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Nate Weisiger
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Added clip and ship notebook
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‎Dockerfile‎

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@@ -73,11 +73,11 @@ RUN ln -s $CONDA_DIR/envs/python2/bin/pip $CONDA_DIR/bin/pip2.7 && \
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pygdal==2.1.3.3 \
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geojsonio
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# Add pip3.5 shortcut and install Python3 packages
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RUN pip3.5 install --upgrade pip && \
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pip3.5 install matplotlib && \
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pip3.5 install scikit-image && \
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pip3.5 install \
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# Add pip shortcut and install Python3 packages
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RUN pip install --upgrade pip && \
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pip install matplotlib && \
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pip install scikit-image && \
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pip install \
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pyproj \
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ipywidgets \
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ipyleaflet \

‎jupyter-notebooks/data-api-tutorials/Cairo.geojson‎

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This file was deleted.

‎jupyter-notebooks/data-api-tutorials/clip_and_ship_introduction.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Item Clipping and Download\n",
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"\n",
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"This example clips, downloads and unzips a given scene to the AOI provided, returning only the image data contained in with the AOI. To so this, we use the [\"Clips\" API](https://www.planet.com/docs/reference/clips-api/).\n",
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"\n",
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"In this example, we are clipping a scene to contain only Golden Gate Park in San Francisco."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Input Parameters "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from os import environ\n",
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"\n",
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"# Set API key (this should to be an environment variable)\n",
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"api_key = environ['PL_API_KEY']\n",
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"\n",
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"# Sent Scene ID\n",
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"scene_id = '20170517_192201_1056516_RapidEye-2'\n",
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"\n",
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"# Set Item Type\n",
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"item_type = 'REOrthoTile'\n",
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"\n",
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"# Set Asset Type\n",
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"asset_type = 'visual'\n",
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"\n",
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"# Area Of Interest (clip geometry) in GeoJSON format\n",
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"aoi_json = '''{\n",
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" \"type\": \"Polygon\",\n",
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" \"coordinates\": [\n",
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" [\n",
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" [\n",
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" -122.51103401184083,\n",
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" 37.771596736802074\n",
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" ],\n",
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" [\n",
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" -122.51060485839844,\n",
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" 37.763997637045456\n",
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" ],\n",
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" [\n",
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" -122.45902061462401,\n",
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" 37.76603318676243\n",
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" ],\n",
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" [\n",
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" -122.45773315429689,\n",
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" 37.7654903789825\n",
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" ],\n",
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" [\n",
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" -122.45275497436523,\n",
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" 37.76637243960179\n",
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" ],\n",
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" [\n",
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" -122.45455741882324,\n",
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" 37.775124624817906\n",
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" ],\n",
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" [\n",
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" -122.46597290039062,\n",
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" 37.7738356083287\n",
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" ],\n",
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" [\n",
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" -122.51103401184083,\n",
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" 37.771596736802074\n",
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" ]\n",
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" ]\n",
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" ]\n",
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" }'''"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Clip scenes to AOI, poll status, and download when complete"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import json\n",
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"import requests\n",
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"import time\n",
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"\n",
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"# Construct clip API payload\n",
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"clip_payload = {\n",
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" 'aoi': json.loads(aoi_json),\n",
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" 'targets': [\n",
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" {\n",
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" 'item_id': scene_id,\n",
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" 'item_type': item_type,\n",
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" 'asset_type': asset_type\n",
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" }\n",
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" ]\n",
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"}\n",
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"\n",
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"# Request clip of scene (This will take some time to complete)\n",
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"request = requests.post('https://api.planet.com/compute/ops/clips/v1', auth=(api_key, ''), json=clip_payload)\n",
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"clip_url = request.json()['_links']['_self']\n",
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"\n",
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"# Poll API to monitor clip status. Once finished, download and upzip the scene\n",
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"clip_succeeded = False\n",
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"while not clip_succeeded:\n",
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"\n",
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" # Poll API\n",
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" check_state_request = requests.get(clip_url, auth=(api_key, ''))\n",
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" \n",
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" # If clipping process succeeded , we are done\n",
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" if check_state_request.json()['state'] == 'succeeded':\n",
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" clip_download_url = check_state_request.json()['_links']['results'][0]\n",
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" clip_succeeded = True\n",
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" print(\"Clip of scene succeeded and is ready to download\") \n",
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" \n",
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" # Still activating. Wait 1 second and check again.\n",
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" else:\n",
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" print(\"...Still waiting for clipping to complete...\")\n",
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" time.sleep(1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Download and upzip the clip\n",
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"Once complete, look in the output directory to see your clipped tif file.\n",
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"\n",
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"NOTE: Clipped scene will only be available for 5 minutes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from tqdm import tqdm\n",
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"import zipfile\n",
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"\n",
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"# Download clip\n",
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"response = requests.get(clip_download_url, stream=True)\n",
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"with open('output/' + scene_id + '.zip', \"wb\") as handle:\n",
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" for data in tqdm(response.iter_content()):\n",
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" handle.write(data)\n",
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"\n",
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"# Unzip file\n",
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"ziped_item = zipfile.ZipFile('output/' + scene_id + '.zip')\n",
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"ziped_item.extractall('output/' + scene_id) \n",
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" \n",
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"# Delete zip file\n",
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"os.remove('output/' + scene_id + '.zip')\n",
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"print('Downloaded clips located in: output/')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎jupyter-notebooks/data-api-tutorials/data/san-francisco.json‎

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}

‎jupyter-notebooks/data-api-tutorials/planet-explorer.png‎ renamed to ‎jupyter-notebooks/data-api-tutorials/images/planet-explorer.png‎

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‎jupyter-notebooks/ndvi/README.md‎ renamed to ‎jupyter-notebooks/data-api-tutorials/output/.gitkeep‎

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