Spaces:
Build error
Build error
Working calculator
Browse files- .gitignore +2 -0
- app.py +313 -17
- conda_environment.yml +3 -2
- indices.yaml +169 -0
.gitignore
CHANGED
@@ -1,3 +1,5 @@
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.venv
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__pycache__/
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service_account.json
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.venv
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__pycache__/
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service_account.json
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ee_service_account.json
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md_service_token.txt
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app.py
CHANGED
@@ -1,17 +1,286 @@
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import gradio as gr
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import plotly.graph_objects as go
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import ee
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# import geemap
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def filter_map(min_price, max_price, boroughs):
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@@ -49,16 +318,43 @@ def filter_map(min_price, max_price, boroughs):
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return fig
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with gr.Blocks() as demo:
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with gr.Column():
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with gr.Row():
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demo.launch()
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import gradio as gr
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import plotly.graph_objects as go
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# import ee
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# # import geemap
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# # GEE
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# service_account = 'climatebase-july-2023@ee-geospatialml-aquarry.iam.gserviceaccount.com'
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# credentials = ee.ServiceAccountCredentials(service_account, 'service_account.json')
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# ee.Initialize(credentials)
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# # Gradio dataset
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# dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")
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# df = dataset.to_pandas()
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import os
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import duckdb
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import pandas as pd
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import datetime
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import ee
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# import geemap
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import yaml
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# Define constants
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MD_SERVICE_TOKEN = 'md_service_token.txt'
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# to-do: set-up with papermill parameters
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DATE='2020-01-01'
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YEAR = 2020
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LOCATION=[-74.653370, 5.845328]
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ROI_RADIUS = 20000
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GEE_SERVICE_ACCOUNT = 'climatebase-july-2023@ee-geospatialml-aquarry.iam.gserviceaccount.com'
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GEE_SERVICE_ACCOUNT_CREDENTIALS_FILE = 'ee_service_account.json'
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INDICES_FILE = 'indices.yaml'
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START_YEAR = 2015
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END_YEAR = 2022
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class IndexGenerator:
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"""
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A class to generate indices and compute zonal means.
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Args:
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centroid (tuple): The centroid coordinates (latitude, longitude) of the region of interest.
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year (int): The year for which indices are generated.
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roi_radius (int, optional): The radius (in meters) for creating a buffer around the centroid as the region of interest. Defaults to 20000.
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project_name (str, optional): The name of the project. Defaults to "".
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map (geemap.Map, optional): Map object for mapping. Defaults to None (i.e. no map created)
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"""
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def __init__(self,
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centroid,
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roi_radius,
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year,
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indices_file,
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project_name="",
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map = None,
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):
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self.indices = self._load_indices(indices_file)
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self.centroid = centroid
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self.roi = ee.Geometry.Point(*centroid).buffer(roi_radius)
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self.year = year
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self.start_date = str(datetime.date(self.year, 1, 1))
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self.end_date = str(datetime.date(self.year, 12, 31))
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self.daterange=[self.start_date, self.end_date]
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self.project_name=project_name
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self.map = map
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if self.map is not None:
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self.show = True
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else:
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self.show = False
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def _cloudfree(self, gee_path):
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"""
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Internal method to generate a cloud-free composite.
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Args:
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gee_path (str): The path to the Google Earth Engine (GEE) image or image collection.
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Returns:
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ee.Image: The cloud-free composite clipped to the region of interest.
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"""
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# Load a raw Landsat ImageCollection for a single year.
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collection = (
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ee.ImageCollection(gee_path)
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.filterDate(*self.daterange)
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.filterBounds(self.roi)
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)
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# Create a cloud-free composite with custom parameters for cloud score threshold and percentile.
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composite_cloudfree = ee.Algorithms.Landsat.simpleComposite(**{
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'collection': collection,
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'percentile': 75,
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'cloudScoreRange': 5
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})
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return composite_cloudfree.clip(self.roi)
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def _load_indices(self, indices_file):
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# Read index configurations
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with open(indices_file, 'r') as stream:
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try:
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return yaml.safe_load(stream)
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except yaml.YAMLError as e:
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print(e)
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return None
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def show_map(self, map=None):
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if map is not None:
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self.map = map
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self.show = True
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def disable_map(self):
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self.show = False
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def generate_index(self, index_config):
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"""
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Generates an index based on the provided index configuration.
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Args:
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index_config (dict): Configuration for generating the index.
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Returns:
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ee.Image: The generated index clipped to the region of interest.
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"""
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match index_config["gee_type"]:
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case 'image':
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dataset = ee.Image(index_config['gee_path']).clip(self.roi)
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if index_config.get('select'):
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dataset = dataset.select(index_config['select'])
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case 'image_collection':
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dataset = ee.ImageCollection(index_config['gee_path']).filterBounds(self.roi).map(lambda image: image.clip(self.roi)).mean()
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if index_config.get('select'):
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dataset = dataset.select(index_config['select'])
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case 'feature_collection':
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dataset = ee.Image().float().paint(ee.FeatureCollection(index_config['gee_path']), index_config['select']).clip(self.roi)
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case 'algebraic':
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image = self._cloudfree(index_config['gee_path'])
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dataset = image.normalizedDifference(['B4', 'B3'])
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case _:
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dataset=None
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if not dataset:
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raise Exception("Failed to generate dataset.")
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if self.show and index_config.get('show'):
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map.addLayer(dataset, index_config['viz'], index_config['name'])
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print(f"Generated index: {index_config['name']}")
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return dataset
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def zonal_mean_index(self, index_key):
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index_config = self.indices[index_key]
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dataset = self.generate_index(index_config)
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# zm = self._zonal_mean(single, index_config.get('bandname') or 'constant')
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out = dataset.reduceRegion(**{
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'reducer': ee.Reducer.mean(),
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'geometry': self.roi,
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'scale': 200 # map scale
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}).getInfo()
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if index_config.get('bandname'):
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return out[index_config.get('bandname')]
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return out
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def generate_composite_index_df(self, indices=[]):
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data={
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"metric": indices,
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"year":self.year,
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"centroid": str(self.centroid),
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"project_name": self.project_name,
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"value": list(map(self.zonal_mean_index, indices)),
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"area": roi.area().getInfo(), # m^2
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"geojson": str(roi.getInfo()),
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}
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print('data', data)
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df = pd.DataFrame(data)
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return df
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def set_up_duckdb(service_token_file=None):
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print('setting up duckdb')
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# use `climatebase` db
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if service_token_file is not None:
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with open(service_token_file, 'r') as f:
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md_service_token=f.read()
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os.environ['motherduck_token'] = md_service_token
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con = duckdb.connect('md:climatebase')
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else:
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con = duckdb.connect(':climatebase:')
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con.sql("USE climatebase;")
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# load extensions
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con.sql("""INSTALL spatial; LOAD spatial;""")
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return con
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def authenticate_gee(gee_service_account, gee_service_account_credentials_file):
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print('authenticate_gee')
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# to-do: alert if dataset filter date nan
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credentials = ee.ServiceAccountCredentials(gee_service_account, gee_service_account_credentials_file)
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ee.Initialize(credentials)
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def load_indices(indices_file):
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# Read index configurations
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with open(indices_file, 'r') as stream:
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try:
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return yaml.safe_load(stream)
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except yaml.YAMLError as e:
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print(e)
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return None
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def create_dataframe(years, project_name):
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dfs=[]
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print(years)
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indices = load_indices(INDICES_FILE)
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for year in years:
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print(year)
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ig = IndexGenerator(centroid=LOCATION, roi_radius=ROI_RADIUS, year=year, indices_file=INDICES_FILE, project_name=project_name)
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df = ig.generate_composite_index_df(list(indices.keys()))
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dfs.append(df)
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return pd.concat(dfs)
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# def preview_table():
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# con.sql("FROM bioindicator;").show()
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# if __name__ == '__main__':
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# Map = geemap.Map()
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# # Create a cloud-free composite with custom parameters for cloud score threshold and percentile.
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# composite_cloudfree = ee.Algorithms.Landsat.simpleComposite(**{
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# 'collection': collection,
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# 'percentile': 75,
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# 'cloudScoreRange': 5
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# })
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# Map.addLayer(composite_cloudfree, {'bands': ['B4', 'B3', 'B2'], 'max': 128}, 'Custom TOA composite')
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# Map.centerObject(roi, 14)
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# ig = IndexGenerator(centroid=LOCATION, year=2015, indices_file=INDICES_FILE, project_name='Test Project', map=Map)
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# dataset = ig.generate_index(indices['Air'])
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# minMax = dataset.clip(roi).reduceRegion(
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# geometry = roi,
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# reducer = ee.Reducer.minMax(),
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# scale= 3000,
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# maxPixels= 10e3,
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# )
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# minMax.getInfo()
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def calculate_biodiversity_score(start_year, end_year, project_name):
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years = []
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for year in range(start_year, end_year):
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row_exists = con.sql(f"SELECT COUNT(1) FROM bioindicator WHERE (year = {year} AND project_name = '{project_name}')").fetchall()[0][0]
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if not row_exists:
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years.append(year)
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if len(years)>0:
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df = create_dataframe(years, project_name)
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# con.sql('FROM df LIMIT 5').show()
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# Write score table to `_temptable`
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con.sql('CREATE OR REPLACE TABLE _temptable AS SELECT *, (value * area) AS score FROM (SELECT year, project_name, AVG(value) AS value, area FROM df GROUP BY year, project_name, area ORDER BY project_name)')
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# Create `bioindicator` table IF NOT EXISTS.
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con.sql("""
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USE climatebase;
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CREATE TABLE IF NOT EXISTS bioindicator (year BIGINT, project_name VARCHAR(255), value DOUBLE, area DOUBLE, score DOUBLE, CONSTRAINT unique_year_project_name UNIQUE (year, project_name));
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""")
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return con.sql(f"SELECT * FROM bioindicator WHERE (year > {start_year} AND year <= {end_year} AND project_name = '{project_name}')").df()
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def view_all():
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print('view_all')
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return con.sql(f"SELECT * FROM bioindicator").df()
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def push_to_md():
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# UPSERT project record
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con.sql("""
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INSERT INTO bioindicator FROM _temptable
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+
ON CONFLICT (year, project_name) DO UPDATE SET value = excluded.value;
|
280 |
+
""")
|
281 |
+
print('Saved records')
|
282 |
+
|
283 |
+
# preview_table()
|
284 |
|
285 |
def filter_map(min_price, max_price, boroughs):
|
286 |
|
|
|
318 |
return fig
|
319 |
|
320 |
with gr.Blocks() as demo:
|
321 |
+
con = set_up_duckdb(MD_SERVICE_TOKEN)
|
322 |
+
authenticate_gee(GEE_SERVICE_ACCOUNT, GEE_SERVICE_ACCOUNT_CREDENTIALS_FILE)
|
323 |
+
# Create circle buffer over point
|
324 |
+
# roi = ee.Geometry.Point(*LOCATION).buffer(ROI_RADIUS)
|
325 |
+
|
326 |
+
# # Load a raw Landsat ImageCollection for a single year.
|
327 |
+
# start_date = str(datetime.date(YEAR, 1, 1))
|
328 |
+
# end_date = str(datetime.date(YEAR, 12, 31))
|
329 |
+
# collection = (
|
330 |
+
# ee.ImageCollection('LANDSAT/LC08/C02/T1')
|
331 |
+
# .filterDate(start_date, end_date)
|
332 |
+
# .filterBounds(roi)
|
333 |
+
# )
|
334 |
+
|
335 |
+
# indices = load_indices(INDICES_FILE)
|
336 |
+
# push_to_md(START_YEAR, END_YEAR, 'Test Project')
|
337 |
with gr.Column():
|
338 |
+
# map = gr.Plot().style()
|
339 |
+
with gr.Row():
|
340 |
+
start_year = gr.Number(value=2017, label="Start Year", precision=0)
|
341 |
+
end_year = gr.Number(value=2022, label="End Year", precision=0)
|
342 |
+
project_name = gr.Textbox(label='Project Name')
|
343 |
+
# boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Methodology:")
|
344 |
+
# btn = gr.Button(value="Update Filter")
|
345 |
with gr.Row():
|
346 |
+
calc_btn = gr.Button(value="Calculate!")
|
347 |
+
view_btn = gr.Button(value="View all")
|
348 |
+
save_btn = gr.Button(value="Save")
|
349 |
+
results_df = gr.Dataframe(
|
350 |
+
headers=["Year", "Project Name", "Score"],
|
351 |
+
datatype=["number", "str", "number"],
|
352 |
+
label="Biodiversity scores by year",
|
353 |
+
)
|
354 |
+
# demo.load(filter_map, [min_price, max_price, boroughs], map)
|
355 |
+
# btn.click(filter_map, [min_price, max_price, boroughs], map)
|
356 |
+
calc_btn.click(calculate_biodiversity_score, inputs=[start_year, end_year, project_name], outputs=results_df)
|
357 |
+
view_btn.click(view_all, outputs=results_df)
|
358 |
+
save_btn.click(push_to_md)
|
359 |
|
360 |
demo.launch()
|
conda_environment.yml
CHANGED
@@ -1,13 +1,14 @@
|
|
1 |
name: openbiodiversity_calculator
|
2 |
channels:
|
3 |
- conda-forge
|
4 |
-
- huggingface
|
5 |
- plotly
|
6 |
dependencies:
|
7 |
-
-
|
8 |
- geemap
|
|
|
9 |
- plotly
|
10 |
- segment-geospatial
|
|
|
11 |
- pip
|
12 |
- pip:
|
13 |
- duckdb==0.8.1
|
|
|
1 |
name: openbiodiversity_calculator
|
2 |
channels:
|
3 |
- conda-forge
|
|
|
4 |
- plotly
|
5 |
dependencies:
|
6 |
+
- earthengine-api
|
7 |
- geemap
|
8 |
+
- geopandas
|
9 |
- plotly
|
10 |
- segment-geospatial
|
11 |
+
- pandas
|
12 |
- pip
|
13 |
- pip:
|
14 |
- duckdb==0.8.1
|
indices.yaml
ADDED
@@ -0,0 +1,169 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
Water:
|
3 |
+
name: Water
|
4 |
+
roi: ''
|
5 |
+
gee_path: JRC/GSW1_1/GlobalSurfaceWater
|
6 |
+
gee_type: image
|
7 |
+
viz:
|
8 |
+
min: 0
|
9 |
+
max: 100
|
10 |
+
palette:
|
11 |
+
- ffffff
|
12 |
+
- ffbbbb
|
13 |
+
- 0000ff
|
14 |
+
bandname: occurrence
|
15 |
+
select: occurrence
|
16 |
+
show: true
|
17 |
+
Protected:
|
18 |
+
name: Protected
|
19 |
+
roi: ''
|
20 |
+
gee_path: WCMC/WDPA/current/polygons
|
21 |
+
gee_type: feature_collection
|
22 |
+
viz:
|
23 |
+
palette:
|
24 |
+
- 2ed033
|
25 |
+
- 5aff05
|
26 |
+
- 67b9ff
|
27 |
+
- 5844ff
|
28 |
+
- 0a7618
|
29 |
+
- 2c05ff
|
30 |
+
min: 0
|
31 |
+
max: 1550000
|
32 |
+
opacity: 0.8
|
33 |
+
select: REP_AREA
|
34 |
+
bandname: constant
|
35 |
+
show: true
|
36 |
+
Air:
|
37 |
+
name: Air
|
38 |
+
roi: ''
|
39 |
+
gee_path: COPERNICUS/S5P/OFFL/L3_AER_AI
|
40 |
+
gee_type: image_collection
|
41 |
+
viz:
|
42 |
+
min: -1
|
43 |
+
max: 2
|
44 |
+
palette:
|
45 |
+
- black
|
46 |
+
- blue
|
47 |
+
- purple
|
48 |
+
- cyan
|
49 |
+
- green
|
50 |
+
- yellow
|
51 |
+
- red
|
52 |
+
bandname: absorbing_aerosol_index
|
53 |
+
select: absorbing_aerosol_index
|
54 |
+
dates: false
|
55 |
+
show: false
|
56 |
+
Soil:
|
57 |
+
name: Soil
|
58 |
+
roi: ''
|
59 |
+
gee_path: OpenLandMap/SOL/SOL_ORGANIC-CARBON_USDA-6A1C_M/v02
|
60 |
+
gee_type: image
|
61 |
+
viz:
|
62 |
+
bands:
|
63 |
+
- b200
|
64 |
+
min: 0
|
65 |
+
max: 12
|
66 |
+
palette:
|
67 |
+
- ffffa0
|
68 |
+
- f7fcb9
|
69 |
+
- d9f0a3
|
70 |
+
- addd8e
|
71 |
+
- 78c679
|
72 |
+
- 41ab5d
|
73 |
+
- '238443'
|
74 |
+
- 005b29
|
75 |
+
- 004b29
|
76 |
+
- 012b13
|
77 |
+
- 00120b
|
78 |
+
select: b0
|
79 |
+
bandname: b0
|
80 |
+
show: false
|
81 |
+
Temperature:
|
82 |
+
name: Temperature
|
83 |
+
roi: ''
|
84 |
+
gee_path: MODIS/061/MYD21C1
|
85 |
+
gee_type: image_collection
|
86 |
+
viz:
|
87 |
+
min: 216
|
88 |
+
max: 348
|
89 |
+
palette:
|
90 |
+
- '040274'
|
91 |
+
- '040281'
|
92 |
+
- 0502a3
|
93 |
+
- 0502b8
|
94 |
+
- 0502ce
|
95 |
+
- 0502e6
|
96 |
+
- 0602ff
|
97 |
+
- 235cb1
|
98 |
+
- 307ef3
|
99 |
+
- 269db1
|
100 |
+
- 30c8e2
|
101 |
+
- 32d3ef
|
102 |
+
- 3be285
|
103 |
+
- 3ff38f
|
104 |
+
- 86e26f
|
105 |
+
- 3ae237
|
106 |
+
- b5e22e
|
107 |
+
- d6e21f
|
108 |
+
- fff705
|
109 |
+
- ffd611
|
110 |
+
- ffb613
|
111 |
+
- ff8b13
|
112 |
+
- ff6e08
|
113 |
+
- ff500d
|
114 |
+
- ff0000
|
115 |
+
- de0101
|
116 |
+
- c21301
|
117 |
+
- a71001
|
118 |
+
- '911003'
|
119 |
+
select: LST_Day
|
120 |
+
bandname: LST_Day
|
121 |
+
dates: true
|
122 |
+
show: true
|
123 |
+
Habitat:
|
124 |
+
name: Habitat
|
125 |
+
roi: ''
|
126 |
+
gee_path: projects/sat-io/open-datasets/IUCN_HABITAT/iucn_habitatclassification_composite_lvl2_ver004
|
127 |
+
gee_type: image
|
128 |
+
viz: {}
|
129 |
+
bandname: comp_first
|
130 |
+
show: true
|
131 |
+
NDVI:
|
132 |
+
name: NDVI
|
133 |
+
roi: ''
|
134 |
+
gee_path: LANDSAT/LC08/C02/T1
|
135 |
+
gee_type: algebraic
|
136 |
+
normalized_difference:
|
137 |
+
- B4
|
138 |
+
- B3
|
139 |
+
viz:
|
140 |
+
palette:
|
141 |
+
- "#d73027"
|
142 |
+
- "#f46d43"
|
143 |
+
- "#fdae61"
|
144 |
+
- "#fee08b"
|
145 |
+
- "#d9ef8b"
|
146 |
+
- "#a6d96a"
|
147 |
+
- "#66bd63"
|
148 |
+
- "#1a9850"
|
149 |
+
bandname: nd
|
150 |
+
NDWI:
|
151 |
+
name: NDWI
|
152 |
+
roi: ''
|
153 |
+
gee_path: LANDSAT/LC08/C02/T1
|
154 |
+
gee_type: algebraic
|
155 |
+
normalized_difference:
|
156 |
+
- B3
|
157 |
+
- B5
|
158 |
+
viz:
|
159 |
+
palette:
|
160 |
+
- "#ece7f2"
|
161 |
+
- "#d0d1e6"
|
162 |
+
- "#a6bddb"
|
163 |
+
- "#74a9cf"
|
164 |
+
- "#3690c0"
|
165 |
+
- "#0570b0"
|
166 |
+
- "#045a8d"
|
167 |
+
- "#023858"
|
168 |
+
bandname: nd
|
169 |
+
show: true
|