Spatio temporal analysis for inferrence of statistical causality using XGenESeSS
Project description
cynet is a spatial-temporal analysis library for inferrence of statistical causality
NOTE: if issues arise with dependencies in python3, be sure that tkinter is installed if not, please run:
sudo apt-get install python3-tk
Usage:
from cynet import cynet from cynet.cynet import uNetworkModels as models from viscynet import viscynet as vcn
cynet module includes: - cynet - viscynet - bokeh_pipe
cynet library classes:
spatioTemporal
uNetworkModels
simulateModels
class spatioTemporal Utilities for spatial-temporal analysis
Attributes:
log_store (Pickle): Pickle storage of class data & dataframes
log_file (string): path to CSV of legacy dataframe
ts_store (string): path to CSV containing most recent ts export
DATE (string):
EVENT (string): column label for category filter
coord1 (string): first coordinate level type; is column name
coord2 (string): second coordinate level type; is column name
coord3 (string): third coordinate level type; (z coordinate)
end_date (datetime.date): upper bound of daterange
freq (string): timeseries increments; e.g. D for date
columns (list): list of column names to use; requires at least 2 coordinates and event type
types (list of strings): event type list of filters
value_limits (tuple): boundaries (magnitude of event above threshold)
grid (dictionary or list of lists): coordinate dictionary with respective ranges and EPS value OR custom list of lists of custom grid tiles as [coord1_start, coord1_stop, coord2_start, coord2_stop]
grid_type (string): parameter to determine if grid should be built up from a coordinate start/stop range (‘auto’) or be built from custom tile coordinates (‘custom’)
threshold (float): significance threshold
Methods:
__init__(self, log_store=’log.p’, log_file=None, ts_store=None, DATE=’Date’, year=None, month=None, day=None, EVENT=’Primary Type’, coord1=’Latitude’, coord2=’Longitude’, coord3=None, init_date=None, end_date=None, freq=None, columns=None, types=None, value_limits=None, grid=None, threshold=None)}
fit(self, grid=None, INIT=None, END=None, THRESHOLD=None,csvPREF=’TS’,auto_adjust_time=False,incr=6,max_incr=24):
getTS(self, _types=None, tile=None, freq=None)
get_rand_tile(tiles=None,LAT=None,LON=None,EPS=None,_types=None)
get_opt_freq(df,incr=6,max_incr=24)
getGrid(self)
pull(self, domain=’data.cityofchicago.org’, dataset_id=’crimes’, token=None, store=True, out_fname=’pull_df.p’, pull_all=False)
timeseries(self, LAT=None, LON=None, EPS=None,_types=None,CSVfile=’TS.csv’,THRESHOLD=None,tiles=None,incr=6,max_incr=24)
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