wip

Author: gedeck

.github/workflows/build.yml

@@ -34,3 +34,16 @@ jobs:
       - run: pip install --upgrade pip pylint 
       - run: pip install -r requirements.txt
       - run: cd src; pylint mistat
+
+  ruff:
+    runs-on: ubuntu-latest
+    name: Ruff
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.12'
+      - run: pip install --upgrade pip ruff
+      - run: pip install -r requirements.txt
+      - run: cd src; ruff check mistat

Makefile

@@ -42,5 +42,8 @@ mypy:
 pylint:
 	docker run -it --rm -v $(PWD)/src:/src $(DEV_IMAGE) pylint mistat
 
+ruff:
+	docker run -it --rm -v $(PWD)/src:/src $(DEV_IMAGE) ruff check mistat
+
 bash-dev:
 	docker run -it --rm -v $(PWD)/src:/src $(DEV_IMAGE) bash

docker/Dockerfile.devtools

@@ -1,5 +1,5 @@
 FROM mistat_test_1
-RUN pip install mypy isort data-science-types types-setuptools pylint
+RUN pip install mypy isort data-science-types types-setuptools pylint ruff
 
 WORKDIR /src
 

src/mistat/__init__.py

@@ -4,6 +4,7 @@
 - Modern Statistics: A Computer Based Approach with Python
 - Industrial Statistics: A Computer Based Approach with Python
 """
+# ruff: noqa:F401
 from .data import describe_data, load_data
 from .design import (FDS_Plot, addTreatments, calculateInteractions, calculateMainEffects, doe, getModelMatrix,
                      interactionPlot, mainEffectsPlot, marginalInteractionPlot, subgroupOfDefining)

src/mistat/acceptanceSampling/__init__.py

@@ -1,3 +1,4 @@
+# ruff: noqa:F401
 from .bandit import optimalOAB, simulateOAB
 from .dodge.dodge_chain import ChainPlanBinomial, ChainPlanPoisson
 from .dodge.dodge_curtailed import curtailedBinomial

src/mistat/design/__init__.py

@@ -1,3 +1,4 @@
+# ruff: noqa:F401
 from . import doe
 from .doeUtilities import addTreatments, aliasesInSubgroup, subgroupOfDefining
 from .graphs import (FDS_Plot, calculateInteractions, calculateMainEffects,

src/mistat/design/doepy/pydoe_corrected.py

@@ -194,7 +194,7 @@ def fracfact_corrected(gen):
     C = [len(item) for item in A]
 
     # Indices of single letters (main factors)
-    I = [i for i, item in enumerate(C) if item == 1]
+    I_idx = [i for i, item in enumerate(C) if item == 1]
 
     # Indices of letter combinations (we need them to fill out H2 properly).
     J = [i for i, item in enumerate(C) if item != 1]
@@ -208,9 +208,9 @@ def fracfact_corrected(gen):
     R2 = _grep(U, "-")
 
     # Fill in design with two level factorial design
-    H1 = ff2n(len(I))
+    H1 = ff2n(len(I_idx))
     H = np.zeros((H1.shape[0], len(C)))
-    H[:, I] = H1
+    H[:, I_idx] = H1
 
     # Recognize combinations and fill in the rest of matrix H2 with the proper
     # products

src/mistat/design/graphs.py

@@ -137,8 +137,8 @@ def _calculateInteractions(df, response, factors=None):
         ax.plot([i] * len(values), values, color='grey')
         common = {'horizontalalignment': 'center', 'verticalalignment': 'center'}
         offset = -abs(offset) if values[0] > values[-1] else abs(offset)
-        for n, (l, value) in enumerate(zip(me['level'], values)):
-            s = f'${l:.5g}$'
+        for n, (level, value) in enumerate(zip(me['level'], values)):
+            s = f'${level:.5g}$'
             if levels:
                 s = f'{s}: {levels[factor][n]:.5g}'
             if n == 0:
@@ -160,10 +160,10 @@ def _calculateInteractions(df, response, factors=None):
         level2 = sorted(subdf['l2'].unique())
 
         shift_1 = {l1: i + s for l1, s in zip(level1, np.linspace(-0.25, 0.25, len(level1)))}
-        markercol_1 = {f'{l:.4f}': 'grey' for l in level1}
+        markercol_1 = {f'{level:.4f}': 'grey' for level in level1}
         markercol_1[f'{level1[0]:.4f}'] = 'red'
         markercol_1[f'{level1[-1]:.4f}'] = 'black'
-        markercol_2 = {f'{l:.4f}': 'grey' for l in level2}
+        markercol_2 = {f'{level:.4f}': 'grey' for level in level2}
         markercol_2[f'{level2[0]:.4f}'] = 'red'
         markercol_2[f'{level2[-1]:.4f}'] = 'black'
         mat = np.zeros([len(level1), len(level2)])

src/mistat/ml/__init__.py

@@ -1 +1,2 @@
+# ruff: noqa:F401
 from .clustering import plot_dendrogram

src/mistat/mqcc/__init__.py

@@ -1,2 +1,3 @@
+# ruff: noqa:F401
 from .mahalanobisT2 import MahalanobisT2
 from .multivariateQualityControlChart import MultivariateQualityControlChart

src/mistat/qcc/__init__.py

@@ -1,3 +1,4 @@
+# ruff: noqa:F401
 from .cusum import Cusum, cusumArl, cusumPfaCed
 from .ewmaChart import EWMA
 from .paretoChart import ParetoChart

src/mistat/qcc/qualityControlChart.py

@@ -101,17 +101,17 @@ def plot(self, title=None, ax=None):
             lcl = [[], []]
             ucl = [[], []]
             last = None
-            for xleft, xright, l, u in zip(df.x.values - delta[:-1], df.x.values + delta[1:],
+            for xleft, xright, lower, upper in zip(df.x.values - delta[:-1], df.x.values + delta[1:],
                                            self.limits.LCL, self.limits.UCL):
                 if last:
                     lcl[0].extend([xleft, xleft])
-                    lcl[1].extend([last, l])
+                    lcl[1].extend([last, lower])
                     ucl[0].extend([xleft, xleft])
-                    ucl[1].extend([last, u])
+                    ucl[1].extend([last, upper])
                 lcl[0].extend([xleft, xright])
-                lcl[1].extend([l, l])
+                lcl[1].extend([lower, lower])
                 ucl[0].extend([xleft, xright])
-                ucl[1].extend([u, u])
+                ucl[1].extend([upper, upper])
             ax.plot(*lcl, color='black', linestyle='--')
             ax.plot(*ucl, color='black', linestyle='--')
 

src/mistat/regression/__init__.py

@@ -1 +1,2 @@
+# ruff: noqa:F401
 from .stepwiseRegression import stepwise_regression

src/mistat/rsm/__init__.py

@@ -1 +1,2 @@
+# ruff: noqa:F401
 from .responseSurfaceMethod import ResponseSurfaceMethod

src/mistat/simulation/__init__.py

@@ -1,3 +1,4 @@
+# ruff: noqa:F401
 from .mistatSimulation import SimulationResult, simulationGroup
 from .pistonSimulation import PistonSimulator, pistonConfigurations
 from .powerCircuitSimulation import PowerCircuitSimulation

src/mistat/timeseries/__init__.py

@@ -1,3 +1,4 @@
+# ruff: noqa:F401
 from .predictors import (dlmLinearGrowth, masPredictor, normRandomWalk,
                          optimalLinearPredictor, predictARMA,
                          quadraticPredictor, simulateARMA)