diff --git a/.gitignore b/.gitignore index 010d2ed8e..dc5279c15 100644 --- a/.gitignore +++ b/.gitignore @@ -36,6 +36,9 @@ MANIFEST pip-log.txt pip-delete-this-directory.txt +# GDAL statistics sidecar files generated when tests compute raster stats +*.aux.xml + # Unit test / coverage reports htmlcov/ .tox/ diff --git a/docs/.$Library_Workflow.xml.bkp b/docs/.$Library_Workflow.xml.bkp deleted file mode 100644 index a51013380..000000000 --- a/docs/.$Library_Workflow.xml.bkp +++ /dev/null @@ -1 +0,0 @@ -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 diff --git a/docs/New folder/description.txt b/docs/New folder/description.txt deleted file mode 100644 index 7286c271c..000000000 --- a/docs/New folder/description.txt +++ /dev/null @@ -1,55 +0,0 @@ -GISpy contains all gis functions and - -Inputs (preparation_functions): - 1-PrepareInputs - 2-changetext2time - 3-rescale - 4-mycolor - -DistParameters(DHBV_functions): - 1-saveobj - 2-loadobj - 3-getraster_data - 4-calculateK - 5-par2d_lumpedK1_lake - 6-par2d_lumpedK1 - -DistRRM(Dist_RRM): - 1-Dist_HBV2 - 2-RunLumpedRRP - -HBV: new HBV structure (with direct runoff component) -HBV_Lake: old HBV structure (without direct runoff component) - -java_functions: - javascript function used in the web interface - 1-javaupload - 2-upload_stations - 3-raster_upload - 4-warning1 - -Performance_criteria: - 1-rmse - 2-rmseHF (fuzzy weighting schemes) - 3-rmseLF (fuzzy weighting schemes) - 4-KGE - 5-WB - 6-nse - -Routing: - 1-muskingum - -RUN: - 1-RunModel - - -Calibration: - 1-RunCalibration - -statistical_tools: - 1-IDW - 2-ISDW - -Wrapper: - 1-Dist_model_lake - 2-Dist_model diff --git a/examples/hydrological-model/HAND_HRUpy.py b/examples/hydrological-model/HAND_HRUpy.py index 914a18438..389d60113 100644 --- a/examples/hydrological-model/HAND_HRUpy.py +++ b/examples/hydrological-model/HAND_HRUpy.py @@ -2,6 +2,7 @@ @author: Mostafa """ + # %links # from IPython import get_ipython # to reset the variable explorer each time @@ -18,13 +19,13 @@ # %library import numpy as np -from osgeo import gdal +from pyramids.dataset import Dataset from scipy.stats import norm # functions -from Hapi.rrm.parameters import Parameters as DP +from hapi.rrm.parameters import Parameters as DP -# from Hapi.catchment import GISCatchment as gc +# from hapi.catchment import GISCatchment as gc # import matplotlib.pyplot as plt @@ -32,26 +33,26 @@ # %% to modify the basins raster # path="C:/Users/Mostafa/Desktop/delineation/Clipped/proj/basins.tif" # pathout="mask.tif" -# basins=gdal.Open(path) +# basins=Dataset.read_file(path) # gc.DeleteBasins(basins,pathout) # %% path = "C:/Users/Mostafa/Desktop/delineation/HRU/HAND/" -DEM = gdal.Open(path + "DEM.tif") +DEM = Dataset.read_file(path + "DEM.tif") # dem_A=DEM.ReadAsArray() # no_val=np.float32(DEM.GetRasterBand(1).GetNoDataValue()) # rows=DEM.RasterYSize # cols=DEM.RasterXSize -FD = gdal.Open(path + "FD.tif") +FD = Dataset.read_file(path + "FD.tif") # fd_A=fd.ReadAsArray() # fd_val=[int(fd_A[i,j]) for i in range(rows) for j in range(cols) if fd_A[i,j] != no_val] -FPL = gdal.Open(path + "fpl.tif") +FPL = Dataset.read_file(path + "fpl.tif") # fpl_A=FPL.ReadAsArray() -River = gdal.Open(path + "river.tif") +River = Dataset.read_file(path + "river.tif") # river_A=River.ReadAsArray() # slope -Slope = gdal.Open(path + "slope.tif") -Slope_A = Slope.ReadAsArray() -no_val_slope = np.float32(Slope.GetRasterBand(1).GetNoDataValue()) +Slope = Dataset.read_file(path + "slope.tif") +Slope_A = Slope.read_array(band=0) +no_val_slope = np.float32(Slope.no_data_value[0]) # %% calculate HAND and DTND HAND, DTND = DP.hru_hand(DEM, FD, FPL, River) # %% calculate the cdf for diff --git a/examples/hydrological-model/coello/calibration/coello-hrus-distributed-model-calibratation-muskingum.py b/examples/hydrological-model/coello/calibration/coello-hrus-distributed-model-calibratation-muskingum.py index 6e00fee6b..06b5d3deb 100644 --- a/examples/hydrological-model/coello/calibration/coello-hrus-distributed-model-calibratation-muskingum.py +++ b/examples/hydrological-model/coello/calibration/coello-hrus-distributed-model-calibratation-muskingum.py @@ -5,12 +5,12 @@ import datetime as dt import numpy as np -from osgeo import gdal +from pyramids.dataset import Dataset from statista.descriptors import rmse -from Hapi.calibration import Calibration -from Hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBV -from Hapi.rrm.parameters import Parameters as DP +from hapi.calibration import Calibration +from hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBV +from hapi.rrm.parameters import Parameters as DP path = Comp + "/Coello/HAPI/Data/00inputs/" # GIS/4000/ SaveTo = Comp + "/Coello/Hapi/Model/results/" @@ -61,7 +61,7 @@ "01_rfcf","02_FC", "03_BETA", "04_ETF", "05_LP", "06_K0","07_K1", "08_K2", "09_UZL","10_PERC", + ["11_Kmuskingum", "12_Xmuskingum"] """ -raster = gdal.Open(path + "GIS/4000/subcatch_classes.tif") +raster = Dataset.read_file(path + "GIS/4000/subcatch_classes.tif") no_parameters = 12 no_lumped_par = 1 lumped_par_pos = [7] diff --git a/examples/hydrological-model/coello/calibration/coello-totally-distributed-model-calibratation-muskingum.py b/examples/hydrological-model/coello/calibration/coello-totally-distributed-model-calibratation-muskingum.py index c9f70cf15..c9516c225 100644 --- a/examples/hydrological-model/coello/calibration/coello-totally-distributed-model-calibratation-muskingum.py +++ b/examples/hydrological-model/coello/calibration/coello-totally-distributed-model-calibratation-muskingum.py @@ -1,12 +1,12 @@ import datetime as dt import numpy as np -from osgeo import gdal +from pyramids.dataset import Dataset from statista.descriptors import rmse -from Hapi.calibration import Calibration -from Hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBV -from Hapi.rrm.parameters import Parameters as DP +from hapi.calibration import Calibration +from hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBV +from hapi.rrm.parameters import Parameters as DP # %% Paths Path = "examples/hydrological-model/data/distributed_model/" @@ -54,7 +54,7 @@ par_dist = SpatialVarFun(par,*SpatialVarArgs,kub=kub,klb=klb) """ -raster = gdal.Open(FlowAccPath) +raster = Dataset.read_file(FlowAccPath) # ------------- # for lumped catchment parameters no_parameters = 12 diff --git a/examples/hydrological-model/coello/prepare-Input-data/01-coello-prepare_inputdata.py b/examples/hydrological-model/coello/prepare-Input-data/01-coello-prepare_inputdata.py index 5a96b36cd..1b7370023 100644 --- a/examples/hydrological-model/coello/prepare-Input-data/01-coello-prepare_inputdata.py +++ b/examples/hydrological-model/coello/prepare-Input-data/01-coello-prepare_inputdata.py @@ -1,43 +1,47 @@ +"""Prepare the Coello meteorological and GIS input rasters. + +Make sure the working directory is set to the root of the Hapi repo: +current_work_directory = Hapi/ """ -Make sure the working directory is set to the examples folder in the Hapi repo" -currunt_work_directory = Hapi/ -""" + +from __future__ import annotations + +from pathlib import Path import numpy as np -from osgeo import gdal -from pyramids.raster import Raster +from pyramids.dataset import Dataset -from Hapi.rrm.inputs import Inputs +from hapi.inputs import Inputs metao_data_path = "examples/hydrological-model/data/meteo_data" gis_data_path = "examples/hydrological-model/data/gis_data" """ -this function prepare downloaded raster data to have the same align and -nodatavalue from a GIS raster (DEM, flow accumulation, flow direction raster) -and return a folder with the output rasters with a name “New_Rasters” +prepare_inputs aligns and crops the downloaded raster data to have the same +alignment and no-data value as a GIS raster (DEM, flow accumulation, or flow +direction raster) and writes the prepared rasters to the given output folder. """ dem_path = f"{gis_data_path}/acc4000.tif" -In = Inputs(dem_path) +inputs = Inputs(dem_path) outputpath = f"{metao_data_path}/meteodata_prepared/" # prec prec_in_path = f"{metao_data_path}/raw_data/prec/" -In.prepareInputs(prec_in_path, outputpath + "prec0") +inputs.prepare_inputs(prec_in_path, outputpath + "prec0") # evap evap_in_path = f"{metao_data_path}/raw_data/evap/" -In.prepareInputs(evap_in_path, outputpath + "evap0") +inputs.prepare_inputs(evap_in_path, outputpath + "evap0") # temp temp_in_path = f"{metao_data_path}/raw_data/temp/" -In.prepareInputs(temp_in_path, outputpath + "temp0") +inputs.prepare_inputs(temp_in_path, outputpath + "temp0") """ -in case you want to manipulate the value in all the rasters of one of the inputs -for example evapotranspiration values in rasters downloaded from ECMWF are -ve -and to change it to +ve in all rasters or if you want to operate any kind of function -in all input rasters that are in the same folder FolderCalculator can do this task +in case you want to manipulate the values in all the rasters of one of the inputs, +for example evapotranspiration values in rasters downloaded from ECMWF are -ve, +and to change them to +ve in all rasters (or apply any kind of function to all +input rasters in the same folder), loop over the files and use `Dataset.apply`. "How can evaporation have both positive and negative values? Evaporation is normally negative due to the convention for fluxes. @@ -46,50 +50,31 @@ Link: https://confluence.ecmwf.int/pages/viewpage.action?pageId=111155327 """ evap_out_path = f"{metao_data_path}/meteodata_prepared/evap/" - - -# define your function -# this function is going to take the absolute value of the values in the raster -# through MapAlgebra function then save the new raster to a given path with the same names -def function(args): - # argument a list of two components - # first argument is the raster object [gdal object] - A = args[0] - # second argument is the path to save the resulted raster - path = args[1] - func = np.abs - # first function - B = Raster.mapAlgebra(A, func) - Raster.saveRaster(B, path) - - -folder_path = evap_out_path new_folder_path = f"{metao_data_path}/meteodata_prepared/new_evap/" -Raster.folderCalculator(folder_path, new_folder_path, function) + +Path(new_folder_path).mkdir(parents=True, exist_ok=True) +for file in sorted(Path(evap_out_path).glob("*.tif")): + dataset = Dataset.read_file(str(file)) + # apply operates on the domain cells only and keeps the no-data cells + dataset.apply(np.abs).to_file(f"{new_folder_path}/{file.name}") """ -in order to run the model all inputs have to have the same number of rows and columns -for this purpose MatchRasterAlignment function was made to resample, change the coordinate -system of the second raster and give it the same alignment like a source raster (DEM raster) +in order to run the model all inputs have to have the same number of rows and columns. +`Dataset.align` resamples a raster and copies the coordinate system, the number of +rows/columns, and the cell size from a source raster (the DEM raster). """ soil_path = f"{gis_data_path}/soil_classes.tif" -DEM = gdal.Open(dem_path) -dem_A = DEM.ReadAsArray() -soil = gdal.Open(soil_path) -soil_A = soil.ReadAsArray() - -# align -aligned_soil = Raster.matchRasterAlignment(DEM, soil) +dem = Dataset.read_file(dem_path) +soil = Dataset.read_file(soil_path) -# to check alignment of DEM raster compared to aligned_soil_A raster -aligned_soil_A = aligned_soil.ReadAsArray() +# align the soil raster to the DEM alignment +aligned_soil = soil.align(dem) -# nodatavalue is still different and some cells are no data value in the soil type raster but it is not in the dem raster -# to match use Match MatchNoDataValue -# match -dst_Aligned_M = Raster.cropAlligned(DEM, aligned_soil) -dst_Aligned_M_A = dst_Aligned_M.ReadAsArray() +# the no-data cells are still different: some cells are no-data in the soil +# raster but not in the DEM raster. Crop with the DEM as a raster mask so both +# rasters share the same no-data cells. +masked_soil = aligned_soil.crop(dem) # save the new raster -Raster.saveRaster(dst_Aligned_M, f"{gis_data_path}/soil_classes_aligned.tif") +masked_soil.to_file(f"{gis_data_path}/soil_classes_aligned.tif") diff --git a/examples/hydrological-model/coello/prepare-Input-data/07-prepare-rasters.py b/examples/hydrological-model/coello/prepare-Input-data/07-prepare-rasters.py index c11d022d4..8de83505c 100644 --- a/examples/hydrological-model/coello/prepare-Input-data/07-prepare-rasters.py +++ b/examples/hydrological-model/coello/prepare-Input-data/07-prepare-rasters.py @@ -6,18 +6,19 @@ currunt_work_directory = Hapi/Example """ -from osgeo import gdal -from pyramids.raster import Raster +from __future__ import annotations + +from pyramids.dataset import Dataset dem_path = "Data/GIS/Hapi_GIS_Data/acc4000.tif" SaveTo = "data/parameters/" # %% -"craeate a raster typicall to the DEM and fill it with 1" -K = 1 -src = gdal.Open(dem_path) +# create a raster typical to the DEM and fill its domain cells with one value +dem = Dataset.read_file(dem_path) -Raster.rasterFill(src, K, SaveTo + "11_K_muskingum.tif") +K = 1 +dem.fill(K, path=SaveTo + "11_K_muskingum.tif") # %% X = 0.2 -Raster.rasterFill(src, X, SaveTo + "12_X_muskingum.tif") +dem.fill(X, path=SaveTo + "12_X_muskingum.tif") diff --git a/examples/hydrological-model/coello/run/coello-distributed-model-run-maxbas.py b/examples/hydrological-model/coello/run/coello-distributed-model-run-maxbas.py index 44158dec8..3fa11e9d7 100644 --- a/examples/hydrological-model/coello/run/coello-distributed-model-run-maxbas.py +++ b/examples/hydrological-model/coello/run/coello-distributed-model-run-maxbas.py @@ -3,11 +3,11 @@ import datetime as dt import pandas as pd -from osgeo import gdal +from pyramids.dataset import Dataset -from Hapi.catchment import Catchment -from Hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBV -from Hapi.run import Run +from hapi.catchment import Catchment +from hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBV +from hapi.run import Run # %% Paths Path = "examples/hydrological-model/data/distributed_model" @@ -78,7 +78,7 @@ Coello.plot_hydrograph(plotstart, plotend, gaugei) # %% store the result into rasters # create list of names -src = gdal.Open(FlowAccPath) +src = Dataset.read_file(FlowAccPath) s = dt.datetime(2012, 6, 14, 19, 00, 00) e = dt.datetime(2013, 12, 23, 00, 00, 00) index = pd.date_range(s, e, freq="1H") diff --git a/examples/hydrological-model/jiboa/Jiboa-distributed-model-muskingum-lake.py b/examples/hydrological-model/jiboa/Jiboa-distributed-model-muskingum-lake.py index 1989dfdb3..ac7f2e427 100644 --- a/examples/hydrological-model/jiboa/Jiboa-distributed-model-muskingum-lake.py +++ b/examples/hydrological-model/jiboa/Jiboa-distributed-model-muskingum-lake.py @@ -170,100 +170,13 @@ Jiboa.plot_hydrograph(plotstart, plotend, gaugei) # %% """ -============================================================================= -plotDistributedResults(StartDate, EndDate, fmt="%Y-%m-%d", Option = 1, Gauges=False, - TicksSpacing = 2, Figsize=(8,8), PlotNumbers=True, - NumSize= 8, Title = 'Total Discharge',title_size = 15, Backgroundcolorthreshold=None, - cbarlabel = 'Discharge m3/s', cbarlabelsize = 12, textcolors=("white","black"), - Cbarlength = 0.75, Interval = 200,cmap='coolwarm_r', Textloc=[0.1,0.2], - Gaugecolor='red',Gaugesize=100, ColorScale = 1,gamma=1./2.,linthresh=0.0001, - linscale=0.001, midpoint=0, orientation='vertical', rotation=-90, - **kwargs): -============================================================================= -plotDistributedResults animate the time series of the meteorological inputs and -the result calculated by the model like the total discharge, upper zone, -and lower zone discharge and the state variables - -Parameters ----------- -StartDate : [str] - starting date -EndDate : [str] - end date -fmt : [str] - format of the gicen date. The default is "%Y-%m-%d" -Option : [str] - 1- Total discharge, 2-Upper zone discharge, 3-ground water, - 4-Snowpack state variable, 5-Soil moisture, 6-Upper zone, - 7-Lower zone, 8-Water content, 9-Precipitation input. 10-ET, - 11-Temperature. The default is 1 -Gauges : [str] - . The default is False -TicksSpacing : [integer], optional - Spacing in the colorbar ticks. The default is 2. -Figsize : [tuple], optional - figure size. The default is (8,8). -PlotNumbers : [bool], optional - True to plot the values intop of each cell. The default is True. -NumSize : integer, optional - size of the numbers plotted intop of each cells. The default is 8. -Title : [str], optional - title of the plot. The default is 'Total Discharge'. -title_size : [integer], optional - title size. The default is 15. -Backgroundcolorthreshold : [float/integer], optional - threshold value if the value of the cell is greater, the plotted - numbers will be black and if smaller the plotted number will be white - if None given the maxvalue/2 will be considered. The default is None. -textcolors : TYPE, optional - Two colors to be used to plot the values i top of each cell. The default is ("white","black"). -cbarlabel : str, optional - label of the color bar. The default is 'Discharge m3/s'. -cbarlabelsize : integer, optional - size of the color bar label. The default is 12. -Cbarlength : [float], optional - ratio to control the height of the colorbar. The default is 0.75. -Interval : [integer], optional - number to controlthe speed of the animation. The default is 200. -cmap : [str], optional - color style. The default is 'coolwarm_r'. -Textloc : [list], optional - location of the date text. The default is [0.1,0.2]. -Gaugecolor : [str], optional - color of the points. The default is 'red'. -Gaugesize : [integer], optional - size of the points. The default is 100. -ColorScale : integer, optional - there are 5 options to change the scale of the colors. The default is 1. - 1- ColorScale 1 is the normal scale - 2- ColorScale 2 is the power scale - 3- ColorScale 3 is the SymLogNorm scale - 4- ColorScale 4 is the PowerNorm scale - 5- ColorScale 5 is the BoundaryNorm scale - ------------------------------------------------------------------ - gamma : [float], optional - value needed for option 2 . The default is 1./2.. - linthresh : [float], optional - value needed for option 3. The default is 0.0001. - linscale : [float], optional - value needed for option 3. The default is 0.001. - midpoint : [float], optional - value needed for option 5. The default is 0. - ------------------------------------------------------------------ -orientation : [string], optional - orintation of the colorbar horizontal/vertical. The default is 'vertical'. -rotation : [number], optional - rotation of the colorbar label. The default is -90. -**kwargs : [dict] - keys: - Points : [dataframe]. - dataframe contains two columns 'cell_row', and cell_col to - plot the point at this location - -Returns -------- -animation.FuncAnimation. +Animate the distributed results. +plot_distributed_results animates the time series of the meteorological +inputs and the results calculated by the model, like the total discharge, +upper zone and lower zone discharge, and the state variables. The keyword +arguments are forwarded to ``cleopatra.array_glyph.ArrayGlyph.animate``; +see its docstring for the full list of supported options. """ plotstart = "2012-07-20" @@ -272,24 +185,24 @@ Anim = Jiboa.plot_distributed_results( plotstart, plotend, - Figsize=(8, 8), + figsize=(8, 8), option=3, - threshold=160, - PlotNumbers=False, - TicksSpacing=10, - Interval=10, + background_color_threshold=160, + display_cell_value=False, + ticks_spacing=10, + interval=10, gauges=False, cmap="inferno", - Textloc=[0.6, 0.8], - Gaugecolor="red", - ColorScale=2, - IDcolor="blue", - IDsize=25, + text_loc=[0.6, 0.8], + point_color="red", + color_scale="power", + pid_color="blue", + pid_size=25, gamma=0.08, ) # %% Path = save_to + "anim.mov" -Jiboa.save_animation(video_format="mov", path=Path, save_frames=3) +Jiboa.save_animation(Path, fps=2) # %% Save Results start_date = "2012-07-20" end_date = "2012-08-20" diff --git a/examples/plot/plot_array.py b/examples/plot/plot_array.py index 3fcc1740f..eb6872511 100644 --- a/examples/plot/plot_array.py +++ b/examples/plot/plot_array.py @@ -1,224 +1,90 @@ -import os +"""Plot a raster with pyramids/cleopatra. + +The old ``Hapi.plot`` module moved to the ``cleopatra`` package. Rasters are +read with ``pyramids.dataset.Dataset`` and plotted either with the +``Dataset.plot`` facade or directly with ``cleopatra.array_glyph.ArrayGlyph``. +""" + +from __future__ import annotations -# os.chdir("F:/01Algorithms/Hydrology/HAPI/examples") import matplotlib matplotlib.use("TkAgg") +import numpy as np import pandas as pd -from osgeo import gdal - -from Hapi.plot.map import Map +from pyramids.dataset import Dataset # %% Paths RasterAPath = "examples/data/GIS/Hapi_GIS_Data/dem_100_f.tif" RasterBPath = "examples/data/GIS/Hapi_GIS_Data/acc4000.tif" pointsPath = "examples/GIS/data/points.csv" -""" -to plot the array you need to read the raster using gdal -""" -# read the raster -src = gdal.Open(RasterAPath) -#%% -""" -then using all the default parameters in the PlotArray method you can directly plot the gdal.Dataset -""" -Map.PlotArray(src) -""" -However as you see in the plot you might need to adjust the color to different color scheme or the -display of the colorbar, colored label. -you might don't need to display the labels showing the values of each cell, and for all of these -decisions there are a lot of customizable parameters -""" -#%% options -""" -first for the size of the figure you have to pass a tuple with the width and height -Figsize : [tuple], optional - figure size. The default is (8,8). -Title : [str], optional - title of the plot. The default is 'Total Discharge'. -title_size : [integer], optional - title size. The default is 15. -""" -Figsize = (8, 8) -Title = "DEM" -titlesize = 15 - -Map.PlotArray(src, Figsize=Figsize, Title=Title, titlesize=titlesize) -#%% -"""color bar - -Cbarlength : [float], optional - ratio to control the height of the colorbar. The default is 0.75. -orientation : [string], optional - orintation of the colorbar horizontal/vertical. The default is 'vertical'. -cbarlabelsize : integer, optional - size of the color bar label. The default is 12. -cbarlabel : str, optional - label of the color bar. The default is 'Discharge m3/s'. -rotation : [number], optional - rotation of the colorbar label. The default is -90. -TicksSpacing : [integer], optional - Spacing in the colorbar ticks. The default is 2. -""" -Cbarlength = 0.75 -orientation = "vertical" -cbarlabelsize = 12 -cbarlabel = "Elevation" -rotation = -80 -TicksSpacing = 500 -""" -rotation : [number], optional - rotation of the colorbar label. The default is -90. -""" -Map.PlotArray( - src, - Cbarlength=Cbarlength, - orientation=orientation, - cbarlabelsize=cbarlabelsize, - cbarlabel=cbarlabel, - rotation=rotation, - TicksSpacing=TicksSpacing, -) # %% -""" -color schame - -ColorScale : integer, optional - there are 5 options to change the scale of the colors. The default is 1. - 1- ColorScale 1 is the normal scale - 2- ColorScale 2 is the power scale - 3- ColorScale 3 is the SymLogNorm scale - 4- ColorScale 4 is the PowerNorm scale - 5- ColorScale 5 is the BoundaryNorm scale - ------------------------------------------------------------------ - gamma : [float], optional - value needed for option 2 . The default is 1./2.. - linthresh : [float], optional - value needed for option 3. The default is 0.0001. - linscale : [float], optional - value needed for option 3. The default is 0.001. - midpoint : [float], optional - value needed for option 5. The default is 0. - ------------------------------------------------------------------ -cmap : [str], optional - color style. The default is 'coolwarm_r'. -""" -# for normal linear scale -ColorScale = 1 -cmap = "terrain" -Map.PlotArray(src, ColorScale=ColorScale, cmap=cmap, TicksSpacing=TicksSpacing) -# %% -# for power scale -""" -the more you lower the value of gamma the more of the color bar you give to the lower value range -""" -ColorScale = 2 -gamma = 0.5 - -Map.PlotArray( - src, - ColorScale=ColorScale, - cmap=cmap, - gamma=gamma, - TicksSpacing=TicksSpacing, - Title=f"gamma = {gamma}", -) -# %% -gamma = 0.4 -Map.PlotArray( - src, - ColorScale=ColorScale, - cmap=cmap, - gamma=gamma, - TicksSpacing=TicksSpacing, - Title=f"gamma = {gamma}", -) -# %% -gamma = 0.2 -Map.PlotArray( - src, - ColorScale=ColorScale, - cmap=cmap, - gamma=gamma, - TicksSpacing=TicksSpacing, - Title=f"gamma = {gamma}", +# read the raster with pyramids +src = Dataset.read_file(RasterAPath) +# using all the default parameters, you can directly plot the Dataset +src.plot(band=0) +# %% figure options +src.plot(band=0, figsize=(8, 8), title="DEM", title_size=15) +# %% color bar options +src.plot( + band=0, + cbar_length=0.75, + cbar_orientation="vertical", + cbar_label_size=12, + cbar_label="Elevation", + cbar_label_rotation=-80, + ticks_spacing=500, ) -# %% SymLogNorm scale -ColorScale = 3 -linscale = 0.001 -linthresh = 0.0001 -Map.PlotArray( - src, - ColorScale=ColorScale, - linscale=linscale, - linthresh=linthresh, - cmap=cmap, - TicksSpacing=TicksSpacing, +# %% color scales +# linear scale +src.plot(band=0, color_scale="linear", cmap="terrain", ticks_spacing=500) +# %% power scale +# the lower the gamma, the more of the color bar is given to the low values +for gamma in [0.5, 0.4, 0.2]: + src.plot( + band=0, + color_scale="power", + gamma=gamma, + cmap="terrain", + ticks_spacing=500, + title=f"gamma = {gamma}", + ) +# %% SymLogNorm scale +src.plot( + band=0, + color_scale="sym-lognorm", + line_scale=0.001, + line_threshold=0.0001, + cmap="terrain", + ticks_spacing=500, ) -# %% PowerNorm scale -ColorScale = 4 -Map.PlotArray(src, ColorScale=ColorScale, cmap=cmap, TicksSpacing=TicksSpacing) -# %% color scale 5 -ColorScale = 5 -midpoint = 20 -Map.PlotArray( - src, ColorScale=ColorScale, midpoint=midpoint, cmap=cmap, TicksSpacing=TicksSpacing -) -# %% -src = gdal.Open(RasterBPath) -arr = src.ReadAsArray() -# %% PowerNorm scale -ColorScale = 4 -TicksSpacing = 10 -Map.PlotArray(src, ColorScale=ColorScale, cmap=cmap, TicksSpacing=TicksSpacing) +# %% midpoint scale +src.plot(band=0, color_scale="midpoint", midpoint=20, cmap="terrain", ticks_spacing=500) # %% -""" -Cell value label - -display_cellvalue : [bool] - True if you want to display the values of the cells as a text -NumSize : integer, optional - size of the numbers plotted intop of each cells. The default is 8. -Backgroundcolorthreshold : [float/integer], optional - threshold value if the value of the cell is greater, the plotted - numbers will be black and if smaller the plotted number will be white - if None given the maxvalue/2 will be considered. The default is None. - -""" -display_cellvalue = True -NumSize = 8 -Backgroundcolorthreshold = None - -Map.PlotArray( - src, - display_cellvalue=display_cellvalue, - NumSize=NumSize, - Backgroundcolorthreshold=Backgroundcolorthreshold, - TicksSpacing=TicksSpacing, +src = Dataset.read_file(RasterBPath) +arr = src.read_array(band=0) +# %% cell value labels +src.plot( + band=0, + display_cell_value=True, + num_size=8, + background_color_threshold=None, + ticks_spacing=10, ) -# %% -""" -if you have points that you want to display in the map you can read it into a dataframe -in condition that it has two columns "x", "y" which are the coordinates of the points of theand they have to be -in the same coordinate system as the raster -""" - -# read the points +# %% display points on the map +# read the points (x/y coordinates in the same CRS as the raster), convert +# them to array indices, and pass them as a [value, row, col] array points = pd.read_csv(pointsPath) - -Gaugecolor = "blue" -Gaugesize = 100 -IDcolor = "green" -IDsize = 20 -Map.PlotArray( - src, - Gaugecolor=Gaugecolor, - Gaugesize=Gaugesize, - IDcolor=IDcolor, - IDsize=IDsize, - points=points, - display_cellvalue=display_cellvalue, - NumSize=NumSize, - Backgroundcolorthreshold=Backgroundcolorthreshold, - TicksSpacing=TicksSpacing, +loc = src.map_to_array_coordinates(points) +points_arr = np.column_stack([points["id"].to_numpy(), loc]) +src.plot( + band=0, + points=points_arr, + point_color="blue", + point_size=100, + pid_color="green", + pid_size=20, + display_cell_value=True, + num_size=8, + ticks_spacing=10, ) diff --git a/pixi.lock b/pixi.lock index 8eff18228..160cfc4ce 100644 --- a/pixi.lock +++ b/pixi.lock @@ -5,223 +5,127 @@ environments: - url: https://conda.anaconda.org/conda-forge/ indexes: - https://pypi.org/simple + options: + pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda - 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"pyramids-gis >=0.6.0,!=0.9.0", + "pyramids-gis >=0.32.0", "requests >=2.31.0", "scipy >=1.11.4", "serapeum-utils >=0.2.0", - "statista >=0.6.0", + "statista >=0.8.0", "Oasis-Optimization >=1.0.3", - "cleopatra >=0.5.1", + "cleopatra >=0.17.0", "matplotlib >=3.7.2" ] @@ -227,5 +227,5 @@ python = "3.12.*" [tool.pixi.feature.py313.dependencies] python = "3.13.*" -[tool.pixi.dependencies] -gdal = ">=3.10.0,<4" +#[tool.pixi.dependencies] +#gdal = ">=3.10.0,<4" diff --git a/src/hapi/catchment.py b/src/hapi/catchment.py index 4fd284e95..b81aafdf7 100644 --- a/src/hapi/catchment.py +++ b/src/hapi/catchment.py @@ -12,6 +12,7 @@ as a lumped model using a rating curve, where the lake and its upstream sub-catchments are treated as one lumped model. """ + from __future__ import annotations import datetime as dt @@ -28,9 +29,8 @@ import statista.descriptors as metrics from cleopatra.array_glyph import ArrayGlyph from loguru import logger -from osgeo import gdal from pyramids.dataset import Dataset -from pyramids.multidataset import MultiDataset as Datacube +from pyramids.dataset import DatasetCollection as Datacube from hapi.dem import DEM @@ -154,6 +154,7 @@ def __init__( self.qlz_translated: np.ndarray | None = None self.state_variables: np.ndarray | None = None self.anim: matplotlib.animation.FuncAnimation | None = None + self._animation_glyph: ArrayGlyph | None = None self.quz: np.ndarray | None = None self.qlz: np.ndarray | None = None self.Qsim: np.ndarray | None = None @@ -402,13 +403,12 @@ def read_flow_acc(self, path: str): # check whether the path exists or not assert os.path.exists(path), path + " you have provided does not exist" - flow_acc = gdal.Open(path) - if flow_acc is None: - raise FileNotFoundError(f"GDAL could not open: {path}") - [self.rows, self.cols] = flow_acc.ReadAsArray().shape + flow_acc = Dataset.read_file(path) + self.rows = flow_acc.rows + self.cols = flow_acc.columns # check flow accumulation input raster - self.NoDataValue = flow_acc.GetRasterBand(1).GetNoDataValue() - self.FlowAccArr = flow_acc.ReadAsArray() + self.NoDataValue = flow_acc.no_data_value[0] + self.FlowAccArr = flow_acc.read_array(band=0) # check if the flow acc array is integer convert it to float if self.FlowAccArr.dtype == "int": @@ -419,8 +419,9 @@ def read_flow_acc(self, path: str): if math.isclose(self.FlowAccArr[i, j], self.NoDataValue, rel_tol=0.001): self.FlowAccArr[i, j] = np.nan - self.no_elem = int(np.size(self.FlowAccArr[:, :]) - np.count_nonzero( - (self.FlowAccArr[np.isnan(self.FlowAccArr)])) + self.no_elem = int( + np.size(self.FlowAccArr[:, :]) + - np.count_nonzero((self.FlowAccArr[np.isnan(self.FlowAccArr)])) ) self.acc_val = [ int(self.FlowAccArr[i, j]) @@ -448,9 +449,8 @@ def read_flow_acc(self, path: str): self.Outlet = np.where(self.FlowAccArr == np.nanmax(self.FlowAccArr)) # calculate area covered by cells - geo_trans = ( - flow_acc.GetGeoTransform() - ) # get the coordinates of the top left corner and cell size [x,dx,y,dy] + # get the coordinates of the top left corner and cell size [x,dx,y,dy] + geo_trans = flow_acc.geotransform dx = np.abs(geo_trans[1]) / 1000.0 # dx in Km dy = np.abs(geo_trans[-1]) / 1000.0 # dy in Km self.CellSize = dx * 1000 @@ -487,14 +487,12 @@ def read_flow_dir(self, path: str): ) # check whether the path exists or not assert os.path.exists(path), path + " you have provided does not exist" - flow_dir = gdal.Open(path) - if flow_dir is None: - raise FileNotFoundError(f"GDAL could not open: {path}") - - [rows, cols] = flow_dir.ReadAsArray().shape - self.FlowDirArr = flow_dir.ReadAsArray().astype(float) + flow_dir = DEM.read_file(path) + rows = flow_dir.rows + cols = flow_dir.columns + self.FlowDirArr = flow_dir.read_array(band=0).astype(float) # check flow direction input raster - fd_noval = flow_dir.GetRasterBand(1).GetNoDataValue() + fd_noval = flow_dir.no_data_value[0] for i in range(rows): for j in range(cols): @@ -514,8 +512,7 @@ def read_flow_dir(self, path: str): ), "flow direction raster should contain values 1,2,4,8,16,32,64,128 only " # create the flow direction table - dem = DEM(flow_dir) - self.FDT = dem.flow_direction_table() + self.FDT = flow_dir.flow_direction_table() logger.debug("Flow Direction input is read successfully") def read_flow_path_length(self, path: str): @@ -544,21 +541,21 @@ def read_flow_path_length(self, path: str): # check whether the path exists or not assert os.path.exists(path), path + " you have provided does not exist" - fpl = gdal.Open(path) - if fpl is None: - raise FileNotFoundError(f"GDAL could not open: {path}") - [self.rows, self.cols] = fpl.ReadAsArray().shape - self.FPLArr = fpl.ReadAsArray() - self.NoDataValue = fpl.GetRasterBand(1).GetNoDataValue() + fpl = Dataset.read_file(path) + self.rows = fpl.rows + self.cols = fpl.columns + self.FPLArr = fpl.read_array(band=0) + self.NoDataValue = fpl.no_data_value[0] for i in range(self.rows): for j in range(self.cols): if math.isclose(self.FPLArr[i, j], self.NoDataValue, rel_tol=0.001): self.FPLArr[i, j] = np.nan # check flow accumulation input raster - self.no_elem = int(np.size(self.FPLArr[:, :]) - np.count_nonzero( - (self.FPLArr[np.isnan(self.FPLArr)]) - )) + self.no_elem = int( + np.size(self.FPLArr[:, :]) + - np.count_nonzero((self.FPLArr[np.isnan(self.FPLArr)])) + ) logger.debug("Flow path length input is read successfully") @@ -594,12 +591,8 @@ def read_river_geometry( ("RiverRoughness", river_roughness_file), ("FloodPlainRoughness", floodplain_roughness_file), ]: - ds = gdal.Open(fpath) - if ds is None: - raise FileNotFoundError( - f"GDAL could not open {name} file: {fpath}" - ) - setattr(self, name, ds.ReadAsArray()) + ds = Dataset.read_file(fpath) + setattr(self, name, ds.read_array(band=0)) def read_parameters(self, path: str, snow: bool = False, maxbas: bool = False): """Read model parameter rasters or a CSV parameter file. @@ -822,13 +815,8 @@ def read_gauge_table( ) if flow_acc_file != "" and "cell_row" not in col_list: # if hasattr(self, 'flow_acc'): - flow_acc = gdal.Open(flow_acc_file) - if flow_acc is None: - raise FileNotFoundError( - f"GDAL could not open: {flow_acc_file}" - ) # calculate the nearest cell to each station - dataset = Dataset(flow_acc) + dataset = Dataset.read_file(flow_acc_file) loc_arr = dataset.map_to_array_coordinates(self.GaugesTable) self.GaugesTable.loc[:, ["cell_row", "cell_col"]] = loc_arr @@ -1038,10 +1026,10 @@ def extract_discharge( metrics.wb(q_obs, q_sim), 3 ) self.Metrics.loc["Pearson-CC", gauge_id] = round( - metrics.pearson_corre(q_obs, q_sim), 3 + metrics.pearson_corr_coeff(q_obs, q_sim), 3 ) self.Metrics.loc["R2", gauge_id] = round( - metrics.R2(q_obs, q_sim), 3 + metrics.r2(q_obs, q_sim), 3 ) elif frame_work_1 or only_outlet: self.Qsim = pd.DataFrame(index=self.Index) @@ -1231,15 +1219,15 @@ def plot_distributed_results( on the animation. Default is False. **kwargs: Additional keyword arguments passed to ``ArrayGlyph.animate``. Common options include: - TicksSpacing (int), Figsize (tuple), - PlotNumbers (bool), NumSize (int), title (str), - title_size (int), Backgroundcolorthreshold (float), - textcolors (tuple), cbarlabel (str), - cbarlabelsize (int), Cbarlength (float), - Interval (int), cmap (str), Textloc (list), - Gaugecolor (str), Gaugesize (int), - ColorScale (int), orientation (str), - rotation (float), Points (DataFrame). + interval (int), text_colors (tuple), + text_loc (list), point_color (str), + point_size (int), pid_color (str), pid_size (int), + title_size (int), cmap (str), vmin (float), + vmax (float), color_scale (str), ticks_spacing (int), + cbar_label (str), cbar_label_size (int), + cbar_length (float), cbar_orientation (str), + display_cell_value (bool), num_size (int), + background_color_threshold (float). Returns: matplotlib.animation.FuncAnimation: The animation object. @@ -1257,47 +1245,47 @@ def plot_distributed_results( raise ValueError("Plotting options are from 1 to 11") if option == 1: - self.Qtot[self.FlowAccArr == self.NoDataValue, :] = np.nan + self.Qtot[np.isnan(self.FlowAccArr), :] = np.nan arr = self.Qtot[:, :, start_i:end_i] title = "Total Discharge" elif option == 2: - self.quz_routed[self.FlowAccArr == self.NoDataValue, :] = np.nan + self.quz_routed[np.isnan(self.FlowAccArr), :] = np.nan arr = self.quz_routed[:, :, start_i:end_i] title = "Surface Flow" elif option == 3: - self.qlz_translated[self.FlowAccArr == self.NoDataValue, :] = np.nan + self.qlz_translated[np.isnan(self.FlowAccArr), :] = np.nan arr = self.qlz_translated[:, :, start_i:end_i] title = "Ground Water Flow" elif option == 4: - self.state_variables[self.FlowAccArr == self.NoDataValue, :, 0] = np.nan + self.state_variables[np.isnan(self.FlowAccArr), :, 0] = np.nan arr = self.state_variables[:, :, start_i:end_i, 0] title = "Snow Pack" elif option == 5: - self.state_variables[self.FlowAccArr == self.NoDataValue, :, 1] = np.nan + self.state_variables[np.isnan(self.FlowAccArr), :, 1] = np.nan arr = self.state_variables[:, :, start_i:end_i, 1] title = "Soil Moisture" elif option == 6: - self.state_variables[self.FlowAccArr == self.NoDataValue, :, 2] = np.nan + self.state_variables[np.isnan(self.FlowAccArr), :, 2] = np.nan arr = self.state_variables[:, :, start_i:end_i, 2] title = "Upper Zone" elif option == 7: - self.state_variables[self.FlowAccArr == self.NoDataValue, :, 3] = np.nan + self.state_variables[np.isnan(self.FlowAccArr), :, 3] = np.nan arr = self.state_variables[:, :, start_i:end_i, 3] title = "Lower Zone" elif option == 8: - self.state_variables[self.FlowAccArr == self.NoDataValue, :, 4] = np.nan + self.state_variables[np.isnan(self.FlowAccArr), :, 4] = np.nan arr = self.state_variables[:, :, start_i:end_i, 4] title = "Water Content" elif option == 9: - self.Prec[self.FlowAccArr == self.NoDataValue, :] = np.nan + self.Prec[np.isnan(self.FlowAccArr), :] = np.nan arr = self.Prec[:, :, start_i:end_i] title = "Precipitation" elif option == 10: - self.ET[self.FlowAccArr == self.NoDataValue, :] = np.nan + self.ET[np.isnan(self.FlowAccArr), :] = np.nan arr = self.ET[:, :, start_i:end_i] title = "ET" elif option == 11: - self.Temp[self.FlowAccArr == self.NoDataValue, :] = np.nan + self.Temp[np.isnan(self.FlowAccArr), :] = np.nan arr = self.Temp[:, :, start_i:end_i] title = "Temperature" else: @@ -1306,16 +1294,43 @@ def plot_distributed_results( time = self.Index[start_i:end_i] if gauges: - kwargs["Points"] = self.GaugesTable + # animate expects a 3-column array: [value to display, cell row, cell column] + kwargs["points"] = self.GaugesTable[ + ["id", "cell_row", "cell_col"] + ].to_numpy() - array = ArrayGlyph(arr) + # animate iterates over the first dimension, so move the time axis to the front + array = ArrayGlyph(np.moveaxis(arr, -1, 0)) anim = array.animate(time, title=title, **kwargs) - # anim = StaticGlyph.AnimateArray(Arr, Time, self.no_elem, Title=Title, **kwargs) + self._animation_glyph = array self.anim = anim return anim + def save_animation(self, path: str, fps: int = 2): + """Save the animation created by `plot_distributed_results`. + + The output format is determined by the file extension. GIF uses + PillowWriter; mov/avi/mp4 require FFmpeg to be installed. + + Args: + path (str): Output file path. The extension determines the + format (gif, mov, avi, or mp4). + fps (int, optional): Frames per second. Default is 2. + + Raises: + ValueError: If `plot_distributed_results` has not been called + yet, or if the file format is not supported. + FileNotFoundError: If a video format is requested but FFmpeg + is not installed. + """ + if self._animation_glyph is None: + raise ValueError( + "There is no animation to save, call `plot_distributed_results` first" + ) + self._animation_glyph.save_animation(path, fps=fps) + def save_results( self, flow_acc_path: str = "", @@ -1376,11 +1391,7 @@ def save_results( "Please enter the FlowAccPath parameter to the saveResults method" ) - src = gdal.Open(flow_acc_path) - if src is None: - raise FileNotFoundError( - f"GDAL could not open: {flow_acc_path}" - ) + src = Dataset.read_file(flow_acc_path) if prefix == "": prefix = "Result_" @@ -1412,7 +1423,7 @@ def save_results( f" The result parameter takes a value between 1 and 8, given: {result}" ) - cube = Datacube(Dataset(src), time_length=arr.shape[2]) + cube = Datacube(src, time_length=arr.shape[2]) arr = np.moveaxis(arr, -1, 0) cube.values = arr cube.to_file(names) diff --git a/src/hapi/dem.py b/src/hapi/dem.py index c96c0e10c..80d81a374 100644 --- a/src/hapi/dem.py +++ b/src/hapi/dem.py @@ -4,43 +4,43 @@ ``pyramids.dataset.Dataset`` with flow-direction utilities used by the distributed rainfall-runoff modelling pipeline. """ + from __future__ import annotations import numpy as np from pyramids.dataset import Dataset - D8_OFFSETS_ESRI: dict[int, tuple[int, int]] = { - 1: (0, 1), # east - 2: (1, 1), # south-east - 4: (1, 0), # south - 8: (1, -1), # south-west - 16: (0, -1), # west - 32: (-1, -1), # north-west - 64: (-1, 0), # north - 128: (-1, 1), # north-east + 1: (0, 1), # east + 2: (1, 1), # south-east + 4: (1, 0), # south + 8: (1, -1), # south-west + 16: (0, -1), # west + 32: (-1, -1), # north-west + 64: (-1, 0), # north + 128: (-1, 1), # north-east } D8_OFFSETS_SAGA: dict[int, tuple[int, int]] = { - 0: (0, 1), # east - 1: (-1, 1), # north-east - 2: (-1, 0), # north - 3: (-1, -1), # north-west - 4: (0, -1), # west - 5: (1, -1), # south-west - 6: (1, 0), # south - 7: (1, 1), # south-east + 0: (0, 1), # east + 1: (-1, 1), # north-east + 2: (-1, 0), # north + 3: (-1, -1), # north-west + 4: (0, -1), # west + 5: (1, -1), # south-west + 6: (1, 0), # south + 7: (1, 1), # south-east } D8_OFFSETS_GRASS: dict[int, tuple[int, int]] = { - 1: (-1, 0), # north - 2: (-1, 1), # north-east - 3: (0, 1), # east - 4: (1, 1), # south-east - 5: (1, 0), # south - 6: (1, -1), # south-west - 7: (0, -1), # west - 8: (-1, -1), # north-west + 1: (-1, 0), # north + 2: (-1, 1), # north-east + 3: (0, 1), # east + 4: (1, 1), # south-east + 5: (1, 0), # south + 6: (1, -1), # south-west + 7: (0, -1), # west + 8: (-1, -1), # north-west } D8_ENCODINGS: dict[str, dict[int, tuple[int, int]]] = { @@ -63,18 +63,11 @@ class DEM(Dataset): - **SAGA**: codes 0--7, starting East counter-clockwise. - **GRASS**: codes 1--8, starting North clockwise. - Args: - src: A GDAL dataset or a file path to a DEM raster that can - be opened by ``pyramids.dataset.Dataset``. + Use ``DEM.read_file(path)`` to open a raster from disk, exactly + like ``pyramids.dataset.Dataset``. """ - def __init__(self, src): - """Initialize the DEM instance.""" - super().__init__(src) - - def flow_direction_index( - self, encoding: str = "esri" - ) -> np.ndarray: + def flow_direction_index(self, encoding: str = "esri") -> np.ndarray: """Convert flow-direction codes into downstream-cell indices. Reads the flow-direction band from the underlying raster and @@ -130,9 +123,7 @@ def flow_direction_index( fd_cell = np.full((rows, cols, 2), np.nan) - row_idx, col_idx = np.meshgrid( - np.arange(rows), np.arange(cols), indexing="ij" - ) + row_idx, col_idx = np.meshgrid(np.arange(rows), np.arange(cols), indexing="ij") d_row = np.full((rows, cols), np.nan) d_col = np.full((rows, cols), np.nan) diff --git a/src/hapi/inputs.py b/src/hapi/inputs.py index 316bd4b9d..6f42d05c1 100644 --- a/src/hapi/inputs.py +++ b/src/hapi/inputs.py @@ -10,6 +10,7 @@ manipulation, and uses the ``HAPI_DATA_DIR`` environment variable to locate pre-downloaded global parameter sets (Beck et al., 2016). """ + from __future__ import annotations import datetime as dt @@ -19,7 +20,7 @@ import pandas as pd from geopandas import GeoDataFrame from pyramids.dataset import Dataset -from pyramids.multidataset import MultiDataset as Datacube +from pyramids.dataset import DatasetCollection as Datacube PARAMETERS_LIST = [ "01_tt", @@ -71,9 +72,7 @@ def __init__(self, src: str): """ self.source_dem = src - def prepare_inputs( - self, inputs_dir: str | Path, outputs_dir: str | Path - ): + def prepare_inputs(self, inputs_dir: str | Path, outputs_dir: str | Path): """Align and crop input rasters to match the source DEM. Reads all rasters from ``inputs_dir``, aligns them to the source @@ -107,9 +106,11 @@ def prepare_inputs( cube = Datacube.read_multiple_files(inputs_dir, with_order=False) cube.open_multi_dataset() - cube.align(mask) + # in-place align/crop clear the collection's file list, so capture the names first + file_names = [Path(file).name for file in cube.files] + cube.align(mask, inplace=True) cube.crop(mask, inplace=True) - path = [f"{outputs_dir}/{file.split('/')[-1]}" for file in cube.files] + path = [f"{outputs_dir}/{name}" for name in file_names] cube.to_file(path) @staticmethod @@ -146,7 +147,9 @@ def extract_parameters_boundaries(basin: GeoDataFrame): file_path = data_dir / f"max/{PARAMETERS_LIST[0]}.tif" if not file_path.exists() or not max_dir.exists() or not min_dir.exists(): - raise FileNotFoundError(f"check the following files{file_path}, {max_dir}, {min_dir} does not exist") + raise FileNotFoundError( + f"check the following files{file_path}, {max_dir}, {min_dir} does not exist" + ) dataset = Dataset.read_file(str(file_path)) basin = basin.to_crs(crs=dataset.crs) @@ -154,18 +157,14 @@ def extract_parameters_boundaries(basin: GeoDataFrame): # max values ub = list() for i in range(len(PARAMETERS_LIST)): - dataset = Dataset.read_file( - f"{data_dir}/max/{PARAMETERS_LIST[i]}.tif" - ) + dataset = Dataset.read_file(f"{data_dir}/max/{PARAMETERS_LIST[i]}.tif") vals = dataset.stats(mask=basin) ub.append(vals.loc[vals.index[0], "max"]) # min values lb = list() for i in range(len(PARAMETERS_LIST)): - dataset = Dataset.read_file( - f"{data_dir}/min/{PARAMETERS_LIST[i]}.tif" - ) + dataset = Dataset.read_file(f"{data_dir}/min/{PARAMETERS_LIST[i]}.tif") vals = dataset.stats(mask=basin) lb.append(vals.loc[vals.index[0], "min"]) diff --git a/src/hapi/rrm/distrrm.py b/src/hapi/rrm/distrrm.py index 62f136bfc..8f211a573 100644 --- a/src/hapi/rrm/distrrm.py +++ b/src/hapi/rrm/distrrm.py @@ -8,10 +8,10 @@ The module belongs to the ``hapi.rrm`` package and supports both Muskingum and triangular (MAXBAS) routing strategies. """ + from __future__ import annotations import numpy as np -from pyramids.dataset import Dataset from hapi.routing import Routing as routing @@ -325,7 +325,7 @@ def Dist_HBV2( giving the grid indices of the lake cell. q_lake (numpy.ndarray): 1-D array of lake discharge time series in m3/s. - DEM: GDAL dataset of the catchment DEM. + DEM (Dataset): pyramids ``Dataset`` of the catchment DEM. flow_acc (dict): Flow direction table mapping ``"row,col"`` keys to lists of upstream cell index pairs. @@ -372,18 +372,15 @@ def Dist_HBV2( dummy_states[:] = np.nan # Get the mask - # mask, no_val = raster.get_mask(DEM) - dataset = Dataset(DEM) - no_val = dataset.no_data_value[0] - mask = dataset.read_array() + no_val = DEM.no_data_value[0] + mask = DEM.read_array(band=0) # shape of the fpl raster (rows, columns)-------------- rows are x and columns are y x_ext, y_ext = mask.shape # y_ext, x_ext = mask.shape # shape of the fpl raster (rows, columns)------------ should change rows are y and columns are x # Get deltas of pixel - geo_trans = ( - DEM.GetGeoTransform() - ) # get the coordinates of the top left corner and cell size [x,dx,y,dy] + # get the coordinates of the top left corner and cell size [x,dx,y,dy] + geo_trans = DEM.geotransform dx = np.abs(geo_trans[1]) / 1000.0 # dx in Km dy = np.abs(geo_trans[-1]) / 1000.0 # dy in Km px_area = dx * dy # area of the cell diff --git a/src/hapi/rrm/parameters.py b/src/hapi/rrm/parameters.py index f8ffae48e..508b2d82e 100644 --- a/src/hapi/rrm/parameters.py +++ b/src/hapi/rrm/parameters.py @@ -5,6 +5,7 @@ lumped, all parameters lumped, hydrologic response units) and saving generated parameters into rasters. """ + from __future__ import annotations import datetime as dt @@ -12,9 +13,10 @@ import os import numpy as np -from osgeo import gdal from pyramids.dataset import Dataset +from hapi.dem import DEM + class Parameters: """Parameter distribution class for hydrological model calibration. @@ -44,9 +46,9 @@ def __init__( raster. Args: - raster: A gdal.Dataset raster to get the spatial information + raster: A pyramids ``Dataset`` to get the spatial information of the catchment (DEM, flow accumulation or flow direction - raster). + raster), read it using ``Dataset.read_file``. no_parameters: Number of parameters in the HBV model. no_lumped_par: Number of lumped parameters. You have to enter the value of the lumped parameter at the end of the list. @@ -76,19 +78,20 @@ def __init__( Defaults to False. Raises: - AssertionError: If `raster` is not a gdal.Dataset, if - `no_parameters` is not an integer, if `no_lumped_par` is - not an integer, or if the length of `lumped_par_pos` does - not match `no_lumped_par`. + TypeError: If `raster` is not a pyramids Dataset. + AssertionError: If `no_parameters` is not an integer, if + `no_lumped_par` is not an integer, or if the length of + `lumped_par_pos` does not match `no_lumped_par`. ValueError: If `lumped_par_pos` is not a list when `no_lumped_par` >= 1. """ if lumped_par_pos is None: lumped_par_pos = [] - assert isinstance( - raster, gdal.Dataset - ), "raster should be read using gdal (gdal dataset please read it using gdal library) " + if not isinstance(raster, Dataset): + raise TypeError( + "raster should be a pyramids Dataset, read it using pyramids.dataset.Dataset.read_file" + ) assert isinstance(no_parameters, int), " no_parameters should be integer number" assert isinstance( no_lumped_par, int @@ -115,12 +118,12 @@ def __init__( self.Maskingum = muskingum # read the raster self.raster = raster - self.raster_A = raster.ReadAsArray().astype(float) + self.raster_A = raster.read_array(band=0).astype(float) # get the shape of the raster - self.rows = raster.RasterYSize - self.cols = raster.RasterXSize + self.rows = raster.rows + self.cols = raster.columns # get the no_value of in the raster - self.noval = raster.GetRasterBand(1).GetNoDataValue() + self.noval = raster.no_data_value[0] for i in range(self.rows): for j in range(self.cols): @@ -509,12 +512,13 @@ def hru_hand(dem, flow_direction, flow_path_length, river): and the flow path distance. Args: - dem: A gdal.Dataset of the DEM raster. - flow_direction: A gdal.Dataset of the flow direction raster. - flow_path_length: A gdal.Dataset of the flow path length + dem: A pyramids ``Dataset`` of the DEM raster. + flow_direction: A pyramids ``Dataset`` of the flow direction raster. - river: A gdal.Dataset of the river location raster, where - cells with value 1 indicate river presence. + flow_path_length: A pyramids ``Dataset`` of the flow path + length raster. + river: A pyramids ``Dataset`` of the river location raster, + where cells with value 1 indicate river presence. Returns: A tuple of two numpy ndarrays: @@ -527,20 +531,19 @@ def hru_hand(dem, flow_direction, flow_path_length, river): (e.g., after cropping with a polygon). """ # Use DEM raster information to run all loops - dem_a = dem.ReadAsArray() - no_val = np.float32(dem.GetRasterBand(1).GetNoDataValue()) - rows = dem.RasterYSize - cols = dem.RasterXSize + dem_a = dem.read_array(band=0) + no_val = np.float32(dem.no_data_value[0]) + rows = dem.rows + cols = dem.columns # get the indices of the flow direction path - dem = dem(flow_direction) - fd_index = dem.flowDirectionIndex() + fd_index = DEM(flow_direction.raster).flow_direction_index() # read the river location raster - river_a = river.ReadAsArray() + river_a = river.read_array(band=0) # read the flow path length raster - fpl_a = flow_path_length.ReadAsArray() + fpl_a = flow_path_length.read_array(band=0) # trace the flow direction to the nearest river reach and store the location # of that nearst reach @@ -702,6 +705,4 @@ def save_parameters(self, path): ] for i in range(np.shape(self.Par3d)[2]): - Dataset.dataset_like( - self.raster, self.Par3d[:, :, i], driver="geotiff", path=pnme[i] - ) + Dataset.dataset_like(self.raster, self.Par3d[:, :, i], path=pnme[i]) diff --git a/src/hapi/wrapper.py b/src/hapi/wrapper.py index 9f8bd9937..e3c46bc02 100644 --- a/src/hapi/wrapper.py +++ b/src/hapi/wrapper.py @@ -5,6 +5,7 @@ supports multiple configurations including Muskingum routing, triangular routing, and lake integration. """ + from __future__ import annotations from collections.abc import Callable @@ -59,12 +60,12 @@ def RRMModel(Model: Catchment, ll_temp=None, q_0=None): Args: Model: Catchment model object containing: - - DEM (gdal.dataset): DEM raster clipped to the - catchment. - - flow_acc (gdal.dataset): Flow accumulation raster - clipped to the catchment. - - flow_direct (gdal.dataset): Flow direction raster - clipped to the catchment. + - DEM (numpy.ndarray): DEM raster array clipped to + the catchment. + - FlowAccArr (numpy.ndarray): Flow accumulation + raster array clipped to the catchment. + - FlowDirArr (numpy.ndarray): Flow direction raster + array clipped to the catchment. - sp_prec (numpy.ndarray): 3D precipitation array with the same 2D dimensions as the raster input. - sp_et (numpy.ndarray): 3D evapotranspiration array diff --git a/tests/calibration/distributed_mode_calib.py b/tests/calibration/distributed_mode_calib.py index 9f9b8c126..607d36f53 100644 --- a/tests/calibration/distributed_mode_calib.py +++ b/tests/calibration/distributed_mode_calib.py @@ -9,7 +9,7 @@ import numpy as np import statista.descriptors as metrics -from osgeo import gdal +from pyramids.dataset import Dataset from hapi.calibration import Calibration from hapi.rrm.hbv_bergestrom92 import HBVBergestrom92 as HBV @@ -61,7 +61,7 @@ par_dist=SpatialVarFun(par,*SpatialVarArgs,kub=kub,klb=klb) """ -raster = gdal.Open(FlowAccPath) +raster = Dataset.read_file(FlowAccPath) # ------------- # for lumped catchment parameters no_parameters = 12 diff --git a/tests/conftest.py b/tests/conftest.py index f7e2ac92d..9acc55a94 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,10 +1,13 @@ -# from tests.gis.conftest import * +# pyramids has to imported the very first thing to make gdal available +from pyramids import __version__ + +from hapi.dem import DEM from tests.rrm.conftest import * @pytest.fixture(scope="module") -def coello_df_4000() -> gdal.Dataset: - return gdal.Open("tests/rrm/data/coello/gis/fd4000.tif") +def coello_df_4000() -> DEM: + return DEM.read_file("tests/rrm/data/coello/gis/fd4000.tif") @pytest.fixture(scope="module") diff --git a/tests/rrm/conftest.py b/tests/rrm/conftest.py index 5c79f0f40..d8e6826bb 100644 --- a/tests/rrm/conftest.py +++ b/tests/rrm/conftest.py @@ -5,7 +5,7 @@ import numpy as np import pytest from geopandas import GeoDataFrame -from osgeo import gdal +from pyramids.dataset import Dataset from hapi.parameters.parameters import Parameter from tests.rrm.calibration.conftest import * @@ -80,8 +80,8 @@ def coello_acc_path() -> str: @pytest.fixture(scope="module") -def coello_acc_raster() -> gdal.Dataset: - return gdal.Open("tests/rrm/data/coello/gis/acc4000.tif") +def coello_acc_raster() -> Dataset: + return Dataset.read_file("tests/rrm/data/coello/gis/acc4000.tif") @pytest.fixture(scope="module") diff --git a/tests/rrm/test_dist_parameters.py b/tests/rrm/test_dist_parameters.py index 4968d30b8..48f50800b 100644 --- a/tests/rrm/test_dist_parameters.py +++ b/tests/rrm/test_dist_parameters.py @@ -1,12 +1,12 @@ import numpy as np -from osgeo import gdal +from pyramids.dataset import Dataset from hapi.rrm.parameters import Parameters as DP def test_create_distparameters_instance( coello_acc_path: str, - coello_acc_raster: gdal.Dataset, + coello_acc_raster: Dataset, coello_no_parameters: int, coello_rows: int, coello_cols: int, @@ -27,7 +27,7 @@ def test_create_distparameters_instance( ) assert SpatialVarFun.no_lumped_par == no_lumped_par assert SpatialVarFun.lumped_par_pos == lumped_par_pos - assert isinstance(SpatialVarFun.raster, gdal.Dataset) + assert isinstance(SpatialVarFun.raster, Dataset) assert SpatialVarFun.rows == coello_rows assert SpatialVarFun.cols == coello_cols assert isinstance(SpatialVarFun.raster_A, np.ndarray) @@ -39,7 +39,7 @@ def test_create_distparameters_instance( def test_par3d( coello_acc_path: str, - coello_acc_raster: gdal.Dataset, + coello_acc_raster: Dataset, coello_no_parameters: int, coello_parameters: np.ndarray, coello_parameters_dist: np.ndarray, diff --git a/tests/rrm/test_rrm_inputs.py b/tests/rrm/test_rrm_inputs.py index 073ff5b19..3fc484af6 100644 --- a/tests/rrm/test_rrm_inputs.py +++ b/tests/rrm/test_rrm_inputs.py @@ -2,7 +2,7 @@ import numpy as np from geopandas import GeoDataFrame -from pyramids.multidataset import MultiDataset as Datacube +from pyramids.dataset import DatasetCollection as Datacube from hapi.inputs import Inputs @@ -144,16 +144,18 @@ def test_create_lumped_parameter(): date=True, file_name_data_fmt="%Y.%m.%d", ) + # exact spatial means of the valid (non-nodata) cells; pyramids >= 0.32 returns + # exact band statistics instead of the sampled values returned by older versions validation_values = [ 0.0, - 0.3153739, - 43.65052, - 0.9195719, - 8.736267, - 6.411966, - 21.155485, - 1.027534, - 4.1313076, - 1.5406969, + 0.2987198, + 44.0648258, + 0.9426745, + 8.8650742, + 6.4935942, + 21.4244297, + 1.0538575, + 4.0846919, + 1.5792784, ] assert np.isclose(lumped_data, validation_values, atol=0.001, rtol=0.001).all() diff --git a/tests/run/distributed_mode_run.py b/tests/run/distributed_mode_run.py index 41624bed7..203e0bf54 100644 --- a/tests/run/distributed_mode_run.py +++ b/tests/run/distributed_mode_run.py @@ -2,6 +2,7 @@ @author: Mostafa """ + Comp = "F:/01Algorithms/Hydrology/HAPI/examples" import hapi.rrm.hbv_bergestrom92 as HBV @@ -82,92 +83,11 @@ Coello.plot_hydrograph(plotstart, plotend, gaugei) # %% """ -============================================================================= -AnimateArray(Arr, Time, NoElem, TicksSpacing = 2, Figsize=(8,8), PlotNumbers=True, - NumSize= 8, Title = 'Total Discharge',title_size = 15, Backgroundcolorthreshold=None, - cbarlabel = 'Discharge m3/s', cbarlabelsize = 12, textcolors=("white","black"), - Cbarlength = 0.75, Interval = 200,cmap='coolwarm_r', Textloc=[0.1,0.2], - Gaugecolor='red',Gaugesize=100, ColorScale = 1,gamma=1./2.,linthresh=0.0001, - linscale=0.001, midpoint=0, orientation='vertical', rotation=-90,IDcolor = "blue", - IDsize =10, **kwargs) -============================================================================= -Parameters ----------- -Arr : [array] - the array you want to animate. -Time : [dataframe] - dataframe contains the date of values. -NoElem : [integer] - Number of the cells that has values. -TicksSpacing : [integer], optional - Spacing in the colorbar ticks. The default is 2. -Figsize : [tuple], optional - figure size. The default is (8,8). -PlotNumbers : [bool], optional - True to plot the values intop of each cell. The default is True. -NumSize : integer, optional - size of the numbers plotted intop of each cells. The default is 8. -Title : [str], optional - title of the plot. The default is 'Total Discharge'. -title_size : [integer], optional - title size. The default is 15. -Backgroundcolorthreshold : [float/integer], optional - threshold value if the value of the cell is greater, the plotted - numbers will be black and if smaller the plotted number will be white - if None given the maxvalue/2 will be considered. The default is None. -textcolors : TYPE, optional - Two colors to be used to plot the values i top of each cell. The default is ("white","black"). -cbarlabel : str, optional - label of the color bar. The default is 'Discharge m3/s'. -cbarlabelsize : integer, optional - size of the color bar label. The default is 12. -Cbarlength : [float], optional - ratio to control the height of the colorbar. The default is 0.75. -Interval : [integer], optional - number to controlthe speed of the animation. The default is 200. -cmap : [str], optional - color style. The default is 'coolwarm_r'. -Textloc : [list], optional - location of the date text. The default is [0.1,0.2]. -Gaugecolor : [str], optional - color of the points. The default is 'red'. -Gaugesize : [integer], optional - size of the points. The default is 100. -IDcolor : [str] - the ID of the Point.The default is "blue". -IDsize : [integer] - size of the ID text. The default is 10. -ColorScale : integer, optional - there are 5 options to change the scale of the colors. The default is 1. - 1- ColorScale 1 is the normal scale - 2- ColorScale 2 is the power scale - 3- ColorScale 3 is the SymLogNorm scale - 4- ColorScale 4 is the PowerNorm scale - 5- ColorScale 5 is the BoundaryNorm scale - ------------------------------------------------------------------ - gamma : [float], optional - value needed for option 2 . The default is 1./2.. - linthresh : [float], optional - value needed for option 3. The default is 0.0001. - linscale : [float], optional - value needed for option 3. The default is 0.001. - midpoint : [float], optional - value needed for option 5. The default is 0. - ------------------------------------------------------------------ -orientation : [string], optional - orintation of the colorbar horizontal/vertical. The default is 'vertical'. -rotation : [number], optional - rotation of the colorbar label. The default is -90. -**kwargs : [dict] - keys: - Points : [dataframe]. - dataframe contains two columns 'cell_row', and cell_col to - plot the point at this location - -Returns -------- -animation.FuncAnimation. +Animate the distributed results. +plot_distributed_results forwards the keyword arguments to +``cleopatra.array_glyph.ArrayGlyph.animate``; see its docstring for the +full list of supported options. """ plotstart = "2009-01-01" @@ -176,24 +96,24 @@ Anim = Coello.plot_distributed_results( plotstart, plotend, - Figsize=(9, 9), + figsize=(9, 9), option=1, - threshold=160, - PlotNumbers=True, - TicksSpacing=5, - Interval=200, + background_color_threshold=160, + display_cell_value=True, + ticks_spacing=5, + interval=200, gauges=True, cmap="inferno", - Textloc=[0.1, 0.2], - Gaugecolor="red", - ColorScale=1, - IDcolor="blue", - IDsize=25, + text_loc=[0.1, 0.2], + point_color="red", + color_scale="linear", + pid_color="blue", + pid_size=25, ) # %% SaveTo = Path + "/results/anim.gif" -Coello.save_animation(video_format="gif", path=SaveTo, save_frames=3) +Coello.save_animation(SaveTo, fps=2) # %% Save the result into rasters StartDate = "2009-01-01" diff --git a/tests/sensitivity_analysis.py b/tests/sensitivity_analysis.py index 6f4f39c85..7b3dd3f16 100644 --- a/tests/sensitivity_analysis.py +++ b/tests/sensitivity_analysis.py @@ -8,13 +8,13 @@ from hapi.run import Run # %% Paths -Parameterpath = "examples/data/lumped/Coello_Lumped2021-03-08_muskingum.txt" -MeteoDataPath = "examples/data/lumped/meteo_data-MSWEP.csv" +Parameterpath = "tests/rrm/data/coello/coello-lumpedparameter-muskingum.txt" +MeteoDataPath = "tests/rrm/data/coello/meteo-lumped-data-MSWEP.csv" Path = "examples/data/lumped/" # %% ### meteorological data start = "2009-01-01" -end = "2011-12-31" +end = "2009-01-10" name = "Coello" Coello = Catchment(name, start, end) Coello.read_lumped_inputs(MeteoDataPath) @@ -29,7 +29,7 @@ Coello.read_lumped_model(HBVLumped, CatArea, InitialCond) ### parameters -Snow = 0 # no snow subroutine +Snow = False # no snow subroutine Coello.read_parameters(Parameterpath, Snow) parameters = pd.read_csv(Parameterpath, index_col=0, header=None) @@ -44,7 +44,9 @@ # %% # observed flow -Coello.read_discharge_gauges(Path + "Qout_c.csv", fmt="%Y-%m-%d") +Coello.read_discharge_gauges( + "examples/hydrological-model/data/lumped_model/Qout_c.csv", fmt="%Y-%m-%d" +) ### Routing Route = 1 # RoutingFn=Routing.triangular_routing_2 @@ -70,39 +72,39 @@ print("WB= " + str(round(Metrics["WB"], 2))) # %% """ -first the SensitivityAnalysis method takes 4 arguments : - 1-parameters:previous obtained parameters - 2-LB: upper bound - 3-UB: lower bound - 4-wrapper: defined function contains the function you want to run with different - parameters and the metric function you want to assess the first function - based on it. +the Sensitivity class takes 4 main arguments: + 1-parameter: previously obtained parameters + 2-lower_bound: lower bound + 3-upper_bound: upper bound + 4-function: defined function containing the function you want to run with + different parameters and the metric function you want to assess the first + function based on it. ## Wrapper function definition - define the function to the OAT sesitivity wrapper and put the parameters argument + define the function for the OAT sensitivity wrapper and put the parameters argument at the first position, and then list all the other arguments required for your function - the following defined function contains two inner function that calculates discharge - for lumped HBV model and calculates the RMSE of the calculated discharge. + the following defined function contains two inner functions that calculate discharge + for the lumped HBV model and the RMSE of the calculated discharge. - the first function "RUN.runLumped" takes some arguments we need to pass it through - the SensitivityAnalysis method [ConceptualModel,data,p2,init_st,snow,Routing, RoutingFn] + the first function "Run.runLumped" takes some arguments we need to pass through + the one_at_a_time method [ConceptualModel,data,p2,init_st,snow,Routing, RoutingFn] with the same order in the defined function "wrapper" the second function is RMSE takes the calculated discharge from the first function and measured discharge array to define the argument of the "wrapper" function - 1- the random parameters valiable i=of the first function should be the first argument + 1- the random parameters variable of the first function should be the first argument "wrapper(Randpar)" 2- the first function arguments with the same order (except that the parameter - argument is taken out and placed at the first potition step-1) + argument is taken out and placed at the first position step-1) 3- list the argument of the second function with the same order that the second function takes them -SensitivityAnalysis method returns a dictionary with the name of the parameters -as keys, -Each parameter has a disctionary with two keys 0: list of parameters woth relative values +the one_at_a_time method stores a dictionary in `Sen.sen` with the name of the +parameters as keys. +Each parameter has a dictionary with two keys 0: list of parameters with relative values 1: list of parameter values """ @@ -133,34 +135,31 @@ def WrapperType2(Randpar, Route, RoutingFn, Qobs): fn = WrapperType2 -Positions = [] - - -Sen = SA(parameters, Coello.LB, Coello.UB, fn, Positions, 5, Type=Type) -Sen.OAT(Route, RoutingFn, Qobs) +Sen = SA(parameters, Coello.LB, Coello.UB, fn, n_values=5, return_values=Type) +Sen.one_at_a_time(Route, RoutingFn, Qobs) # %% From = "" To = "" if Type == 1: - fig, ax1 = Sen.Sobol( - RealValues=False, - Title="Sensitivity Analysis of the rmse to models parameters", + fig, ax1 = Sen.sobol( + real_values=False, + title="Sensitivity Analysis of the rmse to models parameters", xlabel="Maxbas Values", ylabel="rmse", - From=From, - To=To, + plotting_from=From, + plotting_to=To, xlabel2="Time", ylabel2="Discharge m3/s", spaces=[None, None, None, None, None, None], ) elif Type == 2: - fig, (ax1, ax2) = Sen.Sobol( - RealValues=False, - Title="Sensitivity Analysis of the rmse to models parameters", + fig, (ax1, ax2) = Sen.sobol( + real_values=False, + title="Sensitivity Analysis of the rmse to models parameters", xlabel="Maxbas Values", ylabel="rmse", - From=From, - To=To, + plotting_from=From, + plotting_to=To, xlabel2="Time", ylabel2="Discharge m3/s", spaces=[None, None, None, None, None, None], diff --git a/tests/test_dem.py b/tests/test_dem.py index e3a1d5dce..6b7301b50 100644 --- a/tests/test_dem.py +++ b/tests/test_dem.py @@ -1,23 +1,19 @@ import numpy as np -from osgeo import gdal from hapi.dem import DEM -def test_flow_direction_index(coello_df_4000: gdal.Dataset): - dem = DEM(coello_df_4000) - fd_cell = dem.flow_direction_index() +def test_flow_direction_index(coello_df_4000: DEM): + fd_cell = coello_df_4000.flow_direction_index() assert isinstance(fd_cell, np.ndarray) - assert fd_cell.shape == (dem.rows, dem.columns, 2) + assert fd_cell.shape == (coello_df_4000.rows, coello_df_4000.columns, 2) -def test_flow_direction_table_type(coello_df_4000: gdal.Dataset): - dem = DEM(coello_df_4000) - fd_table = dem.flow_direction_table() +def test_flow_direction_table_type(coello_df_4000: DEM): + fd_table = coello_df_4000.flow_direction_table() assert isinstance(fd_table, dict) -def test_flow_direction_table_values(coello_df_4000: gdal.Dataset, coello_fdt): - dem = DEM(coello_df_4000) - fd_table = dem.flow_direction_table() +def test_flow_direction_table_values(coello_df_4000: DEM, coello_fdt): + fd_table = coello_df_4000.flow_direction_table() assert fd_table == coello_fdt diff --git a/tests/test_flow_direction_index.py b/tests/test_flow_direction_index.py index 592a8d862..69a9af8b7 100644 --- a/tests/test_flow_direction_index.py +++ b/tests/test_flow_direction_index.py @@ -4,49 +4,44 @@ D8 encodings (ESRI, SAGA, GRASS), no-data handling, input validation, output shape, and edge cases. """ + from __future__ import annotations import numpy as np import pytest -from osgeo import gdal from hapi.dem import ( - DEM, D8_ENCODINGS, D8_OFFSETS_ESRI, D8_OFFSETS_GRASS, D8_OFFSETS_SAGA, + DEM, ) NO_DATA = -1.0 -def _create_fd_raster( - data: np.ndarray, no_data: float = NO_DATA -) -> gdal.Dataset: - """Create an in-memory GDAL raster from a 2-D flow-direction array. +def _create_fd_raster(data: np.ndarray, no_data: float = NO_DATA) -> DEM: + """Create an in-memory DEM from a 2-D flow-direction array. Args: data: 2-D numpy array of flow direction codes. no_data: Sentinel value for no-data cells. Returns: - An in-memory GDAL Dataset (MEM driver). + An in-memory DEM dataset. """ - rows, cols = data.shape - driver = gdal.GetDriverByName("MEM") - ds = driver.Create("", cols, rows, 1, gdal.GDT_Float64) - ds.SetGeoTransform((0.0, 1.0, 0.0, float(rows), 0.0, -1.0)) - band = ds.GetRasterBand(1) - band.SetNoDataValue(no_data) - band.WriteArray(data) - band.FlushCache() - return ds - - -def _single_code_raster( - code: float, no_data: float = NO_DATA -) -> gdal.Dataset: + rows, _ = data.shape + return DEM.create_from_array( + data, + top_left_corner=(0.0, float(rows)), + cell_size=1.0, + epsg=4326, + no_data_value=no_data, + ) + + +def _single_code_raster(code: float, no_data: float = NO_DATA) -> DEM: """Create a 3x3 raster with one valid code at the center cell. Args: @@ -54,7 +49,7 @@ def _single_code_raster( no_data: Sentinel value for surrounding cells. Returns: - A 3x3 in-memory GDAL Dataset. + A 3x3 in-memory DEM dataset. """ data = np.full((3, 3), no_data) data[1, 1] = code @@ -73,11 +68,10 @@ def test_default_encoding_is_esri(self): encoding argument. """ ds = _single_code_raster(1.0) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() assert not np.isnan(result[1, 1, 0]), ( - "Center cell should have a valid downstream index " - "with default encoding" + "Center cell should have a valid downstream index " "with default encoding" ) @pytest.mark.parametrize("encoding", ["esri", "saga", "grass"]) @@ -93,11 +87,13 @@ def test_supported_encoding_accepted(self, encoding): """ first_code = min(D8_ENCODINGS[encoding]) ds = _single_code_raster(float(first_code)) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding=encoding) - assert result.shape == (3, 3, 2), ( - f"Output shape should be (3, 3, 2), got {result.shape}" - ) + assert result.shape == ( + 3, + 3, + 2, + ), f"Output shape should be (3, 3, 2), got {result.shape}" @pytest.mark.parametrize( "encoding_input, canonical", @@ -116,15 +112,15 @@ def test_encoding_case_insensitive(self, encoding_input, canonical): """ first_code = min(D8_ENCODINGS[canonical]) ds = _single_code_raster(float(first_code)) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding=encoding_input) - assert result.shape == (3, 3, 2), ( - f"Encoding '{encoding_input}' should be accepted" - ) + assert result.shape == ( + 3, + 3, + 2, + ), f"Encoding '{encoding_input}' should be accepted" - @pytest.mark.parametrize( - "bad_encoding", ["arcgis", "qgis", "d8", "", "taudem"] - ) + @pytest.mark.parametrize("bad_encoding", ["arcgis", "qgis", "d8", "", "taudem"]) def test_invalid_encoding_raises_valueerror(self, bad_encoding): """Test that unsupported encoding names raise ValueError. @@ -136,10 +132,8 @@ def test_invalid_encoding_raises_valueerror(self, bad_encoding): ValueError mentioning 'Unsupported encoding'. """ ds = _single_code_raster(1.0) - dem = DEM(ds) - with pytest.raises( - ValueError, match="Unsupported encoding" - ): + dem = ds + with pytest.raises(ValueError, match="Unsupported encoding"): dem.flow_direction_index(encoding=bad_encoding) @@ -162,15 +156,20 @@ class TestFlowDirectionIndexValidation: (10, "grass"), ], ids=[ - "esri-3", "esri-5", "esri-7", "esri-9", - "esri-100", "esri-256", - "saga-9", "saga-10", - "grass-0", "grass-9", "grass-10", + "esri-3", + "esri-5", + "esri-7", + "esri-9", + "esri-100", + "esri-256", + "saga-9", + "saga-10", + "grass-0", + "grass-9", + "grass-10", ], ) - def test_invalid_code_raises_valueerror( - self, invalid_code, encoding - ): + def test_invalid_code_raises_valueerror(self, invalid_code, encoding): """Test that rasters with invalid codes raise ValueError. Args: @@ -182,10 +181,8 @@ def test_invalid_code_raises_valueerror( given encoding should raise ValueError. """ ds = _single_code_raster(float(invalid_code)) - dem = DEM(ds) - with pytest.raises( - ValueError, match="Flow direction raster" - ): + dem = ds + with pytest.raises(ValueError, match="Flow direction raster"): dem.flow_direction_index(encoding=encoding) def test_mixed_valid_and_invalid_codes_raises(self): @@ -199,19 +196,15 @@ def test_mixed_valid_and_invalid_codes_raises(self): data[0, 0] = 1.0 data[1, 1] = 3.0 ds = _create_fd_raster(data) - dem = DEM(ds) - with pytest.raises( - ValueError, match="Flow direction raster" - ): + dem = ds + with pytest.raises(ValueError, match="Flow direction raster"): dem.flow_direction_index(encoding="esri") class TestFlowDirectionIndexOutputShape: """Tests for output array shape and dtype.""" - @pytest.mark.parametrize( - "rows, cols", [(1, 1), (3, 3), (2, 5), (5, 2), (13, 14)] - ) + @pytest.mark.parametrize("rows, cols", [(1, 1), (3, 3), (2, 5), (5, 2), (13, 14)]) def test_output_shape(self, rows, cols): """Test output shape matches (rows, cols, 2). @@ -225,11 +218,10 @@ def test_output_shape(self, rows, cols): """ data = np.full((rows, cols), 1.0) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="esri") assert result.shape == (rows, cols, 2), ( - f"Expected shape ({rows}, {cols}, 2), " - f"got {result.shape}" + f"Expected shape ({rows}, {cols}, 2), " f"got {result.shape}" ) def test_output_dtype_is_float(self): @@ -240,11 +232,11 @@ def test_output_dtype_is_float(self): no-data cells can be represented as NaN. """ ds = _single_code_raster(1.0) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert np.issubdtype(result.dtype, np.floating), ( - f"Expected floating dtype, got {result.dtype}" - ) + assert np.issubdtype( + result.dtype, np.floating + ), f"Expected floating dtype, got {result.dtype}" class TestFlowDirectionIndexNoData: @@ -258,7 +250,7 @@ def test_nodata_cells_produce_nan(self): surrounding no-data cells should be NaN in both layers. """ ds = _single_code_raster(1.0) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() for r in range(3): @@ -282,11 +274,11 @@ def test_all_nodata_produces_all_nan(self): """ data = np.full((3, 3), NO_DATA) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert np.all(np.isnan(result)), ( - "All cells are no-data; output should be entirely NaN" - ) + assert np.all( + np.isnan(result) + ), "All cells are no-data; output should be entirely NaN" def test_valid_cell_unaffected_by_surrounding_nodata(self): """Test valid cell indices are correct despite no-data neighbors. @@ -296,14 +288,10 @@ def test_valid_cell_unaffected_by_surrounding_nodata(self): Expected downstream cell: row=2, col=1. """ ds = _single_code_raster(4.0) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert result[1, 1, 0] == 2.0, ( - f"Row index should be 2, got {result[1, 1, 0]}" - ) - assert result[1, 1, 1] == 1.0, ( - f"Col index should be 1, got {result[1, 1, 1]}" - ) + assert result[1, 1, 0] == 2.0, f"Row index should be 2, got {result[1, 1, 0]}" + assert result[1, 1, 1] == 1.0, f"Col index should be 1, got {result[1, 1, 1]}" def test_1x1_nodata_raster(self): """Test a 1x1 raster with only no-data. @@ -314,11 +302,9 @@ def test_1x1_nodata_raster(self): """ data = np.array([[NO_DATA]]) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert np.all(np.isnan(result)), ( - "Single no-data cell should produce all NaN" - ) + assert np.all(np.isnan(result)), "Single no-data cell should produce all NaN" class TestFlowDirectionIndexEsriDirections: @@ -337,13 +323,17 @@ class TestFlowDirectionIndexEsriDirections: (128, 0, 2), ], ids=[ - "east", "south-east", "south", "south-west", - "west", "north-west", "north", "north-east", + "east", + "south-east", + "south", + "south-west", + "west", + "north-west", + "north", + "north-east", ], ) - def test_direction_offset( - self, code, expected_row, expected_col - ): + def test_direction_offset(self, code, expected_row, expected_col): """Test each ESRI D8 code maps to the correct neighbor. Args: @@ -356,15 +346,13 @@ def test_direction_offset( the downstream cell indices match the D8 offset. """ ds = _single_code_raster(float(code)) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="esri") assert result[1, 1, 0] == expected_row, ( - f"ESRI code {code}: row should be {expected_row}, " - f"got {result[1, 1, 0]}" + f"ESRI code {code}: row should be {expected_row}, " f"got {result[1, 1, 0]}" ) assert result[1, 1, 1] == expected_col, ( - f"ESRI code {code}: col should be {expected_col}, " - f"got {result[1, 1, 1]}" + f"ESRI code {code}: col should be {expected_col}, " f"got {result[1, 1, 1]}" ) def test_all_eight_directions_in_one_raster(self): @@ -377,14 +365,20 @@ def test_all_eight_directions_in_one_raster(self): """ data = np.full((3, 4), NO_DATA) codes_and_positions = [ - (1, 0, 0), (2, 0, 1), (4, 0, 2), (8, 0, 3), - (16, 1, 0), (32, 1, 1), (64, 1, 2), (128, 1, 3), + (1, 0, 0), + (2, 0, 1), + (4, 0, 2), + (8, 0, 3), + (16, 1, 0), + (32, 1, 1), + (64, 1, 2), + (128, 1, 3), ] for code, r, c in codes_and_positions: data[r, c] = float(code) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="esri") for code, r, c in codes_and_positions: @@ -415,13 +409,17 @@ class TestFlowDirectionIndexSagaDirections: (7, 2, 2), ], ids=[ - "east", "north-east", "north", "north-west", - "west", "south-west", "south", "south-east", + "east", + "north-east", + "north", + "north-west", + "west", + "south-west", + "south", + "south-east", ], ) - def test_direction_offset( - self, code, expected_row, expected_col - ): + def test_direction_offset(self, code, expected_row, expected_col): """Test each SAGA D8 code maps to the correct neighbor. Args: @@ -434,15 +432,13 @@ def test_direction_offset( the downstream cell matches the SAGA offset table. """ ds = _single_code_raster(float(code)) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="saga") assert result[1, 1, 0] == expected_row, ( - f"SAGA code {code}: row should be {expected_row}, " - f"got {result[1, 1, 0]}" + f"SAGA code {code}: row should be {expected_row}, " f"got {result[1, 1, 0]}" ) assert result[1, 1, 1] == expected_col, ( - f"SAGA code {code}: col should be {expected_col}, " - f"got {result[1, 1, 1]}" + f"SAGA code {code}: col should be {expected_col}, " f"got {result[1, 1, 1]}" ) def test_all_eight_saga_directions(self): @@ -455,14 +451,20 @@ def test_all_eight_saga_directions(self): """ data = np.full((3, 4), NO_DATA) codes_and_positions = [ - (0, 0, 0), (1, 0, 1), (2, 0, 2), (3, 0, 3), - (4, 1, 0), (5, 1, 1), (6, 1, 2), (7, 1, 3), + (0, 0, 0), + (1, 0, 1), + (2, 0, 2), + (3, 0, 3), + (4, 1, 0), + (5, 1, 1), + (6, 1, 2), + (7, 1, 3), ] for code, r, c in codes_and_positions: data[r, c] = float(code) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="saga") for code, r, c in codes_and_positions: @@ -493,13 +495,17 @@ class TestFlowDirectionIndexGrassDirections: (8, 0, 0), ], ids=[ - "north", "north-east", "east", "south-east", - "south", "south-west", "west", "north-west", + "north", + "north-east", + "east", + "south-east", + "south", + "south-west", + "west", + "north-west", ], ) - def test_direction_offset( - self, code, expected_row, expected_col - ): + def test_direction_offset(self, code, expected_row, expected_col): """Test each GRASS D8 code maps to the correct neighbor. Args: @@ -512,7 +518,7 @@ def test_direction_offset( the downstream cell matches the GRASS offset table. """ ds = _single_code_raster(float(code)) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="grass") assert result[1, 1, 0] == expected_row, ( f"GRASS code {code}: row should be {expected_row}, " @@ -533,14 +539,20 @@ def test_all_eight_grass_directions(self): """ data = np.full((3, 4), NO_DATA) codes_and_positions = [ - (1, 0, 0), (2, 0, 1), (3, 0, 2), (4, 0, 3), - (5, 1, 0), (6, 1, 1), (7, 1, 2), (8, 1, 3), + (1, 0, 0), + (2, 0, 1), + (3, 0, 2), + (4, 0, 3), + (5, 1, 0), + (6, 1, 1), + (7, 1, 2), + (8, 1, 3), ] for code, r, c in codes_and_positions: data[r, c] = float(code) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="grass") for code, r, c in codes_and_positions: @@ -571,8 +583,14 @@ class TestFlowDirectionIndexCrossEncoding: ("north-east", 128, 1, 2), ], ids=[ - "east", "south-east", "south", "south-west", - "west", "north-west", "north", "north-east", + "east", + "south-east", + "south", + "south-west", + "west", + "north-west", + "north", + "north-east", ], ) def test_same_direction_same_result( @@ -597,17 +615,15 @@ def test_same_direction_same_result( ("grass", grass_code), ]: ds = _single_code_raster(float(code)) - dem = DEM(ds) + dem = ds r = dem.flow_direction_index(encoding=enc) results[enc] = (r[1, 1, 0], r[1, 1, 1]) assert results["esri"] == results["saga"], ( - f"{direction}: ESRI {results['esri']} != " - f"SAGA {results['saga']}" + f"{direction}: ESRI {results['esri']} != " f"SAGA {results['saga']}" ) assert results["esri"] == results["grass"], ( - f"{direction}: ESRI {results['esri']} != " - f"GRASS {results['grass']}" + f"{direction}: ESRI {results['esri']} != " f"GRASS {results['grass']}" ) @@ -624,14 +640,12 @@ def test_1x1_raster_valid_code(self): """ data = np.array([[1.0]]) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert result[0, 0, 0] == 0.0, ( - f"Row should be 0, got {result[0, 0, 0]}" - ) - assert result[0, 0, 1] == 1.0, ( - f"Col should be 1 (out of bounds), got {result[0, 0, 1]}" - ) + assert result[0, 0, 0] == 0.0, f"Row should be 0, got {result[0, 0, 0]}" + assert ( + result[0, 0, 1] == 1.0 + ), f"Col should be 1 (out of bounds), got {result[0, 0, 1]}" def test_corner_cell_points_outside_grid(self): """Test corner cells pointing outward yield negative indices. @@ -643,14 +657,10 @@ def test_corner_cell_points_outside_grid(self): data = np.full((3, 3), NO_DATA) data[0, 0] = 32.0 ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert result[0, 0, 0] == -1.0, ( - f"Row should be -1, got {result[0, 0, 0]}" - ) - assert result[0, 0, 1] == -1.0, ( - f"Col should be -1, got {result[0, 0, 1]}" - ) + assert result[0, 0, 0] == -1.0, f"Row should be -1, got {result[0, 0, 0]}" + assert result[0, 0, 1] == -1.0, f"Col should be -1, got {result[0, 0, 1]}" def test_edge_cell_points_outside_grid(self): """Test edge cells pointing outward yield out-of-bounds row. @@ -662,14 +672,10 @@ def test_edge_cell_points_outside_grid(self): data = np.full((3, 3), NO_DATA) data[0, 1] = 64.0 ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert result[0, 1, 0] == -1.0, ( - f"Row should be -1, got {result[0, 1, 0]}" - ) - assert result[0, 1, 1] == 1.0, ( - f"Col should be 1, got {result[0, 1, 1]}" - ) + assert result[0, 1, 0] == -1.0, f"Row should be -1, got {result[0, 1, 0]}" + assert result[0, 1, 1] == 1.0, f"Col should be 1, got {result[0, 1, 1]}" def test_uniform_raster_all_cells_flow_east(self): """Test a uniform raster where every cell flows east. @@ -680,17 +686,15 @@ def test_uniform_raster_all_cells_flow_east(self): """ data = np.full((3, 3), 1.0) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() for r in range(3): for c in range(3): assert result[r, c, 0] == r, ( - f"Cell ({r},{c}): row should be {r}, " - f"got {result[r, c, 0]}" + f"Cell ({r},{c}): row should be {r}, " f"got {result[r, c, 0]}" ) assert result[r, c, 1] == c + 1, ( - f"Cell ({r},{c}): col should be {c + 1}, " - f"got {result[r, c, 1]}" + f"Cell ({r},{c}): col should be {c + 1}, " f"got {result[r, c, 1]}" ) def test_rectangular_raster_wide(self): @@ -702,18 +706,18 @@ def test_rectangular_raster_wide(self): """ data = np.full((2, 5), 4.0) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert result.shape == (2, 5, 2), ( - f"Shape should be (2, 5, 2), got {result.shape}" - ) + assert result.shape == ( + 2, + 5, + 2, + ), f"Shape should be (2, 5, 2), got {result.shape}" for c in range(5): - assert result[0, c, 0] == 1.0, ( - f"Row 0, col {c}: should flow to row 1" - ) - assert result[0, c, 1] == float(c), ( - f"Row 0, col {c}: should stay in col {c}" - ) + assert result[0, c, 0] == 1.0, f"Row 0, col {c}: should flow to row 1" + assert result[0, c, 1] == float( + c + ), f"Row 0, col {c}: should stay in col {c}" def test_rectangular_raster_tall(self): """Test a non-square tall raster (5 rows x 2 cols). @@ -724,18 +728,18 @@ def test_rectangular_raster_tall(self): """ data = np.full((5, 2), 1.0) ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() - assert result.shape == (5, 2, 2), ( - f"Shape should be (5, 2, 2), got {result.shape}" - ) + assert result.shape == ( + 5, + 2, + 2, + ), f"Shape should be (5, 2, 2), got {result.shape}" for r in range(5): - assert result[r, 0, 0] == float(r), ( - f"Row {r}, col 0: should stay in row {r}" - ) - assert result[r, 0, 1] == 1.0, ( - f"Row {r}, col 0: should flow to col 1" - ) + assert result[r, 0, 0] == float( + r + ), f"Row {r}, col 0: should stay in row {r}" + assert result[r, 0, 1] == 1.0, f"Row {r}, col 0: should flow to col 1" def test_checkerboard_nodata_pattern(self): """Test alternating valid/no-data cells in a checkerboard. @@ -753,7 +757,7 @@ def test_checkerboard_nodata_pattern(self): data[r, c] = 4.0 ds = _create_fd_raster(data) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index() for r in range(4): @@ -768,9 +772,9 @@ def test_checkerboard_nodata_pattern(self): f"{c}, got {result[r, c, 1]}" ) else: - assert np.isnan(result[r, c, 0]), ( - f"No-data cell ({r},{c}) should be NaN" - ) + assert np.isnan( + result[r, c, 0] + ), f"No-data cell ({r},{c}) should be NaN" def test_saga_code_zero_not_confused_with_nodata(self): """Test SAGA code 0 (east) is not confused with no-data. @@ -781,16 +785,14 @@ def test_saga_code_zero_not_confused_with_nodata(self): direction, not as no-data. """ ds = _single_code_raster(0.0) - dem = DEM(ds) + dem = ds result = dem.flow_direction_index(encoding="saga") - assert not np.isnan(result[1, 1, 0]), ( - "SAGA code 0 should be valid, not treated as no-data" - ) + assert not np.isnan( + result[1, 1, 0] + ), "SAGA code 0 should be valid, not treated as no-data" assert result[1, 1, 0] == 1.0, ( - f"SAGA code 0 (east): row should be 1, " - f"got {result[1, 1, 0]}" + f"SAGA code 0 (east): row should be 1, " f"got {result[1, 1, 0]}" ) assert result[1, 1, 1] == 2.0, ( - f"SAGA code 0 (east): col should be 2, " - f"got {result[1, 1, 1]}" + f"SAGA code 0 (east): col should be 2, " f"got {result[1, 1, 1]}" )