lint notebooks (isort, pyupgrade) (pymc-devs#123)

tristannew · Nov 7, 2020 · 8aa52a4 · 8aa52a4
1 parent 5f95ecb
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -0,0 +1,13 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v3.3.0
+  hooks:
+    -   id: check-toml
+    -   id: check-yaml
+- repo: https://github.com/nbQA-dev/nbQA
+  rev: 0.4.0
+  hooks:
+    - id: nbqa-isort
+      additional_dependencies: [isort==5.6.4]
+    - id: nbqa-pyupgrade
+      additional_dependencies: [pyupgrade==2.7.3]
diff --git a/BCM/CaseStudies/ExtrasensoryPerception.ipynb b/BCM/CaseStudies/ExtrasensoryPerception.ipynb
@@ -15,8 +15,8 @@
     "import scipy as sp\n",
     "\n",
     "from matplotlib import gridspec\n",
-    "from theano import tensor as tt\n",
     "from scipy import stats\n",
+    "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
     "warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",

diff --git a/BCM/CaseStudies/HeuristicDecisionMaking.ipynb b/BCM/CaseStudies/HeuristicDecisionMaking.ipynb
@@ -12,8 +12,8 @@
     "import pymc3 as pm\n",
     "import scipy.io as sio\n",
     "\n",
-    "from theano import tensor as tt\n",
     "from scipy import stats\n",
+    "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
     "RANDOM_SEED = 8927\n",

diff --git a/BCM/CaseStudies/MemoryRetention.ipynb b/BCM/CaseStudies/MemoryRetention.ipynb
@@ -16,8 +16,8 @@
     "import seaborn as sns\n",
     "import theano\n",
     "\n",
-    "from theano import tensor as tt\n",
     "from scipy import stats\n",
+    "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
     "warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",

diff --git a/BCM/CaseStudies/MultinomialProcessingTrees.ipynb b/BCM/CaseStudies/MultinomialProcessingTrees.ipynb
@@ -15,8 +15,8 @@
     "import scipy.special as sp\n",
     "import theano\n",
     "\n",
-    "from theano import tensor as tt\n",
     "from scipy import stats\n",
+    "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
     "warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",

diff --git a/BCM/CaseStudies/NumberConceptDevelopment.ipynb b/BCM/CaseStudies/NumberConceptDevelopment.ipynb
@@ -15,8 +15,8 @@
     "import scipy.io as sio\n",
     "import theano\n",
     "\n",
-    "from theano import tensor as tt\n",
     "from scipy import stats\n",
+    "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
     "warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",

diff --git a/BDA3/chap_02.ipynb b/BDA3/chap_02.ipynb
@@ -21,10 +21,11 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import numpy as np\n",
+    "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pymc3 as pm\n",
-    "import arviz as az\n",
+    "\n",
     "from scipy.special import expit"
    ]
   },
@@ -627,7 +628,7 @@
     "lldd = expit(df2.loc['trans','hpd_3%'])\n",
     "llii = expit(df2.loc['trans','hpd_97%'])\n",
     "\n",
-    "print('The interval is [{:.3f}, {:.3f}]'.format(lldd, llii))"
+    "print(f'The interval is [{lldd:.3f}, {llii:.3f}]')"
    ]
   },
   {
@@ -647,6 +648,7 @@
    "source": [
     "import theano.tensor as tt\n",
     "\n",
+    "\n",
     "def triangular(central_num, width):\n",
     "    \n",
     "    left_num = central_num - width\n",
@@ -1446,7 +1448,7 @@
    ],
    "source": [
     "val = np.mean(trace_poisson_2['theta'] >= 1)\n",
-    "print('The posterior probability that theta exceeds 1.0 is {:.2f}.'.format(val))"
+    "print(f'The posterior probability that theta exceeds 1.0 is {val:.2f}.')"
    ]
   },
   {

diff --git a/BDA3/chap_03.ipynb b/BDA3/chap_03.ipynb
@@ -35,8 +35,8 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pymc3 as pm\n",
     "import seaborn as sns\n",
     "\n",
@@ -106,7 +106,7 @@
    ],
    "source": [
     "mean_t = np.mean(nums)\n",
-    "print('The mean of the 66 measurements is {:.1f}'.format(mean_t))"
+    "print(f'The mean of the 66 measurements is {mean_t:.1f}')"
    ]
   },
   {
@@ -124,7 +124,7 @@
    ],
    "source": [
     "std_t = np.std(nums, ddof=1)\n",
-    "print('The standard deviation of the 66 measurements is {:.1f}'.format(std_t))"
+    "print(f'The standard deviation of the 66 measurements is {std_t:.1f}')"
    ]
   },
   {
@@ -419,7 +419,7 @@
    ],
    "source": [
     "delta_bin = bins1[1] - bins1[0]\n",
-    "print('This is delta x: {}'.format(delta_bin))"
+    "print(f'This is delta x: {delta_bin}')"
    ]
   },
   {
@@ -484,7 +484,7 @@
     "l_i = bins1[idx - int(idx_sol)]\n",
     "l_d = bins1[idx + int(idx_sol)]\n",
     "\n",
-    "print('The central posterior interval is [{:.2f}, {:.2f}]'.format(l_i, l_d))"
+    "print(f'The central posterior interval is [{l_i:.2f}, {l_d:.2f}]')"
    ]
   },
   {

diff --git a/BDA3/chap_05.ipynb b/BDA3/chap_05.ipynb
@@ -6,12 +6,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "import arviz as az\n",
     "import matplotlib as mpl\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pymc3 as pm\n",
-    "import theano.tensor as tt\n",
-    "import arviz as az"
+    "import theano.tensor as tt"
    ]
   },
   {
@@ -90,7 +90,7 @@
     }
    ],
    "source": [
-    "print('The shape of rat_tumor is {}'.format(rat_tumor.shape))"
+    "print(f'The shape of rat_tumor is {rat_tumor.shape}')"
    ]
   },
   {
@@ -810,7 +810,7 @@
     "ax.grid()\n",
     "ax.set_ylim(1.7, 3.75)\n",
     "ax.set_xlabel('log(α / β)')\n",
-    "_ = ax.set_ylabel('log(α + β)')\n"
+    "_ = ax.set_ylabel('log(α + β)')"
    ]
   },
   {
@@ -1587,7 +1587,7 @@
     "    a3 = np.percentile(post_obs['obs'][:, i], 50)\n",
     "    a4 = np.percentile(post_obs['obs'][:, i], 75)\n",
     "    a5 = np.percentile(post_obs['obs'][:, i], 97.5)\n",
-    "    print('{:^7} {:^10.0f} {:^10.0f} {:^10.0f} {:^10.0f} {:^10.0f}'.format(z, a1, a2, a3, a4, a5))"
+    "    print(f'{z:^7} {a1:^10.0f} {a2:^10.0f} {a3:^10.0f} {a4:^10.0f} {a5:^10.0f}')"
    ]
   },
   {

diff --git a/BDA3/chap_06.ipynb b/BDA3/chap_06.ipynb
@@ -14,12 +14,13 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "import numpy as np\n",
+    "import itertools\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pymc3 as pm\n",
     "import seaborn as sns\n",
     "import theano.tensor as tt\n",
-    "import itertools\n",
     "\n",
     "plt.style.use('seaborn-darkgrid')\n",
     "plt.rc('font', size=12)\n",

diff --git a/BDA3/chap_07.ipynb b/BDA3/chap_07.ipynb
@@ -15,14 +15,15 @@
    "source": [
     "%matplotlib inline\n",
     "\n",
-    "import numpy as np\n",
+    "import random\n",
+    "import statistics as stat\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pymc3 as pm\n",
     "import theano.tensor as tt\n",
     "\n",
-    "import statistics as stat\n",
     "from scipy import stats\n",
-    "import random\n",
     "\n",
     "plt.style.use('seaborn-darkgrid')\n",
     "plt.rc('font', size=12)\n",
@@ -387,7 +388,7 @@
     "y_total = n * np.mean(ppc_1['y_bar'], axis=1) + (N - n) * y_miss_1\n",
     "perc_25 = int(np.percentile(y_total, 2.5))\n",
     "perc_975 = int(np.percentile(y_total, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -416,7 +417,7 @@
     "y_to = n * np.mean(ppc_1['y_bar'], axis=1) + (N - n) * np.mean(ppc_1['y_bar'], axis=1) \n",
     "perc_25 = int(np.percentile(y_to, 2.5))\n",
     "perc_975 = int(np.percentile(y_to, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -635,7 +636,7 @@
     "y_total_2 = n * np.mean(ppc_1_log['y_bar'], axis=1) + (N - n) * y_miss_2\n",
     "perc_25 = int(np.percentile(y_total_2, 2.5))\n",
     "perc_975 = int(np.percentile(y_total_2, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -732,6 +733,7 @@
    ],
    "source": [
     "from scipy import stats\n",
+    "\n",
     "stats.boxcox(sample1)"
    ]
   },
@@ -961,7 +963,7 @@
     "y_total_3 = n * np.mean(invbox_cox(ppc_trans['y_phi'], phi), axis=1) + (N - n) * invbox_cox(y_miss_3, phi)\n",
     "perc_25 = int(np.percentile(y_total_3, 2.5))\n",
     "perc_975 = int(np.percentile(y_total_3, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -1226,7 +1228,7 @@
     "y_total_4 = n * np.mean(ppc_2['y_bar'], axis=1) + (N - n) * y_miss_4\n",
     "perc_25 = int(np.percentile(y_total_4, 2.5))\n",
     "perc_975 = int(np.percentile(y_total_4, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -1445,7 +1447,7 @@
     "y_total_5 = n * np.mean(ppc_2_log['y_bar'], axis=1) + (N - n) * y_miss_5\n",
     "perc_25 = int(np.percentile(y_total_5, 2.5))\n",
     "perc_975 = int(np.percentile(y_total_5, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -1542,6 +1544,7 @@
    ],
    "source": [
     "from scipy import stats\n",
+    "\n",
     "stats.boxcox(sample2)"
    ]
   },
@@ -1776,7 +1779,7 @@
     "y_total_6 = n * np.mean(invbox_cox(ppc_trans_2['y_phi'], phi2), axis=1) + (N - n) * invbox_cox(y_miss_6, phi2)\n",
     "perc_25 = int(np.percentile(y_total_6, 2.5))\n",
     "perc_975 = int(np.percentile(y_total_6, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -1848,7 +1851,7 @@
     "y_total_6 = y_total_6[~np.isnan(y_total_6)]\n",
     "perc_25 = int(np.percentile(y_total_6, 2.5))\n",
     "perc_975 = int(np.percentile(y_total_6, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {
@@ -1888,7 +1891,7 @@
     "y_total_6 = n * np.mean(invbox_cox_mod(ppc_trans_2['y_phi'], phi2), axis=1) + (N - n) * invbox_cox_mod(y_miss_6, phi2)\n",
     "perc_25 = int(np.percentile(y_total_6, 2.5))\n",
     "perc_975 = int(np.percentile(y_total_6, 97.5))\n",
-    "print('The 95% interval is [{:.2e}, {:.2e}]'.format(perc_25, perc_975))"
+    "print(f'The 95% interval is [{perc_25:.2e}, {perc_975:.2e}]')"
    ]
   },
   {

diff --git a/BSM/Chapter_03_00_Bayesian_CLT.ipynb b/BSM/Chapter_03_00_Bayesian_CLT.ipynb
@@ -6,9 +6,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy import stats\n",
-    "import matplotlib.pyplot as plt"
+    "\n",
+    "from scipy import stats"
    ]
   },
   {

diff --git a/BSM/Chapter_03_01_Gibbs_sampling_one_sample_t-test.ipynb b/BSM/Chapter_03_01_Gibbs_sampling_one_sample_t-test.ipynb
@@ -6,9 +6,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy import stats\n",
-    "import matplotlib.pyplot as plt"
+    "\n",
+    "from scipy import stats"
    ]
   },
   {

diff --git a/BSM/Chapter_03_02_Gibbs_sampling_two_sample_t-test.ipynb b/BSM/Chapter_03_02_Gibbs_sampling_two_sample_t-test.ipynb
@@ -6,11 +6,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "from scipy import stats\n",
+    "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
-    "import arviz as az"
+    "from scipy import stats"
    ]
   },
   {

diff --git a/BSM/Chapter_03_03_Gibbs_sampling_for_simple_linear_regression.ipynb b/BSM/Chapter_03_03_Gibbs_sampling_for_simple_linear_regression.ipynb
@@ -6,13 +6,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "from scipy import stats\n",
+    "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
     "import statsmodels.formula.api as smf\n",
     "\n",
-    "import arviz as az"
+    "from scipy import stats"
    ]
   },
   {

diff --git a/BSM/Chapter_03_04_Gibbs_sampling_for_concussions_data.ipynb b/BSM/Chapter_03_04_Gibbs_sampling_for_concussions_data.ipynb
@@ -6,11 +6,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "from scipy import stats\n",
+    "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
-    "import arviz as az"
+    "from scipy import stats"
    ]
   },
   {