Pep8 compliance in notebook examples

Author: lukelbd

docs/1dplots.ipynb

@@ -58,19 +58,18 @@
     "import xarray as xr\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import proplot as plot\n",
     "# DataArray\n",
     "state = np.random.RandomState(51423)\n",
-    "data = np.sin(np.linspace(0, 2*np.pi, 20))[:,None] + state.rand(20,8).cumsum(axis=1)\n",
+    "data = np.sin(np.linspace(0, 2*np.pi, 20))[:, None] \\\n",
+    "    + state.rand(20, 8).cumsum(axis=1)\n",
     "da = xr.DataArray(data, dims=('x', 'cat'), coords={\n",
-    "    'x':xr.DataArray(np.linspace(0,1,20), dims=('x',), attrs={'long_name':'distance', 'units':'km'}),\n",
-    "    'cat':xr.DataArray(np.arange(0,80,10), dims=('cat',), attrs={'long_name':'parameter', 'units':'K'})\n",
-    "    }, name='position series')\n",
+    "    'x': xr.DataArray(np.linspace(0, 1, 20), dims=('x',), attrs={'long_name': 'distance', 'units': 'km'}),\n",
+    "    'cat': xr.DataArray(np.arange(0, 80, 10), dims=('cat',), attrs={'long_name': 'parameter', 'units': 'K'})\n",
+    "}, name='position series')\n",
     "# DataFrame\n",
-    "plot.rc.reset()\n",
     "ts = pd.date_range('1/1/2000', periods=20)\n",
-    "data = (np.cos(np.linspace(0, 2*np.pi, 20))**4)[:,None] + state.rand(20,5)**2\n",
-    "df = pd.DataFrame(data, index=ts, columns=['foo','bar','baz','zap','baf'])\n",
+    "data = (np.cos(np.linspace(0, 2*np.pi, 20))**4)[:, None] + state.rand(20, 5)**2\n",
+    "df = pd.DataFrame(data, index=ts, columns=['foo', 'bar', 'baz', 'zap', 'baf'])\n",
     "df.name = 'time series'\n",
     "df.index.name = 'time (s)'\n",
     "df.columns.name = 'columns'"
@@ -82,12 +81,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Figure\n",
+    "import proplot as plot\n",
     "f, axs = plot.subplots(ncols=2, axwidth=2.2, share=0)\n",
     "axs.format(suptitle='Automatic subplot formatting')\n",
     "# Plot DataArray\n",
     "cycle = plot.Cycle(plot.shade('light blue', 0.4), fade=90, space='hpl')\n",
-    "axs[0].plot(da, cycle=cycle, lw=3, colorbar='ul', colorbar_kw={'locator':20})\n",
+    "axs[0].plot(da, cycle=cycle, lw=3, colorbar='ul', colorbar_kw={'locator': 20})\n",
     "# Plot Dataframe\n",
     "cycle = plot.Cycle(plot.shade('jade', 0.4), fade=90, space='hpl')\n",
     "axs[1].plot(df, cycle=cycle, lw=3, legend='uc')"
@@ -137,8 +136,10 @@
     "plot.rc['title.loc'] = 'uc'\n",
     "plot.rc['axes.ymargin'] = plot.rc['axes.xmargin'] = 0.05\n",
     "state = np.random.RandomState(51423)\n",
-    "data = state.rand(20,8).cumsum(axis=0).cumsum(axis=1)[:,::-1] + 20*state.normal(size=(20,8)) + 30\n",
-    "f, axs = plot.subplots(nrows=3, aspect=1.5, axwidth=3, share=0, hratios=(2,1,1))\n",
+    "data = state.rand(20, 8).cumsum(axis=0).cumsum(axis=1)[:, ::-1] \\\n",
+    "    + 20*state.normal(size=(20, 8)) + 30\n",
+    "f, axs = plot.subplots(nrows=3, aspect=1.5, axwidth=3,\n",
+    "                       share=0, hratios=(2, 1, 1))\n",
     "axs.format(suptitle='Error bars with various plotting commands')\n",
     "# Asking add_errorbars to calculate bars\n",
     "ax = axs[0]\n",
@@ -147,11 +148,11 @@
     "# Showing a standard deviation range instead of percentile range\n",
     "ax = axs[1]\n",
     "ax.scatter(data, color='k', marker='x', markersize=50, barcolor='gray5',\n",
-    "           medians=True, barstd=True, barrange=(-1,1), barzorder=0, boxes=False, capsize=2)\n",
+    "           medians=True, barstd=True, barrange=(-1, 1), barzorder=0, boxes=False, capsize=2)\n",
     "# Supplying error bar data manually\n",
     "ax = axs[2]\n",
-    "boxdata = np.percentile(data, (25,75), axis=0)\n",
-    "bardata = np.percentile(data, (5,95), axis=0)\n",
+    "boxdata = np.percentile(data, (25, 75), axis=0)\n",
+    "bardata = np.percentile(data, (5, 95), axis=0)\n",
     "ax.plot(data.mean(axis=0), boxes=False, marker='o', markersize=5,\n",
     "        edgecolor='k', color='cerulean', boxdata=boxdata, bardata=bardata)\n",
     "# Formatting\n",
@@ -189,15 +190,20 @@
     "import pandas as pd\n",
     "plot.rc.titleloc = 'uc'\n",
     "plot.rc.margin = 0.05\n",
-    "f, axs = plot.subplots(nrows=2, aspect=2, axwidth=3.5, share=0, hratios=(3,2))\n",
+    "f, axs = plot.subplots(nrows=2, aspect=2, axwidth=3.5, share=0, hratios=(3, 2))\n",
     "state = np.random.RandomState(51423)\n",
-    "data = state.rand(5,5).cumsum(axis=0).cumsum(axis=1)[:,::-1]\n",
-    "data = pd.DataFrame(data, columns=pd.Index(np.arange(1,6), name='column'), index=pd.Index(['a','b','c','d','e'], name='row idx'))\n",
+    "data = state.rand(5, 5).cumsum(axis=0).cumsum(axis=1)[:, ::-1]\n",
+    "data = pd.DataFrame(data,\n",
+    "                    columns=pd.Index(np.arange(1, 6), name='column'),\n",
+    "                    index=pd.Index(['a', 'b', 'c', 'd', 'e'], name='row idx'))\n",
     "ax = axs[0]\n",
-    "obj = ax.bar(data, cycle='Reds', colorbar='ul', edgecolor='red9', colorbar_kw={'frameon':False})\n",
-    "ax.format(xlocator=1, xminorlocator=0.5, ytickminor=False, title='Side-by-side', suptitle='Bar plot wrapper demo')\n",
+    "obj = ax.bar(data, cycle='Reds', colorbar='ul',\n",
+    "             edgecolor='red9', colorbar_kw={'frameon': False})\n",
+    "ax.format(xlocator=1, xminorlocator=0.5, ytickminor=False,\n",
+    "          title='Side-by-side', suptitle='Bar plot wrapper demo')\n",
     "ax = axs[1]\n",
-    "obj = ax.barh(data.iloc[::-1,:], cycle='Blues', legend='ur', edgecolor='blue9', stacked=True)\n",
+    "obj = ax.barh(data.iloc[::-1, :], cycle='Blues',\n",
+    "              legend='ur', edgecolor='blue9', stacked=True)\n",
     "ax.format(title='Stacked')\n",
     "axs.format(grid=False)\n",
     "plot.rc.reset()"
@@ -230,21 +236,23 @@
     "import proplot as plot\n",
     "import numpy as np\n",
     "plot.rc.margin = 0\n",
-    "f, axs = plot.subplots(array=[[1,2],[3,3]], hratios=(1,0.8), share=0)\n",
+    "f, axs = plot.subplots(array=[[1, 2], [3, 3]], hratios=(1, 0.8), share=0)\n",
     "axs.format(xlabel='xlabel', ylabel='ylabel', suptitle='Area plot demo')\n",
     "state = np.random.RandomState(51423)\n",
-    "data = state.rand(5,3).cumsum(axis=0)\n",
+    "data = state.rand(5, 3).cumsum(axis=0)\n",
     "cycle = ('gray3', 'gray5', 'gray7')\n",
+    "# 2D arrays\n",
     "ax = axs[0]\n",
-    "ax.areax(np.arange(5), data, data + state.rand(5)[:,None], cycle=cycle, alpha=0.5,\n",
-    "        legend='uc', legend_kw={'center':True, 'ncols':2, 'labels':['z','y','qqqq']},\n",
-    "        )\n",
+    "ax.areax(np.arange(5), data, data + state.rand(5)[:, None], cycle=cycle, alpha=0.5,\n",
+    "         legend='uc', legend_kw={'center': True, 'ncols': 2, 'labels': ['z', 'y', 'qqqq']},\n",
+    "         )\n",
     "ax.format(title='Fill between columns')\n",
     "ax = axs[1]\n",
     "ax.area(np.arange(5), data, stacked=True, cycle=cycle, alpha=0.8,\n",
-    "        legend='ul', legend_kw={'center':True, 'ncols':2, 'labels':['z','y','qqqq']},\n",
+    "        legend='ul', legend_kw={'center': True, 'ncols': 2, 'labels': ['z', 'y', 'qqqq']},\n",
     "        )\n",
     "ax.format(title='Stack between columns')\n",
+    "# Positive and negative colors\n",
     "ax = axs[2]\n",
     "data = 5*(state.rand(20)-0.5)\n",
     "ax.area(data, negpos=True, negcolor='blue7', poscolor='red7')\n",
@@ -281,15 +289,20 @@
     "N = 500\n",
     "state = np.random.RandomState(51423)\n",
     "f, axs = plot.subplots(ncols=2)\n",
-    "data = state.normal(size=(N,5)) + 2*(state.rand(N,5)-0.5)*np.arange(5)\n",
-    "data = pd.DataFrame(data, columns=pd.Index(['a','b','c','d','e'], name='xlabel'))\n",
+    "data = state.normal(size=(N, 5)) + 2*(state.rand(N, 5)-0.5)*np.arange(5)\n",
+    "data = pd.DataFrame(data, columns=pd.Index(\n",
+    "    ['a', 'b', 'c', 'd', 'e'], name='xlabel'))\n",
     "ax = axs[0]\n",
-    "obj1 = ax.boxplot(data, lw=0.7, marker='x', fillcolor='gray5', medianlw=1, mediancolor='k')#, boxprops={'color':'C0'})#, labels=data.columns)\n",
+    "# , boxprops={'color':'C0'})#, labels=data.columns)\n",
+    "obj1 = ax.boxplot(data, lw=0.7, marker='x', fillcolor='gray5',\n",
+    "                  medianlw=1, mediancolor='k')\n",
     "ax.format(title='Box plots', titleloc='uc')\n",
     "ax = axs[1]\n",
-    "obj2 = ax.violinplot(data, lw=0.7, fillcolor='gray7', points=500, bw_method=0.3, means=True)\n",
+    "obj2 = ax.violinplot(data, lw=0.7, fillcolor='gray7',\n",
+    "                     points=500, bw_method=0.3, means=True)\n",
     "ax.format(title='Violin plots', titleloc='uc')\n",
-    "axs.format(ymargin=0.1, xmargin=0.1, grid=False, suptitle='Boxes and violins demo')"
+    "axs.format(ymargin=0.1, xmargin=0.1, grid=False,\n",
+    "           suptitle='Boxes and violins demo')"
    ]
   },
   {
@@ -320,22 +333,26 @@
     "N = 50\n",
     "cmap = 'IceFire'\n",
     "values = np.linspace(-N/2, N/2, N)\n",
-    "f, axs = plot.subplots(share=0, ncols=2, wratios=(2,1), axwidth='6cm', aspect=(2,1))\n",
+    "f, axs = plot.subplots(share=0, ncols=2, wratios=(2, 1),\n",
+    "                       axwidth='6cm', aspect=(2, 1))\n",
     "ax = axs[0]\n",
     "state = np.random.RandomState(51423)\n",
-    "m = ax.plot((state.rand(N)-0.5).cumsum(), state.rand(N), cmap=cmap, values=values, lw=7, extend='both')\n",
-    "ax.format(xlabel='xlabel', ylabel='ylabel', title='Line with smooth color gradations', titleweight='bold')\n",
-    "ax.format(xlim=(-1,5), ylim=(-0.2,1.2))\n",
+    "m = ax.plot((state.rand(N) - 0.5).cumsum(), state.rand(N),\n",
+    "            cmap=cmap, values=values, lw=7, extend='both')\n",
+    "ax.format(xlabel='xlabel', ylabel='ylabel',\n",
+    "          title='Line with smooth color gradations', titleweight='bold')\n",
+    "ax.format(xlim=(-1, 5), ylim=(-0.2, 1.2))\n",
     "ax.colorbar(m, loc='b', label='parametric coordinate', locator=5)\n",
     "N = 12\n",
     "ax = axs[1]\n",
     "values = np.linspace(-N/2, N/2 - 1, N)\n",
-    "radii = np.linspace(1,0.2,N)\n",
-    "angles = np.linspace(0,4*np.pi,N)\n",
+    "radii = np.linspace(1, 0.2, N)\n",
+    "angles = np.linspace(0, 4*np.pi, N)\n",
     "x = radii*np.cos(1.4*angles)\n",
     "y = radii*np.sin(1.4*angles)\n",
     "m = ax.plot(x, y, values=values, linewidth=15, interp=False, cmap=cmap)\n",
-    "ax.format(xlim=(-1,1), ylim=(-1,1), title='Step gradations', titleweight='bold', xlabel='cosine angle', ylabel='sine angle')\n",
+    "ax.format(xlim=(-1, 1), ylim=(-1, 1), title='Step gradations',\n",
+    "          titleweight='bold', xlabel='cosine angle', ylabel='sine angle')\n",
     "ax.colorbar(m, loc='b', maxn=10, label=f'parametric coordinate')"
    ]
   },
@@ -369,18 +386,19 @@
     "f, axs = plot.subplots(ncols=2, share=1)\n",
     "state = np.random.RandomState(51423)\n",
     "x = (state.rand(20)-0).cumsum()\n",
-    "data = (state.rand(20,4)-0.5).cumsum(axis=0)\n",
-    "data = pd.DataFrame(data, columns=pd.Index(['a','b','c','d'], name='label'))\n",
+    "data = (state.rand(20, 4)-0.5).cumsum(axis=0)\n",
+    "data = pd.DataFrame(data, columns=pd.Index(['a', 'b', 'c', 'd'], name='label'))\n",
     "# Scatter demo\n",
     "ax = axs[0]\n",
     "ax.format(title='Extra prop cycle properties', suptitle='Scatter plot demo')\n",
-    "obj = ax.scatter(x, data, legend='ul', cycle='warm', legend_kw={'ncols':2},\n",
-    "                cycle_kw={'marker':['x','o','x','o'], 'markersize':[5,10,20,30]})\n",
+    "obj = ax.scatter(x, data, legend='ul', cycle='warm', legend_kw={'ncols': 2},\n",
+    "                 cycle_kw={'marker': ['x', 'o', 'x', 'o'], 'markersize': [5, 10, 20, 30]})\n",
     "ax = axs[1]\n",
     "ax.format(title='Scatter plot with cmap')\n",
-    "data = (state.rand(2,100) - 0.5)\n",
+    "data = (state.rand(2, 100) - 0.5)\n",
     "obj = ax.scatter(*data, color=data.sum(axis=0), size=state.rand(100), smin=3, smax=30,\n",
-    "                 marker='o', cmap='dusk', colorbar='lr', colorbar_kw={'locator':0.5, 'label':'label'})\n",
+    "                 marker='o', cmap='dusk', colorbar='lr',\n",
+    "                 colorbar_kw={'locator': 0.5, 'label': 'label'})\n",
     "axs.format(xlabel='xlabel', ylabel='ylabel')"
    ]
   }