Merge branch 'github/fork/gsn9/loggingForMatcher' into 'dev'

import os

import json

import sys

import warnings

import logging

from io import StringIO 

from contextlib import redirect_stdout

from autocnet.utils.serializers import JsonEncoder, object_hook

from autocnet.io.db.model import JobsHistory

log = logging.getLogger(__name__)

def parse_args():  # pragma: no cover

    parser = argparse.ArgumentParser()

    parser.add_argument('-r', '--host', help='The host URL for the redis queue to to pull messages from.')

        if msg is None:

            if args['queue'] == False:

                warnings.warn('Expected to process a cluster job, but the message queue is empty.')

                log.warning('Expected to process a cluster job, but the message queue is empty.')

                return

            elif args['queue'] == True:

                print(f'Completed processing from queue: {queue}.')

        with redirect_stdout(stdout):

            # Apply the algorithm

            response = process(msgdict)

            # Should go to a logger someday!

            print(response)

            # Should go to a logger someday! (today is that day!)

            log.info(response)

        out = stdout.getvalue()

        # print to get everything on the logs in the directory

def main():  # pragma: no cover

    args = vars(parse_args())

    # set up the logger

    logging.basicConfig(level=os.environ.get("AUTOCNET_LOGLEVEL", "INFO"))

    # Get the message

    queue = StrictRedis(host=args['host'], port=args['port'], db=0)

    manage_messages(args, queue)

from collections import defaultdict, MutableMapping, Counter

from functools import wraps, singledispatch

import json

import warnings

import logging

import numpy as np

import pandas as pd

from plio.io.io_gdal import GeoDataset

from csmapi import csmapi

# set up the logging file

log = logging.getLogger(__name__)

class Edge(dict, MutableMapping):

    """

    Attributes

        tar_desc = self.destination.descriptors

        if not 'xm' in ref_kps.columns:

            warnings.warn('To ring match body centered coordinates (xm, ym, zm) must be in the keypoints')

            log.warning('To ring match body centered coordinates (xm, ym, zm) must be in the keypoints')

            return

        ref_feats = ref_kps[['x', 'y', 'xm', 'ym', 'zm']].values

        tar_feats = tar_kps[['x', 'y', 'xm', 'ym', 'zm']].values

        node = getattr(self, on)

        camera = getattr(node, 'camera')

        if camera is None:

            warnings.warn('Unable to project matches without a sensor model.')

            log.warning('Unable to project matches without a sensor model.')

            return

        matches = self.matches

    def overlap_check(self):

        """Creates a mask for matches on the overlap"""

        if not (self["source_mbr"] and self["destin_mbr"]):

            warnings.warn(

            log.warning(

                "Cannot use overlap constraint, minimum bounding rectangles"

                " have not been computed for one or more Nodes")

            return

                of reprojective error indexed to the matches data frame

        """

        if self.fundamental_matrix is None:

            warnings.warn('No fundamental matrix has been compute for this edge.')

            log.error('No fundamental matrix has been compute for this edge.')

        matches, mask = self.clean(clean_keys)

        s_keypoints, d_keypoints = self.get_match_coordinates(clean_keys=clean_keys)

            except:

                smbr = self.source.geodata.xy_extent

                dmbr = self.source.geodata.xy_extent

                warnings.warn("Overlap between {} and {} could not be "

                log.warning("Overlap between {} and {} could not be "

                                "computed.  Using the full image extents".format(self.source['image_name'],

                                                      self.destination['image_name']))

                smbr = [smbr[0][0], smbr[1][0], smbr[0][1], smbr[1][1]]

from shutil import copyfile

import threading

from time import gmtime, strftime, time

import warnings

import logging

from itertools import combinations

import networkx as nx

from autocnet.spatial.surface import GdalDem, EllipsoidDem

from autocnet.transformation.spatial import reproject, og2oc

# set up the logging file

log = logging.getLogger(__name__)

#np.warnings.filterwarnings('ignore')

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

            if fp and fp.IsValid():

                valid_datasets.append(i)

            else:

                warnings.warn(

                log.warning(

                    'Missing or invalid geospatial data for {}'.format(i.base_name))

        # Grab the footprints and test for intersection

                    adjacency_dict[i.file_name].append(j.file_name)

                    adjacency_dict[j.file_name].append(i.file_name)

            except:

                warnings.warn(

                log.warning(

                    'Failed to calculate intersection between {} and {}'.format(i, j))

        return cls.from_adjacency(adjacency_dict)

            else:

                image_path = image_name

            if not os.path.exists(image_path):

                warnings.warn("Cannot find {}".format(image_path))

                log.warning("Cannot find {}".format(image_path))

                return

            n = self.graph["node_counter"]

            self.graph["node_counter"] += 1

        if new_node is not None and adj is not None:

            for adj_img in adj:

                if adj_img not in self.graph["node_name_map"].keys():

                    warnings.warn("{} not found in the graph".format(adj_img))

                    log.warning("{} not found in the graph".format(adj_img))

                    continue

                new_idx = new_node["node_id"]

                adj_idx = self.graph["node_name_map"][adj_img]

        """

        if not self.is_connected():

            warnings.warn(

            log.warning(

                'The given graph is not complete and may yield garbage.')

        for s, d, edge in self.edges.data('edge'):

        Push messages to the redis queue for DB objects e.g., Points, Measures

        """

        if filters and query_string:

            warnings.warn('Use of filters and query_string are mutually exclusive.')

            log.warning('Use of filters and query_string are mutually exclusive.')

        with self.session_scope() as session:

            # Support either an SQL query string, or a simple dict based query

        fpaths = [self.nodes[i]['data']['image_path'] for i in ids]

        for f in self.files:

            if f not in fpaths:

                warnings.warn(f'{f} in candidate graph but not in output network.')

                log.warning(f'{f} in candidate graph but not in output network.')

        # Remap the df columns back to ISIS

        df.rename(columns={'pointtype':'pointType',

        elif os.path.exists(filelist):

            filelist = io_utils.file_to_list(filelist)

        else:

            warnings.warn('Unable to parse the passed filelist')

           log.warning('Unable to parse the passed filelist')

        if clear_db:

            self.clear_db()

                    try:

                        session.execute(f'ALTER SEQUENCE {t}_id_seq RESTART WITH 1')

                    except Exception as e:

                        warnings.warn(f'Failed to reset primary id sequence for table {t}')

                        log.warning(f'Failed to reset primary id sequence for table {t}')

    def cnet_to_db(self, cnet):

        """

                                          walltime='00:20:00',

                                          chunksize=1000,

                                          exclude=None):

        warnings.warn('This function is not well tested. No tests currently exists \

        log.warning('This function is not well tested. No tests currently exists \

        in the test suite for this version of the function.')

        # Setup the redis queue

from collections import defaultdict, MutableMapping

import itertools

import os

import warnings

import logging

from csmapi import csmapi

import numpy as np

from autocnet.vis.graph_view import plot_node

from autocnet.utils import utils

# set up the logging file

log = logging.getLogger(__name__)

class Node(dict, MutableMapping):

    """

    def project_keypoints(self):

        if self.camera is None:

            # Without a camera, it is not possible to project

            warnings.warn('Unable to project points, no camera available.')

            log.warning('Unable to project points, no camera available.')

            return False

        # Project the sift keypoints to the ground

        def func(row, args):

                   PATH to the directory for output and base file name

        """

        if self.keypoints.empty:

            warnings.warn('Node {} has not had features extracted.'.format(self['node_id']))

            log.warning('Node {} has not had features extracted.'.format(self['node_id']))

            return

        io_keypoints.to_npy(self.keypoints, self.descriptors,

        try:

            fp, cam_type = self.footprint

        except Exception as e:

            warnings.warn('Unable to generate image footprint.\n{}'.format(e))

            log.warning('Unable to generate image footprint.\n{}'.format(e))

            fp = cam_type = None

        # Create the image

        i = Images(name=self['image_name'],

            with open(isdpath, 'w') as f:

                json.dump(response, f)

        except Exception as e:

            warnings.warn('Failed to write JSON ISD for image {}.\n{}'.format(self['image_path'], e))

           log.warning('Failed to write JSON ISD for image {}.\n{}'.format(self['image_path'], e))

        isd = csmapi.Isd(self['image_path'])

        plugin = csmapi.Plugin.findPlugin('UsgsAstroPluginCSM')

        self._camera = plugin.constructModelFromISD(isd, model_name)

import numpy as np

import pytest

import logging

from autocnet.utils.serializers import JsonEncoder, object_hook

from autocnet.graph import cluster_submit

from autocnet.graph.edge import NetworkEdge

from autocnet.io.db.model import Points, JobsHistory

log = logging.getLogger(__name__)

@pytest.fixture

def args():

    cluster_submit.finalize_message_from_work_queue(queue, args['working_queue'], remove_key)

    assert queue.llen(args['working_queue']) == 0

def test_no_msg(args, queue):

    with pytest.warns(UserWarning, match='Expected to process a cluster job, but the message queue is empty.'):

def test_no_msg(caplog,args, queue):

    cluster_submit.manage_messages(args, queue)

    expected_log = 'Expected to process a cluster job, but the message queue is empty.'

    assert expected_log in caplog.text

# Classes and funcs for testing job submission.

    cluster_submit._instantiate_row.assert_called_once()

@pytest.mark.parametrize()

def _do_something(log_level):

    return getattr(log, log_level)(f'Logging at the {log_level}')

def test_do_something(caplog):

    log_levels = ["critical", "error", "warning", "info", "debug"]

    for level in log_levels:

        os.environ["AUTOCNET_LOGLEVEL"] = level

        _do_something(os.environ["AUTOCNET_LOGLEVEL"])

        for record in caplog.records:

            # casting the env var and record level to a string for comparison 

            assert(str(os.environ["AUTOCNET_LOGLEVEL"]).upper() == str(record.levelname).upper())

            caplog.clear()

@pytest.mark.parametrize("along, func, msg_additions",[

                        ([1,2,3,4,5], _do_nothing, {})

                        ])