update markdown and path

docs/partial-inputs-flood-tutorial.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Monsoon flood analysis using embeddings from partial inputs\n",
     "This notebook contains a complete example for how to run Clay. It\n",
-    "combines the following three different aspects\n",
+    "combines the following three different aspects:\n",
     "\n",
     "1. Create single-chip datacubes with time series data for a location and a date range\n",
     "2. Run the model with partial inputs, in this case RGB + NIR + SWIR\n",
@@ -77,7 +77,7 @@
    "metadata": {},
    "source": [
     "## Search for imagery over an area of interest\n",
-    "In this example we use a location and date range to visualize a major monsoon flood that happened in [Padidan, Pakistan in 2022](https://floodlist.com/asia/pakistan-monsoon-floods-august-2022)"
+    "In this example we use a location and date range to visualize a major monsoon flood that happened in [Padidan, Pakistan in 2022](https://floodlist.com/asia/pakistan-monsoon-floods-august-2022)."
    ]
   },
   {
@@ -123,7 +123,7 @@
    "metadata": {},
    "source": [
     "## Download the data\n",
-    "Get the data into a numpy array and visualize the imagery. The flood is visible in the last seven images. Note: SWIR is very helpful for flood mapping (ref: [Satellite flood detection integrating hydrogeomorphic and spectral indices](https://www.tandfonline.com/doi/full/10.1080/15481603.2022.2143670#:~:text=Methods%20used%20to%20detect%20water,Near%20Infrared%20(NIR)%20wavelengths.))"
+    "Get the data into a numpy array and visualize the imagery. The flood is visible in the last seven images. Note: SWIR is very helpful for flood mapping (ref: [Satellite flood detection integrating hydrogeomorphic and spectral indices](https://www.tandfonline.com/doi/full/10.1080/15481603.2022.2143670#:~:text=Methods%20used%20to%20detect%20water,Near%20Infrared%20(NIR)%20wavelengths.))."
    ]
   },
   {
@@ -287,7 +287,7 @@
    "id": "40a687f7-1558-4261-925a-74b3c524e308",
    "metadata": {},
    "source": [
-    "### Plot the first short-wave infrared band\n",
+    "### Plot the second short-wave infrared band\n",
     "Notice the same significant signal starting on August 26th (first image after the major flood), again appearing more strongly on the next date as August 26th was fairly cloudy."
    ]
   },
@@ -403,7 +403,7 @@
     }
    ],
    "source": [
-    "DATA_DIR = \"/home/ubuntu/data/minicubes_fl_pk\"\n",
+    "DATA_DIR = \"/home/ubuntu/data/minicubes\"\n",
     "CKPT_PATH = \"https://huggingface.co/made-with-clay/Clay1/resolve/main/Clay_v0.1_epoch-24_val-loss-0.46.ckpt\"\n",
     "\n",
     "# Load model\n",
@@ -451,7 +451,7 @@
    "id": "db3f3e5e-8668-4830-9c77-cc1d8cb35234",
    "metadata": {},
    "source": [
-    "### Create the embeddings for the images over the forest fire\n",
+    "### Create the embeddings for the images over the flood event\n",
     "This will loop through the images returned by the data loader\n",
     "and evaluate the model for each one of the images. The raw\n",
     "embeddings are reduced to mean values to simplify the data."