Merge pull request #42 from xmnlab/master

Release 0.1.0

Author: xmnlab

.travis.yml

@@ -6,10 +6,6 @@ python:
   - "3.5"
   - "3.6"
 
-#env:
-#  - TOXENV=py35
-#  - TOXENV=py34
-
 install:
   - sudo apt-get update
   # We do this conditionally because it saves us some downloading if the
@@ -26,21 +22,11 @@ install:
   - conda info -a
 
   # Replace dep1 dep2 ... with your dependencies
-  - conda env create -f environment.yml
+  - conda create -n pywim python=$TRAVIS_PYTHON_VERSION
   - source activate pywim
+  - conda install --file requirements.txt
   - python setup.py install
 
 # command to run tests, e.g. python setup.py test
 script: python setup.py test
 
-# After you create the Github repo and add it to Travis, run the
-# travis_pypi_setup.py script to finish PyPI deployment setup
-deploy:
-  provider: pypi
-  distributions: sdist bdist_wheel
-  user: xmnlab
-  password:
-    secure: PLEASE_REPLACE_ME
-  on:
-    tags: true
-    repo: OpenWIM/pywim

CONTRIBUTING.rst

@@ -101,7 +101,7 @@ Before you submit a pull request, check that it meets these guidelines:
 2. If the pull request adds functionality, the docs should be updated. Put
    your new functionality into a function with a docstring, and add the
    feature to the list in README.rst.
-3. The pull request should work for Python 2.6, 2.7, 3.3, 3.4 and 3.5, and for PyPy. Check
+3. The pull request should work for Python 3.5 and 3.6. Check
    https://travis-ci.org/xmnlab/pywim/pull_requests
    and make sure that the tests pass for all supported Python versions.
 

README.rst

@@ -13,7 +13,7 @@ PyWIM
         :target: https://pywim.readthedocs.io/en/latest/?badge=latest
         :alt: Documentation Status
 
-Open algorithms to use in WIM research
+Open algorithms to use in WIM research.
 
 
 * Free software: MIT license
@@ -29,7 +29,3 @@ PyWIM - SciPy Latin America - Argentina - 2015
 
 * http://tinyurl.com/pjbn8x3
 
-Features
---------
-
-* TODO

docs/modules.rst

@@ -0,0 +1,7 @@
+pywim
+=====
+
+.. toctree::
+   :maxdepth: 4
+
+   pywim

docs/pywim.rst

@@ -0,0 +1,10 @@
+pywim package
+=============
+
+Module contents
+---------------
+
+.. automodule:: pywim
+    :members:
+    :undoc-members:
+    :show-inheritance:

environment.yml

@@ -370,9 +370,11 @@
     "under the signal curve and the speed of the vehicle traveling. A typical signal curve \n",
     "can be viewed as:\n",
     "\n",
-    "![](img/kistler-signal.png)\n",
+    "\\begin{figure}\n",
+    "\\centerline{\\includegraphics[width=10cm]{img/kistler-signal.png}}\n",
+    "\\caption{\\label{fig:kistler-signal} Raw data signal illustration. Source \\cite{kistler2004installation}}\n",
+    "\\end{figure}\n",
     "\n",
-    "<center>Source: \\cite{kistler2004installation}</center>\n",
     "\n",
     "The equation \n",
     "presented by Kistler \\cite{kistler2004installation} is:\n",

notebooks/WeightEstimation.ipynb

@@ -1,4 +1,3 @@
-import numpy as np
 
 
 class Temperature:

pywim/correction/temperature.py

@@ -1,5 +1,5 @@
 from scipy.stats import norm
-from scipy.optimize import root, fsolve
+from scipy.optimize import fsolve
 from scipy import stats
 
 import numpy as np
@@ -199,13 +199,13 @@ def calc_confidence_level(data: pd.DataFrame) -> pd.DataFrame:
     def _calc(v: pd.Series) -> pd.Series:
         return 100 * (
             1 - stats.t.sf(
-                (v.d / v['std'] - v['mean'] / v['std']) - stats.t.isf(0.05 / 2,
-                                                                      v.number - 1) /
+                (v.d / v['std'] - v['mean'] / v['std']) -
+                stats.t.isf(0.05 / 2, v.number - 1) /
                 np.sqrt(v.number),
                 v.number - 1
             ) - stats.t.sf(
-                (v.d / v['std'] + v['mean'] / v['std']) - stats.t.isf(0.05 / 2,
-                                                                      v.number - 1) /
+                (v.d / v['std'] + v['mean'] / v['std']) -
+                stats.t.isf(0.05 / 2, v.number - 1) /
                 np.sqrt(v.number), v.number - 1
             )
         )
@@ -271,13 +271,14 @@ def solver_min_tolerance(data: pd.DataFrame) -> pd.DataFrame:
         _factor = stats.t.isf(0.05 / 2, _number - 1) / np.sqrt(_number)
         _dof = _number - 1
 
-        func = lambda _min_tolerance: _min_confidence - 100 * (
-            1 -
-            stats.t.sf(
-                (_min_tolerance / _std - _mean / _std) - _factor, _dof) -
-            stats.t.sf(
-                (_min_tolerance / _std + _mean / _std) - _factor, _dof)
-        )
+        def func(_min_tolerance):
+            return _min_confidence - 100 * (
+                1 -
+                stats.t.sf(
+                    (_min_tolerance / _std - _mean / _std) - _factor, _dof) -
+                stats.t.sf(
+                    (_min_tolerance / _std + _mean / _std) - _factor, _dof)
+            )
 
         try:
             data.loc[i, 'min_tolerance'] = fsolve(func, [1])[0]

pywim/estimation/accuracy_calculation/cost323.py

@@ -159,7 +159,7 @@ def calc_bl_med(x, mw, sf, sigma):
     e = np.ma.masked_array(x, mask)
 
     # fill in the median vector
-    half_mw = mw/2
+    # half_mw = mw/2
     m = scipy.ndimage.median_filter(e, mw+1, mode="mirror")
     # using the median_filter might give slightly different results than
     # described algorithm but is MUCH faster