def run_analysis(cname, cause, ename, effect, db, rng, ccm_settings):
'''Run analysis for a single causal direction.'''
sys.stderr.write('Running {0}-causes-{1}\n'.format(cname, ename))
# Check if effect has no variation
cause_sd = numpy.std(cause)
effect_sd = numpy.std(effect)
if cause_sd == 0.0 or effect_sd == 0.0:
sys.stderr.write(
'No variation cause or effect time series; skipping analysis.\n')
return
else:
# Identify delay at which autocorrelation drops to 1/e
ac_delay, autocorr = pyembedding.autocorrelation_threshold_delay(
effect, 1.0 / numpy.e)
sys.stderr.write(' ac_delay, autocorr = {0}, {1}\n'.format(
ac_delay, autocorr))
# Calculate Theiler window (limit on closeness of neighbors in time)
theiler_window = min(ccm_settings['max_theiler_window'], 3 * ac_delay)
sys.stderr.write(' theiler_window = {0}\n'.format(theiler_window))
assert theiler_window < effect.shape[0]
embedding_algorithm = ccm_settings['embedding_algorithm']
assert embedding_algorithm == 'max_univariate_prediction_plus_lags'
run_analysis_max_univariate_prediction_plus_lags(
cname, cause, ename, effect, theiler_window, db, rng, ccm_settings)
def run_analysis(cname, cause, ename, effect):
'''Run analysis for a single causal direction.'''
sys.stderr.write('Running {0}-causes-{1}\n'.format(cname, ename))
# Check if effect has no variation
cause_sd = numpy.std(cause)
effect_sd = numpy.std(effect)
if cause_sd < EPS or effect_sd < EPS:
sys.stderr.write(
'No variation cause or effect time series; skipping analysis.\n')
return
else:
# Identify delay at which autocorrelation drops to 1/e
ac_delay, autocorr = pyembedding.autocorrelation_threshold_delay(
effect, 1.0 / numpy.e)
sys.stderr.write(' ac_delay, autocorr = {0}, {1}\n'.format(
ac_delay, autocorr))
# Calculate Theiler window (limit on closeness of neighbors in time)
theiler_window = min(MAX_THEILER_WINDOW, 3 * ac_delay)
sys.stderr.write(' theiler_window = {0}\n'.format(theiler_window))
assert theiler_window < effect.shape[0]
if EMBEDDING_ALGORITHM == 'uzal_nichkawde':
run_analysis_uzal_nichkawde(cname, cause, ename, effect,
theiler_window)
elif EMBEDDING_ALGORITHM == 'uniform_sweep':
run_analysis_uniform_sweep(cname, cause, ename, effect,
theiler_window)
elif EMBEDDING_ALGORITHM == 'max_ccm_rho':
run_analysis_max_ccm_rho(cname, cause, ename, effect,
theiler_window)
elif EMBEDDING_ALGORITHM == 'max_univariate_prediction':
run_analysis_max_univariate_prediction(cname, cause, ename, effect,
theiler_window)
def run_analysis(cname, cause, ename, effect):
'''Run analysis for a single causal direction.'''
sys.stderr.write('Running {0}-causes-{1}\n'.format(cname, ename))
# Check if effect has no variation
cause_sd = numpy.std(cause)
effect_sd = numpy.std(effect)
if cause_sd < EPS or effect_sd < EPS:
sys.stderr.write('No variation cause or effect time series; skipping analysis.\n')
return
else:
# Identify delay at which autocorrelation drops to 1/e
ac_delay, autocorr = pyembedding.autocorrelation_threshold_delay(effect, 1.0/numpy.e)
sys.stderr.write(' ac_delay, autocorr = {0}, {1}\n'.format(ac_delay, autocorr))
# Calculate Theiler window (limit on closeness of neighbors in time)
theiler_window = min(MAX_THEILER_WINDOW, 3 * ac_delay)
sys.stderr.write(' theiler_window = {0}\n'.format(theiler_window))
assert theiler_window < effect.shape[0]
if EMBEDDING_ALGORITHM == 'uzal_nichkawde':
run_analysis_uzal_nichkawde(cname, cause, ename, effect, theiler_window)
elif EMBEDDING_ALGORITHM == 'uniform_sweep':
run_analysis_uniform_sweep(cname, cause, ename, effect, theiler_window)
elif EMBEDDING_ALGORITHM == 'max_ccm_rho':
run_analysis_max_ccm_rho(cname, cause, ename, effect, theiler_window)
elif EMBEDDING_ALGORITHM == 'max_univariate_prediction':
run_analysis_max_univariate_prediction(cname, cause, ename, effect, theiler_window)
def run_analysis(cname, cause, ename, effect, db, rng):
'''Run analysis for a single causal direction.'''
sys.stderr.write('Running {0}-causes-{1}\n'.format(cname, ename))
# Check if effect has no variation
cause_sd = numpy.std(cause)
effect_sd = numpy.std(effect)
if cause_sd == 0.0 or effect_sd == 0.0:
sys.stderr.write('No variation cause or effect time series; skipping analysis.\n')
return
else:
# Identify delay at which autocorrelation drops to 1/e
ac_delay, autocorr = pyembedding.autocorrelation_threshold_delay(effect, 1.0/numpy.e)
sys.stderr.write(' ac_delay, autocorr = {0}, {1}\n'.format(ac_delay, autocorr))
# Calculate Theiler window (limit on closeness of neighbors in time)
theiler_window = 3 * ac_delay
sys.stderr.write(' theiler_window = {0}\n'.format(theiler_window))
assert theiler_window < effect.shape[0]
run_analysis_max_univariate_prediction_plus_lags(cname, cause, ename, effect, theiler_window, db, rng)
def run_analysis(cname, cause, ename, effect, db, rng, ccm_settings):
'''Run analysis for a single causal direction.'''
sys.stderr.write('Running {0}-causes-{1}\n'.format(cname, ename))
# Check if effect has no variation
cause_sd = numpy.std(cause)
effect_sd = numpy.std(effect)
if cause_sd == 0.0 or effect_sd == 0.0:
sys.stderr.write('No variation cause or effect time series; skipping analysis.\n')
return
else:
# Identify delay at which autocorrelation drops to 1/e
ac_delay, autocorr = pyembedding.autocorrelation_threshold_delay(effect, 1.0/numpy.e)
sys.stderr.write(' ac_delay, autocorr = {0}, {1}\n'.format(ac_delay, autocorr))
# Calculate Theiler window (limit on closeness of neighbors in time)
theiler_window = min(ccm_settings['max_theiler_window'], 3 * ac_delay)
sys.stderr.write(' theiler_window = {0}\n'.format(theiler_window))
assert theiler_window < effect.shape[0]
embedding_algorithm = ccm_settings['embedding_algorithm']
assert embedding_algorithm == 'tajima_projection'
if embedding_algorithm == 'tajima_projection':
run_analysis_tajima_projection(cname, cause, ename, effect, theiler_window, db, rng, ccm_settings)