From 4725d7b1f6bc63797cb02b3472f231a7933ae76a Mon Sep 17 00:00:00 2001 From: Mostafa Farrag Date: Wed, 27 Dec 2023 20:43:05 +0100 Subject: [PATCH] Band statistics (#60) * rename datacube object to cube in all tests * add iloc method to dataset class * free the version number for gdal in ci * create stats method * fix typos * correct typo * add docs to stats method * add stats and _iloc to the graph * correct typo * update checklist files --- .github/workflows/pypi-deployment.yml | 4 +- HISTORY.rst | 7 + README.md | 4 +- docs/dataset.rst | 78 +- ...-arrributes.png => dataset-attributes.png} | Bin docs/pyramids.drawio | 186 +++- examples/notebooks/01dataset.ipynb | 810 +++++++++--------- pyramids/dataset.py | 80 +- setup.py | 2 +- tests/conftest.py | 52 ++ tests/dataset/test_datacube.py | 181 ++-- tests/dataset/test_dataset.py | 47 + 12 files changed, 891 insertions(+), 560 deletions(-) rename docs/images/schemes/{dataset-arrributes.png => dataset-attributes.png} (100%) diff --git a/.github/workflows/pypi-deployment.yml b/.github/workflows/pypi-deployment.yml index 2c906792f..e2dde69a3 100644 --- a/.github/workflows/pypi-deployment.yml +++ b/.github/workflows/pypi-deployment.yml @@ -24,7 +24,7 @@ jobs: run: | python -m pip install --upgrade pip pip install --no-cache-dir Cython - pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL==3.7.1 + pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL #==3.7.1 - name: Test GDAL installation run: | @@ -68,7 +68,7 @@ jobs: run: | python -m pip install --upgrade pip pip install --no-cache-dir Cython - pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL==3.7.1 + pip install --find-links=https://girder.github.io/large_image_wheels --no-cache GDAL #==3.7.1 - name: Test GDAL installation run: | diff --git a/HISTORY.rst b/HISTORY.rst index e0ef64457..68a82809e 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -123,3 +123,10 @@ Dataset """"""" * revert the convert_longitude method to not use the gdal_wrap method as it is not working with the new version of gdal (newer tan 3.7.1). * bump up versions. + +0.5.2 (2023-12-27) +------------------ +Dataset +""""""" +* add _iloc method to get the gdal band object by index. +* add stats method to calculate the statistics of the raster bands. diff --git a/README.md b/README.md index 11a55bf48..4fd2ea115 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ Installing pyramids Installing `pyramids` from the `conda-forge` channel can be achieved by: ``` -conda install -c conda-forge pyramids=0.5.1 +conda install -c conda-forge pyramids=0.5.2 ``` It is possible to list all of the versions of `pyramids` available on your platform with: @@ -68,7 +68,7 @@ pip install git+https://github.com/Serapieum-of-alex/pyramids to install the last release you can easly use pip ``` -pip install pyramids-gis==0.5.1 +pip install pyramids-gis==0.5.2 ``` Quick start diff --git a/docs/dataset.rst b/docs/dataset.rst index e1a292ff0..6c077f75e 100644 --- a/docs/dataset.rst +++ b/docs/dataset.rst @@ -16,7 +16,7 @@ dataset - The raster could have different variables (like netcdf file) and these variable can have similar or different dimensions. -- DataCube represent a stack of rasters which have the same dimensions, contains data that have same dimensions (rows +- DataCube represent a stack of raster's which have the same dimensions, contains data that have same dimensions (rows & columns). @@ -24,7 +24,7 @@ dataset Dataset ******* -- The main purpose of the `Dataset` object is to deal with raster objects, single or multibands, has variables/subsets +- The main purpose of the `Dataset` object is to deal with raster objects, single or multi-bands, has variables/subsets like netcdf file or has one variable like most GeoTIFF files. .. image:: /images/dataset/dataset.png @@ -86,13 +86,13 @@ read_file cbar_label="Elevation (m)") -Dataset objest attributes +Dataset object attributes ========================= - The Dataset object has the following attributes, which enables you to access all the stored data in you raster file (GeoTIFF/NetCDF/ASCII) -.. image:: /images/schemes/dataset-arrributes.png +.. image:: /images/schemes/dataset-attributes.png :width: 150pt :alt: dataset attributes :align: center @@ -170,7 +170,7 @@ pivot_point geotransform ------------ - geotransform data of the upper left corner of the raster - (minimum lon/x, pixelsize, rotation, maximum lat/y, rotation, pixelsize). + (minimum lon/x, pixel-size, rotation, maximum lat/y, rotation, pixel-size). .. code:: py @@ -389,8 +389,8 @@ Parameters arr : [array], optional numpy array. The default is ''. geo : [list], optional - geotransform list [minimum lon, pixelsize, rotation, maximum lat, rotation, - pixelsize]. The default is ''. + geotransform list [minimum lon, pixel-size, rotation, maximum lat, rotation, + pixel-size]. The default is ''. nodatavalue : TYPE, optional DESCRIPTION. The default is -9999. epsg: [integer] @@ -408,7 +408,7 @@ Returns .. code:: py - src = Raster.createRaster(arr=arr, geo=geo, epsg=str(epsg), nodatavalue=nodataval) + src = Raster.createRaster(arr=arr, geo=geo, epsg=str(epsg), nodatavalue=no_data_val) Map.plot(src, title="Flow Accumulation") @@ -418,7 +418,7 @@ Returns dataset_like ------------ - `dataset_like` method creates a Geotiff raster like another input raster, new raster will have the same projection, - coordinates or the top left corner of the original raster, cell size, nodata velue, and number of rows and columns + coordinates or the top left corner of the original raster, cell size, nodata value, and number of rows and columns the raster and the dem should have the same number of columns and rows Parameters @@ -533,10 +533,28 @@ read_array -3.402823e+38, -3.402823e+38, -3.402823e+38, -3.402823e+38, -3.402823e+38, -3.402823e+38]], dtype=float32) +Band statistics (stats) +--------------- +- To get a summary statistics (min, max, mean, std) of a band/all bands. +- The method returns a `DataFrame` of statistics values of each band, the dataframe has the following columns: + [min, max, mean, std], the index of the dataframe is the band names. + +.. code:: py + era5_image = "tests/data/geotiff/era5_land_monthly_averaged.tif" + dataset = Dataset.read_file(era5_image) + stats = dataset.stats() + print(stats) + >>> min max mean std + >>> Band_1 270.369720 270.762299 270.551361 0.154270 + >>> Band_2 269.611938 269.744751 269.673645 0.043788 + >>> Band_3 273.641479 274.168823 273.953979 0.198447 + >>> Band_4 273.991516 274.540344 274.310669 0.205754 + + Write raster to disk ==================== -to wtite the dataset object to disk using any of the raster formats (GeoTIFF/NetCDF/ASCII), you can use the `to_file` +to write the dataset object to disk using any of the raster formats (GeoTIFF/NetCDF/ASCII), you can use the `to_file` method. .. image:: /images/schemes/write-to-disk.png @@ -552,7 +570,7 @@ to_file Parameters ^^^^^^^^^^ path: [str] - a path includng the name of the raster and extention. + a path including the name of the raster and extension. >>> path = "data/cropped.tif" driver: [str] driver = "geotiff"/"ascii"/"netcdf". @@ -612,7 +630,7 @@ Spatial properties convert_longitude ----------------- -- some files (espicially netcdf files) uses longitude values from 0 degrees to 360 degrees, instead of the usual, +- some files (especially netcdf files) uses longitude values from 0 degrees to 360 degrees, instead of the usual, GIS-standard, arrangement of -180 degrees to 180 degrees for longitude centered on the Prime Meridian, and -90 degrees to 90 degrees for latitude centered on the Equator. the `convert_longitude` method corrects such behavior. @@ -686,8 +704,8 @@ Returns dst = Raster.resampleRaster(src, cell_size, resample_technique="bilinear") dst_arr = Raster.read_array(dst) - _, newgeo = Raster.getProjectionData(dst) - print("New cell size is " + str(newgeo[1])) + _, new_geo = Raster.getProjectionData(dst) + print("New cell size is " + str(new_geo[1])) Map.plot(dst, title="Flow Accumulation") Original Cell Size =4000.0 @@ -700,7 +718,7 @@ Returns to_crs ------ -- `to_crs` reprojects a raster to any projection (default the WGS84 web mercator projection, without resampling) +- `to_crs` re-projects a raster to any projection (default the WGS84 web mercator projection, without resampling) The function returns a GDAL in-memory file object, where you can ReadAsArray etc. Parameters @@ -714,7 +732,7 @@ Parameters "nearest neibour" for nearest neighbour,"cubic" for cubic convolution, "bilinear" for bilinear maintain_alighment : [bool] - True to maintain the number of rows and columns of the raster the same after reprojection. Default is False. + True to maintain the number of rows and columns of the raster the same after re-projection. Default is False. Returns ^^^^^^^ @@ -726,9 +744,9 @@ Returns print("current EPSG - " + str(epsg)) to_epsg = 4326 dst = Raster.projectRaster(src, to_epsg=to_epsg, option=1) - newepsg, newgeo = Raster.getProjectionData(dst) - print("New EPSG - " + str(newepsg)) - print("New Geotransform - " + str(newgeo)) + new_epsg, new_geo = Raster.getProjectionData(dst) + print("New EPSG - " + str(new_epsg)) + print("New Geotransform - " + str(new_geo)) current EPSG - 32618 New EPSG - 4326 @@ -740,9 +758,9 @@ Returns .. code:: py dst = Raster.projectRaster(src, to_epsg=to_epsg, option=2) - newepsg, newgeo = Raster.getProjectionData(dst) - print("New EPSG - " + str(newepsg)) - print("New Geotransform - " + str(newgeo)) + new_epsg, new_geo = Raster.getProjectionData(dst) + print("New EPSG - " + str(new_epsg)) + print("New Geotransform - " + str(new_geo)) New EPSG - 4326 New Geotransform - (-75.60441003848668, 0.03611587177268461, 0.0, 4.704560448076901, 0.0, -0.03611587177268461) @@ -754,9 +772,9 @@ crop Crop array using a raster ^^^^^^^^^^^^^^^^^^^^^^^^^ -- `crop` clip/crop (matches the location of nodata value from src raster to dst raster), Both rasters have to +- `crop` clip/crop (matches the location of nodata value from src raster to dst raster), Both raster's have to have the same dimensions (no of rows & columns) so MatchRasterAlignment should be used prior to this function to - align both rasters. + align both raster's. Parameters """""""""" @@ -1022,7 +1040,7 @@ Parameters path: [str] a path to ascii file. classes_map: [str/array] - a path includng the name of the ASCII and extention, or an array + a path including the name of the ASCII and extension, or an array >>> path = "classes.asc" exclude_value: [Numeric] values you want to exclude from extracted values. @@ -1034,7 +1052,7 @@ occupied_cells_only: [Bool] Returns ^^^^^^^ ExtractedValues: [Dict] - dictonary with a list of values in the basemap as keys + dictionary with a list of values in the basemap as keys and for each key a list of all the intersected values in the maps from the path. NonZeroCells: [dataframe] @@ -1057,8 +1075,8 @@ To extract the ExtractedValues, Cells = R.OverlayMap(Path+"DepthMax22489.zip", BaseMapF,ExcludedValue, Compressed,OccupiedCellsOnly) -Mathmatical operations -====================== +Mathematical operations +======================= .. image:: /images/schemes/math-operations.png :width: 150pt @@ -1127,7 +1145,7 @@ Returns .. code:: py - path = "examples/data/fillrasterexample.tif" + path = "examples/data/fill-raster-example.tif" value = 20 Raster.rasterFill(src, value, save_to=path) @@ -1191,7 +1209,7 @@ color_table ----------- - The `color_table` property in the `Dataset` object can assign a certain symbology to each band in the raster. -- To assign a certain symbology you have to have to create a `Dataframe` containing the values and coresponding +- To assign a certain symbology you have to have to create a `Dataframe` containing the values and corresponding colors (hexadecimal number) for each band in the raster. - assigning a color_table to the raster file will help when opening the file in GIS software like QGIS or ArcGIS, the raster will be displayed with the colors you assigned to it.without the need to assign the colors manually. diff --git a/docs/images/schemes/dataset-arrributes.png b/docs/images/schemes/dataset-attributes.png similarity index 100% rename from docs/images/schemes/dataset-arrributes.png rename to docs/images/schemes/dataset-attributes.png diff --git a/docs/pyramids.drawio b/docs/pyramids.drawio index 35ba1cbbb..72c108890 100644 --- a/docs/pyramids.drawio +++ b/docs/pyramids.drawio @@ -1,6 +1,6 @@ - + - + @@ -522,7 +522,7 @@ - + @@ -1063,7 +1063,7 @@ - + @@ -1250,25 +1250,31 @@ - + - + - + - + - + + + + + + + @@ -1339,7 +1345,7 @@ - + @@ -1400,7 +1406,7 @@ - + @@ -1409,15 +1415,21 @@ - + - + - + + + + + + + @@ -1456,7 +1468,7 @@ - + @@ -1690,7 +1702,7 @@ - + @@ -1703,12 +1715,15 @@ - + - + + + + @@ -1806,11 +1821,31 @@ + + + + + + + + + + + + + + + + + + + + - + @@ -1837,7 +1872,7 @@ - + @@ -1850,20 +1885,20 @@ - + - + - + - + @@ -1959,12 +1994,12 @@ - + - + @@ -1974,15 +2009,27 @@ - + - + - + + + + + + + + + + + + + @@ -2036,7 +2083,7 @@ - + @@ -2064,6 +2111,85 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/notebooks/01dataset.ipynb b/examples/notebooks/01dataset.ipynb index f4a8d8e3e..c5e520ba7 100644 --- a/examples/notebooks/01dataset.ipynb +++ b/examples/notebooks/01dataset.ipynb @@ -1,408 +1,408 @@ { - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Dataset" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "

\n", - " \"dataset\n", - "

" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "from pyramids.dataset import Dataset\n", - "path = r\"../../examples/data/geotiff/noah-precipitation-1979.tif\"" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Read any raster format" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "dataset = Dataset.read_file(path)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Explore Dataset properties" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " File: ../../examples/data/geotiff/noah-precipitation-1979.tif\n", - " Cell size: 0.5\n", - " EPSG: 4326\n", - " Variables: {}\n", - " Number of Bands: 4\n", - " Band names: ['Band_1', 'Band_2', 'Band_3', 'Band_4']\n", - " Dimension: 360 * 720\n", - " Mask: -9.969209968386869e+36\n", - " Data type: 6\n", - " \n" - ] - } - ], - "source": [ - "print(dataset)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Plot Dataset" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = dataset.plot(\n", - " band=0, figsize=(10, 5), title=\"Noah daily Precipitation 1979-01-01\", cbar_label=\"Raindall mm/day\", vmax=30,\n", - " cbar_length=0.85\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Dataset dimension" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset dimensions: (4, 360, 720)\n", - "Dataset rows: 360\n", - "Dataset columns: 720\n", - "Dataset number of bands: 4\n" - ] - } - ], - "source": [ - "print(f\"Dataset dimensions: {dataset.shape}\")\n", - "print(f\"Dataset rows: {dataset.rows}\")\n", - "print(f\"Dataset columns: {dataset.columns}\")\n", - "print(f\"Dataset number of bands: {dataset.band_count}\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cell size: 0.5\n" - ] - } - ], - "source": [ - "print(f\"Cell size: {dataset.cell_size}\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "['Band_1', 'Band_2', 'Band_3', 'Band_4']" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.band_names" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "### Dataset spatial properties" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EPSG: 4326\n", - "Coordinate reference system: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\n" - ] - } - ], - "source": [ - "print(f\"EPSG: {dataset.epsg}\")\n", - "print(f\"Coordinate reference system: {dataset.crs}\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 21, - "outputs": [ - { - "data": { - "text/plain": "array([2.5000e-01, 7.5000e-01, 1.2500e+00, 1.7500e+00, 2.2500e+00,\n 2.7500e+00, 3.2500e+00, 3.7500e+00, 4.2500e+00, 4.7500e+00,\n 5.2500e+00, 5.7500e+00, 6.2500e+00, 6.7500e+00, 7.2500e+00,\n 7.7500e+00, 8.2500e+00, 8.7500e+00, 9.2500e+00, 9.7500e+00,\n 1.0250e+01, 1.0750e+01, 1.1250e+01, 1.1750e+01, 1.2250e+01,\n 1.2750e+01, 1.3250e+01, 1.3750e+01, 1.4250e+01, 1.4750e+01,\n 1.5250e+01, 1.5750e+01, 1.6250e+01, 1.6750e+01, 1.7250e+01,\n 1.7750e+01, 1.8250e+01, 1.8750e+01, 1.9250e+01, 1.9750e+01,\n 2.0250e+01, 2.0750e+01, 2.1250e+01, 2.1750e+01, 2.2250e+01,\n 2.2750e+01, 2.3250e+01, 2.3750e+01, 2.4250e+01, 2.4750e+01,\n 2.5250e+01, 2.5750e+01, 2.6250e+01, 2.6750e+01, 2.7250e+01,\n 2.7750e+01, 2.8250e+01, 2.8750e+01, 2.9250e+01, 2.9750e+01,\n 3.0250e+01, 3.0750e+01, 3.1250e+01, 3.1750e+01, 3.2250e+01,\n 3.2750e+01, 3.3250e+01, 3.3750e+01, 3.4250e+01, 3.4750e+01,\n 3.5250e+01, 3.5750e+01, 3.6250e+01, 3.6750e+01, 3.7250e+01,\n 3.7750e+01, 3.8250e+01, 3.8750e+01, 3.9250e+01, 3.9750e+01,\n 4.0250e+01, 4.0750e+01, 4.1250e+01, 4.1750e+01, 4.2250e+01,\n 4.2750e+01, 4.3250e+01, 4.3750e+01, 4.4250e+01, 4.4750e+01,\n 4.5250e+01, 4.5750e+01, 4.6250e+01, 4.6750e+01, 4.7250e+01,\n 4.7750e+01, 4.8250e+01, 4.8750e+01, 4.9250e+01, 4.9750e+01,\n 5.0250e+01, 5.0750e+01, 5.1250e+01, 5.1750e+01, 5.2250e+01,\n 5.2750e+01, 5.3250e+01, 5.3750e+01, 5.4250e+01, 5.4750e+01,\n 5.5250e+01, 5.5750e+01, 5.6250e+01, 5.6750e+01, 5.7250e+01,\n 5.7750e+01, 5.8250e+01, 5.8750e+01, 5.9250e+01, 5.9750e+01,\n 6.0250e+01, 6.0750e+01, 6.1250e+01, 6.1750e+01, 6.2250e+01,\n 6.2750e+01, 6.3250e+01, 6.3750e+01, 6.4250e+01, 6.4750e+01,\n 6.5250e+01, 6.5750e+01, 6.6250e+01, 6.6750e+01, 6.7250e+01,\n 6.7750e+01, 6.8250e+01, 6.8750e+01, 6.9250e+01, 6.9750e+01,\n 7.0250e+01, 7.0750e+01, 7.1250e+01, 7.1750e+01, 7.2250e+01,\n 7.2750e+01, 7.3250e+01, 7.3750e+01, 7.4250e+01, 7.4750e+01,\n 7.5250e+01, 7.5750e+01, 7.6250e+01, 7.6750e+01, 7.7250e+01,\n 7.7750e+01, 7.8250e+01, 7.8750e+01, 7.9250e+01, 7.9750e+01,\n 8.0250e+01, 8.0750e+01, 8.1250e+01, 8.1750e+01, 8.2250e+01,\n 8.2750e+01, 8.3250e+01, 8.3750e+01, 8.4250e+01, 8.4750e+01,\n 8.5250e+01, 8.5750e+01, 8.6250e+01, 8.6750e+01, 8.7250e+01,\n 8.7750e+01, 8.8250e+01, 8.8750e+01, 8.9250e+01, 8.9750e+01,\n 9.0250e+01, 9.0750e+01, 9.1250e+01, 9.1750e+01, 9.2250e+01,\n 9.2750e+01, 9.3250e+01, 9.3750e+01, 9.4250e+01, 9.4750e+01,\n 9.5250e+01, 9.5750e+01, 9.6250e+01, 9.6750e+01, 9.7250e+01,\n 9.7750e+01, 9.8250e+01, 9.8750e+01, 9.9250e+01, 9.9750e+01,\n 1.0025e+02, 1.0075e+02, 1.0125e+02, 1.0175e+02, 1.0225e+02,\n 1.0275e+02, 1.0325e+02, 1.0375e+02, 1.0425e+02, 1.0475e+02,\n 1.0525e+02, 1.0575e+02, 1.0625e+02, 1.0675e+02, 1.0725e+02,\n 1.0775e+02, 1.0825e+02, 1.0875e+02, 1.0925e+02, 1.0975e+02,\n 1.1025e+02, 1.1075e+02, 1.1125e+02, 1.1175e+02, 1.1225e+02,\n 1.1275e+02, 1.1325e+02, 1.1375e+02, 1.1425e+02, 1.1475e+02,\n 1.1525e+02, 1.1575e+02, 1.1625e+02, 1.1675e+02, 1.1725e+02,\n 1.1775e+02, 1.1825e+02, 1.1875e+02, 1.1925e+02, 1.1975e+02,\n 1.2025e+02, 1.2075e+02, 1.2125e+02, 1.2175e+02, 1.2225e+02,\n 1.2275e+02, 1.2325e+02, 1.2375e+02, 1.2425e+02, 1.2475e+02,\n 1.2525e+02, 1.2575e+02, 1.2625e+02, 1.2675e+02, 1.2725e+02,\n 1.2775e+02, 1.2825e+02, 1.2875e+02, 1.2925e+02, 1.2975e+02,\n 1.3025e+02, 1.3075e+02, 1.3125e+02, 1.3175e+02, 1.3225e+02,\n 1.3275e+02, 1.3325e+02, 1.3375e+02, 1.3425e+02, 1.3475e+02,\n 1.3525e+02, 1.3575e+02, 1.3625e+02, 1.3675e+02, 1.3725e+02,\n 1.3775e+02, 1.3825e+02, 1.3875e+02, 1.3925e+02, 1.3975e+02,\n 1.4025e+02, 1.4075e+02, 1.4125e+02, 1.4175e+02, 1.4225e+02,\n 1.4275e+02, 1.4325e+02, 1.4375e+02, 1.4425e+02, 1.4475e+02,\n 1.4525e+02, 1.4575e+02, 1.4625e+02, 1.4675e+02, 1.4725e+02,\n 1.4775e+02, 1.4825e+02, 1.4875e+02, 1.4925e+02, 1.4975e+02,\n 1.5025e+02, 1.5075e+02, 1.5125e+02, 1.5175e+02, 1.5225e+02,\n 1.5275e+02, 1.5325e+02, 1.5375e+02, 1.5425e+02, 1.5475e+02,\n 1.5525e+02, 1.5575e+02, 1.5625e+02, 1.5675e+02, 1.5725e+02,\n 1.5775e+02, 1.5825e+02, 1.5875e+02, 1.5925e+02, 1.5975e+02,\n 1.6025e+02, 1.6075e+02, 1.6125e+02, 1.6175e+02, 1.6225e+02,\n 1.6275e+02, 1.6325e+02, 1.6375e+02, 1.6425e+02, 1.6475e+02,\n 1.6525e+02, 1.6575e+02, 1.6625e+02, 1.6675e+02, 1.6725e+02,\n 1.6775e+02, 1.6825e+02, 1.6875e+02, 1.6925e+02, 1.6975e+02,\n 1.7025e+02, 1.7075e+02, 1.7125e+02, 1.7175e+02, 1.7225e+02,\n 1.7275e+02, 1.7325e+02, 1.7375e+02, 1.7425e+02, 1.7475e+02,\n 1.7525e+02, 1.7575e+02, 1.7625e+02, 1.7675e+02, 1.7725e+02,\n 1.7775e+02, 1.7825e+02, 1.7875e+02, 1.7925e+02, 1.7975e+02,\n 1.8025e+02, 1.8075e+02, 1.8125e+02, 1.8175e+02, 1.8225e+02,\n 1.8275e+02, 1.8325e+02, 1.8375e+02, 1.8425e+02, 1.8475e+02,\n 1.8525e+02, 1.8575e+02, 1.8625e+02, 1.8675e+02, 1.8725e+02,\n 1.8775e+02, 1.8825e+02, 1.8875e+02, 1.8925e+02, 1.8975e+02,\n 1.9025e+02, 1.9075e+02, 1.9125e+02, 1.9175e+02, 1.9225e+02,\n 1.9275e+02, 1.9325e+02, 1.9375e+02, 1.9425e+02, 1.9475e+02,\n 1.9525e+02, 1.9575e+02, 1.9625e+02, 1.9675e+02, 1.9725e+02,\n 1.9775e+02, 1.9825e+02, 1.9875e+02, 1.9925e+02, 1.9975e+02,\n 2.0025e+02, 2.0075e+02, 2.0125e+02, 2.0175e+02, 2.0225e+02,\n 2.0275e+02, 2.0325e+02, 2.0375e+02, 2.0425e+02, 2.0475e+02,\n 2.0525e+02, 2.0575e+02, 2.0625e+02, 2.0675e+02, 2.0725e+02,\n 2.0775e+02, 2.0825e+02, 2.0875e+02, 2.0925e+02, 2.0975e+02,\n 2.1025e+02, 2.1075e+02, 2.1125e+02, 2.1175e+02, 2.1225e+02,\n 2.1275e+02, 2.1325e+02, 2.1375e+02, 2.1425e+02, 2.1475e+02,\n 2.1525e+02, 2.1575e+02, 2.1625e+02, 2.1675e+02, 2.1725e+02,\n 2.1775e+02, 2.1825e+02, 2.1875e+02, 2.1925e+02, 2.1975e+02,\n 2.2025e+02, 2.2075e+02, 2.2125e+02, 2.2175e+02, 2.2225e+02,\n 2.2275e+02, 2.2325e+02, 2.2375e+02, 2.2425e+02, 2.2475e+02,\n 2.2525e+02, 2.2575e+02, 2.2625e+02, 2.2675e+02, 2.2725e+02,\n 2.2775e+02, 2.2825e+02, 2.2875e+02, 2.2925e+02, 2.2975e+02,\n 2.3025e+02, 2.3075e+02, 2.3125e+02, 2.3175e+02, 2.3225e+02,\n 2.3275e+02, 2.3325e+02, 2.3375e+02, 2.3425e+02, 2.3475e+02,\n 2.3525e+02, 2.3575e+02, 2.3625e+02, 2.3675e+02, 2.3725e+02,\n 2.3775e+02, 2.3825e+02, 2.3875e+02, 2.3925e+02, 2.3975e+02,\n 2.4025e+02, 2.4075e+02, 2.4125e+02, 2.4175e+02, 2.4225e+02,\n 2.4275e+02, 2.4325e+02, 2.4375e+02, 2.4425e+02, 2.4475e+02,\n 2.4525e+02, 2.4575e+02, 2.4625e+02, 2.4675e+02, 2.4725e+02,\n 2.4775e+02, 2.4825e+02, 2.4875e+02, 2.4925e+02, 2.4975e+02,\n 2.5025e+02, 2.5075e+02, 2.5125e+02, 2.5175e+02, 2.5225e+02,\n 2.5275e+02, 2.5325e+02, 2.5375e+02, 2.5425e+02, 2.5475e+02,\n 2.5525e+02, 2.5575e+02, 2.5625e+02, 2.5675e+02, 2.5725e+02,\n 2.5775e+02, 2.5825e+02, 2.5875e+02, 2.5925e+02, 2.5975e+02,\n 2.6025e+02, 2.6075e+02, 2.6125e+02, 2.6175e+02, 2.6225e+02,\n 2.6275e+02, 2.6325e+02, 2.6375e+02, 2.6425e+02, 2.6475e+02,\n 2.6525e+02, 2.6575e+02, 2.6625e+02, 2.6675e+02, 2.6725e+02,\n 2.6775e+02, 2.6825e+02, 2.6875e+02, 2.6925e+02, 2.6975e+02,\n 2.7025e+02, 2.7075e+02, 2.7125e+02, 2.7175e+02, 2.7225e+02,\n 2.7275e+02, 2.7325e+02, 2.7375e+02, 2.7425e+02, 2.7475e+02,\n 2.7525e+02, 2.7575e+02, 2.7625e+02, 2.7675e+02, 2.7725e+02,\n 2.7775e+02, 2.7825e+02, 2.7875e+02, 2.7925e+02, 2.7975e+02,\n 2.8025e+02, 2.8075e+02, 2.8125e+02, 2.8175e+02, 2.8225e+02,\n 2.8275e+02, 2.8325e+02, 2.8375e+02, 2.8425e+02, 2.8475e+02,\n 2.8525e+02, 2.8575e+02, 2.8625e+02, 2.8675e+02, 2.8725e+02,\n 2.8775e+02, 2.8825e+02, 2.8875e+02, 2.8925e+02, 2.8975e+02,\n 2.9025e+02, 2.9075e+02, 2.9125e+02, 2.9175e+02, 2.9225e+02,\n 2.9275e+02, 2.9325e+02, 2.9375e+02, 2.9425e+02, 2.9475e+02,\n 2.9525e+02, 2.9575e+02, 2.9625e+02, 2.9675e+02, 2.9725e+02,\n 2.9775e+02, 2.9825e+02, 2.9875e+02, 2.9925e+02, 2.9975e+02,\n 3.0025e+02, 3.0075e+02, 3.0125e+02, 3.0175e+02, 3.0225e+02,\n 3.0275e+02, 3.0325e+02, 3.0375e+02, 3.0425e+02, 3.0475e+02,\n 3.0525e+02, 3.0575e+02, 3.0625e+02, 3.0675e+02, 3.0725e+02,\n 3.0775e+02, 3.0825e+02, 3.0875e+02, 3.0925e+02, 3.0975e+02,\n 3.1025e+02, 3.1075e+02, 3.1125e+02, 3.1175e+02, 3.1225e+02,\n 3.1275e+02, 3.1325e+02, 3.1375e+02, 3.1425e+02, 3.1475e+02,\n 3.1525e+02, 3.1575e+02, 3.1625e+02, 3.1675e+02, 3.1725e+02,\n 3.1775e+02, 3.1825e+02, 3.1875e+02, 3.1925e+02, 3.1975e+02,\n 3.2025e+02, 3.2075e+02, 3.2125e+02, 3.2175e+02, 3.2225e+02,\n 3.2275e+02, 3.2325e+02, 3.2375e+02, 3.2425e+02, 3.2475e+02,\n 3.2525e+02, 3.2575e+02, 3.2625e+02, 3.2675e+02, 3.2725e+02,\n 3.2775e+02, 3.2825e+02, 3.2875e+02, 3.2925e+02, 3.2975e+02,\n 3.3025e+02, 3.3075e+02, 3.3125e+02, 3.3175e+02, 3.3225e+02,\n 3.3275e+02, 3.3325e+02, 3.3375e+02, 3.3425e+02, 3.3475e+02,\n 3.3525e+02, 3.3575e+02, 3.3625e+02, 3.3675e+02, 3.3725e+02,\n 3.3775e+02, 3.3825e+02, 3.3875e+02, 3.3925e+02, 3.3975e+02,\n 3.4025e+02, 3.4075e+02, 3.4125e+02, 3.4175e+02, 3.4225e+02,\n 3.4275e+02, 3.4325e+02, 3.4375e+02, 3.4425e+02, 3.4475e+02,\n 3.4525e+02, 3.4575e+02, 3.4625e+02, 3.4675e+02, 3.4725e+02,\n 3.4775e+02, 3.4825e+02, 3.4875e+02, 3.4925e+02, 3.4975e+02,\n 3.5025e+02, 3.5075e+02, 3.5125e+02, 3.5175e+02, 3.5225e+02,\n 3.5275e+02, 3.5325e+02, 3.5375e+02, 3.5425e+02, 3.5475e+02,\n 3.5525e+02, 3.5575e+02, 3.5625e+02, 3.5675e+02, 3.5725e+02,\n 3.5775e+02, 3.5825e+02, 3.5875e+02, 3.5925e+02, 3.5975e+02])" - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.lon" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "data": { - "text/plain": "array([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n 85.75, 85.25, 84.75, 84.25, 83.75, 83.25, 82.75, 82.25,\n 81.75, 81.25, 80.75, 80.25, 79.75, 79.25, 78.75, 78.25,\n 77.75, 77.25, 76.75, 76.25, 75.75, 75.25, 74.75, 74.25,\n 73.75, 73.25, 72.75, 72.25, 71.75, 71.25, 70.75, 70.25,\n 69.75, 69.25, 68.75, 68.25, 67.75, 67.25, 66.75, 66.25,\n 65.75, 65.25, 64.75, 64.25, 63.75, 63.25, 62.75, 62.25,\n 61.75, 61.25, 60.75, 60.25, 59.75, 59.25, 58.75, 58.25,\n 57.75, 57.25, 56.75, 56.25, 55.75, 55.25, 54.75, 54.25,\n 53.75, 53.25, 52.75, 52.25, 51.75, 51.25, 50.75, 50.25,\n 49.75, 49.25, 48.75, 48.25, 47.75, 47.25, 46.75, 46.25,\n 45.75, 45.25, 44.75, 44.25, 43.75, 43.25, 42.75, 42.25,\n 41.75, 41.25, 40.75, 40.25, 39.75, 39.25, 38.75, 38.25,\n 37.75, 37.25, 36.75, 36.25, 35.75, 35.25, 34.75, 34.25,\n 33.75, 33.25, 32.75, 32.25, 31.75, 31.25, 30.75, 30.25,\n 29.75, 29.25, 28.75, 28.25, 27.75, 27.25, 26.75, 26.25,\n 25.75, 25.25, 24.75, 24.25, 23.75, 23.25, 22.75, 22.25,\n 21.75, 21.25, 20.75, 20.25, 19.75, 19.25, 18.75, 18.25,\n 17.75, 17.25, 16.75, 16.25, 15.75, 15.25, 14.75, 14.25,\n 13.75, 13.25, 12.75, 12.25, 11.75, 11.25, 10.75, 10.25,\n 9.75, 9.25, 8.75, 8.25, 7.75, 7.25, 6.75, 6.25,\n 5.75, 5.25, 4.75, 4.25, 3.75, 3.25, 2.75, 2.25,\n 1.75, 1.25, 0.75, 0.25, -0.25, -0.75, -1.25, -1.75,\n -2.25, -2.75, -3.25, -3.75, -4.25, -4.75, -5.25, -5.75,\n -6.25, -6.75, -7.25, -7.75, -8.25, -8.75, -9.25, -9.75,\n -10.25, -10.75, -11.25, -11.75, -12.25, -12.75, -13.25, -13.75,\n -14.25, -14.75, -15.25, -15.75, -16.25, -16.75, -17.25, -17.75,\n -18.25, -18.75, -19.25, -19.75, -20.25, -20.75, -21.25, -21.75,\n -22.25, -22.75, -23.25, -23.75, -24.25, -24.75, -25.25, -25.75,\n -26.25, -26.75, -27.25, -27.75, -28.25, -28.75, -29.25, -29.75,\n -30.25, -30.75, -31.25, -31.75, -32.25, -32.75, -33.25, -33.75,\n -34.25, -34.75, -35.25, -35.75, -36.25, -36.75, -37.25, -37.75,\n -38.25, -38.75, -39.25, -39.75, -40.25, -40.75, -41.25, -41.75,\n -42.25, -42.75, -43.25, -43.75, -44.25, -44.75, -45.25, -45.75,\n -46.25, -46.75, -47.25, -47.75, -48.25, -48.75, -49.25, -49.75,\n -50.25, -50.75, -51.25, -51.75, -52.25, -52.75, -53.25, -53.75,\n -54.25, -54.75, -55.25, -55.75, -56.25, -56.75, -57.25, -57.75,\n -58.25, -58.75, -59.25, -59.75, -60.25, -60.75, -61.25, -61.75,\n -62.25, -62.75, -63.25, -63.75, -64.25, -64.75, -65.25, -65.75,\n -66.25, -66.75, -67.25, -67.75, -68.25, -68.75, -69.25, -69.75,\n -70.25, -70.75, -71.25, -71.75, -72.25, -72.75, -73.25, -73.75,\n -74.25, -74.75, -75.25, -75.75, -76.25, -76.75, -77.25, -77.75,\n -78.25, -78.75, -79.25, -79.75, -80.25, -80.75, -81.25, -81.75,\n -82.25, -82.75, -83.25, -83.75, -84.25, -84.75, -85.25, -85.75,\n -86.25, -86.75, -87.25, -87.75, -88.25, -88.75, -89.25, -89.75])" - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.lat" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Bounding box" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "[0.0, -90.0, 360.0, 90.0]" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.bbox" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": " geometry\n0 POLYGON ((0.00000 90.00000, 0.00000 -90.00000,...", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
geometry
0POLYGON ((0.00000 90.00000, 0.00000 -90.00000,...
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" - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.bounds" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Dataset" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "

\n", + " \"dataset\n", + "

" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "from pyramids.dataset import Dataset\n", + "path = r\"../../examples/data/geotiff/noah-precipitation-1979.tif\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Read any raster format" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "dataset = Dataset.read_file(path)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Explore Dataset properties" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " File: ../../examples/data/geotiff/noah-precipitation-1979.tif\n", + " Cell size: 0.5\n", + " EPSG: 4326\n", + " Variables: {}\n", + " Number of Bands: 4\n", + " Band names: ['Band_1', 'Band_2', 'Band_3', 'Band_4']\n", + " Dimension: 360 * 720\n", + " Mask: -9.969209968386869e+36\n", + " Data type: 6\n", + " \n" + ] + } + ], + "source": [ + "print(dataset)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Plot Dataset" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = dataset.plot(\n", + " band=0, figsize=(10, 5), title=\"Noah daily Precipitation 1979-01-01\", cbar_label=\"Raindall mm/day\", vmax=30,\n", + " cbar_length=0.85\n", + ")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Dataset dimension" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset dimensions: (4, 360, 720)\n", + "Dataset rows: 360\n", + "Dataset columns: 720\n", + "Dataset number of bands: 4\n" + ] + } + ], + "source": [ + "print(f\"Dataset dimensions: {dataset.shape}\")\n", + "print(f\"Dataset rows: {dataset.rows}\")\n", + "print(f\"Dataset columns: {dataset.columns}\")\n", + "print(f\"Dataset number of bands: {dataset.band_count}\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cell size: 0.5\n" + ] + } + ], + "source": [ + "print(f\"Cell size: {dataset.cell_size}\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "['Band_1', 'Band_2', 'Band_3', 'Band_4']" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.band_names" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "### Dataset spatial properties" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EPSG: 4326\n", + "Coordinate reference system: GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]\n" + ] + } + ], + "source": [ + "print(f\"EPSG: {dataset.epsg}\")\n", + "print(f\"Coordinate reference system: {dataset.crs}\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "array([2.5000e-01, 7.5000e-01, 1.2500e+00, 1.7500e+00, 2.2500e+00,\n 2.7500e+00, 3.2500e+00, 3.7500e+00, 4.2500e+00, 4.7500e+00,\n 5.2500e+00, 5.7500e+00, 6.2500e+00, 6.7500e+00, 7.2500e+00,\n 7.7500e+00, 8.2500e+00, 8.7500e+00, 9.2500e+00, 9.7500e+00,\n 1.0250e+01, 1.0750e+01, 1.1250e+01, 1.1750e+01, 1.2250e+01,\n 1.2750e+01, 1.3250e+01, 1.3750e+01, 1.4250e+01, 1.4750e+01,\n 1.5250e+01, 1.5750e+01, 1.6250e+01, 1.6750e+01, 1.7250e+01,\n 1.7750e+01, 1.8250e+01, 1.8750e+01, 1.9250e+01, 1.9750e+01,\n 2.0250e+01, 2.0750e+01, 2.1250e+01, 2.1750e+01, 2.2250e+01,\n 2.2750e+01, 2.3250e+01, 2.3750e+01, 2.4250e+01, 2.4750e+01,\n 2.5250e+01, 2.5750e+01, 2.6250e+01, 2.6750e+01, 2.7250e+01,\n 2.7750e+01, 2.8250e+01, 2.8750e+01, 2.9250e+01, 2.9750e+01,\n 3.0250e+01, 3.0750e+01, 3.1250e+01, 3.1750e+01, 3.2250e+01,\n 3.2750e+01, 3.3250e+01, 3.3750e+01, 3.4250e+01, 3.4750e+01,\n 3.5250e+01, 3.5750e+01, 3.6250e+01, 3.6750e+01, 3.7250e+01,\n 3.7750e+01, 3.8250e+01, 3.8750e+01, 3.9250e+01, 3.9750e+01,\n 4.0250e+01, 4.0750e+01, 4.1250e+01, 4.1750e+01, 4.2250e+01,\n 4.2750e+01, 4.3250e+01, 4.3750e+01, 4.4250e+01, 4.4750e+01,\n 4.5250e+01, 4.5750e+01, 4.6250e+01, 4.6750e+01, 4.7250e+01,\n 4.7750e+01, 4.8250e+01, 4.8750e+01, 4.9250e+01, 4.9750e+01,\n 5.0250e+01, 5.0750e+01, 5.1250e+01, 5.1750e+01, 5.2250e+01,\n 5.2750e+01, 5.3250e+01, 5.3750e+01, 5.4250e+01, 5.4750e+01,\n 5.5250e+01, 5.5750e+01, 5.6250e+01, 5.6750e+01, 5.7250e+01,\n 5.7750e+01, 5.8250e+01, 5.8750e+01, 5.9250e+01, 5.9750e+01,\n 6.0250e+01, 6.0750e+01, 6.1250e+01, 6.1750e+01, 6.2250e+01,\n 6.2750e+01, 6.3250e+01, 6.3750e+01, 6.4250e+01, 6.4750e+01,\n 6.5250e+01, 6.5750e+01, 6.6250e+01, 6.6750e+01, 6.7250e+01,\n 6.7750e+01, 6.8250e+01, 6.8750e+01, 6.9250e+01, 6.9750e+01,\n 7.0250e+01, 7.0750e+01, 7.1250e+01, 7.1750e+01, 7.2250e+01,\n 7.2750e+01, 7.3250e+01, 7.3750e+01, 7.4250e+01, 7.4750e+01,\n 7.5250e+01, 7.5750e+01, 7.6250e+01, 7.6750e+01, 7.7250e+01,\n 7.7750e+01, 7.8250e+01, 7.8750e+01, 7.9250e+01, 7.9750e+01,\n 8.0250e+01, 8.0750e+01, 8.1250e+01, 8.1750e+01, 8.2250e+01,\n 8.2750e+01, 8.3250e+01, 8.3750e+01, 8.4250e+01, 8.4750e+01,\n 8.5250e+01, 8.5750e+01, 8.6250e+01, 8.6750e+01, 8.7250e+01,\n 8.7750e+01, 8.8250e+01, 8.8750e+01, 8.9250e+01, 8.9750e+01,\n 9.0250e+01, 9.0750e+01, 9.1250e+01, 9.1750e+01, 9.2250e+01,\n 9.2750e+01, 9.3250e+01, 9.3750e+01, 9.4250e+01, 9.4750e+01,\n 9.5250e+01, 9.5750e+01, 9.6250e+01, 9.6750e+01, 9.7250e+01,\n 9.7750e+01, 9.8250e+01, 9.8750e+01, 9.9250e+01, 9.9750e+01,\n 1.0025e+02, 1.0075e+02, 1.0125e+02, 1.0175e+02, 1.0225e+02,\n 1.0275e+02, 1.0325e+02, 1.0375e+02, 1.0425e+02, 1.0475e+02,\n 1.0525e+02, 1.0575e+02, 1.0625e+02, 1.0675e+02, 1.0725e+02,\n 1.0775e+02, 1.0825e+02, 1.0875e+02, 1.0925e+02, 1.0975e+02,\n 1.1025e+02, 1.1075e+02, 1.1125e+02, 1.1175e+02, 1.1225e+02,\n 1.1275e+02, 1.1325e+02, 1.1375e+02, 1.1425e+02, 1.1475e+02,\n 1.1525e+02, 1.1575e+02, 1.1625e+02, 1.1675e+02, 1.1725e+02,\n 1.1775e+02, 1.1825e+02, 1.1875e+02, 1.1925e+02, 1.1975e+02,\n 1.2025e+02, 1.2075e+02, 1.2125e+02, 1.2175e+02, 1.2225e+02,\n 1.2275e+02, 1.2325e+02, 1.2375e+02, 1.2425e+02, 1.2475e+02,\n 1.2525e+02, 1.2575e+02, 1.2625e+02, 1.2675e+02, 1.2725e+02,\n 1.2775e+02, 1.2825e+02, 1.2875e+02, 1.2925e+02, 1.2975e+02,\n 1.3025e+02, 1.3075e+02, 1.3125e+02, 1.3175e+02, 1.3225e+02,\n 1.3275e+02, 1.3325e+02, 1.3375e+02, 1.3425e+02, 1.3475e+02,\n 1.3525e+02, 1.3575e+02, 1.3625e+02, 1.3675e+02, 1.3725e+02,\n 1.3775e+02, 1.3825e+02, 1.3875e+02, 1.3925e+02, 1.3975e+02,\n 1.4025e+02, 1.4075e+02, 1.4125e+02, 1.4175e+02, 1.4225e+02,\n 1.4275e+02, 1.4325e+02, 1.4375e+02, 1.4425e+02, 1.4475e+02,\n 1.4525e+02, 1.4575e+02, 1.4625e+02, 1.4675e+02, 1.4725e+02,\n 1.4775e+02, 1.4825e+02, 1.4875e+02, 1.4925e+02, 1.4975e+02,\n 1.5025e+02, 1.5075e+02, 1.5125e+02, 1.5175e+02, 1.5225e+02,\n 1.5275e+02, 1.5325e+02, 1.5375e+02, 1.5425e+02, 1.5475e+02,\n 1.5525e+02, 1.5575e+02, 1.5625e+02, 1.5675e+02, 1.5725e+02,\n 1.5775e+02, 1.5825e+02, 1.5875e+02, 1.5925e+02, 1.5975e+02,\n 1.6025e+02, 1.6075e+02, 1.6125e+02, 1.6175e+02, 1.6225e+02,\n 1.6275e+02, 1.6325e+02, 1.6375e+02, 1.6425e+02, 1.6475e+02,\n 1.6525e+02, 1.6575e+02, 1.6625e+02, 1.6675e+02, 1.6725e+02,\n 1.6775e+02, 1.6825e+02, 1.6875e+02, 1.6925e+02, 1.6975e+02,\n 1.7025e+02, 1.7075e+02, 1.7125e+02, 1.7175e+02, 1.7225e+02,\n 1.7275e+02, 1.7325e+02, 1.7375e+02, 1.7425e+02, 1.7475e+02,\n 1.7525e+02, 1.7575e+02, 1.7625e+02, 1.7675e+02, 1.7725e+02,\n 1.7775e+02, 1.7825e+02, 1.7875e+02, 1.7925e+02, 1.7975e+02,\n 1.8025e+02, 1.8075e+02, 1.8125e+02, 1.8175e+02, 1.8225e+02,\n 1.8275e+02, 1.8325e+02, 1.8375e+02, 1.8425e+02, 1.8475e+02,\n 1.8525e+02, 1.8575e+02, 1.8625e+02, 1.8675e+02, 1.8725e+02,\n 1.8775e+02, 1.8825e+02, 1.8875e+02, 1.8925e+02, 1.8975e+02,\n 1.9025e+02, 1.9075e+02, 1.9125e+02, 1.9175e+02, 1.9225e+02,\n 1.9275e+02, 1.9325e+02, 1.9375e+02, 1.9425e+02, 1.9475e+02,\n 1.9525e+02, 1.9575e+02, 1.9625e+02, 1.9675e+02, 1.9725e+02,\n 1.9775e+02, 1.9825e+02, 1.9875e+02, 1.9925e+02, 1.9975e+02,\n 2.0025e+02, 2.0075e+02, 2.0125e+02, 2.0175e+02, 2.0225e+02,\n 2.0275e+02, 2.0325e+02, 2.0375e+02, 2.0425e+02, 2.0475e+02,\n 2.0525e+02, 2.0575e+02, 2.0625e+02, 2.0675e+02, 2.0725e+02,\n 2.0775e+02, 2.0825e+02, 2.0875e+02, 2.0925e+02, 2.0975e+02,\n 2.1025e+02, 2.1075e+02, 2.1125e+02, 2.1175e+02, 2.1225e+02,\n 2.1275e+02, 2.1325e+02, 2.1375e+02, 2.1425e+02, 2.1475e+02,\n 2.1525e+02, 2.1575e+02, 2.1625e+02, 2.1675e+02, 2.1725e+02,\n 2.1775e+02, 2.1825e+02, 2.1875e+02, 2.1925e+02, 2.1975e+02,\n 2.2025e+02, 2.2075e+02, 2.2125e+02, 2.2175e+02, 2.2225e+02,\n 2.2275e+02, 2.2325e+02, 2.2375e+02, 2.2425e+02, 2.2475e+02,\n 2.2525e+02, 2.2575e+02, 2.2625e+02, 2.2675e+02, 2.2725e+02,\n 2.2775e+02, 2.2825e+02, 2.2875e+02, 2.2925e+02, 2.2975e+02,\n 2.3025e+02, 2.3075e+02, 2.3125e+02, 2.3175e+02, 2.3225e+02,\n 2.3275e+02, 2.3325e+02, 2.3375e+02, 2.3425e+02, 2.3475e+02,\n 2.3525e+02, 2.3575e+02, 2.3625e+02, 2.3675e+02, 2.3725e+02,\n 2.3775e+02, 2.3825e+02, 2.3875e+02, 2.3925e+02, 2.3975e+02,\n 2.4025e+02, 2.4075e+02, 2.4125e+02, 2.4175e+02, 2.4225e+02,\n 2.4275e+02, 2.4325e+02, 2.4375e+02, 2.4425e+02, 2.4475e+02,\n 2.4525e+02, 2.4575e+02, 2.4625e+02, 2.4675e+02, 2.4725e+02,\n 2.4775e+02, 2.4825e+02, 2.4875e+02, 2.4925e+02, 2.4975e+02,\n 2.5025e+02, 2.5075e+02, 2.5125e+02, 2.5175e+02, 2.5225e+02,\n 2.5275e+02, 2.5325e+02, 2.5375e+02, 2.5425e+02, 2.5475e+02,\n 2.5525e+02, 2.5575e+02, 2.5625e+02, 2.5675e+02, 2.5725e+02,\n 2.5775e+02, 2.5825e+02, 2.5875e+02, 2.5925e+02, 2.5975e+02,\n 2.6025e+02, 2.6075e+02, 2.6125e+02, 2.6175e+02, 2.6225e+02,\n 2.6275e+02, 2.6325e+02, 2.6375e+02, 2.6425e+02, 2.6475e+02,\n 2.6525e+02, 2.6575e+02, 2.6625e+02, 2.6675e+02, 2.6725e+02,\n 2.6775e+02, 2.6825e+02, 2.6875e+02, 2.6925e+02, 2.6975e+02,\n 2.7025e+02, 2.7075e+02, 2.7125e+02, 2.7175e+02, 2.7225e+02,\n 2.7275e+02, 2.7325e+02, 2.7375e+02, 2.7425e+02, 2.7475e+02,\n 2.7525e+02, 2.7575e+02, 2.7625e+02, 2.7675e+02, 2.7725e+02,\n 2.7775e+02, 2.7825e+02, 2.7875e+02, 2.7925e+02, 2.7975e+02,\n 2.8025e+02, 2.8075e+02, 2.8125e+02, 2.8175e+02, 2.8225e+02,\n 2.8275e+02, 2.8325e+02, 2.8375e+02, 2.8425e+02, 2.8475e+02,\n 2.8525e+02, 2.8575e+02, 2.8625e+02, 2.8675e+02, 2.8725e+02,\n 2.8775e+02, 2.8825e+02, 2.8875e+02, 2.8925e+02, 2.8975e+02,\n 2.9025e+02, 2.9075e+02, 2.9125e+02, 2.9175e+02, 2.9225e+02,\n 2.9275e+02, 2.9325e+02, 2.9375e+02, 2.9425e+02, 2.9475e+02,\n 2.9525e+02, 2.9575e+02, 2.9625e+02, 2.9675e+02, 2.9725e+02,\n 2.9775e+02, 2.9825e+02, 2.9875e+02, 2.9925e+02, 2.9975e+02,\n 3.0025e+02, 3.0075e+02, 3.0125e+02, 3.0175e+02, 3.0225e+02,\n 3.0275e+02, 3.0325e+02, 3.0375e+02, 3.0425e+02, 3.0475e+02,\n 3.0525e+02, 3.0575e+02, 3.0625e+02, 3.0675e+02, 3.0725e+02,\n 3.0775e+02, 3.0825e+02, 3.0875e+02, 3.0925e+02, 3.0975e+02,\n 3.1025e+02, 3.1075e+02, 3.1125e+02, 3.1175e+02, 3.1225e+02,\n 3.1275e+02, 3.1325e+02, 3.1375e+02, 3.1425e+02, 3.1475e+02,\n 3.1525e+02, 3.1575e+02, 3.1625e+02, 3.1675e+02, 3.1725e+02,\n 3.1775e+02, 3.1825e+02, 3.1875e+02, 3.1925e+02, 3.1975e+02,\n 3.2025e+02, 3.2075e+02, 3.2125e+02, 3.2175e+02, 3.2225e+02,\n 3.2275e+02, 3.2325e+02, 3.2375e+02, 3.2425e+02, 3.2475e+02,\n 3.2525e+02, 3.2575e+02, 3.2625e+02, 3.2675e+02, 3.2725e+02,\n 3.2775e+02, 3.2825e+02, 3.2875e+02, 3.2925e+02, 3.2975e+02,\n 3.3025e+02, 3.3075e+02, 3.3125e+02, 3.3175e+02, 3.3225e+02,\n 3.3275e+02, 3.3325e+02, 3.3375e+02, 3.3425e+02, 3.3475e+02,\n 3.3525e+02, 3.3575e+02, 3.3625e+02, 3.3675e+02, 3.3725e+02,\n 3.3775e+02, 3.3825e+02, 3.3875e+02, 3.3925e+02, 3.3975e+02,\n 3.4025e+02, 3.4075e+02, 3.4125e+02, 3.4175e+02, 3.4225e+02,\n 3.4275e+02, 3.4325e+02, 3.4375e+02, 3.4425e+02, 3.4475e+02,\n 3.4525e+02, 3.4575e+02, 3.4625e+02, 3.4675e+02, 3.4725e+02,\n 3.4775e+02, 3.4825e+02, 3.4875e+02, 3.4925e+02, 3.4975e+02,\n 3.5025e+02, 3.5075e+02, 3.5125e+02, 3.5175e+02, 3.5225e+02,\n 3.5275e+02, 3.5325e+02, 3.5375e+02, 3.5425e+02, 3.5475e+02,\n 3.5525e+02, 3.5575e+02, 3.5625e+02, 3.5675e+02, 3.5725e+02,\n 3.5775e+02, 3.5825e+02, 3.5875e+02, 3.5925e+02, 3.5975e+02])" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.lon" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": "array([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n 85.75, 85.25, 84.75, 84.25, 83.75, 83.25, 82.75, 82.25,\n 81.75, 81.25, 80.75, 80.25, 79.75, 79.25, 78.75, 78.25,\n 77.75, 77.25, 76.75, 76.25, 75.75, 75.25, 74.75, 74.25,\n 73.75, 73.25, 72.75, 72.25, 71.75, 71.25, 70.75, 70.25,\n 69.75, 69.25, 68.75, 68.25, 67.75, 67.25, 66.75, 66.25,\n 65.75, 65.25, 64.75, 64.25, 63.75, 63.25, 62.75, 62.25,\n 61.75, 61.25, 60.75, 60.25, 59.75, 59.25, 58.75, 58.25,\n 57.75, 57.25, 56.75, 56.25, 55.75, 55.25, 54.75, 54.25,\n 53.75, 53.25, 52.75, 52.25, 51.75, 51.25, 50.75, 50.25,\n 49.75, 49.25, 48.75, 48.25, 47.75, 47.25, 46.75, 46.25,\n 45.75, 45.25, 44.75, 44.25, 43.75, 43.25, 42.75, 42.25,\n 41.75, 41.25, 40.75, 40.25, 39.75, 39.25, 38.75, 38.25,\n 37.75, 37.25, 36.75, 36.25, 35.75, 35.25, 34.75, 34.25,\n 33.75, 33.25, 32.75, 32.25, 31.75, 31.25, 30.75, 30.25,\n 29.75, 29.25, 28.75, 28.25, 27.75, 27.25, 26.75, 26.25,\n 25.75, 25.25, 24.75, 24.25, 23.75, 23.25, 22.75, 22.25,\n 21.75, 21.25, 20.75, 20.25, 19.75, 19.25, 18.75, 18.25,\n 17.75, 17.25, 16.75, 16.25, 15.75, 15.25, 14.75, 14.25,\n 13.75, 13.25, 12.75, 12.25, 11.75, 11.25, 10.75, 10.25,\n 9.75, 9.25, 8.75, 8.25, 7.75, 7.25, 6.75, 6.25,\n 5.75, 5.25, 4.75, 4.25, 3.75, 3.25, 2.75, 2.25,\n 1.75, 1.25, 0.75, 0.25, -0.25, -0.75, -1.25, -1.75,\n -2.25, -2.75, -3.25, -3.75, -4.25, -4.75, -5.25, -5.75,\n -6.25, -6.75, -7.25, -7.75, -8.25, -8.75, -9.25, -9.75,\n -10.25, -10.75, -11.25, -11.75, -12.25, -12.75, -13.25, -13.75,\n -14.25, -14.75, -15.25, -15.75, -16.25, -16.75, -17.25, -17.75,\n -18.25, -18.75, -19.25, -19.75, -20.25, -20.75, -21.25, -21.75,\n -22.25, -22.75, -23.25, -23.75, -24.25, -24.75, -25.25, -25.75,\n -26.25, -26.75, -27.25, -27.75, -28.25, -28.75, -29.25, -29.75,\n -30.25, -30.75, -31.25, -31.75, -32.25, -32.75, -33.25, -33.75,\n -34.25, -34.75, -35.25, -35.75, -36.25, -36.75, -37.25, -37.75,\n -38.25, -38.75, -39.25, -39.75, -40.25, -40.75, -41.25, -41.75,\n -42.25, -42.75, -43.25, -43.75, -44.25, -44.75, -45.25, -45.75,\n -46.25, -46.75, -47.25, -47.75, -48.25, -48.75, -49.25, -49.75,\n -50.25, -50.75, -51.25, -51.75, -52.25, -52.75, -53.25, -53.75,\n -54.25, -54.75, -55.25, -55.75, -56.25, -56.75, -57.25, -57.75,\n -58.25, -58.75, -59.25, -59.75, -60.25, -60.75, -61.25, -61.75,\n -62.25, -62.75, -63.25, -63.75, -64.25, -64.75, -65.25, -65.75,\n -66.25, -66.75, -67.25, -67.75, -68.25, -68.75, -69.25, -69.75,\n -70.25, -70.75, -71.25, -71.75, -72.25, -72.75, -73.25, -73.75,\n -74.25, -74.75, -75.25, -75.75, -76.25, -76.75, -77.25, -77.75,\n -78.25, -78.75, -79.25, -79.75, -80.25, -80.75, -81.25, -81.75,\n -82.25, -82.75, -83.25, -83.75, -84.25, -84.75, -85.25, -85.75,\n -86.25, -86.75, -87.25, -87.75, -88.25, -88.75, -89.25, -89.75])" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.lat" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Bounding box" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dataset.bounds.plot()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": "(0.0, 0.5, 0.0, 90.0, 0.0, -0.5)" - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.geotransform" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "data": { - "text/plain": "(0.0, 90.0)" - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.pivot_point" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/pyramids/dataset.py b/pyramids/dataset.py index 6fd3f3639..010802e87 100644 --- a/pyramids/dataset.py +++ b/pyramids/dataset.py @@ -59,7 +59,11 @@ class Dataset: - """Dataset class contains methods to deal with rasters and netcdf files, change projection and coordinate systems.""" + """Dataset. + + class contains methods to deal with rasters and netcdf files, change projection and coordinate + systems. + """ default_no_data_value = DEFAULT_NO_DATA_VALUE @@ -95,7 +99,6 @@ def __init__(self, src: gdal.Dataset): ] self._no_data_value = no_data_value - self._band_names = self._get_band_names() def __str__(self): @@ -512,6 +515,79 @@ def create_driver_from_scratch( return dst + def _iloc(self, i) -> gdal.Band: + """_iloc. + + - Access dataset array using index. + + Parameters + ---------- + i: [int] + index, the index starts from 0. + + Returns + ------- + Band: + Gdal Band. + """ + if i > self.band_count - 1: + raise IndexError( + f"index {i} is out of bounds for axis 0 with size {self.band_count}" + ) + band = self.raster.GetRasterBand(i + 1) + return band + + def stats(self, band: int = None) -> DataFrame: + """stats. + + - Get statistics of a band. + [min, max, mean, std] + Parameters + ---------- + band: [int] + band index, if None the statistics of all bands will be returned. + + Returns + ------- + DataFrame: + DataFrame of statistics values of each band, the dataframe has the following columns: + [min, max, mean, std], the index of the dataframe is the band names. + min max mean std + Band_1 270.369720 270.762299 270.551361 0.154270 + Band_2 269.611938 269.744751 269.673645 0.043788 + Band_3 273.641479 274.168823 273.953979 0.198447 + Band_4 273.991516 274.540344 274.310669 0.205754 + """ + if band is None: + df = pd.DataFrame( + index=self.band_names, + columns=["min", "max", "mean", "std"], + dtype=np.float32, + ) + for i in range(self.band_count): + df.iloc[i, :] = self._get_stats(i) + else: + df = pd.DataFrame( + index=[self.band_names[band]], + columns=["min", "max", "mean", "std"], + dtype=np.float32, + ) + df.iloc[0, :] = self._get_stats(band) + + return df + + def _get_stats(self, band: int = None): + """_get_stats.""" + band_i = self._iloc(band) + vals = band_i.GetStatistics(True, True) + if sum(vals) == 0: + warnings.warn( + f"Band {band} has no statistics, and the statistics are going to be calculate" + ) + vals = band_i.ComputeStatistics(False) + + return vals + def read_array(self, band: int = None) -> np.ndarray: """Read Array diff --git a/setup.py b/setup.py index 6139cab67..dcccf9e45 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ setup( name="pyramids-gis", - version="0.5.1", + version="0.5.2", description="GIS utility package", author="Mostafa Farrag", author_email="moah.farag@gmail.come", diff --git a/tests/conftest.py b/tests/conftest.py index e83b47a6c..73b8daa7a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -4,6 +4,7 @@ import geopandas as gpd import numpy as np import pytest +import pandas as pd from geopandas.geodataframe import GeoDataFrame from osgeo import gdal from osgeo.gdal import Dataset @@ -243,3 +244,54 @@ def era5_image_gdf() -> GeoDataFrame: @pytest.fixture(scope="module") def era5_mask() -> GeoDataFrame: return gpd.read_file("tests/data/geotiff/era5-mask.geojson") + + +@pytest.fixture(scope="function") +def era5_image_stats() -> DataFrame: + """era5 image band statistics""" + df = pd.DataFrame(columns=["min", "max", "mean", "std"], dtype=np.float32) + df["min"] = [ + 270.36972, + 269.611938, + 273.641479, + 273.991516, + 274.979065, + 0.367523, + 0.37233, + 0.380798, + 0.001764, + ] + df["max"] = [ + 270.762299, + 269.744751, + 274.168823, + 274.540344, + 275.666565, + 0.368973, + 0.373856, + 0.394302, + 0.001884, + ] + df["mean"] = [ + 270.551361, + 269.673657, + 273.953979, + 274.310657, + 275.367346, + 0.368094, + 0.372946, + 0.387521, + 0.001822, + ] + df["std"] = [ + 0.15427, + 0.043788, + 0.198447, + 0.205754, + 0.254376, + 0.000499, + 0.000546, + 0.004531, + 0.000044, + ] + return df diff --git a/tests/dataset/test_datacube.py b/tests/dataset/test_datacube.py index 59d0b43c4..5547afdb3 100644 --- a/tests/dataset/test_datacube.py +++ b/tests/dataset/test_datacube.py @@ -1,10 +1,13 @@ -from typing import List +""" Tests for the Datacube class. """ import os -import numpy as np import shutil -from osgeo import gdal +from typing import List + import geopandas as gpd -from pyramids.dataset import Dataset, Datacube +import numpy as np +from osgeo import gdal + +from pyramids.dataset import Datacube, Dataset class TestCreateDataCube: @@ -186,19 +189,21 @@ def test_to_epsg( assert dataset.base.epsg == to_epsg -def test_match_alignment( - match_alignment_datacube, - src: Datacube, -): - dataset = Datacube.read_multiple_files(match_alignment_datacube, with_order=False) - dataset.open_datacube() - mask_obj = Dataset(src) - dataset.align(mask_obj) - assert dataset.base.rows == mask_obj.rows - assert dataset.base.columns == mask_obj.columns +class TestAlign: + def test_match_alignment( + self, + match_alignment_datacube, + src: Datacube, + ): + cube = Datacube.read_multiple_files(match_alignment_datacube, with_order=False) + cube.open_datacube() + mask_obj = Dataset(src) + cube.align(mask_obj) + assert cube.base.rows == mask_obj.rows + assert cube.base.columns == mask_obj.columns -class TestSaveDataset: +class TestSaveDatacube: def test_to_geotiff_with_path( self, rasters_folder_path: str, @@ -209,9 +214,9 @@ def test_to_geotiff_with_path( if os.path.exists(path): shutil.rmtree(path) - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - dataset.to_file(path) + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + cube.to_file(path) files = os.listdir(path) assert len(files) == 6 shutil.rmtree(path) @@ -226,10 +231,10 @@ def test_to_geotiff_with_list_of_paths( if os.path.exists(rpath): shutil.rmtree(rpath) - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - path = [f"{rpath}/{i}.tif" for i in range(dataset.time_length)] - dataset.to_file(path) + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + path = [f"{rpath}/{i}.tif" for i in range(cube.time_length)] + cube.to_file(path) files = os.listdir(rpath) assert len(files) == 6 shutil.rmtree(rpath) @@ -244,9 +249,9 @@ def test_to_ascii( if os.path.exists(path): shutil.rmtree(path) - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - dataset.to_file(path, driver="ascii", band=0) + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + cube.to_file(path, driver="ascii", band=0) files = os.listdir(path) assert len(files) == 6 shutil.rmtree(path) @@ -255,7 +260,7 @@ def test_to_ascii( class TestCrop: def test_crop_with_raster_inplace( self, - raster_mask: Datacube, + raster_mask: Dataset, rasters_folder_path: str, crop_aligned_folder_saveto: str, ): @@ -266,12 +271,12 @@ def test_crop_with_raster_inplace( # os.mkdir(crop_aligned_folder_saveto) mask = Dataset(raster_mask) - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - dataset.crop(mask, inplace=True) - # dataset.to_geotiff(crop_aligned_folder_saveto)_crop_with_polygon - arr = dataset.values[0, :, :] - no_data_value = dataset.base.no_data_value[0] + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + cube.crop(mask, inplace=True) + # cube.to_geotiff(crop_aligned_folder_saveto)_crop_with_polygon + arr = cube.values[0, :, :] + no_data_value = cube.base.no_data_value[0] arr1 = arr[~np.isclose(arr, no_data_value, rtol=0.001)] assert arr1.shape[0] == 720 # shutil.rmtree(crop_aligned_folder_saveto) @@ -289,10 +294,10 @@ def test_crop_with_raster_inplace_false( # os.mkdir(crop_aligned_folder_saveto) mask = Dataset(raster_mask) - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - cropped_dataset = dataset.crop(mask, inplace=False) - # dataset.to_geotiff(crop_aligned_folder_saveto)_crop_with_polygon + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + cropped_dataset = cube.crop(mask, inplace=False) + # cube.to_geotiff(crop_aligned_folder_saveto)_crop_with_polygon arr = cropped_dataset.values[0, :, :] no_data_value = cropped_dataset.base.no_data_value[0] arr1 = arr[~np.isclose(arr, no_data_value, rtol=0.001)] @@ -311,12 +316,12 @@ def test_crop_with_polygon( # else: # os.mkdir(crop_aligned_folder_saveto) - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - dataset.crop(polygon_mask, inplace=True) - # dataset.to_file(crop_aligned_folder_saveto) - arr = dataset.values[0, :, :] - no_data_value = dataset.base.no_data_value[0] + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + cube.crop(polygon_mask, inplace=True) + # cube.to_file(crop_aligned_folder_saveto) + arr = cube.values[0, :, :] + no_data_value = cube.base.no_data_value[0] arr1 = arr[~np.isclose(arr, no_data_value, rtol=0.001)] assert arr1.shape[0] == 696 # shutil.rmtree(crop_aligned_folder_saveto) @@ -337,18 +342,18 @@ def test_1( self, rasters_folder_path: str, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() func = np.abs - dataset.apply(func) + cube.apply(func) def test_overlay(rasters_folder_path: str, germany_classes: str): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() classes_src = Dataset.read_file(germany_classes) - class_dict = dataset.overlay(classes_src) + class_dict = cube.overlay(classes_src) arr = classes_src.read_array() class_values = np.unique(arr) assert len(class_dict.keys()) == len(class_values) - 1 @@ -363,9 +368,9 @@ def test_getitem( rasters_folder_path: str, rasters_folder_dim: tuple, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - arr = dataset[2] + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + arr = cube[2] assert arr.shape == ( rasters_folder_dim[0], rasters_folder_dim[1], @@ -375,15 +380,15 @@ def test_setitem( self, rasters_folder_path: str, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - no_data_value = dataset.base.no_data_value[0] - arr = dataset[2] + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + no_data_value = cube.base.no_data_value[0] + arr = cube[2] arr[~np.isclose(arr, no_data_value, rtol=0.00001)] = ( arr[~np.isclose(arr, no_data_value, rtol=0.00001)] * 10000 ) - dataset[2] = arr - arr2 = dataset.values[2, :, :] + cube[2] = arr + arr2 = cube.values[2, :, :] assert np.array_equal(arr, arr2) def test_len( @@ -391,27 +396,27 @@ def test_len( rasters_folder_path: str, rasters_folder_rasters_number: int, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - assert len(dataset) == rasters_folder_rasters_number + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + assert len(cube) == rasters_folder_rasters_number def test_iter( self, rasters_folder_path: str, rasters_folder_rasters_number: int, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - assert len(list(dataset)) == rasters_folder_rasters_number + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + assert len(list(cube)) == rasters_folder_rasters_number def test_head_tail( self, rasters_folder_path: str, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - head = dataset.head() - tail = dataset.tail() + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + head = cube.head() + tail = cube.tail() assert head.shape[0] == 5 assert tail.shape[0] == 5 @@ -420,10 +425,10 @@ def test_first_last( rasters_folder_path: str, rasters_folder_dim: tuple, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - first = dataset.first() - last = dataset.last() + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + first = cube.first() + last = cube.last() assert first.shape == rasters_folder_dim assert last.shape == rasters_folder_dim @@ -433,12 +438,12 @@ def test_rows_columns( rasters_folder_dim: tuple, rasters_folder_rasters_number: int, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() - assert dataset.rows == rasters_folder_dim[0] - assert dataset.columns == rasters_folder_dim[1] - assert dataset.shape == ( + assert cube.rows == rasters_folder_dim[0] + assert cube.columns == rasters_folder_dim[1] + assert cube.shape == ( rasters_folder_rasters_number, rasters_folder_dim[0], rasters_folder_dim[1], @@ -449,9 +454,9 @@ def test_values_get( rasters_folder_path: str, rasters_folder_dim: tuple, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - arr = dataset.values + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() + arr = cube.values assert isinstance(arr, np.ndarray) assert arr.shape == (6, 125, 93) @@ -460,13 +465,13 @@ def test_values_setter( rasters_folder_path: str, rasters_folder_dim: tuple, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() - arr = dataset.values + cube.open_datacube() + arr = cube.values arr = arr * 0 - dataset.values = arr - assert np.array_equal(dataset.values, arr) + cube.values = arr + assert np.array_equal(cube.values, arr) def test_values_sette_different_dimensions( self, @@ -474,13 +479,13 @@ def test_values_sette_different_dimensions( rasters_folder_rasters_number: int, rasters_folder_dim: tuple, ): - dataset = Datacube.read_multiple_files(rasters_folder_path, with_order=False) - dataset.open_datacube() + cube = Datacube.read_multiple_files(rasters_folder_path, with_order=False) + cube.open_datacube() # access the data attribute - arr = dataset.values + arr = cube.values # modify the array arr = arr[0:4, :, :] * np.nan try: - dataset.values = arr + cube.values = arr except ValueError: pass diff --git a/tests/dataset/test_dataset.py b/tests/dataset/test_dataset.py index 519f5e9fc..f317366ea 100644 --- a/tests/dataset/test_dataset.py +++ b/tests/dataset/test_dataset.py @@ -1217,3 +1217,50 @@ def test_window(self, raster_1band_coello_path): (12, 6, 2, 6), (12, 12, 2, 1), ] + + +class TestIloc: + """extract band from a dataset.""" + + def test_iloc_out_of_bound_index( + self, + src: gdal.Dataset, + src_no_data_value: float, + ): + dataset = Dataset(src) + try: + dataset._iloc(1) + except IndexError: + pass + + def test_iloc( + self, + src: gdal.Dataset, + src_no_data_value: float, + ): + dataset = Dataset(src) + band = dataset._iloc(0) + assert isinstance(band, gdal.Band) + + +class TestStats: + def test_all_bands(self, era5_image: gdal.Dataset, era5_image_stats: DataFrame): + dataset = Dataset(era5_image) + stats = dataset.stats() + assert isinstance(stats, DataFrame) + assert all(stats.columns == ["min", "max", "mean", "std"]) + assert np.isclose( + stats.values, era5_image_stats.values, rtol=0.000001, atol=0.00001 + ).all() + + def test_specific_band(self, era5_image: gdal.Dataset, era5_image_stats: DataFrame): + dataset = Dataset(era5_image) + stats = dataset.stats(0) + assert isinstance(stats, DataFrame) + assert all(stats.columns == ["min", "max", "mean", "std"]) + assert np.isclose( + stats.values, + era5_image_stats.iloc[0, :].values, + rtol=0.000001, + atol=0.00001, + ).all()