diff --git a/docs/raster_example.png b/docs/raster_example.png
index 9d7ed02..135783b 100644
Binary files a/docs/raster_example.png and b/docs/raster_example.png differ
diff --git a/examples/blackmarbley_example.ipynb b/examples/blackmarbley_example.ipynb
index 9de0ed7..01a0786 100644
--- a/examples/blackmarbley_example.ipynb
+++ b/examples/blackmarbley_example.ipynb
@@ -48,9 +48,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found existing installation: blackmarblepy 0.1.0\n",
-      "Uninstalling blackmarblepy-0.1.0:\n",
-      "  Successfully uninstalled blackmarblepy-0.1.0\n"
+      "\u001b[33mWARNING: Skipping blackmarblepy as it is not installed.\u001b[0m\n"
      ]
     }
    ],
@@ -68,10 +66,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting git+https://github.com/ramarty/blackmarblepy.git\n",
-      "  Cloning https://github.com/ramarty/blackmarblepy.git to /private/var/folders/m1/8h14xfm56hd6qfgz6btm1rd80000gn/T/pip-req-build-sxp11589\n",
-      "  Running command git clone -q https://github.com/ramarty/blackmarblepy.git /private/var/folders/m1/8h14xfm56hd6qfgz6btm1rd80000gn/T/pip-req-build-sxp11589\n",
-      "  Resolved https://github.com/ramarty/blackmarblepy.git to commit f11d63dd310e117c2ec0f2ad857db69d8576ae9f\n",
+      "Collecting git+https://github.com/worldbank/blackmarblepy.git@rob-documentation\n",
+      "  Cloning https://github.com/worldbank/blackmarblepy.git (to revision rob-documentation) to /private/var/folders/m1/8h14xfm56hd6qfgz6btm1rd80000gn/T/pip-req-build-ue8ckc4a\n",
+      "  Running command git clone -q https://github.com/worldbank/blackmarblepy.git /private/var/folders/m1/8h14xfm56hd6qfgz6btm1rd80000gn/T/pip-req-build-ue8ckc4a\n",
+      "  Running command git checkout -b rob-documentation --track origin/rob-documentation\n",
+      "  Switched to a new branch 'rob-documentation'\n",
+      "  Branch 'rob-documentation' set up to track remote branch 'rob-documentation' from 'origin'.\n",
+      "  Resolved https://github.com/worldbank/blackmarblepy.git to commit fadb542197a2ad46705f11290aa9d5ed465ce739\n",
       "Requirement already satisfied: pandas in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from blackmarblepy==0.1.0) (1.4.1)\n",
       "Requirement already satisfied: numpy in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from blackmarblepy==0.1.0) (1.21.2)\n",
       "Requirement already satisfied: requests in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from blackmarblepy==0.1.0) (2.27.1)\n",
@@ -80,31 +81,36 @@
       "Requirement already satisfied: rasterstats in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from blackmarblepy==0.1.0) (0.18.0)\n",
       "Requirement already satisfied: h5py in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from blackmarblepy==0.1.0) (2.10.0)\n",
       "Requirement already satisfied: rasterio in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from blackmarblepy==0.1.0) (1.3.6)\n",
+      "Requirement already satisfied: httpx in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from blackmarblepy==0.1.0) (0.25.0)\n",
       "Requirement already satisfied: zope.interface in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from datetime->blackmarblepy==0.1.0) (5.4.0)\n",
       "Requirement already satisfied: pytz in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from datetime->blackmarblepy==0.1.0) (2021.3)\n",
-      "Requirement already satisfied: pyproj>=2.2.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from geopandas->blackmarblepy==0.1.0) (3.3.0)\n",
       "Requirement already satisfied: shapely>=1.6 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from geopandas->blackmarblepy==0.1.0) (1.8.1.post1)\n",
       "Requirement already satisfied: fiona>=1.8 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from geopandas->blackmarblepy==0.1.0) (1.8.21)\n",
-      "Requirement already satisfied: click>=4.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (8.0.4)\n",
-      "Requirement already satisfied: cligj>=0.5 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (0.7.2)\n",
-      "Requirement already satisfied: click-plugins>=1.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (1.1.1)\n",
-      "Requirement already satisfied: setuptools in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (58.0.4)\n",
+      "Requirement already satisfied: pyproj>=2.2.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from geopandas->blackmarblepy==0.1.0) (3.3.0)\n",
       "Requirement already satisfied: six>=1.7 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (1.16.0)\n",
-      "Requirement already satisfied: certifi in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (2021.10.8)\n",
       "Requirement already satisfied: munch in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (2.5.0)\n",
+      "Requirement already satisfied: click-plugins>=1.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (1.1.1)\n",
+      "Requirement already satisfied: setuptools in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (58.0.4)\n",
+      "Requirement already satisfied: click>=4.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (8.0.4)\n",
       "Requirement already satisfied: attrs>=17 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (21.4.0)\n",
+      "Requirement already satisfied: certifi in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (2021.10.8)\n",
+      "Requirement already satisfied: cligj>=0.5 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from fiona>=1.8->geopandas->blackmarblepy==0.1.0) (0.7.2)\n",
       "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from pandas->blackmarblepy==0.1.0) (2.8.2)\n",
+      "Requirement already satisfied: idna in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from httpx->blackmarblepy==0.1.0) (3.3)\n",
+      "Requirement already satisfied: sniffio in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from httpx->blackmarblepy==0.1.0) (1.2.0)\n",
+      "Requirement already satisfied: httpcore<0.19.0,>=0.18.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from httpx->blackmarblepy==0.1.0) (0.18.0)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from httpcore<0.19.0,>=0.18.0->httpx->blackmarblepy==0.1.0) (0.13.0)\n",
+      "Requirement already satisfied: anyio<5.0,>=3.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from httpcore<0.19.0,>=0.18.0->httpx->blackmarblepy==0.1.0) (3.5.0)\n",
       "Requirement already satisfied: snuggs>=1.4.1 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from rasterio->blackmarblepy==0.1.0) (1.4.7)\n",
       "Requirement already satisfied: affine in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from rasterio->blackmarblepy==0.1.0) (2.4.0)\n",
       "Requirement already satisfied: pyparsing>=2.1.6 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from snuggs>=1.4.1->rasterio->blackmarblepy==0.1.0) (3.0.4)\n",
       "Requirement already satisfied: simplejson in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from rasterstats->blackmarblepy==0.1.0) (3.19.1)\n",
-      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from requests->blackmarblepy==0.1.0) (1.26.8)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from requests->blackmarblepy==0.1.0) (3.3)\n",
       "Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from requests->blackmarblepy==0.1.0) (2.0.4)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/robmarty/opt/anaconda3/lib/python3.8/site-packages (from requests->blackmarblepy==0.1.0) (1.26.8)\n",
       "Building wheels for collected packages: blackmarblepy\n",
       "  Building wheel for blackmarblepy (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for blackmarblepy: filename=blackmarblepy-0.1.0-py3-none-any.whl size=15026 sha256=ccda0ca35fa38c50d7adcd0bb2586ae1516f44fc1ff08e41f250fffa5bae8a4e\n",
-      "  Stored in directory: /private/var/folders/m1/8h14xfm56hd6qfgz6btm1rd80000gn/T/pip-ephem-wheel-cache-zvfqv8yd/wheels/2d/74/2a/3f0e00273b80606a21c3dcb8689e2ca07c529a0802ea86bbd3\n",
+      "\u001b[?25h  Created wheel for blackmarblepy: filename=blackmarblepy-0.1.0-py3-none-any.whl size=18932 sha256=10119b412e82b6dd1e60a034e8abfffcc48f6a47e7bd1bcf091c0c1dc2847b60\n",
+      "  Stored in directory: /private/var/folders/m1/8h14xfm56hd6qfgz6btm1rd80000gn/T/pip-ephem-wheel-cache-ku82p4zv/wheels/f1/75/0f/f52b2d3851627c922c5fe6761603a0378ce24e1d61cf883ae6\n",
       "Successfully built blackmarblepy\n",
       "Installing collected packages: blackmarblepy\n",
       "Successfully installed blackmarblepy-0.1.0\n"
@@ -112,7 +118,9 @@
     }
    ],
    "source": [
-    "! pip install git+https://github.com/ramarty/blackmarblepy.git"
+    "#! pip install git+https://github.com/worldbank/blackmarblepy.git\n",
+    "\n",
+    "! pip install git+https://github.com/worldbank/blackmarblepy.git@rob-documentation"
    ]
   },
   {
@@ -206,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "b7014ecc-753d-412a-a123-c7fe84569856",
    "metadata": {},
    "outputs": [
@@ -214,10 +222,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading: VNP46A4.A2021001.h17v07.001.2022094115525.h5\n",
-      "Downloading: VNP46A4.A2021001.h17v08.001.2022094115514.h5\n",
-      "Downloading: VNP46A4.A2021001.h18v07.001.2022094115526.h5\n",
-      "Downloading: VNP46A4.A2021001.h18v08.001.2022094115509.h5\n"
+      "Downloading 1/4: VNP46A4.A2021001.h17v07.001.2022094115525.h5\n",
+      "Downloading 2/4: VNP46A4.A2021001.h17v08.001.2022094115514.h5\n",
+      "Downloading 3/4: VNP46A4.A2021001.h18v07.001.2022094115526.h5\n",
+      "Downloading 4/4: VNP46A4.A2021001.h18v08.001.2022094115509.h5\n"
      ]
     }
    ],
@@ -239,10 +247,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading: VNP46A3.A2021274.h17v07.001.2021321132719.h5\n",
-      "Downloading: VNP46A3.A2021274.h17v08.001.2021321132826.h5\n",
-      "Downloading: VNP46A3.A2021274.h18v07.001.2021321132715.h5\n",
-      "Downloading: VNP46A3.A2021274.h18v08.001.2021321132727.h5\n"
+      "Downloading 1/4: VNP46A3.A2021274.h17v07.001.2021321132719.h5\n",
+      "Downloading 2/4: VNP46A3.A2021274.h17v08.001.2021321132826.h5\n",
+      "Downloading 3/4: VNP46A3.A2021274.h18v07.001.2021321132715.h5\n",
+      "Downloading 4/4: VNP46A3.A2021274.h18v08.001.2021321132727.h5\n"
      ]
     }
    ],
diff --git a/examples/figures_for_readme.ipynb b/examples/figures_for_readme.ipynb
index 29e3f28..769b39a 100644
--- a/examples/figures_for_readme.ipynb
+++ b/examples/figures_for_readme.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "193d6da7-7b63-4e45-bb95-0b23ec283664",
    "metadata": {},
    "outputs": [],
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "643883c1-9f5f-420e-a4e8-41b0f15434fb",
    "metadata": {},
    "outputs": [
@@ -42,10 +42,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Downloading: VNP46A4.A2022001.h17v07.001.2023081124022.h5\n",
-      "Downloading: VNP46A4.A2022001.h17v08.001.2023081124059.h5\n",
-      "Downloading: VNP46A4.A2022001.h18v07.001.2023081223927.h5\n",
-      "Downloading: VNP46A4.A2022001.h18v08.001.2023082112122.h5\n"
+      "Downloading 1/4: VNP46A4.A2022001.h17v07.001.2023081124022.h5\n",
+      "Downloading 2/4: VNP46A4.A2022001.h17v08.001.2023081124059.h5\n",
+      "Downloading 3/4: VNP46A4.A2022001.h18v07.001.2023081223927.h5\n",
+      "Downloading 4/4: VNP46A4.A2022001.h18v08.001.2023082112122.h5\n"
      ]
     }
    ],
@@ -59,13 +59,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 3,
    "id": "0d3be386-4630-4a11-9663-190fdc024ddf",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -83,7 +83,7 @@
     "plt.imshow(r_np, cmap='hot')\n",
     "plt.tight_layout()\n",
     "plt.axis(\"off\")\n",
-    "plt.savefig(\"../raster_example.png\", dpi = 500, bbox_inches='tight')"
+    "plt.savefig(\"../docs/raster_example.png\", dpi = 500, bbox_inches='tight')"
    ]
   },
   {
@@ -179,7 +179,7 @@
     "\n",
     "# Show the plot\n",
     "#plt.show()\n",
-    "plt.savefig(\"../trends_example.png\", dpi = 500, bbox_inches='tight')"
+    "plt.savefig(\"../docs/trends_example.png\", dpi = 500, bbox_inches='tight')"
    ]
   },
   {
diff --git a/examples/make_main_photo.ipynb b/examples/make_main_photo.ipynb
index e68e523..df63037 100644
--- a/examples/make_main_photo.ipynb
+++ b/examples/make_main_photo.ipynb
@@ -490,7 +490,7 @@
     "plt.imshow(r_np, cmap='hot')\n",
     "plt.tight_layout()\n",
     "plt.axis(\"off\")\n",
-    "plt.savefig(\"../ntl_usa.png\", dpi = 500, bbox_inches='tight')"
+    "plt.savefig(\"../docs/ntl_usa.png\", dpi = 500, bbox_inches='tight')"
    ]
   },
   {
@@ -509,41 +509,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9637ac5-6b75-4ce5-9b16-6b6c2e5b30cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#! pip install cartopy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17d65431-c18d-4fdd-8ba8-8b6293b36b9e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.io.shapereader as shpreader\n",
-    "ne_earth_countries = shpreader.natural_earth(resolution = '50m',\n",
-    "                                       category = 'cultural',\n",
-    "                                       name='admin_0_countries')\n",
-    "countries = shpreader.Reader(ne_earth_countries).records()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e16a2b90-929f-4836-ba96-a4789f6d7712",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# https://geopandas.org/en/stable/gallery/cartopy_convert.html\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/blackmarble/bm_extract.py b/src/blackmarble/bm_extract.py
index 21fd64b..a28d606 100644
--- a/src/blackmarble/bm_extract.py
+++ b/src/blackmarble/bm_extract.py
@@ -10,6 +10,7 @@
 import subprocess
 import glob
 import shutil
+import httpx
 from itertools import product
 import geopandas as gpd
 from rasterstats import zonal_stats
diff --git a/src/blackmarble/bm_raster.py b/src/blackmarble/bm_raster.py
index a6852de..991f266 100644
--- a/src/blackmarble/bm_raster.py
+++ b/src/blackmarble/bm_raster.py
@@ -10,6 +10,7 @@
 import subprocess
 import glob
 import shutil
+import httpx
 from itertools import product
 import geopandas as gpd
 from rasterstats import zonal_stats
diff --git a/src/blackmarble/utils.py b/src/blackmarble/utils.py
index a6ce79e..7a068c3 100644
--- a/src/blackmarble/utils.py
+++ b/src/blackmarble/utils.py
@@ -10,6 +10,7 @@
 import subprocess
 import glob
 import shutil
+import httpx
 from itertools import product
 import geopandas as gpd
 from rasterstats import zonal_stats
@@ -380,18 +381,30 @@ def download_raster(
     day = file_name[13:16]
     product_id = file_name[0:7]
 
-    f = os.path.join(temp_dir, product_id, year, day, file_name)
+    #f = os.path.join(temp_dir, product_id, year, day, file_name)
+    f = os.path.join(temp_dir, file_name)
 
     # Download
     if quiet == False:
         print("Downloading " + str(tile_i) + "/" + str(n_tile) + ": " + file_name)
 
-    wget_command = f"/usr/local/bin/wget -e robots=off -m -np .html,.tmp -nH --cut-dirs=3 'https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5000/{product_id}/{year}/{day}/{file_name}' --header 'Authorization: Bearer {bearer}' -P {temp_dir}/"
-    # print(wget_command)
-    # subprocess.run(wget_command, shell=True)
-    subprocess.run(
-        wget_command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
-    )
+    #wget_command = f"/usr/local/bin/wget -e robots=off -m -np .html,.tmp -nH --cut-dirs=3 'https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5000/{product_id}/{year}/{day}/{file_name}' --header 'Authorization: Bearer {bearer}' -P {temp_dir}/"
+    #print(wget_command)
+    #subprocess.run(wget_command, shell=True)
+    #subprocess.run(wget_command, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+
+    url = f'https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/5000/{product_id}/{year}/{day}/{file_name}'
+    headers = {'Authorization': f'Bearer {bearer}'}
+    download_path = os.path.join(temp_dir, file_name)
+
+    with httpx.stream('GET', url, headers=headers) as response:
+        if response.status_code == 200:
+            with open(download_path, 'wb') as file:
+                for chunk in response.iter_bytes(chunk_size=8192):
+                    file.write(chunk)
+            #print(f"Downloaded {file_name} to {download_path}")
+        else:
+            print(f"Failed to download {file_name}. Status code: {response.status_code}")
 
     # Convert to raster
     file_name_tif = re.sub(".h5", ".tif", file_name)
@@ -486,6 +499,20 @@ def bm_extract_i(
 
     return poly_ntl_df
 
+def bm_raster_i(roi_sf,
+                product_id,
+                date,
+                bearer,
+                variable,
+                quality_flag_rm,
+                check_all_tiles_exist,
+                quiet,
+                temp_dir):
+
+    #### Prep files to download
+
+    # Black marble grid: TODO: Add to python repo
+    bm_tiles_sf = gpd.read_file("https://raw.githubusercontent.com/worldbank/blackmarbler/main/data/blackmarbletiles.geojson")
 
 def bm_raster_i(
     roi_sf,