From da85dae2566681a767730aa89e43c6602195f3b1 Mon Sep 17 00:00:00 2001 From: PennyHow Date: Wed, 29 May 2024 16:14:10 -0100 Subject: [PATCH 1/2] assign_regions added --- src/griml/examples/getInventory_funcs.py | 406 ------------------ src/griml/examples/getInventory_obj.py | 182 -------- src/griml/metadata/__init__.py | 1 + src/griml/metadata/add_metadata.py | 52 +-- src/griml/metadata/assign_names.py | 107 ++--- src/griml/metadata/assign_region.py | 97 ----- src/griml/metadata/assign_regions.py | 86 ++++ src/griml/metadata/metadata.py | 38 -- .../test/greenland_basins_polarstereo.cpg | 1 + .../test/greenland_basins_polarstereo.dbf | Bin 0 -> 400320 bytes .../test/greenland_basins_polarstereo.prj | 1 + .../test/greenland_basins_polarstereo.shp | Bin 0 -> 3824176 bytes .../test/greenland_basins_polarstereo.shx | Bin 0 -> 12852 bytes src/griml/test/process_cli.py | 132 ++++++ src/griml/test/process_example.py | 85 ++++ src/griml/test/test.py | 4 +- 16 files changed, 383 insertions(+), 809 deletions(-) delete mode 100644 src/griml/examples/getInventory_funcs.py delete mode 100644 src/griml/examples/getInventory_obj.py delete mode 100644 src/griml/metadata/assign_region.py create mode 100644 src/griml/metadata/assign_regions.py delete mode 100644 src/griml/metadata/metadata.py create mode 100644 src/griml/test/greenland_basins_polarstereo.cpg create mode 100644 src/griml/test/greenland_basins_polarstereo.dbf create mode 100644 src/griml/test/greenland_basins_polarstereo.prj create mode 100644 src/griml/test/greenland_basins_polarstereo.shp create mode 100644 src/griml/test/greenland_basins_polarstereo.shx create mode 100644 src/griml/test/process_cli.py create mode 100644 src/griml/test/process_example.py diff --git a/src/griml/examples/getInventory_funcs.py b/src/griml/examples/getInventory_funcs.py deleted file mode 100644 index 0dea76c..0000000 --- a/src/griml/examples/getInventory_funcs.py +++ /dev/null @@ -1,406 +0,0 @@ -""" -GrIML processing module playground (experimental) - -@author: Penelope How -""" - -import ee, time, sys -from time import gmtime, strftime -import requests, zipfile, io, urllib -from urllib.error import HTTPError - -import numpy as np -from shapely.ops import split -from shapely.geometry import Polygon, LineString, MultiPolygon -import geopandas as gpd -import multiprocessing as mp - -sys.path.append('../') -from retrieve import getScenes, getScene, getInt, getSmooth, getMosaic, \ - getMean, maskImage, splitBBox, getVectors, extractFeatures, getFeatures, \ - getFeaturesSplit -from sar import filterSARscenes, classifySARimage -from vis import filterS2scenes, maskS2clouds, renameS2scenes, \ - resampleS2scenes, classifyVISimage, filterLSscenes, maskL8clouds, \ - renameL8scenes -from dem import getElevation, getSlope, getSinks -from lake import compileFeatures, assignID, assignSources, assignNames - -start=time.time() - -#------------------------------------------------------------------------------ - -# Set AOI - -aoi1 = [-50.94, 67.98] # SW test region -aoi2 = [-49.66, 67.58] - -# aoi1 = [-54.68242187500001,68.8415979398901] -# aoi2 = [-47.21171875000001,60.62185574504481] # SW region - -# aoi1 = [-10.297656250000014,59.03190534154833] # All Greenland -# aoi2 = [-74.98515625000002,83.9867103173338] - -print('Loading bounding box') -aoi = '/home/pho/Desktop/python_workspace/GrIML/other/datasets/aoi/test_mask_4326.shp' -aoi = gpd.read_file(aoi) -aoi_poly = aoi.geometry.iloc[0] -xmin, ymin, xmax, ymax = aoi_poly.bounds - -# Set AOI box splitter parameters -wh=0.3 # Window height -ww=0.3 # Window width -oh = 0.05 # Overlap height -ow = 0.05 # Overlap width - -# Set date range -date1='2017-07-01' -date2='2017-08-31' - -# Set maximum cloud cover -cloud=50 - -# Set mask location -ice_mask = 'users/pennyruthhow/GIMP_iceMask_edit_buffer7km' - -# Set output projection -proj = 'EPSG:3413' # Polar stereographic - - -#--------------------------- Initialise GEE ------------------------------- - -ee.Initialize() -print('EE initialized') - -# Set the Area of Interest (AOI) through box coordinates -box = ee.Geometry.Rectangle([xmin, ymin, xmax, ymax]) - -# Split box into grid for exporting -bbox = splitBBox(aoi_poly, wh, ww, oh, ow) -print('grid completed') -# # Remove boxes that don't overlap with AOI polygon -# grid = [Polygon([[g[0],g[1]], [g[0],g[3]], [g[2],g[3]], -# [g[2],g[1]], [g[0],g[1]]]) for g in grid] -# bbox = [ee.Geometry.Rectangle(min(g.exterior.coords.xy[0]), -# min(g.exterior.coords.xy[1]), -# max(g.exterior.coords.xy[0]), -# max(g.exterior.coords.xy[1])) for g in grid if g.intersects(aoi_poly)] - - -print(f'Computed {len(bbox)} bounding boxes') -print(f'Total area covered: {aoi_poly.area} sq km') - - - - -# bbox = bbox[100:105] - -# # Set the Area of Interest (AOI) through box coordinates -# box = ee.Geometry.Rectangle([aoi1[0], aoi1[1], -# aoi2[0], aoi2[1]]) - -# bbox = [ee.Geometry.Rectangle(g[0], g[1], g[2], g[3]) for g in grid] - -# print(f'Computed {len(bbox)} bounding boxes from {aoi1}, {aoi2}') -# print(f'Total area covered: {box.area().getInfo()/10**6} sq km') - - -#--------------------- Get basic ocean mask --------------------------------- - -# Retrieve ice-ocean mask -img = ee.Image("OSU/GIMP/2000_ICE_OCEAN_MASK") -ocean_mask = img.select('ocean_mask').eq(0) -# ice_mask = img.select('ice_mask').eq(0) - -# imask0 = ice_mask.reduceToVectors(geometryType='polygon', labelProperty='v', scale=10, bestEffort=True) -# def _mapBuffer(feature): -# return feature.buffer(-10000) -# imask01 = imask0.map(_mapBuffer) -# imask0 = imask01.reduceToImage(properties=['v'], reducer=ee.Reducer.mean()) - -# sys.exit(1) - -# # Construct vector features -# def getFeaturesSplit(image, bbox): -# features=[] -# for b in range(len(bbox)): -# print(f'Fetching vectors from bbox {b}...') -# v = getVectors(image, 10, bbox[b]) -# features.append(extractFeatures(v)) -# features = [val for sublist in features for val in sublist] -# print(f'{len(features)} vector features identified') -# return features - -# def getFeatures(image, bbox): -# features=[] -# v = getVectors(image, 10, bbox) -# features = extractFeatures(v) -# print(f'{len(features)} vector features identified') -# return features - -# def getParallelFeatures1(image, bbox): -# # Parallel process vector generation -# pool = mp.Pool(mp.cpu_count()) -# v = [pool.apply(getVectors, args=(image, 10, bbox[b])) for b in range(len(bbox))] -# pool.close() - -# # Parallel process feature extraction -# pool = mp.Pool(mp.cpu_count()) -# features = pool.map(extractFeatures, [row for row in v]) -# pool.close() - -# features = [val for sublist in features for val in sublist] -# print(f'{len(features)} vector features identified') -# return features - -# def getDownload(v): -# link = v.getDownloadURL('csv') -# req = urllib.request.Request(url=link) -# try: -# handler = urllib.request.urlopen(req) -# except HTTPError as e: -# handler = e.read() -# lines = [] -# for l in handler: -# lines.append(str(l)) -# features_dem.append(lines) - -#--------------------------- DEM processing ------------------------------- - -# Set collection -dem_col = 'UMN/PGC/ArcticDEM/V3/2m_mosaic' - -# Get image collection -scenes = getScene(dem_col, box) -print(f'\nScenes gathered from {dem_col}') - -# Get elevation and slope -elevation = getElevation(scenes) -slope = getSlope(elevation) - -elevation = getSmooth(elevation, 110) - -# Mask out ocean pixels -elevation = maskImage(elevation, ocean_mask) - -# Get sinks -elevation = getInt(elevation) -sinks = getSinks(elevation, 10, 50) - -# Remove speckle with smoothing -sinks = getSmooth(sinks, 50).rename('dem_sinks') -sinks = getInt(sinks) -print(f'{dem_col} scenes classified') -print('Band names: ' + str(sinks.bandNames().getInfo())) - -try: - features_dem = getFeatures(sinks, 10, box) -except: - features_dem = getFeaturesSplit(sinks, 10, bbox) - - -#--------------------------- SAR processing ------------------------------- - -# Set collection -sar_col = 'COPERNICUS/S1_GRD' - -# Get image collection -scenes = getScenes(sar_col, date1, date2, box) -if scenes.size().getInfo() > 0: - scenes = filterSARscenes(scenes) - print(f'\nScenes gathered from {sar_col}') - print('Total number of scenes: ', scenes.size().getInfo()) - print('Number of bands per scene: ' - + str(len(scenes.first().bandNames().getInfo()))) - print('Band names: ' + str(scenes.first().bandNames().getInfo())) - - # Get average - aver = getMosaic(scenes, 'HH') - - # Mask out ocean pixels - aver = maskImage(aver, ocean_mask) - - # Classify water from SAR average image - water_sar = classifySARimage(aver, -20, 'HH', 50) - print(f'{sar_col} scenes classified') - print('Band names: ' + str(water_sar.bandNames().getInfo())) - - try: - features_sar = getFeatures(water_sar, 10, box) - except: - features_sar = getFeaturesSplit(water_sar, 10, bbox) - -else: - features_sar = None - print(f'\nNo {sar_col} scenes identified between {date1} and {date2}') - -#------------------------- VIS processing (S2) ----------------------------- - -# Set collection -vis_col1 = "COPERNICUS/S2" - -# Get image collection -scenes = getScenes(vis_col1, date1, date2, box) -if scenes.size().getInfo() > 0: - scenes = filterS2scenes(scenes, cloud) - print(f'\nScenes gathered from {vis_col1}') - print('Total number of scenes: ', scenes.size().getInfo()) - print('Number of bands per scene: ' - + str(len(scenes.first().bandNames().getInfo()))) - print('Band names: ' + str(scenes.first().bandNames().getInfo())) - - # Mask scenes for clouds - scenes = maskS2clouds(scenes) - # ee.Terrain.hillshade(image, azimuth, elevation) - - # Get average of spectific bands - scenes = renameS2scenes(scenes) - scenes = resampleS2scenes(scenes) - aver = getMean(scenes, ['blue','green','red','vnir','swir1_1','swir2_1']) - print('Scenes resampled and mosiacked') - print('Band names: ' + str(aver.bandNames().getInfo())) - - # Classify water from VIS average image, and mask out ocean pixels - water_s2 = classifyVISimage(aver) - water_s2 = maskImage(water_s2, ocean_mask) - print(f'{vis_col1} scenes classified') - print('Band names: ' + str(water_s2.bandNames().getInfo())) - - try: - features_s2 = getFeatures(water_s2, 10, box) - except: - features_s2 = getFeaturesSplit(water_s2, 10, bbox) -else: - features_s2 = None - print(f'\nNo {vis_col1} scenes identified between {date1} and {date2}') - -#----------------------- VIS processing (Landsat 8) ----------------------- - -# Set collection -vis_col2 = "LANDSAT/LC08/C01/T1_TOA" - -# Get image collection -scenes = getScenes(vis_col2, date1, date2, box) -if scenes.size().getInfo() > 0: - scenes = filterLSscenes(scenes, cloud) - print(f'\nScenes gathered from {vis_col2}') - print('Total number of scenes: ', scenes.size().getInfo()) - print('Number of bands per scene: ' - + str(len(scenes.first().bandNames().getInfo()))) - print('Band names: ' + str(scenes.first().bandNames().getInfo())) - - # Mask scenes for clouds - scenes = maskL8clouds(scenes) - # ee.Terrain.hillshade(image, azimuth, elevation) - - # Get average of spectific bands - scenes = renameL8scenes(scenes) - aver = getMean(scenes, ['blue','green','red','vnir','swir1','swir2']) - print('Scenes resampled and mosiacked') - print('Band names: ' + str(aver.bandNames().getInfo())) - - # Classify water from VIS average image, and mask out ocean pixels - ndwi = aver.normalizedDifference(['green_mean', 'vnir_mean']).rename('ndwi') - mndwi = aver.normalizedDifference(['green_mean','swir1_mean']).rename('mndwi') - aweish = aver.expression('BLUE + 2.5 * GREEN - 1.5 * (VNIR + SWIR1) - 0.25 * SWIR2', - {'BLUE' : aver.select('blue_mean'), - 'GREEN' : aver.select('green_mean'), - 'SWIR1' : aver.select('swir1_mean'), - 'VNIR' : aver.select('vnir_mean'), - 'SWIR2' : aver.select('swir2_mean')}).rename('aweish') - aweinsh = aver.expression('4.0 * (GREEN - SWIR1) - (0.25 * VNIR + 2.75 * SWIR2)', - {'GREEN' : aver.select('green_mean'), - 'SWIR1' : aver.select('swir1_mean'), - 'VNIR' : aver.select('vnir_mean'), - 'SWIR2' : aver.select('swir2_mean')}).rename('aweinsh') - bright = aver.expression('(RED + GREEN + BLUE) / 3', - {'BLUE' : aver.select('blue_mean'), - 'GREEN' : aver.select('green_mean'), - 'RED' : aver.select('red_mean')}).rename('bright') - - aver = aver.addBands([ndwi, mndwi, aweish, aweinsh, bright]) - classified = aver.expression("(BRIGHT > 5000) ? 0" - ": (NDWI > 0.3) ? 1 " - ": (MNDWI < 0.1) ? 0 " - ": (AWEISH < 2000) ? 0" - ": (AWEISH > 5000) ? 0" - ": (AWEINSH < 4000) ? 0" - ": (AWEINSH > 6000) ? 0" - ": 1", - {'NDWI' : aver.select('ndwi'), - 'MNDWI' : aver.select('mndwi'), - 'AWEISH' : aver.select('aweish'), - 'AWEINSH' : aver.select('aweinsh'), - 'BRIGHT' : aver.select('bright')}).rename('water') - water_ls8 = classified.updateMask(classified) - water_ls8 = maskImage(water_ls8, ocean_mask) - print(f'{vis_col2} scenes classified') - print('Band names: ' + str(water_ls8.bandNames().getInfo())) - - try: - features_ls8 = getFeatures(water_ls8, 10, box) - except: - features_ls8 = getFeaturesSplit(water_ls8, 10, bbox) - -else: - features_ls8 = None - print(f'\nNo {vis_col2} scenes identified between {date1} and {date2}') - - -#------------------------ Compile geodatabase ----------------------------- - -iml = compileFeatures([features_s2, features_ls8, features_sar, features_dem], - ['VIS','VIS','SAR','DEM'], - [vis_col1, vis_col2, sar_col, dem_col], - [date1, date2]) -print(f'\nCompiled {iml.shape[0]} geodatabase features') - -# Reproject geometries -iml = iml.to_crs(proj) -print(f'{iml.shape[0]} unfiltered features') - -iml.to_file(f"out/iiml_{date1}_{date2}_{aoi1[0]}_{aoi1[1]}_unfiltered.shp") - - -#-------------------- Filter database by ice margin ----------------------- - -# Load margin and add buffer -margin = gpd.read_file("../../../other/datasets/ice_margin/gimp_icemask_line_polstereo.shp") -margin_buff = margin.buffer(500) -margin_buff = gpd.GeoDataFrame(geometry=margin_buff, crs=margin.crs) - -# Perform spatial join -iml = gpd.sjoin(iml, margin_buff, how='left') -iml = iml[iml['index_right']==0] -iml = iml.drop(columns='index_right') -print(f'{iml.shape[0]} features within 500 m of margin') - -# Filter lakes by area -iml['area_sqkm'] = iml['geometry'].area/10**6 -iml['length_km'] = iml['geometry'].length/1000 -iml = iml[(iml.area_sqkm >= 0.05)] - -# Calculate geometry info -iml.reset_index(inplace=True, drop=True) -print(f'{iml.shape[0]} features over 0.05 sq km') -iml.to_file(f"out/iiml_{date1}_{date2}_{aoi1[0]}_{aoi1[1]}_filtered.shp") - -#------------------------- Populate metadata ------------------------------ - -iml = assignID(iml) -iml = assignSources(iml) - -names = gpd.read_file('../../../other/datasets/placenames/oqaasileriffik_placenames.shp') -iml = assignNames(iml, names) - -#----------------------- Write data to shapefile -------------------------- - -iml.to_file(f"/home/pho/Desktop/python_workspace/GrIML/other/out/iiml_{date1}_{date2}_{aoi1[0]}_{aoi1[1]}_final.shp") -print(f'Saved to out/iiml_{date1}_{date2}_{aoi1[0]}_{aoi1[1]}_final.shp') - -# Plot lakes -iml.plot(figsize=(10, 10), alpha=0.5, edgecolor='k') - -print('\nFinished') -end=time.time() -print('Total run time: ' + strftime("%H:%M:%S", gmtime(round(end-start)))) diff --git a/src/griml/examples/getInventory_obj.py b/src/griml/examples/getInventory_obj.py deleted file mode 100644 index f8b507c..0000000 --- a/src/griml/examples/getInventory_obj.py +++ /dev/null @@ -1,182 +0,0 @@ -""" -GrIML processing module playground (experimental) - -@author: Penelope How -""" - -import time,sys -from time import gmtime, strftime -import geopandas as gpd - -try: - sys.path.append('../') - from process import gee - from lake import dissolvePolygons, compileFeatures, assignID, assignSources, assignNames -except: - from griml.process import gee - from griml.lake import dissolvePolygons, compileFeatures, assignID, \ - assignSources, assignNames - -start=time.time() - -#------------------------------------------------------------------------------ - -# Set AOI box splitter parameters -wh=0.3 # Window height -ww=0.3 # Window width -oh = 0.05 # Overlap height -ow = 0.05 # Overlap width - -# Set date range -date1='2017-07-01' -date2='2017-08-31' - -# Set output projection -proj = 'EPSG:3413' # Polar stereographic - -# Set AOI -# aoi = '/home/pho/python_workspace/GrIML/other/datasets/aoi/AOI_mask_split2.shp' -aoi = '/home/pho/python_workspace/GrIML/other/datasets/aoi/test_mask_4326.shp' -aoi = gpd.read_file(aoi)#.to_crs('EPSG:4326') - -# Load ice margin with buffer -print('Preparing ice margin buffer...') -margin_buff = gpd.read_file("/home/pho/python_workspace/GrIML/other/datasets/ice_margin/gimp_icemask_line_polstereo_simple_buffer.shp") -# margin_buff = margin.buffer(500) -# margin_buff = gpd.GeoDataFrame(geometry=margin_buff, crs=margin.crs) - -# Load name database -print('Loading placename database...') -names = gpd.read_file('/home/pho/python_workspace/GrIML/other/datasets/placenames/oqaasileriffik_placenames.shp') - - -#--------------------------- Initialise GEE ------------------------------- - -parameters = [#{'collection' : 'UMN/PGC/ArcticDEM/V3/2m_mosaic', - #'smooth' : 100, 'fill' : 100, 'kernel' : 100, 'speckle' : 50}, - # {'collection' : 'UMN/PGC/ArcticDEM/V3/2m', - # 'smooth' : 100, 'fill' : 100, 'kernel' : 100, 'speckle' : 50}, - {'collection' : 'COPERNICUS/S1_GRD', - 'polar' : 'HH', 'threshold' : -20, 'smooth' : 50}, - #{'collection' : 'COPERNICUS/S2', ` - #'cloud' : 20}, - {'collection' : 'LANDSAT/LC08/C01/T1_TOA', - 'cloud' : 20}] - # {'collection' : 'LANDSAT/LE07/C02/T1_TOA', - # 'cloud' : 50}] - - - -for i in range(len(aoi.index)): - print(f'\nCommencing processing of {list(aoi["catch"])[i]} catchment area') - aoi_poly = aoi.geometry.iloc[i] - xmin, ymin, xmax, ymax = aoi_poly.bounds - - print('Clipping margin to catchment area...') - aoi_poly_proj = gpd.GeoDataFrame(geometry=[aoi_poly], crs=aoi.crs).to_crs(proj) - margin_clip = gpd.clip(margin_buff, aoi_poly_proj) - - print('Conducting classifications...') - proc = gee([date1, date2], aoi_poly, parameters, [wh, ww, oh, ow], True) - - print('Processing...') - water = proc.processAll() - - print('Retrieving...') - features = proc.retrieveAll(water) - - # Filter features - filtered=[] - for f in features: - print('Filtering features...') - - # Remove duplicates - print('Dissolving polygons...') - gdf = gpd.GeoDataFrame(geometry=f, crs='EPSG:4326') - gdf = dissolvePolygons(gdf) - - # Remove lakes below size threshold - print('Filtering by size...') - gdf = gdf.set_crs('EPSG:4326') - gdf = gdf.to_crs(proj) - gdf['area_sqkm'] = gdf['geometry'].area/10**6 - gdf = gdf[(gdf.area_sqkm >= 0.05)] - - # # Remove lakes outside of margin boundary - # print('Filtering by bounds...') - # gdf = gpd.sjoin(gdf, margin_clip, how='left') - # gdf = gdf[gdf['index_right']==0] - # gdf = gdf.drop(columns='index_right') - filtered.append(gdf.geometry) - - -#------------------------ Compile geodatabase ----------------------------- -# lakes=[] -# for f in features: -# gdf = gpd.GeoDataFrame(geometry=f, crs='EPSG:4326') -# gdf = dissolvePolygons(gdf) -# gdf['area'] = gdf['geometry'].area/10**6 -# gdf = gdf[(gdf.area >= 0.05)] - -# gdf = gpd.sjoin(gdf, margin_buff, how='left') -# gdf = gdf[gdf['index_right']==0] -# gdf = gdf.drop(columns='index_right') -# lakes.append(list(gdf['geometry'])) - - # Compile all features - iml = compileFeatures(filtered, - ['DEM', 'SAR', 'VIS'], - ['UMN/PGC/ArcticDEM/V3/2m_mosaic', 'COPERNICUS/S1_GRD', - 'LANDSAT/LC08/C01/T1_TOA'], - [date1, date2], proj) - print(f'\nCompiled {iml.shape[0]} geodatabase features') - - # Calculate geometry attributes - iml['area_sqkm'] = iml['geometry'].area/10**6 - iml['length_km'] = iml['geometry'].length/1000 - iml = iml[(iml.area_sqkm >= 0.05)] - iml['catch'] = str(list(aoi["catch"])[i]) - - # Calculate geometry info - iml.reset_index(inplace=True, drop=True) - - print(f'{iml.shape[0]} filtered features') - - iml.to_file(f'out/iiml_{date1}_{date2}_{list(aoi["catch"])[i]}_filtered.shp') - - -# #-------------------- Filter database by ice margin ----------------------- - -# # Perform spatial join -# aoi_poly_proj = aoi_poly.to_crs(proj) -# margin_clip = gpd.clip(margin_buff, aoi_poly_proj) - -# iml = gpd.sjoin(iml, margin_clip, how='left') -# iml = iml[iml['index_right']==0] -# iml = iml.drop(columns='index_right') -# print(f'{iml.shape[0]} features within 500 m of margin') - -# # Calculate geometry info -# iml.reset_index(inplace=True, drop=True) -# print(f'{iml.shape[0]} features over 0.05 sq km') -# iml.to_file(f'out/iiml_{date1}_{date2}_{list(aoi["catch"])[i]}_filtered.shp') - - -#-------------------- Populate metadata and write to file ----------------- - -# iml = assignID(iml) - # iml = assignSources(iml) - - # iml = assignNames(iml, names) - - # iml.to_file(f'out/iiml_{date1}_{date2}_{list(aoi["catch"])[i]}_final.shp') - # print(f'Saved to out/iiml_{date1}_{date2}_{list(aoi["catch"])[i]}_final.shp') - - # # Plot lakes - # iml.plot(figsize=(10, 10), alpha=0.5, edgecolor='k') - - -#------------------------------------------------------------------------------ -print('\nFinished') -end=time.time() -print('Total run time: ' + strftime("%H:%M:%S", gmtime(round(end-start)))) diff --git a/src/griml/metadata/__init__.py b/src/griml/metadata/__init__.py index 3d16982..7e1e3d8 100644 --- a/src/griml/metadata/__init__.py +++ b/src/griml/metadata/__init__.py @@ -1,5 +1,6 @@ from griml.metadata.assign_certainty import * from griml.metadata.assign_id import * from griml.metadata.assign_names import * +from griml.metadata.assign_regions import * from griml.metadata.assign_sources import * from griml.metadata.add_metadata import * diff --git a/src/griml/metadata/add_metadata.py b/src/griml/metadata/add_metadata.py index 2f15ee2..50e6d9a 100644 --- a/src/griml/metadata/add_metadata.py +++ b/src/griml/metadata/add_metadata.py @@ -1,30 +1,17 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -from griml.metadata import assign_id, assign_sources, assign_certainty, \ - assign_names from griml.load import load +from griml.metadata import assign_id, assign_sources, assign_certainty, \ + assign_names, assign_regions import geopandas as gpd -def add_metadata(iml_file, names_file, outfile=None): - '''Add metadata to collection of features - - Parameters - ---------- - iml_file : str, geopandas.dataframe.DataFrame - Filepath or geopandas.dataframe.DataFrame object to assign metadata to - - Returns - ------- - iml: geopandas.dataframe.GeoDataFrame - Geodataframe with metadata - ''' - - # Load files - iml = load(iml_file) - names = load(names_file) +def add_metadata(iml, names, regions, outfile=None): + + iml = load(iml) + names = load(names) + regions = load(regions) - # Perform metadata steps print('Assigning ID...') iml = assign_id(iml) @@ -35,24 +22,27 @@ def add_metadata(iml_file, names_file, outfile=None): n = ['S1','S2','ARCTICDEM'] scores = [0.298, 0.398, 0.304] iml = assign_certainty(iml, n, scores) + + print('Assigning regions...') + iml = assign_regions(iml, regions) print('Assigning placenames...') iml = assign_names(iml, names) - # Save to file if given - if outfile is not None: + if outfile: print('Saving file...') iml.to_file(outfile) - print('Saved to '+str(outfile)) - - return iml + print('Saved to '+str(outfile)+"_metadata.shp") if __name__ == "__main__": - infile1 = '../test/test_merge_2.shp' - infile2 = '../test/test_placenames.shp' - add_metadata(infile1, infile2) + # indir = "/home/pho/python_workspace/GrIML/other/iml_2017/merged_vectors/griml_2017_inventory.shp" + indir = "/home/pho/python_workspace/GrIML/other/iml_2017/inspected_vectors/lakes_all-0000000000-0000037888_filtered.shp" + + + infile_names = '/home/pho/python_workspace/GrIML/other/datasets/placenames/oqaasileriffik_placenames.shp' + infile_regions = '/home/pho/python_workspace/GrIML/other/datasets/drainage_basins/greenland_basins_polarstereo.shp' + - iml = gpd.read_file(infile1) - names = gpd.read_file(infile2) - add_metadata(iml, names) \ No newline at end of file + outfile = "/home/pho/python_workspace/GrIML/other/iml_2017/metadata_vectors/griml_2017_inventory_final.shp" + add_metadata(indir, infile_names, infile_regions, outfile) diff --git a/src/griml/metadata/assign_names.py b/src/griml/metadata/assign_names.py index 9bd294f..c151a48 100644 --- a/src/griml/metadata/assign_names.py +++ b/src/griml/metadata/assign_names.py @@ -1,7 +1,18 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -def assign_names(gdf, gdf_names, distance=500.0): +import itertools +from operator import itemgetter + +import geopandas as gpd +import numpy as np +import pandas as pd + +from scipy.spatial import cKDTree +from shapely.geometry import Point, LineString, Polygon +from griml.load import load + +def assign_names(gdf, gdf_names): '''Assign placenames to geodataframe geometries based on names in another geodataframe point geometries @@ -19,62 +30,41 @@ def assign_names(gdf, gdf_names, distance=500.0): gdf : pandas.GeoDataFrame Vectors with assigned IDs ''' - placenames = _compile_names(gdf_names) + + # Load geodataframes + gdf1 = load(gdf) + gdf2 = load(gdf_names) + + # Compile placenames into new dataframe + names = _compile_names(gdf2) + placenames = gpd.GeoDataFrame({"geometry": list(gdf2['geometry']), + "placename": names}) - lakename=[] - for i,v in gdf.iterrows(): - - geom = v['geometry'] - - polynames=[] - for pt in range(len(placenames)): - if geom.contains(gdf_names['geometry'][pt]) == True: - polynames.append(placenames[pt]) - - if len(polynames)==0: - for pt in range(len(placenames)): - if gdf_names['geometry'][pt].distance(geom) < distance: - polynames.append(placenames[pt]) - lakename.append(polynames) - - elif len(polynames)==1: - lakename.append(polynames) - - else: - out=[] - for p in polynames: - out.append(p) - lakename.append(out) - - lakeid = gdf['unique_id'].tolist() - lakename2 = [] + # Assign names based on proximity + a = _get_nearest_point(gdf1, placenames) + return a + + +def _get_nearest_point(gdA, gdB, distance=500.0): + '''Return properties of nearest point in Y to geometry in X''' + nA = np.array(list(gdA.geometry.centroid.apply(lambda x: (x.x, x.y)))) + nB = np.array(list(gdB.geometry.apply(lambda x: (x.x, x.y)))) + + btree = cKDTree(nB) + dist, idx = btree.query(nA, k=1) + gdB_nearest = gdB.iloc[idx].drop(columns="geometry").reset_index(drop=True) + gdf = pd.concat( + [ + gdA.reset_index(drop=True), + gdB_nearest, + pd.Series(dist, name='dist') + ], + axis=1) - for x in gdf.index: - indx = _get_indices(lakeid, x) - findname=[] - for l in indx: - if len(lakename[l])!=0: - findname.append(lakename[l]) - - for i in range(len(indx)): - if len(findname)==0: - lakename2.append('') - - else: - unique = set(findname[0]) - unique = list(unique) - - if len(unique)==1: - lakename2.append(findname[0][0]) - - else: - out2 = ', ' - out2 = out2.join(unique) - lakename2.append(out2) - gdf['placename'] = lakename2 + gdf.loc[gdf['dist']>=distance, 'placename'] = 'Unknown' + gdf = gdf.drop(columns=['dist']) return gdf - def _get_indices(mylist, value): '''Get indices for value in list''' return[i for i, x in enumerate(mylist) if x==value] @@ -95,3 +85,14 @@ def _compile_names(gdf): else: placenames.append(None) return placenames + + +if __name__ == "__main__": + + # Define file attributes + infile = '/home/pho/Desktop/python_workspace/GrIML/src/griml/test/test_merge_1.shp' + names = '/home/pho/Desktop/python_workspace/GrIML/other/datasets/placenames/oqaasileriffik_placenames_polarstereo.shp' + + + # Perform conversion + a = assign_names(infile, names) \ No newline at end of file diff --git a/src/griml/metadata/assign_region.py b/src/griml/metadata/assign_region.py deleted file mode 100644 index 9bd294f..0000000 --- a/src/griml/metadata/assign_region.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -def assign_names(gdf, gdf_names, distance=500.0): - '''Assign placenames to geodataframe geometries based on names in another - geodataframe point geometries - - Parameters - ---------- - gdf : pandas.GeoDataFrame - Vectors to assign uncertainty to - gdf_names : pandas.GeoDataFrame - Vector geodataframe with placenames - distance : int - Distance threshold between a given vector and a placename - - Returns - ------- - gdf : pandas.GeoDataFrame - Vectors with assigned IDs - ''' - placenames = _compile_names(gdf_names) - - lakename=[] - for i,v in gdf.iterrows(): - - geom = v['geometry'] - - polynames=[] - for pt in range(len(placenames)): - if geom.contains(gdf_names['geometry'][pt]) == True: - polynames.append(placenames[pt]) - - if len(polynames)==0: - for pt in range(len(placenames)): - if gdf_names['geometry'][pt].distance(geom) < distance: - polynames.append(placenames[pt]) - lakename.append(polynames) - - elif len(polynames)==1: - lakename.append(polynames) - - else: - out=[] - for p in polynames: - out.append(p) - lakename.append(out) - - lakeid = gdf['unique_id'].tolist() - lakename2 = [] - - for x in gdf.index: - indx = _get_indices(lakeid, x) - findname=[] - for l in indx: - if len(lakename[l])!=0: - findname.append(lakename[l]) - - for i in range(len(indx)): - if len(findname)==0: - lakename2.append('') - - else: - unique = set(findname[0]) - unique = list(unique) - - if len(unique)==1: - lakename2.append(findname[0][0]) - - else: - out2 = ', ' - out2 = out2.join(unique) - lakename2.append(out2) - gdf['placename'] = lakename2 - return gdf - - -def _get_indices(mylist, value): - '''Get indices for value in list''' - return[i for i, x in enumerate(mylist) if x==value] - - -def _compile_names(gdf): - '''Get preferred placenames from placename geodatabase''' - placenames=[] - for i,v in gdf.iterrows(): - if v['Ny_grønla'] != None: - placenames.append(v['Ny_grønla']) - else: - if v['Dansk'] != None: - placenames.append(v['Dansk']) - else: - if v['Alternativ'] != None: - placenames.append(v['Alternativ']) - else: - placenames.append(None) - return placenames diff --git a/src/griml/metadata/assign_regions.py b/src/griml/metadata/assign_regions.py new file mode 100644 index 0000000..6458989 --- /dev/null +++ b/src/griml/metadata/assign_regions.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import itertools +from operator import itemgetter + +import geopandas as gpd +import numpy as np +import pandas as pd + +from griml.load import load +from scipy.spatial import cKDTree +from shapely.geometry import Point, LineString, Polygon + +def assign_regions(gdf, gdf_regions, region_name='region'): + '''Assign region to geodataframe geometries based on regions in another + geodataframe object + + Parameters + ---------- + gdf : pandas.GeoDataFrame + Vectors to assign region name to + gdf_regions : pandas.GeoDataFrame + Vector geodataframe with regions + + Returns + ------- + gdf : pandas.GeoDataFrame + Vectors with assigned IDs + ''' + + # Load geodataframes + gdf1 = load(gdf) + gdf2 = load(gdf_regions) + + g = _get_nearest_polygon(gdf1, gdf2) + + return g + + +def _get_nearest_polygon(gdfA, gdfB, gdfB_cols=['subregion'], distance=100000.0): + '''Return given properties of nearest polygon in Y to geometry in X''' + + A = np.array(list(gdfA.geometry.centroid.apply(lambda x: (x.x, x.y)))) + # B = [np.array(geom.boundary.coords) for geom in gdfB.geometry.to_list()] + + B=[] + for geom in gdfB.geometry.to_list(): + try: + g = np.array(geom.boundary.coords) + except: + g = np.array(geom.boundary.geoms[0].coords) + B.append(g) + + + B_ix = tuple(itertools.chain.from_iterable( + [itertools.repeat(i, x) for i, x in enumerate(list(map(len, B)))])) + B = np.concatenate(B) + ckd_tree = cKDTree(B) + + dist, idx = ckd_tree.query(A, k=1) + idx = itemgetter(*idx)(B_ix) + + gdf = pd.concat( + [gdfA, gdfB.loc[idx, gdfB_cols].reset_index(drop=True), + pd.Series(dist, name='dist')], axis=1) + + gdf.loc[gdf['dist']>=distance, 'subregion'] = 'Unknown' + + gdf = gdf.drop(columns=['dist']) + return gdf + + +if __name__ == "__main__": + + # Define file attributes + infile = '/home/pho/Desktop/python_workspace/GrIML/src/griml/test/test_merge_1.shp' + regions = '/home/pho/python_workspace/GrIML/other/datasets/drainage_basins/greenland_basins_polarstereo.shp' + regions=gpd.read_file(regions) + + regions_dissolve = regions.dissolve(by='subregion') + + + # Perform conversion + a = assign_regions(infile, regions) + diff --git a/src/griml/metadata/metadata.py b/src/griml/metadata/metadata.py deleted file mode 100644 index 9b0ca30..0000000 --- a/src/griml/metadata/metadata.py +++ /dev/null @@ -1,38 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -from griml.metadata import assign_id, assign_sources, assign_certainty, \ - assign_names -import geopandas as gpd - -def add_metadata(iml, names, outfile): - - print('Assigning ID...') - iml = assign_id(iml) - - print('Assigning sources...') - iml = assign_sources(iml) - - print('Assigning certainty scores...') - n = ['S1','S2','ARCTICDEM'] - scores = [0.298, 0.398, 0.304] - iml = assign_certainty(iml, n, scores) - - print('Assigning placenames...') - iml = assign_names(iml, names) - - print('Saving file...') - iml.to_file(outfile) - print('Saved to '+str(outfile)+"_metadata.shp") - - -if __name__ == "__main__": - # indir = "/home/pho/python_workspace/GrIML/other/iml_2017/merged_vectors/griml_2017_inventory.shp" - indir = "/home/pho/python_workspace/GrIML/other/iml_2017/inspected_vectors/lakes_all-0000000000-0000037888_filtered.shp" - iml = gpd.read_file(indir) - - infile_names = '/home/pho/python_workspace/GrIML/other/datasets/placenames/oqaasileriffik_placenames.shp' - names = gpd.read_file(infile_names) - - 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