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kr-stn/sentinelsat_order_by_bug.ipynb

Created June 2, 2018 11:28
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Sentinelsat Issue #200 - order_by bug
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sentinelsat_order_by_bug.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Explore the cause of the `sentinelsat` bug related to `order_by` https://github.com/sentinelsat/sentinelsat/issues/200"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt\n",
"import os\n",
"import pandas as pd\n",
"\n",
"api = SentinelAPI(os.environ['DHUS_USER'], os.environ['DHUS_PASSWORD'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Query all available GRD scenes - returns 183 products.\n",
"\n",
"This is equivalent to the URL: `https://scihub.copernicus.eu/dhus/search?q=producttype:GRD%20AND%20footprint:%22Intersects(POLYGON((117.5000%2041.0000,117.5000%2031.0000,127.5000%2031.0000,127.5000%2038.5000,133.0000%2038.5000,133.0000%2043.3000,127.2000%2043.3000,127.2000%2041.0000,117.5000%2041.0000)))%22%20AND%20ingestiondate:%20[2017-08-01T00:00:00Z%20TO%202017-09-01T00:00:00Z]`"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Querying products: 100%|███████████████████████████████████████████████████| 183/183 [00:01<00:00, 74.07 products/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"183\n"
]
}
],
"source": [
"products = api.query(date=('2017-08-01T00:00:00Z', '2017-09-01T00:00:00Z'),\n",
" area='POLYGON((117.5000 41.0000,117.5000 31.0000,127.5000 31.0000,127.5000 38.5000,133.0000 38.5000,133.0000 43.3000,127.2000 43.3000,127.2000 41.0000,117.5000 41.0000))',\n",
" producttype=\"GRD\")\n",
"print(len(products))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Query, but also `order_by`. The attribute you order on influences the amount of products returned. The available server side order keywords are listed at: https://scihub.copernicus.eu/twiki/do/view/SciHubUserGuide/3FullTextSearch\n",
"\n",
"`footprint` and `collection` are excluded since they cause exceptions when used as `order_by` parameter.\n",
"\n",
"URL to list the first 100 products and ordering by `ingestiondate` in ascending order `https://scihub.copernicus.eu/dhus/search?q=producttype:GRD%20AND%20footprint:\"Intersects(POLYGON((117.5000%2041.0000,117.5000%2031.0000,127.5000%2031.0000,127.5000%2038.5000,133.0000%2038.5000,133.0000%2043.3000,127.2000%2043.3000,127.2000%2041.0000,117.5000%2041.0000)))\"%20AND%20ingestiondate:%20[2017-08-01T00:00:00Z%20TO%202017-09-01T00:00:00Z]&rows=100&start=0&orderby=ingestiondate%20asc`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 628.54 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 237.50 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 229.82 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 621.24 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 640.02 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 700.97 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 253.93 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 688.38 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 227.25 products/s]\n",
"Querying products: 100%|███████████████████████████████████████████████████| 183/183 [00:01<00:00, 42.64 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 511.37 products/s]\n",
"Querying products: 100%|███████████████████████████████████████████████████| 183/183 [00:01<00:00, 68.95 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 696.67 products/s]\n",
"Querying products: 100%|███████████████████████████████████████████████████| 183/183 [00:01<00:00, 68.89 products/s]\n",
"Querying products: 100%|██████████████████████████████████████████████████| 183/183 [00:00<00:00, 684.53 products/s]\n"
]
}
],
"source": [
"order_kw = [\"platformname\", \"beginposition\", \"endposition\", \"ingestiondate\", \"filename\", \"orbitnumber\", \"lastorbitnumber\", \"relativeorbitnumber\", \"lastrelativeorbitnumber\", \"orbitdirection\", \"polarisationmode\", \"producttype\", \"sensoroperationalmode\", \"swathidentifier\", \"cloudcoverpercentage\"]\n",
"\n",
"ordered_products = {}\n",
"\n",
"for kw in order_kw:\n",
" try:\n",
" products_ordered = api.query(date=('2017-08-01T00:00:00Z', '2017-09-01T00:00:00Z'),\n",
" area='POLYGON((117.5000 41.0000,117.5000 31.0000,127.5000 31.0000,127.5000 38.5000,133.0000 38.5000,133.0000 43.3000,127.2000 43.3000,127.2000 41.0000,117.5000 41.0000))',\n",
" producttype=\"GRD\",\n",
" order_by=kw)\n",
" ordered_products[kw] = products_ordered\n",
" except:\n",
" ordered_products[kw] = \"Error\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The amount of products returned is dependent on the order keyword. However - it always lists 183 found products.\n",
"\n",
"- it seems like date time objects return 100 products, i.e. only the first page - this relates to `beginposition`, `ingestiondate` and `endposition`\n",
"- `orbitnumbers` return 134 and `relativeorbitnumbers` 136 products\n",
"- string objects return 115 products, except `filename` which returns 132\n",
"- `cloudcoverpercentage` also returns 115 products even though it is not present/empty in Sentinel-1 metadata"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"polarisationmode : 115\n",
"producttype : 115\n",
"filename : 132\n",
"lastorbitnumber : 134\n",
"sensoroperationalmode : 115\n",
"relativeorbitnumber : 136\n",
"swathidentifier : 115\n",
"beginposition : 100\n",
"endposition : 100\n",
"cloudcoverpercentage : 115\n",
"ingestiondate : 100\n",
"orbitnumber : 134\n",
"platformname : 115\n",
"lastrelativeorbitnumber : 136\n",
"orbitdirection : 132\n"
]
}
],
"source": [
"for k in ordered_products.keys():\n",
" print(k,\":\",len(ordered_products[k]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Explore the differences further by converting the product lists into Pandas DataFrames."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"df_prod = api.to_dataframe(products)\n",
"df_ordered = api.to_dataframe(ordered_products[\"ingestiondate\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a DF with all the products present in the full list (183) that are missing in the ordered list."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df_diff = df_prod[~df_prod.isin(df_ordered)].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['link_icon', 'link_alternative', 'format', 'identifier', 'filename',\n",
" 'slicenumber', 'swathidentifier', 'endposition', 'gmlfootprint',\n",
" 'instrumentname', 'lastorbitnumber', 'link', 'missiondatatakeid',\n",
" 'producttype', 'lastrelativeorbitnumber', 'platformname', 'title',\n",
" 'size', 'footprint', 'acquisitiontype', 'summary', 'polarisationmode',\n",
" 'status', 'sensoroperationalmode', 'relativeorbitnumber',\n",
" 'beginposition', 'instrumentshortname', 'platformidentifier',\n",
" 'ingestiondate', 'orbitnumber', 'uuid', 'productclass',\n",
" 'orbitdirection'],\n",
" dtype='object')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_diff.columns"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"6a1511ab-b30c-45a7-a6b2-2137fbf268cb 2017-08-01 17:25:22.896\n",
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"b04403c3-54c1-4e7c-9c5e-f0ffa66be170 2017-08-07 05:10:44.126\n",
" ... \n",
"e84ec47c-fcad-4298-8ec0-bd93912236fc 2017-08-26 13:26:21.822\n",
"212e194c-34c8-429d-9449-61b557da5bd2 2017-08-26 13:26:23.588\n",
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"fee7729a-636b-43fa-82ea-1478590e0c84 2017-08-30 03:40:21.288\n",
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"9a0f7453-f8af-4265-951c-89f15f8808f1 2017-08-30 03:42:16.424\n",
"980967d2-9505-4cfe-91a2-af83884d7c41 2017-08-30 03:42:16.591\n",
"f8203295-c440-429f-8ac2-c6c6642ae3e2 2017-08-30 04:02:08.813\n",
"72c855d3-7623-476b-a5bc-5810efe1182b 2017-08-30 12:42:44.535\n",
"97716c9e-5e55-4f8f-89db-01e98dd64929 2017-08-31 06:32:21.203\n",
"cfad23f5-b00b-4b4d-9514-7ec0d3b5508b 2017-08-31 06:34:26.786\n",
"974f2faa-ade2-4579-bd58-965082bce640 2017-08-31 06:34:30.111\n",
"ed11cef7-a920-4fc8-adad-1d429a4e8954 2017-08-31 06:34:30.835\n",
"69c297b4-cc8c-4285-8106-8b38a615ba14 2017-08-31 17:58:46.189\n",
"10839c2d-b865-489e-b25e-a77670bf530b 2017-08-31 17:58:48.261\n",
"e67e8877-6f53-4aea-b783-ee490c78f0ea 2017-08-31 17:59:34.038\n",
"3062b18e-13f5-4f9f-b4ec-ac937410e630 2017-08-31 17:59:45.167\n",
"4d60d407-0fd6-448f-aea5-0438690097aa 2017-08-31 18:02:30.969\n",
"37979890-b46a-4204-a628-c80556c97164 2017-08-31 18:05:30.728\n",
"cefe2060-5bee-4ea9-9da4-b6ac89eddc46 2017-08-31 18:07:07.679\n",
"80f7c36f-88c0-4c02-a98f-1fb7367733f7 2017-08-31 18:07:17.640\n",
"a12e809e-661c-4778-a93e-60db52588877 2017-09-01 01:32:10.000\n",
"Name: ingestiondate, Length: 183, dtype: datetime64[ns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_prod.sort_values(by=\"ingestiondate\", ascending=True)[\"ingestiondate\"]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"6a1511ab-b30c-45a7-a6b2-2137fbf268cb 2017-08-01 17:25:22.896\n",
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" ... \n",
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"acdd3977-899f-4399-ab2f-bfa1d0ebf6e8 2017-08-13 12:34:47.959\n",
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"Name: ingestiondate, Length: 100, dtype: datetime64[ns]"
]
},
"execution_count": 35,
"metadata": {},
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}
],
"source": [
"df_ordered.sort_values(by=\"ingestiondate\", ascending=True)[\"ingestiondate\"]"
]
},
{
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"execution_count": 36,
"metadata": {
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},
"outputs": [
{
"data": {
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"89cf90c2-85ab-4462-a659-3932c9d2b291 2017-08-19 04:44:32.874\n",
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" ... \n",
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"Name: ingestiondate, Length: 83, dtype: datetime64[ns]"
]
},
"execution_count": 36,
"metadata": {},
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}
],
"source": [
"df_diff.sort_values(by=\"ingestiondate\", ascending=True)[\"ingestiondate\"]"
]
}
],
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