Ran black on project (#379)

* Ran black on project

* yaml flake8 conf

* newline

* Removed extraneous whitespace

.pep8speaks.yml

@@ -0,0 +1,4 @@
+# File : .pep8speaks.yml
+
+flake8:
+        max-line-length: 88

setup.py

@@ -1,105 +1,104 @@
-'''Web server Tableau uses to run Python scripts.
+"""Web server Tableau uses to run Python scripts.
 
 TabPy (the Tableau Python Server) is an external service implementation
 which expands Tableau's capabilities by allowing users to execute Python
 scripts and saved functions via Tableau's table calculations.
-'''
+"""
 
 import os
 from setuptools import setup, find_packages
 
 
-DOCLINES = (__doc__ or '').split('\n')
+DOCLINES = (__doc__ or "").split("\n")
 
 
 def setup_package():
     def read(fname):
         return open(os.path.join(os.path.dirname(__file__), fname)).read()
 
     setup(
-        name='tabpy',
-        version=read('tabpy/VERSION'),
+        name="tabpy",
+        version=read("tabpy/VERSION"),
         description=DOCLINES[0],
-        long_description='\n'.join(DOCLINES[1:]) + '\n' + read('CHANGELOG'),
-        long_description_content_type='text/markdown',
-        url='https://github.com/tableau/TabPy',
-        author='Tableau',
-        author_email='github@tableau.com',
-        maintainer='Tableau',
-        maintainer_email='github@tableau.com',
-        download_url='https://pypi.org/project/tabpy',
+        long_description="\n".join(DOCLINES[1:]) + "\n" + read("CHANGELOG"),
+        long_description_content_type="text/markdown",
+        url="https://github.com/tableau/TabPy",
+        author="Tableau",
+        author_email="github@tableau.com",
+        maintainer="Tableau",
+        maintainer_email="github@tableau.com",
+        download_url="https://pypi.org/project/tabpy",
         project_urls={
             "Bug Tracker": "https://github.com/tableau/TabPy/issues",
             "Documentation": "https://tableau.github.io/TabPy/",
             "Source Code": "https://github.com/tableau/TabPy",
         },
         classifiers=[
-            'Development Status :: 4 - Beta',
-            'Intended Audience :: Developers',
-            'Intended Audience :: Science/Research',
-            'License :: OSI Approved :: MIT License',
-            'Programming Language :: Python :: 3.6',
-            'Topic :: Scientific/Engineering',
-            'Topic :: Scientific/Engineering :: Information Analysis',
-            'Operating System :: Microsoft :: Windows',
-            'Operating System :: POSIX',
-            'Operating System :: Unix',
-            'Operating System :: MacOS'
+            "Development Status :: 4 - Beta",
+            "Intended Audience :: Developers",
+            "Intended Audience :: Science/Research",
+            "License :: OSI Approved :: MIT License",
+            "Programming Language :: Python :: 3.6",
+            "Topic :: Scientific/Engineering",
+            "Topic :: Scientific/Engineering :: Information Analysis",
+            "Operating System :: Microsoft :: Windows",
+            "Operating System :: POSIX",
+            "Operating System :: Unix",
+            "Operating System :: MacOS",
         ],
-        platforms=['Windows', 'Linux', 'Mac OS-X', 'Unix'],
-        keywords=['tabpy tableau'],
-        packages=find_packages(
-            exclude=['docs', 'misc', 'tests']),
+        platforms=["Windows", "Linux", "Mac OS-X", "Unix"],
+        keywords=["tabpy tableau"],
+        packages=find_packages(exclude=["docs", "misc", "tests"]),
         package_data={
-            'tabpy': [
-                'VERSION',
-                'tabpy_server/state.ini.template',
-                'tabpy_server/static',
-                'tabpy_server/common/default.conf'
+            "tabpy": [
+                "VERSION",
+                "tabpy_server/state.ini.template",
+                "tabpy_server/static",
+                "tabpy_server/common/default.conf",
             ]
         },
-        python_requires='>=3.6',
-        license='MIT',
+        python_requires=">=3.6",
+        license="MIT",
         # Note: many of these required packages are included in base python
         # but are listed here because different linux distros use custom
         # python installations.  And users can remove packages at any point
         install_requires=[
-            'backports_abc',
-            'cloudpickle',
-            'configparser',
-            'decorator',
-            'future',
-            'genson',
-            'jsonschema',
-            'pyopenssl',
-            'python-dateutil',
-            'requests',
-            'singledispatch',
-            'six',
-            'tornado',
-            'urllib3<1.25,>=1.21.1'
+            "backports_abc",
+            "cloudpickle",
+            "configparser",
+            "decorator",
+            "future",
+            "genson",
+            "jsonschema",
+            "pyopenssl",
+            "python-dateutil",
+            "requests",
+            "singledispatch",
+            "six",
+            "tornado",
+            "urllib3<1.25,>=1.21.1",
         ],
         entry_points={
-            'console_scripts': [
-                'tabpy=tabpy.tabpy:main',
-                'tabpy-deploy-models=tabpy.models.deploy_models:main',
-                'tabpy-user-management=tabpy.utils.user_management:main'
+            "console_scripts": [
+                "tabpy=tabpy.tabpy:main",
+                "tabpy-deploy-models=tabpy.models.deploy_models:main",
+                "tabpy-user-management=tabpy.utils.user_management:main",
             ],
         },
-        setup_requires=['pytest-runner'],
+        setup_requires=["pytest-runner"],
         tests_require=[
-            'mock',
-            'nltk',
-            'numpy',
-            'pandas',
-            'pytest',
-            'scipy',
-            'sklearn',
-            'textblob'
+            "mock",
+            "nltk",
+            "numpy",
+            "pandas",
+            "pytest",
+            "scipy",
+            "sklearn",
+            "textblob",
         ],
-        test_suite='pytest'
+        test_suite="pytest",
     )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     setup_package()

tabpy/models/deploy_models.py

@@ -14,40 +14,38 @@
 
 
 def install_dependencies(packages):
-    pip_arg = ['install'] + packages + ['--no-cache-dir']
-    if hasattr(pip, 'main'):
+    pip_arg = ["install"] + packages + ["--no-cache-dir"]
+    if hasattr(pip, "main"):
         pip.main(pip_arg)
     else:
         pip._internal.main(pip_arg)
 
 
 def main():
-    install_dependencies(['sklearn', 'pandas', 'numpy',
-                          'textblob', 'nltk', 'scipy'])
-    print('==================================================================')
+    install_dependencies(["sklearn", "pandas", "numpy", "textblob", "nltk", "scipy"])
+    print("==================================================================")
     # Determine if we run python or python3
-    if platform.system() == 'Windows':
-        py = 'python'
+    if platform.system() == "Windows":
+        py = "python"
     else:
-        py = 'python3'
+        py = "python3"
 
     if len(sys.argv) > 1:
         config_file_path = sys.argv[1]
     else:
         config_file_path = setup_utils.get_default_config_file_path()
-    print(f'Using config file at {config_file_path}')
+    print(f"Using config file at {config_file_path}")
     port, auth_on, prefix = setup_utils.parse_config(config_file_path)
     if auth_on:
         auth_args = setup_utils.get_creds()
     else:
         auth_args = []
 
-    directory = str(Path(__file__).resolve().parent / 'scripts')
+    directory = str(Path(__file__).resolve().parent / "scripts")
     # Deploy each model in the scripts directory
     for filename in os.listdir(directory):
-        subprocess.run([py, f'{directory}/{filename}', config_file_path]
-                       + auth_args)
+        subprocess.run([py, f"{directory}/{filename}", config_file_path] + auth_args)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

tabpy/models/scripts/ANOVA.py

@@ -3,11 +3,11 @@
 
 
 def anova(_arg1, _arg2, *_argN):
-    '''
+    """
     ANOVA is a statistical hypothesis test that is used to compare
     two or more group means for equality.For more information on
     the function and how to use it please refer to tabpy-tools.md
-    '''
+    """
 
     cols = [_arg1, _arg2] + list(_argN)
     for col in cols:
@@ -18,8 +18,5 @@ def anova(_arg1, _arg2, *_argN):
     return p_value
 
 
-if __name__ == '__main__':
-    setup_utils.deploy_model(
-        'anova',
-        anova,
-        'Returns the p-value form an ANOVA test')
+if __name__ == "__main__":
+    setup_utils.deploy_model("anova", anova, "Returns the p-value form an ANOVA test")

tabpy/models/scripts/PCA.py

@@ -8,16 +8,16 @@
 
 
 def PCA(component, _arg1, _arg2, *_argN):
-    '''
+    """
     Principal Component Analysis is a technique that extracts the key
     distinct components from a high dimensional space whie attempting
     to capture as much of the variance as possible. For more information
     on the function and how to use it please refer to tabpy-tools.md
-    '''
+    """
     cols = [_arg1, _arg2] + list(_argN)
     encodedCols = []
     labelEncoder = LabelEncoder()
-    oneHotEncoder = OneHotEncoder(categories='auto', sparse=False)
+    oneHotEncoder = OneHotEncoder(categories="auto", sparse=False)
 
     for col in cols:
         if isinstance(col[0], (int, float)):
@@ -27,8 +27,10 @@ def PCA(component, _arg1, _arg2, *_argN):
             encodedCols.append(intCol.astype(int))
         else:
             if len(set(col)) > 25:
-                print('ERROR: Non-numeric arguments cannot have more than '
-                      '25 unique values')
+                print(
+                    "ERROR: Non-numeric arguments cannot have more than "
+                    "25 unique values"
+                )
                 raise ValueError
             integerEncoded = labelEncoder.fit_transform(array(col))
             integerEncoded = integerEncoded.reshape(len(col), 1)
@@ -38,11 +40,10 @@ def PCA(component, _arg1, _arg2, *_argN):
 
     dataDict = {}
     for i in range(len(encodedCols)):
-        dataDict[f'col{1 + i}'] = list(encodedCols[i])
+        dataDict[f"col{1 + i}"] = list(encodedCols[i])
 
     if component <= 0 or component > len(dataDict):
-        print('ERROR: Component specified must be >= 0 and '
-              '<= number of arguments')
+        print("ERROR: Component specified must be >= 0 and " "<= number of arguments")
         raise ValueError
 
     df = pd.DataFrame(data=dataDict, dtype=float)
@@ -55,8 +56,5 @@ def PCA(component, _arg1, _arg2, *_argN):
     return pcaComponents[:, component - 1].tolist()
 
 
-if __name__ == '__main__':
-    setup_utils.deploy_model(
-        'PCA',
-        PCA,
-        'Returns the specified principal component')
+if __name__ == "__main__":
+    setup_utils.deploy_model("PCA", PCA, "Returns the specified principal component")

tabpy/models/scripts/SentimentAnalysis.py

@@ -5,47 +5,48 @@
 
 
 import ssl
+
 _ctx = ssl._create_unverified_context
 ssl._create_default_https_context = _ctx
 
 
-nltk.download('vader_lexicon')
-nltk.download('punkt')
+nltk.download("vader_lexicon")
+nltk.download("punkt")
 
 
-def SentimentAnalysis(_arg1, library='nltk'):
-    '''
+def SentimentAnalysis(_arg1, library="nltk"):
+    """
     Sentiment Analysis is a procedure that assigns a score from -1 to 1
     for a piece of text with -1 being negative and 1 being positive. For
     more information on the function and how to use it please refer to
     tabpy-tools.md
-    '''
+    """
     if not (isinstance(_arg1[0], str)):
         raise TypeError
 
-    supportedLibraries = {'nltk', 'textblob'}
+    supportedLibraries = {"nltk", "textblob"}
 
     library = library.lower()
     if library not in supportedLibraries:
         raise ValueError
 
     scores = []
-    if library == 'nltk':
+    if library == "nltk":
         sid = SentimentIntensityAnalyzer()
         for text in _arg1:
             sentimentResults = sid.polarity_scores(text)
-            score = sentimentResults['compound']
+            score = sentimentResults["compound"]
             scores.append(score)
-    elif library == 'textblob':
+    elif library == "textblob":
         for text in _arg1:
             currScore = TextBlob(text)
             scores.append(currScore.sentiment.polarity)
     return scores
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     setup_utils.deploy_model(
-        'Sentiment Analysis',
+        "Sentiment Analysis",
         SentimentAnalysis,
-        'Returns a sentiment score between -1 and 1 for '
-        'a given string')
+        "Returns a sentiment score between -1 and 1 for " "a given string",
+    )

tabpy/models/scripts/tTest.py

@@ -3,12 +3,12 @@
 
 
 def ttest(_arg1, _arg2):
-    '''
+    """
     T-Test is a statistical hypothesis test that is used to compare
     two sample means or a sample’s mean against a known population mean.
     For more information on the function and how to use it please refer
     to tabpy-tools.md
-    '''
+    """
     # one sample test with mean
     if len(_arg2) == 1:
         test_stat, p_value = stats.ttest_1samp(_arg1, _arg2)
@@ -35,8 +35,5 @@ def ttest(_arg1, _arg2):
         return p_value
 
 
-if __name__ == '__main__':
-    setup_utils.deploy_model(
-        'ttest',
-        ttest,
-        'Returns the p-value form a t-test')
+if __name__ == "__main__":
+    setup_utils.deploy_model("ttest", ttest, "Returns the p-value form a t-test")