[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

scripts/worldcover/embeddings_db.py

@@ -31,7 +31,7 @@
 db.table_names()
 
 # Drop existing table if exists
-#db.drop_table("worldcover-2020-v001")
+# db.drop_table("worldcover-2020-v001")
 
 # Create embeddings table and insert the vector data
 tbl = db.create_table("worldcover-2020-v001", data=data, mode="overwrite")
@@ -55,4 +55,4 @@ def plot(df, cols=10):
 # Select a vector by index, and search 10 similar pairs, and plot
 v = tbl.to_pandas()["vector"].values[5]
 result = tbl.search(query=v).limit(5).to_pandas()
-plot(result, 5)
+plot(result, 5)

scripts/worldcover/run.py

@@ -1,26 +1,25 @@
 #!/usr/bin/env python3
 
 import sys
+
 sys.path.append("../../")
 
 import os
 import tempfile
 from math import floor
 from pathlib import Path
-import requests
 
 import boto3
 import einops
 import geopandas as gpd
-import pandas as pd
 import numpy
-import pyarrow as pa
+import pandas as pd
 import rasterio
+import requests
 import shapely
 import torch
 import xarray as xr
 from rasterio.windows import Window
-from shapely import box
 from torchvision.transforms import v2
 
 from src.datamodule import ClayDataset
@@ -141,6 +140,7 @@ def tiles_and_windows(input: Window):
 
     return result
 
+
 def download_image(url):
     # Download an image from a URL
     response = requests.get(url)
@@ -150,52 +150,54 @@ def download_image(url):
     else:
         raise Exception("Failed to download the image")
 
+
 def patch_bounds_from_url(url, chunk_size=(PATCH_SIZE, PATCH_SIZE)):
     # Download an image from a URL
     image_data = download_image(url)
-    
+
     # Open the image using rasterio from memory
     with rasterio.io.MemoryFile(image_data) as memfile:
         with memfile.open() as src:
             # Read the image data and metadata
             img_data = src.read()
             img_meta = src.profile
             img_crs = src.crs
-    
+
     # Convert raster data and metadata into an xarray DataArray
     img_da = xr.DataArray(img_data, dims=("band", "y", "x"), attrs=img_meta)
-    
+
     # Tile the data
     ds_chunked = img_da.chunk({"y": chunk_size[0], "x": chunk_size[1]})
-    
+
     # Get the geospatial information from the original dataset
     transform = img_meta["transform"]
-    
+
     # Iterate over the chunks and compute the geospatial bounds for each chunk
     chunk_bounds = {}
-    
+
     for x in range(ds_chunked.sizes["x"] // chunk_size[1]):
         for y in range(ds_chunked.sizes["y"] // chunk_size[0]):
             # Compute chunk coordinates
             x_start = x * chunk_size[1]
             y_start = y * chunk_size[0]
             x_end = min(x_start + chunk_size[1], ds_chunked.sizes["x"])
             y_end = min(y_start + chunk_size[0], ds_chunked.sizes["y"])
-    
+
             # Compute chunk geospatial bounds
             lon_start, lat_start = transform * (x_start, y_start)
             lon_end, lat_end = transform * (x_end, y_end)
-    
+
             # Store chunk bounds
             chunk_bounds[(x, y)] = {
                 "lon_start": lon_start,
                 "lat_start": lat_start,
                 "lon_end": lon_end,
                 "lat_end": lat_end,
             }
-    
+
     return chunk_bounds, img_crs
 
+
 def make_batch(result):
     pixels = []
     for url, win in result:
@@ -230,10 +232,10 @@ def make_batch(result):
         "timestep": torch.as_tensor(data=[ds.normalize_timestamp(f"{YEAR}-06-01")]).to(
             rgb_model.device
         ),
-        "date": f"{YEAR}-06-01"
-        ,
+        "date": f"{YEAR}-06-01",
     }
 
+
 def get_pixels(result):
     pixels = []
     for url, win in result:
@@ -319,42 +321,41 @@ def get_pixels(result):
     )
 
     yoff += CHIP_SIZE
-    
-
 
     print(len(embeddings), len(results))
     embeddings_ = numpy.vstack(embeddings)
-    #embeddings_ = embeddings[0]
+    # embeddings_ = embeddings[0]
     print("Embeddings shape: ", embeddings_.shape)
 
     # remove date and lat/lon
     embeddings_ = embeddings_[:, :-2, :].mean(axis=0)
-    
+
     print(f"Embeddings have shape {embeddings_.shape}")
-    
+
     # reshape to disaggregated patches
     embeddings_patch = embeddings_.reshape([2, 16, 16, 768])
-    
+
     # average over the band groups
     embeddings_mean = embeddings_patch.mean(axis=0)
-    
-    print(f"Average patch embeddings have shape {embeddings_mean.shape}")
 
+    print(f"Average patch embeddings have shape {embeddings_mean.shape}")
 
     if result is not None:
         print("result: ", result[0][0])
         pix = get_pixels(result)
         chunk_bounds, epsg = patch_bounds_from_url(result[0][0])
-        #print("chunk_bounds: ", chunk_bounds)
+        # print("chunk_bounds: ", chunk_bounds)
         print("chunk bounds length:", len(chunk_bounds))
-        
+
         # Iterate through each patch
         for i in range(embeddings_mean.shape[0]):
             for j in range(embeddings_mean.shape[1]):
                 embeddings_output_patch = embeddings_mean[i, j]
-        
+
                 item_ = [
-                    element for element in list(chunk_bounds.items()) if element[0] == (i, j)
+                    element
+                    for element in list(chunk_bounds.items())
+                    if element[0] == (i, j)
                 ]
                 box_ = [
                     item_[0][1]["lon_start"],
@@ -364,42 +365,44 @@ def get_pixels(result):
                 ]
 
                 data = {
-                    #"source_url": batch["source_url"][0],
-                    #"date": pd.to_datetime(arg=date, format="%Y-%m-%d").astype(
+                    # "source_url": batch["source_url"][0],
+                    # "date": pd.to_datetime(arg=date, format="%Y-%m-%d").astype(
                     #    dtype="date32[day][pyarrow]"
-                    #),
-                    #"date": pd.to_datetime(date, format="%Y-%m-%d", dtype="date32[day][pyarrow]"),
+                    # ),
+                    # "date": pd.to_datetime(date, format="%Y-%m-%d", dtype="date32[day][pyarrow]"),
                     "date": pd.to_datetime(batch["date"], format="%Y-%m-%d"),
                     "embeddings": [numpy.ascontiguousarray(embeddings_output_patch)],
                 }
-        
+
                 # Define the bounding box as a Polygon (xmin, ymin, xmax, ymax)
                 # The box_ list is encoded as
                 # [bottom left x, bottom left y, top right x, top right y]
                 box_emb = shapely.geometry.box(box_[0], box_[1], box_[2], box_[3])
 
                 print(str(epsg)[-4:])
-        
+
                 # Create the GeoDataFrame
-                gdf = gpd.GeoDataFrame(data, geometry=[box_emb], crs=f"EPSG:{str(epsg)[-4:]}")
-        
+                gdf = gpd.GeoDataFrame(
+                    data, geometry=[box_emb], crs=f"EPSG:{str(epsg)[-4:]}"
+                )
+
                 # Reproject to WGS84 (lon/lat coordinates)
                 gdf = gdf.to_crs(epsg=4326)
-        
+
                 with tempfile.TemporaryDirectory() as tmp:
                     # tmp = "/home/tam/Desktop/wcctmp"
-                
+
                     outpath = f"{tmp}/worldcover_patch_embeddings_{YEAR}_{index}_{i}_{j}_v{VERSION}.gpq"
                     print(f"Uploading embeddings to {outpath}")
-                    #print(gdf)
-                
-                    gdf.to_parquet(path=outpath, compression="ZSTD", schema_version="1.0.0")
-                
+                    # print(gdf)
+
+                    gdf.to_parquet(
+                        path=outpath, compression="ZSTD", schema_version="1.0.0"
+                    )
+
                     s3_client = boto3.client("s3")
                     s3_client.upload_file(
                         outpath,
                         BUCKET,
                         f"v{VERSION}/{YEAR}/{os.path.basename(outpath)}",
                     )
-                
-