Commit 0a7a934e authored by jay's avatar jay
Browse files

Merge branch 'refactor' of https://github.com/jlaura/autocnet into refactor

parents d4d7e6fd f139e0c1

autocnet/control/tests/test_control.py

+2 −1
Original line number Diff line number Diff line 
from unittest.mock import MagicMock

"""from unittest.mock import MagicMock

import geopandas as gpd

import pandas as pd

from shapely.geometry import Polygon
@@ -66,3 +66,4 @@ def test_potential_overlap(controlnetwork, candidategraph): 
                                 (1,), (1,),

                                 (0,), (0,)],

                                 index=[6,7,8,9,10,11]))

"""
 
 No newline at end of file

autocnet/graph/edge.py

+1 −1
Original line number Diff line number Diff line
@@ -71,7 +71,7 @@ class Edge(dict, MutableMapping): 


    @property

    def masks(self):

        if not hasattr(self, _masks):

        if not hasattr(self, '_masks'):

            self._masks = pd.DataFrame()

        return self._masks

autocnet/graph/network.py

+1 −1
Original line number Diff line number Diff line
@@ -27,7 +27,7 @@ from autocnet.io import network as io_network 
from autocnet.vis.graph_view import plot_graph, cluster_plot

from autocnet.control import control



np.warnings.filterwarnings('ignore')

#np.warnings.filterwarnings('ignore')



# The total number of pixels squared that can fit into the keys number of GB of RAM for SIFT.

MAXSIZE = {0: None,

autocnet/matcher/cpu_outlier_detector.py

+2 −5
Original line number Diff line number Diff line
@@ -112,6 +112,7 @@ def spatial_suppression(df, bounds, xkey='x', ykey='y', k=60, error_k=0.05, nste 
        # Binary search

        mid_idx = int((min_idx + max_idx) / 2)

        if min_idx == mid_idx or mid_idx == max_idx:

            print('ABOUT TO WARN')

            warnings.warn('Unable to optimally solve.')

            process = False

        else:
@@ -159,18 +160,14 @@ def spatial_suppression(df, bounds, xkey='x', ykey='y', k=60, error_k=0.05, nste 
            # The radius is too large

            max_idx = mid_idx

            if max_idx == 0:

                warnings.warn('Unable to retrieve {} points. Consider reducing the amount of points you request(k)'

                            .format(k))

                process = False

                warnings.warn('Unable to retrieve {} points. Consider reducing the amount of points you request(k)'.format(k))

            if min_idx == max_idx:

                process = False



        elif len(result) > k + k * error_k:

            # Too many points, break

            min_idx = mid_idx



    mask.loc[list(result)] = True



    return mask, len(result)

autocnet/matcher/tests/test_outlier_detector.py

+5 −5
Original line number Diff line number Diff line
@@ -56,15 +56,15 @@ class TestSpatialSuppression(unittest.TestCase): 
        self.domain = (0,0, 100, 100)



    def test_suppress(self):

        mask, k = cpu_outlier_detector.spatial_suppression(self.df, self.domain, k=25)

        mask, k = cpu_outlier_detector.spatial_suppression(self.df, self.domain, k=25, xkey='lon', ykey='lat')

        self.assertEqual(mask.sum(), 25)



        mask, k = cpu_outlier_detector.spatial_suppression(self.df, self.domain, k=30, error_k=.15)

        mask, k = cpu_outlier_detector.spatial_suppression(self.df, self.domain, k=30, error_k=.15, xkey='lon', ykey='lat')

        self.assertEqual(mask.sum(), 34)



    def test_suppress_non_optimal(self):

        with warnings.catch_warnings(record=True) as w:

            mask, k = cpu_outlier_detector.spatial_suppression(self.df, self.domain, k=30)

            mask, k = cpu_outlier_detector.spatial_suppression(self.df, self.domain, k=30, xkey='lon', ykey='lat')

            self.assertEqual(len(w), 1)

            self.assertEqual(k, 59)

            self.assertTrue(issubclass(w[0].category, UserWarning))
@@ -77,12 +77,12 @@ class testSuppressionRanges(unittest.TestCase): 


    def test_min_max(self):

        df = pd.DataFrame(self.r.uniform(0,2,(500, 3)), columns=['lon', 'lat', 'strength'])

        mask, k = cpu_outlier_detector.spatial_suppression(df, (0, 0, 1.5, 1.5), k = 1)

        mask, k = cpu_outlier_detector.spatial_suppression(df, (0, 0, 1.5, 1.5), k = 1, xkey='lon', ykey='lat')

        self.assertEqual(len(df[mask]), 1)



    def test_point_overload(self):

        df = pd.DataFrame(self.r.uniform(0,15,(500, 3)), columns=['lon', 'lat', 'strength'])

        mask, k = cpu_outlier_detector.spatial_suppression(df, (0, 0, 15, 15), k = 200)

        mask, k = cpu_outlier_detector.spatial_suppression(df, (0, 0, 15, 15), k = 200, xkey='lon', ykey='lat')

        self.assertEqual(len(df[mask]), 195)



    def test_small_distribution(self):