From d3d818a57ef67ff5524a46356385b428700df7ed Mon Sep 17 00:00:00 2001
From: Cyberes <cyberes@evulid.cc>
Date: Mon, 6 Nov 2023 20:40:07 -0700
Subject: [PATCH] clarification

---
 README.md     | 10 +++++++---
 exfiltrate.py |  8 +++++---
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index d591533..1f98211 100644
--- a/README.md
+++ b/README.md
@@ -35,13 +35,17 @@ python3 exfiltrate.py \
   --threads 30
 ```
 
-Building the GeoTIFF will take dozens of gigs of memory for any significant extent! For example, a 21 mile extent
-required about 400GB of memory. You can use swap for this, but don't expect it to be very quick if you go this route.
+Building the GeoTIFF will take dozens of gigs of memory for any significant extent! For example, a 336 square mile
+extent required about 400GB of memory. You can use swap for this, but don't expect it to be very quick if you go this
+route.
+
+Be careful not to go overboard with your spatial extent. Use only what you need to avoid unnecessary processing time or
+else you will end up with a situation where it will take a week to download all the tiles and build a TIFF.
 
 ### ArcGIS
 
 TODO
 
-### Credits
+### Inspiration
 
 https://jimmyutterstrom.com/blog/2019/06/05/map-tiles-to-geotiff/
\ No newline at end of file
diff --git a/exfiltrate.py b/exfiltrate.py
index de682fa..0602f5d 100644
--- a/exfiltrate.py
+++ b/exfiltrate.py
@@ -114,7 +114,7 @@ if __name__ == '__main__':
     print(f' {num_cols}x{num_rows}')
 
     # Create an empty array to store the image data
-    print(f'Allocating an array with the shape {num_rows * tile_size, num_cols * tile_size} and dimension 3...')
+    print(f'Allocating an array with shape {num_rows * tile_size, num_cols * tile_size} and dimension 3...')
     image_data = np.empty((num_rows * tile_size, num_cols * tile_size, 3), dtype=np.uint8)
 
 
@@ -149,8 +149,10 @@ if __name__ == '__main__':
 
     with ThreadPoolExecutor(max_workers=args.tiff_threads) as executor:
         futures = {executor.submit(build_tiff_data, task) for task in tiles}
-        for future in tqdm(as_completed(futures), total=len(futures), desc='Building TIFF'):
-            pass
+        bar = tqdm(total=len(futures), desc='Building TIFF', postfix='There may be a lengthy startup time, please be patient!')
+        for future in as_completed(futures):
+            bar.set_postfix()
+            bar.update()
 
     # Transpose the image data array to the format (bands, rows, cols).
     print('Transposing...')