allow custom colormaps

wiheto · Nov 30, 2022 · 708826b · 708826b
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diff --git a/docs/gallery.md b/docs/gallery.md
@@ -16,7 +16,11 @@
 
 [![](./gallery/figures/specifying_node_color.png)](./specifying_node_color/)
 
-### [Dual row figures](.//two_rows/)
+### [Specifying colormaps](./node_cmap/)
+
+[![](./gallery/figures/node_cmap.png)](./node_cmap/)
+
+### [Dual row figures](./two_rows/)
 
 [![](./gallery/figures/rows1.png)](./two_rows/)
 

diff --git a/docs/gallery/jupytext.toml → docs/gallery/.jupytext.toml b/docs/gallery/jupytext.toml → docs/gallery/.jupytext.toml
diff --git a/docs/gallery/figures/node_cmap.png b/docs/gallery/figures/node_cmap.png
diff --git a/docs/gallery/figures/specifying_node_color.png b/docs/gallery/figures/specifying_node_color.png
diff --git a/docs/gallery/highlevel_size.py b/docs/gallery/highlevel_size.py
@@ -1,19 +1,16 @@
-# %% [markdown]
-""" 
-# Node properties: size
-
-Netplotbrain allows for specifying different figure properties by only specifyign the column name.
-
-Below we demonstrate how node_size can change based on different columns in the nodes dataframe.
-
-The key lines of code here are when:
-
-`node_size='centrality_measure1'`
-
-and
-
-`node_size='centrality_measure2'`
-
-These columns could be called anything such as "module_degree_zscore" or "efficiency"
-
-"""
+#  
+# # Node properties: size
+#
+# Netplotbrain allows for specifying different figure properties by only specifyign the column name.
+#
+# Below we demonstrate how node_size can change based on different columns in the nodes dataframe.
+#
+# The key lines of code here are when:
+#
+# `node_size='centrality_measure1'`
+#
+# and
+#
+# `node_size='centrality_measure2'`
+#
+# These columns could be called anything such as "module_degree_zscore" or "efficiency"
diff --git a/docs/gallery/infant.ipynb b/docs/gallery/infant.ipynb
diff --git a/docs/gallery/nbs.ipynb b/docs/gallery/nbs.ipynb
diff --git a/docs/gallery/node_cmap.ipynb b/docs/gallery/node_cmap.ipynb
diff --git a/docs/gallery/node_cmap.py b/docs/gallery/node_cmap.py
@@ -0,0 +1,54 @@
+#  
+# # Specifying node colormaps
+#
+# [Open interactive notebook in Binder](https://mybinder.org/v2/gh/wiheto/netplotbrain/main?filepath=docs/gallery/node_cmap.ipynb)
+#
+# See [example about colors for different way the color columns in the dataframe can be specified](https://www.netplotbrain.org/gallery/specifying_node_color/) 
+#
+# There are two different ways to specify colormaps. This holds for both nodes (node_cmap) edges (edge_cmap)
+# 
+# The first way entails that you specify a matplotlib colormap (e.g. Set1, Inferno, (see options [here](https://matplotlib.org/stable/tutorials/colors/colormaps.html)) 
+# 
+# The second way is to provide a list of matplotlib colors to create your own colormap (see named color options [here](https://matplotlib.org/stable/gallery/color/named_colors.html))
+#
+# This notebook will shows an example of each of these. We will use 100 ROIs from the Schaefer atlas and color the 7 Yeo networks
+
+# Import necessary packages 
+import netplotbrain
+import pandas as pd
+import templateflow.api as tf
+
+# Load templateflow information about the Schaefer atlas
+atlasinfo = tf.get(template='MNI152NLin2009cAsym',
+                   atlas='Schaefer2018',
+                   desc='100Parcels7Networks',
+                   extension='.tsv')
+atlas_df = pd.read_csv(str(atlasinfo), sep='\t')
+atlas_df.head()
+
+# Create a column called "network" by extracting the relevant information from the column "name"
+atlas_df['network'] = list(map(lambda x: x.split('_')[2], atlas_df.name.values))
+atlas_df.head()
+
+# Define the TemplateFlow parcels that we want to plot 
+nodes = {'template': 'MNI152NLin2009cAsym',
+         'atlas': 'Schaefer2018',
+         'desc': '100Parcels7Networks',
+         'resolution': 1}
+
+# Plot with a the Set2 colormap
+netplotbrain.plot(nodes=nodes, 
+                  node_type='parcels', 
+                  nodes_df=atlas_df, 
+                  node_color='network', 
+                  node_cmap='Set2')
+
+# Create custom colormap
+node_cmap = ['blue', 'red', 'green', 'black', 'purple', 'yellow', 'orange']
+
+# Plot with a the custom colormap
+netplotbrain.plot(nodes=nodes, 
+                  node_type='parcels', 
+                  nodes_df=atlas_df, 
+                  node_color='network', 
+                  node_cmap=node_cmap)
diff --git a/docs/gallery/node_styles.ipynb b/docs/gallery/node_styles.ipynb
diff --git a/docs/gallery/simple.ipynb b/docs/gallery/simple.ipynb
diff --git a/docs/gallery/specifying_node_color.ipynb b/docs/gallery/specifying_node_color.ipynb
@@ -2,33 +2,35 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "00714681",
+   "id": "4d96e067",
    "metadata": {},
    "source": [
     " \n",
     "# Specifying node colours\n",
     "\n",
-    "[Open interactive notebook in Binder](https://mybinder.org/v2/gh/wiheto/netplotbrain/main?filepath=docs/gallery/node_colors.ipynb)\n",
+    "[Open interactive notebook in Binder](https://mybinder.org/v2/gh/wiheto/netplotbrain/main?filepath=docs/gallery/specifying_node_color.ipynb)\n",
     "\n",
     "There are three different ways to specify different colours of nodes.\n",
     "Both are done by setting node_color keyword argument.\n",
     "If this column consists of category data, different categories will be plotted.\n",
     "If this column consists of continuous data, a colour spectrum will be plotted. \n",
     "If this column consists of differernt colours, these colours will be plotted. \n",
     "\n",
-    "In this notebook we will see an example of each of the three different ways"
+    "In this notebook we will see an example of each of the three different ways.\n",
+    "\n",
+    "See [example about cmap for specifying colormaps](https://www.netplotbrain.org/gallery/node_cmap/) "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "55baafb7",
+   "id": "b666792b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-11-28T14:48:22.838614Z",
-     "iopub.status.busy": "2022-11-28T14:48:22.837492Z",
-     "iopub.status.idle": "2022-11-28T14:48:25.394354Z",
-     "shell.execute_reply": "2022-11-28T14:48:25.393942Z"
+     "iopub.execute_input": "2022-11-30T20:32:31.379155Z",
+     "iopub.status.busy": "2022-11-30T20:32:31.378760Z",
+     "iopub.status.idle": "2022-11-30T20:32:34.823308Z",
+     "shell.execute_reply": "2022-11-30T20:32:34.822830Z"
     }
    },
    "outputs": [],
@@ -44,13 +46,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "352bbd25",
+   "id": "2b297af0",
    "metadata": {
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-     "iopub.status.idle": "2022-11-28T14:48:25.415233Z",
-     "shell.execute_reply": "2022-11-28T14:48:25.414844Z"
+     "iopub.execute_input": "2022-11-30T20:32:34.826450Z",
+     "iopub.status.busy": "2022-11-30T20:32:34.825883Z",
+     "iopub.status.idle": "2022-11-30T20:32:34.865359Z",
+     "shell.execute_reply": "2022-11-30T20:32:34.864879Z"
     },
     "lines_to_next_cell": 2
    },
@@ -143,13 +145,13 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "96b553e9",
+   "id": "13682bb7",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2022-11-28T14:48:25.416884Z",
-     "iopub.status.idle": "2022-11-28T14:48:25.419734Z",
-     "shell.execute_reply": "2022-11-28T14:48:25.419313Z"
+     "iopub.execute_input": "2022-11-30T20:32:34.868467Z",
+     "iopub.status.busy": "2022-11-30T20:32:34.867934Z",
+     "iopub.status.idle": "2022-11-30T20:32:34.872783Z",
+     "shell.execute_reply": "2022-11-30T20:32:34.872076Z"
     }
    },
    "outputs": [],
@@ -162,13 +164,13 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e6418347",
+   "id": "676456dc",
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2022-11-28T14:48:25.421198Z",
-     "iopub.status.idle": "2022-11-28T14:48:25.423519Z",
-     "shell.execute_reply": "2022-11-28T14:48:25.423254Z"
+     "iopub.execute_input": "2022-11-30T20:32:34.876287Z",
+     "iopub.status.busy": "2022-11-30T20:32:34.875759Z",
+     "iopub.status.idle": "2022-11-30T20:32:34.879829Z",
+     "shell.execute_reply": "2022-11-30T20:32:34.879037Z"
     }
    },
    "outputs": [],
@@ -180,13 +182,118 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "67dbea75",
+   "id": "1724e757",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-11-30T20:32:34.886455Z",
+     "iopub.status.busy": "2022-11-30T20:32:34.886091Z",
+     "iopub.status.idle": "2022-11-30T20:32:34.896063Z",
+     "shell.execute_reply": "2022-11-30T20:32:34.895530Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>name</th>\n",
+       "      <th>color</th>\n",
+       "      <th>network</th>\n",
+       "      <th>centrality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7Networks_LH_Vis_1</td>\n",
+       "      <td>#781283</td>\n",
+       "      <td>Vis</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7Networks_LH_Vis_2</td>\n",
+       "      <td>#781284</td>\n",
+       "      <td>Vis</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>7Networks_LH_Vis_3</td>\n",
+       "      <td>#781285</td>\n",
+       "      <td>Vis</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>7Networks_LH_Vis_4</td>\n",
+       "      <td>#781287</td>\n",
+       "      <td>Vis</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>7Networks_LH_Vis_5</td>\n",
+       "      <td>#781288</td>\n",
+       "      <td>Vis</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index                name    color network  centrality\n",
+       "0      1  7Networks_LH_Vis_1  #781283     Vis           0\n",
+       "1      2  7Networks_LH_Vis_2  #781284     Vis           1\n",
+       "2      3  7Networks_LH_Vis_3  #781285     Vis           2\n",
+       "3      4  7Networks_LH_Vis_4  #781287     Vis           3\n",
+       "4      5  7Networks_LH_Vis_5  #781288     Vis           4"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets have a look at the data frame now with three different columns for colours\n",
+    "atlas_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "09195103",
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2022-11-28T14:48:25.426827Z"
+     "iopub.execute_input": "2022-11-30T20:32:34.899668Z",
+     "iopub.status.busy": "2022-11-30T20:32:34.899408Z",
+     "iopub.status.idle": "2022-11-30T20:32:34.902870Z",
+     "shell.execute_reply": "2022-11-30T20:32:34.902347Z"
     }
    },
    "outputs": [],
@@ -200,14 +307,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "472a8989",
+   "execution_count": 7,
+   "id": "ffab46b1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2022-11-28T14:48:25.428727Z",
-     "iopub.status.busy": "2022-11-28T14:48:25.428595Z",
-     "iopub.status.idle": "2022-11-28T14:50:01.581763Z",
-     "shell.execute_reply": "2022-11-28T14:50:01.581317Z"
+     "iopub.execute_input": "2022-11-30T20:32:34.905177Z",
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+     "iopub.status.idle": "2022-11-30T20:34:06.148065Z",
+     "shell.execute_reply": "2022-11-30T20:34:06.147715Z"
     }
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    "outputs": [

diff --git a/docs/gallery/specifying_node_color.py b/docs/gallery/specifying_node_color.py
@@ -1,15 +1,17 @@
 #  
 # # Specifying node colours
 #
-# [Open interactive notebook in Binder](https://mybinder.org/v2/gh/wiheto/netplotbrain/main?filepath=docs/gallery/node_colors.ipynb)
+# [Open interactive notebook in Binder](https://mybinder.org/v2/gh/wiheto/netplotbrain/main?filepath=docs/gallery/specifying_node_color.ipynb)
 #
 # There are three different ways to specify different colours of nodes.
 # Both are done by setting node_color keyword argument.
 # If this column consists of category data, different categories will be plotted.
 # If this column consists of continuous data, a colour spectrum will be plotted. 
 # If this column consists of differernt colours, these colours will be plotted. 
 #
-# In this notebook we will see an example of each of the three different ways
+# In this notebook we will see an example of each of the three different ways.
+#
+# See [example about cmap for specifying colormaps](https://www.netplotbrain.org/gallery/node_cmap/) 
 
 # Import packages
 import templateflow.api as tf
@@ -34,6 +36,9 @@
 # Add a continuous variable between 0-10 
 atlas_df['centrality'] = np.tile(np.arange(0,10), 10)
 
+# Lets have a look at the data frame now with three different columns for colours
+atlas_df.head()
+
 # define the nodes as templateflow dict
 nodes = {'template': 'MNI152NLin2009cAsym',
          'atlas': 'Schaefer2018',