thesamovar · August 14, 2024 22:35 · Apr 29, 2020 · Apr 27, 2020 · Apr 26, 2020 · Apr 25, 2020
diff --git a/Automatic scientific axes layout for matplotlib.ipynb b/Automatic scientific axes layout for matplotlib.ipynb
@@ -14,8 +14,8 @@
         "trusted": true
       },
       "cell_type": "code",
-      "source": "def panel_specs(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)-set('.')\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axis specs\n    gs = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    specs = {}\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        specs[letter] = gs[top:bottom, left:right]\n    return specs, gs\n\ndef panels(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    specs, gs = panel_specs(layout, fig=fig)\n    for letter, spec in specs.items():\n        axes[letter] = fig.add_subplot(spec)\n    return axes, gs\n\ndef label_panel(ax, letter, *,\n                offset_left=0.8, offset_up=0.2, prefix='', postfix='.', **font_kwds):\n    kwds = dict(fontsize=18)\n    kwds.update(font_kwds)\n    # this mad looking bit of code says that we should put the code offset a certain distance in\n    # inches (using the fig.dpi_scale_trans transformation) from the top left of the frame\n    # (which is (0, 1) in ax.transAxes transformation space)\n    fig = ax.figure\n    trans = ax.transAxes + transforms.ScaledTranslation(-offset_left, offset_up, fig.dpi_scale_trans)\n    ax.text(0, 1, prefix+letter+postfix, transform=trans, **kwds)\n\ndef label_panels(axes, letters=None, **kwds):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        label_panel(ax, letter, **kwds)\n    \nlayout = '''\n AAB\n AA.\n .CC\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = panels(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABC')\nplt.tight_layout()",
-      "execution_count": 100,
+      "source": "def panel_specs(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)-set('.')\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axis specs\n    gs = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    specs = {}\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        specs[letter] = gs[top:bottom, left:right]\n    return specs, gs\n\ndef panels(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    specs, gs = panel_specs(layout, fig=fig)\n    axes = {}\n    for letter, spec in specs.items():\n        axes[letter] = fig.add_subplot(spec)\n    return axes, gs\n\ndef label_panel(ax, letter, *,\n                offset_left=0.8, offset_up=0.2, prefix='', postfix='.', **font_kwds):\n    kwds = dict(fontsize=18)\n    kwds.update(font_kwds)\n    # this mad looking bit of code says that we should put the code offset a certain distance in\n    # inches (using the fig.dpi_scale_trans transformation) from the top left of the frame\n    # (which is (0, 1) in ax.transAxes transformation space)\n    fig = ax.figure\n    trans = ax.transAxes + transforms.ScaledTranslation(-offset_left, offset_up, fig.dpi_scale_trans)\n    ax.text(0, 1, prefix+letter+postfix, transform=trans, **kwds)\n\ndef label_panels(axes, letters=None, **kwds):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        label_panel(ax, letter, **kwds)\n    \nlayout = '''\n AAB\n AA.\n .CC\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = panels(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABC')\nplt.tight_layout()",
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "display_data",
@@ -35,7 +35,7 @@
       },
       "cell_type": "code",
       "source": "layout = '''\n AAAB\n CDEB\n '''\nfig = plt.figure(figsize=(10, 5))\naxes, spec = panels(layout, fig=fig)\nlabel_panels(axes)\nplt.tight_layout()",
-      "execution_count": 101,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "display_data",
@@ -54,14 +54,23 @@
         "trusted": true
       },
       "cell_type": "code",
-      "source": "layout = '''\n    AAAB\n    AAAC\n    AAAD\n    '''\nN = 5\nfig = plt.figure(figsize=(8, 6))\nspecs, gs = panel_specs(layout, fig=fig)\naxes = {}\nfor letter in 'BCD':\n    axes[letter] = ax = fig.add_subplot(specs[letter])\n    label_panel(ax, letter)\nsubgs = specs['A'].subgridspec(N, N, wspace=0, hspace=0)\ntriaxes = {}\nfor i in range(N):\n    for j in range(i+1):\n        triaxes[i, j] = ax = fig.add_subplot(subgs[i, j])\n        ax.set_xticks([])\n        ax.set_yticks([])\n        if i==N-1:\n            ax.set_xlabel(chr(ord('α')+j))\n        if j==0:\n            ax.set_ylabel(chr(ord('α')+i))\nlabel_panel(triaxes[0, 0], 'A')\nplt.tight_layout()",
-      "execution_count": 102,
+      "source": "def tight_xticklabels(ax=None):\n    if ax is None:\n        ax = plt.gca()\n    ticklabels = ax.get_xticklabels()\n    ticklabels[0].set_ha('left')\n    ticklabels[0].set_text(' '+ticklabels[0].get_text())\n    ticklabels[-1].set_ha('right')\n    ticklabels[-1].set_text(ticklabels[-1].get_text()+' ')\n    ax.set_xticklabels(ticklabels)\n    \ndef tight_yticklabels(ax=None):\n    if ax is None:\n        ax = plt.gca()\n    ticklabels = ax.get_yticklabels()\n    ticklabels[0].set_va('bottom')\n    ticklabels[-1].set_va('top')",
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "layout = '''\n    AAAB\n    AAAC\n    AAAD\n    '''\nN = 5\nfig = plt.figure(figsize=(8, 6))\nspecs, gs = panel_specs(layout, fig=fig)\naxes = {}\nfor letter in 'BCD':\n    axes[letter] = ax = fig.add_subplot(specs[letter])\n    label_panel(ax, letter)\nsubgs = specs['A'].subgridspec(N, N, wspace=0, hspace=0)\ntriaxes = {}\ntighten = []\nfor i in range(N):\n    for j in range(i+1):\n        triaxes[i, j] = ax = fig.add_subplot(subgs[i, j])\n        if i==N-1:\n            ax.set_xlabel(chr(ord('α')+j))\n            tighten.append((tight_xticklabels, ax))\n        else:\n            ax.set_xticks([])\n        if j==0:\n            ax.set_ylabel(chr(ord('α')+i))\n            tighten.append((tight_yticklabels, ax))\n        else:\n            ax.set_yticks([])\nlabel_panel(triaxes[0, 0], 'A')\nplt.tight_layout()\nfor f, ax in tighten:\n    f(ax)\nplt.tight_layout()",
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "text/plain": "<Figure size 576x432 with 18 Axes>",
-            "image/png": 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\n"
+            "image/png": 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3zVWotjHgSJI6YXQm5ydHj+XaXgNcM+OS1GJeopIkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb3TqYCTZFuSR5PsT3LjhP1JctNo/4NJzmuiTkmS1KzOBJwkG4BbgEuBc4Ark5yzpNmlwObRYwfwobkWKUnqtSS7kzyT5KGj7PcX7ZboTMBhuGDa/qp6rKpeBu4Alq4qewVwWw3dC5yY5NR5FypJ6q1bgW3H2O8v2i3RpYBzGvDE2OsDo20rbSNJ0qpU1d0MF/g8Gn/RbokuBZxM2FaraCNJ0qz4i3ZLdGk18QPAGWOvTweeWkUbSZJmZepftJPsYHgZi40bN56/ZcuWWdbVWfv27Xuuqk5e6XFdCjh7gc1JzgaeBLYD71jSZg9wQ5I7gAuAg1X19HzLlCStY1P/ol1Vu4BdAIPBoBYXF2dfXQcl+cJqjuvMJaqqOgTcANwFfA74SFU9nOT6JNePmi0AjwH7gX8D/N1GipUkrVd7gKtHd1NdiL9oN6ZLZ3CoqgWGIWZ8286x5wW8e951SZLWhyS3AxcDm5IcAN4PHA+vfB8tAJcx/EX7JeDaZipVpwLOWkryYeBtABs3bmQwGDRcUfs8/vjj9ssEJ5100vmDwcDB62NWe41c6pqqunKZ/f6i3RKdCjhJdgOXA89U1Zsm7A/wQYbp+SXgmqq6b9J7VdVVwFXgtc+jGQwG2C9Hsl+OtNpr5JI0K50ZgzNyK06wJEmSltGpgOMES5IkaRqdCjhTcIIlSZLUu4DjTMaSJKl3AceZjCVJUu8CjhMsSZKkzt0m7gRLkiRpWZ0KOE6wJEmSptG3S1SSJEkGHEmS1D8GHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DudCjhJtiV5NMn+JDdO2H9xkoNJ7h893tdEnZKkfvJ7qDuOa7qAaSXZANwCXAIcAPYm2VNVjyxpek9VXT73AiVJveb3ULc0cgYnyXuTfOsKD9sK7K+qx6rqZeAO4Iq1r06SpIn8HuqQpi5RvRa4J8meJH89yTRnkk4Dnhh7fWC0bamLkjyQ5M4k5x7tzZLsSLKYZPHZZ59dWfWSpPXI76EOaSTgVNWPVdUbgA8CPwB8PslPJXlPkvcc5bBMeqslr+8Dzqqq7wJ+Gvj4MWrYVVWDqhqcfPLJq/gpJEnrjN9DHdLYIOPRWZtvBA4BfwC8Dvim0WOSA8AZY69PB54ab1BVL1TVi6PnC8DxSTatcemSpPXJ76EOaWSQcZJ/AFwP/HfgVuCaqvraMoftBTYnORt4EtgOvGPJ+54CfLGqKslWhgHuS2tcviRpffJ7qEOauovqUuC7quor0x5QVYeS3ADcBWwAdlfVw0muH+3fCbwdeFeSQ8CXge1VtfT0oSRJK+b3ULc0EnCq6ntWedwCsLBk286x5zcDN7+66iRJmszvoe7o1ER/kiRJ0zDgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3jHgSJKk3ulUwEmyLcmjSfYnuXHC/iS5abT/wSTnNVGnJElqVmcCTpINwC3ApcA5wJVJzlnS7FJg8+ixA/jQXIuUJEmt0JmAA2wF9lfVY1X1MnAHcMWSNlcAt9XQvcCJSU6dd6GSJKlZXQo4pwFPjL0+MNq20jaSJKnnuhRwMmFbraKNJEnquS4FnAPAGWOvTweeWkUbSZLUc10KOHuBzUnOTnICsB3Ys6TNHuDq0d1UFwIHq+rpeRcqSZKadVzTBUyrqg4luQG4C9gA7K6qh5NcP9q/E1gALgP2Ay8B1zZVryRJak5nAg5AVS0wDDHj23aOPS/g3fOuS5IktUunAs5aSvJh4G0AGzduZDAYNFxR+zz++OP2ywT2y5FOOumk8weDgQP6l9i3b99zVXVy03VI61GnAk6S3cDlwDNV9aYJ+wN8kOFlqpeAa6rqvknvVVVXAVcBDAaDWlxcnFndXTUYDLBfjmS/HMk+mSzJF5quQVqvujTIGOBWYNsx9juTsSRpZlwyqDs6FXCq6m7g+WM0cSZjSdJMuGRQt3Qq4EzBmYwlSbPikkEd0qkxOFNwJmNJ0qxM+iX6ginanAYcMSdbkh0Mz/IAfDXJQ2tX6quyCXiu6SLGvHE1B/Ut4DiTsSRpVtZ0yaCq2gXsAkiyWFWtuD2zTbXAsJ7VHNe3S1TOZCxJmhWXDOqQTp3BSXI7cDGwKckB4P3A8eBMxpKkmXtlySDgSYZLBr1jSZs9wA1J7mB4+cpftBvSqYBTVVcus9+ZjCVJMzHjJYN2zaDk1WpTLbDKejoVcCRJatKslgwajcdphTbVAquvp29jcCRJkgw4kiSpfww4kiTNQduWeZiinouTHExy/+jxvhnWsjvJM0ebC2g1fWPAkSRpxtq2zMOU9QDcU1VvHj1+dFb1MIO1Jg04kiTNXtuWeZimnrmZxVqTBhxJkmZvmrUS57me4rR/10VJHkhyZ5JzZ1TLNFbcN94mLknS7K3pMg9rYJq/6z7grKp6McllwMcZXiJqwor7xjM4kiTNXtuWeVj276qqF6rqxdHzBeD4JJtmVM9yVtw3BhxJkmbvlWUekpzAcJmHPUvazHM9xWXrSXJKkoyeb2WYGb40o3qWs+K+8RKVJEkzNuNlHmZVz9uBdyU5BHwZ2D6aqXnNzWKtSQOOJElzMKtlHmZYz83AzXOqZc3XmvQSlSRJ6h0DjiRJ6h0DjiRJ6h0DjiRJ6h0DjiRJ6h0DjiRJ6h0DjiRJ6h0DjiRJ6h0DjiRJ6h0DjiRJ6p1OBZwk25I8mmR/khsn7L84ycEk948e72uiTkmS1KzOrEWVZANwC3AJw2XT9ybZU1WPLGl6T1VdPvcCJUlSazQWcJJsAf4yEODuqnpwmUO2Avur6rHR8XcAVwBLA44kSVrn5nqJKsm/TfKaJN8HfBT41tHj3ydZbpXQ04Anxl4fGG1b6qIkDyS5M8m5x6hlR5LFJIvPPvvsCn8SSdI8jYYg1NjjD5P8bpKHkvzCaAhDmq5T7THvMzhvrqqvjsLMW6rqdwGS/Evg1xhegjqaSR/cWvL6PuCsqnoxyWXAx4HNk96sqnYBuwAGg8HS95EktdPtwALD74RvAt4IvBW4GvilJD9QVb/XYH1qiXkHnNckORP4tsPhZuQPpzj2AHDG2OvTgafGG1TVC2PPF5L8TJJNVfXcqylaktQa91XVL45vSPIe4P8H3sMwAF3aRGFql3kHnP8MLAIfSvIp4FdHNbwV+Nlljt0LbE5yNvAksB14x3iDJKcAX6yqSrKV4SW4L63tjyBJapOq+kPgH47+v78tyVuq6learkvNmmvAqap/AvwTgCTfxjDYHAdcXVUPLHPsoSQ3AHcBG4DdVfVwkutH+3cCbwfeleQQ8GVge1V5+UmS1od/C7wF+KuAAWeda+wuqqp6CviZFR6zwPDa6/i2nWPPbwZuXpMCJUldc/hu3Dc0WoVaoVMT/UmSdAyHx2F+c6NVqBUMOJKkvjgcbF44ZiutCwYcSVJffOfoz0cbrUKtYMCRJPXFdaM/P9loFWoFA44kqdOSbEjyrxjeQbVQVb862v66JFuSnNpshWpCZxbblCQJOC/JO0fPx2cyPgv4b3z9/Ghbgf8J/AJwzRxrVAsYcCRJXXLl6PE14EWGs9z/MnB7VX2qycLULgYcSVLrVdX/YvKahGt6jPrDMTiSJKl3DDiSJKl3DDiSJKl3DDiSJKl3DDiSJKl3DDiSJKl3DDiSJKl3DDiSJKl3DDiSJKl3DDiSJE0pye4kzyR56Cj7k+SmJPuTPJjkvHnXqCEDjiRJ07sV2HaM/ZcCm0ePHcCH5lCTJjDgSJI0paq6G3j+GE2uAG6roXuBE5OcOp/qNK5Ti20m2QZ8ENgA/FxV/csl+zPafxnwEnBNVd0390IlSevVacATY68PjLY9vbRhkh0Mz/KwcePG87ds2TKXArtm3759z1XVySs9rjMBJ8kG4BbgEoYfmL1J9lTVI2PNxk8NXsDw1OAF865VkrRuTVq9vCY1rKpdwC6AwWBQi4uLs6yrs5J8YTXHdekS1VZgf1U9VlUvA3cwPBU4zlODkqQmHQDOGHt9OvBUQ7Wsa10KOEc77bfSNpIkzcoe4OrR3VQXAger6ojLU5q9zlyiYrrTflOfGpQkaaWS3A5cDGxKcgB4P3A8QFXtBBYYjgPdz3As6LXNVKouBZxpTvt5alCSNDNVdeUy+wt495zK0TF06RLVXmBzkrOTnABsZ3gqcJynBiVJUnfO4FTVoSQ3AHcxvE18d1U9nOT60X5PDUqSJKBDAQegqhYYhpjxbTvHnntqUJIkdeoSlSRJ0lQ6dQYnyW7gcuCZqnrThP1Tz2Sc5MPA2wA2btzIYDCYWd1d9fjjj9svE9gvR7JPJjvppJPOHwwG3sm5xGpnppVWolMBh+EiZzcDtx1l/9QzGVfVVcBV4AySRzMYDLBfjmS/HMk+mcx+mWy1M9NKK9GpS1QuciZJkqbRqYAzBWcyliRJvQs4zmQsSZJ6F3CcyViSJPUu4DiTsSRJ6tZdVC5yJkmSptGpgOMiZ5IkaRp9u0QlSZJkwJEkSf1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkSb1jwJEkaUpJtiV5NMn+JDdO2H9xkoNJ7h893tdEnYLjmi5AkqQuSLIBuAW4BDgA7E2yp6oeWdL0nqq6fO4F6ut4BkeSpOlsBfZX1WNV9TJwB3BFwzXpKAw4kiRN5zTgibHXB0bblrooyQNJ7kxy7tHeLMmOJItJFp999tm1rnXdM+BIkjSdTNhWS17fB5xVVd8F/DTw8aO9WVXtqqpBVQ1OPvnkNSxT0LGA4+AuSVKDDgBnjL0+HXhqvEFVvVBVL46eLwDHJ9k0vxJ1WGcGGTu4S5LUsL3A5iRnA08C24F3jDdIcgrwxaqqJFsZnkj40twrVTNncJL8gyWvT0zys8sc5uAuSVJjquoQcANwF/A54CNV9XCS65NcP2r2duChJA8ANwHbq2rpZSzNQVNncLYDP5nkp6rq71fV742S7rFMGtx1wYR2F40+WE8B762qhye9WZIdwA6AM888c8U/gCRp/RlddlpYsm3n2PObgZvnXZeO1NQYnD8xuuR0dZLjknwD8NpljnFwlyRJmkpTAefXgM8C/xX4BPBp4FeWOcbBXZIkaSpNXaK6BtjC8BrmFuD1wKeWOcbBXZIkaSqNBJyq+hpw+O6nz40eyx1zKMnhwV0bgN2HB3eN9u9kOLjrXUkOAV/GwV2SJK1LnblNHBzcJUmSptOpif4kSZKmYcCRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm906mAk2RbkkeT7E9y44T9SXLTaP+DSc5rok5JktSszgScJBuAW4BLgXOAK5Ocs6TZpcDm0WMH8KG5FilJklqhMwEH2Arsr6rHqupl4A7giiVtrgBuq6F7gROTnDrvQiVJUrO6FHBOA54Ye31gtG2lbSRJUs91KeBkwrZaRRtJktRzXQo4B4Azxl6fDjy1ijaSJKnnuhRw9gKbk5yd5ARgO7BnSZs9wNWju6kuBA5W1dPzLlSS1E/ezdsdxzVdwLSq6lCSG4C7gA3A7qp6OMn1o/07gQXgMmA/8BJwbVP1SpL6Zexu3ksYXjHYm2RPVT0y1mz8bt4LGN7Ne8G8a1WHAg5AVfb7WpcAAAZySURBVC0wDDHj23aOPS/g3fOuS5K0LrxyNy9AksN3844HnFfu5gXuTXJiklO9mjB/nQo4aynJDoZz5QB8NclDTdYzZhPwXNNFjJyfZF/TRYzYL5O1pV/sk8nsl8ne2HQBqzTpTt2lZ2eOdjfvEQHH76Gprerzsm4DTlXtAnYBJFmsqkHDJQGtq+X3W1SL/TJBW/rFPpnMfpksyWLTNazSmt7N6/fQdFb7eenSIGNJkprk3bwdYsCRJGk63s3bIev2EtUSu5ouYEybavlY0wWMsV8ma0u/2CeT2S+TtamWqc34bt429UmbaoFV1pPhQG9JkqT+8BKVJEnqHQOOJEnqHQPOBEnOT/LZ0VTbNyWZdNsfSf7xqM2jSb53Be+/3FTfFyc5mOT+0eN9K6x/d5JnjjWnwmqmE+9yv8yqT0bH2S9HHjPTPhkda79MPrZz/bJeTPFvM9e+m+VnZRW1HPOztaq+qSofSx7ArwMXMZzP4E7g0gltzgEeAF4DnA18HtgwxXtvGLX9DuCE0Xucs6TNxcAnXkX9fxE4D3joGG0uG/1sAS4EPtPnfplVn9gv8+8T+6V//bIeHlP+28yt72b9WVnrz9Zq+sYzOEskORX45qr6tRr26m3AWyc0vQK4o6q+WlW/xXDE/NYp/opXpvquqpeBw1N9r5mquht4fplmr0wnXlX3AieOfvaJut4vs+gTsF8mmUOfgP1yNJ3rl3Vkmn+befbdzL+LVmKKz9aK+8aAc6TTGE7UdNjhabYntZs0Hfc07z/NcRcleSDJnUnOneJ9V2ql9a+HfllN7fbL5Paz7JOVHGu/tL9f1otp+mWefdeGz8pKrLhvnAfnSNNOsz31dNyrOO4+4KyqejHJZcDHGa5Mu5ZWWv966JfV1G6/rL79avtk2mPtl8nHtq1f1os1XeZhDbThs7ISK+4bz+Ac6QDDqbUPO9o026udjnvZ46rqhap6cfR8ATg+yaYp3nslVlr/euiX1dRuv0xuP8s+mepY+2XysS3sl/Wibcs8tOGzshIr75vlBumsxwfD6bgv5I8GAl42oc25fP1AwMeYbtDocaO2Z/NHA7vOXdLmFP5oEsatwG8ffr2Cn+HbOfZAwL/K1w/Y+vW+98ss+sR+mX+f2C/965f18Jjy32ZufTePz8oqajrqZ2tV/701/Y/exgcwAB5iOML85rF/4O8DfnSs3Q+P2jzKhLshjvH+lwG/MTr2h0fbrgeuHz2/AXh49IG7F/hzK6z/duBp4A8Ypt7rJvwdAW4Z1fBZYNDnfplVn9gvzfSJ/dKvflkvjyn+bebad7P8rKyiliM+W6+2b1yqQZIk9Y5jcCRJUu8YcCRJUu8YcCRJUu8YcCRJUu8YcCRJUu8YcNRqSW5I8htJHk7ygabrUbsl+bHRZ+V/J/n5puuR1BxvE1drJXkNw5kqNwMHGa5Dcm5V/W6jham1kjzHcH6Mx5O8tqq+3HRNkprhGZyWSPIjST47+s3zLUn+a9M1tcC3Ar9TVc8DW0bbDjZYTysk+XSS94y9fl+SH2qyphb5GeATST4NfE/TxbRBkg1Jbkvy+SR7k/zDpmuS5sHFNlsgyYXA9wN/Fng78J+A9zdaVDt8A1BJ/jHw48CuqvpawzW1wfcBDyS5meGsn1cBf7HZkpqX5DSGU7h/J3Ai8CtJfrOqHmm2ssZtYTjN/paq+oOmi5HmxTM47XAR8MmqOgR8CviTwCeaLak9quoDwMnAtyf5203X07TRZZf/AXwv8JeBR6vq6WaraoW3Ar9aVV8bnfX7KMP+We8+B/w68EySm5ouRpoXz+C0x1fH/nyyqp5sspi2qarnk9zOMAwK/iNwDcP/hh1MO3QcX///tIwe690WhosmnlJVX12usdQXnsFph0Xgz4+efx/wbUlObrCeNjkjyRtGzy9guPig4H8Bfw54C+B4raFfBt6a5HVJNgJ/Hbi74Zra4PXA8cAhgCTf0mw50nx4BqcFquqe0a2tC8BG4GrgY0m+t6peari8ph0C/kuSQ8B+4B81XE8rVNUfJvkfwJer6uWm62mDqro/yS7gMwzP3PxsVd3fcFlt8CmGl+8eSfIVhper1v2lXvWft4mrtZJ8O/CJqnpTw6W0UpJ/CrxYVf+q6VokqW28RCVJknrHMziSJKl3PIMjSZJ6x4AjSZJ6x4AjSZJ6x4AjSZJ6x4AjSZJ65/8Bfad19j8lzDkAAAAASUVORK5CYII=\n"
           },
           "metadata": {
             "needs_background": "light"
@@ -71,6 +80,16 @@
     }
   ],
   "metadata": {
+    "_draft": {
+      "nbviewer_url": "https://gist.github.com/52dbbb3a58a73c590d54c34f5f719bac"
+    },
+    "gist": {
+      "id": "52dbbb3a58a73c590d54c34f5f719bac",
+      "data": {
+        "description": "Automatic scientific axes layout for matplotlib.ipynb",
+        "public": true
+      }
+    },
     "kernelspec": {
       "name": "conda-env-brian-py",
       "display_name": "Python [conda env:brian]",
@@ -87,16 +106,6 @@
       "pygments_lexer": "ipython3",
       "nbconvert_exporter": "python",
       "file_extension": ".py"
-    },
-    "gist": {
-      "id": "52dbbb3a58a73c590d54c34f5f719bac",
-      "data": {
-        "description": "Automatic scientific axes layout for matplotlib.ipynb",
-        "public": true
-      }
-    },
-    "_draft": {
-      "nbviewer_url": "https://gist.github.com/52dbbb3a58a73c590d54c34f5f719bac"
     }
   },
   "nbformat": 4,

diff --git a/Automatic scientific axes layout for matplotlib.ipynb b/Automatic scientific axes layout for matplotlib.ipynb
@@ -5,7 +5,7 @@
         "trusted": true
       },
       "cell_type": "code",
-      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec",
+      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nfrom matplotlib import transforms",
       "execution_count": 1,
       "outputs": []
     },
@@ -14,14 +14,14 @@
         "trusted": true
       },
       "cell_type": "code",
-      "source": "def panel_specs(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)-set('.')\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axis specs\n    gs = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    specs = {}\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        specs[letter] = gs[top:bottom, left:right]\n    return specs, gs\n\ndef panels(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    specs, gs = panel_specs(layout, fig=fig)\n    for letter, spec in specs.items():\n        axes[letter] = fig.add_subplot(spec)\n    return axes, gs\n\ndef label_panel(ax, letter, *, prefix='', postfix='.', spaces=6, pad=10, fontsize=18):\n    ax.set_title(prefix+letter+postfix+' '*spaces, loc='left', pad=pad,\n                 fontdict={'horizontalalignment': 'right',\n                           'fontsize': fontsize})\n\ndef label_panels(axes, letters=None, *, prefix='', postfix='.', spaces=6, pad=10, fontsize=18):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        label_panel(ax, letter, prefix=prefix, postfix=postfix, spaces=spaces, pad=pad, fontsize=fontsize)\n    \nlayout = '''\n AAB\n AA.\n .DD\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = panels(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABD')\nplt.tight_layout()",
-      "execution_count": 20,
+      "source": "def panel_specs(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)-set('.')\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axis specs\n    gs = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    specs = {}\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        specs[letter] = gs[top:bottom, left:right]\n    return specs, gs\n\ndef panels(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    specs, gs = panel_specs(layout, fig=fig)\n    for letter, spec in specs.items():\n        axes[letter] = fig.add_subplot(spec)\n    return axes, gs\n\ndef label_panel(ax, letter, *,\n                offset_left=0.8, offset_up=0.2, prefix='', postfix='.', **font_kwds):\n    kwds = dict(fontsize=18)\n    kwds.update(font_kwds)\n    # this mad looking bit of code says that we should put the code offset a certain distance in\n    # inches (using the fig.dpi_scale_trans transformation) from the top left of the frame\n    # (which is (0, 1) in ax.transAxes transformation space)\n    fig = ax.figure\n    trans = ax.transAxes + transforms.ScaledTranslation(-offset_left, offset_up, fig.dpi_scale_trans)\n    ax.text(0, 1, prefix+letter+postfix, transform=trans, **kwds)\n\ndef label_panels(axes, letters=None, **kwds):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        label_panel(ax, letter, **kwds)\n    \nlayout = '''\n AAB\n AA.\n .CC\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = panels(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABC')\nplt.tight_layout()",
+      "execution_count": 100,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "text/plain": "<Figure size 720x504 with 3 Axes>",
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {
             "needs_background": "light"
@@ -35,13 +35,13 @@
       },
       "cell_type": "code",
       "source": "layout = '''\n AAAB\n CDEB\n '''\nfig = plt.figure(figsize=(10, 5))\naxes, spec = panels(layout, fig=fig)\nlabel_panels(axes)\nplt.tight_layout()",
-      "execution_count": 14,
+      "execution_count": 101,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "text/plain": "<Figure size 720x360 with 5 Axes>",
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {
             "needs_background": "light"
@@ -54,14 +54,14 @@
         "trusted": true
       },
       "cell_type": "code",
-      "source": "layout = '''\n    AAAB\n    AAAC\n    AAAD\n    '''\nN = 5\nfig = plt.figure(figsize=(8, 6))\nspecs, gs = panel_specs(layout, fig=fig)\naxes = {}\nfor letter in 'BCD':\n    axes[letter] = ax = fig.add_subplot(specs[letter])\n    label_panel(ax, letter)\nsubgs = specs['A'].subgridspec(N, N, wspace=0, hspace=0)\ntriaxes = {}\nfor i in range(N):\n    for j in range(i+1):\n        triaxes[i, j] = ax = fig.add_subplot(subgs[i, j])\n        ax.set_xticks([])\n        ax.set_yticks([])\n        if i==N-1:\n            ax.set_xlabel(chr(ord('α')+j))\n        if j==0:\n            ax.set_ylabel(chr(ord('α')+i))\nlabel_panel(triaxes[0, 0], 'A', spaces=2)\nplt.tight_layout()",
-      "execution_count": 42,
+      "source": "layout = '''\n    AAAB\n    AAAC\n    AAAD\n    '''\nN = 5\nfig = plt.figure(figsize=(8, 6))\nspecs, gs = panel_specs(layout, fig=fig)\naxes = {}\nfor letter in 'BCD':\n    axes[letter] = ax = fig.add_subplot(specs[letter])\n    label_panel(ax, letter)\nsubgs = specs['A'].subgridspec(N, N, wspace=0, hspace=0)\ntriaxes = {}\nfor i in range(N):\n    for j in range(i+1):\n        triaxes[i, j] = ax = fig.add_subplot(subgs[i, j])\n        ax.set_xticks([])\n        ax.set_yticks([])\n        if i==N-1:\n            ax.set_xlabel(chr(ord('α')+j))\n        if j==0:\n            ax.set_ylabel(chr(ord('α')+i))\nlabel_panel(triaxes[0, 0], 'A')\nplt.tight_layout()",
+      "execution_count": 102,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "text/plain": "<Figure size 576x432 with 18 Axes>",
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {
             "needs_background": "light"
@@ -89,11 +89,14 @@
       "file_extension": ".py"
     },
     "gist": {
-      "id": "",
+      "id": "52dbbb3a58a73c590d54c34f5f719bac",
       "data": {
         "description": "Automatic scientific axes layout for matplotlib.ipynb",
         "public": true
       }
+    },
+    "_draft": {
+      "nbviewer_url": "https://gist.github.com/52dbbb3a58a73c590d54c34f5f719bac"
     }
   },
   "nbformat": 4,

diff --git a/Automatic scientific axes layout for matplotlib.ipynb b/Automatic scientific axes layout for matplotlib.ipynb
@@ -6,26 +6,26 @@
       },
       "cell_type": "code",
       "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec",
-      "execution_count": 21,
+      "execution_count": 1,
       "outputs": []
     },
     {
       "metadata": {
         "trusted": true
       },
       "cell_type": "code",
-      "source": "from collections import namedtuple\ndef make_panelled_axes(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axes\n    axes = {}\n    spec = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        axes[letter] = fig.add_subplot(spec[top:bottom, left:right])\n    return axes, spec\n\ndef label_panels(axes, letters=None, *, prefix='', postfix='.', spaces=6, pad=10, fontsize=18):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        ax.set_title(prefix+letter+postfix+' '*spaces, loc='left', pad=pad,\n                     fontdict={'horizontalalignment': 'right',\n                               'fontsize': fontsize})\n    \nlayout = '''\n AAB\n AAx\n CDD\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = make_panelled_axes(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABCD')\nplt.tight_layout()",
-      "execution_count": 56,
+      "source": "def panel_specs(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)-set('.')\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axis specs\n    gs = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    specs = {}\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        specs[letter] = gs[top:bottom, left:right]\n    return specs, gs\n\ndef panels(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    specs, gs = panel_specs(layout, fig=fig)\n    for letter, spec in specs.items():\n        axes[letter] = fig.add_subplot(spec)\n    return axes, gs\n\ndef label_panel(ax, letter, *, prefix='', postfix='.', spaces=6, pad=10, fontsize=18):\n    ax.set_title(prefix+letter+postfix+' '*spaces, loc='left', pad=pad,\n                 fontdict={'horizontalalignment': 'right',\n                           'fontsize': fontsize})\n\ndef label_panels(axes, letters=None, *, prefix='', postfix='.', spaces=6, pad=10, fontsize=18):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        label_panel(ax, letter, prefix=prefix, postfix=postfix, spaces=spaces, pad=pad, fontsize=fontsize)\n    \nlayout = '''\n AAB\n AA.\n .DD\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = panels(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABD')\nplt.tight_layout()",
+      "execution_count": 20,
       "outputs": [
         {
+          "output_type": "display_data",
           "data": {
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l18GCLEmSdOomYZnqUZ7D2Kuqe4DnTjDklF8HC7IkSdKpW24J6lMd06dR8/1ykh8k+XaSd3cTbUWd8uvg+yBLkiSduhVbprpHo+T7PvCOqvppkiuBuxjcqrCWnPLr4BVkSZKkUzcJy1SfNF9V/aSqfjr8+CBwRpJzu4u4Ik75dbAgS5Iknbq/XqY6yZkMlqk+sGjMAeCa4bsovI/xW6b6pM8hyc8nyfDjyxl0x2c7T/ranPLr4C0WkiRJp2gVl6nuzIjP4Srgt5IcB/4C2Fljtgxzkq8DW4FzkywAnwPOgNN/HVxqWpIkjcylpjUNvMVCkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkTaUk+5I8neThZY4nyVeSzCd5MMklXWdUP8a6ICfZluSx4cS86QTjLkvyUpKruswnSZLWtNuBbSc4vh3YPHzsBr7aQSaNgbEtyEnWAbcwmJxbgF1Jtiwz7gvA3d0mlCRJa1lV3QM8d4IhO4Cv1cB9wNlJ3tZNOvVpfd8BTuByYL6qjgAk2c9goj6yaNyngTuAy7qNJ0mSJtwG4Klme2G470eLBybZzeAqM2edddalF154YScBdWL333//M1U1c6qfN84FealJ+d52QJINwEeBX8OCLEmSVlaW2FdLDayqvcBegNnZ2Zqbm1vNXBpRkj8/nc8b21ssGG1Sfhm4sapeOuEXSnYnmUsyd+zYsRULKEmSJtoCcF6zvRE42lMWdWicC/Iok3IW2J/kz4CrgFuT/PriL1RVe6tqtqpmZ2ZO+Sq7JEmaTgeAa4bvZvE+4PmqetXtFZo843yLxSFgc5JNwA+BncDV7YCq2vTKx0luB75VVXd1GVKSJK1NSb4ObAXOTbIAfA44A6Cq9gAHgSuBeeBnwLX9JFXXxrYgV9XxJDcweHeKdcC+qjqc5Prh8T29BpQkSWtaVe06yfECPtVRHI2RsS3IAFV1kMH/3tp9SxbjqvpEF5kkSZI02cb5HmRJkiSpcxZkSZIkqWFBliRJkhoWZEmSJKlhQZYkSZIaFmRJkiSpYUGWJEmSGhZkSZIkqWFBliRJkhoWZEmSJKlhQZYkSZIaFmRJkiSpYUGWJElTK8m2JI8lmU9y0xLH35Tkm0l+kORwkmv7yKluWZAlSdJUSrIOuAXYDmwBdiXZsmjYp4BHqupiYCvwpSRndhpUnbMgS5KkaXU5MF9VR6rqRWA/sGPRmALekCTA64HngOPdxlTXLMiSJGlabQCearYXhvtaNwPvAo4CDwGfqaqXF3+hJLuTzCWZO3bs2GrlVUcsyJIkaVpliX21aPsK4AHgF4D3ADcneeOrPqlqb1XNVtXszMzMyidVpyzIkiRpWi0A5zXbGxlcKW5dC9xZA/PAE8CFHeVTTyzIkiRpWh0CNifZNPzFu53AgUVjngQ+CJDkrcAFwJFOU6pz6/sOIEmS1IeqOp7kBuBuYB2wr6oOJ7l+eHwP8Hng9iQPMbgl48aqeqa30OqEBVmSJE2tqjoIHFy0b0/z8VHgw13nUr+8xUKSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRNrSTbkjyWZD7JTcuM2ZrkgSSHk3yv64zq3vq+A0iSJPUhyTrgFuBDwAJwKMmBqnqkGXM2cCuwraqeTPKWftKqS15BliRJ0+pyYL6qjlTVi8B+YMeiMVcDd1bVkwBV9XTHGdUDC7IkSZpWG4Cnmu2F4b7W+cA5Sb6b5P4k1yz1hZLsTjKXZO7YsWOrFFddsSBLkqRplSX21aLt9cClwEeAK4DfSXL+qz6pam9VzVbV7MzMzMonVae8B1mSJE2rBeC8ZnsjcHSJMc9U1QvAC0nuAS4GHu8movrgFWRJkjStDgGbk2xKciawEziwaMw3gPcnWZ/kdcB7gUc7zqmOeQVZkiRNpao6nuQG4G5gHbCvqg4nuX54fE9VPZrkO8CDwMvAbVX1cH+p1QULsiRJmlpVdRA4uGjfnkXbXwS+2GUu9ctbLCRJkqSGBVmSJElqWJAlSZKkxlgX5JOtj57kY0keHD7uTXJxHzklSZI0Oca2IDfro28HtgC7kmxZNOwJ4Fer6iLg88DeblNKkiRp0oxtQWaE9dGr6t6q+vFw8z4Gb/AtSZIknbZxLsijrI/eug749lIHXB9dkiRJoxrngjzK+uiDgckHGBTkG5c67vrokiRJGtU4LxQyyvroJLkIuA3YXlXPdpRNkiRJE2qcryCfdH30JG8H7gQ+XlWP95BRkiRJE2ZsryCPsj468FngzcCtSQCOV9VsX5klSZK09o1tQYaTr49eVZ8EPtl1LkmSJE2ucb7FQpIkSeqcBVmSJE2tk63a24y7LMlLSa7qMp/6YUGWJElTacRVe18Z9wUGvxelKWBBliRJ0+qkq/YOfRq4A3i6y3DqjwVZkiRNq5Ou2ptkA/BRYA8n4Kq9k8WCLEmSptUoq/Z+Gbixql460Rdy1d7JMtZv8yZJkrSKRlm1dxbYP1xv4VzgyiTHq+qubiKqDxZkSZI0rf561V7ghwxW7b26HVBVm175OMntwLcsx5PPgixJkqbSiKv2agpZkCVJ0tQ62aq9i/Z/ootM6p+/pCdJkiQ1LMiSJElSw4IsSZIkNSzIkiRJUsOCLEmSJDUsyJIkSVLDgixJkiQ1LMiSJElSw4IsSZIkNSzIkiRJUsOCLEmSJDUsyJIkSVLDgixJkiQ1LMiSJElSw4IsSZKmVpJtSR5LMp/kpiWOfyzJg8PHvUku7iOnumVBliRJUynJOuAWYDuwBdiVZMuiYU8Av1pVFwGfB/Z2m1J9sCBLkqRpdTkwX1VHqupFYD+wox1QVfdW1Y+Hm/cBGzvOqB5YkCVJ0rTaADzVbC8M9y3nOuDbSx1IsjvJXJK5Y8eOrWBE9cGCLEmSplWW2FdLDkw+wKAg37jU8araW1WzVTU7MzOzghHVh/V9B5AkSerJAnBes70ROLp4UJKLgNuA7VX1bEfZ1COvIEuSpGl1CNicZFOSM4GdwIF2QJK3A3cCH6+qx3vIqB54BVmSJE2lqjqe5AbgbmAdsK+qDie5fnh8D/BZ4M3ArUkAjlfVbF+Z1Q0LsiRJmlpVdRA4uGjfnubjTwKf7DqX+uUtFpIkSVLDgixJkiQ1LMiSJElSw4IsSZIkNSzIkiRJUsOCLEmSJDUsyJIkSVLDgixJkiQ1LMiSJElSw4IsSZIkNSzIkiRJUsOCLEmSJDUsyJIkSVJjrAtykm1JHksyn+SmJY4nyVeGxx9MckkfOSVJ0tpk19BSxrYgJ1kH3AJsB7YAu5JsWTRsO7B5+NgNfLXTkJIkac2ya2g5Y1uQgcuB+ao6UlUvAvuBHYvG7AC+VgP3AWcneVvXQSVJ0ppk19CS1vcd4AQ2AE812wvAe0cYswH4UTsoyW4G/+sD+MskD69sVJ3EucAzfYeYMp7zfnjeu+c5794FfQdYQXaN5U3K363Tmq/jXJCzxL46jTFU1V5gL0CSuaqafe3xNCrPefc85/3wvHfPc969JHN9Z1hBdo1lTMJzgNOfr+N8i8UCcF6zvRE4ehpjJEmSlmLX0JLGuSAfAjYn2ZTkTGAncGDRmAPANcPfMH0f8HxV/WjxF5IkSVqCXUNLGttbLKrqeJIbgLuBdcC+qjqc5Prh8T3AQeBKYB74GXDtCF967ypF1vI8593znPfD8949z3n3Juac2zVOaBKeA5zm80jVq26jkSRJkqbWON9iIUmSJHXOgixJkiQ1JrYgu3Rk90Y45x8bnusHk9yb5OI+ck6Sk53zZtxlSV5KclWX+SbRKOc8ydYkDyQ5nOR7XWecRCP8+/KmJN9M8oPheR/lPlEtI8m+JE8v916+fg8dmISuMcJz2Jrk+eG/aQ8k+WwfOU9kVeZrVU3cg8GN9n8K/BJwJvADYMuiMVcC32bw/obvA/6k79xr+THiOf9bwDnDj7d7zlf/nDfj/gWDXzS5qu/ca/kx4jw/G3gEePtw+y19517rjxHP+z8AvjD8eAZ4Djiz7+xr9QH8beAS4OFljk/999BJ6BojPoetwLf6znqS57Hi83VSryC7dGT3TnrOq+reqvrxcPM+Bu8lqdM3yjwH+DRwB/B0l+Em1Cjn/Grgzqp6EqCqPO+v3SjnvYA3JAnwegYF+Xi3MSdHVd3D4Bwux++hk9E1Rv0+MtZWY75OakFeblnIUx2j0Z3q+byOwf/mdPpOes6TbAA+CuzpMNckG2Wenw+ck+S7Se5Pck1n6SbXKOf9ZuBdDBZweAj4TFW93E28qeT30MnoGqPm++Xh7UvfTvLubqKtqFN+Hcb2fZBfoxVbOlIjG/l8JvkAg4L8K6uaaPKNcs6/DNxYVS8NLqzpNRrlnK8HLgU+CPwc8K+T3FdVj692uAk2ynm/AngA+DXgncAfJvnjqvrJaoebUn4PnYyuMUq+7wPvqKqfJrkSuAvYvOrJVtYpvw6TegXZpSO7N9L5THIRcBuwo6qe7SjbpBrlnM8C+5P8GXAVcGuSX+8m3kQa9d+W71TVC1X1DHAP4C+kvjajnPdrGdzaUlU1DzwBXNhRvmnk99DJ6BonzVdVP6mqnw4/PgickeTc7iKuiFN+HSa1ILt0ZPdOes6TvB24E/i4V9NWxEnPeVVtqqpfrKpfBH4f+O2quqv7qBNjlH9bvgG8P8n6JK8D3gs82nHOSTPKeX+SwVV7krwVuAA40mnK6eL30MnoGqN87/754b39JLmcQXdcaxe4Tvl1mMhbLGr1lo7UMkY8558F3szgKibA8aqa7SvzWjfiOdcKGuWcV9WjSb4DPAi8DNxWVUu+9ZBGM+Jc/zxwe5KHGPw49cbhFXydhiRfZ/DuBecmWQA+B5wBfg99xSR0jRGfw1XAbyU5DvwFsLOGbw0xLlZjvrrUtCRJktSY1FssJEmSpNNiQZYkSZIaFmRJkiSpYUGWJEmSGhZkSZIkqWFBliRJkhoWZEmSJKlhQZYkSZIaFmRJkiSpYUGWJEmSGhZkSZIkqWFBliRJkhoWZEmSJKkxckFO8rok/2WSP07yXJK/SvL/JDmY5BNJ1q9mUOlEkmxNUs3jpSQ/TvJwkt9Lsi1J+s4pSZLG30ilNsnfBP4AOB/4I+B3gWeAtwD/MfBPgS3Af7s6MaWRfR04CAR4A3AB8OvANcAfJfm7VfVve8wnSZLG3EkLcpKfA74F/BLwG1V156IhX0hyGXDZKuSTTtX3q+qftTuS/H3gHwF/n0GB3t5HMEmStDaMcovFJxlchfvSEuUYgKo6VFW3rmgyaYVU1UtV9V8D/yewLcmv9J1JkiSNr1EK8lXDP/euZhCpA/9k+OdHek0hSZLG2igF+T8A/t+qOrLaYaRV9uDwz/N7TSFJksbaKAX5jcBPVjuI1IFX5vEbe00hSZLG2igF+ScM3g1AWuteKcb+h0+SJC1rlIL8MPDGJL+02mGkVXbR8M/Hek0hSZLG2igF+Y7hn59czSBSB64b/vkHvaaQJEljbZSCfBuDK27/TZIdSw1IcmmS326235bkwiSvW6Gc0mlLsi7J/wL8CnCwqv5Vc+x1w7n6tv4SSpKkcXLShUKq6mdJ/hMGV93uSvLPgT8EngVmgA8AVzBYiOEVvwv8veGx765wZulELknym8OP25X03gH8c+DqReMvB/4l8HvAJzrKKEmSxthIS01X1XyS/wj4L4DfAP474PXAc8AcgzL8v69WSOkU7Bo+XgZ+CiwA3wO+XlXf6TOYJElaG1JVfWeQJEmSxsYo9yBLkiRJU8OCLEmSJDUsyJIkSVLDgixJkiQ1LEDUnfoAAA1hSURBVMiSJElSY2wLcpJ9SZ5O8vAyx5PkK0nmkzyY5JKuM0rgXJUkadKMbUEGbge2neD4dmDz8LEb+GoHmaSl3I5zVZKkiTG2Bbmq7mGwEMlydgBfq4H7gLNdLlh9cK5KkjRZRlpJb0xtAJ5qtheG+360eGCS3Qyu3HHWWWddeuGFF3YSUMu7//77n6mqmb5zdMS5KklSD063b6zlgpwl9i25LGBV7QX2AszOztbc3Nxq5tIIkvx53xk65FyVJKkHp9s3xvYWixEsAOc12xuBoz1lkU7EuSpJ0hqylgvyAeCa4TsEvA94vqpe9SNraQw4VyVJWkPG9haLJF8HtgLnJlkAPgecAVBVe4CDwJXAPPAz4Np+kmraOVclSZosY1uQq2rXSY4X8KmO4kjLcq5KkjRZ1vItFpIkSdKKsyBLkiRJDQuyJEmS1LAgS5IkSQ0LsiRJktSwIEuSJEkNC7IkSZLUsCBLkiRJDQuyJEmS1LAgS5IkSQ0LsiRJktSwIEuSJEkNC7IkSZLUsCBLkiRJjbEuyEm2JXksyXySm5Y4/qYk30zygySHk1zbR07JuSpJ0uQY24KcZB1wC7Ad2ALsSrJl0bBPAY9U1cXAVuBLSc7sNKimnnNVkqTJMrYFGbgcmK+qI1X1IrAf2LFoTAFvSBLg9cBzwPFuY0rOVUmSJsk4F+QNwFPN9sJwX+tm4F3AUeAh4DNV9fLiL5Rkd5K5JHPHjh1brbyaXs5VSZImyDgX5CyxrxZtXwE8APwC8B7g5iRvfNUnVe2tqtmqmp2ZmVn5pJp2zlVJkibIOBfkBeC8Znsjg6tvrWuBO2tgHngCuLCjfNIrnKuSJE2QcS7Ih4DNSTYNf5lpJ3Bg0ZgngQ8CJHkrcAFwpNOUknNVkqSJsr7vAMupquNJbgDuBtYB+6rqcJLrh8f3AJ8Hbk/yEIMfc99YVc/0FlpTybkqSdJkGduCDFBVB4GDi/btaT4+Cny461zSYs5VSZImxzjfYiFJkiR1zoIsSZIkNSzIkiRJUsOCLEmSJDUsyJIkSVLDgixJkiQ1LMiSJElSw4IsSZIkNSzIkiRJUsOCLEmSJDUsyJIkSVLDgixJkiQ1LMiSJElSY6wLcpJtSR5LMp/kpmXGbE3yQJLDSb7XdUYJnKuSJE2S9X0HWE6SdcAtwIeABeBQkgNV9Ugz5mzgVmBbVT2Z5C39pNU0c65KkjRZxvkK8uXAfFUdqaoXgf3AjkVjrgburKonAarq6Y4zSuBclSRpooxzQd4APNVsLwz3tc4Hzkny3ST3J7lmqS+UZHeSuSRzx44dW6W4mmLOVUmSJsg4F+Qssa8Wba8HLgU+AlwB/E6S81/1SVV7q2q2qmZnZmZWPqmmnXNVkqQJMrb3IDO4Cndes70ROLrEmGeq6gXghST3ABcDj3cTUQKcq5IkTZRxvoJ8CNicZFOSM4GdwIFFY74BvD/J+iSvA94LPNpxTsm5KknSBBnbK8hVdTzJDcDdwDpgX1UdTnL98Pieqno0yXeAB4GXgduq6uH+UmsaOVclSZosqVp8q+Rkm52drbm5ub5jTL0k91fVbN85xplzVZKk1+Z0+8Y432IhSZIkdc6CLEmSJDUsyJIkSVLDgixJkiQ1LMiSJElSw4IsSZIkNSzIkiRJUsOCLEmSJDUsyJIkSVLDgixJkiQ1LMiSJElSw4IsSZIkNSzIkiRJUsOCLEmSJDXGuiAn2ZbksSTzSW46wbjLkryU5Kou80mvcK5KkjQ5xrYgJ1kH3AJsB7YAu5JsWWbcF4C7u00oDThXJUmaLGNbkIHLgfmqOlJVLwL7gR1LjPs0cAfwdJfhpIZzVZKkCTLOBXkD8FSzvTDc99eSbAA+Cuw50RdKsjvJXJK5Y8eOrXhQTT3nqiRJE2ScC3KW2FeLtr8M3FhVL53oC1XV3qqararZmZmZFQsoDTlXJUmaIOv7DnACC8B5zfZG4OiiMbPA/iQA5wJXJjleVXd1E1ECnKuSJE2UcS7Ih4DNSTYBPwR2Ale3A6pq0ysfJ7kd+JaFQz1wrkqSNEHGtiBX1fEkNzD4jf91wL6qOpzk+uHxE97LKXXFuSpJ0mQZ24IMUFUHgYOL9i1ZNqrqE11kkpbiXJUkaXKM8y/pSZIkSZ2zIEuSJEkNC7IkSZLUsCBLkiRJDQuyJEmS1LAgS5IkSQ0LsiRJktSwIEuSJEkNC7IkSZLUsCBLkiRJDQuyJEmS1LAgS5IkSQ0LsiRJktQY64KcZFuSx5LMJ7lpieMfS/Lg8HFvkov7yCk5VyVJmhxjW5CTrANuAbYDW4BdSbYsGvYE8KtVdRHweWBvtykl56okSZNmbAsycDkwX1VHqupFYD+wox1QVfdW1Y+Hm/cBGzvOKIFzVZKkiTLOBXkD8FSzvTDct5zrgG8vdSDJ7iRzSeaOHTu2ghElwLkqSdJEGeeCnCX21ZIDkw8wKB03LnW8qvZW1WxVzc7MzKxgRAlwrkqSNFHW9x3gBBaA85rtjcDRxYOSXATcBmyvqmc7yia1nKuSJE2Qcb6CfAjYnGRTkjOBncCBdkCStwN3Ah+vqsd7yCiBc1WSpIkytleQq+p4khuAu4F1wL6qOpzk+uHxPcBngTcDtyYBOF5Vs31l1nRyrkqSNFlSteStkhNrdna25ubm+o4x9ZLcb0E8MeeqJEmvzen2jXG+xUKSJEnqnAVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaliQJUmSpIYFWZIkSWpYkCVJkqSGBVmSJElqWJAlSZKkhgVZkiRJaox1QU6yLcljSeaT3LTE8ST5yvD4g0ku6SOn5FyVJGlyjG1BTrIOuAXYDmwBdiXZsmjYdmDz8LEb+GqnISWcq5IkTZqxLcjA5cB8VR2pqheB/cCORWN2AF+rgfuAs5O8reugmnrOVUmSJsj6vgOcwAbgqWZ7AXjvCGM2AD9qByXZzeCqHcBfJnl4ZaN27lzgmb5DvEYX9B1gBTlXJ8sk/P1aazzn3fOcd89z3o/T6hvjXJCzxL46jTFU1V5gL0CSuaqafe3x+jMpz6HvDCvIuTpBPO/d85x3z3PePc95P063b4zzLRYLwHnN9kbg6GmMkVabc1WSpAkyzgX5ELA5yaYkZwI7gQOLxhwArhm+Q8D7gOer6keLv5C0ypyrkiRNkLG9xaKqjie5AbgbWAfsq6rDSa4fHt8DHASuBOaBnwHXjvCl965S5C75HMaIc3XieN675znvnue8e57zfpzWeU/Vq26DlCRJkqbWON9iIUmSJHXOgixJkiQ1JrYgT8LSvyM8h61Jnk/ywPDx2T5yLifJviRPL/devmvhNejCJMzVtWaEc/6x4bl+MMm9SS7uI+ckOdk5b8ZdluSlJFd1mW9SjXLeh99LHkhyOMn3us44aUb49+VNSb6Z5AfDcz7K76ToBFalb1TVxD0Y/KLUnwK/BJwJ/ADYsmjMlcC3Gbw/7fuAP+k792k8h63At/rOeoLn8LeBS4CHlzk+1q/BGL3OU3+eejjnfws4Z/jxds/56p/zZty/YPBLrVf1nXutP0ac62cDjwBvH26/pe/ca/kx4jn/B8AXhh/PAM8BZ/adfS0/VqNvTOoV5ElY+neU5zDWquoeBn/xlzPur0EXJmGurjUnPedVdW9V/Xi4eR+D963W6Rv137NPA3cAT3cZboKNct6vBu6sqicBqspz/9qMcs4LeEOSAK9n8H3yeLcxJ8tq9I1JLcjLLet7qmP6NGq+Xx7+mObbSd7dTbQVM+6vQRcmYa6uNad6Pq9jcOVBp++k5zzJBuCjwJ4Oc026Ueb6+cA5Sb6b5P4k13SWbjKNcs5vBt7FYLGoh4DPVNXL3cSbWqf8fXRs3wf5NVqxpX97NEq+7wPvqKqfJrkSuAvYvOrJVs64vwZdmIS5utaMfD6TfIBBQf6VVU00+UY5518GbqyqlwYX1rQCRjnv64FLgQ8CPwf86yT3VdXjqx1uQo1yzq8AHgB+DXgn8IdJ/riqfrLa4abYKX8fndQryJOw9O9J81XVT6rqp8OPDwJnJDm3u4iv2bi/Bl2YhLm61ox0PpNcBNwG7KiqZzvKNqlGOeezwP4kfwZcBdya5Ne7iTexRv335TtV9UJVPQPcA/hLqadvlHN+LYPbWqqq5oEngAs7yjetTvn76KQW5ElY+vekzyHJzw/vYSLJ5Qxez7X0jXzcX4MuTMJcXWtG+bv1duBO4ONeSVsRJz3nVbWpqn6xqn4R+H3gt6vqru6jTpRR/n35BvD+JOuTvA54L/BoxzknySjn/EkGV+xJ8lbgAuBIpymnzyl/H53IWyxq9Zb+7cyIz+Eq4LeSHAf+AthZw1/XHAdJvs7gnTbOTbIAfA44A9bGa9CFSZira82I5/yzwJsZXMUEOF5Vs31lXutGPOdaYaOc96p6NMl3gAeBl4HbqmrJt8rSyY041z8P3J7kIQY/+r9xePVep2k1+oZLTUuSJEmNSb3FQpIkSTotFmRJkiSpYUGWJEmSGhZkSZIkqWFBliRJkhoWZEmSJKlhQZYkSZIa/x/U9HWxIdeUUAAAAABJRU5ErkJggg==\n",
-            "text/plain": "<Figure size 720x504 with 5 Axes>"
+            "text/plain": "<Figure size 720x504 with 3 Axes>",
+            "image/png": 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\n"
           },
           "metadata": {
             "needs_background": "light"
-          },
-          "output_type": "display_data"
+          }
         }
       ]
     },
@@ -34,18 +34,38 @@
         "trusted": true
       },
       "cell_type": "code",
-      "source": "layout = '''\n AAAB\n CDEB\n '''\nfig = plt.figure(figsize=(10, 5))\naxes, spec = make_panelled_axes(layout, fig=fig)\nlabel_panels(axes)\nplt.tight_layout()",
-      "execution_count": 57,
+      "source": "layout = '''\n AAAB\n CDEB\n '''\nfig = plt.figure(figsize=(10, 5))\naxes, spec = panels(layout, fig=fig)\nlabel_panels(axes)\nplt.tight_layout()",
+      "execution_count": 14,
       "outputs": [
         {
+          "output_type": "display_data",
           "data": {
-            "image/png": 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\n",
-            "text/plain": "<Figure size 720x360 with 5 Axes>"
+            "text/plain": "<Figure size 720x360 with 5 Axes>",
+            "image/png": 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SdFxtDsWTzP/ZC1w2/BaKNwHfqapvjT+QJEmSdDytnT4xyfwfBn8GuQiYB74PXD7BQ+9apZL1bJ7r6fJ8T4/neno819Pl+VZvpepZU3AlSZK0jLm5uTpwoLPTsGdSkrtO9nMIbZ4+IUmSJE2FoViSJEm919lQ7BLR0zPBuX7P8Bzfk+SOJK4QdJKWO9cj485L8lSSd0+zvq6Z5HwnuSDJ3UkOJvn6tGvsigneR16S5AtJvjE815N8hkSLSLI7yaNLfWe/vx/VV50MxS4RPT0TnuuHgLdW1dnAdfhBjpMy4bl+ZtxHGXxIVSdpkvOd5DTgRuAfVtXrgJ+eeqEdMGFvfwD4ZlWdA1wAfHz4zUQ6cTcDW49z3N+P6qVOhmJcInqalj3XVXVHVX17uHkng++T1ombpK8BPgjcAjw6zeI6aJLzfSlwa1U9DFBVnvOTM8m5LuBFSQK8EHgcODbdMruhqm5ncP6W4u9H9VJXQ/FSyz+f6Bgt70TP4xXAl1e1ou5a9lwnWQ+8C/iBVZp0Uibp7TOBlyb5WpK7klw2teq6ZZJzfT3wWgYLNN0LfKiqnp5Oeb3j70f1Umu/p/g5WrElorWsic9jkrcxCMVvXtWKumuSc/0J4JqqempwQU3PwSTney3wBuDtwPOB30tyZ1U9uNrFdcwk5/pC4G7gx4FXA7+Z5Heq6rurXVwP+ftRvdTVUOwS0dMz0XlMcjZwE7Ctqh6bUm1dM8m5ngP2DAPx6cBFSY5V1eemU2KnTPo+crSqngCeSHI7cA5gKD4xk5zry4H/WIMv159P8hBwFvA/p1Nir/j7Ub3U1ekTLhE9Pcue6yRnALcC7/MK2nOy7Lmuqk1V9SNV9SPArwM/ayA+aZO8j3weeEuStUlOBd4I3D/lOrtgknP9MIMr8iR5BfAa4NBUq+wPfz+qlzp5pXgVl4jWmAnP9YeBlwE3Dq9gHjvZ1Wb6bMJzrRUyyfmuqvuTfAW4B3gauKmqFv2aKy1twt6+Drg5yb0M/rx/TVUdbazoGZbkswy+weP0JAvAR4Dngb8f1W8u8yxJknQSXOa5feIyz5IkSdLJMxRLkiSp9wzFkiRJ6j1DsSRJknrPUCxJkqTeMxRLkiSp9wzFkiRJ6j1DsSRJknrPUCxJkqTeMxRLkiSp9wzFkiRJ6j1DsSRJknrPUCxJkqTemzgUJzk1yb9I8jtJHk/yl0n+NMm+JO9PsnY1C5VORpILktTI7akk305yX5JPJ9maJE3XKU1qkZ4evx1rukZJmkUTBdkkPwZ8CTgT+CrwS8BR4OXA3wN+FdgC/JvVKVN6zj4L7AMCvAh4DfCPgMuAryb56ar6swbrk07UMz097ulpFyJJXbBsKE7yfOCLwI8CP1VVt44N+WiS84DzVqE+aaX8QVX92uiOJD8H/Cfg5xgEjG1NFCadpGf1tCTp5E0yfeJKBlfVPr5IIAagqvZX1Y0rWpm0yqrqqar6V8D/ALYmeXPTNUmSpGZMEorfPfy5azULkRr0K8OfP9FoFdKJOTXJ6YvcXtx0YZI0iyYJxX8T+D9VdWi1i5Eacs/w55mNViGdmF8Ejixy+29NFiVJs2qSD9q9GPjT1S5EatB3hz+9wqZZsgv474vsPzLtQiSpCyYJxd9l8Gl9qaueCcPfPe4oqV3+sKq+2nQRktQVk0yfuA94cZIfXe1ipIacPfz5QKNVSJKkxkwSim8Z/rxyNQuRGnTF8OeXGq1CkiQ1ZpJQfBODK2j/Osn2xQYkeUOSnx3ZfmWSs5KcukJ1SisuyZok/xl4M7Cvqn535Nipwx5+ZXMVSs9dklcnOavpOiSp7ZadU1xV30/y9xlcRftckt8AfhN4DFgHvA24kMEiCM/4JeBnhse+tsI1Syfj3CTvHd4fXdHuVcBvAJeOjT8f+G3g08D7p1SjdCJGe3rc56rqe8P7v8Wgz13OXJKOY6JlnqtqPsnfBv4Z8FPAvwVeCDwOHGAQgP0aILXZJcPb08D3gAXg68Bnq+orTRYmnaRnenoxm4H5KdYiSTMvVdV0DZIkSTNnbm6uDhw40HQZGpHkrqqaO5l/O8mcYkmSJKnTDMWSJEnqPUOxJEmSes9QLEmSpN4zFEuSJKn3WhuKk+xO8miS+5Y4niSfTDKf5J4k5067Rmk59rFmnT0sqS9aG4qBm4Gtxzm+jcF3cW4GdgCfmkJN0om6GftYs+1m7GFJPdDaUFxVtzNYHGQp24HP1MCdwGkuyau2sY816+xhSX0x0Yp2LbUeeGRke2G471vjA5PsYHAFgxe84AVvOOuss6ZSoCZz1113Ha2qdU3X0RD7uCN63Mf2cIf0uI+lmQ7FWWTfosvzVdUuYBe4+kwbJfnjpmtokH3cET3uY3u4Q7rcx0m2Ar8MrAFuqqr/uMS484A7gX9cVb8+xRLVsNZOn5jAArBxZHsDcLihWqSTZR9r1tnDar0ka4AbGMyB3wJckmTLEuM+Ctw23QrVBrMcivcClw0/+fwm4DtV9aw/10ktZx9r1tnDmgXnA/NVdaiqngT2MJgPP+6DwC3Ao9MsTu3Q2ukTST4LXACcnmQB+AjwPICq2gnsAy4C5oHvA5c3U6m0NPtYs84eVkcsNvf9jaMDkqwH3gX8OHDeUg80Ojf+jDPOWPFC1ZzWhuKqumSZ4wV8YErlSCfFPtass4fVEZPMff8EcE1VPZUsNnz4j8bmxq9YhWpca0OxJEnSCplk7vscsGcYiE8HLkpyrKo+N50S1TRDsSRJ6rr9wOYkm4A/AS4GLh0dUFWbnrmf5GbgiwbifjEUS5KkTquqY0muZvCtEmuA3VV1MMlVw+M7Gy1QrWAoliRJnVdV+xh8MHR036JhuKreP42a1C6z/JVskiRJ0oowFEuSJKn3DMWSJEnqPUOxJEmSes9QLEmSpN4zFEuSJKn3DMWSJEnqPUOxJEmSes9QLEmSpN4zFEuSJKn3DMWSJEnqvVaH4iRbkzyQZD7JtYscf0mSLyT5RpKDSS5vok5pKfawusA+ltQHrQ3FSdYANwDbgC3AJUm2jA37APDNqjoHuAD4eJJTplqotAR7WF1gH0vqi9aGYuB8YL6qDlXVk8AeYPvYmAJelCTAC4HHgWPTLVNakj2sLrCPJfVCm0PxeuCRke2F4b5R1wOvBQ4D9wIfqqqnp1OetCx7WF1gH0vqhTaH4iyyr8a2LwTuBn4YeD1wfZIXP+uBkh1JDiQ5cOTIkZWvVFrcivUw2MdqjO/FknqhzaF4Adg4sr2BwVWIUZcDt9bAPPAQcNb4A1XVrqqaq6q5devWrVrB0pgV62Gwj9UY34sl9UKbQ/F+YHOSTcMPbFwM7B0b8zDwdoAkrwBeAxyaapXS0uxhdYF9LKkX1jZdwFKq6liSq4HbgDXA7qo6mOSq4fGdwHXAzUnuZfAnvmuq6mhjRUsj7GF1gX0sqS9aG4oBqmofsG9s386R+4eBd067LmlS9rC6wD6W1Adtnj4hSZIkTYWhWJIkSb1nKJYkSVLvGYolSZLUe4ZiSZIk9Z6hWJIkSb1nKJYkSVLvGYolSZLUe4ZiSZIk9Z6hWJIkSb1nKJYkSVLvGYolSZLUe4ZiSZIk9Z6hWJIkSb1nKJYkSVLvGYolSZLUe60OxUm2JnkgyXySa5cYc0GSu5McTPL1adcoHY89rC6wjyX1wdqmC1hKkjXADcA7gAVgf5K9VfXNkTGnATcCW6vq4SQvb6Za6dnsYXWBfSypL9p8pfh8YL6qDlXVk8AeYPvYmEuBW6vqYYCqenTKNUrHYw+rC+xjSb3Q5lC8HnhkZHthuG/UmcBLk3wtyV1JLlvsgZLsSHIgyYEjR46sUrnSs6xYD4N9rMb4XiypF9ocirPIvhrbXgu8AfgJ4ELg55Oc+ax/VLWrquaqam7dunUrX6m0uBXrYbCP1RjfiyX1QmvnFDO4GrFxZHsDcHiRMUer6gngiSS3A+cAD06nROm47GF1gX0sqRfafKV4P7A5yaYkpwAXA3vHxnweeEuStUlOBd4I3D/lOqWl2MPqAvtYUi+09kpxVR1LcjVwG7AG2F1VB5NcNTy+s6ruT/IV4B7gaeCmqrqvuaql/88eVhfYx5L6orWhGKCq9gH7xvbtHNv+GPCxadYlTcoeVhfYx5L6oM3TJyRJkqSpMBRLkiSp9wzFkiRJ6j1DsSRJknrPUCxJkqTeMxRLkqTOS7I1yQNJ5pNcu8jx9yS5Z3i7I8k5TdSp5hiKJUlSpyVZA9wAbAO2AJck2TI27CHgrVV1NnAdsGu6VapphmJJktR15wPzVXWoqp4E9gDbRwdU1R1V9e3h5p0MljRXjxiKJUlS160HHhnZXhjuW8oVwJcXO5BkR5IDSQ4cOXJkBUtU0wzFkiSp67LIvlp0YPI2BqH4msWOV9Wuqpqrqrl169atYIlqWquXeZYkSVoBC8DGke0NwOHxQUnOBm4CtlXVY1OqTS3hlWJJktR1+4HNSTYlOQW4GNg7OiDJGcCtwPuq6sEGalTDvFIsSZI6raqOJbkauA1YA+yuqoNJrhoe3wl8GHgZcGMSgGNVNddUzZo+Q7EkSeq8qtoH7Bvbt3Pk/pXAldOuS+3h9AlJkiT1XqtD8XKrz4yMOy/JU0nePc36pOXYw+oC+1hSH7Q2FE+4+swz4z7KYJ6Q1Br2sLrAPpbUF60NxUyw+szQB4FbgEenWZw0AXtYXWAfS+qFNofiZVefSbIeeBewE6l97GF1gX0sqRfaHIonWX3mE8A1VfXUcR/IJRnVjBXrYbCP1RjfiyX1Qpu/km2S1WfmgD3D7xM8HbgoybGq+tzooKraBewCmJubW3RZR2kVrFgPg32sxvheLKkX2hyK/2r1GeBPGKw+c+nogKra9Mz9JDcDX1wsTEgNsYfVBfaxpF5obSiecPUZqbXsYXWBfSypL1obimH51WfG9r9/GjVJJ8IeVhfYx5L6oM0ftJMkSZKmwlAsSZKk3jMUS5IkqfcMxZIkSeo9Q7EkSZJ6z1AsSZKk3jMUS5IkqfcMxZIkSeo9Q7EkSZJ6z1AsSZKk3jMUS5IkqfcMxZIkSeo9Q7EkSZJ6z1AsSZKk3jMUS5IkqfcMxZIkSeq9VofiJFuTPJBkPsm1ixx/T5J7hrc7kpzTRJ3SUuxhdYF9LKkPWhuKk6wBbgC2AVuAS5JsGRv2EPDWqjobuA7YNd0qpaXZw+oC+1hSX7Q2FAPnA/NVdaiqngT2ANtHB1TVHVX17eHmncCGKdcoHY89rC6wjyX1QptD8XrgkZHtheG+pVwBfHmxA0l2JDmQ5MCRI0dWsETpuFash8E+VmN8L5bUC20OxVlkXy06MHkbgzfiaxY7XlW7qmququbWrVu3giVKx7ViPQz2sRrje7GkXljbdAHHsQBsHNneABweH5TkbOAmYFtVPTal2qRJ2MPqAvtYUi+0+UrxfmBzkk1JTgEuBvaODkhyBnAr8L6qerCBGqXjsYfVBfaxpF5o7ZXiqjqW5GrgNmANsLuqDia5anh8J/Bh4GXAjUkAjlXVXFM1S6PsYXWBfSypL1obigGqah+wb2zfzpH7VwJXTrsuaVL2sLrAPpbUB22ePiFJkiRNhaFYkiRJvWcoliRJUu8ZiiVJktR7hmJJkiT1nqFYkiRJvWcoliRJUu8ZiiVJktR7hmJJkiT1nqFYkiRJvWcoliRJUu8ZiiVJktR7hmJJkiT1nqFYkiRJvWcoliRJUu8ZiiVJktR7rQ7FSbYmeSDJfJJrFzmeJJ8cHr8nyblN1CktxR5WF9jH6gL7WMtpbShOsga4AdgGbAEuSbJlbNg2YPPwtgP41FSLlI7DHlYX2MfqAvtYk2htKAbOB+ar6lBVPQnsAbaPjdkOfKYG7gROS/LKaRcqLcEeVhfYx+oC+1jLWtt0AcexHnhkZHsBeOMEY9YD3xodlGQHg//1AfxFkvtWttTGnA4cbbqIFfCapgtYJSvWw9DZPu5KD4N97HtxN9jHPzjGPp49J93DbQ7FWWRfncQYqmoXsAsgyYGqmnvu5TWvK88lyYGma1glK9bD0M0+7srzAPt4gjGd7GHo3nNpuoZVYh8voyvP5bn0cJunTywAG0e2NwCHT2KM1BR7WF1gH6sL7GMtq82heD+wOVPGYmkAAALGSURBVMmmJKcAFwN7x8bsBS4bfmL0TcB3qupZf3aWGmIPqwvsY3WBfaxltXb6RFUdS3I1cBuwBthdVQeTXDU8vhPYB1wEzAPfBy6f4KF3rVLJTejKc+nK8/gBq9jD0J1z1pXnAd16Ln/F9+KJ+Fxazj6eSFeey0k/j1QtOn1RkiRJ6o02T5+QJEmSpsJQLEmSpN7rbCjuynKOEzyPC5J8J8ndw9uHm6hzEkl2J3l0qe90nJXXZFq60sPQnT62h0+cfdw+9vGJ60ofd6WHYZX6uKo6d2Mwif6PgB8FTgG+AWwZG3MR8GUG30v4JuD3m677JJ/HBcAXm651wufzd4FzgfuWON7616Rlr/1MnK8u9bE9vCqv/UycM/u4+bpb/tq3/px1qYeHta54H3f1SnFXlnOc5HnMjKq6HXj8OENm4TWZlq70MHSoj+3hE2Yft5B9fMK60sed6WFYnT7uaiheaqnGEx3TtElr/DtJvpHky0leN53SVsUsvCbT0pUehn718ay8JtNiH8+mWXlNpqUrfdynHoaTeE1a+z3Fz9GKLq/boElq/APgVVX1vSQXAZ8DNq96ZatjFl6TaelKD0O/+nhWXpNpsY/t4y7oSh/3qYfhJF6Trl4p7spyjsvWWFXfrarvDe/vA56X5PTplbiiZuE1mZau9DD0q49n5TWZFvvYPu6CrvRxn3oYTuI16Woo7spyjss+jyR/I0mG989n8Jo+NvVKV8YsvCbT0pUehn718ay8JtNiH9vHXdCVPu5TD8NJvCadnD5Rq7u87tRM+DzeDfzzJMeA/wtcXMOPXbZNks8y+GTr6UkWgI8Az4PZeU2mpSs9DN3qY3v4xNjH9nEXdKWPu9TDsDp97DLPkiRJ6r2uTp+QJEmSJmYoliRJUu8ZiiVJktR7hmJJkiT1nqFYkiRJvWcoliRJUu8ZiiVJktR7/w/uGxvp50OZDAAAAABJRU5ErkJggg==\n"
           },
           "metadata": {
             "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "layout = '''\n    AAAB\n    AAAC\n    AAAD\n    '''\nN = 5\nfig = plt.figure(figsize=(8, 6))\nspecs, gs = panel_specs(layout, fig=fig)\naxes = {}\nfor letter in 'BCD':\n    axes[letter] = ax = fig.add_subplot(specs[letter])\n    label_panel(ax, letter)\nsubgs = specs['A'].subgridspec(N, N, wspace=0, hspace=0)\ntriaxes = {}\nfor i in range(N):\n    for j in range(i+1):\n        triaxes[i, j] = ax = fig.add_subplot(subgs[i, j])\n        ax.set_xticks([])\n        ax.set_yticks([])\n        if i==N-1:\n            ax.set_xlabel(chr(ord('α')+j))\n        if j==0:\n            ax.set_ylabel(chr(ord('α')+i))\nlabel_panel(triaxes[0, 0], 'A', spaces=2)\nplt.tight_layout()",
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 576x432 with 18 Axes>",
+            "image/png": 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\n"
           },
-          "output_type": "display_data"
+          "metadata": {
+            "needs_background": "light"
+          }
         }
       ]
     }

diff --git a/Automatic scientific axes layout for matplotlib.ipynb b/Automatic scientific axes layout for matplotlib.ipynb
@@ -0,0 +1,81 @@
+{
+  "cells": [
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec",
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "from collections import namedtuple\ndef make_panelled_axes(layout, fig=None):\n    # default arguments\n    if fig is None:\n        fig = plt.gcf()\n    # format and sanity check grid\n    lines = layout.split('\\n')\n    lines = [line.strip() for line in lines if line.strip()]\n    linewidths = set(len(line) for line in lines)\n    if len(linewidths)>1:\n        raise ValueError('Invalid layout (all lines must have same width)')\n    width = linewidths.pop()\n    height = len(lines)\n    panel_letters = set(c for line in lines for c in line)\n    # find bounding boxes for each panel\n    panel_grid = {}\n    for letter in panel_letters:\n        left = min(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        right = 1+max(x for x in range(width) for y in range(height) if lines[y][x]==letter)\n        top = min(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        bottom = 1+max(y for x in range(width) for y in range(height) if lines[y][x]==letter)\n        panel_grid[letter] = (left, right, top, bottom)\n        # check that this layout is consistent, i.e. all squares are filled\n        valid = all(lines[y][x]==letter for x in range(left, right) for y in range(top, bottom))\n        if not valid:\n            raise ValueError('Invalid layout (not all square)')\n    # build axes\n    axes = {}\n    spec = gridspec.GridSpec(ncols=width, nrows=height, figure=fig)\n    for letter, (left, right, top, bottom) in panel_grid.items():\n        axes[letter] = fig.add_subplot(spec[top:bottom, left:right])\n    return axes, spec\n\ndef label_panels(axes, letters=None, *, prefix='', postfix='.', spaces=6, pad=10, fontsize=18):\n    if letters is None:\n        letters = axes.keys()\n    for letter in letters:\n        ax = axes[letter]\n        ax.set_title(prefix+letter+postfix+' '*spaces, loc='left', pad=pad,\n                     fontdict={'horizontalalignment': 'right',\n                               'fontsize': fontsize})\n    \nlayout = '''\n AAB\n AAx\n CDD\n '''\nfig = plt.figure(figsize=(10, 7))\naxes, spec = make_panelled_axes(layout, fig=fig)\nspec.set_width_ratios([1, 3, 1])\nlabel_panels(axes, letters='ABCD')\nplt.tight_layout()",
+      "execution_count": 56,
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": "<Figure size 720x504 with 5 Axes>"
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "trusted": true
+      },
+      "cell_type": "code",
+      "source": "layout = '''\n AAAB\n CDEB\n '''\nfig = plt.figure(figsize=(10, 5))\naxes, spec = make_panelled_axes(layout, fig=fig)\nlabel_panels(axes)\nplt.tight_layout()",
+      "execution_count": 57,
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": "<Figure size 720x360 with 5 Axes>"
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "name": "conda-env-brian-py",
+      "display_name": "Python [conda env:brian]",
+      "language": "python"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.8.2",
+      "mimetype": "text/x-python",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "file_extension": ".py"
+    },
+    "gist": {
+      "id": "",
+      "data": {
+        "description": "Automatic scientific axes layout for matplotlib.ipynb",
+        "public": true
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}