From ca3ce577110117a130dfa4e977ffe81aec472fb3 Mon Sep 17 00:00:00 2001 From: Penny How Date: Wed, 22 May 2024 14:46:27 -0100 Subject: [PATCH] Load module added (#11) * relevant figures added * summary written * ref formatting changed * load module added --- .gitignore | 1 + .../figures/griml_workflow_with_gee.png | Bin 0 -> 206088 bytes .../figures/griml_workflow_without_gee.png | Bin 0 -> 166643 bytes other/reporting/griml_bimonthly_may24.pdf | Bin 0 -> 846824 bytes paper/paper.bib | 92 ++++++++++++++---- paper/paper.md | 26 ++++- setup.py | 4 +- src/griml/convert/convert.py | 19 +++- src/griml/filter/filter_vectors.py | 64 +++++++++--- src/griml/load/__init__.py | 1 + src/griml/load/load.py | 80 +++++++++++++++ src/griml/merge/merge_vectors.py | 90 ++++++++++------- src/griml/metadata/add_metadata.py | 39 ++++++-- src/griml/test/test.py | 36 ++----- 14 files changed, 332 insertions(+), 120 deletions(-) create mode 100644 other/reporting/figures/griml_workflow_with_gee.png create mode 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{10.5194/essd-13-3819-2021}, - language = {English}, - number = {8}, - urldate = {2022-12-15}, - journal = {Earth System Science Data}, - author = {Fausto, Robert S. and van As, Dirk and Mankoff, Kenneth D. and Vandecrux, Baptiste and Citterio, Michele and Ahlstrøm, Andreas P. and Andersen, Signe B. and Colgan, William and Karlsson, Nanna B. and Kjeldsen, Kristian K. and Korsgaard, Niels J. and Larsen, Signe H. and Nielsen, Søren and Pedersen, Allan Ø and Shields, Christopher L. and Solgaard, Anne M. and Box, Jason E.}, - year = {2021}, - note = {Publisher: Copernicus GmbH}, - pages = {3819--3845}, +@article{wilkinson_fair_2016, + title = {The {FAIR} {Guiding} {Principles} for scientific data management and stewardship}, + volume = {3}, + copyright = {2016 The Author(s)}, + issn = {2052-4463}, + url = {https://www.nature.com/articles/sdata201618}, + doi = {10.1038/sdata.2016.18}, + abstract = {There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.}, + language = {en}, + number = {1}, + urldate = {2024-05-17}, + journal = {Scientific Data}, + author = {Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg, IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E. and Bouwman, Jildau and Brookes, Anthony J. and Clark, Tim and Crosas, Mercè and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J. G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa, Jaap and ’t Hoen, Peter A. C. and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A. and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend}, + month = mar, + year = {2016}, + note = {Publisher: Nature Publishing Group}, + keywords = {Publication characteristics, Research data}, + pages = {160018}, + file = {Full Text PDF:/home/pho/Zotero/storage/2YD97CTA/Wilkinson et al. - 2016 - The FAIR Guiding Principles for scientific data ma.pdf:application/pdf}, } -@article{how-greenland-2021, +@article{how_greenland-wide_2021, title = {Greenland-wide inventory of ice marginal lakes using a multi-method approach}, volume = {11}, - url = {https://doi.org/10.1038/s41598-021-83509-1}, + copyright = {2021 The Author(s)}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-021-83509-1}, doi = {10.1038/s41598-021-83509-1}, - language = {English}, - number = {4481}, - urldate = {2022-02-24}, + abstract = {Ice marginal lakes are a dynamic component of terrestrial meltwater storage at the margin of the Greenland Ice Sheet. Despite their significance to the sea level budget, local flood hazards and bigeochemical fluxes, there is a lack of Greenland-wide research into ice marginal lakes. Here, a detailed multi-sensor inventory of Greenland’s ice marginal lakes is presented based on three well-established detection methods to form a unified remote sensing approach. The inventory consists of 3347 (\$\${\textbackslash}pm 8\$\$\%) ice marginal lakes (\$\${\textgreater}0.05{\textbackslash},\{\{{\textbackslash}text\{ km \}\}{\textasciicircum}\{2\}\}\$\$) detected for the year 2017. The greatest proportion of lakes lie around Greenland’s ice caps and mountain glaciers, and the southwest margin of the ice sheet. Through comparison to previous studies, a \$\${\textbackslash}sim 75\$\$\% increase in lake frequency is evident over the west margin of the ice sheet since 1985. This suggests it is becoming increasingly important to include ice marginal lakes in future sea level projections, where these lakes will form a dynamic storage of meltwater that can influence outlet glacier dynamics. Comparison to existing global glacial lake inventories demonstrate that up to 56\% of ice marginal lakes could be unaccounted for in global estimates of ice marginal lake change, likely due to the reliance on a single lake detection method.}, + language = {en}, + number = {1}, + urldate = {2024-05-17}, journal = {Scientific Reports}, author = {How, Penelope and Messerli, Alexandra and Mätzler, Eva and Santoro, Maurizio and Wiesmann, Andreas and Caduff, Rafael and Langley, Kirsty and Bojesen, Mikkel Høegh and Paul, Frank and Kääb, Andreas and Carrivick, Jonathan L.}, + month = feb, year = {2021}, -} + note = {Publisher: Nature Publishing Group}, + keywords = {Cryospheric science, Hydrology}, + pages = {4481}, + file = {Full Text PDF:/home/pho/Zotero/storage/48EXCPRT/How et al. - 2021 - Greenland-wide inventory of ice marginal lakes usi.pdf:application/pdf}, +} + +@article{shugar_rapid_2020, + title = {Rapid worldwide growth of glacial lakes since 1990}, + volume = {10}, + copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited}, + issn = {1758-6798}, + url = {https://www.nature.com/articles/s41558-020-0855-4}, + doi = {10.1038/s41558-020-0855-4}, + abstract = {Glacial lakes are rapidly growing in response to climate change and glacier retreat. The role of these lakes as terrestrial storage for glacial meltwater is currently unknown and not accounted for in global sea level assessments. Here, we map glacier lakes around the world using 254,795 satellite images and use scaling relations to estimate that global glacier lake volume increased by around 48\%, to 156.5 km3, between 1990 and 2018. This methodology provides a near-global database and analysis of glacial lake extent, volume and change. Over the study period, lake numbers and total area increased by 53 and 51\%, respectively. Median lake size has increased 3\%; however, the 95th percentile has increased by around 9\%. Currently, glacial lakes hold about 0.43 mm of sea level equivalent. As glaciers continue to retreat and feed glacial lakes, the implications for glacial lake outburst floods and water resources are of considerable societal and ecological importance.}, + language = {en}, + number = {10}, + urldate = {2024-05-17}, + journal = {Nature Climate Change}, + author = {Shugar, Dan H. and Burr, Aaron and Haritashya, Umesh K. and Kargel, Jeffrey S. and Watson, C. Scott and Kennedy, Maureen C. and Bevington, Alexandre R. and Betts, Richard A. and Harrison, Stephan and Strattman, Katherine}, + month = oct, + year = {2020}, + note = {Publisher: Nature Publishing Group}, + keywords = {Climate-change impacts, Cryospheric science, Hydrology}, + pages = {939--945}, +} + +@article{rick_dam_2022, + title = {Dam type and lake location characterize ice-marginal lake area change in {Alaska} and {NW} {Canada} between 1984 and 2019}, + volume = {16}, + issn = {1994-0416}, + url = {https://tc.copernicus.org/articles/16/297/2022/}, + doi = {10.5194/tc-16-297-2022}, + abstract = {Ice-marginal lakes impact glacier mass balance, water resources, and ecosystem dynamics and can produce catastrophic glacial lake outburst floods (GLOFs) via sudden drainage. Multitemporal inventories of ice-marginal lakes are a critical first step in understanding the drivers of historic change, predicting future lake evolution, and assessing GLOF hazards. Here, we use Landsat-era satellite imagery and supervised classification to semi-automatically delineate lake outlines for four ∼5-year time periods between 1984 and 2019 in Alaska and northwest Canada. Overall, ice-marginal lakes in the region have grown in total number (+183 lakes, 38 \% increase) and area (+483 km2, 59 \% increase) between the time periods of 1984–1988 and 2016–2019. However, changes in lake numbers and area were notably unsteady and nonuniform. We demonstrate that lake area changes are connected to dam type (moraine, bedrock, ice, or supraglacial) and topological position (proglacial, detached, unconnected, ice, or supraglacial), with important differences in lake behavior between the sub-groups. In strong contrast to all other dam types, ice-dammed lakes decreased in number (six fewer, 9 \% decrease) and area (−51 km2, 40 \% decrease), while moraine-dammed lakes increased (56 more, 26 \% and +479 km2, 87 \% increase for number and area, respectively) at a faster rate than the average when considering all dam types together. Proglacial lakes experienced the largest area changes and rate of change out of any lake position throughout the period of study and moraine-dammed lakes which experienced the largest increases are associated with clean-ice glaciers (\<19 \% debris cover). By tracking individual lakes through time and categorizing lakes by dam type, subregion, and topological position, we are able to parse trends that would otherwise be aliased if these characteristics were not considered. This work highlights the importance of such lake characterization when performing ice-marginal lake inventories and provides insight into the physical processes driving recent ice-marginal lake evolution.}, + language = {English}, + number = {1}, + urldate = {2024-05-17}, + journal = {The Cryosphere}, + author = {Rick, Brianna and McGrath, Daniel and Armstrong, William and McCoy, Scott W.}, + month = jan, + year = {2022}, + note = {Publisher: Copernicus GmbH}, + pages = {297--314}, + file = {Full Text PDF:/home/pho/Zotero/storage/SW8L4RIG/Rick et al. - 2022 - Dam type and lake location characterize ice-margin.pdf:application/pdf}, +} diff --git a/paper/paper.md b/paper/paper.md index dc57778..4a01f41 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,7 +1,7 @@ --- title: "GrIML: A Python package for investigating Greenland's ice marginal lakes under a changing climate" tags: - - Python + - python - glaciology - remote sensing - greenland @@ -12,7 +12,7 @@ authors: corresponding: true affiliation: 1 affiliations: - - name: Department of Glaciology and Climate, Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark + - name: Department of Glaciology and Climate, Geological Survey of Denmark and Greenland (GEUS), Nuuk, Greenland index: 1 date: 01 September 2024 @@ -22,15 +22,31 @@ bibliography: paper.bib # Summary -The **GrIML** processing package is for classifying water bodies from satellite imagery using a multi-sensor, multi-method remote sensing approach. This workflow is part of the [ESA GrIML project](https://eo4society.esa.int/projects/griml/) (Investigating Greenland's ice marginal lakes under a changing climate). +The `GrIML` Python package is for the post-processing of classified water bodies from satellite imagery. Initial rasterised binary classifications denoting water bodies can be inputted to `GrIML` to convert, filter and merge into a cohesive ice marginal lake vector dataset, populated with useful metadata and analysed with relevant statistical information (\autoref{fig:workflow}). -Sea level is predicted to rise drastically by 2100, with significant contribution from the melting of the Greenland Ice Sheet (GrIS). In these predictions, melt runoff is assumed to contribute directly to sea level change, with little consideration for meltwater storage at the terrestrial margin of the ice sheet; such as ice marginal lakes. +![An overview of the GrIML Python package workflow \label{fig:workflow}](https://github.com/PennyHow/GrIML/blob/joss-paper/other/reporting/figures/griml_workflow_without_gee.png?raw=true) -In 2017, 3347 ice marginal lakes were identified in Greenland along the ice margin ([How et al., 2021](https://www.nature.com/articles/s41598-021-83509-1), see map figure for all mapped lakes). Globally, these ice marginal lakes hold up to 0.43 mm of sea level equivalent, which could have a marked impact on future predictions ([Shugar et al., 2021](https://www.nature.com/articles/s41558-020-0855-4)). Therefore, they need to be monitored to understand how changes in ice marginal lake water storage affect melt contribution, and how their dynamics evolve under a changing climate. +This package is part of the [ESA GrIML project](https://eo4society.esa.int/projects/griml/) (Investigating Greenland's ice marginal lakes under a changing climate), whose aim is to map and monitor ice marginal lakes across Greenland through a series of annual ice marginal lake inventories (2016-2023). This workflow was used to make the inventory series, and will continue to be used to generate inventories in the future. # Statement of need +`GrIML` meets four main needs to users in the remote sensing and cryospheric science communities: + +1. Provide a usable workflow for post-processing of rasterised water body classifications +2. Document the criteria for classifying an ice marginal lake +3. Showcase a transparent workflow that, in turn, produces an open and reproducible ice marginal lake dataset that adheres to the FAIR principles [@wilkinson_fair_2016] +4. Produce inventories that map the areal extent and frequency of ice marginal lakes across Greenland, which demonstrate ice marginal lake evolution under a changing cliamte + + +- Many different approaches to classifying ice marginal lakes [@shugar_rapid_2020;@rick_dam_2022] +- Previously this workflow was a closed method [@how_greenland-wide_2021] + + +Sea level is predicted to rise drastically by 2100, with significant contribution from the melting of the Greenland Ice Sheet (GrIS). In these predictions, melt runoff is assumed to contribute directly to sea level change, with little consideration for meltwater storage at the terrestrial margin of the ice sheet; such as ice marginal lakes. + +In 2017, 3347 ice marginal lakes were identified in Greenland along the ice margin ([How et al., 2021](https://www.nature.com/articles/s41598-021-83509-1), see map figure for all mapped lakes). Globally, these ice marginal lakes hold up to 0.43 mm of sea level equivalent, which could have a marked impact on future predictions ([Shugar et al., 2021](https://www.nature.com/articles/s41558-020-0855-4)). Therefore, they need to be monitored to understand how changes in ice marginal lake water storage affect melt contribution, and how their dynamics evolve under a changing climate. + `GrIML` proposes to examine ice marginal lake changes across Greenland using a multi-sensor and multi-method remote sensing approach to better address their influence on sea level contribution forecasting. 1. Greenland-wide inventories of ice marginal lakes will be generated for selected years during the satellite era, building upon established classification methods in a unified cloud processing workflow diff --git a/setup.py b/setup.py index ac24c9b..9206eff 100644 --- a/setup.py +++ b/setup.py @@ -17,6 +17,8 @@ }, keywords="glaciology ice lake ESA", classifiers=[ + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", @@ -30,6 +32,6 @@ package_dir={"": "src"}, packages=setuptools.find_packages(where="src"), package_data={"griml.test": ["*"]}, - python_requires=">=3.10", + python_requires=">=3.8", install_requires=['geopandas', 'pandas', 'scipy', 'Shapely', 'rasterio'], ) diff --git a/src/griml/convert/convert.py b/src/griml/convert/convert.py index 6e85554..90ef190 100644 --- a/src/griml/convert/convert.py +++ b/src/griml/convert/convert.py @@ -5,7 +5,19 @@ import glob from pathlib import Path -def convert(indir, outdir, proj, band_info, startdate, enddate, outfile=None): +def convert(indir, proj, band_info, startdate, enddate, outdir=None): + '''Compile features from multiple processings into one geodataframe + + Parameters + ---------- + inlist : list + List of files or geopandas.dataframe.DataFrame objects to merge + + Returns + ------- + all_gdf : geopandas.dataframe.GeoDataFrame + Compiled goedataframe + ''' # Iterate through files converted=[] @@ -14,18 +26,17 @@ def convert(indir, outdir, proj, band_info, startdate, enddate, outfile=None): print('\n'+str(count) + '. Converting ' + str(Path(i).name)) # Convert raster to vector - if outfile is not None: + if outdir is not None: outfile = str(Path(outdir).joinpath(Path(i).stem+'.shp')) g = raster_to_vector(str(i), proj, band_info, startdate, enddate, None) print('Saved to '+str(Path(outfile).name)) else: - g = raster_to_vector(str(i), proj, band_info, startdate, enddate, outfile) + g = raster_to_vector(str(i), proj, band_info, startdate, enddate) converted.append(g) count=count+1 - print('Finished') return (converted) if __name__ == "__main__": diff --git a/src/griml/filter/filter_vectors.py b/src/griml/filter/filter_vectors.py index 79264a5..2337c55 100644 --- a/src/griml/filter/filter_vectors.py +++ b/src/griml/filter/filter_vectors.py @@ -2,38 +2,72 @@ # -*- coding: utf-8 -*- from griml.filter import filter_margin, filter_area +from griml.load import load import geopandas as gpd from pathlib import Path -import glob -def filter_vectors(indir, outdir, margin_buff): +def filter_vectors(inlist, margin_file, outdir=None, min_area=0.05): + '''Filter vectors by area and margin proximity + + Parameters + ---------- + inlist : list + List of either file paths of GeoDataFrame objects to filter + margin_file : str, geopandas.GeoSeries + Bufferred margin to perform margin proximity filter + outdir : str, optional + Output directory to write files to + min_area: int, optional + Threshold area (sq km) to filter by + + Returns + ------- + filtered : list + List of filtered GeoDataFrame objects + + ''' + + # Load margin + margin_buff = load(margin_file) + + # Iterate through input list count=1 - for infile in list(glob.glob(indir)): + filtered=[] + for infile in inlist: + + # Load and define name + if type(infile)==str: + print('\n'+str(count)+'/'+str(len(inlist)) + + ': Filtering vectors in '+str(Path(infile).name)) + name = str(Path(infile).stem)+"_filtered.shp" + + else: + print('\n'+str(count)+'/'+str(len(inlist))) + name = 'lakes_' + str(count) + "_filtered.shp" - print('\n'+str(count)+'. Filtering vectors in '+str(Path(infile).stem)) - vectors = gpd.read_file(infile) + vectors = load(infile) - vectors = filter_area(vectors) + # Perform filtering steps + vectors = filter_area(vectors, min_area) print(f'{vectors.shape[0]} features over 0.05 sq km') vectors = filter_margin(vectors, margin_buff) print(f'{vectors.shape[0]} features within 500 m of margin') + # Retain and save if vectors are present after filtering if vectors.shape[0]>0: if outdir is not None: - vectors.to_file(outdir+str(Path(infile).stem)+"_filtered.shp") + vectors.to_file(Path(outdir).joinpath(name)) + filtered.append(vectors) else: print('No vectors present after filter. Moving to next file.') count=count+1 + if __name__ == "__main__": - indir = "/home/pho/python_workspace/GrIML/other/iml_2017/vectors/*.shp" - - infile_margin = "/home/pho/python_workspace/GrIML/other/datasets/ice_margin/gimp_icemask_line_polstereo_simple_buffer.shp" - print('Preparing ice margin buffer...') - margin_buff = gpd.read_file(infile_margin) - # margin_buff = margin.buffer(500) - # margin_buff = gpd.GeoDataFrame(geometry=margin_buff, crs=margin.crs) + infile1 = '../test/test_icemask.shp' + margin_buff = gpd.read_file(infile1) - filter_vectors(indir, margin_buff) + infile2 = ['../test/test_filter.shp'] + filter_vectors(infile2, margin_buff) diff --git a/src/griml/load/__init__.py b/src/griml/load/__init__.py new file mode 100644 index 0000000..8f43f1e --- /dev/null +++ b/src/griml/load/__init__.py @@ -0,0 +1 @@ +from griml.load.load import * diff --git a/src/griml/load/load.py b/src/griml/load/load.py new file mode 100644 index 0000000..86dac35 --- /dev/null +++ b/src/griml/load/load.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import geopandas as gpd +import pandas as pd +import os, errno +from pathlib import Path + +def load(i): + '''Load vectors into appropriate format for processing + + Parameters + ---------- + i : str, geopandas.geodataframe.GeoDataFrame, pandas.core.series.Series + Input vectors (either from file or vector object) + + Returns + ------- + out : geopandas.geodataframe.GeoDataFrame + Output vector object + ''' + if is_string(i): + if is_filepath(i): + out = load_vector_from_file(i) + return out + + else: + ValueError('Expected valid vector filepath, but instead got '+i) + + elif is_geo_object(i): + out = i + return out + + else: + TypeError('Expected str, geopandas.geodataframe.GeoDataFrame or pandas.core.series.Series object, but instead got '+str(type(i))) + +def load_vector_from_file(infile): + '''Load vector object from file''' + gdf = gpd.read_file(infile) + return gdf + +def is_string(n): + '''Check if input for loading is string''' + if type(n)==str: + return True + else: + return False + +def is_filepath(n): + '''Check if string is valid file''' + if os.path.isfile(n): + return True + else: + raise FileNotFoundError( + errno.ENOENT, os.strerror(errno.ENOENT), n) + +def is_dir(n): + '''Check if string is valid directory''' + if os.path.isdir(str(Path(n).parent)): + return True + else: + raise FileNotFoundError( + errno.ENOENT, os.strerror(errno.ENOENT), n) + +def is_geo_object(typ): + '''Check if object is valid vector object''' + if type(typ)==gpd.geodataframe.GeoDataFrame or type(typ)==pd.core.series.Series: + return True + else: + return False + +if __name__ == "__main__": + infile1 = '../test/test_icemask.shp' + test1 = load(infile1) + + g = gpd.read_file(infile1) + test2 = load(g) + + infile2 = '../test/test_icemsak.shp' + test2 = load(infile2) \ No newline at end of file diff --git a/src/griml/merge/merge_vectors.py b/src/griml/merge/merge_vectors.py index c456bd9..89686b2 100644 --- a/src/griml/merge/merge_vectors.py +++ b/src/griml/merge/merge_vectors.py @@ -4,29 +4,24 @@ import geopandas as gpd import pandas as pd from glob import glob +from griml.load import load -def merge_vectors(feature_list, method_list, collection_list, start_date_list, - end_date_list, proj='EPSG:3413'): +def merge_vectors(inlist, outfile=None, proj='EPSG:3413'): '''Compile features from multiple processings into one geodataframe Parameters ---------- - feature_list : list - List of shapely features - method_list : list - List of strings denoting processing method - collection_list : list - List of strings denoting dataset collection - date_list : list - List of start and end date for processing + inlist : list + List of files or geopandas.dataframe.DataFrame objects to merge Returns ------- - all_gdf : geopandas.GeoDataFrame + all_gdf : geopandas.dataframe.GeoDataFrame Compiled goedataframe ''' + feature, method, collection, start_date, end_date = _load_all(inlist) dfs=[] - for a,b,c,d,e in zip(feature_list, method_list, collection_list, start_date_list, end_date_list): + for a,b,c,d,e in zip(feature, method, collection, start_date, end_date): if a is not None: #Construct geodataframe with basic metadata @@ -48,10 +43,51 @@ def merge_vectors(feature_list, method_list, collection_list, start_date_list, all_gdf['area_sqkm'] = all_gdf['geometry'].area/10**6 all_gdf['length_km'] = all_gdf['geometry'].length/1000 - all_gdf = gpd.GeoDataFrame(all_gdf, geometry=all_gdf.geometry, - crs=proj) + # all_gdf = gpd.GeoDataFrame(all_gdf, geometry=all_gdf.geometry, + # crs=proj) + + if outfile is not None: + print('Saving file...') + all_gdf.to_file(outfile) + print('Saved to '+str(outfile)) + return all_gdf +def _load_all(inlist): + '''Load info from all features for merging + + Parameters + ---------- + inlist : list + List of files or geodataframe.dataframe.DataFrame objects to load info + from + + Returns + ------- + feature_list : list + List of shapely features + method_list : list + List of strings denoting processing method + collection_list : list + List of strings denoting dataset collection + date_list : list + List of start and end date for processing + ''' + features=[] + methods=[] + sources=[] + starts=[] + ends=[] + + for f in inlist: + i = load(f) + features.append(list(i['geometry'])) + methods.append(list(i['method'])) + sources.append(list(i['source'])) + starts.append(list(i['startdate'])) + ends.append(list(i['enddate'])) + + return features, methods, sources, starts, ends def _dissolve_vectors(gdf): '''Dissolve overlapping polygons in a Pandas GeoDataFrame @@ -72,26 +108,6 @@ def _dissolve_vectors(gdf): return gdf2 if __name__ == "__main__": - indir = "/home/pho/python_workspace/GrIML/other/iml_2017/test/*.shp" - outfile = "/home/pho/python_workspace/GrIML/other/iml_2017/merged_vectors/griml_2017_inventory.shp" - features=[] - methods=[] - sources=[] - starts=[] - ends=[] - - for f in list(glob(indir)): - i = gpd.read_file(f) - - features.append(list(i['geometry'])) - methods.append(list(i['method'])) - sources.append(list(i['source'])) - starts.append(list(i['startdate'])) - ends.append(list(i['enddate'])) - vectors = merge_vectors(features, methods, sources, starts, ends) - - # features.append(i) - # vectors = pd.concat(features, ignore_index=True) - - - vectors.to_file(outfile) + infile1 = '../test/test_merge_1.shp' + infile2 = '../test/test_merge_2.shp' + vectors = merge_vectors([infile1,infile2]) \ No newline at end of file diff --git a/src/griml/metadata/add_metadata.py b/src/griml/metadata/add_metadata.py index 0bdb248..2f15ee2 100644 --- a/src/griml/metadata/add_metadata.py +++ b/src/griml/metadata/add_metadata.py @@ -3,10 +3,28 @@ from griml.metadata import assign_id, assign_sources, assign_certainty, \ assign_names +from griml.load import load import geopandas as gpd -def add_metadata(iml, names, outfile): - +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) + + # Perform metadata steps print('Assigning ID...') iml = assign_id(iml) @@ -21,19 +39,20 @@ def add_metadata(iml, names, outfile): print('Assigning placenames...') iml = assign_names(iml, names) + # Save to file if given if outfile is not None: print('Saving file...') iml.to_file(outfile) print('Saved to '+str(outfile)) + + return iml 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/merged_vectors/griml_2017_inventory_first_intervention.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) + infile1 = '../test/test_merge_2.shp' + infile2 = '../test/test_placenames.shp' + add_metadata(infile1, infile2) - outfile = "/home/pho/python_workspace/GrIML/other/iml_2017/metadata_vectors/griml_2017_inventory_final_first_intervention.shp" - add_metadata(iml, names, outfile) + iml = gpd.read_file(infile1) + names = gpd.read_file(infile2) + add_metadata(iml, names) \ No newline at end of file diff --git a/src/griml/test/test.py b/src/griml/test/test.py index 0585700..336fdee 100644 --- a/src/griml/test/test.py +++ b/src/griml/test/test.py @@ -19,45 +19,25 @@ def test_convert(self): end='20170831' infile = os.path.join(os.path.dirname(griml.__file__),'test/test_north_greenland.tif') - convert([infile], None, proj, band_info, start, end) + convert([infile], proj, band_info, start, end) def test_filter(self): '''Test vector filtering''' - infile1 = os.path.join(os.path.dirname(griml.__file__),'test/test_icemask.shp') - margin_buff = gpd.read_file(infile1) - - infile2 = os.path.join(os.path.dirname(griml.__file__),'test/test_filter.shp') - filter_vectors(infile2, None, margin_buff) + infile1 = os.path.join(os.path.dirname(griml.__file__),'test/test_filter.shp') + infile2 = os.path.join(os.path.dirname(griml.__file__),'test/test_icemask.shp') + filter_vectors([infile1], infile2) def test_merge(self): '''Test vector merging''' infile1 = os.path.join(os.path.dirname(griml.__file__),'test/test_merge_1.shp') - merge1 = gpd.read_file(infile1) - - infile2 = os.path.join(os.path.dirname(griml.__file__),'test/test_merge_2.shp') - merge2 = gpd.read_file(infile2) - - features=[] - methods=[] - sources=[] - starts=[] - ends=[] - for infile in [merge1, merge2]: - features.append(list(infile['geometry'])) - methods.append(list(infile['method'])) - sources.append(list(infile['source'])) - starts.append(list(infile['startdate'])) - ends.append(list(infile['enddate'])) - vectors = merge_vectors(features, methods, sources, starts, ends) + infile2 = os.path.join(os.path.dirname(griml.__file__),'test/test_merge_2.shp') + merge_vectors([infile1,infile2]) def test_metadata(self): '''Test metadata population''' - infile1 = os.path.join(os.path.dirname(griml.__file__),'test/test_merge_2.shp') - iml = gpd.read_file(infile1) - + infile1 = os.path.join(os.path.dirname(griml.__file__),'test/test_merge_2.shp') infile2 = os.path.join(os.path.dirname(griml.__file__),'test/test_placenames.shp') - names = gpd.read_file(infile2) - add_metadata(iml, names, None) + add_metadata(infile1, infile2) if __name__ == "__main__": unittest.main()