diff --git a/README.md b/README.md index e54a7be..92d3893 100644 --- a/README.md +++ b/README.md @@ -30,11 +30,15 @@ fimeval/ ├── docs/ # Documentation (contains 'FIMserv' Tool usage sample codes) │ └── sampledata/ # Contains the sample data to demonstrate how this frameworks works │ └── fimeval_usage.ipynb #Sample code usage of the Evaluation framework +│ └── fimbench_usage.ipynb #Sample code usage of the FIMbench Query and getting benchmark dataset ├── Images/ # have sample images for documentation ├── src/ -│ └── fimeval/ +│ └── fimeval/ +│ ├──BenchFIMQuery/ #Module to interact with the extensive FIMdatabase, hosted in AWS S3 +│ │ └── access_benchfim.py #Different classes to query right benchmark FIM for any given location and set of filter +│ │ └── utilis.py #Support utility │ ├──BuildingFootprint/ # Contains the evaluation of model predicted FIM with microsoft building footprint -│ │ └── microsoftBF.py +│ │ └── arcgis_API.py #seamless integration of building footprint in evaluation through ArcGIS REST API │ │ └── evaluationwithBF.py │ └── ContingencyMap/ # Contains all the metrics calculation and contingency map generation │ │ ├── evaluationFIM.py # main evaluation moodule @@ -42,14 +46,14 @@ fimeval/ │ │ └── metrics.py # metrics calculation module │ │ └── plotevaluationmetrics.py # use to vizualize the different performance metrics │ │ └── printcontingency.py # prints the contingency map to quickly generate the Map layout -│ │ └── PWBs3.py # module which helps to get permanent water bodies from s3 bucket +│ │ └── water_bodies.py # module which to get permanent water bodies from s3 bucket and ArcGIS REST API │ └── utilis.py #Includes the resampling and reprojection of FIMs └── tests/ # Includes test cases for different functionality ``` The graphical representation of fimeval pipeline can be summarized as follows in **```Figure 1```**. Here, it will show all the steps incorporated within the ```fimeval``` during packaging and all functionality are interconnected to each other, resulting the automation of the framework.
- image + image
Figure 1: Flowchart showing the entire framework pipeline. @@ -91,7 +95,7 @@ main_dir = Path('./path/to/main/dir') ``` #### **Permanent Water Bodies (PWB)** -This framework uses PWB to first to delineate the PWB in the FIM and assign into different class so that the evaluation will be more fair. For the Contiguous United States (CONUS), the PWB is already integrated within the framework however, if user have more accurate PWB or using fimeval for outside US they can initialize and use PWB within fimeval framework. Currently it is using PWB publicly hosted by ESRI: https://hub.arcgis.com/datasets/esri::usa-detailed-water-bodies/about +This framework uses PWB to first to delineate the PWB in the FIM and assign into different class so that the evaluation will be more fair. For the Contiguous United States (CONUS), the PWB is already integrated within the framework however, if user have more accurate PWB or using fimeval for outside US they can initialize and use PWB within fimeval framework. Currently it is using PWB publicly hosted by ESRI through REST API: https://hub.arcgis.com/datasets/esri::usa-detailed-water-bodies/about If user have more precise PWB, they can input their own PWB boundary as .shp and .gpkg format and need to assign the shapefile of the PWB and define directory as, ```bash @@ -136,7 +140,7 @@ Table 1: Modules in `fimeval` are in order of execution. | `EvaluateFIM` | It runs all the evaluation of FIM between B-FIM and M-FIMs. | `main_dir`: Main directory containing the case study folders,
`method_name`: How users wants to evaluate their FIM,
`outpur_dir`: Output directory where all the results and the intermidiate files will be saved for further calculation,
*`PWB_dir`*: The permanenet water bodies vectory file directory if user wants to user their own boundary,
*`target_crs`*: this fimeval framework needs the floodmaps to be in projected CRS so define the projected CRS in epsg code format,
*`target_resolution`*: sometime if the benchmark is very high resolution than candidate FIMs, it needs heavy computational time, so user can define the resolution if there FIMs are in different spatial resolution, else it will use the coarser resolution among all FIMS within that case. |The outputs includes generated files in TIFF, SHP, CSV, and PNG formats, all stored within the output folder. Users can visualize the TIFF files using any geospatial platform. The TIFF files consist of the binary Benchmark-FIM (Benchmark.tif), Model-FIM (Candidate.tif), and Agreement-FIM (Contingency.tif). The shp files contain the boundary of the generated flood extent.| | `PlotContingencyMap` | For better understanding, It will print the agreement maps derived in first step. | `main_dir`, `method_name`, `output_dir` : Based on the those arguments, once all the evaluation is done, it will dynamically get the corresponding contingency raster for printing.| This prints the contingency map showing different class of evaluation (TP, FP, no data, PWB etc). The outputs look like- Figure 4 first row.| | `PlotEvaluationMetrics` | For quick understanding of the evaluation metrics, to plot bar of evaluation scores. | `main_dir`, `method_name`, `output_dir` : Based on the those arguments, once all the evaluation is done, it will dynamically get the corresponding file for printing based on all those info.| This prints the bar plots which includes different performance metrics calculated by EvaluateFIM module. The outputs look like- Figure 4 second row.| -| `EvaluationWithBuildingFootprint` | For Building Footprint Analysis, user can specify shapefile of building footprints as .shp or .gpkg format. By default it consider global Microsoft building footprint dataset. Those data are hosted in Google Earth Engine (GEE) so, It pops up to authenticate the GEE account, please allow it and it will download the data based on evaluation boundary and evaluation is done. | `main_dir`, `method_name`, `output_dir`: Those arguments are as it is, same as all other modules.
*`building_footprint`*: If user wants to use their own building footprint file then pass the directory here, *`country`*: It is the 3 letter based country ISO code (eg. 'USA', NEP' etc), for the building data automation using GEE based on the evaluation extent, *`shapefile_dir`*: this is the directory of user defined AOI if user is working with their own boundary and automatic Building footprint download and evaluation, *`geeprojectID`*: this is the google earth engine google cloud project ID, which helps to access the GEE data and resources to work with building footprint download and process. | It will calculate the different metrics (e.g. TP, FP, CSI, F1, Accuracy etc) based on hit and miss of building on different M-FIM and B-FIM. Those all metrics will be saved as CSV format in `output_dir` and finally using that info it prints the counts of building foorpint in each FIMs as well as scenario on the evaluation end via bar plot.| +| `EvaluationWithBuildingFootprint` | For Building Footprint Analysis, user can specify shapefile of building footprints as .shp or .gpkg format. By default it consider global Microsoft building footprint dataset hosted in ArcGIS Online. It is seamlessly integrated within framework through ArcGIS REST API. | `main_dir`, `method_name`, `output_dir`: Those arguments are as it is, same as all other modules.
*`building_footprint`*: If user wants to use their own building footprint file then pass the directory here, *`shapefile_dir`*: this is the directory of user defined AOI if user is working with their own boundary and automatic Building footprint download and evaluation,| It will calculate the different metrics (e.g. TP, FP, CSI, F1, Accuracy etc) based on hit and miss of building on different M-FIM and B-FIM. Those all metrics will be saved as CSV format in `output_dir` and finally using that info it prints the counts of building foorpint in each FIMs as well as scenario on the evaluation end via bar plot.|

@@ -219,7 +223,9 @@ pip install fimeval | ![alt text](https://ciroh.ua.edu/wp-content/uploads/2022/08/CIROHLogo_200x200.png) | Funding for this project was provided by the National Oceanic & Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research to Operations in Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama. ### For More Information + Contact Sagy Cohen (sagy.cohen@ua.edu) + Supath Dhital, (sdhital@crimson.ua.edu) Dipsikha Devi, (ddevi@ua.edu) -Supath Dhital, (sdhital@crimson.ua.edu) + diff --git a/dist/fimeval-0.1.56-py3-none-any.whl b/dist/fimeval-0.1.56-py3-none-any.whl index 8d7e468..8da4d5d 100644 Binary files a/dist/fimeval-0.1.56-py3-none-any.whl and b/dist/fimeval-0.1.56-py3-none-any.whl differ diff --git a/dist/fimeval-0.1.56.tar.gz b/dist/fimeval-0.1.56.tar.gz index 5221736..3cf4a36 100644 Binary files a/dist/fimeval-0.1.56.tar.gz and b/dist/fimeval-0.1.56.tar.gz differ diff --git a/dist/fimeval-0.1.57-py3-none-any.whl b/dist/fimeval-0.1.57-py3-none-any.whl new file mode 100644 index 0000000..fee5083 Binary files /dev/null and b/dist/fimeval-0.1.57-py3-none-any.whl differ diff --git a/dist/fimeval-0.1.57.tar.gz b/dist/fimeval-0.1.57.tar.gz new file mode 100644 index 0000000..dbdc071 Binary files /dev/null and b/dist/fimeval-0.1.57.tar.gz differ diff --git a/docs/fimeval_usage.ipynb b/docs/fimeval_usage.ipynb index b4fd3f1..3ad3bd4 100644 --- a/docs/fimeval_usage.ipynb +++ b/docs/fimeval_usage.ipynb @@ -238,7 +238,7 @@ " This directory where all the output as well as intermidiate files will be saved.\n", "\n", "4. **Building Footprint** \n", - " The directory of the building footprint (in vector format) used for FIM evaluation with buildings. Within the ```fimpef``` the microsoft derived global building footprint is integrated, so if user didnot provide any and willing to evaluate with it, it can automatically retrieve and do further analysis. Currently it uses ```msfootprint``` framework (https://github.com/supathdhitalGEO/msfootprint) to retrieve the data. \n", + " The directory of the building footprint (in vector format) used for FIM evaluation with buildings. Within the ```fimeval``` the microsoft derived global building footprint is integrated, so if user didnot provide any and willing to evaluate with it, it can automatically retrieve using ArcGIS Hosted Feature Layer through REST API. \n", "\n", " If user have better preference, they can use their own data as well. \n", "\n", @@ -333,7 +333,13 @@ "outputs": [], "source": [ "#For instance\n", - "method_name = \"smallest_extent\"" + "method_name = \"smallest_extent\"\n", + "\n", + "#If the candidate and benchmark resolution are different, user can define them here\n", + "target_resolution = 10 #WIll be in meters\n", + "\n", + "#By default, for CONUS it uses 5070 projection, in other region user can define the projection here\n", + "target_crs = \"EPSG:32633\" #WIll be in EPSG code" ] }, { @@ -358,10 +364,16 @@ "metadata": {}, "outputs": [], "source": [ + "\"\"\"\n", + "USE ONE OF THE OPTION BASED ON REQUIREMENT\n", + "\"\"\"\n", "fp.EvaluateFIM(main_dir, method_name, output_dir) #It uses the default Permanent Water Bodies (PWB) dataset for United States.\n", "\n", "# #If the User has their PWB shapefile, then give the following argument\n", - "fp.EvaluateFIM(main_dir, method_name, output_dir, PWB_dir = PWD_dir)" + "fp.EvaluateFIM(main_dir, method_name, output_dir, PWB_dir = PWD_dir)\n", + "\n", + "# If the FIMS are not in projected crs or are in different spatial resolution\n", + "fp.EvaluateFIM(main_dir, method_name, output_dir, target_crs=target_crs, shapefile_dir = AOI, target_resolution=target_resolution)\n" ] }, { @@ -429,7 +441,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**For building automation, need to install java runtime (preferred version is 8: https://www.java.com/en/download/manual.jsp). For Windows need to setup the path and environmental variables carefully to avoid the RunTimeError**" + "**For building automation**" ] }, { @@ -439,13 +451,13 @@ "outputs": [], "source": [ "# FIM Evaluation with Building Footprint (by default, it uses the Microsoft Building Footprint dataset)\n", - "#3 letter country ISO code\n", - "countryISO = \"USA\" #For instance\n", - "geeprojectID = \"your_gee_project_id\" # Replace with your actual GEE cloud project ID\n", "\n", "fp.EvaluationWithBuildingFootprint(\n", - " main_dir, method_name, output_dir, country=countryISO, geeprojectID=geeprojectID\n", - ")" + " main_dir, method_name, output_dir\n", + ")\n", + "\n", + "# It will use the evaluation boundary to retrieve the building footprints\n", + "fp.EvaluationWithBuildingFootprint(main_dir, method_name, output_dir, shapefile_dir=AOI)" ] } ], diff --git a/pyproject.toml b/pyproject.toml index 5196873..47581b5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "fimeval" -version = "0.1.56" +version = "0.1.57" description = "A Framework for Automatic Evaluation of Flood Inundation Mapping Predictions Evaluation" readme = "README.md" requires-python = ">=3.10" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index a718ce3..0000000 --- a/requirements.txt +++ /dev/null @@ -1,524 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml -o requirements.txt -affine==2.4.0 - # via rasterio -anyio==4.10.0 - # via - # httpx - # jupyter-server -anywidget==0.9.18 - # via geemap -appnope==0.1.4 - # via ipykernel -argon2-cffi==25.1.0 - # via jupyter-server -argon2-cffi-bindings==25.1.0 - # via argon2-cffi -arrow==1.3.0 - # via isoduration -asttokens==3.0.0 - # via stack-data -async-lru==2.0.5 - # via jupyterlab -attrs==25.3.0 - # via - # jsonschema - # rasterio - # referencing -babel==2.17.0 - # via jupyterlab-server -beautifulsoup4==4.13.4 - # via nbconvert -bleach==6.2.0 - # via nbconvert -boto3==1.40.11 - # via fimeval (pyproject.toml) -botocore==1.40.11 - # via - # boto3 - # s3transfer -bqplot==0.12.45 - # via geemap -branca==0.8.1 - # via - # folium - # ipyleaflet -cachetools==5.5.2 - # via google-auth -certifi==2025.8.3 - # via - # httpcore - # httpx - # pyogrio - # pyproj - # rasterio - # requests -cffi==1.17.1 - # via argon2-cffi-bindings -charset-normalizer==3.4.3 - # via requests -click==8.2.1 - # via - # click-plugins - # cligj - # geocoder - # rasterio -click-plugins==1.1.1.2 - # via rasterio -cligj==0.7.2 - # via rasterio -comm==0.2.3 - # via - # ipykernel - # ipywidgets -contourpy==1.3.3 - # via matplotlib -cycler==0.12.1 - # via matplotlib -debugpy==1.8.16 - # via ipykernel -decorator==5.2.1 - # via - # ipython - # ratelim -defusedxml==0.7.1 - # via nbconvert -earthengine-api==1.6.3 - # via - # eerepr - # geemap -eerepr==0.1.2 - # via geemap -executing==2.2.0 - # via stack-data -fastjsonschema==2.21.2 - # via nbformat -folium==0.20.0 - # via geemap -fonttools>=4.60.2 - # via matplotlib -fqdn==1.5.1 - # via jsonschema -future==1.0.0 - # via geocoder -geemap==0.36.1 - # via fimeval (pyproject.toml) -geocoder==1.38.1 - # via geemap -geopandas==1.1.1 - # via fimeval (pyproject.toml) -google-api-core==2.25.1 - # via - # google-api-python-client - # google-cloud-core - # google-cloud-storage -google-api-python-client==2.179.0 - # via earthengine-api -google-auth==2.40.3 - # via - # earthengine-api - # google-api-core - # google-api-python-client - # google-auth-httplib2 - # google-cloud-core - # google-cloud-storage -google-auth-httplib2==0.2.0 - # via - # earthengine-api - # google-api-python-client -google-cloud-core==2.4.3 - # via google-cloud-storage -google-cloud-storage==3.3.0 - # via earthengine-api -google-crc32c==1.7.1 - # via - # google-cloud-storage - # google-resumable-media -google-resumable-media==2.7.2 - # via google-cloud-storage -googleapis-common-protos==1.70.0 - # via google-api-core -h11==0.16.0 - # via httpcore -httpcore==1.0.9 - # via httpx -httplib2==0.22.0 - # via - # earthengine-api - # google-api-python-client - # google-auth-httplib2 -httpx==0.28.1 - # via jupyterlab -idna==3.10 - # via - # anyio - # httpx - # jsonschema - # requests -ipyevents==2.0.2 - # via geemap -ipyfilechooser==0.6.0 - # via geemap -ipykernel==6.30.1 - # via jupyterlab -ipyleaflet==0.20.0 - # via geemap -ipython==9.4.0 - # via - # ipykernel - # ipywidgets -ipython-pygments-lexers==1.1.1 - # via ipython -ipytree==0.2.2 - # via geemap -ipywidgets==8.1.7 - # via - # anywidget - # bqplot - # ipyevents - # ipyfilechooser - # ipyleaflet - # ipytree -isoduration==20.11.0 - # via jsonschema -jedi==0.19.2 - # via ipython -jinja2==3.1.6 - # via - # branca - # folium - # jupyter-server - # jupyterlab - # jupyterlab-server - # nbconvert -jmespath==1.0.1 - # via - # boto3 - # botocore -json5==0.12.1 - # via jupyterlab-server -jsonpointer==3.0.0 - # via jsonschema -jsonschema==4.25.0 - # via - # jupyter-events - # jupyterlab-server - # nbformat -jsonschema-specifications==2025.4.1 - # via jsonschema -jupyter-client==8.6.3 - # via - # ipykernel - # jupyter-server - # nbclient -jupyter-core==5.8.1 - # via - # ipykernel - # jupyter-client - # jupyter-server - # jupyterlab - # nbclient - # nbconvert - # nbformat -jupyter-events==0.12.0 - # via jupyter-server -jupyter-leaflet==0.20.0 - # via ipyleaflet -jupyter-lsp==2.2.6 - # via jupyterlab -jupyter-server==2.16.0 - # via - # jupyter-lsp - # jupyterlab - # jupyterlab-server - # notebook - # notebook-shim -jupyter-server-terminals==0.5.3 - # via jupyter-server -jupyterlab>=4.4.8 - # via notebook -jupyterlab-pygments==0.3.0 - # via nbconvert -jupyterlab-server==2.27.3 - # via - # jupyterlab - # notebook -jupyterlab-widgets==3.0.15 - # via ipywidgets -kaleido==0.2.1 - # via fimeval (pyproject.toml) -kiwisolver==1.4.9 - # via matplotlib -lark==1.2.2 - # via rfc3987-syntax -markupsafe==3.0.2 - # via - # jinja2 - # nbconvert -matplotlib==3.10.5 - # via - # fimeval (pyproject.toml) - # geemap -matplotlib-inline==0.1.7 - # via - # ipykernel - # ipython -mistune==3.1.3 - # via nbconvert -nbclient==0.10.2 - # via nbconvert -nbconvert==7.16.6 - # via jupyter-server -nbformat==5.10.4 - # via - # fimeval (pyproject.toml) - # jupyter-server - # nbclient - # nbconvert -nest-asyncio==1.6.0 - # via ipykernel -notebook==7.4.5 - # via fimeval (pyproject.toml) -notebook-shim==0.2.4 - # via - # jupyterlab - # notebook -numpy==2.3.2 - # via - # fimeval (pyproject.toml) - # bqplot - # contourpy - # folium - # geemap - # geopandas - # matplotlib - # pandas - # pyogrio - # rasterio - # shapely -overrides==7.7.0 - # via jupyter-server -packaging==25.0 - # via - # geopandas - # ipykernel - # jupyter-events - # jupyter-server - # jupyterlab - # jupyterlab-server - # matplotlib - # nbconvert - # plotly - # pyogrio -pandas==2.3.1 - # via - # bqplot - # geemap - # geopandas -pandocfilters==1.5.1 - # via nbconvert -parso==0.8.4 - # via jedi -pexpect==4.9.0 - # via ipython -pillow==11.3.0 - # via matplotlib -platformdirs==4.3.8 - # via jupyter-core -plotly==5.24.1 - # via - # fimeval (pyproject.toml) - # geemap -prometheus-client==0.22.1 - # via jupyter-server -prompt-toolkit==3.0.51 - # via ipython -proto-plus==1.26.1 - # via google-api-core -protobuf==6.32.0 - # via - # google-api-core - # googleapis-common-protos - # proto-plus -psutil==7.0.0 - # via ipykernel -psygnal==0.14.1 - # via anywidget -ptyprocess==0.7.0 - # via - # pexpect - # terminado -pure-eval==0.2.3 - # via stack-data -pyasn1==0.6.1 - # via - # pyasn1-modules - # rsa -pyasn1-modules==0.4.2 - # via google-auth -pycparser==2.22 - # via cffi -pygments==2.19.2 - # via - # ipython - # ipython-pygments-lexers - # nbconvert -pyogrio==0.11.1 - # via geopandas -pyparsing==3.2.3 - # via - # httplib2 - # matplotlib - # rasterio -pyperclip==1.9.0 - # via geemap -pyproj==3.7.2 - # via - # fimeval (pyproject.toml) - # geopandas -pyshp==2.3.1 - # via geemap -python-box==7.3.2 - # via geemap -python-dateutil==2.9.0.post0 - # via - # arrow - # botocore - # jupyter-client - # matplotlib - # pandas -python-json-logger==3.3.0 - # via jupyter-events -pytz==2025.2 - # via pandas -pyyaml==6.0.2 - # via jupyter-events -pyzmq==27.0.1 - # via - # ipykernel - # jupyter-client - # jupyter-server -rasterio==1.4.3 - # via fimeval (pyproject.toml) -ratelim==0.1.6 - # via geocoder -referencing==0.36.2 - # via - # jsonschema - # jsonschema-specifications - # jupyter-events -requests==2.32.4 - # via - # earthengine-api - # folium - # geocoder - # google-api-core - # google-cloud-storage - # jupyterlab-server -rfc3339-validator==0.1.4 - # via - # jsonschema - # jupyter-events -rfc3986-validator==0.1.1 - # via - # jsonschema - # jupyter-events -rfc3987-syntax==1.1.0 - # via jsonschema -rpds-py==0.27.0 - # via - # jsonschema - # referencing -rsa==4.9.1 - # via google-auth -s3transfer==0.13.1 - # via boto3 -scooby==0.10.1 - # via geemap -send2trash==1.8.3 - # via jupyter-server -setuptools==80.9.0 - # via jupyterlab -shapely==2.1.1 - # via - # fimeval (pyproject.toml) - # geopandas -six==1.17.0 - # via - # geocoder - # python-dateutil - # rfc3339-validator -sniffio==1.3.1 - # via anyio -soupsieve==2.7 - # via beautifulsoup4 -stack-data==0.6.3 - # via ipython -tenacity==9.1.2 - # via plotly -terminado==0.18.1 - # via - # jupyter-server - # jupyter-server-terminals -tinycss2==1.4.0 - # via bleach -tornado==6.5.2 - # via - # ipykernel - # jupyter-client - # jupyter-server - # jupyterlab - # notebook - # terminado -traitlets==5.14.3 - # via - # bqplot - # ipykernel - # ipython - # ipywidgets - # jupyter-client - # jupyter-core - # jupyter-events - # jupyter-server - # jupyterlab - # matplotlib-inline - # nbclient - # nbconvert - # nbformat - # traittypes -traittypes==0.2.1 - # via - # bqplot - # ipyleaflet -types-python-dateutil==2.9.0.20250809 - # via arrow -typing-extensions==4.14.1 - # via - # anywidget - # beautifulsoup4 -tzdata==2025.2 - # via pandas -uri-template==1.3.0 - # via jsonschema -uritemplate==4.2.0 - # via google-api-python-client -urllib3>=2.6.0 - # via - # botocore - # requests -wcwidth==0.2.13 - # via prompt-toolkit -webcolors==24.11.1 - # via jsonschema -webencodings==0.5.1 - # via - # bleach - # tinycss2 -websocket-client==1.8.0 - # via jupyter-server -widgetsnbextension==4.0.14 - # via ipywidgets -xyzservices==2025.4.0 - # via - # folium - # ipyleaflet diff --git a/src/fimeval/BenchFIMQuery/__init__.py b/src/fimeval/BenchFIMQuery/__init__.py index 10a5219..771b733 100644 --- a/src/fimeval/BenchFIMQuery/__init__.py +++ b/src/fimeval/BenchFIMQuery/__init__.py @@ -2,4 +2,4 @@ __all__ = [ "benchFIMquery", -] \ No newline at end of file +] diff --git a/src/fimeval/BenchFIMQuery/access_benchfim.py b/src/fimeval/BenchFIMQuery/access_benchfim.py index a6df24b..e3fcc34 100644 --- a/src/fimeval/BenchFIMQuery/access_benchfim.py +++ b/src/fimeval/BenchFIMQuery/access_benchfim.py @@ -8,7 +8,7 @@ from typing import Optional, Dict, Any, List, Tuple from pathlib import Path import os -import json +import json import rasterio from rasterio.warp import transform_bounds @@ -24,13 +24,14 @@ _to_hour_or_none, _record_day, _record_hour_or_none, - _pretty_date_for_print, + _pretty_date_for_print, ) # Preferred area CRSs for area calculations -AREA_CRS_US = "EPSG:5070" # for CONUS +AREA_CRS_US = "EPSG:5070" # for CONUS AREA_CRS_GLOBAL = "EPSG:6933" # WGS 84 / NSIDC EASE-Grid 2.0 Global (equal-area) for rest of world: in future if the data is added into the catalog. + # Helper: pretty-print container so that print(response) shows the structured text. # If "printable" is empty (e.g., download=True), nothing is printed. class PrettyDict(dict): @@ -40,6 +41,7 @@ def __str__(self) -> str: return txt # Empty string when we do not want anything printed (e.g., download=True) return "" + __repr__ = __str__ @@ -106,7 +108,8 @@ def _record_bbox_polygon(rec: Dict[str, Any]) -> Polygon: minx, miny, maxx, maxy = _get_record_bbox_xy(rec) return box(minx, miny, maxx, maxy) -#Return AOI polygon in WGS84 from a raster file --> this will be useful when user have model predicted raster and looking for the benchmrk FIM into the database + +# Return AOI polygon in WGS84 from a raster file --> this will be useful when user have model predicted raster and looking for the benchmrk FIM into the database def _raster_aoi_polygon_wgs84(path: str) -> Polygon: with rasterio.open(path) as ds: if ds.crs is None: @@ -117,24 +120,29 @@ def _raster_aoi_polygon_wgs84(path: str) -> Polygon: ) return box(minx, miny, maxx, maxy) -#Return AOI polygon in WGS84 from a vector file --> this will be useful when user have model predicted vector and looking for the benchmrk FIM into the database + +# Return AOI polygon in WGS84 from a vector file --> this will be useful when user have model predicted vector and looking for the benchmrk FIM into the database def _vector_aoi_polygon_wgs84(path: str) -> Polygon: gdf = gpd.read_file(path) if gdf.empty: raise ValueError(f"Vector file {path} contains no features.") if gdf.crs is None: - raise ValueError(f"Vector file {path} has no CRS; cannot derive WGS84 geometry.") + raise ValueError( + f"Vector file {path} has no CRS; cannot derive WGS84 geometry." + ) gdf = gdf.to_crs("EPSG:4326") geom = unary_union(gdf.geometry) if geom.is_empty: raise ValueError(f"Vector file {path} has empty geometry after union.") return geom + def _ensure_dir(path: str | Path) -> Path: p = Path(path) p.mkdir(parents=True, exist_ok=True) return p + # benchmark FIM filtering by date def _filter_by_date_exact( records: List[Dict[str, Any]], @@ -167,7 +175,8 @@ def _filter_by_date_exact( out.append(r) return out -#Filter available benchmark FIMs by date range + +# Filter available benchmark FIMs by date range def _filter_by_date_range( records: List[Dict[str, Any]], start_date: Optional[str], @@ -191,6 +200,7 @@ def _filter_by_date_range( out.append(r) return out + # Dynamic area CRS selection and overlap stats def _pick_area_crs_for_bounds(bounds: Tuple[float, float, float, float]) -> str: """ @@ -219,7 +229,8 @@ def _pick_area_crs_for_bounds(bounds: Tuple[float, float, float, float]) -> str: return AREA_CRS_US return AREA_CRS_GLOBAL -#Compute the area overlap statistics between user passed AOI/raster and benchmark AOI + +# Compute the area overlap statistics between user passed AOI/raster and benchmark AOI def _compute_area_overlap_stats( aoi_geom: Polygon, benchmark_geom: Polygon, @@ -280,6 +291,7 @@ def _aoi_context_str( return ", ".join(parts) # fall back to the non-AOI context from utils from .utilis import _context_str as _ctx + return _ctx( huc8=huc8, date_input=date_input, @@ -290,9 +302,9 @@ def _aoi_context_str( # Build a single printable block, optionally with overlap appended -def _format_block_with_overlap(rec: Dict[str, Any], - pct: Optional[float], - km2: Optional[float]) -> str: +def _format_block_with_overlap( + rec: Dict[str, Any], pct: Optional[float], km2: Optional[float] +) -> str: tier = rec.get("tier") or rec.get("quality") or "Unknown" res = rec.get("resolution_m") res_txt = f"{res}m" if res is not None else "NA" @@ -307,22 +319,27 @@ def _format_block_with_overlap(rec: Dict[str, Any], date_str = _pretty_date_for_print(rec) lines.append(f"Benchmark FIM date: {date_str}") - lines.extend([ - f"Spatial Resolution: {res_txt}", - f"Raster Filename in DB: {fname}", - ]) + lines.extend( + [ + f"Spatial Resolution: {res_txt}", + f"Raster Filename in DB: {fname}", + ] + ) if pct is not None and km2 is not None: - lines.append(f"Overlap with respect to benchmark FIM: {pct:.1f}% / {km2:.2f} km²") + lines.append( + f"Overlap with respect to benchmark FIM: {pct:.1f}% / {km2:.2f} km²" + ) return "\n".join(lines) -#For Tier-4- adding synthetic event year while reflecting the outcomes + +# For Tier-4- adding synthetic event year while reflecting the outcomes def _is_synthetic_tier(rec: Dict[str, Any]) -> bool: """Return True when the record is a synthetic (Tier_4) event.""" tier = str(rec.get("tier") or rec.get("quality") or "").lower() return "tier_4" in tier or tier.strip() == "4" - + def _return_period_text(rec: Dict[str, Any]) -> str: """ @@ -344,13 +361,19 @@ def _return_period_text(rec: Dict[str, Any]) -> str: except Exception: return f"{rp} synthetic flow" -#helpers to read AOI GPKG geometry directly + +# helpers to read AOI GPKG geometry directly def _storage_options_for_uri(uri: str) -> Optional[Dict[str, Any]]: if isinstance(uri, str) and uri.startswith("s3://"): - anon = str(os.environ.get("AWS_NO_SIGN_REQUEST", "")).upper() in {"YES", "TRUE", "1"} + anon = str(os.environ.get("AWS_NO_SIGN_REQUEST", "")).upper() in { + "YES", + "TRUE", + "1", + } return {"anon": anon} return None + def _gpkg_urls_from_record(rec: Dict[str, Any]) -> List[str]: urls: List[str] = [] for key in ("aoi_gpkg", "aoi_gpkg_url", "gpkg_url"): @@ -381,6 +404,7 @@ def _gpkg_urls_from_record(rec: Dict[str, Any]) -> List[str]: out.append(u) return out + def _read_benchmark_aoi_union_geom(rec: Dict[str, Any]) -> Optional[Polygon]: # Read and union AOI geometries referenced by the record (kept permissive) urls = _gpkg_urls_from_record(rec) @@ -390,10 +414,18 @@ def _read_benchmark_aoi_union_geom(rec: Dict[str, Any]) -> Optional[Polygon]: for uri in urls: try: storage_opts = _storage_options_for_uri(uri) - gdf = gpd.read_file(uri, storage_options=storage_opts) if storage_opts else gpd.read_file(uri) + gdf = ( + gpd.read_file(uri, storage_options=storage_opts) + if storage_opts + else gpd.read_file(uri) + ) if gdf.empty: continue - gdf = gdf.to_crs("EPSG:4326") if gdf.crs else gdf.set_crs("EPSG:4326", allow_override=True) + gdf = ( + gdf.to_crs("EPSG:4326") + if gdf.crs + else gdf.set_crs("EPSG:4326", allow_override=True) + ) u = unary_union(gdf.geometry) if not u.is_empty: geoms.append(u) @@ -419,6 +451,7 @@ class benchFIMquery: into a local directory - or, as a special-case, fetch a specific benchmark by its filename. """ + def __init__(self, catalog: Optional[Dict[str, Any]] = None) -> None: """ Create a new service. @@ -510,12 +543,14 @@ def query( ) if download and not out_dir: - return PrettyDict({ - "status": "error", - "message": "When download=True, you must provide out_dir.", - "matches": [], - "printable": "", - }) + return PrettyDict( + { + "status": "error", + "message": "When download=True, you must provide out_dir.", + "matches": [], + "printable": "", + } + ) # Direct filename-only workflow (no AOI, no dates) if ( @@ -535,9 +570,7 @@ def query( ] if not candidates: candidates = [ - r - for r in recs - if str(r.get("file_name", "")).strip() == fname + r for r in recs if str(r.get("file_name", "")).strip() == fname ] else: candidates = [ @@ -545,39 +578,41 @@ def query( ] if not candidates: - return PrettyDict({ - "status": "not_found", - "message": f"File name {fname!r} not found in catalog.", - "matches": [], - "printable": "", - }) + return PrettyDict( + { + "status": "not_found", + "message": f"File name {fname!r} not found in catalog.", + "matches": [], + "printable": "", + } + ) target = candidates[0] out_dir_path = _ensure_dir(out_dir) dl = download_fim_assets(target, str(out_dir_path)) - return PrettyDict({ - "status": "ok", - "message": f"Downloaded benchmark FIM '{fname}' to '{out_dir_path}'.", - "matches": [ - { - "record": target, - "bbox_intersects": False, - "intersection_area_pct": None, - "intersection_area_km2": None, - "downloads": dl, - } - ], - "printable": "", - }) + return PrettyDict( + { + "status": "ok", + "message": f"Downloaded benchmark FIM '{fname}' to '{out_dir_path}'.", + "matches": [ + { + "record": target, + "bbox_intersects": False, + "intersection_area_pct": None, + "intersection_area_km2": None, + "downloads": dl, + } + ], + "printable": "", + } + ) # AOI-based workflows records = self.records if huc8: huc8_str = str(huc8).strip() - records = [ - r for r in records if str(r.get("huc8", "")).strip() == huc8_str - ] + records = [r for r in records if str(r.get("huc8", "")).strip() == huc8_str] # Date filters if event_date: @@ -586,12 +621,14 @@ def query( records = _filter_by_date_range(records, start_date, end_date) if not records: - return PrettyDict({ - "status": "not_found", - "message": "No catalog records match the provided filters.", - "matches": [], - "printable": "", - }) + return PrettyDict( + { + "status": "not_found", + "message": "No catalog records match the provided filters.", + "matches": [], + "printable": "", + } + ) # If no AOI is provided at all if aoi_geom is None: @@ -615,14 +652,14 @@ def query( end_date=end_date, file_name=file_name, ) - printable = format_records_for_print([m["record"] for m in matches], context=ctx) + printable = format_records_for_print( + [m["record"] for m in matches], context=ctx + ) if download: out_dir_path = _ensure_dir(out_dir) for m in matches: - m["downloads"] = download_fim_assets( - m["record"], str(out_dir_path) - ) + m["downloads"] = download_fim_assets(m["record"], str(out_dir_path)) msg = ( f"Downloaded {len(matches)} benchmark record(s) " f"to '{out_dir_path}'." @@ -633,12 +670,14 @@ def query( f"for the provided filters." ) - return PrettyDict({ - "status": "ok", - "message": msg, - "matches": matches, - "printable": "" if download else printable, - }) + return PrettyDict( + { + "status": "ok", + "message": msg, + "matches": matches, + "printable": "" if download else printable, + } + ) # AOI is present: intersect with bbox intersecting: List[Dict[str, Any]] = [] @@ -652,12 +691,14 @@ def query( intersecting.append(r) if not intersecting: - return PrettyDict({ - "status": "not_found", - "message": "No benchmark FIM bbox intersects the provided AOI.", - "matches": [], - "printable": "", - }) + return PrettyDict( + { + "status": "not_found", + "message": "No benchmark FIM bbox intersects the provided AOI.", + "matches": [], + "printable": "", + } + ) out_matches: List[Dict[str, Any]] = [] out_dir_path = _ensure_dir(out_dir) if (download and out_dir) else None @@ -725,12 +766,14 @@ def query( blocks.append(_format_block_with_overlap(rec, None, None)) printable = header + "\n\n".join(blocks) - return PrettyDict({ - "status": "ok", - "message": msg, - "matches": out_matches, - "printable": printable, - }) + return PrettyDict( + { + "status": "ok", + "message": msg, + "matches": out_matches, + "printable": printable, + } + ) def __call__( self, @@ -758,4 +801,6 @@ def __call__( download=download, out_dir=out_dir, ) -benchFIMquery = benchFIMquery() \ No newline at end of file + + +benchFIMquery = benchFIMquery() diff --git a/src/fimeval/BenchFIMQuery/utilis.py b/src/fimeval/BenchFIMQuery/utilis.py index eebabe3..b936c94 100644 --- a/src/fimeval/BenchFIMQuery/utilis.py +++ b/src/fimeval/BenchFIMQuery/utilis.py @@ -196,14 +196,17 @@ def _return_period_text_local(r: Dict[str, Any]) -> str: date_str = _pretty_date_for_print(r) lines.append(f"Benchmark FIM date: {date_str}") - lines.extend([ - f"Spatial Resolution: {res_txt}", - f"Benchmark FIM raster name in DB: {fname}", - ]) + lines.extend( + [ + f"Spatial Resolution: {res_txt}", + f"Benchmark FIM raster name in DB: {fname}", + ] + ) blocks.append("\n".join(lines)) return (header + "\n\n".join(blocks)).strip() + # S3 and json catalog def load_catalog_core() -> Dict[str, Any]: obj = _S3.get_object(Bucket=BUCKET, Key=CATALOG_KEY) @@ -224,6 +227,7 @@ def _download(bucket: str, key: str, dest_path: str) -> str: _S3.download_file(bucket, key, dest_path) return dest_path + # Get the files from s3 bucket def _folder_from_record(rec: Dict[str, Any]) -> str: s3_key = rec.get("s3_key") @@ -241,7 +245,8 @@ def _tif_key_from_record(rec: Dict[str, Any]) -> Optional[str]: return None return _folder_from_record(rec) + fname -#Download that tif and the boundary file --> need to add building footprint automation as well. + +# Download that tif and the boundary file --> need to add building footprint automation as well. def download_fim_assets(record: Dict[str, Any], dest_dir: str) -> Dict[str, Any]: """ Download the .tif (if present) and any .gpkg from the record's folder to dest_dir. @@ -266,4 +271,4 @@ def download_fim_assets(record: Dict[str, Any], dest_dir: str) -> Dict[str, Any] _download(BUCKET, key, local) out["gpkg_files"].append(local) - return out \ No newline at end of file + return out diff --git a/src/fimeval/BuildingFootprint/__init__.py b/src/fimeval/BuildingFootprint/__init__.py index dce798b..254f1d4 100644 --- a/src/fimeval/BuildingFootprint/__init__.py +++ b/src/fimeval/BuildingFootprint/__init__.py @@ -1,3 +1,4 @@ from .evaluationwithBF import EvaluationWithBuildingFootprint +from .arcgis_API import getBuildingFootprint -__all__ = ["EvaluationWithBuildingFootprint"] +__all__ = ["EvaluationWithBuildingFootprint", "getBuildingFootprint"] diff --git a/src/fimeval/BuildingFootprint/arcgis_API.py b/src/fimeval/BuildingFootprint/arcgis_API.py new file mode 100644 index 0000000..2175979 --- /dev/null +++ b/src/fimeval/BuildingFootprint/arcgis_API.py @@ -0,0 +1,195 @@ +""" +Author: Supath Dhital +Date CreatedL January 2026 + +Description: This will extract Microsoft Building Footprints using ArcGIS REST API for a given boundary. +""" + +import geopandas as gpd +import requests +import pandas as pd +from pathlib import Path +from typing import Union, Optional + + +# Main class +class getBuildingFootprint: + """Extract Microsoft Building Footprints within a boundary using spatial queries.""" + + MSBFP_URL = "https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/MSBFP2/FeatureServer/0" + + def __init__( + self, + boundary: Union[str, Path, gpd.GeoDataFrame], + layer: Optional[str] = None, + output_dir: Optional[Union[str, Path]] = None, + service_url: Optional[str] = None, + ): + """ + Parameters + ---------- + boundary : str, Path, or GeoDataFrame + Boundary as file path or GeoDataFrame + layer : str, optional + Layer name if boundary is a geopackage with multiple layers + service_url : str, optional + Custom ArcGIS feature service URL + """ + self.boundary = self._load_boundary(boundary, layer) + self.service_url = service_url or self.MSBFP_URL + + # Setup output directory + if output_dir is None: + output_dir = Path.cwd() / "BFOutputs" + else: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Run the code + self.extract(output_dir=output_dir) + + def _load_boundary( + self, boundary: Union[str, Path, gpd.GeoDataFrame], layer: Optional[str] + ) -> gpd.GeoDataFrame: + """Load and validate boundary.""" + if isinstance(boundary, gpd.GeoDataFrame): + gdf = boundary.copy() + else: + kwargs = {"layer": layer} if layer else {} + gdf = gpd.read_file(boundary, **kwargs) + + # Ensure WGS84 + if gdf.crs != "EPSG:4326": + gdf = gdf.to_crs("EPSG:4326") + + return gdf + + def extract( + self, + output_dir: Optional[Union[str, Path]] = None, + output_filename: str = "building_footprints.gpkg", + batch_size: int = 2000, + timeout: int = 60, + verbose: bool = True, + ) -> gpd.GeoDataFrame: + """ + Extract building footprints within the boundary. + + Parameters + ---------- + output_dir : str or Path, optional + Output directory (defaults to cwd/building_footprints) + output_filename : str, default="building_footprints.gpkg" + Output filename + batch_size : int, default=2000 + Number of features to retrieve per request + timeout : int, default=60 + Request timeout in seconds + verbose : bool, default=True + Print progress messages + + Returns + ------- + GeoDataFrame + Extracted building footprints + """ + + # Get bounding box + xmin, ymin, xmax, ymax = self.boundary.total_bounds + + if verbose: + print(f"Querying {self.service_url}...") + + # Query the service + all_features = [] + offset = 0 + query_url = f"{self.service_url}/query" + + while True: + params = { + "f": "geojson", + "where": "1=1", + "geometry": f"{xmin},{ymin},{xmax},{ymax}", + "geometryType": "esriGeometryEnvelope", + "inSR": "4326", + "spatialRel": "esriSpatialRelIntersects", + "outFields": "*", + "returnGeometry": "true", + "outSR": "4326", + "resultOffset": offset, + "resultRecordCount": batch_size, + } + + try: + response = requests.get(query_url, params=params, timeout=timeout) + + if response.status_code != 200: + if verbose: + print(f"Error {response.status_code}") + break + + data = response.json() + + if "error" in data: + if verbose: + print(f"Server error: {data['error'].get('message')}") + break + + if "features" in data and data["features"]: + batch_gdf = gpd.GeoDataFrame.from_features( + data["features"], crs="EPSG:4326" + ) + all_features.append(batch_gdf) + + if verbose: + total = sum(len(gdf) for gdf in all_features) + + if len(data["features"]) < batch_size: + break + offset += batch_size + else: + break + + except requests.exceptions.Timeout: + if verbose: + print(" Request timed out, retrying...") + continue + except Exception as e: + if verbose: + print(f" Error: {e}") + break + + if not all_features: + if verbose: + print("No features found.") + return gpd.GeoDataFrame() + + # Combine and process + gdf = pd.concat(all_features, ignore_index=True) + gdf = gpd.GeoDataFrame(gdf, crs="EPSG:4326") + + # Remove duplicates + for id_field in ["OBJECTID", "FID", "ID"]: + if id_field in gdf.columns: + initial = len(gdf) + gdf = gdf.drop_duplicates(subset=[id_field]) + if verbose and (initial - len(gdf)) > 0: + print(f"Removed {initial - len(gdf)} duplicates") + break + + # Clip to exact boundary + if verbose: + print(f"Clipping {len(gdf)} features to boundary...") + gdf = gpd.clip(gdf, self.boundary) + + # Save + output_path = output_dir / output_filename + gdf.to_file(output_path, driver="GPKG") + + if verbose: + print(f"\n{'='*60}") + print(f"SUCCESS: Saved {len(gdf)} buildings to:") + print(f" {output_path}") + print(f"{'='*60}") + + return gdf diff --git a/src/fimeval/BuildingFootprint/evaluationwithBF.py b/src/fimeval/BuildingFootprint/evaluationwithBF.py index 7bb2f3d..ceb1e8a 100644 --- a/src/fimeval/BuildingFootprint/evaluationwithBF.py +++ b/src/fimeval/BuildingFootprint/evaluationwithBF.py @@ -10,6 +10,9 @@ import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec +# Import building footprint module +from .arcgis_API import getBuildingFootprint + def Changeintogpkg(input_path, output_dir, layer_name): input_path = str(input_path) @@ -355,38 +358,12 @@ def detect_shapefile(folder): return None -def ensure_pyspark(version: str | None = "3.5.4") -> None: - """Install pyspark at runtime via `uv pip` into this env (no-op if present).""" - import importlib, shutil, subprocess, sys, re - - try: - import importlib.util - - if importlib.util.find_spec("pyspark"): - return - except Exception: - pass - uv = shutil.which("uv") - if not uv: - raise RuntimeError( - "`uv` not found on PATH. Please install uv or add it to PATH." - ) - if version is None: - spec = "pyspark" - else: - v = version.strip() - spec = f"pyspark{v}" if re.match(r"^[<>=!~]", v) else f"pyspark=={v}" - subprocess.check_call([uv, "pip", "install", "--python", sys.executable, spec]) - - def EvaluationWithBuildingFootprint( main_dir, method_name, output_dir, - country=None, building_footprint=None, shapefile_dir=None, - geeprojectID=None, ): tif_files_main = glob.glob(os.path.join(main_dir, "*.tif")) if tif_files_main: @@ -410,31 +387,23 @@ def EvaluationWithBuildingFootprint( building_footprintMS = building_footprint + # If no building footprint provided, extract using ArcGIS API if building_footprintMS is None: - ensure_pyspark() - from .microsoftBF import BuildingFootprintwithISO - out_dir = os.path.join(method_path, "BuildingFootprint") if not os.path.exists(out_dir): os.makedirs(out_dir) EX_building_footprint = find_existing_footprint(out_dir) - if not EX_building_footprint: + if EX_building_footprint: + building_footprintMS = EX_building_footprint + else: boundary_dir = shapefile_dir if shapefile_dir else boundary - - if geeprojectID: - BuildingFootprintwithISO( - country, - boundary_dir, - out_dir, - geeprojectID=geeprojectID, - ) - else: - BuildingFootprintwithISO(country, boundary_dir, out_dir) - building_footprintMS = os.path.join( - out_dir, f"building_footprint.gpkg" + getBuildingFootprint( + boundary=boundary_dir, + output_dir=out_dir, ) - else: - building_footprintMS = EX_building_footprint + # After downloading, find the newly created footprint + building_footprintMS = find_existing_footprint(out_dir) + process_TIFF( tif_files, contingency_files, @@ -471,33 +440,22 @@ def EvaluationWithBuildingFootprint( building_footprintMS = building_footprint if building_footprintMS is None: - ensure_pyspark() - from .microsoftBF import BuildingFootprintwithISO - out_dir = os.path.join(method_path, "BuildingFootprint") if not os.path.exists(out_dir): os.makedirs(out_dir) EX_building_footprint = find_existing_footprint(out_dir) - if not EX_building_footprint: + if EX_building_footprint: + building_footprintMS = EX_building_footprint + else: boundary_dir = ( shapefile_dir if shapefile_dir else boundary ) - if geeprojectID: - BuildingFootprintwithISO( - country, - boundary_dir, - out_dir, - geeprojectID=geeprojectID, - ) - else: - BuildingFootprintwithISO( - country, boundary_dir, out_dir - ) - building_footprintMS = os.path.join( - out_dir, f"building_footprint.gpkg" + getBuildingFootprint( + boundary=boundary_dir, + output_dir=out_dir, ) - else: - building_footprintMS = EX_building_footprint + # After downloading, find the newly created footprint + building_footprintMS = find_existing_footprint(out_dir) process_TIFF( tif_files, diff --git a/src/fimeval/BuildingFootprint/microsoftBF.py b/src/fimeval/BuildingFootprint/microsoftBF.py deleted file mode 100644 index 4d8164a..0000000 --- a/src/fimeval/BuildingFootprint/microsoftBF.py +++ /dev/null @@ -1,134 +0,0 @@ -# Importing necessary libraries -import geemap -import ee -import os -from shapely.geometry import box -import pandas as pd -from pathlib import Path -import geopandas as gpd -from pyspark.sql import SparkSession -from shapely.wkt import loads -import shutil - -# Suppress the warnings -import warnings - -warnings.filterwarnings("ignore") - -# Authenticate and initialize Earth Engine -ee.Authenticate() - - -# %% -def split_into_tiles(boundary, tile_size=0.1): - bounds = boundary.total_bounds - x_min, y_min, x_max, y_max = bounds - tiles = [] - x = x_min - while x < x_max: - y = y_min - while y < y_max: - tile = box(x, y, x + tile_size, y + tile_size) - if tile.intersects(boundary.unary_union): - tiles.append(tile) - y += tile_size - x += tile_size - return gpd.GeoDataFrame(geometry=tiles, crs=boundary.crs) - - -# Merge the final geojson files -def mergeGeoJSONfiles(output_dir, merged_file): - output_dir = Path(output_dir) - files = list(output_dir.glob("*.geojson")) - gdfs = [gpd.read_file(file) for file in files] - merged_gdf = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True), crs="EPSG:4326") - merged_gdf.to_file(merged_file, driver="GPKG") - - -# Process each batch with number of tiles -def process_batch(partition, collection_name, output_dir, boundary_wkt, projectID=None): - try: - if projectID: - ee.Initialize(project=projectID) - else: - ee.Initialize() - - except Exception: - print("To initialize, please provide the earth engine project ID") - - # Convert WKT boundary to geometry - boundary = loads(boundary_wkt) - results = [] - - for tile_wkt in partition: - try: - tile = loads(tile_wkt) - aoi = ee.Geometry(tile.__geo_interface__) - collection = ee.FeatureCollection(collection_name).filterBounds(aoi) - - # Download features and filter by boundary - gdf = geemap.ee_to_gdf(collection) - gdf = gdf[gdf.geometry.intersects(boundary)] - - # Save each tile as a GeoJSON file - tile_id = f"tile_{hash(tile)}" - output_file = Path(output_dir) / f"{tile_id}.geojson" - gdf.to_file(output_file, driver="GeoJSON") - results.append(f"Saved: {output_file}") - except Exception as e: - results.append(f"Error processing tile: {e}") - - return results - - -def getBuildingFootprintSpark( - countryISO, boundary_file, out_dir, tile_size, projectID=None -): - spark = SparkSession.builder.appName("BuildingFootprints").getOrCreate() - - # Make temporary directory - temp_dir = out_dir / "temp" - temp_dir.mkdir(parents=True, exist_ok=True) - - # Load and process boundary - boundary = gpd.read_file(boundary_file).to_crs("EPSG:4326") - tiles = split_into_tiles(boundary, tile_size) - boundary_wkt = boundary.unary_union.wkt - - collection_names = [f"projects/sat-io/open-datasets/VIDA_COMBINED/{countryISO}"] - - # Distribute processing - for collection_name in collection_names: - tiles_rdd = spark.sparkContext.parallelize( - tiles.geometry.apply(lambda x: x.wkt).tolist(), numSlices=10 - ) - results = tiles_rdd.mapPartitions( - lambda partition: process_batch( - partition, collection_name, str(temp_dir), boundary_wkt, projectID - ) - ).collect() - - # Merge GeoJSON files - mergeGeoJSONfiles(temp_dir, out_dir / "building_footprint.gpkg") - - # Clean up the temp directory - shutil.rmtree(temp_dir, ignore_errors=True) - - print(f"Building footprint data saved to {out_dir / 'building_footprint.gpkg'}") - - -# %% -# Export the building footprint -def BuildingFootprintwithISO(countryISO, ROI, out_dir, geeprojectID=None): - out_dir = Path(out_dir) - out_dir.mkdir(parents=True, exist_ok=True) - filename = out_dir / "building_footprint.gpkg" - - if filename.exists(): - os.remove(filename) - - getBuildingFootprintSpark( - countryISO, ROI, out_dir, tile_size=0.05, projectID=geeprojectID - ) - -BuildingFootprintwithISO("USA", "/Users/supath/Downloads/S1A_9_6m_20190530T23573_910244W430506N_AOI.gpkg", "/Users/supath/Downloads/AOI", geeprojectID="supathdh") \ No newline at end of file diff --git a/src/fimeval/ContingencyMap/PWBs3.py b/src/fimeval/ContingencyMap/PWBs3.py deleted file mode 100644 index 83ecdca..0000000 --- a/src/fimeval/ContingencyMap/PWBs3.py +++ /dev/null @@ -1,42 +0,0 @@ -# import Libraries -import geopandas as gpd -import boto3 -import botocore -import os -import tempfile - -# Initialize an anonymous S3 client -s3 = boto3.client( - "s3", config=botocore.config.Config(signature_version=botocore.UNSIGNED) -) - -bucket_name = "sdmlab" -pwb_folder = "PWB/" - - -def PWB_inS3(s3_client, bucket, prefix): - """Download all components of a shapefile from S3 into a temporary directory.""" - tmp_dir = tempfile.mkdtemp() - response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) - if "Contents" not in response: - raise ValueError("No files found in the specified S3 folder.") - - for obj in response["Contents"]: - file_key = obj["Key"] - file_name = os.path.basename(file_key) - if file_name.endswith((".shp", ".shx", ".dbf", ".prj", ".cpg")): - local_path = os.path.join(tmp_dir, file_name) - s3_client.download_file(bucket, file_key, local_path) - - shp_files = [f for f in os.listdir(tmp_dir) if f.endswith(".shp")] - if not shp_files: - raise ValueError("No .shp file found after download.") - - shp_path = os.path.join(tmp_dir, shp_files[0]) - return shp_path - - -def get_PWB(): - shp_path = PWB_inS3(s3, bucket_name, pwb_folder) - pwb = gpd.read_file(shp_path) - return pwb diff --git a/src/fimeval/ContingencyMap/__init__.py b/src/fimeval/ContingencyMap/__init__.py index 8c86e8d..9eaecfd 100644 --- a/src/fimeval/ContingencyMap/__init__.py +++ b/src/fimeval/ContingencyMap/__init__.py @@ -1,6 +1,6 @@ from .evaluationFIM import EvaluateFIM from .printcontingency import PrintContingencyMap from .plotevaluationmetrics import PlotEvaluationMetrics -from .PWBs3 import get_PWB +from .water_bodies import get_PWB, ExtractPWB -__all__ = ["EvaluateFIM", "PrintContingencyMap", "PlotEvaluationMetrics", "get_PWB"] +__all__ = ["EvaluateFIM", "PrintContingencyMap", "PlotEvaluationMetrics", "get_PWB", "ExtractPWB"] diff --git a/src/fimeval/ContingencyMap/evaluationFIM.py b/src/fimeval/ContingencyMap/evaluationFIM.py index 57dd19f..9994ffd 100644 --- a/src/fimeval/ContingencyMap/evaluationFIM.py +++ b/src/fimeval/ContingencyMap/evaluationFIM.py @@ -1,3 +1,7 @@ +""" +Author: Supath Dhital +Date Updated: January 2026 +""" import os import re import numpy as np @@ -11,6 +15,7 @@ from rasterio.warp import reproject, Resampling from rasterio.io import MemoryFile from rasterio import features +from shapely.geometry import shape from rasterio.mask import mask os.environ["CHECK_DISK_FREE_SPACE"] = "NO" @@ -19,9 +24,9 @@ warnings.filterwarnings("ignore", category=rasterio.errors.ShapeSkipWarning) -from .methods import AOI, smallest_extent, convex_hull, get_smallest_raster_path +from .methods import AOI, convex_hull, smallest_extent, get_smallest_raster_path from .metrics import evaluationmetrics -from .PWBs3 import get_PWB +from .water_bodies import ExtractPWB from ..utilis import MakeFIMsUniform, benchmark_name, find_best_boundary from ..setup_benchFIM import ensure_benchmark @@ -68,7 +73,7 @@ def fix_permissions(path): # Function for the evalution of the model def evaluateFIM( - benchmark_path, candidate_paths, gdf, folder, method, output_dir, shapefile=None + benchmark_path, candidate_paths, PWB_Dir, folder, method, output_dir, shapefile=None ): # Lists to store evaluation metrics csi_values = [] @@ -129,8 +134,23 @@ def evaluateFIM( benchmark_nodata = src1.nodata benchmark_crs = src1.crs b_profile = src1.profile + + #Getting the correct geometry shape and crs to extract PWB + boundary_shape = shape(bounding_geom[0]) + boundary_gdf = gpd.GeoDataFrame(geometry=[boundary_shape], crs=benchmark_crs) + + #Proceed the masking out_image1[out_image1 == benchmark_nodata] = 0 out_image1 = np.where(out_image1 > 0, 2, 0).astype(np.float32) + + #If PWB_Dir is provided, use the local PWB shapefile, else download from ArcGIS API + if PWB_Dir is not None: + gdf = gpd.read_file(PWB_Dir) + else: + #Get the permanent water bodies from ArcGIS REST API + pwb_obj = ExtractPWB(boundary = boundary_gdf, save = False) + gdf = pwb_obj.gdf + gdf = gdf.to_crs(benchmark_crs) shapes1 = [ geom for geom in gdf.geometry if geom is not None and not geom.is_empty @@ -394,23 +414,18 @@ def safe_delete_folder(folder_path): def EvaluateFIM( main_dir, - method_name=None, - output_dir=None, + method_name=None, + output_dir=None, PWB_dir=None, shapefile_dir=None, target_crs=None, target_resolution=None, benchmark_dict=None, ): - if output_dir is None: + if output_dir is None: output_dir = os.path.join(os.getcwd(), "Evaluation_Results") main_dir = Path(main_dir) - # Read the permanent water bodies - if PWB_dir is None: - gdf = get_PWB() - else: - gdf = gpd.read_file(PWB_dir) # Grant the permission to the main directory fix_permissions(main_dir) @@ -429,8 +444,8 @@ def process_TIFF(tif_files, folder_dir): if benchmark_path and candidate_path: if method_name is None: local_method = "AOI" - - #For single case, if user have explicitly send boundary, use that, else use the boundary from the benchmark FIM evaluation + + # For single case, if user have explicitly send boundary, use that, else use the boundary from the benchmark FIM evaluation if shapefile_dir is not None: local_shapefile = shapefile_dir else: @@ -444,20 +459,29 @@ def process_TIFF(tif_files, folder_dir): local_shapefile = str(boundary) else: local_method = method_name - local_shapefile = shapefile_dir + local_shapefile = shapefile_dir print(f"**Flood Inundation Evaluation of {folder_dir.name}**") try: Metrics = evaluateFIM( benchmark_path, candidate_path, - gdf, + PWB_dir, folder_dir, local_method, output_dir, - shapefile=local_shapefile, + shapefile=local_shapefile, ) - print("\n", Metrics, "\n") + + # Print results in structured table format with 3 decimal points + candidate_names = [os.path.splitext(os.path.basename(path))[0] for path in candidate_path] + df_display = pd.DataFrame.from_dict(Metrics, orient='index') + df_display.columns = candidate_names + df_display.reset_index(inplace=True) + df_display.rename(columns={'index': 'Metrics'}, inplace=True) + print("\n") + print(df_display.to_string(index=False, float_format='%.3f')) + print("\n") except Exception as e: print(f"Error evaluating {folder_dir.name}: {e}") else: @@ -470,7 +494,7 @@ def process_TIFF(tif_files, folder_dir): if TIFFfiles_main_dir: # Ensure benchmark is present if needed - TIFFfiles_main_dir = ensure_benchmark( + TIFFfiles_main_dir = ensure_benchmark( main_dir, TIFFfiles_main_dir, benchmark_dict ) @@ -494,12 +518,10 @@ def process_TIFF(tif_files, folder_dir): tif_files = list(folder.glob("*.tif")) if tif_files: - processing_folder = folder / "processing" + processing_folder = folder / "processing" try: # Ensure benchmark is present if needed - tif_files = ensure_benchmark( - folder, tif_files, benchmark_dict - ) + tif_files = ensure_benchmark(folder, tif_files, benchmark_dict) MakeFIMsUniform( folder, @@ -517,5 +539,4 @@ def process_TIFF(tif_files, folder_dir): else: print( f"Skipping {folder.name} as it doesn't contain any tif files." - ) - + ) \ No newline at end of file diff --git a/src/fimeval/ContingencyMap/printcontingency.py b/src/fimeval/ContingencyMap/printcontingency.py index aaf6d0e..19664a1 100644 --- a/src/fimeval/ContingencyMap/printcontingency.py +++ b/src/fimeval/ContingencyMap/printcontingency.py @@ -101,7 +101,9 @@ def getContingencyMap(raster_path, method_path): base_name = os.path.basename(raster_path).split(".")[0] output_path = os.path.join(plot_dir, f"{base_name}.png") plt.savefig(output_path, dpi=500, bbox_inches="tight") - plt.show() + plt.show(block=False) + plt.pause(5.0) + plt.close() def PrintContingencyMap(main_dir, method_name, out_dir): diff --git a/src/fimeval/ContingencyMap/water_bodies.py b/src/fimeval/ContingencyMap/water_bodies.py new file mode 100644 index 0000000..bf0501d --- /dev/null +++ b/src/fimeval/ContingencyMap/water_bodies.py @@ -0,0 +1,175 @@ +""" +Author: Supath Dhital +Date Created: January 2026 + +Description: This module extracts permanent water bodies +using the ArcGIS REST API and AWS S3 for a given boundary file. The mechanism is using AWS, it retrieve all the US permanent water bodies shapefile from S3 bucket and then clip on the boundary. + +This FIMeval module now uses the ArcGIS REST API to extract water bodies within a specified boundary. As it query data for only specified boundary, it is more efficient and faster than downloading the entire US water bodies dataset. +""" + +# import Libraries +import geopandas as gpd +import boto3 +import botocore +import os +import tempfile +import requests +import pandas as pd +import numpy as np +import json +from pathlib import Path +from typing import Union, Optional +from shapely.geometry import box + +#USING ANONYMOUS S3 CLIENT TO ACCESS PUBLIC DATA +# Initialize an anonymous S3 client +s3 = boto3.client( + "s3", config=botocore.config.Config(signature_version=botocore.UNSIGNED) +) + +bucket_name = "sdmlab" +pwb_folder = "PWB/" + + +def PWB_inS3(s3_client, bucket, prefix): + """Download all components of a shapefile from S3 into a temporary directory.""" + tmp_dir = tempfile.mkdtemp() + response = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix) + if "Contents" not in response: + raise ValueError("No files found in the specified S3 folder.") + + for obj in response["Contents"]: + file_key = obj["Key"] + file_name = os.path.basename(file_key) + if file_name.endswith((".shp", ".shx", ".dbf", ".prj", ".cpg")): + local_path = os.path.join(tmp_dir, file_name) + s3_client.download_file(bucket, file_key, local_path) + + shp_files = [f for f in os.listdir(tmp_dir) if f.endswith(".shp")] + if not shp_files: + raise ValueError("No .shp file found after download.") + + shp_path = os.path.join(tmp_dir, shp_files[0]) + return shp_path + + +def get_PWB(): + shp_path = PWB_inS3(s3, bucket_name, pwb_folder) + pwb = gpd.read_file(shp_path) + return pwb + + +#USING ARCGIS REST TO ACCESS PUBLIC DATA- More fast +class ExtractPWB: + SERVICE_URL = "https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Detailed_Water_Bodies/FeatureServer/0" + + def __init__( + self, + boundary: Union[str, Path, gpd.GeoDataFrame], + layer: Optional[str] = None, + output_dir: Optional[Union[str, Path]] = None, + save: bool = True, + output_filename: str = "permanent_water.gpkg" + ): + self.boundary_gdf = self._load_boundary(boundary, layer) + self.output_dir = Path(output_dir) if output_dir else Path.cwd() / "PWBOutputs" + + # We store the final result in self.gdf so it can be accessed after init + self.gdf = self.extract(save=save, output_filename=output_filename) + + def _load_boundary(self, boundary, layer): + if isinstance(boundary, gpd.GeoDataFrame): + gdf = boundary.copy() + else: + kwargs = {"layer": layer} if layer else {} + gdf = gpd.read_file(boundary, **kwargs) + return gdf.to_crs("EPSG:4326") if gdf.crs != "EPSG:4326" else gdf + + def _get_query_envelopes(self, threshold=1.0): + xmin, ymin, xmax, ymax = self.boundary_gdf.total_bounds + cols = list(np.arange(xmin, xmax, threshold)) + [xmax] + rows = list(np.arange(ymin, ymax, threshold)) + [ymax] + + grid = [] + for i in range(len(cols)-1): + for j in range(len(rows)-1): + grid.append({ + "xmin": cols[i], "ymin": rows[j], + "xmax": cols[i+1], "ymax": rows[j+1], + "spatialReference": {"wkid": 4326} + }) + return grid + + def extract(self, save: bool = True, output_filename: str = "permanent_water.gpkg", verbose: bool = True) -> gpd.GeoDataFrame: + all_features = [] + query_url = f"{self.SERVICE_URL}/query" + envelopes = self._get_query_envelopes() + + permanent_filter = "FTYPE IN ('Lake/Pond', 'Stream/River', 'Reservoir', 'Canal/Ditch')" + + for env_idx, env in enumerate(envelopes): + offset = 0 + limit = 1000 + + while True: + payload = { + "f": "geojson", + "where": permanent_filter, + "geometry": json.dumps(env), + "geometryType": "esriGeometryEnvelope", + "inSR": "4326", + "spatialRel": "esriSpatialRelIntersects", + "outFields": "NAME,FTYPE,FCODE,SQKM", + "returnGeometry": "true", + "outSR": "4326", + "resultOffset": offset, + "resultRecordCount": limit + } + + try: + response = requests.post(query_url, data=payload, timeout=60) + response.raise_for_status() + data = response.json() + + features = data.get("features", []) + if not features: + break + + batch_gdf = gpd.GeoDataFrame.from_features(features, crs="EPSG:4326") + all_features.append(batch_gdf) + + if verbose and offset > 0: + print(f" Grid {env_idx}: Paginated to offset {offset}...") + + if len(features) < limit: + break + + offset += limit + + except Exception as e: + print(f"Error at grid {env_idx}, offset {offset}: {e}") + break + + if not all_features: + print("No water bodies found.") + return gpd.GeoDataFrame() + + # Combine and Deduplicate + full_gdf = pd.concat(all_features, ignore_index=True) + full_gdf = gpd.GeoDataFrame(full_gdf, crs="EPSG:4326") + full_gdf = full_gdf.loc[full_gdf.geometry.to_wkt().drop_duplicates().index] + + # Clip to exact AOI + final_gdf = gpd.clip(full_gdf, self.boundary_gdf) + + # Conditional Saving + if save: + self.output_dir.mkdir(parents=True, exist_ok=True) + output_path = self.output_dir / output_filename + final_gdf.to_file(output_path, driver="GPKG") + if verbose: print(f"Saved {len(final_gdf)} features to {output_path}") + else: + if verbose: print(f"PWB Extraction complete.") + + return final_gdf \ No newline at end of file diff --git a/src/fimeval/__init__.py b/src/fimeval/__init__.py index c3203cb..fe2987d 100644 --- a/src/fimeval/__init__.py +++ b/src/fimeval/__init__.py @@ -2,7 +2,7 @@ from .ContingencyMap.evaluationFIM import EvaluateFIM from .ContingencyMap.printcontingency import PrintContingencyMap from .ContingencyMap.plotevaluationmetrics import PlotEvaluationMetrics -from .ContingencyMap.PWBs3 import get_PWB +from .ContingencyMap.water_bodies import get_PWB, ExtractPWB # Utility modules from .utilis import compress_tif_lzw @@ -10,9 +10,12 @@ # Evaluation with Building foorprint module from .BuildingFootprint.evaluationwithBF import EvaluationWithBuildingFootprint -#Access benchmark FIM module +# Access benchmark FIM module from .BenchFIMQuery.access_benchfim import benchFIMquery +# Building Footprint module +from .BuildingFootprint.arcgis_API import getBuildingFootprint + __all__ = [ "EvaluateFIM", "PrintContingencyMap", @@ -21,4 +24,6 @@ "EvaluationWithBuildingFootprint", "compress_tif_lzw", "benchFIMquery", + "getBuildingFootprint", + "ExtractPWB", ] diff --git a/src/fimeval/setup_benchFIM.py b/src/fimeval/setup_benchFIM.py index cae5050..56221d6 100644 --- a/src/fimeval/setup_benchFIM.py +++ b/src/fimeval/setup_benchFIM.py @@ -4,11 +4,13 @@ Author: Supath Dhital Date updated: 25 Nov, 2025 """ + from pathlib import Path from .BenchFIMQuery.access_benchfim import benchFIMquery from .utilis import benchmark_name + def ensure_benchmark(folder_dir, tif_files, benchmark_map): """ If no local benchmark is found in `tif_files`, and `folder_dir.name` diff --git a/src/fimeval/utilis.py b/src/fimeval/utilis.py index f4c6894..4641604 100644 --- a/src/fimeval/utilis.py +++ b/src/fimeval/utilis.py @@ -184,8 +184,9 @@ def MakeFIMsUniform(fim_dir, target_crs=None, target_resolution=None): else: print("All rasters already have the same resolution. No resampling needed.") -#Function to find the best boundary file in the folder if multiple boundary files are present -def find_best_boundary(folder: Path, benchmark_path: Path): + +# Function to find the best boundary file in the folder if multiple boundary files are present +def find_best_boundary(folder: Path, benchmark_path: Path): """ Choose the best boundary file in `folder`: - prefer .gpkg (from benchFIM downloads), @@ -207,11 +208,9 @@ def find_best_boundary(folder: Path, benchmark_path: Path): ) def score(path: Path): - name_tokens = set( - t for t in re.split(r"[_\-\.\s]+", path.stem.lower()) if t - ) + name_tokens = set(t for t in re.split(r"[_\-\.\s]+", path.stem.lower()) if t) common = len(bench_tokens & name_tokens) - bonus = 1 if path.suffix.lower() == ".gpkg" else 0 + bonus = 1 if path.suffix.lower() == ".gpkg" else 0 return (common, bonus) best = max(candidates, key=score) @@ -219,7 +218,7 @@ def score(path: Path): return best -#To test whether the tif is benchmark or not +# To test whether the tif is benchmark or not def benchmark_name(f: Path) -> bool: name = f.stem.lower() @@ -230,4 +229,3 @@ def benchmark_name(f: Path) -> bool: # Treating underscores/dashes/dots as separators and look for a 'bm' token tokens = re.split(r"[_\-\.\s]+", name) return "bm" in tokens - diff --git a/tests/test_accessbenchmarkFIM.py b/tests/test_accessbenchmarkFIM.py index a83f5db..ad60528 100644 --- a/tests/test_accessbenchmarkFIM.py +++ b/tests/test_accessbenchmarkFIM.py @@ -1,11 +1,11 @@ import fimeval as fe -#For testing boundary/raster inputs -raster_path = "/Users/supath/Downloads/MSResearch/FIMpef/CodeUsage/SM_prediction/HUC11110203_AR/hf_RS_HUC11110203_AR_binary_BM.tif" #./paths/to/your/model_predicted_fim" +# For testing boundary/raster inputs +raster_path = "/Users/supath/Downloads/MSResearch/FIMpef/CodeUsage/SM_prediction/HUC11110203_AR/hf_RS_HUC11110203_AR_binary_BM.tif" # ./paths/to/your/model_predicted_fim" boundary_path = "./paths/to/your/boundaryfile" -#Benchmark FIM querying +# Benchmark FIM querying """ Query benchmark FIMs from the catalog. Supports multiple conbinations of filters.: @@ -35,17 +35,19 @@ out_dir: Directory for downloads (required if ``download=True``). """ + + def test_benchmark_fimquery(): response = fe.benchFIMquery( - raster_path = raster_path, - boundary_path = None, - huc8 = None, #Example HUC8 ID: "03020202" + raster_path=raster_path, + boundary_path=None, + huc8=None, # Example HUC8 ID: "03020202" # event_date = "2017-05-01", # start_date = "2017-04-01", # end_date = "2017-05-01", # file_name = None, - area = True, #Default is false; if True, returns overlap stats + area=True, # Default is false; if True, returns overlap stats # download = False, # out_dir = None, ) - print(response) \ No newline at end of file + print(response) diff --git a/tests/test_evaluationfim.py b/tests/test_evaluationfim.py index b390757..a321f56 100644 --- a/tests/test_evaluationfim.py +++ b/tests/test_evaluationfim.py @@ -3,7 +3,6 @@ Main_dir = ( # "../docs/sampledata" - "/Users/supath/Downloads/MSResearch/FIMpef/test/SM_prediction" ) PWD_dir = "./path/to/PWB" output_dir = ( @@ -15,28 +14,27 @@ building_footprint = "path/to/building_footprint.shp" # If user is working with user defined building footprint shapefile # If user is working with user defined shapefile -AOI = "path/to/shapefile.shp" # This shapefile should be in projected CRS, if not, it will be reprojected to the target CRS +AOI = "path/to/your/boundary" # This shapefile should be in projected CRS, if not, it will be reprojected to the target CRS # Three methods are available to evaluate the FIM, # 1. Smallest extent # 2. Convex Hull # 3. AOI (User defined shapefile) -method_name = "smallest_extent" +method_name = "AOI" # 3 letter country ISO code countryISO = "USA" -#Evaluate with out extensive benchmark FIM +# Evaluate with out extensive benchmark FIM """ A dictionary containing all the test cases as a key (foldername) and appropritate decidede benchmark FIM file as a value(got from the query). If some cases already have benchmark; do not need to add them here. """ -benchmark_dict = { - "HUC11110203_AR": "AI_0_4m_20160103_923809W350109N_BM.tif" -} +benchmark_dict = {"HUC11110203_AR": "AI_0_4m_20160103_923809W350109N_BM.tif"} + def test_evaluation_framework(): # Run the evaluation [Few possible cases documentations are given below] @@ -47,26 +45,25 @@ def test_evaluation_framework(): # fe.EvaluateFIM(Main_dir, method_name, output_dir) # If the FIMS are not in projected crs or are in different spatial resolution - # fe.EvaluateFIM(Main_dir, method_name, output_dir, target_crs=target_crs, shapefile_dir = AOI, target_resolution=target_resolution) - + fe.EvaluateFIM(Main_dir, method_name, output_dir, target_crs=target_crs, shapefile_dir = AOI, target_resolution=target_resolution) + """ If user is passing with specified benchmark FIMs used from the benchmark FIM catalog as a dictionary. - Here, the method_name will be passes, for those cases which are not in the dictionary, it will follow the - method_name for evaluation, but for those cases which are in the dictionary, it will use the AOI. + Here, the method_name will be passes, for those cases which are not in the dictionary, it will follow the + method_name for evaluation, but for those cases which are in the dictionary, it will use the AOI. - Target CRS and Target resolution will be applied as usual as without dictionaries. 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