pRJEpZL2CrlpTPQsn1sZ9Q2dSGLCujbUGWyFi3gTnsTxVkz2xdAlC+pp0+Z4AdY9YN3P8CqXFFl5Dr8jh/Y2dH7JK5DbUpTgRKwI1Cqqc32FTtFyZ6qaFM/uB/Vf1cM7t3JWPvX1HpYtHIDnl62FotWbsjZTkHu3yCDVGBn8J/r/ziFHUFytgoKZSWZv4+yksK8Biu22puIdmDmlZyR1NOnpC2XkDB1qmr5hJRjOyW0aVC6MqlBB6lxraBARLkxeCVnuHT6lEoSl0vY7PhVLp+QdGyn7WAqyIBAZ2mqMINUlyooEFF0DF7JGa6VTlJF6nIJGx2/6moT0o7ttBVMBRkQ6M6kRikBxkwqUTIweCWnuDi9GlVSl0vUp2P5RJyP7dR1VKruTGqUQSozqUTJwOCVnCMpQDAhqcsl6tOxfCKux3bqPCrVRCY1iYNUIsqf1WoD06ZNw4EHHoiioiJ06dIFJ5xwAj755JOMr/E8D5MnT0bXrl3RunVrDBs2DB988IGlKyYpkrTb16Ud/vnuPg9D1/KJqDvPdQvapjp39gPBZwLC3r9HV5Rj4cThmHP2INwxpj/mnD0ICycOt/77ICL7rGZeFyxYgPPPPx8HHnggfvzxR1xzzTU48sgj8eGHH6Jt27YAgJtvvhkzZszAQw89hD333BM33HADRowYgU8++QRFRUU2L5/IGBcyUbrr0OpcPiE1mx+0TU0clWoykyohi01E8qQ8z1OXGono66+/RpcuXbBgwQIceuih8DwPXbt2xSWXXIKJEycCALZt24bS0lJMnz4d55xzTpM/s7q6GiUlJdi0aROKi4t1/y+QRbZPnzJB6v9jrnWT/pWpyGDW1HoYMn1ek0HTwonDRbRJVGHadNHKDRh7/+Imf/acswdhcO9Ogb++/rUB2Zda5Pp9S71/ici+IPGaqDWvmzZtAgB07LhjxL5q1SqsW7cORx55ZPprCgoKMHToULz55ptZg9dt27Zh27Zt6T9XV1drvmqSQMLpUyZIzESZqkPrcrWJoEFb2DY1ubOfmVQiskVM8Op5HiZMmIAhQ4agoqICALBu3ToAQGlpacbXlpaW4vPPP8/6c6ZNm4YpU6bovVgSRXX5JArGZB1aF5ZP1BdmYBW2TU3u7Je61IKI4k9M8HrBBRfg/fffx8KFCxv8XSqV+TL0PK/B53xXXXUVJkyYkP5zdXU1unfvrvZiSQxpp08lkek6tC4FTWEHVmHb1PTOfmZSicgGEcHrhRdeiGeeeQavv/46unXrlv58WVkZgB0Z2PLynS/Q9evXN8jG+goKClBQUKD3gkkMiadPmSRhDaGNOrSmgqYo7RtlYBW2TXlUKhElgdXg1fM8XHjhhXjqqacwf/589OrVK+Pve/XqhbKyMrzyyivYf//9AQA//PADFixYgOnTp9u4ZBJG6ulTJkhZ5xvXOrRR2zfKwCpKm3I9KhHFndXg9fzzz8djjz2Gp59+GkVFRek1riUlJWjdujVSqRQuueQSTJ06FX369EGfPn0wdepUtGnTBqeeeqrNSychknr6lKR1vi5vpMpFRftGGVhFbVNmUokozqweUjBz5kxs2rQJw4YNQ3l5efrj8ccfT3/NFVdcgUsuuQTnnXceBgwYgLVr1+Lll19mjVcCsDNDlatLTmFHtsy1rF9jmpqOBnZMR6s8IKAp0gv9B6GqfaMOrKK2aZIO8iCiZBFV51UH1nmNv7A1J10VtjanCTbX4Kr6t1W1r6q6tBLWNRMR6eZsnVeiMFwsnxSF5HW+ttZNqlz/q6p9VS2n4FpUIqJMDF4pFpK0xi+p63xzUb3+V2X7Jm1gRURkAoNXio2kZKjiurs/DB11flW3b5IGVkSkHpcONcTglcgxLu7u1/Xy1VHnV0f7JmVgRURqSSmJKA2DV4q1uI5YXZqO1vny1bX+16X2JaJ4klQSURoGrxRbcR+xujAdrfvlq3P9rwvtS0TxxKPPG8fglWIpKSNWydPRJl6+utf/Sm5fIoqvpB993hSrhxQQ6SCxiH8SBXn5huWvTwXQ4KAKqet/iciemloPi1ZuwNPL1mLRyg1i+wHJJRElYOaVYocjVhlMvXy5PpWI8uHSUjKWRGwcg1eKHY5YZWxUM/ny5fpUImqMa0vJWBKxcQxeKXaSPmKVkl0w/fLl+lQiysbFzU8ulkQ0iWteKXb8oCnXI53CjmAujiNWP7tQf9mEn12oXFFl7Fq4HpWIcjG59tTE+nsd/CVRZSWZiZaykkJxmWLTmHml2EnqiFVidoHrUYmoPtOzQy4vJeOSqOwYvFIsJTFokrpRjS9fIvLZWHvq+lIyLolqiMErxVbSgibJ2QW+fInI1uwQNz/FD9e8Uqz5QdOo/rticO9OsQ1cAfezC0Qki+p1qbbWnnL9ffww80oUE8wuEJEqOtal2pwdSuJSsjhj8EoUE0ndqEZEO6mo8axrXart2aGkLSWLMwavRDHC7AJRcqnIlupclyphdojr7+OBwSslloRTqHRgdoEoeVRlS3VWLeHsEKnC4JUSScopVLowu0DkrqADa5XZUt3rUjk7RCoweKXEce2MayJKjjADa5XZUhPrUjk7RFGxVBYlSlMZCmBHhkLnUYVElAxBS02FPd5ZZbbU1PHaSSpjSOox80qJIvUUKiKKl6AZ1ChT/yqzpVyXSi5g5pUSRfIpVEQkV5AsapgMapQC/qqzpf661LKSzGC3rKSQy6pIBGZeKVFs1xkkIvcEyaKGzaBGGVjryJZyXSpJxswrJYqp9VxEJFu+mdSgWdSwGdSoA2sd2VKuSyWpmHmlROF6LiLKN5MaJosaNoOqooA/s6WUFMy8UuJwPRdRPOWTTQ2SSQ2TRQ2bQfUH1gAazAwFGVgzW0pJwMwrJRIzFETxkk82NWgmNUwWNUoGlQX8ifLD4JUSi6dQEcmXz2lT+R48ErRUXpgsatSlSRxYEzWNwSsREYmkOpsaNJMaNosaNYPKgTVR4xi8EhGRUbayqUEzqVGyqMygEunD4JWIiIyxmU09bt+ugTOpUbKozKAS6cHglYiIInMhmxo2k8osKpEsDF6JiCgSl7KpYTOpzKISycHglYiIGsgnkwq4mU1lJpXIbQxeiYgog44TqKRlU5lJJXIXg1ciogRQnUkFgp1AxWwqEanC4JWIKOZ0ZFKDnkDFbCoRqcLglYgoxnRlUoOeQMVsKhGp0sz2BRARUX5qaj0sWrkBTy9bi0UrN6CmNlsOM/PrG8ukAjsyqf7PCXsCVa5QMoUdGd762dSyksygt6ykMCOIrsvPpo7qvysG9+7EwJWImHklInJBvlP/denMpALhTqBiNpWIomLmlYhIOH/qv34g6k/9V66oyvp9ujOpALOpRGQeM69ERAblu+u/7tcH2URVl4lMKsBsKhGZxeCViMgQE1P/dfmZ1Hx3+AM8gYqI5GPwSkRkQJBd/3UFnfqvi5lUIoojrnklItIs6K7/uoJO/dcXZk0qwHWpREkWtLKJacy8EhE1Ieg61fpMT/3Xx0wqEeUrzPIm0xi8EhE1QsWL3MbUf7afwzWpRNSYsMubTOOyASKiHMKWqKrP1tQ/EVG+oixvMo2ZVyKiLKKUqKqPU/9E8Rd1eZFtUZY3mcbglYgoC5Uvck79E8WbC+tEmxJleZNpXDZARM7TsTNW9YucU/9E8aRqeZFtUZc3mcTMKxE5TVfGQ8eLnFP/ROrZnK5XubzINhXLm0xh8EpEztK5M1bXi5xT/0Tq2J6ud2mdaFNULW8ygcsGiMhJunfG+i9yYOeL2yftRU6URBKm611aJ5oPV5Y3MfNKRE4ykfHwX+T1Mztljm3EILJF15S+lOl6l9aJ5suF5U0MXonISaYyHi68yIkk0jmlL2W63qV1okFIX97EZQNEpITps7BNZjz8F/mo/rticO9ODFwptlQ9x7qn9KVM13N5kR3MvBJRZDY2TcQ140Fki6rn2MSUvqTpei4vMo/BKxFFYussbJd2xhJJp/I5NjGlL23wyuVFZnHZABGFZvssbFd2xhKZEHbKX/VzbGJKX+J0PZcXmcPMKxGFJmHTBDMeRNGm/FU/x6am9Dldn1xWg9fXX38dt9xyC9555x1UVVXhqaeewgknnJD++/Hjx2P27NkZ33PQQQdh8eLFhq+UiLKRtGlC8s5YonyFKS0Vdcpf9XNsckqfg9dkshq8bt26Ffvttx/OPPNMnHTSSVm/5uijj8asWbPSf27VqpWpyyOiJkjaNEHkujDZUxWbo1Q/x6bXo3PwmjxWg9eRI0di5MiRjX5NQUEBysrKDF0REQUhbdMEkSRBsqhhs6cqpvx1PMec0iedxK95nT9/Prp06YL27dtj6NChuPHGG9GlS5ecX79t2zZs27Yt/efq6moTl0mUSNzxT5RdkCxqlOypiil/Xc8xp/RJF9HVBkaOHIlHH30U8+bNw2233YYlS5Zg+PDhGcFpfdOmTUNJSUn6o3v37gavmCh5uOOfkiLf3fxBC/QHyZ7Wp2rKX9dzzB34pIPozOspp5yS/u+KigoMGDAAPXr0wPPPP4/Ro0dn/Z6rrroKEyZMSP+5urqaASyRZsywUNzlm0kNk0WNkj1VOeXP55hcITp4ra+8vBw9evTAp59+mvNrCgoKUFBQYPCqiNQKs9tYAm6aINfk+6wFWY8aZg1qlOyp6il/PsfkAqeC1w0bNmDNmjUoL+c0JMWTjWNWiZJIVyY1TBY1avaUm6MoaawGr1u2bMFnn32W/vOqVauwbNkydOzYER07dsTkyZNx0kknoby8HKtXr8bVV1+Nzp0748QTT7R41UR62DpmlSguJGRSw2RRVWRPOeVPSWI1eH377bdx2GGHpf/sr1UdN24cZs6cieXLl+Phhx/Gxo0bUV5ejsMOOwyPP/44ioqKbF0ykRYqajUSJZmUTGrYLKqK7Cmn/CkprAavw4YNg+flPiv5pZdeMng1RPZIOGaVSKJ8sqmSMqlRsqjMnhLlx6k1r0RxJeWYVSJJ8smmSsykRsmiMntK1DQGr0QC8JhVSgrV61KlZlKZRSXSh8ErkQA8ZpWSQMe6VMmZVGZRifRg8EokAI9ZJRcFqUmsa10qM6lEycPglUgI1moklwSpSaxzXepx+3ZlJpUoYRi8EgnC7A65IGhNYp3rUplJJUoeBq9EwjC7QzbkuwQgTE1i3etSmUklShYGr0RECRdkCUCYmsQm1qUyk0qUHM1sXwAREdnjLwGoH5D6SwAqV1RlfD5MTWI/k5orjExhR7CcbV1qWUlm4FtWUpjzqGQ/kzqq/64Y3LsTA1eimGLmlYgoJoLs/ve/PugSgDA1ibkulYhUYvBKRBQDQab+fWGWAIStScx1qUSkCoNXIiLHBd397wuzBCBKTWJmUolIBa55JSJyWFNT/8COqf+a2oZfEfZY4jDrUX1cl0pEUTHzSkQkQND1qr4wU/++KMcSM4tKRLYweCUisizMelVfmKl/X9RjibkelYhs4LIBIiKLgpaqqi/s1L8vyhIAIiIbmHklIrIkTKmq+qJM/fu4BICIXMLMKxFRCDW1Hhat3ICnl63FopUbsm6IakqQ9aq5+FP/ABocApDP1H/dn8ONVETkAmZeiYgCirJGta4o61XrCltDlYjIRQxeiYgCCFtTNZuo61Xr4tQ/ESUFg1ciojypWKNal4r1qnVx9z8RJQHXvBIR5UnFGtW6VK1XJSJKEgavRER5UrVGtS6WqiIiCobLBogodsKeVtUUlWtU6+J6VSKi/DF4JaJYUVUJIBvVa1Tr4npVIqL8cNkAEcVG1NOqmsI1qkRE9jF4JaJYaKoSALCjEkCYwwTq4hpVIiK7uGyAiGIhSCWAqNPzXKNKRGQPg1ciigUdlQAawzWqRER2cNkAEcWCrkoAREQkC4NXIooFvxJAron7FHZUHQhTCYCIiORg8EpEscBKAEREycDglYiUqan1sGjlBjy9bC0WrdwQeWd/UKwEQEQUf9ywRURK6DwcIAhWAiAiireU53lmUyOGVVdXo6SkBJs2bUJxcbHtyyGKJf9wgPovEz9cZNaTiIgaEyRe47IBIorE1OEAREREAINXIoooyOEAREREUTF4JaJITB8OQEREycbglYgi4eEARERkEoNXIoqEhwMQEZFJDF6JKBIeDkBERCYxeCWiyHg4ABERmcJDCohICR4OQEREJjB4JSJlmjdLYXDvTrYvg4iIYozLBoiIiIjIGQxeiYiIiMgZDF6JiIiIyBlc80pkSU2tx81NREREATF4JbKgckUVpjz7Iao27TwytbykEJOO78uyUkRERI3gsgEiwypXVOHcR97NCFwBYN2m73HuI++ickWVpSsjIiKSj8ErkUE1tR6mPPshvCx/539uyrMfoqY221cQERERg1cig/656t8NMq51eQCqNn2Pf676t7mLIiIicgiDVyKD1m/OHbiG+ToiIqKkYfBKZFCXokKlX0dERJQ0DF6JDBrYqyPKSwqRqyBWCjuqDgzs1dHkZRERETmDwSuRQc2bpTDp+L4A0CCA9f886fi+rPdKRESUA4NXIsOOrijHzJ/9BGUlmUsDykoKMfNnP2GdVyIiokbwkAIiC46uKMeIvmU8YYuIiCggBq9EljRvlsLg3p1sXwYREZFTuGyAiIiIiJzB4JWIiIiInMHglYiIiIicweCViIiIiJzB4JWIiIiInGE1eH399ddx/PHHo2vXrkilUvjb3/6W8fee52Hy5Mno2rUrWrdujWHDhuGDDz6wc7FEREREZJ3V4HXr1q3Yb7/9cPfdd2f9+5tvvhkzZszA3XffjSVLlqCsrAwjRozA5s2bDV8pEREREUlgtc7ryJEjMXLkyKx/53kebr/9dlxzzTUYPXo0AGD27NkoLS3FY489hnPOOcfkpRIRERGRAGLXvK5atQrr1q3DkUcemf5cQUEBhg4dijfffDPn923btg3V1dUZH0REREQUD2KD13Xr1gEASktLMz5fWlqa/rtspk2bhpKSkvRH9+7dtV4nEREREZkjNnj1pVKZZ717ntfgc3VdddVV2LRpU/pjzZo1ui+RiIiIiAyxuua1MWVlZQB2ZGDLy8vTn1+/fn2DbGxdBQUFKCgo0H59RERERGSe2Mxrr169UFZWhldeeSX9uR9++AELFizAwQcfbPHKiIiIiMgWq5nXLVu24LPPPkv/edWqVVi2bBk6duyI3XbbDZdccgmmTp2KPn36oE+fPpg6dSratGmDU089Ne9/w/M8AODGLSIiIiKh/DjNj9sa5Vn02muveQAafIwbN87zPM+rra31Jk2a5JWVlXkFBQXeoYce6i1fvjzQv7FmzZqs/wY/+MEPfvCDH/zgBz9kfaxZs6bJ2C7lefmEuO6qra3Fl19+Cc/zsNtuu2HNmjUoLi62fVmxVF1dje7du7ONNWM7m8F21o9tbAbb2Qy2czSe52Hz5s3o2rUrmjVrfFWr2A1bqjRr1gzdunVLp6OLi4t5U2nGNjaD7WwG21k/trEZbGcz2M7hlZSU5PV1YjdsERERERHVx+CViIiIiJyRmOC1oKAAkyZNYg1YjdjGZrCdzWA768c2NoPtbAbb2ZzYb9giIiIiovhITOaViIiIiNzH4JWIiIiInMHglYiIiIicweCViIiIiJwRu+D1xhtvxMEHH4w2bdqgffv2Wb/m4osvxgEHHICCggL0798/69csX74cQ4cORevWrbHrrrvi+uuvz++83YTIp53/9a9/4fjjj0fbtm3RuXNnXHTRRfjhhx/Sf7969WqkUqkGH5WVlYb+L2RT0cYA7+Uw3n33XYwYMQLt27dHp06d8Mtf/hJbtmzJ+Jps9+69995r6Yrdk08b53N/U+P++7//G6NGjULnzp1RXFyMQw45BK+99lrG1/BejiafNua9rFbsgtcffvgBJ598Ms4999ycX+N5Hs466yyccsopWf++uroaI0aMQNeuXbFkyRLcdddduPXWWzFjxgxdl+2cptq5pqYGxx57LLZu3YqFCxdi7ty5ePLJJ3HZZZc1+NpXX30VVVVV6Y/hw4frvnwnqGhj3svBffnllzjiiCOwxx574K233kJlZSU++OADjB8/vsHXzpo1K+PeHTdunPkLdlA+bRzkHUK5HXvssfjxxx8xb948vPPOO+jfvz+OO+44rFu3LuPreC+H11Qb817WwIupWbNmeSUlJY1+zaRJk7z99tuvwefvuecer6SkxPv+++/Tn5s2bZrXtWtXr7a2VvGVui1XO7/wwgtes2bNvLVr16Y/N2fOHK+goMDbtGmT53met2rVKg+At3TpUkNX66Yobcx7Obg//OEPXpcuXbyampr055YuXeoB8D799NP05wB4Tz31lIUrdF8+bZzP/U2N+/rrrz0A3uuvv57+XHV1tQfAe/XVV9Of470cXj5tzHtZvdhlXlVYtGgRhg4dmlFo+KijjsKXX36J1atX27swhyxatAgVFRXo2rVr+nNHHXUUtm3bhnfeeSfja//jP/4DXbp0wSGHHIK//OUvpi/VWfm0Me/l4LZt24ZWrVqhWbOdr8fWrVsDABYuXJjxtRdccAE6d+6MAw88EPfeey9qa2uNXqur8mnjIO8Qyq5Tp07YZ5998PDDD2Pr1q348ccf8Yc//AGlpaU44IADMr6W93I4+bQx72X1GLxmsW7dOpSWlmZ8zv9z/akWyi5bG3bo0AGtWrVKt2G7du0wY8YM/OUvf8ELL7yAww8/HKeccgoeeeQRG5fsnHzamPdycMOHD8e6detwyy234IcffsA333yDq6++GgBQVVWV/rrf/va3eOKJJ/Dqq69izJgxuOyyyzB16lRbl+2UfNo4n/ubGpdKpfDKK69g6dKlKCoqQmFhIX73u9+hsrIyYx097+Xw8mlj3svqORG8Tp48OeuC8rofb7/9ttJ/M5VKZfzZ+78NLvU/Hyeq2zlbW3mel/58586dcemll2LgwIEYMGAArr/+epx33nm4+eablf0/SWO6jbN9TRLu5Wzybft+/fph9uzZuO2229CmTRuUlZVh9913R2lpKZo3b57+eb/5zW8wePBg9O/fH5dddhmuv/563HLLLRb/D+1T3cb53N9JlG87e56H8847D126dMEbb7yBf/7znxg1ahSOO+64jIEY7+WGVLcx72W1Wti+gHxccMEFGDNmTKNf07NnT2X/XllZWYPR0Pr16wGgwegpTlS2c1lZGd56662Mz33zzTfYvn17o204aNAg/PGPf8zr33CR6TZO6r2cTZC2P/XUU3Hqqafiq6++Qtu2bZFKpTBjxgz06tUr5/cOGjQI1dXV+OqrrxLXtj6VbRz2HZIE+bbzvHnz8Nxzz+Gbb75BcXExAOCee+7BK6+8gtmzZ+PKK6/M+r28l9W2Me9l9ZwIXjt37ozOnTsb+/cGDx6Mq6++Gj/88ANatWoFAHj55ZfRtWtXpUGyNCrbefDgwbjxxhtRVVWF8vJyADvasKCgoMFaq7qWLl2a/vo4Mt3GSb2XswnT9n7H8uCDD6KwsBAjRozI+bVLly5FYWFhzrJmSaCyjcO+Q5Ig33b+9ttvASBjbbH/58bWtPJeVtvGvJc1sLdXTI/PP//cW7p0qTdlyhSvXbt23tKlS72lS5d6mzdvTn/Np59+6i1dutQ755xzvD333DP9Ndu2bfM8z/M2btzolZaWemPHjvWWL1/u/fWvf/WKi4u9W2+91db/ljhNtfOPP/7oVVRUeIcffrj37rvveq+++qrXrVs374ILLkj/jIceesh79NFHvQ8//ND7+OOPvVtuucVr2bKlN2PGDFv/W6KoaGPey+Hcdddd3jvvvON98skn3t133+21bt3au+OOO9J//8wzz3j33Xeft3z5cu+zzz7z7r//fq+4uNi76KKLLF61W5pq43zub2rc119/7XXq1MkbPXq0t2zZMu+TTz7xLr/8cq9ly5besmXLPM/jvRxVPm3Me1m92AWv48aN8wA0+HjttdfSXzN06NCsX7Nq1ar017z//vveT3/6U6+goMArKyvzJk+ezNJCdeTTzp9//rl37LHHeq1bt/Y6duzoXXDBBRklmx566CFvn3328dq0aeMVFRV5BxxwgPenP/3Jwv+NTCra2PN4L4dx+umnex07dvRatWrl7bvvvt7DDz+c8fcvvvii179/f69du3ZemzZtvIqKCu/222/3tm/fbumK3dNUG3tefvc3NW7JkiXekUce6XXs2NErKiryBg0a5L3wwgvpv+e9HF1Tbex5vJdVS3kej9ohIiIiIjc4UW2AiIiIiAhg8EpEREREDmHwSkRERETOYPBKRERERM5g8EpEREREzmDwSkRERETOYPBKRERERM5g8EpEREREzmDwSkTkmNWrVyOVSmHZsmVafn4qlcLf/vY3LT+biCgqBq9ERAGNHz8eJ5xwgrV/v3v37qiqqkJFRQUAYP78+UilUti4caO1ayIiMqWF7QsgIqJgmjdvjrKyMtuXQURkBTOvREQKLViwAAMHDkRBQQHKy8tx5ZVX4scff0z//bBhw3DRRRfhiiuuQMeOHVFWVobJkydn/IyPP/4YQ4YMQWFhIfr27YtXX301Yyq/7rKB1atX47DDDgMAdOjQAalUCuPHjwcA9OzZE7fffnvGz+7fv3/Gv/fpp5/i0EMPTf9br7zySoP/p7Vr1+KUU05Bhw4d0KlTJ4waNQqrV6+O2lRERKEweCUiUmTt2rU45phjcOCBB+K9997DzJkz8cADD+CGG27I+LrZs2ejbdu2eOutt3DzzTfj+uuvTweNtbW1OOGEE9CmTRu89dZbuO+++3DNNdfk/De7d++OJ598EgDwySefoKqqCnfccUde11tbW4vRo0ejefPmWLx4Me69915MnDgx42u+/fZbHHbYYWjXrh1ef/11LFy4EO3atcPRRx+NH374IUjzEBEpwWUDRESK3HPPPejevTvuvvtupFIp7L333vjyyy8xceJEXHfddWjWbEe+YN9998WkSZMAAH369MHdd9+Nv//97xgxYgRefvllrFy5EvPnz08vDbjxxhsxYsSIrP9m8+bN0bFjRwBAly5d0L59+7yv99VXX8VHH32E1atXo1u3bgCAqVOnYuTIkemvmTt3Lpo1a4Y//vGPSKVSAIBZs2ahffv2mD9/Po488shgjUREFBGDVyIiRT766CMMHjw4HeQBwCGHHIItW7bgiy++wG677QZgR/BaV3l5OdavXw9gR/a0e/fuGWtaBw4cqO16d9ttt3TgCgCDBw/O+Jp33nkHn332GYqKijI+//3332PlypVarouIqDEMXomIFPE8LyNw9T8HIOPzLVu2zPiaVCqF2tranD8jrGbNmqX/fd/27dsbXFv9a6mrtrYWBxxwAB599NEGX7vLLrsouU4ioiAYvBIRKdK3b188+eSTGQHom2++iaKiIuy66655/Yy9994b//rXv/DVV1+htLQUALBkyZJGv6dVq1YAgJqamozP77LLLqiqqkr/ubq6GqtWrcq43n/961/48ssv0bVrVwDAokWLMn7GT37yEzz++OPo0qULiouL8/p/ICLSiRu2iIhC2LRpE5YtW5bx8ctf/hJr1qzBhRdeiI8//hhPP/00Jk2ahAkTJqTXuzZlxIgR6N27N8aNG4f3338f//jHP9IbtnJlZHv06IFUKoXnnnsOX3/9NbZs2QIAGD58OP70pz/hjTfewIoVKzBu3Dg0b948/X1HHHEE9tprL5xxxhl477338MYbbzTYHHbaaaehc+fOGDVqFN544w2sWrUKCxYswMUXX4wvvvgiTNMREUXC4JWIKIT58+dj//33z/iYNGkSXnjhBfzzn//Efvvth1/96lf4+c9/jt/85jd5/9zmzZvjb3/7G7Zs2YIDDzwQv/jFL9LfX1hYmPV7dt11V0yZMgVXXnklSktLccEFFwAArrrqKhx66KE47rjjcMwxx+CEE05A796909/XrFkzPPXUU9i2bRsGDhyIX/ziF7jxxhszfnabNm3w+uuvY7fddsPo0aOxzz774KyzzsJ3333HTCwRWZHysi16IiIiMf7xj39gyJAh+OyzzzKCTyKiJGLwSkQkzFNPPYV27dqhT58++Oyzz3DxxRejQ4cOWLhwoe1LIyKyjhu2iIiE2bx5M6644gqsWbMGnTt3xhFHHIHbbrvN9mUREYnAzCsREREROYMbtoiIiIjIGQxeiYiIiMgZDF6JiIiIyBkMXomIiIjIGQxeiYiIiMgZDF6JiIiIyBkMXomIiIjIGQxeiYiIiMgZ/x/w2/KJF7X6fwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot = hypercoast.view_pace_pixel_locations(filepath, step=20)" ] @@ -110,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -223,25 +194,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "df13805906dd46d5ae8af98481d8da4d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map(center=[25.194741, -92.490864], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title'…" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m = hypercoast.Map()\n", "m.add_basemap(\"Hybrid\")\n", diff --git a/docs/examples/pace_cyano.ipynb b/docs/examples/pace_cyano.ipynb index 276b386..e031dbc 100644 --- a/docs/examples/pace_cyano.ipynb +++ b/docs/examples/pace_cyano.ipynb @@ -106,7 +106,7 @@ }, "outputs": [], "source": [ - "df_filtered = df.dropna(subset=['Lon', 'Lat']).reset_index(drop=True)\n", + "df_filtered = df.dropna(subset=[\"Lon\", \"Lat\"]).reset_index(drop=True)\n", "df_filtered.head()" ] }, @@ -629,7 +629,13 @@ "m.add(\"spectral\")\n", "style = {\"weight\": 2, \"color\": \"red\"}\n", "m.add_kml(kml_path, style=style, layer_name=\"Hypoxia Path\", info_mode=None)\n", - "m.add_points_from_xy(df_filtered, x=\"Lon\", y=\"Lat\", max_cluster_radius=50, layer_name=\"Hypoxia Data Points\")\n", + "m.add_points_from_xy(\n", + " df_filtered,\n", + " x=\"Lon\",\n", + " y=\"Lat\",\n", + " max_cluster_radius=50,\n", + " layer_name=\"Hypoxia Data Points\",\n", + ")\n", "m.set_center(-91.46118, 28.89758, zoom=8)\n", "m\n", "dataset" @@ -662,7 +668,11 @@ "outputs": [], "source": [ "da = dataset[\"Rrs\"]\n", - "data = (da.sel(wavelength=650) > da.sel(wavelength=620)) & (da.sel(wavelength=701) > da.sel(wavelength=681)) & (da.sel(wavelength=701) > da.sel(wavelength=450))" + "data = (\n", + " (da.sel(wavelength=650) > da.sel(wavelength=620))\n", + " & (da.sel(wavelength=701) > da.sel(wavelength=681))\n", + " & (da.sel(wavelength=701) > da.sel(wavelength=450))\n", + ")" ] }, { @@ -704,29 +714,35 @@ "import cartopy.feature as cfeature\n", "\n", "# Create a plot\n", - "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", "\n", - "#ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())\n", + "# ax.set_extent([-93, -87, 28, 32], crs=ccrs.PlateCarree())\n", "\n", "# Plot the data\n", - "data.plot(ax=ax, transform=ccrs.PlateCarree(), cmap='coolwarm', cbar_kwargs={'label': 'Cyano'},)\n", + "data.plot(\n", + " ax=ax,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=\"coolwarm\",\n", + " cbar_kwargs={\"label\": \"Cyano\"},\n", + ")\n", "\n", "# Add coastlines\n", "ax.coastlines()\n", "\n", "# Add state boundaries\n", "states_provinces = cfeature.NaturalEarthFeature(\n", - " category='cultural',\n", - " name='admin_1_states_provinces_lines',\n", - " scale='50m',\n", - " facecolor='none')\n", + " category=\"cultural\",\n", + " name=\"admin_1_states_provinces_lines\",\n", + " scale=\"50m\",\n", + " facecolor=\"none\",\n", + ")\n", "\n", - "ax.add_feature(states_provinces, edgecolor='gray')\n", + "ax.add_feature(states_provinces, edgecolor=\"gray\")\n", "\n", "# Optionally, add gridlines, labels, etc.\n", "ax.gridlines(draw_labels=True)\n", "\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -767,8 +783,7 @@ "base_uri": "https://localhost:8080/" }, "id": "8SYB8MKyZc0-", - "outputId": "02c93296-323b-41e9-e175-eaa18e0d19eb", - "scrolled": true + "outputId": "02c93296-323b-41e9-e175-eaa18e0d19eb" }, "outputs": [], "source": [ @@ -830,7 +845,7 @@ }, "outputs": [], "source": [ - "reshaped_data = da.values.reshape(-1, da.shape[-1])\n" + "reshaped_data = da.values.reshape(-1, da.shape[-1])" ] }, { @@ -841,7 +856,7 @@ }, "outputs": [], "source": [ - "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n" + "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]" ] }, { @@ -919,24 +934,35 @@ "\n", "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n", "# Also assume that 'da' has the original latitude and longitude data\n", - "latitudes = da.coords['latitude'].values\n", - "longitudes = da.coords['longitude'].values\n", + "latitudes = da.coords[\"latitude\"].values\n", + "longitudes = da.coords[\"longitude\"].values\n", "\n", "# Create a custom discrete color map for K-means clusters\n", - "cmap = mcolors.ListedColormap(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "cmap = mcolors.ListedColormap(\n", + " [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n", + ")\n", "bounds = np.arange(-0.5, n_clusters, 1)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", "# Create a figure and axis with the correct map projection\n", - "fig, ax = plt.subplots(figsize=(12, 10), dpi=400, subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(\n", + " figsize=(12, 10), dpi=400, subplot_kw={\"projection\": ccrs.PlateCarree()}\n", + ")\n", "\n", "# Plot the K-means classification results on the map\n", - "im = ax.pcolormesh(longitudes, latitudes, cluster_labels, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "im = ax.pcolormesh(\n", + " longitudes,\n", + " latitudes,\n", + " cluster_labels,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", "# Add geographic features for context\n", "ax.add_feature(cfeature.COASTLINE)\n", - "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", - "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n", "\n", "# Add gridlines\n", "ax.gridlines(draw_labels=True)\n", @@ -945,15 +971,22 @@ "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", "\n", "# Add color bar with labels\n", - "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", - "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", - "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "cbar = plt.colorbar(\n", + " im,\n", + " ax=ax,\n", + " orientation=\"vertical\",\n", + " pad=0.02,\n", + " fraction=0.05,\n", + " ticks=np.arange(n_clusters),\n", + ")\n", + "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n", + "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n", "\n", "# Add title\n", - "ax.set_title('Water Type Classification using K-means', fontsize=16)\n", + "ax.set_title(\"Water Type Classification using K-means\", fontsize=16)\n", "\n", "# Show the plot\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -979,34 +1012,45 @@ "\n", "# Assume 'cluster_labels' contains the K-means classification results reshaped to the original spatial dimensions\n", "# Also assume that 'da' has the original latitude and longitude data\n", - "latitudes = da.coords['latitude'].values\n", - "longitudes = da.coords['longitude'].values\n", + "latitudes = da.coords[\"latitude\"].values\n", + "longitudes = da.coords[\"longitude\"].values\n", "\n", "# Filtering condition based on wavelength values\n", "filter_condition = (\n", - " (da.sel(wavelength=650) > da.sel(wavelength=620)) & \n", - " (da.sel(wavelength=701) > da.sel(wavelength=681)) & \n", - " (da.sel(wavelength=701) > da.sel(wavelength=450))\n", + " (da.sel(wavelength=650) > da.sel(wavelength=620))\n", + " & (da.sel(wavelength=701) > da.sel(wavelength=681))\n", + " & (da.sel(wavelength=701) > da.sel(wavelength=450))\n", ")\n", "\n", "# Apply the filtering condition to the K-means classification results\n", "filtered_cluster_labels = np.where(filter_condition, cluster_labels, np.nan)\n", "\n", "# Create a custom discrete color map for K-means clusters\n", - "cmap = mcolors.ListedColormap(['#e41a1c', '#377eb8', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "cmap = mcolors.ListedColormap(\n", + " [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n", + ")\n", "bounds = np.arange(-0.5, n_clusters, 1)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", "# Create a figure and axis with the correct map projection\n", - "fig, ax = plt.subplots(figsize=(12, 10), dpi=400, subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(\n", + " figsize=(12, 10), dpi=400, subplot_kw={\"projection\": ccrs.PlateCarree()}\n", + ")\n", "\n", "# Plot the filtered K-means classification results on the map\n", - "im = ax.pcolormesh(longitudes, latitudes, filtered_cluster_labels, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "im = ax.pcolormesh(\n", + " longitudes,\n", + " latitudes,\n", + " filtered_cluster_labels,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", "# Add geographic features for context\n", "ax.add_feature(cfeature.COASTLINE)\n", - "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", - "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n", "\n", "# Add gridlines\n", "ax.gridlines(draw_labels=True)\n", @@ -1015,15 +1059,22 @@ "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", "\n", "# Add color bar with labels\n", - "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", - "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", - "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "cbar = plt.colorbar(\n", + " im,\n", + " ax=ax,\n", + " orientation=\"vertical\",\n", + " pad=0.02,\n", + " fraction=0.05,\n", + " ticks=np.arange(n_clusters),\n", + ")\n", + "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n", + "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n", "\n", "# Add title\n", - "ax.set_title('Water Type Classification using K-means', fontsize=16)\n", + "ax.set_title(\"Water Type Classification using K-means\", fontsize=16)\n", "\n", "# Show the plot\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1062,7 +1113,7 @@ "reshaped_data = da.values.reshape(-1, da.shape[-1])\n", "\n", "# Handle NaNs by removing them\n", - "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]\n" + "reshaped_data_no_nan = reshaped_data[~np.isnan(reshaped_data).any(axis=1)]" ] }, { @@ -1086,11 +1137,11 @@ "\n", "# Visualize PCA components to manually identify endmembers\n", "plt.figure(figsize=(10, 8))\n", - "plt.scatter(pca_data[:, 0], pca_data[:, 1], c='blue', s=1)\n", - "plt.title('PCA of Spectral Data')\n", - "plt.xlabel('Principal Component 1')\n", - "plt.ylabel('Principal Component 2')\n", - "plt.show()\n" + "plt.scatter(pca_data[:, 0], pca_data[:, 1], c=\"blue\", s=1)\n", + "plt.title(\"PCA of Spectral Data\")\n", + "plt.xlabel(\"Principal Component 1\")\n", + "plt.ylabel(\"Principal Component 2\")\n", + "plt.show()" ] }, { @@ -1107,7 +1158,7 @@ "kmeans.fit(pca_data)\n", "\n", "# The cluster centers in the original spectral space are your endmembers\n", - "endmembers = pca.inverse_transform(kmeans.cluster_centers_)\n" + "endmembers = pca.inverse_transform(kmeans.cluster_centers_)" ] }, { @@ -1125,12 +1176,15 @@ " angle = np.arccos(np.clip(cos_theta, -1, 1))\n", " return angle\n", "\n", + "\n", "# Apply SAM for each pixel and each endmember\n", "angles = np.zeros((reshaped_data_no_nan.shape[0], endmembers.shape[0]))\n", "\n", "for i in range(reshaped_data_no_nan.shape[0]):\n", " for j in range(endmembers.shape[0]):\n", - " angles[i, j] = spectral_angle_mapper(reshaped_data_no_nan[i, :], endmembers[j, :])\n", + " angles[i, j] = spectral_angle_mapper(\n", + " reshaped_data_no_nan[i, :], endmembers[j, :]\n", + " )\n", "\n", "# Find the minimum angle (best match) for each pixel\n", "best_match = np.argmin(angles, axis=1)\n", @@ -1139,7 +1193,7 @@ "original_shape = da.shape[:-1] # Get the spatial dimensions\n", "best_match_full = np.full(reshaped_data.shape[0], np.nan)\n", "best_match_full[~np.isnan(reshaped_data).any(axis=1)] = best_match\n", - "best_match_full = best_match_full.reshape(original_shape)\n" + "best_match_full = best_match_full.reshape(original_shape)" ] }, { @@ -1162,38 +1216,54 @@ "\n", "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n", "# Also assume that 'da' has the original latitude and longitude data\n", - "latitudes = da.coords['latitude'].values\n", - "longitudes = da.coords['longitude'].values\n", + "latitudes = da.coords[\"latitude\"].values\n", + "longitudes = da.coords[\"longitude\"].values\n", "\n", "# Create a custom discrete color map\n", - "cmap = mcolors.ListedColormap(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "cmap = mcolors.ListedColormap(\n", + " [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n", + ")\n", "bounds = np.arange(-0.5, n_clusters, 1)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", "# Create a figure and axis with the correct map projection\n", - "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", "\n", "# Plot the SAM classification results\n", - "im = ax.pcolormesh(longitudes, latitudes, best_match_full, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "im = ax.pcolormesh(\n", + " longitudes,\n", + " latitudes,\n", + " best_match_full,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", "# Add geographic features for context\n", "ax.add_feature(cfeature.COASTLINE)\n", - "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", - "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n", "\n", "# Add gridlines\n", "ax.gridlines(draw_labels=True)\n", "\n", "# Add color bar with labels\n", - "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", - "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", - "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "cbar = plt.colorbar(\n", + " im,\n", + " ax=ax,\n", + " orientation=\"vertical\",\n", + " pad=0.02,\n", + " fraction=0.05,\n", + " ticks=np.arange(n_clusters),\n", + ")\n", + "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n", + "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n", "\n", "# Add title\n", - "ax.set_title('Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n", "\n", "# Show the plot\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1217,24 +1287,33 @@ "\n", "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n", "# Also assume that 'da' has the original latitude and longitude data\n", - "latitudes = da.coords['latitude'].values\n", - "longitudes = da.coords['longitude'].values\n", + "latitudes = da.coords[\"latitude\"].values\n", + "longitudes = da.coords[\"longitude\"].values\n", "\n", "# Create a custom discrete color map\n", - "cmap = mcolors.ListedColormap(['#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "cmap = mcolors.ListedColormap(\n", + " [\"#377eb8\", \"#ff7f00\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n", + ")\n", "bounds = np.arange(-0.5, n_clusters, 1)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", "# Create a figure and axis with the correct map projection\n", - "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", "\n", "# Plot the SAM classification results\n", - "im = ax.pcolormesh(longitudes, latitudes, best_match_full, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "im = ax.pcolormesh(\n", + " longitudes,\n", + " latitudes,\n", + " best_match_full,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", "# Add geographic features for context\n", "ax.add_feature(cfeature.COASTLINE)\n", - "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", - "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n", "\n", "# Add gridlines\n", "ax.gridlines(draw_labels=True)\n", @@ -1243,15 +1322,22 @@ "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", "\n", "# Add color bar with labels\n", - "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", - "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", - "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "cbar = plt.colorbar(\n", + " im,\n", + " ax=ax,\n", + " orientation=\"vertical\",\n", + " pad=0.02,\n", + " fraction=0.05,\n", + " ticks=np.arange(n_clusters),\n", + ")\n", + "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n", + "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n", "\n", "# Add title\n", - "ax.set_title('Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n", "\n", "# Show the plot\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1270,34 +1356,43 @@ "\n", "# Assume 'best_match_full' contains the SAM classification results reshaped to the original spatial dimensions\n", "# Also assume that 'da' has the original latitude and longitude data\n", - "latitudes = da.coords['latitude'].values\n", - "longitudes = da.coords['longitude'].values\n", + "latitudes = da.coords[\"latitude\"].values\n", + "longitudes = da.coords[\"longitude\"].values\n", "\n", "# Filtering condition based on wavelength values\n", "filter_condition = (\n", - " (da.sel(wavelength=650) > da.sel(wavelength=620)) & \n", - " (da.sel(wavelength=701) > da.sel(wavelength=681)) & \n", - " (da.sel(wavelength=701) > da.sel(wavelength=450))\n", + " (da.sel(wavelength=650) > da.sel(wavelength=620))\n", + " & (da.sel(wavelength=701) > da.sel(wavelength=681))\n", + " & (da.sel(wavelength=701) > da.sel(wavelength=450))\n", ")\n", "\n", "# Apply the filtering condition to the SAM classification results\n", "filtered_best_match_full = np.where(filter_condition, best_match_full, np.nan)\n", "\n", "# Create a custom discrete color map\n", - "cmap = mcolors.ListedColormap(['#e41a1c', '#377eb8', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "cmap = mcolors.ListedColormap(\n", + " [\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n", + ")\n", "bounds = np.arange(-0.5, n_clusters, 1)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", "# Create a figure and axis with the correct map projection\n", - "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", "\n", "# Plot the filtered SAM classification results\n", - "im = ax.pcolormesh(longitudes, latitudes, filtered_best_match_full, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "im = ax.pcolormesh(\n", + " longitudes,\n", + " latitudes,\n", + " filtered_best_match_full,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", "# Add geographic features for context\n", "ax.add_feature(cfeature.COASTLINE)\n", - "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", - "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n", "\n", "# Add gridlines\n", "ax.gridlines(draw_labels=True)\n", @@ -1306,15 +1401,22 @@ "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", "\n", "# Add color bar with labels\n", - "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(n_clusters))\n", - "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(n_clusters)])\n", - "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "cbar = plt.colorbar(\n", + " im,\n", + " ax=ax,\n", + " orientation=\"vertical\",\n", + " pad=0.02,\n", + " fraction=0.05,\n", + " ticks=np.arange(n_clusters),\n", + ")\n", + "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(n_clusters)])\n", + "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n", "\n", "# Add title\n", - "ax.set_title('PCA + Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "ax.set_title(\"PCA + Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n", "\n", "# Show the plot\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1346,9 +1448,9 @@ "outputs": [], "source": [ "# Load the netCDF data\n", - "file_path = './PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc'\n", + "file_path = \"./PACE_OCI.20240730T181157.L2.OC_AOP.V2_0.NRT.nc\"\n", "dataset = hypercoast.read_pace(file_path)\n", - "da = dataset['Rrs'] # Assuming 'Rrs' contains the reflectance data\n" + "da = dataset[\"Rrs\"] # Assuming 'Rrs' contains the reflectance data" ] }, { @@ -1367,7 +1469,7 @@ "outputs": [], "source": [ "# Extract PACE wavelengths\n", - "pace_wavelengths = da['wavelength'].values\n" + "pace_wavelengths = da[\"wavelength\"].values" ] }, { @@ -1390,13 +1492,15 @@ " df = pd.read_csv(csv_path)\n", " original_wavelengths = df.iloc[:, 0].values # First column is wavelength\n", " spectra_values = df.iloc[:, 1].values # Second column is spectral values\n", - " \n", + "\n", " # Interpolation function\n", - " interp_func = interp1d(original_wavelengths, spectra_values, kind='linear', fill_value='extrapolate')\n", - " \n", + " interp_func = interp1d(\n", + " original_wavelengths, spectra_values, kind=\"linear\", fill_value=\"extrapolate\"\n", + " )\n", + "\n", " # Resample to the target (PACE) wavelengths\n", " resampled_spectra = interp_func(target_wavelengths)\n", - " \n", + "\n", " return resampled_spectra" ] }, @@ -1406,18 +1510,22 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Load and resample all 6 endmembers\n", "endmember_paths = [\n", - " './SAM_spectral_library/Dataset_1.csv',\n", - " './SAM_spectral_library/Dataset_2.csv',\n", - " './SAM_spectral_library/Dataset_3.csv',\n", - " './SAM_spectral_library/Dataset_4.csv',\n", - " './SAM_spectral_library/Dataset_5.csv',\n", - " './SAM_spectral_library/Dataset_6.csv'\n", + " \"./SAM_spectral_library/Dataset_1.csv\",\n", + " \"./SAM_spectral_library/Dataset_2.csv\",\n", + " \"./SAM_spectral_library/Dataset_3.csv\",\n", + " \"./SAM_spectral_library/Dataset_4.csv\",\n", + " \"./SAM_spectral_library/Dataset_5.csv\",\n", + " \"./SAM_spectral_library/Dataset_6.csv\",\n", "]\n", "\n", - "endmembers = np.array([load_and_resample_spectral_library(path, pace_wavelengths) for path in endmember_paths])" + "endmembers = np.array(\n", + " [\n", + " load_and_resample_spectral_library(path, pace_wavelengths)\n", + " for path in endmember_paths\n", + " ]\n", + ")" ] }, { @@ -1438,24 +1546,32 @@ "# Plot sample spectra from the CSV files and their resampled versions\n", "def plot_sample_spectra(csv_paths, pace_wavelengths):\n", " plt.figure(figsize=(14, 8))\n", - " \n", + "\n", " for i, csv_path in enumerate(csv_paths):\n", " df = pd.read_csv(csv_path)\n", " original_wavelengths = df.iloc[:, 0].values\n", " spectra_values = df.iloc[:, 1].values\n", - " resampled_spectra = load_and_resample_spectral_library(csv_path, pace_wavelengths)\n", - " \n", - " plt.plot(original_wavelengths, spectra_values, label=f'Original Spectra {i+1}', linestyle='--')\n", - " plt.plot(pace_wavelengths, resampled_spectra, label=f'Resampled Spectra {i+1}')\n", - " \n", - " plt.xlabel('Wavelength (nm)')\n", - " plt.ylabel('Spectral Reflectance')\n", - " plt.title('Comparison of Original and Resampled Spectra')\n", + " resampled_spectra = load_and_resample_spectral_library(\n", + " csv_path, pace_wavelengths\n", + " )\n", + "\n", + " plt.plot(\n", + " original_wavelengths,\n", + " spectra_values,\n", + " label=f\"Original Spectra {i+1}\",\n", + " linestyle=\"--\",\n", + " )\n", + " plt.plot(pace_wavelengths, resampled_spectra, label=f\"Resampled Spectra {i+1}\")\n", + "\n", + " plt.xlabel(\"Wavelength (nm)\")\n", + " plt.ylabel(\"Spectral Reflectance\")\n", + " plt.title(\"Comparison of Original and Resampled Spectra\")\n", " plt.legend()\n", " plt.grid(True)\n", " plt.show()\n", "\n", - "plot_sample_spectra(endmember_paths, pace_wavelengths)\n" + "\n", + "plot_sample_spectra(endmember_paths, pace_wavelengths)" ] }, { @@ -1479,9 +1595,8 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Reshape data to (n_pixels, n_bands)\n", - "reshaped_data = da.values.reshape(-1, da.shape[-1])\n" + "reshaped_data = da.values.reshape(-1, da.shape[-1])" ] }, { @@ -1490,13 +1605,12 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Apply SAM for each pixel and each endmember\n", "angles = np.zeros((reshaped_data.shape[0], endmembers.shape[0]))\n", "\n", "for i in range(reshaped_data.shape[0]):\n", " for j in range(endmembers.shape[0]):\n", - " angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :])\n" + " angles[i, j] = spectral_angle_mapper(reshaped_data[i, :], endmembers[j, :])" ] }, { @@ -1505,7 +1619,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# Find the minimum angle (best match) for each pixel\n", "best_match = np.argmin(angles, axis=1)" ] @@ -1517,7 +1630,7 @@ "outputs": [], "source": [ "# Reshape best_match back to the original spatial dimensions\n", - "best_match = best_match.reshape(da.shape[:-1])\n" + "best_match = best_match.reshape(da.shape[:-1])" ] }, { @@ -1534,39 +1647,55 @@ "\n", "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n", "# Also assume that 'da' has the original latitude and longitude data\n", - "latitudes = da.coords['latitude'].values\n", - "longitudes = da.coords['longitude'].values\n", + "latitudes = da.coords[\"latitude\"].values\n", + "longitudes = da.coords[\"longitude\"].values\n", "\n", "# Create a custom discrete color map\n", - "cmap = mcolors.ListedColormap(['#377eb8', '#e41a1c', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "cmap = mcolors.ListedColormap(\n", + " [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n", + ")\n", "bounds = np.arange(-0.5, len(endmembers), 1)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", "# Create a figure and axis with the correct map projection\n", - "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", "\n", "# Plot the SAM classification results\n", - "im = ax.pcolormesh(longitudes, latitudes, best_match, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "im = ax.pcolormesh(\n", + " longitudes,\n", + " latitudes,\n", + " best_match,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", "# Add geographic features for context\n", "ax.add_feature(cfeature.COASTLINE)\n", - "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", - "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n", "\n", "# Adding axis labels\n", - "ax.set_xlabel('Longitude')\n", - "ax.set_ylabel('Latitude')\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", "\n", "# Adding a title\n", - "ax.set_title('Spectral Angle Mapper (SAM) Water Type Classification', fontsize=16)\n", + "ax.set_title(\"Spectral Angle Mapper (SAM) Water Type Classification\", fontsize=16)\n", "\n", "# Adding a color bar with discrete values\n", - "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(len(endmembers)))\n", - "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(len(endmembers))])\n", - "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "cbar = plt.colorbar(\n", + " im,\n", + " ax=ax,\n", + " orientation=\"vertical\",\n", + " pad=0.02,\n", + " fraction=0.05,\n", + " ticks=np.arange(len(endmembers)),\n", + ")\n", + "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))])\n", + "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n", "\n", "# Adding gridlines\n", - "ax.gridlines(draw_labels=True, linestyle='--', linewidth=0.5)\n", + "ax.gridlines(draw_labels=True, linestyle=\"--\", linewidth=0.5)\n", "\n", "# Set the extent to zoom in to the specified region (adjust as needed)\n", "ax.set_extent([-95, -85, 27, 33], crs=ccrs.PlateCarree())\n", @@ -1589,8 +1718,8 @@ "\n", "# Assume 'best_match' contains the SAM classification results reshaped to the original spatial dimensions\n", "# Also assume that 'da' has the original latitude and longitude data\n", - "latitudes = da.coords['latitude'].values\n", - "longitudes = da.coords['longitude'].values\n", + "latitudes = da.coords[\"latitude\"].values\n", + "longitudes = da.coords[\"longitude\"].values\n", "\n", "# Extract specific wavelengths for the conditions\n", "wavelength_450 = da.sel(wavelength=450).values\n", @@ -1600,47 +1729,67 @@ "wavelength_701 = da.sel(wavelength=701).values\n", "\n", "# Apply the condition to filter the pixels\n", - "condition = (wavelength_650 > wavelength_620) & (wavelength_701 > wavelength_681) & (wavelength_701 > wavelength_450)\n", + "condition = (\n", + " (wavelength_650 > wavelength_620)\n", + " & (wavelength_701 > wavelength_681)\n", + " & (wavelength_701 > wavelength_450)\n", + ")\n", "\n", "# Filter the best_match data based on the condition\n", "filtered_best_match = np.where(condition, best_match, np.nan)\n", "\n", "# Create a custom discrete color map\n", - "cmap = mcolors.ListedColormap(['#377eb8', '#e41a1c', '#4daf4a', '#f781bf', '#a65628', '#984ea3'])\n", + "cmap = mcolors.ListedColormap(\n", + " [\"#377eb8\", \"#e41a1c\", \"#4daf4a\", \"#f781bf\", \"#a65628\", \"#984ea3\"]\n", + ")\n", "bounds = np.arange(-0.5, len(endmembers), 1)\n", "norm = mcolors.BoundaryNorm(bounds, cmap.N)\n", "\n", "# Create a figure and axis with the correct map projection\n", - "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={'projection': ccrs.PlateCarree()})\n", + "fig, ax = plt.subplots(figsize=(12, 10), subplot_kw={\"projection\": ccrs.PlateCarree()})\n", "\n", "# Plot the SAM classification results for the filtered pixels\n", - "im = ax.pcolormesh(longitudes, latitudes, filtered_best_match, cmap=cmap, norm=norm, transform=ccrs.PlateCarree())\n", + "im = ax.pcolormesh(\n", + " longitudes,\n", + " latitudes,\n", + " filtered_best_match,\n", + " cmap=cmap,\n", + " norm=norm,\n", + " transform=ccrs.PlateCarree(),\n", + ")\n", "\n", "# Add geographic features for context\n", "ax.add_feature(cfeature.COASTLINE)\n", - "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", - "ax.add_feature(cfeature.STATES, linestyle='--')\n", + "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", + "ax.add_feature(cfeature.STATES, linestyle=\"--\")\n", "\n", "# Adding axis labels\n", - "ax.set_xlabel('Longitude')\n", - "ax.set_ylabel('Latitude')\n", + "ax.set_xlabel(\"Longitude\")\n", + "ax.set_ylabel(\"Latitude\")\n", "\n", "# Adding a title\n", - "ax.set_title('SAM Water Type Classification', fontsize=16)\n", + "ax.set_title(\"SAM Water Type Classification\", fontsize=16)\n", "\n", "# Adding a color bar with discrete values\n", - "cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, fraction=0.05, ticks=np.arange(len(endmembers)))\n", - "cbar.ax.set_yticklabels([f'Class {i+1}' for i in range(len(endmembers))])\n", - "cbar.set_label('Water Types', rotation=270, labelpad=20)\n", + "cbar = plt.colorbar(\n", + " im,\n", + " ax=ax,\n", + " orientation=\"vertical\",\n", + " pad=0.02,\n", + " fraction=0.05,\n", + " ticks=np.arange(len(endmembers)),\n", + ")\n", + "cbar.ax.set_yticklabels([f\"Class {i+1}\" for i in range(len(endmembers))])\n", + "cbar.set_label(\"Water Types\", rotation=270, labelpad=20)\n", "\n", "# Adding gridlines\n", - 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Depth (m): nan
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Lat: 29.54599
Depth (m): 18.31
Secchi (m): nan
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Rrs: 106-124; took 3 times, all good!
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Time: 11:11AM
Date: 2024-07-23 00:00:00
Lon: -90.83367
Lat: 28.60835
Depth (m): 19.19
Secchi (m): nan
Salinity: 32.29
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Time: 13:20:00
Date: 2024-07-22 00:00:00
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Lat: 28.99091
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Secchi (m): nan
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Salinity: 23.57
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Station.1: St2
Time: 09:47:00
Date: 2024-07-21 00:00:00
Lon: -89.45306
Lat: 28.90001
Depth (m): nan
Secchi (m): 2
Salinity: 25.91325
Water Temp: 30.91475
Rrs: cloudy
Microscopy: yes
Absorption: 150ml
CDOM: yes
LISST: yes
Nano: yes
Surface \npH: nan
Sufface \nO2: nan
Bottom \nO2: nan
FL-ECO: nan
FL-CDOM: nan
Notes: River plume, seaside
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Time: nan
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Lat: 29.69968
Depth (m): 7.34
Secchi (m): nan
Salinity: 25.75
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CDOM: yes
LISST: no
Nano: no
Surface \npH: 8.06
Sufface \nO2: 5.91
Bottom \nO2: 5.28
FL-ECO: nan
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Lon: -90.03359
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Depth (m): 15.03
Secchi (m): nan
Salinity: 28.34
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Rrs: no
Microscopy: yes
Absorption: 400ml
CDOM: yes
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Time: 10:25:00
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Time: 02:30:00
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Lon: -88.7747
Lat: 29.93375
Depth (m): 12.14
Secchi (m): nan
Salinity: 24.79
Water Temp: 30.756
Rrs: night
Microscopy: nan
Absorption: 250ml
CDOM: nan
LISST: nan
Nano: nan
Surface \npH: 8.5
Sufface \nO2: 8.38
Bottom \nO2: 0.29
FL-ECO: nan
FL-CDOM: nan
Notes: nan
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Depth (m): 34.35
Secchi (m): nan
Salinity: 32.1
Water Temp: 31.08
Rrs: 160-175; took 3 times; the 1st, problematic Ed; 2nd and 3rd a few more time ware refl
Microscopy: no
Absorption: 300ml
CDOM: yes
LISST: yes
Nano: yes
Surface \npH: 8.24
Sufface \nO2: 4.99
Bottom \nO2: 1.23
FL-ECO: nan
FL-CDOM: nan
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Date: 2024-07-24 00:00:00
Lon: -91.57862
Lat: 28.99891
Depth (m): 15.68
Secchi (m): nan
Salinity: 25.97
Water Temp: 29.771
Rrs: no
Microscopy: no
Absorption: 300ml
CDOM: yes
LISST: yes
Nano: no
Surface \npH: 8.29
Sufface \nO2: 6.19
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FL-ECO: 1.3616
FL-CDOM: 17.5101
Notes: nan
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Date: 2024-07-21 00:00:00
Lon: -89.49781
Lat: 29.09392
Depth (m): 10.05
Secchi (m): nan
Salinity: 23.17603
Water Temp: 31.22302
Rrs: cloudy
Microscopy: yes
Absorption: 150ml
CDOM: yes
LISST: yes
Nano: yes
Surface \npH: 8.36
Sufface \nO2: 8.91
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FL-CDOM: 20.9142
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Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "gdf.explore()" ] @@ -291,38 +97,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ChunkedEncodingError", - "evalue": "('Connection broken: IncompleteRead(398458880 bytes read, 1477995760 more expected)', IncompleteRead(398458880 bytes read, 1477995760 more expected))", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIncompleteRead\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:710\u001b[0m, in \u001b[0;36mHTTPResponse._error_catcher\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 709\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 710\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[0;32m 712\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 713\u001b[0m \u001b[38;5;66;03m# FIXME: Ideally we'd like to include the url in the ReadTimeoutError but\u001b[39;00m\n\u001b[0;32m 714\u001b[0m \u001b[38;5;66;03m# there is yet no clean way to get at it from this context.\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:835\u001b[0m, in \u001b[0;36mHTTPResponse._raw_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 825\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 826\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menforce_content_length\n\u001b[0;32m 827\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength_remaining \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 833\u001b[0m \u001b[38;5;66;03m# raised during streaming, so all calls with incorrect\u001b[39;00m\n\u001b[0;32m 834\u001b[0m \u001b[38;5;66;03m# Content-Length are caught.\u001b[39;00m\n\u001b[1;32m--> 835\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IncompleteRead(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp_bytes_read, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength_remaining)\n\u001b[0;32m 837\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n", - "\u001b[1;31mIncompleteRead\u001b[0m: IncompleteRead(398458880 bytes read, 1477995760 more expected)", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mProtocolError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\requests\\models.py:816\u001b[0m, in \u001b[0;36mResponse.iter_content..generate\u001b[1;34m()\u001b[0m\n\u001b[0;32m 815\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 816\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 817\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:936\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[1;34m(self, amt, decode_content)\u001b[0m\n\u001b[0;32m 935\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m--> 936\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 938\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:879\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[0;32m 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer\u001b[38;5;241m.\u001b[39mget(amt)\n\u001b[1;32m--> 879\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 881\u001b[0m flush_decoder \u001b[38;5;241m=\u001b[39m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (amt \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data)\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:813\u001b[0m, in \u001b[0;36mHTTPResponse._raw_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m 811\u001b[0m fp_closed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m--> 813\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_error_catcher\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 814\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fp_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfp_closed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\contextlib.py:158\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen\u001b[38;5;241m.\u001b[39mthrow(typ, value, traceback)\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 160\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 161\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 162\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\urllib3\\response.py:727\u001b[0m, in \u001b[0;36mHTTPResponse._error_catcher\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (HTTPException, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 726\u001b[0m \u001b[38;5;66;03m# This includes IncompleteRead.\u001b[39;00m\n\u001b[1;32m--> 727\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ProtocolError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection broken: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 729\u001b[0m \u001b[38;5;66;03m# If no exception is thrown, we should avoid cleaning up\u001b[39;00m\n\u001b[0;32m 730\u001b[0m \u001b[38;5;66;03m# unnecessarily.\u001b[39;00m\n", - "\u001b[1;31mProtocolError\u001b[0m: ('Connection broken: IncompleteRead(398458880 bytes read, 1477995760 more expected)', IncompleteRead(398458880 bytes read, 1477995760 more expected))", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mChunkedEncodingError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 2\u001b[0m filepath \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/PACE_OCI.20240615T182549.L1B.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[43mhypercoast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\DATA\\Hyperspectral\\HyperCoast\\hypercoast\\common.py:89\u001b[0m, in \u001b[0;36mdownload_file\u001b[1;34m(url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume, unzip, overwrite, subfolder)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://drive.google.com/file/d/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m url:\n\u001b[0;32m 87\u001b[0m fuzzy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 89\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mgdown\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 90\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquiet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproxy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspeed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_cookies\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverify\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mid\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfuzzy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresume\u001b[49m\n\u001b[0;32m 91\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unzip:\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.zip\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\gdown\\download.py:344\u001b[0m, in \u001b[0;36mdownload\u001b[1;34m(url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume, format, user_agent, log_messages)\u001b[0m\n\u001b[0;32m 342\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm\u001b[38;5;241m.\u001b[39mtqdm(total\u001b[38;5;241m=\u001b[39mtotal, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mB\u001b[39m\u001b[38;5;124m\"\u001b[39m, unit_scale\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 343\u001b[0m t_start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m--> 344\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mres\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter_content\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCHUNK_SIZE\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m 345\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 346\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mquiet\u001b[49m\u001b[43m:\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\requests\\models.py:818\u001b[0m, in \u001b[0;36mResponse.iter_content..generate\u001b[1;34m()\u001b[0m\n\u001b[0;32m 816\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 817\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m--> 818\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n\u001b[0;32m 819\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m DecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 820\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ContentDecodingError(e)\n", - "\u001b[1;31mChunkedEncodingError\u001b[0m: ('Connection broken: IncompleteRead(398458880 bytes read, 1477995760 more expected)', IncompleteRead(398458880 bytes read, 1477995760 more expected))" - ] - } - ], + "outputs": [], "source": [ "url = \"https://github.com/opengeos/datasets/releases/download/hypercoast/PACE_OCI.20240615T182549.L1B.nc\"\n", "filepath = \"data/PACE_OCI.20240615T182549.L1B.nc\"\n", @@ -338,28 +115,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] Unable to synchronously open file (unable to open file: name = 'data/PACE_OCI.20240615T182549.L1B.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mhypercoast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnetcdf_groups\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\DATA\\Hyperspectral\\HyperCoast\\hypercoast\\common.py:144\u001b[0m, in \u001b[0;36mnetcdf_groups\u001b[1;34m(filepath)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 130\u001b[0m \u001b[38;5;124;03mGet the list of groups in a NetCDF file.\u001b[39;00m\n\u001b[0;32m 131\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[38;5;124;03m ['group1', 'group2', 'group3']\u001b[39;00m\n\u001b[0;32m 141\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 142\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mh5netcdf\u001b[39;00m\n\u001b[1;32m--> 144\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mh5netcdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[0;32m 145\u001b[0m groups \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(file)\n\u001b[0;32m 146\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m groups\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\h5netcdf\\core.py:1054\u001b[0m, in \u001b[0;36mFile.__init__\u001b[1;34m(self, path, mode, invalid_netcdf, phony_dims, **kwargs)\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preexisting_file \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(path) \u001b[38;5;129;01mand\u001b[39;00m mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1053\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h5py \u001b[38;5;241m=\u001b[39m h5py\n\u001b[1;32m-> 1054\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h5file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_h5py\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1055\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrack_order\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrack_order\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m 1056\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1057\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# file-like object\u001b[39;00m\n\u001b[0;32m 1058\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preexisting_file \u001b[38;5;241m=\u001b[39m mode \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\h5py\\_hl\\files.py:562\u001b[0m, in \u001b[0;36mFile.__init__\u001b[1;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[0m\n\u001b[0;32m 553\u001b[0m fapl \u001b[38;5;241m=\u001b[39m make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[0;32m 554\u001b[0m locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[0;32m 555\u001b[0m alignment_threshold\u001b[38;5;241m=\u001b[39malignment_threshold,\n\u001b[0;32m 556\u001b[0m alignment_interval\u001b[38;5;241m=\u001b[39malignment_interval,\n\u001b[0;32m 557\u001b[0m meta_block_size\u001b[38;5;241m=\u001b[39mmeta_block_size,\n\u001b[0;32m 558\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 559\u001b[0m fcpl \u001b[38;5;241m=\u001b[39m make_fcpl(track_order\u001b[38;5;241m=\u001b[39mtrack_order, fs_strategy\u001b[38;5;241m=\u001b[39mfs_strategy,\n\u001b[0;32m 560\u001b[0m fs_persist\u001b[38;5;241m=\u001b[39mfs_persist, fs_threshold\u001b[38;5;241m=\u001b[39mfs_threshold,\n\u001b[0;32m 561\u001b[0m fs_page_size\u001b[38;5;241m=\u001b[39mfs_page_size)\n\u001b[1;32m--> 562\u001b[0m fid \u001b[38;5;241m=\u001b[39m \u001b[43mmake_fid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muserblock_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfcpl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mswmr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mswmr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(libver, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m 565\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_libver \u001b[38;5;241m=\u001b[39m libver\n", - "File \u001b[1;32mc:\\Users\\C00553090\\AppData\\Local\\miniconda3\\envs\\Hyper\\Lib\\site-packages\\h5py\\_hl\\files.py:235\u001b[0m, in \u001b[0;36mmake_fid\u001b[1;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[0;32m 233\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m swmr \u001b[38;5;129;01mand\u001b[39;00m swmr_support:\n\u001b[0;32m 234\u001b[0m flags \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mACC_SWMR_READ\n\u001b[1;32m--> 235\u001b[0m fid \u001b[38;5;241m=\u001b[39m \u001b[43mh5f\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 236\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m 237\u001b[0m fid \u001b[38;5;241m=\u001b[39m h5f\u001b[38;5;241m.\u001b[39mopen(name, h5f\u001b[38;5;241m.\u001b[39mACC_RDWR, fapl\u001b[38;5;241m=\u001b[39mfapl)\n", - "File \u001b[1;32mh5py\\_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mh5py\\_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mh5py\\h5f.pyx:102\u001b[0m, in \u001b[0;36mh5py.h5f.open\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] Unable to synchronously open file (unable to open file: name = 'data/PACE_OCI.20240615T182549.L1B.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)" - ] - } - ], + "outputs": [], "source": [ "hypercoast.netcdf_groups(filepath)" ] From 727f3154602c2c47deceffa0bdd936b1e9826dba Mon Sep 17 00:00:00 2001 From: Qiusheng Wu Date: Sun, 25 Aug 2024 01:08:14 -0400 Subject: [PATCH 3/3] Skip notebook execute --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index 5555cfd..a2679c0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -60,6 +60,7 @@ plugins: "image_slicing.ipynb", "temperature.ipynb", "pace_oci_l1.ipynb", + "pace_cyano.ipynb" ] markdown_extensions: