cynet 1.0.48

Spatio temporal analysis for inferrence of statistical causality using XGenESeSS

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)