diff --git a/.env b/.env index 1fd078f..5e62c71 100644 --- a/.env +++ b/.env @@ -1 +1,2 @@ LD_LIBRARY_PATH=${CONDA_PREFIX}/lib/ +GMT_LIBRARY_PATH=${CONDA_PREFIX}/lib/ diff --git a/Dockerfile b/Dockerfile index f9b27cc..741c9ca 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM buildpack-deps:bionic-scm@sha256:f37982278d0dfd71d282ee551a927a44294876d07b98ea9c001087282e482817 +FROM buildpack-deps:bionic@sha256:b3514c949630fd6accaac959387e66dc42fc32894c5ef9f275b267e5d1c971d4 LABEL maintainer "https://github.com/weiji14" ENV LANG C.UTF-8 ENV LC_ALL C.UTF-8 @@ -24,6 +24,7 @@ RUN cd /tmp && \ echo "e1045ee415162f944b6aebfe560b8fee *Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh" | md5sum -c - && \ /bin/bash Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh -f -b -p $CONDA_DIR && \ rm Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh && \ + $CONDA_DIR/bin/conda config --prepend channels conda-forge/label/dev && \ $CONDA_DIR/bin/conda config --system --prepend channels conda-forge && \ $CONDA_DIR/bin/conda config --system --set auto_update_conda false && \ $CONDA_DIR/bin/conda config --system --set show_channel_urls true && \ diff --git a/Pipfile b/Pipfile index 43fc3c4..3d25241 100644 --- a/Pipfile +++ b/Pipfile @@ -5,14 +5,17 @@ name = "pypi" [packages] cython = "==0.29" +gmt = {editable = true, ref = "0.1a3-131-g9772fa3", git = "https://github.com/weiji14/gmt-python.git"} ipython = "==7.1.1" jupyterlab = "==0.35.4" keras = "==2.2.4" livelossplot = "==0.2.0" matplotlib = "==3.0.2" +netcdf4 = "==1.4.1" numpy = "==1.14.5" packaging = "==18.0" pandas = "==0.23.4" +pyproj = "==1.9.5.1" quilt = "==2.9.12" rasterio = "==1.0.9" requests = "==2.20.1" diff --git a/Pipfile.lock b/Pipfile.lock index 484a0ea..9228662 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "e93f292e7815f0044587e87091cdf3be82516f04668362b69a807653f9361d0b" + "sha256": "378323f3a49483c239bf7994f111e7e29c209eb356ab4cefce42a2fbe8833c77" }, "pipfile-spec": 6, "requires": { @@ -71,6 +71,22 @@ ], "version": "==2018.10.15" }, + "cftime": { + "hashes": [ + "sha256:1c95964596527ad6ca01be1935c50251baae3fbde9243264712312ecfe9af5e9", + "sha256:2883278a80bb3f099d2450766791274fd9a4b722b940b8d01113ae84b54ef830", + "sha256:2c81d4879a2c1753961d647e55e0125039ddeda195944c3d526f2cf087dfb7bb", + "sha256:48e0a4d4cde77e3ff5e242dd1e22f82eb9e7ebd51567359475a49df646af0db4", + "sha256:4b31ba52673e2dc3eb5ccd1eaff3b552fb3d2d4aa1c227dcbae03ff845c24ac3", + "sha256:8ef1a39d0647b3bba6b5dbb29d93ff97bb6f60f7cc0c3a9884be409001790ae5", + "sha256:b103fc3974672bb03ac6a50419486e16a83a0cef0acf8915e967c518545fd3ac", + "sha256:b28f3512ede5e930a54fd3f2c82cd94788546eb320da6c3596f298b4ec99ce70", + "sha256:d31471e532f18d1562344ebe362670fded9afe8c467c77fc1058935913663ca1", + "sha256:d4791423ce18c18414a5610140b7df7455f5aaf8fc00c106be54d8cff667249a", + "sha256:f87c6da5a69fffdc1858b3b669a7d9f959e40de2f9f1f8caac14b9fd86cd4de1" + ], + "version": "==1.0.2.1" + }, "chardet": { "hashes": [ "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae", @@ -153,10 +169,10 @@ "array" ], "hashes": [ - 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"sha256:b85e4de3d54cf4f14fe1d0515a980ccb49ddd4cdd21250cc0d4fb6374d50b1a7", - "sha256:ddb713d15a3205d7d3beab11f7fa9e3b10dbe0a2fff034a7db22ec8a2bc47a8b" + "sha256:661341909008d1e7bfa1541904006f9789fa3de1cbec8379d2879819454cc04b", + "sha256:91705b109fc785198faed892489cddb233265564d5e2dad5e4f7974af05ee8dd" ], - "version": "==5.7.0" + "version": "==5.7.2" }, "numpy": { "hashes": [ @@ -702,11 +754,18 @@ ], "version": "==2.3.0" }, + "pyproj": { + "hashes": [ + "sha256:53fa54c8fa8a1dfcd6af4bf09ce1aae5d4d949da63b90570ac5ec849efaf3ea8" + ], + "index": "pypi", + "version": "==1.9.5.1" + }, "pyrsistent": { "hashes": [ - "sha256:f64dd1b706c31f7aa24495a7da58c0407c072981289b675331e2a16364355102" + "sha256:5a31f6b093da3401fefdeb53a0980e3145bb9d2bf852b579cc7b39c7f0016c87" ], - "version": "==0.14.5" + "version": "==0.14.6" }, "python-dateutil": { "hashes": [ @@ -1048,11 +1107,18 @@ }, "wheel": { "hashes": [ - "sha256:196c9842d79262bb66fcf59faa4bd0deb27da911dbc7c6cdca931080eb1f0783", - "sha256:c93e2d711f5f9841e17f53b0e6c0ff85593f3b416b6eec7a9452041a59a42688" + "sha256:029703bf514e16c8271c3821806a1c171220cc5bdd325cbf4e7da1e056a01db6", + "sha256:1e53cdb3f808d5ccd0df57f964263752aa74ea7359526d3da6c02114ec1e1d44" ], "markers": "python_version >= '3'", - "version": "==0.32.2" + "version": "==0.32.3" + }, + "xarray": { + "hashes": [ + "sha256:51013a4fbdad6def83a49233490da6f15650a0d4a65966c26d8e2b6cf7992269", + "sha256:636964baccfca0e5d69220ac4ecb948d561addc76f47704064dcbe399e03a818" + ], + "version": "==0.11.0" }, "xlrd": { "hashes": [ @@ -1278,9 +1344,9 @@ }, "pyrsistent": { "hashes": [ - "sha256:f64dd1b706c31f7aa24495a7da58c0407c072981289b675331e2a16364355102" + "sha256:5a31f6b093da3401fefdeb53a0980e3145bb9d2bf852b579cc7b39c7f0016c87" ], - "version": "==0.14.5" + "version": "==0.14.6" }, "pytest": { "hashes": [ diff --git a/data_list.yml b/data_list.yml index a310d4f..6c87eed 100644 --- a/data_list.yml +++ b/data_list.yml @@ -1,154 +1,158 @@ -- +- citekey: Fretwell2013BEDMAP2 folder: lowres location: Antarctica resolution: 1000m - doi: + doi: dataset: "https://doi.org/10.7488/ds/1916" literature: "https://doi.org/10.5194/tc-7-375-2013" - files: - - + files: + - filename: bedmap2_bed.tif url: "http://data.pgc.umn.edu/elev/dem/bedmap2/bedmap2_bed.tif" sha256: 28e2ca7656d61b0bc7f8f8c1db41914023e0cab1634e0ee645f38a87d894b416 -- +- citekey: Noh2018REMA folder: misc location: Antarctica resolution: 200m - doi: + doi: dataset: "https://doi.org/10.7910/DVN/SAIK8B" literature: "https://doi.org/10.1016/j.isprsjprs.2017.12.008" - files: - - + files: + - filename: REMA_200m_dem_filled.tif url: "http://data.pgc.umn.edu/elev/dem/setsm/REMA/mosaic/v1.0/200m/REMA_200m_dem_filled.tif" sha256: 8ac252e40810ac5e59934879a066f496c847936771f318dab2ab4a257052d964 -- +- citekey: Rignot2011MEASURES folder: misc location: Antarctica resolution: 450m - doi: + doi: dataset: "https://doi.org/10.5067/D7GK8F5J8M8R" literature: "https://doi.org/10.1126/science.1208336" - files: - - + files: + - filename: MEaSUREs_IceFlowSpeed_450m.tif url: "http://data.pgc.umn.edu/gis/packages/quantarctica/Quantarctica3/Glaciology/MEaSUREs%20Ice%20Flow%20Velocity/MEaSUREs_IceFlowSpeed_450m.tif" sha256: 4a4efc3a84204c3d67887e8d7fa1186467b51e696451f2832ebbea3ca491c8a8 -- +- citekey: King2016Rutford folder: highres location: Rutford Ice Stream resolution: nan - doi: + doi: dataset: "https://doi.org/10.5285/54757cbe-0b13-4385-8b31-4dfaa1dab55e" literature: "https://doi.org/10.5194/essd-8-151-2016" - files: - - + files: + - filename: bed_WGS84_grid.txt url: "http://ramadda.nerc-bas.ac.uk/repository/entry/get/Polar%20Data%20Centre/DOI/Rutford%20Ice%20Stream%20bed%20elevation%20DEM%20from%20radar%20data/bed_WGS84_grid.txt?entryid=synth%3A54757cbe-0b13-4385-8b31-4dfaa1dab55e%3AL2JlZF9XR1M4NF9ncmlkLnR4dA%3D%3D" - sha256: 0d3e98ca727fc1201b436170af5a63f23348aaf146a3ac6234f6c4da283e8b34 -- + sha256: 7396e56cda5adb82cecb01f0b3e01294ed0aa6489a9629f3f7e8858ea6cb91cf +- citekey: Bingham2018PIG folder: highres location: Pine Island Glacier resolution: nan - doi: + doi: dataset: nan literature: "https://doi.org/10.1038/s41467-017-01597-y" - files: - - + files: + - filename: 2007t1.txt url: nan sha256: 04bdbd3c8e814cbc8f0d324277e339a46cc90a8dc23434d11815a8966951e766 - - + - filename: 2007tr.txt url: nan sha256: 3858a1e58e17b2816920e1b309534cee0391f72a6a0aa68d57777b030e70e9a3 - - + - filename: 2010tr.txt url: nan sha256: 751ea56acc5271b3fb54893ed59e05ff485187a6fc5daaedf75946d730805b80 - - + - filename: istar08.txt url: nan sha256: ed03c64332e8d406371c74a66f3cd21fb3f78ee498ae8408c355879bb89eb13d - - + - filename: istar18.txt url: nan sha256: 3e69d86f28e26810d29b0b9309090684dcb295c0dd39007fe9ee0d1285c57804 - - + - filename: istar15.txt url: nan sha256: 59c981e8c96f73f3a5bd98be6570e101848b4f67a12d98a577292e7bcf776b17 - - + - filename: istar13.txt url: nan sha256: f5bcf80c7ea5095e2eabf72b69a264bf36ed56af5cb67976f9428f560e5702a2 - - + - filename: istar17.txt url: nan sha256: f51a674dc27d6e0b99d199949a706ecf96ea807883c1901fea186efc799a36e8 - - + - filename: istar07.txt url: nan sha256: c81ec04290433f598ce4368e4aae088adeeabb546913edc44c54a5a5d7593e93 -- +- citekey: Shi2010CRESIS folder: highres location: Antarctica resolution: nan - doi: + doi: dataset: "https://doi.org/10.5067/GDQ0CUCVTE2Q" literature: "https://doi.org/10.1109/IGARSS.2010.5649518" - files: - - + files: + - filename: 2009_Antarctica_DC8.csv url: "https://data.cresis.ku.edu/data/rds/2009_Antarctica_DC8/csv_good/2009_Antarctica_DC8.csv" sha256: 1b9fe0faf4ef217794c2a1de9ef8cfa45f5949efdc4e925930d31c0554cf0ca2 - - + - filename: 2009_Antarctica_TO.csv url: "https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO/csv_good/2009_Antarctica_TO.csv" sha256: 7a90c5955fa881b4fb88e45ff11629e60ff9ad045c07bf4c6e3aa1f7d1a9361d - - + - filename: 2009_Antarctica_TO_Gambit.csv url: "https://data.cresis.ku.edu/data/rds/2009_Antarctica_TO_Gambit/csv_good/2009_Antarctica_TO_Gambit.csv" sha256: 93da613223733a4850283b700060afdb14f1002fe5613b8d78c6d3be83e34072 - - + - filename: 2010_Antarctica_DC8.csv url: "https://data.cresis.ku.edu/data/rds/2010_Antarctica_DC8/csv_good/2010_Antarctica_DC8.csv" sha256: f725a8dbc21d31601b99ccaf9f5282ecd516f2ff966d268b4e735ea1af2014e6 - - + - filename: 2011_Antarctica_DC8.csv url: "https://data.cresis.ku.edu/data/rds/2011_Antarctica_DC8/csv_good/2011_Antarctica_DC8.csv" sha256: 38aba2a39b0d58b72827f25cfcd667fc943f25c0024d3c52cb1b9e65e9e76163 - - + - filename: 2011_Antarctica_TO.csv url: "https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv" sha256: 4bf37750b9986ce582c9fd1f3a6ac622fc17f3b3ecb07b7a7132eb3797ee31d1 - - + - filename: 2012_Antarctica_DC8.csv url: "https://data.cresis.ku.edu/data/rds/2012_Antarctica_DC8/csv_good/2012_Antarctica_DC8.csv" sha256: 5c6701b8c34bd57517b93e8e18f32e4579d6e2f56e4796bd7140b3e338544007 - - + - filename: 2013_Antarctica_Basler.csv url: "https://data.cresis.ku.edu/data/rds/2013_Antarctica_Basler/csv_good/2013_Antarctica_Basler.csv" sha256: 56609027b4af04ba078ae093772916341bd1d6ab5f110de11b21294507733cc8 - - + - filename: 2013_Antarctica_P3.csv url: "https://data.cresis.ku.edu/data/rds/2013_Antarctica_P3/csv_good/2013_Antarctica_P3.csv" sha256: 9de95030f49ce0bbf107eb72418db2845c39822872a6c9aa10f023148262f658 - - + - filename: 2014_Antarctica_DC8.csv url: "https://data.cresis.ku.edu/data/rds/2014_Antarctica_DC8/csv_good/2014_Antarctica_DC8.csv" sha256: bd8c8674ba66508c64303725bfe45b3365467d01f69cfa8ec4258a3ced05e5bf - - + - filename: 2016_Antarctica_DC8.csv url: "https://data.cresis.ku.edu/data/rds/2016_Antarctica_DC8/csv_good/2016_Antarctica_DC8.csv" sha256: ec3b514dfcae265f5b8643eeb3503be8a0a6531e563faf9f12cb67f2b618a741 - - + - filename: 2017_Antarctica_P3.csv url: "https://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/csv_good/2017_Antarctica_P3.csv" sha256: 9208a64fefe2f4a6e7f08d44c0af0c35400cd814590c32b8eb02f1545bfc8bec + - + filename: 2017_Antarctica_Basler.csv + url: "https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv" + sha256: c97d0d92f3095ee8c3941d915028728423758594cc95e7b819889b51693f0712 diff --git a/data_prep.ipynb b/data_prep.ipynb index d5dd527..c96fe86 100644 --- a/data_prep.ipynb +++ b/data_prep.ipynb @@ -26,9 +26,11 @@ "output_type": "stream", "text": [ "Python : 3.6.6 | packaged by conda-forge | (default, Oct 11 2018, 14:33:06) \n", + "GMT : 0.1a3+131.g9772fa3\n", "Numpy : 1.14.5\n", "Rasterio : 1.0.9\n", - "Scikit-image : 0.14.1\n" + "Scikit-image : 0.14.1\n", + "Xarray : 0.11.0\n" ] } ], @@ -36,28 +38,34 @@ "import glob\n", "import hashlib\n", "import io\n", + "import json\n", "import os\n", + "import shutil\n", "import sys\n", "\n", "import requests\n", "import tqdm\n", "import yaml\n", "\n", + "import gmt\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "from PIL import Image\n", + "import pyproj\n", "import quilt\n", "import rasterio\n", "import rasterio.mask\n", "import rasterio.plot\n", "import shapely.geometry\n", "import skimage.util.shape\n", + "import xarray as xr\n", "\n", "print('Python :', sys.version.split('\\n')[0])\n", + "print('GMT :', gmt.__version__)\n", "print('Numpy :', np.__version__)\n", "print('Rasterio :', rasterio.__version__)\n", - "print('Scikit-image :', skimage.__version__)" + "print('Scikit-image :', skimage.__version__)\n", + "print('Xarray :', xr.__version__)" ] }, { @@ -77,12 +85,12 @@ " r\"\"\"\n", " Download from a url to a path\n", " \n", - " >>> download_to_path(path=\"highres/2017_Antarctica_Basler.csv\",\n", - " ... url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv\")\n", + " >>> download_to_path(path=\"highres/Data_20171204_02.csv\",\n", + " ... url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\")\n", " \n", - " >>> open('highres/2017_Antarctica_Basler.csv').readlines()\n", + " >>> open(\"highres/Data_20171204_02.csv\").readlines()\n", " ['LAT,LON,UTCTIMESOD,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY\\n']\n", - " >>> os.remove(path=\"highres/2017_Antarctica_Basler.csv\")\n", + " >>> os.remove(path=\"highres/Data_20171204_02.csv\")\n", " \"\"\"\n", " #if not os.path.exists(path=path):\n", " r = requests.get(url=url, stream=True)\n", @@ -102,12 +110,12 @@ " \"\"\"\n", " Returns SHA256 checksum of a file\n", " \n", - " >>> download_to_path(path=\"highres/2017_Antarctica_Basler.csv\",\n", - " ... url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv\")\n", + " >>> download_to_path(path=\"highres/Data_20171204_02.csv\",\n", + " ... url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\")\n", " \n", - " >>> check_sha256('highres/2017_Antarctica_Basler.csv')\n", + " >>> check_sha256(\"highres/Data_20171204_02.csv\")\n", " '53cef7a0d28ff92b30367514f27e888efbc32b1bda929981b371d2e00d4c671b'\n", - " >>> os.remove(path=\"highres/2017_Antarctica_Basler.csv\")\n", + " >>> os.remove(path=\"highres/Data_20171204_02.csv\")\n", " \"\"\"\n", " with open(file=path, mode=\"rb\") as afile:\n", " sha = hashlib.sha256(afile.read())\n", @@ -128,16 +136,29 @@ "metadata": {}, "outputs": [], "source": [ - "with open(\"data_list.yml\", \"r\") as yml:\n", - " y = yaml.load(stream=yml)\n", - " \n", - " #For the machines (used by the download and hash check scripts)\n", - " datalist = pd.io.json.json_normalize(data=y, record_path=[\"files\"],\n", - " meta=[\"citekey\", \"folder\", \"location\"])\n", - " datalist = datalist.reindex(columns=[\"folder\", \"filename\", \"url\", \"sha256\"]) #reorder columns\n", - " \n", - " #For the humans (parse to README.md in highres/lowres/misc folders)\n", - " df = pd.io.json.json_normalize(data=y, sep=\"_\")" + "def parse_datalist(\n", + " yaml_file: str = \"data_list.yml\",\n", + " record_path: str = \"files\",\n", + " schema: list = [\n", + " \"citekey\",\n", + " \"folder\",\n", + " \"location\",\n", + " \"resolution\",\n", + " [\"doi\", \"dataset\"],\n", + " [\"doi\", \"literature\"],\n", + " ],\n", + ") -> pd.DataFrame:\n", + "\n", + " assert yaml_file.endswith((\".yml\", \".yaml\"))\n", + "\n", + " with open(file=yaml_file, mode=\"r\") as yml:\n", + " y = yaml.load(stream=yml)\n", + "\n", + " datalist = pd.io.json.json_normalize(\n", + " data=y, record_path=record_path, meta=schema, sep=\"_\"\n", + " )\n", + "\n", + " return datalist" ] }, { @@ -145,13 +166,28 @@ "execution_count": 5, "metadata": {}, "outputs": [], + "source": [ + "# Pretty print table with nice column order and clickable url links\n", + "pprint_table = (\n", + " lambda df, folder: df.loc[df[\"folder\"] == folder]\n", + " .reindex(columns=[\"folder\", \"filename\", \"url\", \"sha256\"])\n", + " .style.format({\"url\": lambda url: f'{url}'})\n", + ")\n", + "dataframe = parse_datalist()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], "source": [ "# Code to autogenerate README.md files in highres/lowres/misc folders from data_list.yml\n", "columns = [\"Filename\", \"Location\", \"Resolution\", \"Literature Citation\", \"Data Citation\"]\n", "for folder, md_header in [(\"lowres\", \"Low Resolution\"),\n", " (\"highres\", \"High Resolution\"),\n", " (\"misc\", \"Miscellaneous\")]:\n", - " assert(folder in pd.unique(df[\"folder\"]))\n", + " assert(folder in pd.unique(dataframe[\"folder\"]))\n", " md_name = f\"{folder}/README.md\"\n", " \n", " with open(file=md_name, mode=\"w\") as md_file:\n", @@ -162,14 +198,15 @@ " \n", " md_table = pd.DataFrame(columns=columns)\n", " md_table.loc[0] = ['---','---','---','---','---']\n", - "\n", - " for row in df.loc[df[\"folder\"] == folder].itertuples():\n", - " filecount = len(row.files)\n", - " extension = os.path.splitext(row.files[0]['filename'])[-1]\n", - " row_dict = {\"Filename\": row.files[0][\"filename\"] if filecount == 1 else f\"{filecount} *{extension} files\",\n", + " \n", + " keydf = dataframe.groupby(\"citekey\").aggregate(lambda x: set(x).pop())\n", + " for row in keydf.loc[keydf[\"folder\"] == folder].itertuples():\n", + " filecount = len(dataframe[dataframe[\"citekey\"] == row.Index])\n", + " extension = os.path.splitext(row.filename)[-1]\n", + " row_dict = {\"Filename\": row.filename if filecount == 1 else f\"{filecount} *{extension} files\",\n", " \"Location\": row.location,\n", " \"Resolution\": row.resolution,\n", - " \"Literature Citation\": f\"[{row.citekey}]({row.doi_literature})\",\n", + " \"Literature Citation\": f\"[{row.Index}]({row.doi_literature})\",\n", " \"Data Citation\": f\"[DOI]({row.doi_dataset})\" if row.doi_dataset!='nan' else None}\n", " md_table = md_table.append(other=row_dict, ignore_index=True)\n", " \n", @@ -185,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -214,23 +251,23 @@ "" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "for dataset in datalist.loc[datalist[\"folder\"] == \"lowres\"].itertuples():\n", + "for dataset in dataframe.loc[dataframe[\"folder\"] == \"lowres\"].itertuples():\n", " path = f\"{dataset.folder}/{dataset.filename}\" #path to download the file to\n", " if not os.path.exists(path=path):\n", " download_to_path(path=path, url=dataset.url)\n", " assert(check_sha256(path=path) == dataset.sha256)\n", - "datalist.loc[datalist[\"folder\"] == \"lowres\"].style.format({\"url\": lambda url: f'{url}'})" + "pprint_table(dataframe, \"lowres\")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -260,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -295,18 +332,18 @@ "" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "for dataset in datalist.loc[datalist[\"folder\"] == \"misc\"].itertuples():\n", + "for dataset in dataframe.loc[dataframe[\"folder\"] == \"misc\"].itertuples():\n", " path = f\"{dataset.folder}/{dataset.filename}\" #path to download the file to\n", " if not os.path.exists(path=path):\n", " download_to_path(path=path, url=dataset.url)\n", " assert(check_sha256(path=path) == dataset.sha256)\n", - "datalist.loc[datalist[\"folder\"] == \"misc\"].style.format({\"url\": lambda url: f'{url}'})" + "pprint_table(dataframe, \"misc\")" ] }, { @@ -318,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -326,7 +363,7 @@ "text/html": [ " \n", - 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foldersha256
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21highres2013_Antarctica_P3.csvhttps://data.cresis.ku.edu/data/rds/2013_Antarctica_P3/csv_good/2013_Antarctica_P3.csv9de95030f49ce0bbf107eb72418db2845c39822872a6c9aa10f023148262f65822highres2014_Antarctica_DC8.csvhttps://data.cresis.ku.edu/data/rds/2014_Antarctica_DC8/csv_good/2014_Antarctica_DC8.csvbd8c8674ba66508c64303725bfe45b3365467d01f69cfa8ec4258a3ced05e5bf
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23highres2016_Antarctica_DC8.csvhttps://data.cresis.ku.edu/data/rds/2016_Antarctica_DC8/csv_good/2016_Antarctica_DC8.csvec3b514dfcae265f5b8643eeb3503be8a0a6531e563faf9f12cb67f2b618a74124highres2017_Antarctica_P3.csvhttps://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/csv_good/2017_Antarctica_P3.csv9208a64fefe2f4a6e7f08d44c0af0c35400cd814590c32b8eb02f1545bfc8bec
24highres2017_Antarctica_P3.csvhttps://data.cresis.ku.edu/data/rds/2017_Antarctica_P3/csv_good/2017_Antarctica_P3.csv9208a64fefe2f4a6e7f08d44c0af0c35400cd814590c32b8eb02f1545bfc8bec25highres2017_Antarctica_Basler.csvhttps://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csvc97d0d92f3095ee8c3941d915028728423758594cc95e7b819889b51693f0712
" ], "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "for dataset in datalist.loc[datalist[\"folder\"] == \"highres\"].itertuples():\n", + "for dataset in dataframe.loc[dataframe[\"folder\"] == \"highres\"].itertuples():\n", " path = f\"{dataset.folder}/{dataset.filename}\" #path to download the file to\n", " if not os.path.exists(path=path):\n", " download_to_path(path=path, url=dataset.url)\n", " assert(check_sha256(path=path) == dataset.sha256)\n", - "datalist.loc[datalist[\"folder\"] == \"highres\"].style.format({\"url\": lambda url: f'{url}'})" + "pprint_table(dataframe, \"highres\")" ] }, { @@ -493,57 +536,267 @@ "source": [ "## 2. Process high resolution data into grid format\n", "\n", - "[ASCII Text](https://pdal.io/stages/readers.text.html) ----> [GeoTIFF](https://pdal.io/stages/writers.gdal.html)\n", + "Our processing step involves two stages:\n", + "\n", + "1) Cleaning up the raw **vector** data, performing necessary calculations and reprojections to EPSG:3031.\n", + "\n", + "2) Convert the cleaned vector data table via an interpolation function to a **raster** grid." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 [Raw ASCII Text](https://pdal.io/stages/readers.text.html) to [Clean XYZ table](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#table-data)\n", + "\n", + "![Raw ASCII to Clean Table via pipeline file](https://yuml.me/diagram/scruffy;dir:LR/class/[Raw-ASCII-Text|*.csv/*.txt]->[Pipeline-File|*.json],[Pipeline-File]->[Clean-XYZ-Table|*.xyz])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def ascii_to_xyz(pipeline_file: str) -> pd.DataFrame:\n", + " \"\"\"\n", + " Converts ascii txt/csv files to xyz pandas.DataFrame via\n", + " a JSON Pipeline file similar to the one used by PDAL.\n", + "\n", + " >>> os.makedirs(name=\"/tmp/highres\", exist_ok=True)\n", + " >>> download_to_path(path=\"/tmp/highres/2011_Antarctica_TO.csv\",\n", + " ... url=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv\")\n", + " \n", + " >>> _ = shutil.copy(src=\"highres/20xx_Antarctica_TO.json\", dst=\"/tmp/highres\")\n", + " >>> df = ascii_to_xyz(pipeline_file=\"/tmp/highres/20xx_Antarctica_TO.json\")\n", + " >>> df.head(2)\n", + " x y z\n", + " 0 345580.826265 -1.156471e+06 -377.2340\n", + " 1 345593.322948 -1.156460e+06 -376.6332\n", + " >>> shutil.rmtree(path=\"/tmp/highres\")\n", + " \"\"\"\n", + " assert os.path.exists(pipeline_file)\n", + " assert pipeline_file.endswith((\".json\"))\n", + "\n", + " # Read json file first\n", + " j = json.loads(open(pipeline_file).read())\n", + " jdf = pd.io.json.json_normalize(j, record_path=\"pipeline\")\n", + " jdf = jdf.set_index(keys=\"type\")\n", + " reader = jdf.loc[\"readers.text\"] # check how to read the file(s)\n", + "\n", + " ## Basic table read\n", + " skip = int(reader.skip) # number of header rows to skip\n", + " sep = reader.separator # delimiter to use\n", + " names = reader.header.split(sep=sep) # header/column names as list\n", + " usecols = reader.usecols.split(sep=sep) # column names to use\n", + "\n", + " path_pattern = os.path.join(os.path.dirname(pipeline_file), reader.filename)\n", + " files = [file for file in glob.glob(path_pattern)]\n", + " assert len(files) > 0 # check that there are actually files being matched!\n", + "\n", + " df = pd.concat(\n", + " pd.read_table(f, sep=sep, header=skip, names=names, usecols=usecols)\n", + " for f in files\n", + " )\n", + " df.reset_index(drop=True, inplace=True) # reset index after concatenation\n", + "\n", + " ## Advanced table read with conversions\n", + " try:\n", + " # Perform math operations\n", + " newcol, expr = reader.converters.popitem()\n", + " df[newcol] = df.eval(expr=expr)\n", + " # Drop unneeded columns\n", + " dropcols = reader.dropcols.split(sep=sep)\n", + " df.drop(columns=dropcols, inplace=True)\n", + " except AttributeError:\n", + " pass\n", "\n", - "![Processing pipeline](https://yuml.me/diagram/scruffy;dir:LR/class/[ASCII-Text|*.csv/*.txt/*.grd]->[PDAL-Pipeline|*.json],[PDAL-Pipeline]->[GeoTIFF|*.tif])" + " assert len(df.columns) == 3 # check that we have 3 columns i.e. x, y, z\n", + " df.sort_index(axis=\"columns\", inplace=True) # sort cols alphabetically\n", + " df.set_axis(labels=[\"x\", \"y\", \"z\"], axis=\"columns\", inplace=True) # lower case\n", + "\n", + " ## Reproject x and y coordinates if necessary\n", + " try:\n", + " reproject = jdf.loc[\"filters.reprojection\"]\n", + " p1 = pyproj.Proj(init=reproject.in_srs)\n", + " p2 = pyproj.Proj(init=reproject.out_srs)\n", + " reproj_func = lambda x, y: pyproj.transform(p1=p1, p2=p2, x=x, y=y)\n", + "\n", + " x2, y2 = reproj_func(np.array(df[\"x\"]), np.array(df[\"y\"]))\n", + " df[\"x\"] = pd.Series(x2)\n", + " df[\"y\"] = pd.Series(y2)\n", + "\n", + " except KeyError:\n", + " pass\n", + "\n", + " return df" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 5.52 s, sys: 1.09 s, total: 6.61 s\n", - "Wall time: 3min 44s\n" + "Processing highres/2007tx.json pipeline ... 42995 datapoints\n", + "Processing highres/2010tr.json pipeline ... 84922 datapoints\n", + "Processing highres/201x_Antarctica_Basler.json pipeline ... 2325792 datapoints\n", + "Processing highres/20xx_Antarctica_DC8.json pipeline ... 12840213 datapoints\n", + "Processing highres/20xx_Antarctica_TO.json pipeline ... 2895926 datapoints\n", + "Processing highres/bed_WGS84_grid.json pipeline ... 244279 datapoints\n", + "Processing highres/istarxx.json pipeline ... 396369 datapoints\n" ] } ], "source": [ - "%%time\n", - "# change to highres directory, list all the json pipeline files, run pdal pipeline on each of those files\n", - "!cd highres && ls *.json | xargs -n1 pdal pipeline --nostream -i" + "xyz_dict = {}\n", + "for pf in sorted(glob.glob(\"highres/*.json\")):\n", + " print(f\"Processing {pf} pipeline\", end=' ... ')\n", + " name = os.path.splitext(os.path.basename(pf))[0]\n", + " xyz_dict[name] = ascii_to_xyz(pipeline_file=pf)\n", + " print(f\"{len(xyz_dict[name])} datapoints\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "TODO:\n", - "- use Python bindings instead of shell" + "### 2.2 [Clean XYZ table](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#table-data) to [Raster Grid](https://gmt.soest.hawaii.edu/doc/latest/GMT_Docs.html#grid-files)\n", + "\n", + "![Clean XYZ Table to Raster Grid via interpolation function](https://yuml.me/diagram/scruffy;dir:LR/class/[Clean-XYZ-Table|*.xyz]->[Interpolation-Function],[Interpolation-Function]->[Raster-Grid|*.tif/*.nc])" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_region(xyz_data: pd.DataFrame) -> str:\n", + " \"\"\"\n", + " Gets the bounding box region of an xyz pandas.DataFrame in string\n", + " format xmin/xmax/ymin/ymax rounded to 5 decimal places.\n", + " Used for the -R 'region of interest' parameter in GMT.\n", + " \n", + " >>> xyz_data = pd.DataFrame(np.random.RandomState(seed=42).rand(30).reshape(10, 3))\n", + " >>> get_region(xyz_data=xyz_data)\n", + " '0.05808/0.83244/0.02058/0.95071'\n", + " \"\"\"\n", + " xmin, ymin, _ = xyz_data.min(axis=\"rows\")\n", + " xmax, ymax, _ = xyz_data.max(axis=\"rows\")\n", + " return f\"{xmin:.5f}/{xmax:.5f}/{ymin:.5f}/{ymax:.5f}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def xyz_to_grid(\n", + " xyz_data: pd.DataFrame,\n", + " region: str,\n", + " spacing: int = 250,\n", + " tension: float = 0.35,\n", + " outfile: str = None,\n", + " mask_cell_radius: int = 3,\n", + "):\n", + " \"\"\"\n", + " Performs interpolation of x, y, z point data to a raster grid.\n", + "\n", + " >>> xyz_data = 1000*pd.DataFrame(np.random.RandomState(seed=42).rand(60).reshape(20, 3))\n", + " >>> region = get_region(xyz_data=xyz_data)\n", + " >>> grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)\n", + " >>> grid.to_array().shape\n", + " (1, 5, 5)\n", + " >>> grid.to_array().values\n", + " array([[[403.17618 , 544.92535 , 670.7824 , 980.75055 , 961.47723 ],\n", + " [379.0757 , 459.26407 , 314.38297 , 377.78555 , 546.0469 ],\n", + " [450.67664 , 343.26 , 88.391594, 260.10492 , 452.3337 ],\n", + " [586.09906 , 469.74008 , 216.8168 , 486.9802 , 642.2116 ],\n", + " [451.4794 , 652.7244 , 325.77896 , 879.8973 , 916.7921 ]]],\n", + " dtype=float32)\n", + " \"\"\"\n", + " ## Preprocessing with blockmedian\n", + " with gmt.helpers.GMTTempFile(suffix=\".txt\") as tmpfile:\n", + " with gmt.clib.Session() as lib:\n", + " file_context = lib.virtualfile_from_matrix(matrix=xyz_data.values)\n", + " with file_context as infile:\n", + " kwargs = {\"V\": \"\", \"R\": region, \"I\": f\"{spacing}+e\"}\n", + " arg_str = \" \".join(\n", + " [infile, gmt.helpers.build_arg_string(kwargs), \"->\" + tmpfile.name]\n", + " )\n", + " lib.call_module(module=\"blockmedian\", args=arg_str)\n", + " x, y, z = np.loadtxt(fname=tmpfile.name, unpack=True)\n", + "\n", + " ## XYZ point data to NetCDF grid via GMT surface\n", + " grid = gmt.surface(\n", + " x=x,\n", + " y=y,\n", + " z=z,\n", + " region=region,\n", + " spacing=f\"{spacing}+e\",\n", + " T=tension,\n", + " V=\"\",\n", + " M=f\"{mask_cell_radius}c\",\n", + " )\n", + "\n", + " ## Save grid to NetCDF with projection information\n", + " if outfile is not None:\n", + " grid.to_netcdf(path=outfile) ##TODO add CRS!!\n", + "\n", + " return grid" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 4.63 s, sys: 3.35 s, total: 7.97 s\n", - "Wall time: 7.98 s\n" + "Gridding 2007tx ... done! (1, 266, 74)\n", + "Gridding 2010tr ... done! (1, 92, 115)\n", + "Gridding 201x_Antarctica_Basler ... done! (1, 9062, 7437)\n", + "Gridding 20xx_Antarctica_DC8 ... done! (1, 12388, 15326)\n", + "Gridding 20xx_Antarctica_TO ... done! (1, 7671, 12287)\n", + "Gridding bed_WGS84_grid ... done! (1, 123, 163)\n", + "Gridding istarxx ... done! (1, 552, 377)\n" ] - }, + } + ], + "source": [ + "grid_dict = {}\n", + "for name in xyz_dict.keys():\n", + " print(f\"Gridding {name}\", end=' ... ')\n", + " xyz_data = xyz_dict[name]\n", + " region = get_region(xyz_data)\n", + " grid_dict[name] = xyz_to_grid(xyz_data=xyz_data, region=region, outfile=f\"highres/{name}.nc\")\n", + " print(f\"done! {grid_dict[name].to_array().shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Plot raster grids" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] }, "metadata": { @@ -553,13 +806,12 @@ } ], "source": [ - "%%time\n", - "tifs = glob.glob(\"highres/*.tif\")\n", - "fig, axarr = plt.subplots(nrows=1+((len(tifs)-1)//3), ncols=3, squeeze=False, figsize=(15,15))\n", + "grids = sorted(glob.glob(\"highres/*.nc\"))\n", + "fig, axarr = plt.subplots(nrows=1+((len(grids)-1)//3), ncols=3, squeeze=False, figsize=(15,15))\n", "\n", - "for i, tif in enumerate(tifs):\n", - " with rasterio.open(tif) as raster_source:\n", - " rasterio.plot.show(source=raster_source, cmap='BrBG_r', ax=axarr[i//3,i%3], title=tif)" + "for i, grid in enumerate(grids):\n", + " with rasterio.open(grid) as raster_source:\n", + " rasterio.plot.show(source=raster_source, cmap='BrBG_r', ax=axarr[i//3,i%3], title=grid)" ] }, { @@ -578,65 +830,93 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "def get_window_bounds(filepath:str, height:int=32, width:int=32, step=4) -> list:\n", + "def get_window_bounds(\n", + " filepath: str, height: int = 32, width: int = 32, step: int = 4\n", + ") -> list:\n", " \"\"\"\n", " Reads in a raster and finds tiles for them according to a stepped moving window.\n", " Returns a list of bounding box coordinates corresponding to a tile that looks like\n", " [(minx, miny, maxx, maxy), (minx, miny, maxx, maxy), ...]\n", - " \"\"\"\n", - " assert(height==width) #make sure it's a square!\n", - " assert(height%2==0) #make sure we are passing in an even number\n", " \n", - " with rasterio.open(filepath) as dataset:\n", - " print(f'Tiling: {filepath} ... ', end='')\n", - " #Vectorized 'loop' along the the raster image from top to bottom, and left to right\n", - " \n", - " #Get boolean true/false mask of where the data/nodata pixels lie\n", - " mask = dataset.read(indexes=list(range(1,dataset.count+1)), masked=True).mask\n", - " mask = np.rollaxis(a=mask, axis=0, start=3)[:,:,0] #change to shape (height, width)\n", - " \n", - " #Sliding window view of the input geographical raster image\n", - " window_views = skimage.util.shape.view_as_windows(arr_in=mask, window_shape=(height, width), step=step)\n", - " filled_tiles = ~window_views.any(axis=(-2,-1)) #find tiles which are fully filled, i.e. no blank/NODATA pixels\n", - " tile_indexes = np.argwhere(filled_tiles) #get x and y index of filled tiles\n", - " \n", - " #Convert x,y tile indexes to bounding box coordinates\n", - " windows = [rasterio.windows.Window(col_off=ulx*step, row_off=uly*step, width=width, height=height) for uly, ulx in tile_indexes]\n", - " window_bounds = [rasterio.windows.bounds(window=window, transform=dataset.transform) for window in windows]\n", + " >>> xr.DataArray(\n", + " ... data=np.zeros(shape=(36, 32)),\n", + " ... coords={\"x\": np.arange(1, 37), \"y\": np.arange(1, 33)},\n", + " ... dims=[\"x\", \"y\"],\n", + " ... ).to_netcdf(path=\"/tmp/tmp_wb.nc\")\n", + " >>> get_window_bounds(filepath=\"/tmp/tmp_wb.nc\")\n", + " Tiling: /tmp/tmp_wb.nc ... 2\n", + " [(0.5, 4.5, 32.5, 36.5), (0.5, 0.5, 32.5, 32.5)]\n", + " >>> os.remove(\"/tmp/tmp_wb.nc\")\n", + " \"\"\"\n", + " assert height == width # make sure it's a square!\n", + " assert height % 2 == 0 # make sure we are passing in an even number\n", + "\n", + " with xr.open_rasterio(filepath) as dataset:\n", + " print(f\"Tiling: {filepath} ... \", end=\"\")\n", + " # Vectorized 'loop' along the raster image from top to bottom, and left to right\n", + "\n", + " # Get boolean true/false mask of where the data/nodata pixels lie\n", + " mask = dataset.to_masked_array(copy=False).mask\n", + " mask = np.rollaxis(a=mask, axis=0, start=3)[\n", + " :, :, 0\n", + " ] # change to shape (height, width)\n", + "\n", + " # Sliding window view of the input geographical raster image\n", + " window_views = skimage.util.shape.view_as_windows(\n", + " arr_in=mask, window_shape=(height, width), step=step\n", + " )\n", + " filled_tiles = ~window_views.any(\n", + " axis=(-2, -1)\n", + " ) # find tiles which are fully filled, i.e. no blank/NODATA pixels\n", + " tile_indexes = np.argwhere(filled_tiles) # get x and y index of filled tiles\n", + "\n", + " # Convert x,y tile indexes to bounding box coordinates\n", + " windows = [\n", + " rasterio.windows.Window(\n", + " col_off=ulx * step, row_off=uly * step, width=width, height=height\n", + " )\n", + " for uly, ulx in tile_indexes\n", + " ]\n", + " window_bounds = [\n", + " rasterio.windows.bounds(\n", + " window=window,\n", + " transform=rasterio.Affine(*dataset.transform),\n", + " width=width,\n", + " height=height,\n", + " )\n", + " for window in windows\n", + " ]\n", " print(len(window_bounds))\n", - " \n", + "\n", " return window_bounds" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Tiling: highres/20xx_Antarctica_TO.tif ... 963\n", - "Tiling: highres/2010tr.tif ... 131\n", - "Tiling: highres/bed_WGS84_grid.tif ... 121\n", - "Tiling: highres/20xx_Antarctica_DC8.tif ... 15\n", - "Tiling: highres/201x_Antarctica_Basler.tif ... 762\n", - "Tiling: highres/istarxx.tif ... 119\n", - "Total number of tiles: 2111\n", - "CPU times: user 7.19 s, sys: 1.3 s, total: 8.48 s\n", - "Wall time: 8.48 s\n" + "Tiling: highres/2010tr.nc ... 164\n", + "Tiling: highres/201x_Antarctica_Basler.nc ... 961\n", + "Tiling: highres/20xx_Antarctica_DC8.nc ... 19\n", + "Tiling: highres/20xx_Antarctica_TO.nc ... 989\n", + "Tiling: highres/bed_WGS84_grid.nc ... 172\n", + "Tiling: highres/istarxx.nc ... 175\n", + "Total number of tiles: 2480\n" ] } ], "source": [ - "%%time\n", - "filepaths = glob.glob(\"highres/*.tif\")\n", - "window_bounds = [get_window_bounds(filepath=tif) for tif in filepaths]\n", + "filepaths = sorted([g for g in glob.glob(\"highres/*.nc\") if g != \"highres/2007tx.nc\"])\n", + "window_bounds = [get_window_bounds(filepath=grid) for grid in filepaths]\n", "window_bounds_concat = np.concatenate([w for w in window_bounds]).tolist()\n", "print(f'Total number of tiles: {len(window_bounds_concat)}')" ] @@ -650,25 +930,24 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "" + "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#shapely.geometry.box(*window_bound)\n", "shapely.geometry.MultiPolygon([shapely.geometry.box(*bound) for bound in window_bounds_concat])" ] }, @@ -681,51 +960,86 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "def selective_tile(filepath:str, window_bounds:list, out_shape:tuple=None) -> np.ndarray:\n", + "def selective_tile(\n", + " filepath: str, window_bounds: list, out_shape: tuple = None\n", + ") -> np.ndarray:\n", " \"\"\"\n", " Reads in raster and tiles them selectively.\n", " Tiles will go according to list of window_bounds.\n", - " Output shape can be set to e.g. (16,16) to resample input raster to desired shape/resolution.\n", + " Output shape can be set to e.g. (16,16) to resample input raster to\n", + " some desired shape/resolution.\n", + "\n", + " >>> xr.DataArray(\n", + " ... data=np.random.RandomState(seed=42).rand(64).reshape(8, 8),\n", + " ... coords={\"x\": np.arange(8), \"y\": np.arange(8)},\n", + " ... dims=[\"x\", \"y\"],\n", + " ... ).to_netcdf(path=\"/tmp/tmp_st.nc\", mode=\"w\")\n", + " >>> selective_tile(\n", + " ... filepath=\"/tmp/tmp_st.nc\",\n", + " ... window_bounds=[(1.0, 4.0, 3.0, 6.0), (2.0, 5.0, 4.0, 7.0)],\n", + " ... )\n", + " Tiling: /tmp/tmp_st.nc\n", + " array([[[[0.18485446],\n", + " [0.96958464]],\n", + " \n", + " [[0.4951769 ],\n", + " [0.03438852]]],\n", + " \n", + " \n", + " [[[0.04522729],\n", + " [0.32533032]],\n", + " \n", + " [[0.96958464],\n", + " [0.77513283]]]], dtype=float32)\n", + " >>> os.remove(\"/tmp/tmp_st.nc\")\n", " \"\"\"\n", - " \n", " array_list = []\n", - " \n", + "\n", " with rasterio.open(filepath) as dataset:\n", - " print(f'Tiling: {filepath}')\n", + " print(f\"Tiling: {filepath}\")\n", " for window_bound in window_bounds:\n", - " window = rasterio.windows.from_bounds(*window_bound, transform=dataset.transform, precision=6)\n", - " \n", - " #Read the raster according to the crop window\n", - " array = dataset.read(indexes=list(range(1,dataset.count+1)), masked=True, window=window, out_shape=out_shape)\n", - " array = np.rollaxis(a=array, axis=0, start=3) #change to shape (height, width, 1)\n", - " \n", - " assert(not array.mask.any())\n", - " assert(array.shape[0]==array.shape[1]) #check that height==width\n", + " window = rasterio.windows.from_bounds(\n", + " *window_bound, transform=dataset.transform, precision=None\n", + " ).round_offsets()\n", + "\n", + " # Read the raster according to the crop window\n", + " array = dataset.read(\n", + " indexes=list(range(1, dataset.count + 1)),\n", + " masked=True,\n", + " window=window,\n", + " out_shape=out_shape,\n", + " )\n", + " array = np.rollaxis(\n", + " a=array, axis=0, start=3\n", + " ) # change to shape (height, width, 1)\n", + "\n", + " assert not array.mask.any()\n", + " assert array.shape[0] == array.shape[1] # check that height==width\n", " array_list.append(array.data.astype(dtype=np.float32))\n", - " \n", + "\n", " return np.stack(arrays=array_list)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Tiling: highres/20xx_Antarctica_TO.tif\n", - "Tiling: highres/2010tr.tif\n", - "Tiling: highres/bed_WGS84_grid.tif\n", - "Tiling: highres/20xx_Antarctica_DC8.tif\n", - "Tiling: highres/201x_Antarctica_Basler.tif\n", - "Tiling: highres/istarxx.tif\n", - "(2111, 32, 32, 1) float32\n" + "Tiling: highres/2010tr.nc\n", + "Tiling: highres/201x_Antarctica_Basler.nc\n", + "Tiling: highres/20xx_Antarctica_DC8.nc\n", + "Tiling: highres/20xx_Antarctica_TO.nc\n", + "Tiling: highres/bed_WGS84_grid.nc\n", + "Tiling: highres/istarxx.nc\n", + "(2480, 32, 32, 1) float32\n" ] } ], @@ -744,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -752,7 +1066,7 @@ "output_type": "stream", "text": [ "Tiling: lowres/bedmap2_bed.tif\n", - "(2111, 8, 8, 1) float32\n" + "(2480, 8, 8, 1) float32\n" ] } ], @@ -770,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -778,7 +1092,7 @@ "output_type": "stream", "text": [ "Tiling: misc/REMA_200m_dem_filled.tif\n", - "(2111, 40, 40, 1) float32\n" + "(2480, 40, 40, 1) float32\n" ] } ], @@ -789,7 +1103,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -797,7 +1111,7 @@ "output_type": "stream", "text": [ "Tiling: misc/MEaSUREs_IceFlowSpeed_450m.tif\n", - "(2111, 16, 16, 1) float32\n" + "(2480, 16, 16, 1) float32\n" ] } ], @@ -823,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -845,7 +1159,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -861,7 +1175,7 @@ "name": "stdin", "output_type": "stream", "text": [ - "Enter the code from the webpage: eyJpZCI6ICIyOWI4YzUyNS1lZmM1LTQ5NTItOGQ4Yy03NzQyYTg1YmI1MmEiLCAiY29kZSI6ICI2ODk5YzJjNi1jZjM5LTRiZDgtODkxMS1kZjQxNTk0MWRmOTAifQ==\n" + "Enter the code from the webpage: eyJpZCI6ICIyOWI4YzUyNS1lZmM1LTQ5NTItOGQ4Yy03NzQyYTg1YmI1MmEiLCAiY29kZSI6ICJjN2ViZDU3Mi0xMGFjLTQ0ODItYjk2My02YTUzN2I0NjJlN2YifQ==\n" ] } ], @@ -871,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -883,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -897,38 +1211,35 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0.00/27.2M [00:00 + When we process the data through + And interpolate the xyz data table to + Then a high resolution raster grid is returned + + Examples: ASCII text files to grid + | input_pattern | pipeline_file | output_file | + | bed_WGS84_grid.txt | bed_WGS84_grid.json | bed_WGS84_grid.nc | diff --git a/features/steps/data_prep.py b/features/steps/data_prep.py index fe51446..8cbbe86 100644 --- a/features/steps/data_prep.py +++ b/features/steps/data_prep.py @@ -1,19 +1,55 @@ from behave import given, when, then import os +import rasterio -@given(u"this {url} link to a file hosted on the web") +@given("this {url} link to a file hosted on the web") def set_url(context, url): context.url = url -@when(u"we download it to {filepath}") +@when("we download it to {filepath}") def download_from_url_to_path(context, filepath): context.filepath = filepath context.data_prep.download_to_path(path=filepath, url=context.url) -@then(u"the local file should have this {sha256} checksum") +@then("the local file should have this {sha256} checksum") def check_sha256_of_file(context, sha256): assert context.data_prep.check_sha256(path=context.filepath) == sha256 os.remove(path=context.filepath) # remove downloaded file + + +@given("a collection of raw high resolution datasets {input_pattern}") +def collection_of_high_resolution_datasets(context, input_pattern): + df = context.data_prep.parse_datalist() # retrieve from data_list.yml + subset_df = df[df.filename.str.match(input_pattern)] # pattern match filename + + context.input_files = [] # setup empty list to store path to downloaded input files + for file in subset_df.itertuples(): + filepath = os.path.join(file.folder, file.filename) # join folder and filename + context.data_prep.download_to_path(path=filepath, url=file.url) # download + assert context.data_prep.check_sha256(path=filepath) == file.sha256 + context.input_files.append(filepath) # append filepath to the input list + + +@when("we process the data through {pipeline_file}") +def process_data_through_pipeline_and_get_output(context, pipeline_file): + pf = os.path.join("highres", pipeline_file) # join folder and filename + context.xyz_data = context.data_prep.ascii_to_xyz(pipeline_file=pf) + assert list(context.xyz_data.columns) == ["x", "y", "z"] + + +@when("interpolate the xyz data table to {output_file}") +def interpolate_xyz_data_to_grid(context, output_file): + region = context.data_prep.get_region(context.xyz_data) + context.outfile = os.path.join("highres", output_file) + context.data_prep.xyz_to_grid( + xyz_data=context.xyz_data, region=region, outfile=context.outfile + ) + + +@then("a high resolution raster grid is returned") +def open_raster_grid_to_check(context): + with rasterio.open(context.outfile) as raster_source: + assert raster_source.closed == False # check that it can be opened diff --git a/highres/2007tx.json b/highres/2007tx.json new file mode 100644 index 0000000..7dc87a6 --- /dev/null +++ b/highres/2007tx.json @@ -0,0 +1,12 @@ +{ + "pipeline":[ + { + "type":"readers.text", + "filename":"2007t?.txt", + "separator":"\t", + "skip":1, + "header":"x\ty\tz_surf\ttime\th\th_fc\tz\tz_fc", + "usecols":"x\ty\tz_fc" + } + ] +} \ No newline at end of file diff --git a/highres/2010tr.json b/highres/2010tr.json index 4683b6f..f35809f 100644 --- a/highres/2010tr.json +++ b/highres/2010tr.json @@ -5,16 +5,8 @@ "filename":"2010tr.txt", "separator":"\t", "skip":1, - "header":"x\ty\tz_surf\ttime\th\th_fc\tz_bed\tz_bed_fc\tz-surf" - }, - { - "type":"writers.gdal", - "filename":"2010tr.tif", - "resolution": 250, - "data_type": "float", - "dimension": "z_bed_fc", - "output_type": "idw", - "window_size": 0 + "header":"x\ty\tz_surf\ttime\th\th_fc\tz_bed\tz_bed_fc\tz-surf", + "usecols":"x\ty\tz_bed_fc" } ] } \ No newline at end of file diff --git a/highres/201x_Antarctica_Basler.json b/highres/201x_Antarctica_Basler.json index 33f2cdf..9ccb8dd 100644 --- a/highres/201x_Antarctica_Basler.json +++ b/highres/201x_Antarctica_Basler.json @@ -5,27 +5,15 @@ "filename":"201?_Antarctica_Basler.csv", "separator":",", "skip":1, - "header":"Y,X,TIME,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY" - }, - { - "type":"filters.python", - "script":"custom_filters.py", - "function":"bottom_minus_surface_elevation", - "module":"anything" + "header":"Y,X,TIME,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY", + "usecols":"X,Y,ELEVATION,BOTTOM", + "converters": {"Z": "ELEVATION-BOTTOM"}, + "dropcols":"ELEVATION,BOTTOM" }, { "type":"filters.reprojection", "in_srs":"EPSG:4326", "out_srs":"EPSG:3031" - }, - { - "type":"writers.gdal", - "filename":"201x_Antarctica_Basler.tif", - "resolution": 250, - "data_type": "float", - "dimension": "BOTTOM", - "output_type": "idw", - "window_size": 2 } ] } \ No newline at end of file diff --git a/highres/20xx_Antarctica_DC8.json b/highres/20xx_Antarctica_DC8.json index 5308711..72be914 100644 --- a/highres/20xx_Antarctica_DC8.json +++ b/highres/20xx_Antarctica_DC8.json @@ -5,27 +5,15 @@ "filename":"20??_Antarctica_DC8.csv", "separator":",", "skip":1, - "header":"Y,X,TIME,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY" - }, - { - "type":"filters.python", - "script":"custom_filters.py", - "function":"bottom_minus_surface_elevation", - "module":"anything" + "header":"Y,X,TIME,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY", + "usecols":"X,Y,ELEVATION,BOTTOM", + "converters": {"Z": "ELEVATION-BOTTOM"}, + "dropcols":"ELEVATION,BOTTOM" }, { "type":"filters.reprojection", "in_srs":"EPSG:4326", "out_srs":"EPSG:3031" - }, - { - "type":"writers.gdal", - "filename":"20xx_Antarctica_DC8.tif", - "resolution": 250, - "data_type": "float", - "dimension": "BOTTOM", - "output_type": "idw", - "window_size": 2 } ] } \ No newline at end of file diff --git a/highres/20xx_Antarctica_TO.json b/highres/20xx_Antarctica_TO.json index 5ca5619..258655f 100644 --- a/highres/20xx_Antarctica_TO.json +++ b/highres/20xx_Antarctica_TO.json @@ -5,27 +5,15 @@ "filename":"20??_Antarctica_TO*.csv", "separator":",", "skip":1, - "header":"Y,X,TIME,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY" - }, - { - "type":"filters.python", - "script":"custom_filters.py", - "function":"bottom_minus_surface_elevation", - "module":"anything" + "header":"Y,X,TIME,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY", + "usecols":"X,Y,ELEVATION,BOTTOM", + "converters": {"Z": "ELEVATION-BOTTOM"}, + "dropcols":"ELEVATION,BOTTOM" }, { "type":"filters.reprojection", "in_srs":"EPSG:4326", "out_srs":"EPSG:3031" - }, - { - "type":"writers.gdal", - "filename":"20xx_Antarctica_TO.tif", - "resolution": 250, - "data_type": "float", - "dimension": "BOTTOM", - "output_type": "idw", - "window_size": 2 } ] } \ No newline at end of file diff --git a/highres/README.md b/highres/README.md index e1003f9..95265b8 100644 --- a/highres/README.md +++ b/highres/README.md @@ -4,6 +4,6 @@ Note: This file was automatically generated from [data_list.yml](/data_list.yml) Filename|Location|Resolution|Literature Citation|Data Citation ---|---|---|---|--- -bed_WGS84_grid.txt|Rutford Ice Stream|nan|[King2016Rutford](https://doi.org/10.5194/essd-8-151-2016)|[DOI](https://doi.org/10.5285/54757cbe-0b13-4385-8b31-4dfaa1dab55e) 9 *.txt files|Pine Island Glacier|nan|[Bingham2018PIG](https://doi.org/10.1038/s41467-017-01597-y)| -12 *.csv files|Antarctica|nan|[Shi2010CRESIS](https://doi.org/10.1109/IGARSS.2010.5649518)|[DOI](https://doi.org/10.5067/GDQ0CUCVTE2Q) +bed_WGS84_grid.txt|Rutford Ice Stream|nan|[King2016Rutford](https://doi.org/10.5194/essd-8-151-2016)|[DOI](https://doi.org/10.5285/54757cbe-0b13-4385-8b31-4dfaa1dab55e) +13 *.csv files|Antarctica|nan|[Shi2010CRESIS](https://doi.org/10.1109/IGARSS.2010.5649518)|[DOI](https://doi.org/10.5067/GDQ0CUCVTE2Q) diff --git a/highres/bed_WGS84_grid.json b/highres/bed_WGS84_grid.json index 73ea5a9..ad527dc 100644 --- a/highres/bed_WGS84_grid.json +++ b/highres/bed_WGS84_grid.json @@ -5,15 +5,8 @@ "filename":"bed_WGS84_grid.txt", "separator":"\t", "skip":20, - "header":"x\ty\tz\tcolumn\trow" - }, - { - "type":"writers.gdal", - "filename":"bed_WGS84_grid.tif", - "resolution": 250, - "data_type": "float", - "output_type": "idw", - "window_size": 0 + "header":"x\ty\tz\tcolumn\trow", + "usecols":"x\ty\tz" } ] } \ No newline at end of file diff --git a/highres/custom_filters.py b/highres/custom_filters.py deleted file mode 100644 index 974e7c7..0000000 --- a/highres/custom_filters.py +++ /dev/null @@ -1,10 +0,0 @@ -def bottom_minus_surface_elevation(ins,outs): - """ - Used for CReSIS Radar Depth Sounder (RDS) data. - Calculate actual ice bottom height referenced to WGS84 Ellipsoid. - See https://data.cresis.ku.edu/data/rds/rds_readme.pdf for more info. - """ - zb = ins['BOTTOM'] #range to ice bottom (from sensor) - zs = ins['ELEVATION'] #range to ice surface (from sensor) - outs['BOTTOM'] = zs - zb #actual ice bottom height is Elevation minus Bottom - return True \ No newline at end of file diff --git a/highres/istarxx.json b/highres/istarxx.json index d959fab..d1eeedd 100644 --- a/highres/istarxx.json +++ b/highres/istarxx.json @@ -5,16 +5,8 @@ "filename":"istar??.txt", "separator":"\t", "skip":1, - "header":"x\ty\tz_surf\ttime\th\th_fc\tz_bed\tz_bed_fc" - }, - { - "type":"writers.gdal", - "filename":"istarxx.tif", - "resolution": 250, - "data_type": "float", - "dimension": "z_bed_fc", - "output_type": "idw", - "window_size": 0 + "header":"x\ty\tz_surf\ttime\th\th_fc\tz_bed\tz_bed_fc", + "usecols":"x\ty\tz_bed_fc" } ] } \ No newline at end of file diff --git a/test_ipynb.ipynb b/test_ipynb.ipynb index e9e6fab..7bc0b6a 100644 --- a/test_ipynb.ipynb +++ b/test_ipynb.ipynb @@ -34,22 +34,27 @@ " Unit tests on loaded modules from a .ipynb file.\n", " Uses doctest.\n", " \"\"\"\n", - " assert(path.endswith(\".ipynb\"))\n", - " \n", + " assert path.endswith(\".ipynb\")\n", + "\n", " module = _load_ipynb_modules(ipynb_path=path)\n", " num_failures, num_attempted = doctest.testmod(m=module, verbose=True)\n", " if num_failures > 0:\n", " sys.exit(num_failures)\n", - " \n", - "def _integration_test_ipynb(path: str):\n", + "\n", + "def _integration_test_ipynb(path: str, summary: bool = False):\n", " \"\"\"\n", " Integration tests on various feature behaviours inside a .feature file.\n", " Uses behave.\n", " \"\"\"\n", - " assert(os.path.exists(path=path))\n", - " assert(path.endswith(\".feature\"))\n", - " \n", - " num_failures = behave.__main__.main(f\"--no-summary {path}\")\n", + " assert os.path.exists(path=path)\n", + " assert path.endswith(\".feature\")\n", + "\n", + " if summary == False:\n", + " args = f\"--tags ~@skip --no-summary {path}\"\n", + " elif summary == True:\n", + " args = f\"--tags ~@skip {path}\"\n", + "\n", + " num_failures = behave.__main__.main(args=args)\n", " if num_failures > 0:\n", " sys.exit(num_failures)" ] @@ -74,44 +79,163 @@ "output_type": "stream", "text": [ "Trying:\n", - " download_to_path(path=\"highres/2017_Antarctica_Basler.csv\",\n", - " url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv\")\n", + " os.makedirs(name=\"/tmp/highres\", exist_ok=True)\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " download_to_path(path=\"/tmp/highres/2011_Antarctica_TO.csv\",\n", + " url=\"https://data.cresis.ku.edu/data/rds/2011_Antarctica_TO/csv_good/2011_Antarctica_TO.csv\")\n", + "Expecting:\n", + " \n", + "ok\n", + "Trying:\n", + " _ = shutil.copy(src=\"highres/20xx_Antarctica_TO.json\", dst=\"/tmp/highres\")\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " df = ascii_to_xyz(pipeline_file=\"/tmp/highres/20xx_Antarctica_TO.json\")\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " df.head(2)\n", + "Expecting:\n", + " x y z\n", + " 0 345580.826265 -1.156471e+06 -377.2340\n", + " 1 345593.322948 -1.156460e+06 -376.6332\n", + "ok\n", + "Trying:\n", + " shutil.rmtree(path=\"/tmp/highres\")\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " download_to_path(path=\"highres/Data_20171204_02.csv\",\n", + " url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\")\n", "Expecting:\n", " \n", "ok\n", "Trying:\n", - " check_sha256('highres/2017_Antarctica_Basler.csv')\n", + " check_sha256(\"highres/Data_20171204_02.csv\")\n", "Expecting:\n", " '53cef7a0d28ff92b30367514f27e888efbc32b1bda929981b371d2e00d4c671b'\n", "ok\n", "Trying:\n", - " os.remove(path=\"highres/2017_Antarctica_Basler.csv\")\n", + " os.remove(path=\"highres/Data_20171204_02.csv\")\n", "Expecting nothing\n", "ok\n", "Trying:\n", - " download_to_path(path=\"highres/2017_Antarctica_Basler.csv\",\n", - " url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv\")\n", + " download_to_path(path=\"highres/Data_20171204_02.csv\",\n", + " url=\"https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv\")\n", "Expecting:\n", " \n", "ok\n", "Trying:\n", - " open('highres/2017_Antarctica_Basler.csv').readlines()\n", + " open(\"highres/Data_20171204_02.csv\").readlines()\n", "Expecting:\n", " ['LAT,LON,UTCTIMESOD,THICK,ELEVATION,FRAME,SURFACE,BOTTOM,QUALITY\\n']\n", "ok\n", "Trying:\n", - " os.remove(path=\"highres/2017_Antarctica_Basler.csv\")\n", + " os.remove(path=\"highres/Data_20171204_02.csv\")\n", "Expecting nothing\n", "ok\n", - "3 items had no tests:\n", + "Trying:\n", + " xyz_data = pd.DataFrame(np.random.RandomState(seed=42).rand(30).reshape(10, 3))\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " get_region(xyz_data=xyz_data)\n", + "Expecting:\n", + " '0.05808/0.83244/0.02058/0.95071'\n", + "ok\n", + "Trying:\n", + " xr.DataArray(\n", + " data=np.zeros(shape=(36, 32)),\n", + " coords={\"x\": np.arange(1, 37), \"y\": np.arange(1, 33)},\n", + " dims=[\"x\", \"y\"],\n", + " ).to_netcdf(path=\"/tmp/tmp_wb.nc\")\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " get_window_bounds(filepath=\"/tmp/tmp_wb.nc\")\n", + "Expecting:\n", + " Tiling: /tmp/tmp_wb.nc ... 2\n", + " [(0.5, 4.5, 32.5, 36.5), (0.5, 0.5, 32.5, 32.5)]\n", + "ok\n", + "Trying:\n", + " os.remove(\"/tmp/tmp_wb.nc\")\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " xr.DataArray(\n", + " data=np.random.RandomState(seed=42).rand(64).reshape(8, 8),\n", + " coords={\"x\": np.arange(8), \"y\": np.arange(8)},\n", + " dims=[\"x\", \"y\"],\n", + " ).to_netcdf(path=\"/tmp/tmp_st.nc\", mode=\"w\")\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " selective_tile(\n", + " filepath=\"/tmp/tmp_st.nc\",\n", + " window_bounds=[(1.0, 4.0, 3.0, 6.0), (2.0, 5.0, 4.0, 7.0)],\n", + " )\n", + "Expecting:\n", + " Tiling: /tmp/tmp_st.nc\n", + " array([[[[0.18485446],\n", + " [0.96958464]],\n", + " \n", + " [[0.4951769 ],\n", + " [0.03438852]]],\n", + " \n", + " \n", + " [[[0.04522729],\n", + " [0.32533032]],\n", + " \n", + " [[0.96958464],\n", + " [0.77513283]]]], dtype=float32)\n", + "ok\n", + "Trying:\n", + " os.remove(\"/tmp/tmp_st.nc\")\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " xyz_data = 1000*pd.DataFrame(np.random.RandomState(seed=42).rand(60).reshape(20, 3))\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " region = get_region(xyz_data=xyz_data)\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " grid = xyz_to_grid(xyz_data=xyz_data, region=region, spacing=250)\n", + "Expecting nothing\n", + "ok\n", + "Trying:\n", + " grid.to_array().shape\n", + "Expecting:\n", + " (1, 5, 5)\n", + "ok\n", + "Trying:\n", + " grid.to_array().values\n", + "Expecting:\n", + " array([[[403.17618 , 544.92535 , 670.7824 , 980.75055 , 961.47723 ],\n", + " [379.0757 , 459.26407 , 314.38297 , 377.78555 , 546.0469 ],\n", + " [450.67664 , 343.26 , 88.391594, 260.10492 , 452.3337 ],\n", + " [586.09906 , 469.74008 , 216.8168 , 486.9802 , 642.2116 ],\n", + " [451.4794 , 652.7244 , 325.77896 , 879.8973 , 916.7921 ]]],\n", + " dtype=float32)\n", + "ok\n", + "2 items had no tests:\n", " data_prep\n", - " data_prep.get_window_bounds\n", - " data_prep.selective_tile\n", - "2 items passed all tests:\n", + " data_prep.parse_datalist\n", + "7 items passed all tests:\n", + " 6 tests in data_prep.ascii_to_xyz\n", " 3 tests in data_prep.check_sha256\n", " 3 tests in data_prep.download_to_path\n", - "6 tests in 5 items.\n", - "6 passed and 0 failed.\n", + " 2 tests in data_prep.get_region\n", + " 3 tests in data_prep.get_window_bounds\n", + " 3 tests in data_prep.selective_tile\n", + " 5 tests in data_prep.xyz_to_grid\n", + "25 tests in 9 items.\n", + "25 passed and 0 failed.\n", "Test passed.\n" ] } @@ -301,10 +425,21 @@ " In order to have reproducible data inputs for everyone\n", " As a data scientist,\n", " We want to share cryptographically secured pieces of the datasets\n", - " Scenario Outline: Download and check data -- @1.1 Files to download and check # features/data_prep.feature:15\n", - " Given this https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/2017_Antarctica_Basler.csv link to a file hosted on the web # features/steps/data_prep.py:5\n", - " When we download it to highres/2017_Antarctica_Basler.csv # features/steps/data_prep.py:10\n", - " Then the local file should have this 53cef7a0d28ff92b30367514f27e888efbc32b1bda929981b371d2e00d4c671b checksum # features/steps/data_prep.py:16\n", + " Scenario Outline: Download and check data -- @1.1 Files to download and check # features/data_prep.feature:15\n", + " Given this https://data.cresis.ku.edu/data/rds/2017_Antarctica_Basler/csv_good/Data_20171204_02.csv link to a file hosted on the web # features/steps/data_prep.py:6\n", + " When we download it to highres/Data_20171204_02.csv # features/steps/data_prep.py:11\n", + " Then the local file should have this 53cef7a0d28ff92b30367514f27e888efbc32b1bda929981b371d2e00d4c671b checksum # features/steps/data_prep.py:17\n", + "\n", + " Scenario Outline: Download and check data -- @1.2 Files to download and check # features/data_prep.feature:16\n", + " Given this http://ramadda.nerc-bas.ac.uk/repository/entry/get/Polar%20Data%20Centre/DOI/Rutford%20Ice%20Stream%20bed%20elevation%20DEM%20from%20radar%20data/bed_WGS84_grid.txt?entryid=synth%3A54757cbe-0b13-4385-8b31-4dfaa1dab55e%3AL2JlZF9XR1M4NF9ncmlkLnR4dA%3D%3D link to a file hosted on the web # features/steps/data_prep.py:6\n", + " When we download it to highres/bed_WGS84_grid.txt # features/steps/data_prep.py:11\n", + " Then the local file should have this 7396e56cda5adb82cecb01f0b3e01294ed0aa6489a9629f3f7e8858ea6cb91cf checksum # features/steps/data_prep.py:17\n", + "\n", + " Scenario Outline: Grid datasets -- @1.1 ASCII text files to grid # features/data_prep.feature:26\n", + " Given a collection of raw high resolution datasets bed_WGS84_grid.txt # features/steps/data_prep.py:23\n", + " When we process the data through bed_WGS84_grid.json # features/steps/data_prep.py:36\n", + " And interpolate the xyz data table to bed_WGS84_grid.nc # features/steps/data_prep.py:43\n", + " Then a high resolution raster grid is returned # features/steps/data_prep.py:52\n", "\n" ] }