DOC: restruct tut a bit; grid ex; finish sentence

fedarko · Sep 2, 2023 · a030218 · a030218
1 parent e455e46
commit a030218
Showing 1 changed file with 52 additions and 4 deletions.
diff --git a/docs/Tutorial.ipynb b/docs/Tutorial.ipynb
@@ -675,7 +675,9 @@
    "id": "39af47f4",
    "metadata": {},
    "source": [
-    "# 3. Tiling multiple dot plots\n",
+    "# 3. Other fancy visualization options\n",
+    "\n",
+    "## 3.1. Tiling multiple dot plots\n",
     "\n",
     "One of the main reasons I wrote this library was so that I could create figures containing grids of many dot plots using matplotlib. wotplot makes this process fairly painless!\n",
     "\n",
@@ -721,7 +723,7 @@
    "id": "123d9ec4",
    "metadata": {},
    "source": [
-    "# 4. Passing arbitrary keyword arguments to `imshow()` / `spy()`\n",
+    "## 3.2. Passing arbitrary keyword arguments to `imshow()` / `spy()`\n",
     "\n",
     "These functions have a lot of options available (see the matplotlib docs for [`imshow()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.imshow.html) and [`spy()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.spy.html)), and we can make use of these options without too much effort:"
    ]
@@ -795,12 +797,57 @@
     "wotplot.viz_spy(m, markersize=13, marker=\"o\", alpha=0.3)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4d426aa3",
+   "metadata": {},
+   "source": [
+    "## 3.3. Drawing a grid\n",
+    "Let's show this by reusing the above example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ad05eb6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = wotplot.viz_spy(m, markersize=13, marker=\"o\", alpha=0.3)\n",
+    "# In order to draw a grid, matplotlib needs ticks. Adding properly labelled ticks\n",
+    "# to these plots can be a bit tricky since we need to account for (1) the y-axis'\n",
+    "# direction and (2) zero-indexing, so here we just add some unlabelled ticks and\n",
+    "# call it a day.\n",
+    "#\n",
+    "# The ticks' positions will look like [-0.5, 0.5, 1.5, ..., 13.5, 14.5, 15.5]. We\n",
+    "# create them in this way because the cells of the matrix occur at integer coordinates.\n",
+    "ax.set_xticks([t - 0.5 for t in range(0, 17)])\n",
+    "ax.set_yticks([t - 0.5 for t in range(0, 17)])\n",
+    "# I don't like the little tick lines protruding from the plot, so this hides them:\n",
+    "# from https://stackoverflow.com/a/29988431\n",
+    "ax.tick_params(length=0)\n",
+    "ax.grid()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "04bd6970",
    "metadata": {},
    "source": [
-    "# 5. Creating dot plots of longer sequences\n",
+    "# 4. Creating dot plots of longer sequences\n",
     "\n",
     "As a final example, let's create a dot plot of two _E. coli_ strains' genomes. We'll use _E. coli_ K-12 [(from this assembly)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000005845.2/) and _E. coli_ O157:H7 [(from this assembly)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000008865.2/).\n",
     "\n",
@@ -830,7 +877,8 @@
    "outputs": [],
    "source": [
     "# Extract the sequences from these pyfaidx.Fasta objects. (Both only contain\n",
-    "# one sequence / \"record\")\n",
+    "# one sequence / \"record\", hence the \"list(e1.records)[0]\" operations -- it's\n",
+    "# a way of saying \"give me the name of the only sequence in this FASTA file.\")\n",
     "e1s = str(e1[list(e1.records)[0]])\n",
     "e2s = str(e2[list(e2.records)[0]])"
    ]