Merge pull request #20 from Social-Evolution-and-Behavior/docedits

small changes to make deep dive docs more cohesive

Author: neurorishika

docs/deep_dive/advanced_queries.ipynb

@@ -9,10 +9,10 @@
     "\n",
     "This tutorial explores the powerful and flexible querying capabilities of CRANTpy. We'll go beyond simple filters and dive into:\n",
     "\n",
-    "1.  **Complex, multi-parameter queries**\n",
-    "2.  **Using regular expressions** for flexible matching\n",
-    "3.  **Inspecting available data** to build your queries\n",
-    "4.  **Analyzing populations** of neurons returned from queries\n",
+    "*  **Complex, multi-parameter queries**\n",
+    "*  **Using regular expressions** for flexible matching\n",
+    "*  **Inspecting available data** to build your queries\n",
+    "*  **Analyzing populations** of neurons returned from queries\n",
     "\n",
     "Let's get started by setting up our environment."
    ]

docs/deep_dive/connectivity.ipynb

@@ -6,7 +6,12 @@
    "metadata": {},
    "source": [
     "# Deep Dive: Analyzing Connectivity\n",
-    "This tutorial will guide you through the process of querying and analyzing connectivity data using the CRANTpy package. We will cover how to retrieve synapse data, filter it, and visualize the results."
+    "This tutorial will guide you through the process of querying and analyzing connectivity data using the CRANTpy package. We will cover \n",
+    "* how to **retrieve synapse data**\n",
+    "* **filter synapse data**, and \n",
+    "* **visualize** the results.\n",
+    "\n",
+    "Let's get started by setting up our environment."
    ]
   },
   {

docs/deep_dive/morphology.ipynb

@@ -9,15 +9,15 @@
     "\n",
     "This comprehensive tutorial explores the morphological analysis capabilities of CRANTpy. We'll cover:\n",
     "\n",
-    "1. **Mesh representations** - Fetching and analyzing neuron meshes\n",
-    "2. **Skeleton representations** - Multiple skeletonization methods\n",
-    "3. **L2 graph-based skeletons** - High-quality morphology from L2 chunks\n",
-    "4. **Dotprops** - Point cloud representations for fast analysis\n",
-    "5. **Morphometric analysis** - Cable length, surface area, volume, branching\n",
-    "6. **Soma detection** - Automated soma identification\n",
-    "7. **Visualization** - 2D and 3D visualization techniques\n",
-    "8. **Population analysis** - Comparing morphology across neuron groups\n",
-    "9. **Navis integration** - Leveraging navis for advanced analysis\n",
+    "* **Mesh representations** - Fetching and analyzing neuron meshes\n",
+    "* **Skeleton representations** - Multiple skeletonization methods\n",
+    "* **L2 graph-based skeletons** - High-quality morphology from L2 chunks\n",
+    "* **Dotprops** - Point cloud representations for fast analysis\n",
+    "* **Morphometric analysis** - Cable length, surface area, volume, branching\n",
+    "* **Soma detection** - Automated soma identification\n",
+    "* **Visualization** - 2D and 3D visualization techniques\n",
+    "* **Population analysis** - Comparing morphology across neuron groups\n",
+    "* **Navis integration** - Leveraging navis for advanced analysis\n",
     "\n",
     "Let's start by setting up our environment."
    ]

docs/deep_dive/ngllink.ipynb

@@ -9,16 +9,18 @@
     "\n",
     "This comprehensive tutorial will guide you through creating, manipulating, and sharing neuroglancer visualizations for CRANT neurons. We'll cover:\n",
     "\n",
-    "1. Basic scene creation and URL generation\n",
-    "2. Adding and coloring neurons\n",
-    "3. Working with annotations (points, lines, ellipsoids)\n",
-    "4. Adding neuron skeletons\n",
-    "5. Grouping and organizing neurons\n",
-    "6. Scene customization (layouts, layers, coordinates)\n",
-    "7. URL shortening and sharing\n",
-    "8. Integration with NeuronCriteria queries\n",
-    "9. Advanced connectivity visualization\n",
-    "10. Tips and best practices"
+    "* Basic scene creation and URL generation\n",
+    "* Adding and coloring neurons\n",
+    "* Working with annotations (points, lines, ellipsoids)\n",
+    "* Adding neuron skeletons\n",
+    "* Grouping and organizing neurons\n",
+    "* Scene customization (layouts, layers, coordinates)\n",
+    "* URL shortening and sharing\n",
+    "* Integration with NeuronCriteria queries\n",
+    "* Advanced connectivity visualization\n",
+    "* Tips and best practices\n",
+    "\n",
+    "Let's get started by setting up our environment."
    ]
   },
   {

docs/deep_dive/segmentation.ipynb

@@ -9,13 +9,13 @@
     "\n",
     "This comprehensive tutorial explores the powerful segmentation capabilities of CRANTpy using CAVE. We'll cover:\n",
     "\n",
-    "1. **Root and supervoxel conversions** - Working with different segmentation levels\n",
-    "2. **Location-based queries** - Finding segmentation at specific coordinates\n",
-    "3. **ID updates and validation** - Keeping root IDs current\n",
-    "4. **Neuron mapping** - Connecting spatial data to segmentation\n",
-    "5. **Voxel operations** - Working with voxel-level data\n",
-    "6. **Temporal analysis** - Understanding segmentation history\n",
-    "7. **Spatial corrections** - Snapping coordinates to segmentation\n",
+    "* **Root and supervoxel conversions** - Working with different segmentation levels\n",
+    "* **Location-based queries** - Finding segmentation at specific coordinates\n",
+    "* **ID updates and validation** - Keeping root IDs current\n",
+    "* **Neuron mapping** - Connecting spatial data to segmentation\n",
+    "* **Voxel operations** - Working with voxel-level data\n",
+    "* **Temporal analysis** - Understanding segmentation history\n",
+    "* **Spatial corrections** - Snapping coordinates to segmentation\n",
     "\n",
     "These tools are essential for working with dynamic segmentation data in CAVE/ChunkedGraph systems.\n",
     "\n",