Updates for provenance tracking.

  - conda info -a

  # Create a virtual env and install dependencies

  - conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose numpy pillow scipy pandas networkx scikit-image sqlalchemy

  - conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose numpy pillow scipy pandas networkx scikit-image sqlalchemy numexpr

  # Activate the env

  - source activate test-env

                                     index=mask[mask == True].index)

    def subpixel_register(self, clean_keys=[], threshold=0.8, upsampling=16,

                                 template_size=19, search_size=53):

                          template_size=19, search_size=53, max_x_shift=1.0,

                          max_y_shift=1.0):

        """

        For the entire graph, compute the subpixel offsets using pattern-matching and add the result

        as an attribute to each edge of the graph.

        search_size : int

                      The size of the search

        max_x_shift : float

                      The maximum (positive) value that a pixel can shift in the x direction

                      without being considered an outlier

        max_y_shift : float

                      The maximum (positive) value that a pixel can shift in the y direction

                      without being considered an outlier

        """

        matches = self.matches

        self.subpixel_offsets.to_sparse(fill_value=0.0)

        # Compute the mask for correlations less than the threshold

        mask = self.subpixel_offsets['correlation'] >= threshold

        threshold_mask = self.subpixel_offsets['correlation'] >= threshold

        # Compute the mask for the point shifts that are too large

        subp= self.subpixel_offsets

        query_string = 'x_offset <= -{0} or x_offset >= {0} or y_offset <= -{1} or y_offset >= {1}'.format(max_x_shift,

                                                                                                           max_y_shift)

        sp_shift_outliers = subp.query(query_string)

        shift_mask = pd.Series(True, index=self.subpixel_offsets.index)

        shift_mask[sp_shift_outliers.index] = False

        # Generate the composite mask and write the masks to the mask data structure

        mask = threshold_mask & shift_mask

        self.masks = ('shift', shift_mask)

        self.masks = ('threshold', threshold_mask)

        self.masks = ('subpixel', mask)

    def coverage_ratio(self, clean_keys=[]):

numpy>=1.10.0

numexpr

scipy>=0.17.0

gdal>=1.11.2

pvl>=0.2.0