Sort imports in another 9 notebooks (#4074)

* another 10 notebooks

* 10th notebook

* put magic at bottom of cell

* separate first-party imports

Author: MarcoGorelli

docs/source/notebooks/GLM-robust.ipynb

@@ -40,11 +40,9 @@
    "source": [
     "%matplotlib inline\n",
     "\n",
-    "import pymc3 as pm\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "\n",
+    "import pymc3 as pm\n",
     "import theano"
    ]
   },

docs/source/notebooks/GP-MeansAndCovs.ipynb

@@ -11,16 +11,16 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
     "import matplotlib.cm as cmap\n",
-    "%matplotlib inline\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "np.random.seed(206)\n",
-    "\n",
+    "import pymc3 as pm\n",
     "import theano\n",
     "import theano.tensor as tt\n",
-    "import pymc3 as pm"
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "np.random.seed(206)"
    ]
   },
   {

docs/source/notebooks/bayes_param_survival_pymc3.ipynb

@@ -30,7 +30,8 @@
     "from matplotlib import pyplot as plt\n",
     "from matplotlib.ticker import StrMethodFormatter\n",
     "from statsmodels import datasets\n",
-    "from theano import shared, tensor as tt\n",
+    "from theano import shared\n",
+    "from theano import tensor as tt\n",
     "\n",
     "print('Running on PyMC3 v{}'.format(pm.__version__))"
    ]

docs/source/notebooks/dependent_density_regression.ipynb

@@ -39,8 +39,10 @@
     "import seaborn as sns\n",
     "\n",
     "from IPython.display import HTML\n",
-    "from matplotlib import animation as ani, pyplot as plt\n",
-    "from theano import shared, tensor as tt\n",
+    "from matplotlib import animation as ani\n",
+    "from matplotlib import pyplot as plt\n",
+    "from theano import shared\n",
+    "from theano import tensor as tt\n",
     "\n",
     "print('Running on PyMC3 v{}'.format(pm.__version__))"
    ]
@@ -1109,8 +1111,7 @@
     "\n",
     "with model:\n",
     "    step = pm.Metropolis()\n",
-    "    trace = pm.sample(SAMPLES, step, chains=1, tune=BURN, random_seed=SEED)\n",
-    "    "
+    "    trace = pm.sample(SAMPLES, step, chains=1, tune=BURN, random_seed=SEED)"
    ]
   },
   {

docs/source/notebooks/dp_mix.ipynb

@@ -82,8 +82,8 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc3 as pm\n",
-    "import seaborn as sns\n",
     "import scipy as sp\n",
+    "import seaborn as sns\n",
     "\n",
     "from matplotlib import pyplot as plt\n",
     "from theano import tensor as tt\n",

docs/source/notebooks/factor_analysis.ipynb

@@ -14,17 +14,18 @@
     }
    ],
    "source": [
+    "import warnings\n",
+    "\n",
     "import arviz as az\n",
     "import matplotlib\n",
     "import numpy as np\n",
     "import pymc3 as pm\n",
-    "import seaborn as sbn\n",
     "import scipy as sp\n",
     "import scipy.sparse\n",
     "import scipy.sparse.linalg\n",
+    "import seaborn as sbn\n",
     "import theano as T\n",
     "import theano.tensor as tt\n",
-    "import warnings\n",
     "\n",
     "from matplotlib import pyplot as plt\n",
     "\n",

docs/source/notebooks/getting_started.ipynb

@@ -875,6 +875,7 @@
    ],
    "source": [
     "import warnings\n",
+    "\n",
     "with warnings.catch_warnings():\n",
     "    warnings.filterwarnings('ignore', category=UserWarning)\n",
     "    returns.plot(figsize=(10, 6))\n",
@@ -3407,7 +3408,7 @@
     "        labels = [label.get_text() for label in ax.get_xticklabels()]\n",
     "        ax.set_xticklabels(labels, rotation=45, ha='right')\n",
     "        break\n",
-    "plt.draw()                         "
+    "plt.draw()"
    ]
   },
   {
@@ -3491,6 +3492,7 @@
    "outputs": [],
    "source": [
     "import theano.tensor as tt\n",
+    "\n",
     "from theano.compile.ops import as_op\n",
     "\n",
     "\n",

docs/source/notebooks/multilevel_modeling.ipynb

@@ -64,13 +64,14 @@
     }
    ],
    "source": [
+    "import warnings\n",
+    "\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pymc3 as pm\n",
     "import xarray as xr\n",
-    "import warnings\n",
     "\n",
     "from theano import tensor as tt\n",
     "\n",

docs/source/notebooks/probabilistic_matrix_factorization.ipynb

@@ -22,9 +22,10 @@
    ],
    "source": [
     "%matplotlib inline\n",
-    "import pymc3 as pm\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import pymc3 as pm\n",
+    "\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
     "plt.style.use('seaborn-darkgrid')\n",
@@ -767,11 +768,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import time\n",
     "import logging\n",
-    "import theano\n",
-    "import scipy as sp\n",
+    "import time\n",
     "\n",
+    "import scipy as sp\n",
+    "import theano\n",
     "\n",
     "# Enable on-the-fly graph computations, but ignore\n",
     "# absence of intermediate test values.\n",
@@ -1056,7 +1057,7 @@
     "    Method = baseline_methods[name]\n",
     "    method = Method(train)\n",
     "    baselines[name] = method.rmse(test)\n",
-    "    print('%s RMSE:\\t%.5f' % (method, baselines[name]))\n"
+    "    print('%s RMSE:\\t%.5f' % (method, baselines[name]))"
    ]
   },
   {