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DOC: binary=False / vals updates in tutorial
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‎docs/Tutorial.ipynb‎

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@@ -292,7 +292,7 @@
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"id": "7d808534",
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"metadata": {},
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"source": [
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"In the above matrix, `0` values represent cells where there aren't any forward or reverse-complement $k$-mer matches, and `1` values represent cells where there is at least one such match.\n",
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"In the above matrix: `0` values represent cells where there aren't any forward or reverse-complement $k$-mer matches, and `1` values represent cells where there is at least one such match.\n",
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"\n",
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"For more details about these values, you can run `help(wotplot.DotPlotMatrix)`."
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]
@@ -440,18 +440,86 @@
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"id": "ec251ecc",
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"metadata": {},
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"source": [
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"# 2. Visualizing matrices in color\n",
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"# 2. Visualizing matrices using multiple colors of matches\n",
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"\n",
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"If we set `binary=False` when creating a `DotPlotMatrix` object, then wotplot will distinguish between forward, reverse-complementary, and palindromic matches. This can be useful for certain analyses; also, we can color these matches differently in the visualization! `viz_imshow()` will automatically do this for you.\n",
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"If we set `binary=False` when creating a `DotPlotMatrix` object, then wotplot will distinguish between forward, reverse-complementary, and palindromic matches. This can be useful for certain analyses; also, we can color these matches differently in the visualization!\n",
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"\n",
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"## 2.1. Using `binary=False` with the same dataset as above\n",
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"\n",
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"The default colormap used colors non-match cells white, forward match cells red, reverse-complementary match cells blue, and palindromic match cells purple. This is based on Figure 6.20 in Chapter 6 of _Bioinformatics Algorithms_ (Compeau & Pevzner), edition 2."
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"## 2.1. Using `binary=False` with the same dataset as above"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "dd5c9aa8",
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"metadata": {},
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"outputs": [],
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"source": [
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"n = wotplot.DotPlotMatrix(s1, s2, k, binary=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9f3f163a",
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"metadata": {},
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"source": [
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"If we convert `n`'s matrix to a dense format, we see that now it doesn't just contain `0` and `1` values:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "b830209a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
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" [ 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
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" [ 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0],\n",
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" [ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0],\n",
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" [ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0],\n",
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" [ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"n.mat.toarray()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aad2225c",
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"metadata": {},
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"source": [
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"In this not-binary matrix:\n",
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"\n",
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"- `1` values indicate forward matches,\n",
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"- `-1` values indicate reverse-complementary matches, and\n",
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"- `2` values indicate palindromic matches (there aren't any of those in this example).\n",
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"\n",
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"For reference, these values actually correspond to constants in wotplot's package (`wotplot.FWD`, `wotplot.REV`, and `wotplot.BOTH`, respectively; for binary matrices, `1` values correspond to `wotplot.MATCH`). If you're writing code that works with these matrices, I suggest using these constants instead of `1`, `-1`, etc.; this might improve readability a bit. (But it's not a huge deal.)\n",
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"\n",
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"Okay, now let's draw this matrix! `viz_imshow()` will automatically notice that this matrix was created using `binary=False`, and will draw it in color."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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}
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],
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"source": [
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"n = wotplot.DotPlotMatrix(s1, s2, k, binary=False)\n",
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"fig, ax = wotplot.viz_imshow(n)\n",
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"# Save the visualization to a file -- we can do this using the fig object returned by\n",
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"# viz_imshow() or viz_spy() \n",
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"# Since we include it in the README, we'l save this drawing to a file.\n",
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"# We can do this using the fig object returned by viz_imshow() (or by viz_spy()).\n",
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"fig.savefig(os.path.join(\"img\", \"small_example_dotplot.png\"), **savefig_kwargs)"
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"source": [
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"Note that `viz_spy()` can only use one color for all match cells (by default this is set to black), so visualizing a `binary=False` matrix with `viz_spy()` doesn't look different from visualizing the equivalent `binary=True` (default) matrix:"
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"Note that `viz_spy()` can only use one color for all match cells (by default this color is set to black), so visualizing a `binary=False` matrix with `viz_spy()` doesn't look different from visualizing the equivalent `binary=True` (default) matrix:"
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"cell_type": "code",
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"id": "2e1c70c9",
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"metadata": {
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"scrolled": true
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},
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"data": {
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" <AxesSubplot:xlabel='$s_1$ (18 nt) →', ylabel='$s_2$ (18 nt) →'>)"
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" <AxesSubplot:xlabel='$s_1$ (21 nt) →', ylabel='$s_2$ (21 nt) →'>)"
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" <AxesSubplot:xlabel='$s_1$ (21 nt) →', ylabel='$s_2$ (21 nt) →'>)"
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" <AxesSubplot:xlabel='$s_1$ (18 nt) →', ylabel='$s_2$ (18 nt) →'>)"
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" <AxesSubplot:xlabel='$s_1$ (18 nt) →', ylabel='$s_2$ (18 nt) →'>)"
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