Merge branch 'release/0.4.7'

.travis.yml

@@ -0,0 +1,16 @@
+language: python
+python:
+  - "2.7"
+  - "3.3"
+  - "3.4"
+  - "3.5"
+#  - "3.5-dev" # 3.5 development branch
+# command to install dependencies
+install: "pip install -r requirements.txt"
+# command to run tests
+script: nosetests
+#
+branches:
+  only:
+    - master
+    - develop

MANIFEST.in

@@ -0,0 +1 @@
+include lamana/tests/controls_LT/*.csv

README.md

@@ -0,0 +1 @@
+[![Build Status](https://travis-ci.org/par2/lamana-test.svg?branch=develop)](https://travis-ci.org/par2/lamana-test)

lamana/__init__.py

@@ -1,7 +1,7 @@
 # -----------------------------------------------------------------------------
 
 __title__ = 'lamana'
-__version__ = '0.4.6'
+__version__ = '0.4.7'
 __author__ = 'P. Robinson II'
 __license__ = 'BSD3'
 __copyright__ = 'Copyright 2015 P. Robinson II'

lamana/tests/test_controls.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 import pandas as pd
-import nose.tools as nt 
+import nose.tools as nt
 
 import lamana as la
 import lamana.utils.tools as ut
@@ -17,21 +17,21 @@
 # SETUP -----------------------------------------------------------------------
 def fix_discontinuities(laminate, inner_i):
     '''Replace t_ Nan values at discontinuities with adjacent value for inner_i.
-    
+
     Accounts for the following found in controls directory:
     - missing t(um) and d(mm) columns
     - discontinuities in multi and special plies
     - and more ...
-    
+
     '''
     df = laminate.copy()
 
     # Tensile side
-    discTensidx = df.loc[(df['label'] == 'discont.') 
+    discTensidx = df.loc[(df['label'] == 'discont.')
                        & (df['type'] == 'inner')
                        & (df['side'] == 'Tens.'), 't(um)'].index.tolist()
     # Compressive side
-    discCompidx = df.loc[(df['label'] == 'discont.') 
+    discCompidx = df.loc[(df['label'] == 'discont.')
                        & (df['type'] == 'inner')
                        & (df['side'] == 'Comp.'), 't(um)'].index.tolist()
     #print(discTensidx)
@@ -49,13 +49,13 @@ def fix_discontinuities(laminate, inner_i):
 def extract_dataframe(df):
     '''Parse corrected DataFrame from a csv file; legacy, automated or custom.'''
     df_expected = df.copy()
-    
+
     # Mild cleanup
     if 'd(mm)' in df_expected.columns:
         df_expected['d(m)'] = df_expected['d(mm)']/1000.
         del df_expected['d(mm)']
 
-    if 't(um)' not in df_expected.columns:             # for custom controls from legacy scripts 
+    if 't(um)' not in df_expected.columns:             # for custom controls from legacy scripts
         df_expected['t(um)'] = df_expected['h(m)']/1e-6
         # Assign Nan to layer thickness of the discontinuity row
         df_expected.loc[df_expected['label'] == 'discont.', 't(um)'
@@ -64,18 +64,18 @@ def extract_dataframe(df):
         df_expected.loc[df_expected['type'] == 'middle', 't(um)'
                        ] = df_expected.loc[df_expected['type'] == 'middle', 'h(m)'].multiply(2)/1e-6
 
-    # Parse data mainly for the Case 
+    # Parse data mainly for the Case
     nplies = len(df_expected['layer'].unique())
     p = df_expected.groupby('layer').size().iloc[0]
     t_total = df_expected.iloc[-1]['d(m)']             # (in m)
 
-    # Get a geometry string to feed the API            
+    # Get a geometry string to feed the API
     if nplies < 5:
         geometry = ut.get_special_geometry(df_expected)
     elif nplies >= 5:
         geometry = ut.get_multi_geometry(df_expected)
     elif nplies < 1:
-        raise Exception('Number of plies < 1.  No plies detected.') 
+        raise Exception('Number of plies < 1.  No plies detected.')
     #print(geometry)
 
     # Plugin holes; overwrite placeholder Nans at discontinuities
@@ -88,50 +88,52 @@ def extract_dataframe(df):
         '''Why is float needed here and not int?'''
         '''Getting 200L error for float.'''
         #print(inners)
-        inner_i = [float(t) for t in inners] 
+        inner_i = [float(t) for t in inners]
     else:
         inner_i = float(geo[1])
     #print(inner_i)
     df_expected.loc[(df_expected['label'] == 'discont.')
                     & (df_expected['type'] == 'outer'),'t(um)'] = outer
     if ('discont.' in df_expected['label'].values) and ('inner' in df_expected['type'].values):
         df_expected = fix_discontinuities(df_expected, inner_i)
-    
+
     return df_expected, geometry, nplies, p, t_total
 
 # TESTS -----------------------------------------------------------------------
 # Test Columns
 def test_apply_LaminateModels_cols_dimensions1():
-    '''Test actual against expected DataFrames in .cvs files found in 
+    '''Test actual against expected DataFrames in .cvs files found in
     tests/controls_LT; IDs and dimensional columns.
-        
+
     '''
     # Prepare file path.
     # Depends which directory nosetests is rum
-    path = os.getcwd()                                     # use for the test in the correct path
+    #path = os.getcwd()                                     # use for the test in the correct path
+    path = os.path.join(os.getcwd(), 'lamana', 'tests', 'controls_LT') # for builds
     #path = path + r'\lamana\tests\controls_LT'             # for Main Script. Comment out in tests
-    path = path + r'\tests\controls_LT'                    # for test  
-    #path = path + r'\controls_LT'                          # for test  
+    #path = path + r'\tests\controls_LT'                    # for test
+    #path = os.path.join(os.getcwd(), 'tests', 'controls_LT')          # for test
+    #path = path + r'\controls_LT'                          # for test
 
     # Read all files in the path (REF 013)
     for file in ut.read_csv_dir(path):
         #df_expected = file
         df = file
         #print(df_expected)
-                
+
         df_expected, geometry, nplies, p, t_total = extract_dataframe(df)
-    
+
         # Build actual Case using distributions API
         dft.load_params['p'] = p
-        case = la.distributions.Case(dft.load_params, dft.mat_props) 
-        case.apply([geometry]) 
+        case = la.distributions.Case(dft.load_params, dft.mat_props)
+        case.apply([geometry])
         df = case.frames[0]
 
         # Compare the dimensional columns only
         '''Bypassing z(m), z(m)*, intf and k for now'''
         '''UPDATE: k add back in 0.4.4b'''
         ###
-        cols = ['layer', 'side','type', 'matl', 
+        cols = ['layer', 'side','type', 'matl',
         #        'label', 't(um)', 'h(m)', 'd(m)', 'intf', 'k', 'Z(m)', 'z(m)']
         #        'label', 't(um)', 'h(m)', 'd(m)', 'intf', 'k', 'Z(m)', ]
         #        'label', 't(um)', 'h(m)', 'd(m)', 'intf', 'Z(m)', ]      # removed; k redefined in 0.4.3c4d
@@ -143,25 +145,25 @@ def test_apply_LaminateModels_cols_dimensions1():
 
         actual = df[cols]
         expected = df_expected[cols]
-        #print ('expected (file) \n', expected)        
+        #print ('expected (file) \n', expected)
         #print('actual (API) \n', actual)
         #print(expected.dtypes)
         #print(actual.dtypes)
         print('\n')
         ut.assertFrameEqual(actual, expected)
-        
+
 def test_apply_LaminateModels_cols_models1():
     '''Test .cvs files found in tests/controls_LT with API DataFrames.
-    Comparing models columns only. 
-    
+    Comparing models columns only.
+
     Due to a different algorithms for calculating internal values,
     rows yielding maximum and minimum stress are the most reliable comparisons.
-    
-    Tests for internal points can varying depending on choice of z(m).  
+
+    Tests for internal points can varying depending on choice of z(m).
     So they will be excluded from this test.
     '''
     '''Wait for skipcols kwarg in read_csv in pandas 0.17'''
-    
+
     def remove_units(cols):
         '''Return a dict of stress column lables with units removed.'''
         #cols = cols.tolist()
@@ -173,51 +175,53 @@ def remove_units(cols):
                 #print(name)
                 dict_[colname] = unitless_name
         return dict_
-    
+
     # Prepare file path.
     # Depends which directory nosetests is rum
-    path = os.getcwd()                                     # use for the test in the correct path
+    #path = os.getcwd()                                     # use for the test in the correct path
+    path = os.path.join(os.getcwd(), 'lamana', 'tests', 'controls_LT') # for builds
     #path = path + r'\lamana\tests\controls_LT'             # for Main Script. Comment out in tests
-    path = path + r'\tests\controls_LT'                    # for test  
-    #path = path + r'\controls_LT'                          # for test  
+    #path = path + r'\tests\controls_LT'                    # for test
+    #path = os.path.join(os.getcwd(), 'tests', 'controls_LT')          # for test
+    #path = path + r'\controls_LT'                          # for test
 
     # Read all files in the path (REF 013)
     for file in ut.read_csv_dir(path):
         #df_expected = file
         df = file
         #print(df_expected)
-                
+
         df_expected, geometry, nplies, p, t_total = extract_dataframe(df)
-    
+
         # Build actual Case using distributions API
         dft.load_params['p'] = p
-        case = la.distributions.Case(dft.load_params, dft.mat_props) 
-        case.apply([geometry]) 
+        case = la.distributions.Case(dft.load_params, dft.mat_props)
+        case.apply([geometry])
         df = case.frames[0]
-        
+
         # Compare only model-related columns; skip API columns
         IDs =  ['layer','side', 'type', 'matl', 't(um)']   # except label_
         Dimensionals = ['h(m)', 'd(m)', 'intf', 'k', 'Z(m)', 'z(m)', 'z(m)*']
         #bypassed = ['z(m)', 'z(m)*', 'intf', 'k']
         skippedcols = IDs + Dimensionals
         actual_remainingcols = df.columns.difference(skippedcols)
         expected_remainingcols = df_expected.columns.difference(skippedcols)
-        
+
         # Get model columns only and strip units from stress columns
         df_models = df[actual_remainingcols].copy()
         df_expected = df_expected[expected_remainingcols].copy()
         df_models.rename(columns=remove_units(actual_remainingcols), inplace=True)
         df_expected.rename(columns=remove_units(expected_remainingcols), inplace=True)
         #print(df_expected)
-        
+
         print('A .csv file is being processed with the following dimensional properties:')
         print(' Number of plies: {} \n p: {} \n total \
                t (m): {} \n geometry: {} \n'.format(nplies, p, t_total, geometry))
 
         # Use all rows (including internals, optional)
         #actual = df_models
         #expected = df_expected[expected_remainingcols]
-        
+
         # Only use max stress rows (interfacial)
         actual1 = df_models.loc[df_models['label'] == 'interface']
         expected1 = df_expected.loc[df_expected['label'] == 'interface']
@@ -229,9 +233,9 @@ def remove_units(cols):
             expected2 = df_expected.loc[df_expected['label'] == 'discont.']
             #expected2 = df_expected[expected_remainingcols].loc[df_expected['label'] == 'discont.']
 
-        #print ('expected (file) \n', expected1)        
+        #print ('expected (file) \n', expected1)
         #print('actual (API) \n', actual1)
-        #print ('expected (file) \n', expected2)        
+        #print ('expected (file) \n', expected2)
         #print('actual (API) \n', actual2)
         #print(type(expected1))
         #print(type(actual1))
@@ -240,6 +244,6 @@ def remove_units(cols):
         print('\n')
         ut.assertFrameEqual(actual1, expected1, check_dtype=False)  # max stress rows
         if p > 1:                                                   # min stress rows
-            ut.assertFrameEqual(actual2, expected2, check_dtype=False)  
-        
-        # Internal rows depend on the `z_` algorithm.  They are not compared to prevent breakage.
+            ut.assertFrameEqual(actual2, expected2, check_dtype=False)
+
+        # Internal rows depend on the `z_` algorithm.  They are not compared to prevent breakage.

setup.py

@@ -2,14 +2,16 @@
 
 setup(
     name='lamana',
-    packages=['lamana'],                   # this must be the same as the name above
-    version='0.4.6',                       ### edit
+    version='0.4.7',                                  ### edit
     description='An extensible Python package for Laminate Analysis.',
     author='P. Robinson II',
     author_email='[email protected]',
-    url='https://github.com/par2/lamana',  # use the URL to the github repo
-    download_url='https://github.com/par2/lamana/tarball/0.4.6',
+    url='https://github.com/par2/lamana',             # use the URL to the github repo
+    download_url='https://github.com/par2/lamana/tarball/0.4.7',
+    packages=['lamana', 'lamana.models', 'lamana.utils', 'lamana.tests',
+              'lamana.tests.controls_LT', 'lamana.models.tests'],
     keywords=['laminate analysis', 'visualization'],
+    #install_requires=['matplotlib', 'pandas', 'numpy'],
     classifiers=[
         'Framework :: IPython',
         'Intended Audience :: Science/Research',
@@ -29,3 +31,5 @@
 # (004) Create package                      http://zetcode.com/articles/packageinpython/
 # (005) Detailed package guide              https://python-packaging-user-guide.readthedocs.org/en/latest/
 # (006) Updated guide: twine, pip, wheel    http://joebergantine.com/blog/2015/jul/17/releasing-package-pypi/
+# (007) packages                            Python Cookbook, 3rd ed, p. 435
+# (008) install_requires                    https://python-packaging-user-guide.readthedocs.org/en/latest/requirements/