diff --git a/maap_jupyter/.ipynb_checkpoints/maap_notebook-checkpoint.ipynb b/maap_jupyter/.ipynb_checkpoints/maap_notebook-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c8afb0defb080c9dad98f3e389ab44e3df67cbb7
--- /dev/null
+++ b/maap_jupyter/.ipynb_checkpoints/maap_notebook-checkpoint.ipynb
@@ -0,0 +1,297 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1a70970d",
+ "metadata": {},
+ "source": [
+ "# <center> Tree Classification Demo </center>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf3c2a51",
+ "metadata": {},
+ "source": [
+ "<center>Tests for geopandas and gdal<center>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c55216cb",
+ "metadata": {},
+ "source": [
+ "## Import Packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4d9291fa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from datetime import datetime\n",
+ "import json\n",
+ "import time\n",
+ "import configparser\n",
+ "import requests\n",
+ "# On NASA , install with pip install geopandas\n",
+ "# On ESA; use a PolinSAR stack ( geopandas already embedded) \n",
+ "import geopandas as gpd\n",
+ "import pandas as pd\n",
+ "from creodias_finder import query\n",
+ "from shapely.geometry import Polygon,shape\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "711e5ecd",
+ "metadata": {},
+ "source": [
+ "## Define Functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "2b694047",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def __get_result_S1( geometry_search, str_plateformeS1, str_product_type, str_sensor_mode, start_date, end_date):\n",
+ " '''\n",
+ " Recupere dans un dataframe les produits S1 qui intersectent la zone de recherche\n",
+ "\n",
+ " :geometry_search: geometrie de la zone de recherche\n",
+ " :str_plateformeS1: nom de la plateforme\n",
+ " :str_product_type: nom du product type\n",
+ " :str_sensor_mode: nom du sensor mode\n",
+ " :start_date: date debut interval recherche au format datetime\n",
+ " :end_date: date fin interval recherche au format datetime\n",
+ "\n",
+ "\n",
+ " return: df_groupby\n",
+ " :rtype: dataframe\n",
+ " '''\n",
+ "\n",
+ " # liste des produits de l'api creodias\n",
+ " results = query.query(\n",
+ " str_plateformeS1,\n",
+ " start_date=start_date,\n",
+ " end_date=end_date,\n",
+ " productType=str_product_type,\n",
+ " sensorMode=str_sensor_mode,\n",
+ " status=\"ONLINE\",\n",
+ " geometry=geometry_search\n",
+ " )\n",
+ "\n",
+ " # init des listes des parametres a conserver \n",
+ " list_title = []\n",
+ " list_date = []\n",
+ " list_orbit = []\n",
+ " list_orbitDirection = []\n",
+ " list_geom = []\n",
+ " list_url = []\n",
+ "\n",
+ " # remplissage des listes avec les parametres des produits du results de l'api\n",
+ " for idproduct, dict_product in results.items():\n",
+ " list_title.append(dict_product['properties']['title'])\n",
+ " list_orbit.append(dict_product['properties']['relativeOrbitNumber'])\n",
+ " list_orbitDirection.append(dict_product['properties']['orbitDirection'])\n",
+ " list_geom.append(shape(dict_product['geometry']))\n",
+ " list_url.append(dict_product['properties']['productIdentifier'])\n",
+ " list_date.append(dict_product['properties']['title'][17:25])\n",
+ "\n",
+ " # genere le gdf avec les produits s1\n",
+ " gdf_product = gpd.GeoDataFrame({'title' : list_title, 'date' : list_date, 'orbit' : list_orbit, 'direction' : list_orbitDirection,'geometry' : list_geom, 'url' : list_url}, crs = \"4326\")\n",
+ " # regroupe les produits qui ont les memes parametres de date, orbit et direction\n",
+ " df_groupby = gdf_product.groupby(['date', 'orbit', 'direction']).agg({'title' : list, 'url' : list, 'geometry' : list}).reset_index()\n",
+ "\n",
+ " return df_groupby\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2573ab84",
+ "metadata": {},
+ "source": [
+ "## Define the study area or list of tile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7e14efe8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "search_zone = [[4.751930185604931,45.37522170186796],[4.468812157001305,45.24092464324643],[4.018935832120382,44.88158298080388],[3.852035966569818,44.75014456444579],[3.898047329546759,44.56923769773521],[4.038870891160542,44.31329708744024],[4.241954580160842,44.26894961414283],[4.674279531570155,44.26246269728098],[4.882500597010154,44.8652889083827],[4.905098845459158,45.06277528749476],[4.782524828980907,45.368587240900965],[4.751930185604931,45.37522170186796]]\n",
+ "search_poly = Polygon(search_zone)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b8ee178",
+ "metadata": {},
+ "source": [
+ "## Search S1 products"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "f7fd2a1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# parametres de recherche\n",
+ "str_plateformeS1 = 'Sentinel1'\n",
+ "str_product_type = 'GRD'\n",
+ "str_sensor_mode = 'IW'\n",
+ "start_date = datetime(2022, 6, 3)\n",
+ "end_date = datetime(2023, 1, 31)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "3cb6aa8f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "query_url http://datahub.creodias.eu/resto/api/collections/Sentinel1/search.json?maxRecords=1000&startDate=2022-06-03T00%3A00%3A00&completionDate=2023-01-31T23%3A59%3A59&geometry=POLYGON+%28%284.751930185604931+45.37522170186796%2C+4.468812157001305+45.24092464324643%2C+4.018935832120382+44.88158298080388%2C+3.852035966569818+44.75014456444579%2C+3.898047329546759+44.56923769773521%2C+4.038870891160542+44.31329708744024%2C+4.241954580160842+44.26894961414283%2C+4.674279531570155+44.26246269728098%2C+4.882500597010154+44.8652889083827%2C+4.905098845459158+45.06277528749476%2C+4.782524828980907+45.368587240900965%2C+4.751930185604931+45.37522170186796%29%29&status=ONLINE&productType=GRD&sensorMode=IW\n",
+ "<Response [200]>\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "df_product = __get_result_S1( search_poly, str_plateformeS1, str_product_type, str_sensor_mode, start_date, end_date)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "568856bd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_product\n",
+ "df_product = df_product[df_product['orbit']==37]\n",
+ "df_product\n",
+ "print(len(df_product))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f85d0273",
+ "metadata": {},
+ "source": [
+ "## Extract list of URL "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6cfbf9c7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\"Sentinel-1/SAR/GRD/2022/06/04/S1A_IW_GRDH_1SDV_20220604T055236_20220604T055301_043509_0531E5_DC1B.SAFE,\"Sentinel-1/SAR/GRD/2022/06/16/S1A_IW_GRDH_1SDV_20220616T055236_20220616T055301_043684_05371A_E51A.SAFE,\"Sentinel-1/SAR/GRD/2022/06/28/S1A_IW_GRDH_1SDV_20220628T055237_20220628T055302_043859_053C5F_3096.SAFE,\"Sentinel-1/SAR/GRD/2022/07/22/S1A_IW_GRDH_1SDV_20220722T055238_20220722T055303_044209_0546C9_677E.SAFE,\"Sentinel-1/SAR/GRD/2022/08/03/S1A_IW_GRDH_1SDV_20220803T055239_20220803T055304_044384_054BF3_4ACD.SAFE,\"Sentinel-1/SAR/GRD/2022/08/15/S1A_IW_GRDH_1SDV_20220815T055240_20220815T055305_044559_05516B_B0D3.SAFE,\"Sentinel-1/SAR/GRD/2022/08/27/S1A_IW_GRDH_1SDV_20220827T055240_20220827T055305_044734_055755_77D7.SAFE,\"Sentinel-1/SAR/GRD/2022/09/08/S1A_IW_GRDH_1SDV_20220908T055241_20220908T055306_044909_055D33_8C27.SAFE,\"Sentinel-1/SAR/GRD/2022/09/20/S1A_IW_GRDH_1SDV_20220920T055241_20220920T055306_045084_056320_8ED8.SAFE,\"Sentinel-1/SAR/GRD/2022/10/02/S1A_IW_GRDH_1SDV_20221002T055242_20221002T055307_045259_0568FD_84DA.SAFE,\"Sentinel-1/SAR/GRD/2022/10/14/S1A_IW_GRDH_1SDV_20221014T055242_20221014T055307_045434_056EDB_346E.SAFE,\"Sentinel-1/SAR/GRD/2022/10/26/S1A_IW_GRDH_1SDV_20221026T055242_20221026T055307_045609_05740C_5826.SAFE,\"Sentinel-1/SAR/GRD/2022/11/07/S1A_IW_GRDH_1SDV_20221107T055241_20221107T055306_045784_0579F9_649C.SAFE,\"Sentinel-1/SAR/GRD/2022/11/19/S1A_IW_GRDH_1SDV_20221119T055241_20221119T055306_045959_057FDC_E997.SAFE,\"Sentinel-1/SAR/GRD/2022/12/01/S1A_IW_GRDH_1SDV_20221201T055240_20221201T055305_046134_0585CE_836F.SAFE,\"Sentinel-1/SAR/GRD/2022/12/13/S1A_IW_GRDH_1SDV_20221213T055240_20221213T055305_046309_058BCE_F90F.SAFE,\"Sentinel-1/SAR/GRD/2022/12/25/S1A_IW_GRDH_1SDV_20221225T055239_20221225T055304_046484_0591C3_7C2C.SAFE,\"Sentinel-1/SAR/GRD/2023/01/06/S1A_IW_GRDH_1SDV_20230106T055239_20230106T055304_046659_0597A7_7ED7.SAFE,\"Sentinel-1/SAR/GRD/2023/01/18/S1A_IW_GRDH_1SDV_20230118T055238_20230118T055303_046834_059D93_5C5F.SAFE,\"Sentinel-1/SAR/GRD/2023/01/30/S1A_IW_GRDH_1SDV_20230130T055238_20230130T055303_047009_05A37A_61B6.SAFE\"]\n"
+ ]
+ }
+ ],
+ "source": [
+ "tmp_list_product_path = []\n",
+ "for prd in df_product['url']:\n",
+ " hour_1 = int(prd[0].split('/')[-1].split('_')[4].split('T')[1])\n",
+ " hour_2 = int(prd[1].split('/')[-1].split('_')[4].split('T')[1])\n",
+ " \n",
+ " if hour_1 > hour_2:\n",
+ " tmp_list_product_path.append(prd[0])\n",
+ " else:\n",
+ " tmp_list_product_path.append(prd[1])\n",
+ "\n",
+ "list_product_path = []\n",
+ "for path_url in tmp_list_product_path:\n",
+ " list_product_path.append(path_url[8:])\n",
+ "\n",
+ "prod_payload = \"[\\\"\"+\",\\\"\".join(list_product_path)+\"\\\"]\"\n",
+ "print(prod_payload)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "0142ecbc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Test Gdal"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "ae1c9c71",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating output file that is 4680P x 5400L.\n",
+ "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+ "\n",
+ "[02/28 16:51:44] [INFO dem.py] Bounds: -156.0 18.8 -154.7 20.3\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] Creating /projects/treeclassificationdemo/driver/L1_MSI_pixel_value_Composite_123.tif\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] Fetching remote tiles...\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] Running GDAL command:\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] gdalwarp /vsicurl/https://raw.githubusercontent.com/scottstanie/sardem/master/sardem/data/cop_global.vrt /projects/treeclassificationdemo/driver/L1_MSI_pixel_value_Composite_123.tif -of GTiff -ot Int16 -te -156 18.8000000000000007 -154.699999999999989 20.3000000000000007 -tr 0.000277777777777777778 0.000277777777777777778 -s_srs \"epsg:4326+3855\" -t_srs \"epsg:4326\" -wo NUM_THREADS=4 -r nearest -wm 5000 -multi\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!conda run -n maap sardem --bbox -156 18.8 -154.7 20.3 --data-source COP -o \"/projects/treeclassificationdemo/driver/maap_utils/L1_MSI_pixel_value_Composite_123.tif\" --output-format GTiff\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Maap",
+ "language": "python",
+ "name": "maap"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/maap_jupyter/maap_notebook.ipynb b/maap_jupyter/maap_notebook.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c8afb0defb080c9dad98f3e389ab44e3df67cbb7
--- /dev/null
+++ b/maap_jupyter/maap_notebook.ipynb
@@ -0,0 +1,297 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "1a70970d",
+ "metadata": {},
+ "source": [
+ "# <center> Tree Classification Demo </center>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf3c2a51",
+ "metadata": {},
+ "source": [
+ "<center>Tests for geopandas and gdal<center>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c55216cb",
+ "metadata": {},
+ "source": [
+ "## Import Packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "4d9291fa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from datetime import datetime\n",
+ "import json\n",
+ "import time\n",
+ "import configparser\n",
+ "import requests\n",
+ "# On NASA , install with pip install geopandas\n",
+ "# On ESA; use a PolinSAR stack ( geopandas already embedded) \n",
+ "import geopandas as gpd\n",
+ "import pandas as pd\n",
+ "from creodias_finder import query\n",
+ "from shapely.geometry import Polygon,shape\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "711e5ecd",
+ "metadata": {},
+ "source": [
+ "## Define Functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "2b694047",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def __get_result_S1( geometry_search, str_plateformeS1, str_product_type, str_sensor_mode, start_date, end_date):\n",
+ " '''\n",
+ " Recupere dans un dataframe les produits S1 qui intersectent la zone de recherche\n",
+ "\n",
+ " :geometry_search: geometrie de la zone de recherche\n",
+ " :str_plateformeS1: nom de la plateforme\n",
+ " :str_product_type: nom du product type\n",
+ " :str_sensor_mode: nom du sensor mode\n",
+ " :start_date: date debut interval recherche au format datetime\n",
+ " :end_date: date fin interval recherche au format datetime\n",
+ "\n",
+ "\n",
+ " return: df_groupby\n",
+ " :rtype: dataframe\n",
+ " '''\n",
+ "\n",
+ " # liste des produits de l'api creodias\n",
+ " results = query.query(\n",
+ " str_plateformeS1,\n",
+ " start_date=start_date,\n",
+ " end_date=end_date,\n",
+ " productType=str_product_type,\n",
+ " sensorMode=str_sensor_mode,\n",
+ " status=\"ONLINE\",\n",
+ " geometry=geometry_search\n",
+ " )\n",
+ "\n",
+ " # init des listes des parametres a conserver \n",
+ " list_title = []\n",
+ " list_date = []\n",
+ " list_orbit = []\n",
+ " list_orbitDirection = []\n",
+ " list_geom = []\n",
+ " list_url = []\n",
+ "\n",
+ " # remplissage des listes avec les parametres des produits du results de l'api\n",
+ " for idproduct, dict_product in results.items():\n",
+ " list_title.append(dict_product['properties']['title'])\n",
+ " list_orbit.append(dict_product['properties']['relativeOrbitNumber'])\n",
+ " list_orbitDirection.append(dict_product['properties']['orbitDirection'])\n",
+ " list_geom.append(shape(dict_product['geometry']))\n",
+ " list_url.append(dict_product['properties']['productIdentifier'])\n",
+ " list_date.append(dict_product['properties']['title'][17:25])\n",
+ "\n",
+ " # genere le gdf avec les produits s1\n",
+ " gdf_product = gpd.GeoDataFrame({'title' : list_title, 'date' : list_date, 'orbit' : list_orbit, 'direction' : list_orbitDirection,'geometry' : list_geom, 'url' : list_url}, crs = \"4326\")\n",
+ " # regroupe les produits qui ont les memes parametres de date, orbit et direction\n",
+ " df_groupby = gdf_product.groupby(['date', 'orbit', 'direction']).agg({'title' : list, 'url' : list, 'geometry' : list}).reset_index()\n",
+ "\n",
+ " return df_groupby\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2573ab84",
+ "metadata": {},
+ "source": [
+ "## Define the study area or list of tile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7e14efe8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "search_zone = [[4.751930185604931,45.37522170186796],[4.468812157001305,45.24092464324643],[4.018935832120382,44.88158298080388],[3.852035966569818,44.75014456444579],[3.898047329546759,44.56923769773521],[4.038870891160542,44.31329708744024],[4.241954580160842,44.26894961414283],[4.674279531570155,44.26246269728098],[4.882500597010154,44.8652889083827],[4.905098845459158,45.06277528749476],[4.782524828980907,45.368587240900965],[4.751930185604931,45.37522170186796]]\n",
+ "search_poly = Polygon(search_zone)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b8ee178",
+ "metadata": {},
+ "source": [
+ "## Search S1 products"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "f7fd2a1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# parametres de recherche\n",
+ "str_plateformeS1 = 'Sentinel1'\n",
+ "str_product_type = 'GRD'\n",
+ "str_sensor_mode = 'IW'\n",
+ "start_date = datetime(2022, 6, 3)\n",
+ "end_date = datetime(2023, 1, 31)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "3cb6aa8f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "query_url http://datahub.creodias.eu/resto/api/collections/Sentinel1/search.json?maxRecords=1000&startDate=2022-06-03T00%3A00%3A00&completionDate=2023-01-31T23%3A59%3A59&geometry=POLYGON+%28%284.751930185604931+45.37522170186796%2C+4.468812157001305+45.24092464324643%2C+4.018935832120382+44.88158298080388%2C+3.852035966569818+44.75014456444579%2C+3.898047329546759+44.56923769773521%2C+4.038870891160542+44.31329708744024%2C+4.241954580160842+44.26894961414283%2C+4.674279531570155+44.26246269728098%2C+4.882500597010154+44.8652889083827%2C+4.905098845459158+45.06277528749476%2C+4.782524828980907+45.368587240900965%2C+4.751930185604931+45.37522170186796%29%29&status=ONLINE&productType=GRD&sensorMode=IW\n",
+ "<Response [200]>\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "df_product = __get_result_S1( search_poly, str_plateformeS1, str_product_type, str_sensor_mode, start_date, end_date)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "568856bd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_product\n",
+ "df_product = df_product[df_product['orbit']==37]\n",
+ "df_product\n",
+ "print(len(df_product))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f85d0273",
+ "metadata": {},
+ "source": [
+ "## Extract list of URL "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6cfbf9c7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\"Sentinel-1/SAR/GRD/2022/06/04/S1A_IW_GRDH_1SDV_20220604T055236_20220604T055301_043509_0531E5_DC1B.SAFE,\"Sentinel-1/SAR/GRD/2022/06/16/S1A_IW_GRDH_1SDV_20220616T055236_20220616T055301_043684_05371A_E51A.SAFE,\"Sentinel-1/SAR/GRD/2022/06/28/S1A_IW_GRDH_1SDV_20220628T055237_20220628T055302_043859_053C5F_3096.SAFE,\"Sentinel-1/SAR/GRD/2022/07/22/S1A_IW_GRDH_1SDV_20220722T055238_20220722T055303_044209_0546C9_677E.SAFE,\"Sentinel-1/SAR/GRD/2022/08/03/S1A_IW_GRDH_1SDV_20220803T055239_20220803T055304_044384_054BF3_4ACD.SAFE,\"Sentinel-1/SAR/GRD/2022/08/15/S1A_IW_GRDH_1SDV_20220815T055240_20220815T055305_044559_05516B_B0D3.SAFE,\"Sentinel-1/SAR/GRD/2022/08/27/S1A_IW_GRDH_1SDV_20220827T055240_20220827T055305_044734_055755_77D7.SAFE,\"Sentinel-1/SAR/GRD/2022/09/08/S1A_IW_GRDH_1SDV_20220908T055241_20220908T055306_044909_055D33_8C27.SAFE,\"Sentinel-1/SAR/GRD/2022/09/20/S1A_IW_GRDH_1SDV_20220920T055241_20220920T055306_045084_056320_8ED8.SAFE,\"Sentinel-1/SAR/GRD/2022/10/02/S1A_IW_GRDH_1SDV_20221002T055242_20221002T055307_045259_0568FD_84DA.SAFE,\"Sentinel-1/SAR/GRD/2022/10/14/S1A_IW_GRDH_1SDV_20221014T055242_20221014T055307_045434_056EDB_346E.SAFE,\"Sentinel-1/SAR/GRD/2022/10/26/S1A_IW_GRDH_1SDV_20221026T055242_20221026T055307_045609_05740C_5826.SAFE,\"Sentinel-1/SAR/GRD/2022/11/07/S1A_IW_GRDH_1SDV_20221107T055241_20221107T055306_045784_0579F9_649C.SAFE,\"Sentinel-1/SAR/GRD/2022/11/19/S1A_IW_GRDH_1SDV_20221119T055241_20221119T055306_045959_057FDC_E997.SAFE,\"Sentinel-1/SAR/GRD/2022/12/01/S1A_IW_GRDH_1SDV_20221201T055240_20221201T055305_046134_0585CE_836F.SAFE,\"Sentinel-1/SAR/GRD/2022/12/13/S1A_IW_GRDH_1SDV_20221213T055240_20221213T055305_046309_058BCE_F90F.SAFE,\"Sentinel-1/SAR/GRD/2022/12/25/S1A_IW_GRDH_1SDV_20221225T055239_20221225T055304_046484_0591C3_7C2C.SAFE,\"Sentinel-1/SAR/GRD/2023/01/06/S1A_IW_GRDH_1SDV_20230106T055239_20230106T055304_046659_0597A7_7ED7.SAFE,\"Sentinel-1/SAR/GRD/2023/01/18/S1A_IW_GRDH_1SDV_20230118T055238_20230118T055303_046834_059D93_5C5F.SAFE,\"Sentinel-1/SAR/GRD/2023/01/30/S1A_IW_GRDH_1SDV_20230130T055238_20230130T055303_047009_05A37A_61B6.SAFE\"]\n"
+ ]
+ }
+ ],
+ "source": [
+ "tmp_list_product_path = []\n",
+ "for prd in df_product['url']:\n",
+ " hour_1 = int(prd[0].split('/')[-1].split('_')[4].split('T')[1])\n",
+ " hour_2 = int(prd[1].split('/')[-1].split('_')[4].split('T')[1])\n",
+ " \n",
+ " if hour_1 > hour_2:\n",
+ " tmp_list_product_path.append(prd[0])\n",
+ " else:\n",
+ " tmp_list_product_path.append(prd[1])\n",
+ "\n",
+ "list_product_path = []\n",
+ "for path_url in tmp_list_product_path:\n",
+ " list_product_path.append(path_url[8:])\n",
+ "\n",
+ "prod_payload = \"[\\\"\"+\",\\\"\".join(list_product_path)+\"\\\"]\"\n",
+ "print(prod_payload)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "0142ecbc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Test Gdal"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "ae1c9c71",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Creating output file that is 4680P x 5400L.\n",
+ "0...10...20...30...40...50...60...70...80...90...100 - done.\n",
+ "\n",
+ "[02/28 16:51:44] [INFO dem.py] Bounds: -156.0 18.8 -154.7 20.3\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] Creating /projects/treeclassificationdemo/driver/L1_MSI_pixel_value_Composite_123.tif\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] Fetching remote tiles...\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] Running GDAL command:\n",
+ "[02/28 16:51:44] [INFO cop_dem.py] gdalwarp /vsicurl/https://raw.githubusercontent.com/scottstanie/sardem/master/sardem/data/cop_global.vrt /projects/treeclassificationdemo/driver/L1_MSI_pixel_value_Composite_123.tif -of GTiff -ot Int16 -te -156 18.8000000000000007 -154.699999999999989 20.3000000000000007 -tr 0.000277777777777777778 0.000277777777777777778 -s_srs \"epsg:4326+3855\" -t_srs \"epsg:4326\" -wo NUM_THREADS=4 -r nearest -wm 5000 -multi\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "!conda run -n maap sardem --bbox -156 18.8 -154.7 20.3 --data-source COP -o \"/projects/treeclassificationdemo/driver/maap_utils/L1_MSI_pixel_value_Composite_123.tif\" --output-format GTiff\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Maap",
+ "language": "python",
+ "name": "maap"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/maap_jupyter/maap_utils/.ipynb_checkpoints/L1_MSI_pixel_value_Composite_123-checkpoint.tif b/maap_jupyter/maap_utils/.ipynb_checkpoints/L1_MSI_pixel_value_Composite_123-checkpoint.tif
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diff --git a/maap_jupyter/maap_utils/L1_MSI_pixel_value_Composite_123.tif b/maap_jupyter/maap_utils/L1_MSI_pixel_value_Composite_123.tif
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index 0000000000000000000000000000000000000000..5447e95147c80263594956abd221e88699450e74
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