more cleanup of dependencies

examples/python/conditional_correlation_matrix.py

@@ -20,13 +20,12 @@
 import correlationMatrix as cm
 
 from correlationMatrix import source_path
-dataset_path = source_path + "datasets/"
 
+dataset_path = source_path + "datasets/"
 
 # Initialize a correlation matrix from the available examples
 myMatrix = cm.CorrelationMatrix()
 
-
 # Select method to stress
 
 # TODO Perform PCA Analysis
@@ -40,4 +39,3 @@
 
 # TODO stress API
 # myMatrix.stress(Scenario, Method)
-

examples/python/empirical_correlation_matrix.py

@@ -18,13 +18,10 @@
 timeseries data. The datasets are produced in examples/generate_synthetic_data.py
 
 """
-import matplotlib.pyplot as plt
-import numpy as np
 import pandas as pd
 
 import correlationMatrix as cm
 from correlationMatrix import source_path
-from correlationMatrix.utils.converters import datetime_to_float
 
 dataset_path = source_path + "datasets/"
 

examples/python/factor_models.py

@@ -61,4 +61,3 @@
  print("> Step 2: Estimate Credit Metrics style model")
  myMatrix = cm.FactorCorrelationMatrix()
  myMatrix.fit(data, method='CreditMetrics')
-

examples/python/generate_synthetic_data.py

@@ -91,4 +91,3 @@
  rho = cm.CorrelationMatrix(type='UniformSingleFactor', rho=0.3, n=m)
  data = dataset_generators.sector_model(n=n, b=b, rho=rho, sample=10000)
  data.to_csv(dataset_path + 'synthetic_data4.csv', index=False)
-

examples/python/generate_visuals.py

@@ -65,5 +65,3 @@
  )
  # plt.show()
  plt.savefig('dendrogram.png')
-
-

examples/python/matrix_from_json_data.py

@@ -28,7 +28,6 @@
 from correlationMatrix.utils.preprocessing import construct_returns, \
  normalize_log_returns, json_file_to_frame
 
-
 input_dataset_path = source_path + "datasets/"
 output_dataset_path = source_path + "datasets/"
 

examples/python/matrix_from_sector_data.py

@@ -58,5 +58,3 @@
  myMatrix = cm.FactorCorrelationMatrix()
  data = pd.read_csv(in_filename)
  myMatrix.fit(data, method='CreditMetrics')
-
-

examples/python/matrix_operations.py

@@ -50,7 +50,6 @@
 E = cm.CorrelationMatrix(values=[[1.0, 0.25], [0.0, 1.0]])
 print(E.validate())
 
-
 print("> Values can be loaded from json or csv files")
 F = cm.CorrelationMatrix(json_file=dataset_path + "SingleFactor.json")
 F.print()
@@ -79,4 +78,3 @@
 # Apply Cholesky decomposition
 print("> Calculate its Cholesky decomposition")
 matrix_print(G.decompose('cholesky'), accuracy=2)
-

requirements.txt

@@ -1,45 +1,8 @@
-alabaster==0.7.12
-Babel==2.9.1
-certifi==2021.10.8
-charset-normalizer==2.0.12
-cycler==0.11.0
-docutils==0.17.1
-fonttools==4.29.1
-idna==3.3
-imagesize==1.3.0
-importlib-metadata==4.11.1
-Jinja2==3.0.3
-joblib==1.1.0
-kiwisolver==1.3.2
-lxml==4.8.0
-MarkupSafe==2.1.0
-matplotlib==3.5.1
-mpmath==1.2.1
-numpy==1.21.0
-packaging==21.3
-pandas==0.25.3
-pandas-datareader==0.10.0
-patsy==0.5.2
-Pillow==9.0.1
-Pygments==2.11.2
-pyparsing==3.0.7
-python-dateutil==2.8.2
-pytz==2021.3
-requests==2.27.1
-scikit-learn==1.0.2
-scipy==1.8.0
-six==1.16.0
-snowballstemmer==2.2.0
-Sphinx==4.4.0
-sphinx-rtd-theme==1.0.0
-sphinxcontrib-applehelp==1.0.2
-sphinxcontrib-devhelp==1.0.2
-sphinxcontrib-htmlhelp==2.0.0
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==1.0.3
-sphinxcontrib-serializinghtml==1.1.5
-statsmodels==0.13.2
-sympy==1.9
-threadpoolctl==3.1.0
-urllib3==1.26.8
-zipp==3.7.0
+matplotlib
+numpy
+pandas
+requests
+scipy
+setuptools
+Sphinx
+sphinx-rtd-theme

tests/test_datasets.py

@@ -23,9 +23,9 @@
 
 
 class TestDatasets(unittest.TestCase):
- '''
+ """
  Load in-memory matrices
- '''
+ """
 
  def test_minimal_matrix(self):
  a = cm.CorrelationMatrix(values=Minimal)

tests/test_model.py

@@ -21,9 +21,9 @@
 
 
 class TestcorrelationMatrix(unittest.TestCase):
- '''
+ """
  Default instance (2x2 identity matrix)
- '''
+ """
 
  def test_instantiate_matrix(self):
  a = cm.CorrelationMatrix()

tests/test_utils.py

@@ -13,9 +13,6 @@
 # limitations under the License.
 
 import unittest
-import correlationMatrix as cm
-from correlationMatrix import source_path
-import pandas as pd
 
 ACCURATE_DIGITS = 7