#!/usr/bin/env python3

# CatAmount analyzes GPS collar data to find time/space relationships.

# Copyright (C) 2012-2019 Michael Rickard

#

# This program is free software: you can redistribute it and/or modify

# it under the terms of the GNU General Public License as published by

# the Free Software Foundation, either version 3 of the License, or

# (at your option) any later version.

#

# This program is distributed in the hope that it will be useful,

# but WITHOUT ANY WARRANTY; without even the implied warranty of

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

# GNU General Public License for more details.

#

# You should have received a copy of the GNU General Public License

# along with this program. If not, see <http://www.gnu.org/licenses/>.

# This code was based on a reading of work done by Mike Warren at the

# University of Alberta and Kyle Knopff at The Central East Slopes

# Cougar Study.

# This file provides facilities to identify one cat's time/space groupings, called clusters.

# IMPORT

import os

import sys

import argparse

from csv import reader as csvreader

import catamount.common as catcm

import catamount.find_clusters as catfc

import catamount.sunmetrics as catsm

# BEGIN SCRIPT

argman = argparse.ArgumentParser(

)

argman.add_argument(

'-f', '--datafile_path',

dest='datafile_path', action='store',

type=catcm.check_file_arg, default=catcm.cfg_datafile_path,

help='Interpret this data file.'

)

argman.add_argument(

'-o', '--outdir_path',

dest='outdir_path', action='store',

type=catcm.check_dir_arg, default=catcm.cfg_outdir_path,

help='Specify an output directory.'

)

argman.add_argument(

'-c', '--catid',

dest='catid', action='store',

type=str, required=True,

help='Show clusters for this cat.'

)

argman.add_argument(

'-r', '--radius',

dest='radius', action='store',

type=int, default=catcm.cfg_cluster_radius,

help='Design radius of a cluster.'

)

argman.add_argument(

'-t', '--time_cutoff',

dest='time_cutoff', action='store',

type=catcm.hours_arg_to_seconds, default=catcm.cfg_cluster_time_cutoff,

help='Design time cutoff of a cluster in hours.'

)

argman.add_argument(

'-mc', '--minimum_count',

dest='minimum_count', action='store',

type=int, default=catcm.cfg_cluster_minimum_count,

help='Minimum number of points to qualify as a cluster.'

)

argman.add_argument(

'-ms', '--minimum_stay',

dest='minimum_stay', action='store',

type=catcm.hours_arg_to_seconds, default=catcm.cfg_cluster_minimum_stay,

help='Minimum elapsed hours of clusters.'

)

argman.add_argument(

'-d1', '--start_date',

dest='start_date', action='store',

type=catcm.date_string_to_objects, default=catcm.cfg_cluster_start_date,

help='Limit clusters to ones that start after this date. YYYY-MM-DD'

)

argman.add_argument(

'-d2', '--end_date',

dest='end_date', action='store',

type=catcm.date_string_to_objects, default=catcm.cfg_cluster_end_date,

help='Limit clusters to ones that start before this date. YYYY-MM-DD'

)

argman.add_argument(

'-x', '--text_style',

dest='text_style', action='store',

choices=['csv', 'csv-all', 'descriptive', 'descriptive-all'], default='csv',

help='Text output style: csv, csv-all, descriptive, descriptive-all'

)

argman.add_argument(

'-z', '--clusterid',

dest='clusterid', action='store',

type=str, default=False,

help='Zoom in on a specific cluster.'

)

# Using the argument parser to do a lot of work:

# * Prefer command line arguments

# * Fall back on config file values

# * Convert to appropriate types

# * Check validity of arguments

args = argman.parse_args()

# Make sure integer arguments are in a reasonable range.

args.radius = catcm.constrain_integer(args.radius, 0, 1000)

args.time_cutoff = catcm.constrain_integer(args.time_cutoff, 0, 31536000)

args.minimum_count = catcm.constrain_integer(args.minimum_count, 0, 100)

args.minimum_stay = catcm.constrain_integer(args.minimum_stay, 0, 8640000)

# Create a SunMetrics object so any fixes can compute day and night

sun_metrics = catsm.SunMetrics()

# Open and process the data file

with open(args.datafile_path, 'rt') as datafile:

csvrows = csvreader(datafile)

# Limit to just one cat

csvrows = [csvrow for csvrow in csvrows if csvrow[int(catcm.cfg_data_column_catid)] == args.catid]

# If no rows were retrieved, warn user that cat is not represented in the current data

if not csvrows:

sys.exit('No CSV data was found for cat with id {}.'.format(args.catid))

# Create a new Trail object, which is a series of fixes

trail = catfc.FCTrail(args.catid, args.radius, args.time_cutoff, args.minimum_count, args.minimum_stay)

# For every row, create a Fix object and add it to the Trail

for csvrow in csvrows:

try:

new_fix = catcm.Fix(csvrow, sun_metrics)

except IndexError:

sys.stderr.write('CSV row doesn’t have expected number of columns: {}\n'.format(csvrow))

continue

except:

sys.stderr.write('CSV row doesn’t look like data: {}\n'.format(csvrow))

continue

# Add the fix to the trail

trail.fixes.append(new_fix)

# Limit by date, if requested

if args.start_date:

trail.start_dateobj = args.start_date[0]

trail.start_time = args.start_date[1]

if args.end_date:

trail.end_dateobj = args.end_date[0]

trail.end_time = args.end_date[1]

trail.filter_by_date()

# Filter by date may have removed everything

if len(trail.fixes) < 1:

sys.exit('No data remaining after filtering by date. Try adjusting the date range.')

# Put the current collecton of fixes in order by time

trail.order_by_time()

# Remove any duplicate entries, seen in some data sets

trail.remove_duplicates()

# Find the farthest distance in each direction

trail.find_bounds()

# Find clusters

trail.find_clusters()

# Calculate averages, after all points have been added

trail.calculate_cluster_averages()

# Get image name and path ready

imagename = catfc.create_filename(args.catid, args.start_date, args.end_date, args.clusterid)

imagepath = os.path.join(args.outdir_path, imagename + '.png')

# Prepare date limiting strings for use in image legends

if args.start_date:

trail.legend_start_date = args.start_date[0].strftime(catcm.DATE_FMT_ISO_SHORT)

if args.end_date:

trail.legend_end_date = args.end_date[0].strftime(catcm.DATE_FMT_ISO_SHORT)

# If we're zooming in on a certain cluster, do so now

if args.clusterid:

# Limit to one cluster

cluster = trail.return_cluster_by_id(args.clusterid)

# Create feedback image

cluster.create_image(imagepath, 'auto')

# Do a text report of this cluster; show all points by default

if args.text_style == 'csv':

cluster.csv_report

else:

cluster.descriptive_report(True)

# Account of what was done

sys.stderr.write('Cluster {} shown.\n'.format(args.clusterid))

# Otherwise report on all clusters

else:

# Apply limiting filters to collection of clusters

trail.filter_clusters_by_count()

trail.filter_clusters_by_stay()

# Create feedback image

trail.create_image(imagepath, 50)

# Do a text report of all clusters found

if args.text_style == 'descriptive-all':

trail.descriptive_report(True)

elif args.text_style == 'descriptive':

trail.descriptive_report(False)

elif args.text_style == 'csv-all':

trail.csv_report(True)

else:

trail.csv_report(False)

# Account of what was done

sys.stderr.write('{} clusters found.\n'.format(len(trail.clusters)))