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{
"filename": "enc_int_wrapper.py",
"repo_name": "masoncarney/stepped_luneburg",
"repo_path": "stepped_luneburg_extracted/stepped_luneburg-master/scripts/enc_int_wrapper.py",
"type": "Python"
}
|
import os, sys
import numpy as np
import pylab as pl
try:
from enclosed_intensity import enc_int
except:
raise ImportError("Cannot import module enc_int. Try updating the PYTHONPATH variable to include the path to the directory containing enclosed_intensity.py")
'''
Wrapper script for function enc_int(). Requires multiple files output from stepped_luneburg() to be read in and creates a new enclosed intensity output file for each iteration.
Default parameters:
enc_int(infile=None,outfile="luneburg_enc_int.dat",frac=0.5, mode=None, star_num=None,plot=True,figoutfile="luneburg_enc_int.eps"):
'''
###################################################################
# loop through power-law exponents and layers for Luneburg model
datadir = "/my/path/to/output/files/"
mode = 'grid'
nrays = 1024.
layers = [5.,10.,20.,40.]
plaw_exp = [0.30,0.35,0.40,0.45,0.50,0.55,0.60,0.65,0.70]
frac = 0.5
for i in range(len(layers)):
for j in range(len(plaw_exp)):
myinfile = (os.path.join(datadir,"luneburg_out_%i_%.2f_%i_%s.dat"%(layers[i],plaw_exp[j],nrays,mode))).replace('_0.','_0d')
myoutfile = (os.path.join(datadir,"luneburg_enc_int_%i_%.2f_%i_%s.dat"%(layers[i],plaw_exp[j],nrays,mode))).replace('_0.','_0d')
enc_int(infile=myinfile,outfile=myoutfile,frac=frac,mode=mode,plot=True)
|
masoncarneyREPO_NAMEstepped_luneburgPATH_START.@stepped_luneburg_extracted@stepped_luneburg-master@scripts@enc_int_wrapper.py@.PATH_END.py
|
{
"filename": "_reversescale.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scatter/marker/line/_reversescale.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ReversescaleValidator(_plotly_utils.basevalidators.BooleanValidator):
def __init__(
self, plotly_name="reversescale", parent_name="scatter.marker.line", **kwargs
):
super(ReversescaleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scatter@marker@line@_reversescale.py@.PATH_END.py
|
{
"filename": "util.py",
"repo_name": "xgds/xgds_core",
"repo_path": "xgds_core_extracted/xgds_core-master/xgds_core/util.py",
"type": "Python"
}
|
# __BEGIN_LICENSE__
# Copyright (c) 2015, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration.
# All rights reserved.
#
# The xGDS platform is licensed under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing, software distributed
# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.
# __END_LICENSE__
import os
import sys
import pytz
import datetime
import time
from dateutil.relativedelta import relativedelta
from django.utils import timezone
from django.conf import settings
from django.core.cache import caches
import urlparse
import json
import requests
if settings.XGDS_CORE_REDIS:
from redisUtil import queueRedisData
from geocamUtil.datetimeJsonEncoder import DatetimeJsonEncoder
def get100Years():
theNow = timezone.now() + relativedelta(years=100)
return theNow
def addPort(url, port, http=True):
''' Add a port to a url '''
if port:
parsed = urlparse.urlparse(url)
replaced = parsed._replace(netloc="{}:{}".format(parsed.hostname, port))
if http:
replaced = replaced._replace(scheme='http')
return replaced.geturl()
return url
def callUrl(url, username, password, method='GET', data=None, shareSession=False):
''' WARNING If you are calling this a lot of times then you will be opening a new connection and instead it should
be run outside with a pycroraptor service.'''
if shareSession and settings.XGDS_CORE_REDIS:
# POST THIS ON SHARED SESSION FOR REDIS
config = {'url':url,
'username': username,
'password': password,
'method': method,
'data': data}
queueRedisData(settings.XGDS_CORE_REDIS_SESSION_MANAGER, json.dumps(config, cls=DatetimeJsonEncoder))
return
else:
s = requests.Session()
if username:
s.auth = (username, password)
if method == 'POST':
return s.post(url, data=data)
elif method == 'GET':
return s.get(url)
def deletePostKey(post, theKey):
try:
if theKey in post:
mutable = post._mutable
post._mutable = True
del post[theKey]
post._mutable = mutable
except:
pass
return post
def insertIntoPath(original, insertion='rest'):
""" Insert a string after the first block in a path, for example
/my/original/path,insertion
/my/INSERTION/original/path
"""
slashIndex = original.index('/',1)
newString = '%s/%s%s' % (original[0:slashIndex], insertion, original[slashIndex:len(original)])
return newString
def get_all_subclasses(the_class, check_meta_abstract=True, top=True):
"""
Returns all the subclasses of the given class
:param the_class: parent class
:param check_meta_abstract: False to not check the abstract setting on a django model
:param top: true if this is the 'top call', ie non recursive call
:return: set of all subclasses
"""
kids = the_class.__subclasses__()
result = set(kids).union(
[s for c in kids for s in get_all_subclasses(c, check_meta_abstract, False)])
if top:
if check_meta_abstract:
non_abstract_result = []
for k in result:
if not k._meta.abstract:
non_abstract_result.append(k)
result = non_abstract_result
return sorted(result)
def build_relative_path(full_path, prefix='/', split_on='/data/'):
"""
Given a full file path on the hard drive return a relative path to the data directory
ie /full/path/to/data/my/file
:param full_path: The original full path to a file
:param prefix: The prefix of the result
:param split_on: The string in the path to split on, included in the result.
:return: the relative path, ie '/data/my/file
"""
splits = full_path.split(split_on)
return os.path.join(prefix, split_on, splits[-1])
def get_file_size(input_file):
"""
Return the file size in bytes from an open file
:param input_file:
:return:
"""
old_file_position = input_file.tell()
input_file.seek(0, os.SEEK_END)
size = input_file.tell()
input_file.seek(old_file_position, os.SEEK_SET)
return size
def persist_error(error, stacktrace=None):
cache = caches['default']
# Get current list of error keys or default to empty list
error_keys = cache.get('error_keys', [])
# Use the current process name as the key for this error
key = os.path.basename(sys.argv[0])
timestamp = time.time()
if isinstance(error, Exception):
error_string = str(error.__class__.__name__) + " (" + str(error) + ")"
else:
error_string = str(error)
value = {
'timestamp': int(timestamp),
'error': str(error_string),
'stacktrace': str(stacktrace),
}
# Store the error
cache.set(key, value)
# If this is a new error key, update the error keys to include it
if key not in error_keys:
error_keys.append(key)
cache.set('error_keys', error_keys)
def get_persisted_error_keys():
return caches['default'].get('error_keys', [])
def get_persisted_error(key):
return caches['default'].get(key)
def get_persisted_errors():
keys = get_persisted_error_keys()
errors = {}
for k in keys:
errors[k] = get_persisted_error(k)
return errors
def delete_persisted_error(key):
cache = caches['default']
cache.delete(key)
error_keys = cache.get('error_keys', [])
if key in error_keys:
error_keys.remove(key)
cache.set('error_keys', error_keys)
|
xgdsREPO_NAMExgds_corePATH_START.@xgds_core_extracted@xgds_core-master@xgds_core@util.py@.PATH_END.py
|
{
"filename": "test_predicted_zeropoints.py",
"repo_name": "lsst/rubin_sim",
"repo_path": "rubin_sim_extracted/rubin_sim-main/tests/phot_utils/test_predicted_zeropoints.py",
"type": "Python"
}
|
import unittest
import numpy as np
from rubin_scheduler.utils import SysEngVals
from rubin_sim.phot_utils import (
predicted_zeropoint,
predicted_zeropoint_e2v,
predicted_zeropoint_hardware,
predicted_zeropoint_hardware_e2v,
predicted_zeropoint_hardware_itl,
predicted_zeropoint_itl,
)
class PredictedZeropointsTst(unittest.TestCase):
def test_predicted_zeropoints(self):
bands = ["u", "g", "r", "i", "z", "y"]
sev = SysEngVals()
for b in bands:
zp = predicted_zeropoint(band=b, airmass=1.0, exptime=1)
self.assertAlmostEqual(zp, sev.zp_t[b], delta=0.005)
zp_hardware = predicted_zeropoint_hardware(b, exptime=1)
self.assertTrue(zp < zp_hardware)
# Check the vendors
zp_v = predicted_zeropoint_itl(band=b, airmass=1.0, exptime=1)
self.assertAlmostEqual(zp, zp_v, delta=0.1)
zp_v = predicted_zeropoint_e2v(band=b, airmass=1.0, exptime=1)
self.assertAlmostEqual(zp, zp_v, delta=0.1)
zp_v = predicted_zeropoint_hardware_itl(band=b, exptime=1)
self.assertAlmostEqual(zp_hardware, zp_v, delta=0.1)
zp_v = predicted_zeropoint_hardware_e2v(band=b, exptime=1)
self.assertAlmostEqual(zp_hardware, zp_v, delta=0.1)
# Check some of the scaling
zp_test = predicted_zeropoint(band=b, airmass=1.5, exptime=1)
self.assertTrue(zp > zp_test)
zp_test = predicted_zeropoint(band=b, airmass=1.0, exptime=30)
self.assertAlmostEqual(zp, zp_test - 2.5 * np.log10(30), places=7)
funcs = [predicted_zeropoint, predicted_zeropoint_itl, predicted_zeropoint_e2v]
for zpfunc in funcs:
zp = []
for x in np.arange(1.0, 2.5, 0.1):
for exptime in np.arange(1.0, 130, 30):
zp.append(zpfunc(b, x, exptime))
zp = np.array(zp)
self.assertTrue(zp.max() - zp.min() < 6)
self.assertTrue(zp.max() < 35)
self.assertTrue(zp.min() > 25)
if __name__ == "__main__":
unittest.main()
|
lsstREPO_NAMErubin_simPATH_START.@rubin_sim_extracted@rubin_sim-main@tests@phot_utils@test_predicted_zeropoints.py@.PATH_END.py
|
{
"filename": "_outlinewidth.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/streamtube/colorbar/_outlinewidth.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class OutlinewidthValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="outlinewidth", parent_name="streamtube.colorbar", **kwargs
):
super(OutlinewidthValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@streamtube@colorbar@_outlinewidth.py@.PATH_END.py
|
{
"filename": "test_plot.py",
"repo_name": "iraf-community/pyraf",
"repo_path": "pyraf_extracted/pyraf-main/pyraf/tests/test_plot.py",
"type": "Python"
}
|
import pytest
import os
from .utils import HAS_IRAF
from pyraf import gki
from ..tools import capable
if HAS_IRAF:
from pyraf import iraf
else:
pytestmark = pytest.mark.skip('Need IRAF to run')
try:
import tkinter
tkinter.Tk().destroy()
except Exception:
pytestmark = pytest.mark.skip('No graphics available')
markers = [None, 'point', 'box', 'plus', 'cross', 'circle']
graphics = ['tkplot', 'matplotlib']
@pytest.fixture(autouse=True)
def setup():
orig_graphics = capable.OF_GRAPHICS
capable.OF_GRAPHICS = True
iraf.plot() # load plot pkg
# gki._resetGraphicsKernel()
# clean slate
graphenv = os.environ.get('PYRAFGRAPHICS')
if graphenv is not None:
del os.environ['PYRAFGRAPHICS']
yield
gki.kernel.clear()
capable.OF_GRAPHICS = orig_graphics
if graphenv is not None:
os.environ['PYRAFGRAPHICS'] = graphenv
@pytest.mark.parametrize('marker', markers)
@pytest.mark.parametrize('graphics', graphics)
def test_plot_prow(marker, graphics):
os.environ['PYRAFGRAPHICS'] = graphics
apd = False
for row in range(150, 331, 90):
if marker is None:
iraf.prow('dev$pix', row, wy2=400, append=apd, pointmode=False)
else:
iraf.prow('dev$pix', row, wy2=400, append=apd, pointmode=True,
marker=marker)
apd = True
@pytest.mark.parametrize('marker', markers)
@pytest.mark.parametrize('graphics', graphics)
def test_plot_graph(marker, graphics):
os.environ['PYRAFGRAPHICS'] = graphics
tstr = ''
for row in range(150, 331, 90):
tstr += 'dev$pix[*,' + str(row) + '],'
tstr = tstr[:-1] # rm final comma
if marker is None:
iraf.graph(tstr, wy2=400, pointmode=False, ltypes='1')
else:
iraf.graph(tstr, wy2=400, pointmode=True, marker=marker)
@pytest.mark.parametrize('graphics', graphics)
def test_plot_contour(graphics):
os.environ['PYRAFGRAPHICS'] = graphics
iraf.contour("dev$pix", Stderr='/dev/null')
@pytest.mark.parametrize('graphics', graphics)
def test_plot_surface(graphics):
os.environ['PYRAFGRAPHICS'] = graphics
iraf.surface("dev$pix", Stderr='/dev/null')
|
iraf-communityREPO_NAMEpyrafPATH_START.@pyraf_extracted@pyraf-main@pyraf@tests@test_plot.py@.PATH_END.py
|
{
"filename": "cli-reference_eval-metrics.md",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/catboost/docs/en/concepts/cli-reference_eval-metrics.md",
"type": "Markdown"
}
|
# Calculate metrics
## {{ dl--purpose }} {#purpose}
Calculate metrics for a given dataset using a previously trained model.
## {{ dl__cli__execution-format }} {#execution-format}
```
catboost eval-metrics --metrics <comma-separated list of metrics> [optional parameters]
```
## {{ common-text__title__reference__parameters }} {#options}
{% include [cli__eval-metrics-cli__eval-metrics__options](../_includes/work_src/reusage/cli__eval-metrics__options.md) %}
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@catboost@docs@en@concepts@cli-reference_eval-metrics.md@.PATH_END.py
|
{
"filename": "XID+SED_example.ipynb",
"repo_name": "H-E-L-P/XID_plus",
"repo_path": "XID_plus_extracted/XID_plus-master/docs/build/doctrees/nbsphinx/notebooks/examples/XID+SED_example.ipynb",
"type": "Jupyter Notebook"
}
|
# XID+SED Example Run Script
(This is based on a Jupyter notebook, available in the [XID+ package](https://github.com/H-E-L-P/XID_plus/tree/master/docs/notebooks/examples/) and can be interactively run and edited)
XID+ is a probababilistic deblender for confusion dominated maps. It is designed to:
1. Use a MCMC based approach to get FULL posterior probability distribution on flux
2. Provide a natural framework to introduce additional prior information
3. Allows more representative estimation of source flux density uncertainties
4. Provides a platform for doing science with the maps (e.g XID+ Hierarchical stacking, Luminosity function from the map etc)
This notebook shows an example on how to run XID+ with an SED emulator on the GALFORM simulation of the COSMOS field
* SAM simulation (with dust) ran through SMAP pipeline_ similar depth and size as COSMOS
Import required modules
```python
from astropy.io import ascii, fits
from astropy.table import Table
import pylab as plt
%matplotlib inline
from astropy import wcs
import seaborn as sns
import glob
from astropy.coordinates import SkyCoord
import numpy as np
import xidplus
from xidplus import moc_routines
import pickle
import os
from xidplus.numpyro_fit.neuralnet_models import CIGALE_emulator_kasia
```
/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
data = yaml.load(f.read()) or {}
WARNING: AstropyDeprecationWarning: block_reduce was moved to the astropy.nddata.blocks module. Please update your import statement. [astropy.nddata.utils]
```python
#Folder containing maps
imfolder=xidplus.__path__[0]+'/../test_files/'
pswfits=imfolder+'cosmos_itermap_lacey_07012015_simulated_observation_w_noise_PSW_hipe.fits.gz'#SPIRE 250 map
pmwfits=imfolder+'cosmos_itermap_lacey_07012015_simulated_observation_w_noise_PMW_hipe.fits.gz'#SPIRE 350 map
plwfits=imfolder+'cosmos_itermap_lacey_07012015_simulated_observation_w_noise_PLW_hipe.fits.gz'#SPIRE 500 map
#Folder containing prior input catalogue
catfolder=xidplus.__path__[0]+'/../test_files/'
#prior catalogue
prior_cat='lacey_07012015_MillGas.ALLVOLS_cat_PSW_COSMOS_test.fits'
#output folder
output_folder='./'
```
Load in images, noise maps, header info and WCS information
```python
#-----250-------------
hdulist = fits.open(pswfits)
im250phdu=hdulist[0].header
im250hdu=hdulist[1].header
im250=hdulist[1].data*1.0E3 #convert to mJy
nim250=hdulist[2].data*1.0E3 #convert to mJy
w_250 = wcs.WCS(hdulist[1].header)
pixsize250=3600.0*w_250.wcs.cd[1,1] #pixel size (in arcseconds)
hdulist.close()
#-----350-------------
hdulist = fits.open(pmwfits)
im350phdu=hdulist[0].header
im350hdu=hdulist[1].header
im350=hdulist[1].data*1.0E3 #convert to mJy
nim350=hdulist[2].data*1.0E3 #convert to mJy
w_350 = wcs.WCS(hdulist[1].header)
pixsize350=3600.0*w_350.wcs.cd[1,1] #pixel size (in arcseconds)
hdulist.close()
#-----500-------------
hdulist = fits.open(plwfits)
im500phdu=hdulist[0].header
im500hdu=hdulist[1].header
im500=hdulist[1].data*1.0E3 #convert to mJy
nim500=hdulist[2].data*1.0E3 #convert to mJy
w_500 = wcs.WCS(hdulist[1].header)
pixsize500=3600.0*w_500.wcs.cd[1,1] #pixel size (in arcseconds)
hdulist.close()
```
Load in catalogue you want to fit (and make any cuts)
```python
catalogue=Table.read(catfolder+prior_cat)
catalogue['ID']=np.arange(0,len(catalogue))
catalogue.add_index('ID')
# select only sources with 100micron flux greater than 50 microJy
sgood=catalogue['S100']>0.010
inra=catalogue['RA'][sgood]
indec=catalogue['DEC'][sgood]
inz=catalogue['Z_OBS'][sgood]
```
XID+ uses Multi Order Coverage (MOC) maps for cutting down maps and catalogues so they cover the same area. It can also take in MOCs as selection functions to carry out additional cuts. Lets use the python module pymoc to create a MOC, centered on a specific position we are interested in. We will use a HEALPix order of 15 (the resolution: higher order means higher resolution), have a radius of 100 arcseconds centered around an R.A. of 150.74 degrees and Declination of 2.03 degrees.
```python
from astropy.coordinates import SkyCoord
from astropy import units as u
c = SkyCoord(ra=[150.74]*u.degree, dec=[2.03]*u.degree)
import pymoc
moc=pymoc.util.catalog.catalog_to_moc(c,100,15)
```
XID+ is built around Python classes:
* A `prior class`: Contains all the information specific to a map being fitted (e.g. the map, noise map, a multi order coverage map, a prior positional catalogue, point spread function and pointing matrix)
* A `phys_prior` : Contains all the prior information on physical parameters( e.g. redshift, SFR etc), and the SED emulator
There should be a prior class for each map being fitted.It is initiated with a map, noise map, primary header and map header and can be set with a MOC. It also requires an input prior catalogue and point spread function.
```python
#---prior250--------
prior250=xidplus.prior(im250,nim250,im250phdu,im250hdu, moc=moc)#Initialise with map, uncertianty map, wcs info and primary header
prior250.prior_cat(inra,indec,prior_cat,ID=catalogue['ID'][sgood])#Set input catalogue
prior250.prior_bkg(-5.0,5)#Set prior on background (assumes Gaussian pdf with mu and sigma)
#---prior350--------
prior350=xidplus.prior(im350,nim350,im350phdu,im350hdu, moc=moc)
prior350.prior_cat(inra,indec,prior_cat,ID=catalogue['ID'][sgood])
prior350.prior_bkg(-5.0,5)
#---prior500--------
prior500=xidplus.prior(im500,nim500,im500phdu,im500hdu, moc=moc)
prior500.prior_cat(inra,indec,prior_cat,ID=catalogue['ID'][sgood])
prior500.prior_bkg(-5.0,5)
```
Set PRF. For SPIRE, the PRF can be assumed to be Gaussian with a FWHM of 18.15, 25.15, 36.3 '' for 250, 350 and 500 $\mathrm{\mu m}$ respectively. Lets use the astropy module to construct a Gaussian PRF and assign it to the three XID+ prior classes.
```python
#pixsize array (size of pixels in arcseconds)
pixsize=np.array([pixsize250,pixsize350,pixsize500])
#point response function for the three bands
prfsize=np.array([18.15,25.15,36.3])
#use Gaussian2DKernel to create prf (requires stddev rather than fwhm hence pfwhm/2.355)
from astropy.convolution import Gaussian2DKernel
##---------fit using Gaussian beam-----------------------
prf250=Gaussian2DKernel(prfsize[0]/2.355,x_size=101,y_size=101)
prf250.normalize(mode='peak')
prf350=Gaussian2DKernel(prfsize[1]/2.355,x_size=101,y_size=101)
prf350.normalize(mode='peak')
prf500=Gaussian2DKernel(prfsize[2]/2.355,x_size=101,y_size=101)
prf500.normalize(mode='peak')
pind250=np.arange(0,101,1)*1.0/pixsize[0] #get 250 scale in terms of pixel scale of map
pind350=np.arange(0,101,1)*1.0/pixsize[1] #get 350 scale in terms of pixel scale of map
pind500=np.arange(0,101,1)*1.0/pixsize[2] #get 500 scale in terms of pixel scale of map
prior250.set_prf(prf250.array,pind250,pind250)#requires PRF as 2d grid, and x and y bins for grid (in pixel scale)
prior350.set_prf(prf350.array,pind350,pind350)
prior500.set_prf(prf500.array,pind500,pind500)
```
```python
print('fitting '+ str(prior250.nsrc)+' sources \n')
print('using ' + str(prior250.snpix)+', '+ str(prior250.snpix)+' and '+ str(prior500.snpix)+' pixels')
```
fitting 101 sources
using 870, 870 and 219 pixels
Before fitting, the prior classes need to take the PRF and calculate how much each source contributes to each pixel. This process provides what we call a pointing matrix. Lets calculate the pointing matrix for each prior class
```python
prior250.get_pointing_matrix()
prior350.get_pointing_matrix()
prior500.get_pointing_matrix()
```
```python
priors=[prior250,prior350,prior500]
```
To create the `phys_prior` class, you need to provide a table with some prior information for the physical parameters that contains the following:
* an ID column, `ID` that matches the `ID` in the prior class
* an `log10SFR_mu` and `log10SFR_sig` column that contains the mean and standard deviation for a Normal prior distribution
* an `z_mu` and `z_sig` column that contains the mean and standard deviation for a Normal prior distribution (truncated at 0.01 so not to go too small)
* a `fracAGN_alpha` and `fracAGN_beta` column that contains the alpha and beta values for a Beta prior distribution (i.e. that is constrained to be between 0 and 1
* a `atten_mu` and `atten_sig` column that contains the mean and standard deviation for a Normal prior distribution,
* a `dust_alpha_mu` and `dust_alpha_sig` column that contains the mean and standard deviation for a Normal prior distribution
* a `tau_main_mu` and `tau_main_sig` column that contains the mean and standard deviation for a Normal prior distribution
```python
phys_prior_table = Table([priors[0].ID,
np.full(priors[0].nsrc,1),
np.full(priors[0].nsrc,1),
catalogue.loc[priors[0].ID]['Z_OBS'].data,
catalogue.loc[priors[0].ID]['Z_OBS'].data*0.1,
np.full(priors[0].nsrc,1.0),
np.full(priors[0].nsrc,3.0),
np.full(priors[0].nsrc,1.95),
np.full(priors[0].nsrc,1.0),
np.full(priors[0].nsrc,2),
np.full(priors[0].nsrc,1),
np.full(priors[1].nsrc,3.5),
np.full(priors[1].nsrc,0.25)
],
names=('ID','log10SFR_mu', 'log10SFR_sig', 'z_mu', 'z_sig','fracAGN_alpha','fracAGN_beta','atten_mu','atten_sig','dust_alpha_mu','dust_alpha_sig','tau_main_mu','tau_main_sig'),
meta={'name': 'phys prior table'},
dtype=[np.longlong]+[float for i in range(0,12)])
phys_prior_table.add_index('ID')
```
```python
phys_prior_table
```
<i>Table length=101</i>
<table id="table140524044733016" class="table-striped table-bordered table-condensed">
<thead><tr><th>ID</th><th>log10SFR_mu</th><th>log10SFR_sig</th><th>z_mu</th><th>z_sig</th><th>fracAGN_alpha</th><th>fracAGN_beta</th><th>atten_mu</th><th>atten_sig</th><th>dust_alpha_mu</th><th>dust_alpha_sig</th><th>tau_main_mu</th><th>tau_main_sig</th></tr></thead>
<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
<tr><td>8177</td><td>1.0</td><td>1.0</td><td>2.1290905475616455</td><td>0.2129090577363968</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>8179</td><td>1.0</td><td>1.0</td><td>2.127817392349243</td><td>0.21278174221515656</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>8206</td><td>1.0</td><td>1.0</td><td>2.1299428939819336</td><td>0.2129942923784256</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>8215</td><td>1.0</td><td>1.0</td><td>2.126209259033203</td><td>0.21262092888355255</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>11643</td><td>1.0</td><td>1.0</td><td>1.0341293811798096</td><td>0.1034129410982132</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>20716</td><td>1.0</td><td>1.0</td><td>1.437013864517212</td><td>0.14370138943195343</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>22635</td><td>1.0</td><td>1.0</td><td>2.521920919418335</td><td>0.25219210982322693</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>22636</td><td>1.0</td><td>1.0</td><td>2.522642135620117</td><td>0.25226423144340515</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>22638</td><td>1.0</td><td>1.0</td><td>2.522317886352539</td><td>0.25223180651664734</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>22864</td><td>1.0</td><td>1.0</td><td>0.4999462366104126</td><td>0.04999462515115738</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>
<tr><td>277272</td><td>1.0</td><td>1.0</td><td>2.1351029872894287</td><td>0.21351030468940735</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>277365</td><td>1.0</td><td>1.0</td><td>2.358628749847412</td><td>0.2358628809452057</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>282324</td><td>1.0</td><td>1.0</td><td>1.383598804473877</td><td>0.1383598893880844</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>286312</td><td>1.0</td><td>1.0</td><td>2.115485668182373</td><td>0.2115485668182373</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>286322</td><td>1.0</td><td>1.0</td><td>2.1113414764404297</td><td>0.2111341506242752</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>286345</td><td>1.0</td><td>1.0</td><td>2.1165578365325928</td><td>0.21165578067302704</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>286346</td><td>1.0</td><td>1.0</td><td>2.115492343902588</td><td>0.21154923737049103</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>286347</td><td>1.0</td><td>1.0</td><td>2.114086151123047</td><td>0.2114086151123047</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>286348</td><td>1.0</td><td>1.0</td><td>2.112424850463867</td><td>0.21124248206615448</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
<tr><td>286349</td><td>1.0</td><td>1.0</td><td>2.1147334575653076</td><td>0.21147334575653076</td><td>1.0</td><td>3.0</td><td>1.95</td><td>1.0</td><td>2.0</td><td>1.0</td><td>3.5</td><td>0.25</td></tr>
</table>
The `phys_prior` class takes in the phys prior table, the model emulator, the path to the emulator parameters file, and dictionary with parameters for the hierarchical model (in this case a schecter like function)
```python
hier_params={'z_star_mu':4.0,'z_star_sig':0.3,'sfr_star_mu':7,'sfr_star_sig':2.0,'sfr_disp':0.5,'alpha':0.5}
emulator_path=xidplus.__path__[0]+'/emulator/params/CIGALE_emulator_kasia_20220127.npz'
phys_prior=xidplus.hier_prior(phys_prior_table,CIGALE_emulator_kasia,emulator_path,hier_params)
```
W0513 16:31:55.089982 4539567552 xla_bridge.py:234] No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
```python
from numpyro.infer import Predictive
from jax import random, vmap
from xidplus.numpyro_fit import SED_prior
rng_key = random.PRNGKey(0)
rng_key, rng_key_ = random.split(rng_key)
```
```python
## For numpyro to sample from the prior, the data needs to be set to None
import copy
#make a deepcopy of the priors
priors_prior_pred=copy.deepcopy(priors)
#set data to None
for i in range(0,len(priors)):
priors_prior_pred[i].sim=None
```
```python
%%time
#sample from the prior using numpyro's Predictive function
prior_predictive=Predictive(SED_prior.spire_model_CIGALE_kasia_schect,posterior_samples = {}, num_samples = 1000,)
prior_pred=prior_predictive(random.PRNGKey(0),priors_prior_pred,phys_prior)
```
CPU times: user 19.5 s, sys: 544 ms, total: 20 s
Wall time: 25.6 s
## First pass
```python
import numpyro
from numpyro.diagnostics import summary
import jax.numpy as jnp
from numpyro.infer import NUTS,MCMC
import os
numpyro.set_host_device_count(4)
from operator import attrgetter
def fit_model(priors,phys_prior,num_samples=500, num_warmup=500,num_chains=4,chain_method='parallel'):
nuts_kernel = NUTS(SED_prior.spire_model_CIGALE_kasia_schect,init_strategy=numpyro.infer.init_to_median())
mcmc = MCMC(nuts_kernel, num_samples=num_samples, num_warmup=num_warmup,num_chains=num_chains,chain_method=chain_method)
rng_key = random.PRNGKey(0)
mcmc.run(rng_key,priors,phys_prior)
return mcmc
```
```python
n_chains=2
mcmc=fit_model(priors,phys_prior,num_samples=100, num_warmup=500,num_chains=n_chains,chain_method='parallel')
```
sample: 100%|██████████| 600/600 [05:15<00:00, 1.90it/s, 255 steps of size 1.58e-02. acc. prob=0.86]
sample: 100%|██████████| 600/600 [05:23<00:00, 1.85it/s, 255 steps of size 1.52e-02. acc. prob=0.86]
```python
from operator import attrgetter
from numpyro.diagnostics import summary
import jax.numpy as jnp
import os
samples=mcmc.get_samples()
divergences=mcmc.get_extra_fields()['diverging']
prob = 0.9
exclude_deterministic = True
sites = mcmc._states[mcmc._sample_field]
if isinstance(sites, dict) and exclude_deterministic:
state_sample_field = attrgetter(mcmc._sample_field)(mcmc._last_state)
# XXX: there might be the case that state.z is not a dictionary but
# its postprocessed value `sites` is a dictionary.
# TODO: in general, when both `sites` and `state.z` are dictionaries,
# they can have different key names, not necessary due to deterministic
# behavior. We might revise this logic if needed in the future.
if isinstance(state_sample_field, dict):
sites = {k: v for k, v in mcmc._states[mcmc._sample_field].items()
if k in state_sample_field}
stats_summary = summary(sites, prob=prob)
```
## Check diagnostics
When running any MCMC algorithm, you need to check that it has explored the full posterior probability space, and not got stuck in local minima. The standard MCMC diagnostics are:
* $\hat{R}$ compares variation within and between chains. You want $\hat{R} < 1.1$
* $n_{eff}$ effective sample size which is a measure of autocorrelation within the chains. More autocorrelation increases uncertainty in estimates. Typical MCMC has a low $n_{eff}$ and requires thinning (as to keep all samples would be too memory intensive). Since NUTS HMC is efficient, there is little need to chuck samples away. If $n_{eff} / N < 0.001$, then there is a problem with model
The other diagnostic, exclusive to The NUTS sampler, is the identification of `divergent transitions`. These identify areas in parameter space where because of some the gradient is very different to the rest of parameter space, such that the tuned NUTS sampler cannot perform its next step accuractely. These tend to occur in funnel like or VERY correlated posterior spaces. Another good explanation can be found [here](https://dev.to/martinmodrak/taming-divergences-in-stan-models-5762). If many `divergent transitions` are found, the posterior cannot be trusted and some reparamaterisation is required. Note: Standard MCMC algorithms cannot identify divergent transitions, but that does not mean they do not occur.
```python
fig,axes=plt.subplots(2,len(samples),figsize=(2*len(samples),10))
for i,k in enumerate(samples):
axes[0,i].hist(stats_summary[k]['r_hat'].flatten(),color='Blue',bins=np.arange(0.5,2,0.1))
axes[1,i].hist(stats_summary[k]['n_eff'].flatten(),color='Blue',bins=np.arange(0,samples['z_star'].shape[0],100))
axes[0,i].set_xlabel(k+' $ \hat{R}$')
axes[0,i].set_title('Div. Trans= {}'.format(np.sum(divergences)))
axes[1,i].set_xlabel(k+' $ n_{eff}$')
plt.subplots_adjust(hspace=0.5,wspace=0.5)
```
All diagnostics look fine.
## Posterior Probability distributions
#### Hierarchical parameters
```python
import pandas as pd
hier_param_names=['z_star','sfr_star','sfr_sig']
df_prior=pd.DataFrame(np.array([prior_pred[s] for s in hier_param_names]).T,columns=hier_param_names)
g=sns.PairGrid(df_prior)
g.map_lower(plt.scatter,alpha=0.5,color='Red')
g.map_diag(plt.hist,alpha=0.5,histtype='step',linewidth=3.0,color='Red',density=True)
df_post=pd.DataFrame(np.array([samples[s] for s in hier_param_names]).T,columns=hier_param_names)
g.data=df_post
g.map_lower(plt.scatter,alpha=0.5,color='Blue')
g.map_diag(plt.hist,alpha=0.5,histtype='step',linewidth=3.0,color='Blue',density=True)
g.map_upper(sns.kdeplot,alpha=0.5,color='Blue',n_levels=5, shade=False,linewidth=3.0,shade_lowest=False)
#for some reason the contour plots will delete other plots so do last
g.data=df_prior
g.map_upper(sns.kdeplot,alpha=0.5,color='Red',n_levels=5, shade=False,linewidth=3.0,shade_lowest=False)
```
<seaborn.axisgrid.PairGrid at 0x7fce3ce93e80>
```python
def Schecter(zstar,alpha,sfr_star,z):
return (sfr_star*(z/zstar)**alpha)*np.exp(-z/zstar)-2
```
```python
z=np.arange(0.1,6,0.1)
for i in range(0,2000,50):
if samples['z_star'][i]<3:
col='g'
else:
col='b'
plt.plot(z,Schecter(prior_pred['z_star'][i],phys_prior.hier_params['alpha'],prior_pred['sfr_star'][i],z),'r',alpha=0.5)
plt.fill_between(z,Schecter(prior_pred['z_star'][i],phys_prior.hier_params['alpha'],prior_pred['sfr_star'][i],z)-prior_pred['sfr_sig'][i],
Schecter(prior_pred['z_star'][i],phys_prior.hier_params['alpha'],prior_pred['sfr_star'][i],z)+prior_pred['sfr_sig'][i],color='r',alpha=0.2)
plt.fill_between(z,Schecter(samples['z_star'][i],phys_prior.hier_params['alpha'],samples['sfr_star'][i],z)-samples['sfr_sig'][i],Schecter(samples['z_star'][i],phys_prior.hier_params['alpha'],samples['sfr_star'][i],z)+samples['sfr_sig'][i],color=col,alpha=0.2)
plt.plot(z,Schecter(samples['z_star'][i],phys_prior.hier_params['alpha'],samples['sfr_star'][i],z),col,alpha=0.9)
plt.plot(np.median(samples['redshift'],axis=0),np.median(samples['sfr'],axis=0),'kx')
plt.xlabel('Redshift')
plt.ylabel(r'$\log_{10}$ SFR')
```
Text(0, 0.5, '$\\log_{10}$ SFR')
#### Source parameters
```python
phys_params=['sfr','agn','redshift','atten','dust_alpha','tau_main']
fig,axes=plt.subplots(len(phys_params),1,figsize=(50,10))
for i ,p in enumerate(phys_params):
v_plot=axes[i].violinplot(prior_pred[p].T,showextrema=False);
plt.setp(v_plot['bodies'], facecolor='Red')
v_plot=axes[i].violinplot(samples[p].T,showextrema=False);
plt.setp(v_plot['bodies'], facecolor='Blue')
axes[i].set_ylabel(phys_params[i])
```
### Posterior Predicitive Checks
```python
%%time
#sample from the prior using numpyro's Predictive function
prior_predictive_samp=Predictive(SED_prior.spire_model_CIGALE_kasia_schect,posterior_samples = samples, num_samples = 50)
prior_pred_samp=prior_predictive_samp(random.PRNGKey(0),priors_prior_pred,phys_prior)
mod_map_array_samp=[prior_pred_samp['obs_psw'].T,prior_pred_samp['obs_pmw'].T,prior_pred_samp['obs_plw'].T]
```
CPU times: user 3.5 s, sys: 87.2 ms, total: 3.59 s
Wall time: 3.8 s
In order to compare how good our fit is, its often useful to look at original map. There is a routine within XID+ that makes the original fits map from the data stored within the prior class. Lets use that to make the SPIRE maps for the region we have fit.
Now lets use the [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) plotting package and [APLpy](http://aplpy.readthedocs.io/en/stable/) package to view those maps, plotting the sources we have fit on top of those maps.
```python
figures,fig=xidplus.plot_map(priors)
```
We can use each sample we have from the posterior, and use it to make a replicated map, including simulating the instrumental noise, and the estimated confusion noise. You can think of these maps as all the possible maps that are allowed by the data.
> NOTE: You will require the `FFmpeg` library installed to run the movie
```python
#feed the model map arrays the animation function
xidplus.plots.make_map_animation(priors,mod_map_array_samp,50)
```
<video style="max-width:100%" controls>
<source 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type="video/mp4">
Your browser does not support the video tag.
</video>
### Posterior Predictive checking and Bayesian P-value maps
When examining goodness of fits, the typical method is to look at the residuals. i.e. $\frac{data - model}{\sigma}$. Because we have distribution of $y^{rep}$, we can do this in a more probabilisitic way using posterior predictive checks. For more information on posterior predictive checks, [Gelman et al. 1996](http://www.stat.columbia.edu/~gelman/research/published/A6n41.pdf) is a good starting point.
For our case, the best way to carry out posterior predictive checks is to think about one pixel. We can look at where the real flux value for our pixel is in relation to the distribution from $y^{rep}$.
We can calculate fraction of $y^{rep}$ samples above and below real map value. This is often referred to as the Bayesian p-value and is telling us the probability of drawing the real pixel value, from our model which has been inferred on the data. This is tells us if the model is inconsistent with the data, given the uncertianties in parameters and data.
* $\sim 0.5$ means our model is consistent with the data
* 0.99 or 0.01 means model is missing something.
We can convert this to a typical '$\sigma$' level and create map versions of these Bayesian p-values:
```python
from xidplus import postmaps
import aplpy
sns.set_style("white")
cmap = sns.diverging_palette(220, 20, as_cmap=True)
Bayes_pvals = []
hdulists = list(map(lambda prior: postmaps.make_fits_image(prior, prior.sim), priors))
fig = plt.figure(figsize=(10 * len(priors), 10))
figs = []
for i in range(0, len(priors)):
figs.append(aplpy.FITSFigure(hdulists[i][1], figure=fig, subplot=(1, len(priors), i + 1)))
Bayes_pvals.append(postmaps.make_Bayesian_pval_maps(priors[i], mod_map_array_samp[i]))
for i in range(0, len(priors)):
figs[i].show_markers(priors[i].sra, priors[i].sdec, edgecolor='black', facecolor='black',
marker='o', s=20, alpha=0.5)
figs[i].tick_labels.set_xformat('dd.dd')
figs[i].tick_labels.set_yformat('dd.dd')
figs[i]._data[
priors[i].sy_pix - np.min(priors[i].sy_pix) - 1, priors[i].sx_pix - np.min(priors[i].sx_pix) - 1] = \
Bayes_pvals[i]
figs[i].show_colorscale(vmin=-6, vmax=6, cmap=cmap)
figs[i].add_colorbar()
figs[i].colorbar.set_location('top')
```
Red indicates the flux value in the real map is higher than our model thinks is possible. This could be indicating there is a source there that is not in our model.
Blue indicates the flux in the real map is lower than in our model. This is either indicating a very low density region or that too much flux has been assigned to one of the sources.
## Save the samples using arviz
```python
import arviz as az
numpyro_data = az.from_numpyro(
mcmc,
prior=prior_pred,
posterior_predictive=prior_pred_samp,
coords={"src": np.arange(0,priors[0].nsrc),
"band":np.arange(0,3)},
dims={"agn": ["src"],
"bkg":["band"],
"redshift":["src"],
"sfr":["src"]},
)
#stack params and make vector ready to be used by emualator
params = jnp.stack([i.values.reshape(numpyro_data.posterior.chain.size * numpyro_data.posterior.draw.size,numpyro_data.posterior.src.size).T for i in [numpyro_data.posterior.sfr,numpyro_data.posterior.agn,numpyro_data.posterior.redshift,numpyro_data.posterior.atten,numpyro_data.posterior.dust_alpha,numpyro_data.posterior.tau_main]]).T
# Use emulator to get fluxes. As emulator provides log flux, convert.
src_f = np.array(jnp.exp(phys_prior.emulator['net_apply'](phys_prior.emulator['params'], params)))
#stack params and make vector ready to be used by emualator
params_prior = jnp.stack([i.values.reshape(numpyro_data.prior.chain.size * numpyro_data.prior.draw.size,numpyro_data.prior.src.size).T for i in [numpyro_data.prior.sfr,numpyro_data.prior.agn,numpyro_data.prior.redshift,numpyro_data.prior.atten,numpyro_data.prior.dust_alpha,numpyro_data.prior.tau_main]]).T
# Use emulator to get fluxes. As emulator provides log flux, convert.
src_f_prior = np.array(jnp.exp(phys_prior.emulator['net_apply'](phys_prior.emulator['params'], params_prior)))
dims=("chain", "draw","src", "band")
numpyro_data.posterior['src_f']=(dims,src_f[:,:,[-3,-2,-1]].reshape(numpyro_data.posterior.chain.size,numpyro_data.posterior.draw.size,numpyro_data.posterior.src.size,3))
numpyro_data.prior['src_f']=(dims,src_f_prior[:,:,[-3,-2,-1]].reshape(numpyro_data.prior.chain.size,numpyro_data.prior.draw.size,numpyro_data.prior.src.size,3))
numpyro_data.to_netcdf('xid+sed_sim_output.nc')
```
'xid+sed_sim_output.nc'
|
H-E-L-PREPO_NAMEXID_plusPATH_START.@XID_plus_extracted@XID_plus-master@docs@build@doctrees@nbsphinx@notebooks@examples@XID+SED_example.ipynb@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "RafiKueng/SpaghettiLens",
"repo_path": "SpaghettiLens_extracted/SpaghettiLens-master/SpaghettiLensProject/Lenses/tests/__init__.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
|
RafiKuengREPO_NAMESpaghettiLensPATH_START.@SpaghettiLens_extracted@SpaghettiLens-master@SpaghettiLensProject@Lenses@tests@__init__.py@.PATH_END.py
|
{
"filename": "_error_y.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/scattergl/_error_y.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class Error_YValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="error_y", parent_name="scattergl", **kwargs):
super(Error_YValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "ErrorY"),
data_docs=kwargs.pop(
"data_docs",
"""
array
Sets the data corresponding the length of each
error bar. Values are plotted relative to the
underlying data.
arrayminus
Sets the data corresponding the length of each
error bar in the bottom (left) direction for
vertical (horizontal) bars Values are plotted
relative to the underlying data.
arrayminussrc
Sets the source reference on Chart Studio Cloud
for arrayminus .
arraysrc
Sets the source reference on Chart Studio Cloud
for array .
color
Sets the stoke color of the error bars.
symmetric
Determines whether or not the error bars have
the same length in both direction (top/bottom
for vertical bars, left/right for horizontal
bars.
thickness
Sets the thickness (in px) of the error bars.
traceref
tracerefminus
type
Determines the rule used to generate the error
bars. If *constant`, the bar lengths are of a
constant value. Set this constant in `value`.
If "percent", the bar lengths correspond to a
percentage of underlying data. Set this
percentage in `value`. If "sqrt", the bar
lengths correspond to the square of the
underlying data. If "data", the bar lengths are
set with data set `array`.
value
Sets the value of either the percentage (if
`type` is set to "percent") or the constant (if
`type` is set to "constant") corresponding to
the lengths of the error bars.
valueminus
Sets the value of either the percentage (if
`type` is set to "percent") or the constant (if
`type` is set to "constant") corresponding to
the lengths of the error bars in the bottom
(left) direction for vertical (horizontal) bars
visible
Determines whether or not this set of error
bars is visible.
width
Sets the width (in px) of the cross-bar at both
ends of the error bars.
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@scattergl@_error_y.py@.PATH_END.py
|
{
"filename": "freetype.py",
"repo_name": "macrocosme/shwirl",
"repo_path": "shwirl_extracted/shwirl-master/shwirl/extern/vispy/ext/freetype.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
# Copyright (c) 2015, Vispy Development Team.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
"""
Handle loading freetype package from system or from the bundled copy
"""
try:
from ._bundled.freetype import * # noqa
except ImportError:
from freetype import * # noqa
|
macrocosmeREPO_NAMEshwirlPATH_START.@shwirl_extracted@shwirl-master@shwirl@extern@vispy@ext@freetype.py@.PATH_END.py
|
{
"filename": "conftest.py",
"repo_name": "jzuhone/pyxsim",
"repo_path": "pyxsim_extracted/pyxsim-main/pyxsim/tests/conftest.py",
"type": "Python"
}
|
import os
import pytest
def pytest_addoption(parser):
parser.addoption("--check_dir", help="Directory where spectrum checks are stored.")
parser.addoption(
"--answer_store",
action="store_true",
help="Generate new answers, but don't test.",
)
@pytest.fixture()
def answer_store(request):
return request.config.getoption("--answer_store")
@pytest.fixture()
def check_dir(request):
cd = request.config.getoption("--check_dir")
if cd is not None:
cd = os.path.abspath(cd)
if not os.path.exists(cd):
os.makedirs(cd)
cd = os.path.join(cd, f"{request.node.name}.dat")
return cd
|
jzuhoneREPO_NAMEpyxsimPATH_START.@pyxsim_extracted@pyxsim-main@pyxsim@tests@conftest.py@.PATH_END.py
|
{
"filename": "splittable.py",
"repo_name": "gwastro/pycbc",
"repo_path": "pycbc_extracted/pycbc-master/pycbc/workflow/splittable.py",
"type": "Python"
}
|
# Copyright (C) 2013 Ian Harry
#
# 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, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# =============================================================================
#
# Preamble
#
# =============================================================================
#
"""
This module is responsible for setting up the splitting output files stage of
workflows. For details about this module and its capabilities see here:
https://ldas-jobs.ligo.caltech.edu/~cbc/docs/pycbc/NOTYETCREATED.html
"""
import os
import logging
from pycbc.workflow.core import FileList, make_analysis_dir
from pycbc.workflow.jobsetup import (PycbcSplitBankExecutable,
PycbcSplitBankXmlExecutable, PycbcSplitInspinjExecutable,
PycbcHDFSplitInjExecutable)
logger = logging.getLogger('pycbc.workflow.splittable')
def select_splitfilejob_instance(curr_exe):
"""
This function returns an instance of the class that is appropriate for
splitting an output file up within workflow (for e.g. splitbank).
Parameters
----------
curr_exe : string
The name of the Executable that is being used.
curr_section : string
The name of the section storing options for this executble
Returns
--------
exe class : sub-class of pycbc.workflow.core.Executable
The class that holds the utility functions appropriate
for the given Executable. This class **must** contain
* exe_class.create_job()
and the job returned by this **must** contain
* job.create_node()
"""
if curr_exe == 'pycbc_hdf5_splitbank':
exe_class = PycbcSplitBankExecutable
elif curr_exe == 'pycbc_splitbank':
exe_class = PycbcSplitBankXmlExecutable
elif curr_exe == 'pycbc_split_inspinj':
exe_class = PycbcSplitInspinjExecutable
elif curr_exe == 'pycbc_hdf_splitinj':
exe_class = PycbcHDFSplitInjExecutable
else:
# Should we try some sort of default class??
err_string = "No class exists for Executable %s" %(curr_exe,)
raise NotImplementedError(err_string)
return exe_class
def setup_splittable_workflow(workflow, input_tables, out_dir=None, tags=None):
'''
This function aims to be the gateway for code that is responsible for taking
some input file containing some table, and splitting into multiple files
containing different parts of that table. For now the only supported operation
is using lalapps_splitbank to split a template bank xml file into multiple
template bank xml files.
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance that the jobs will be added to.
input_tables : pycbc.workflow.core.FileList
The input files to be split up.
out_dir : path
The directory in which output will be written.
Returns
--------
split_table_outs : pycbc.workflow.core.FileList
The list of split up files as output from this job.
'''
if tags is None:
tags = []
logger.info("Entering split output files module.")
make_analysis_dir(out_dir)
# Parse for options in .ini file
splitMethod = workflow.cp.get_opt_tags("workflow-splittable",
"splittable-method", tags)
if splitMethod == "IN_WORKFLOW":
# Scope here for choosing different options
logger.info("Adding split output file jobs to workflow.")
split_table_outs = setup_splittable_dax_generated(workflow,
input_tables, out_dir, tags)
elif splitMethod == "NOOP":
# Probably better not to call the module at all, but this option will
# return the input file list.
split_table_outs = input_tables
else:
errMsg = "Splittable method not recognized. Must be one of "
errMsg += "IN_WORKFLOW or NOOP."
raise ValueError(errMsg)
logger.info("Leaving split output files module.")
return split_table_outs
def setup_splittable_dax_generated(workflow, input_tables, out_dir, tags):
'''
Function for setting up the splitting jobs as part of the workflow.
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance that the jobs will be added to.
input_tables : pycbc.workflow.core.FileList
The input files to be split up.
out_dir : path
The directory in which output will be written.
Returns
--------
split_table_outs : pycbc.workflow.core.FileList
The list of split up files as output from this job.
'''
cp = workflow.cp
# Get values from ini file
try:
num_splits = cp.get_opt_tags("workflow-splittable",
"splittable-num-banks", tags)
except BaseException:
inj_interval = int(cp.get_opt_tags("workflow-splittable",
"splitinjtable-interval", tags))
if cp.has_option_tags("em_bright_filter", "max-keep", tags) and \
cp.has_option("workflow-injections", "em-bright-only"):
num_injs = int(cp.get_opt_tags("em_bright_filter", "max-keep",
tags))
else:
# This needed to be changed from num-injs to ninjections in order
# to work properly with pycbc_create_injections
num_injs = int(cp.get_opt_tags("workflow-injections",
"ninjections", tags))
inj_tspace = float(abs(workflow.analysis_time)) / num_injs
num_splits = int(inj_interval // inj_tspace) + 1
split_exe_tag = cp.get_opt_tags("workflow-splittable",
"splittable-exe-tag", tags)
split_exe = os.path.basename(cp.get("executables", split_exe_tag))
# Select the appropriate class
exe_class = select_splitfilejob_instance(split_exe)
# Set up output structure
out_file_groups = FileList([])
# Set up the condorJob class for the current executable
curr_exe_job = exe_class(workflow.cp, split_exe_tag, num_splits,
out_dir=out_dir)
for input in input_tables:
node = curr_exe_job.create_node(input, tags=tags)
workflow.add_node(node)
out_file_groups += node.output_files
return out_file_groups
|
gwastroREPO_NAMEpycbcPATH_START.@pycbc_extracted@pycbc-master@pycbc@workflow@splittable.py@.PATH_END.py
|
{
"filename": "_namelengthsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/pie/hoverlabel/_namelengthsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class NamelengthsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="namelengthsrc", parent_name="pie.hoverlabel", **kwargs
):
super(NamelengthsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@pie@hoverlabel@_namelengthsrc.py@.PATH_END.py
|
{
"filename": "_showtickprefix.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/carpet/baxis/_showtickprefix.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ShowtickprefixValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="showtickprefix", parent_name="carpet.baxis", **kwargs
):
super(ShowtickprefixValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
values=kwargs.pop("values", ["all", "first", "last", "none"]),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@carpet@baxis@_showtickprefix.py@.PATH_END.py
|
{
"filename": "_meta.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattermapbox/_meta.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class MetaValidator(_plotly_utils.basevalidators.AnyValidator):
def __init__(self, plotly_name="meta", parent_name="scattermapbox", **kwargs):
super(MetaValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
array_ok=kwargs.pop("array_ok", True),
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattermapbox@_meta.py@.PATH_END.py
|
{
"filename": "_thickness.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattergeo/marker/colorbar/_thickness.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ThicknessValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self,
plotly_name="thickness",
parent_name="scattergeo.marker.colorbar",
**kwargs,
):
super(ThicknessValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattergeo@marker@colorbar@_thickness.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatterpolar/marker/colorbar/title/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._text import TextValidator
from ._side import SideValidator
from ._font import FontValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
["._text.TextValidator", "._side.SideValidator", "._font.FontValidator"],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatterpolar@marker@colorbar@title@__init__.py@.PATH_END.py
|
{
"filename": "analysis.particles.md",
"repo_name": "dmentipl/plonk",
"repo_path": "plonk_extracted/plonk-main/docs/source/api/analysis.particles.md",
"type": "Markdown"
}
|
# Extra particle quantities
```{eval-rst}
.. automodule:: plonk.analysis.particles
:members:
```
|
dmentiplREPO_NAMEplonkPATH_START.@plonk_extracted@plonk-main@docs@source@api@analysis.particles.md@.PATH_END.py
|
{
"filename": "test_utils_numba.py",
"repo_name": "arviz-devs/arviz",
"repo_path": "arviz_extracted/arviz-main/arviz/tests/base_tests/test_utils_numba.py",
"type": "Python"
}
|
# pylint: disable=redefined-outer-name, no-member
"""Tests for arviz.utils."""
import importlib
from unittest.mock import Mock
import numpy as np
import pytest
from ...stats.stats_utils import stats_variance_2d as svar
from ...utils import Numba, _numba_var, numba_check
from ..helpers import importorskip
from .test_utils import utils_with_numba_import_fail # pylint: disable=unused-import
importorskip("numba")
def test_utils_fixture(utils_with_numba_import_fail):
"""Test of utils fixture to ensure mock is applied correctly"""
# If Numba doesn't exist in dev environment this will raise an ImportError
import numba # pylint: disable=unused-import,W0612
with pytest.raises(ImportError):
utils_with_numba_import_fail.importlib.import_module("numba")
def test_conditional_jit_numba_decorator_keyword(monkeypatch):
"""Checks else statement and JIT keyword argument"""
from ... import utils
# Mock import lib to return numba with hit method which returns a function that returns kwargs
numba_mock = Mock()
monkeypatch.setattr(utils.importlib, "import_module", lambda x: numba_mock)
def jit(**kwargs):
"""overwrite numba.jit function"""
return lambda fn: lambda: (fn(), kwargs)
numba_mock.jit = jit
@utils.conditional_jit(keyword_argument="A keyword argument")
def placeholder_func():
"""This function does nothing"""
return "output"
# pylint: disable=unpacking-non-sequence
function_results, wrapper_result = placeholder_func()
assert wrapper_result == {"keyword_argument": "A keyword argument", "nopython": True}
assert function_results == "output"
def test_numba_check():
"""Test for numba_check"""
numba = importlib.util.find_spec("numba")
flag = numba is not None
assert flag == numba_check()
def test_numba_utils():
"""Test for class Numba."""
flag = Numba.numba_flag
assert flag == numba_check()
Numba.disable_numba()
val = Numba.numba_flag
assert not val
Numba.enable_numba()
val = Numba.numba_flag
assert val
assert flag == Numba.numba_flag
@pytest.mark.parametrize("axis", (0, 1))
@pytest.mark.parametrize("ddof", (0, 1))
def test_numba_var(axis, ddof):
"""Method to test numba_var."""
flag = Numba.numba_flag
data_1 = np.random.randn(100, 100)
data_2 = np.random.rand(100)
with_numba_1 = _numba_var(svar, np.var, data_1, axis=axis, ddof=ddof)
with_numba_2 = _numba_var(svar, np.var, data_2, ddof=ddof)
Numba.disable_numba()
non_numba_1 = _numba_var(svar, np.var, data_1, axis=axis, ddof=ddof)
non_numba_2 = _numba_var(svar, np.var, data_2, ddof=ddof)
Numba.enable_numba()
assert flag == Numba.numba_flag
assert np.allclose(with_numba_1, non_numba_1)
assert np.allclose(with_numba_2, non_numba_2)
|
arviz-devsREPO_NAMEarvizPATH_START.@arviz_extracted@arviz-main@arviz@tests@base_tests@test_utils_numba.py@.PATH_END.py
|
{
"filename": "_textcase.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/carpet/legendgrouptitle/font/_textcase.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextcaseValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self,
plotly_name="textcase",
parent_name="carpet.legendgrouptitle.font",
**kwargs,
):
super(TextcaseValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
values=kwargs.pop("values", ["normal", "word caps", "upper", "lower"]),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@carpet@legendgrouptitle@font@_textcase.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "COSMOGRAIL/PyCS",
"repo_path": "PyCS_extracted/PyCS-master/pycs/regdiff2/__init__.py",
"type": "Python"
}
|
"""
Regression difference curve shifting technique, using gaussian process regression.
"""
__all__ = ["rslc", "multiopt", "gpr"]
import rslc
import multiopt
import gpr
|
COSMOGRAILREPO_NAMEPyCSPATH_START.@PyCS_extracted@PyCS-master@pycs@regdiff2@__init__.py@.PATH_END.py
|
{
"filename": "_len.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/densitymap/colorbar/_len.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LenValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="len", parent_name="densitymap.colorbar", **kwargs):
super(LenValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@densitymap@colorbar@_len.py@.PATH_END.py
|
{
"filename": "idealized.py",
"repo_name": "pynbody/genetIC",
"repo_path": "genetIC_extracted/genetIC-master/tools/toy_implementation/constrainedzoom/methods/idealized.py",
"type": "Python"
}
|
import copy
import numpy as np
from . import ZoomConstrained, UnfilteredZoomConstrained
from ..fft_wrapper import FFTArray, in_real_space, in_fourier_space, complex_dot
from ..power_spectrum import powerlaw_covariance
class IdealizedZoomConstrained(ZoomConstrained):
"""Calculate the low-res/high-res split by making a full box at the high resolution,
then downgrading the resolution of the low-res region"""
description = "Idealized"
def __init__(self, cov_fn=powerlaw_covariance, nP=256, nW=768, hires_window_scale=4, offset=10):
super().__init__(cov_fn, nP, nW, hires_window_scale, offset)
self._underlying = UnfilteredZoomConstrained(cov_fn, nW*hires_window_scale, nW, hires_window_scale, offset)
def _iter_cov_elements(self):
test_field_lo = np.zeros(self._underlying.nP)
test_field_hi = np.zeros(self._underlying.nW)
for i in range(self._underlying.nP):
test_field_lo[i] = 1.0
yield test_field_lo, test_field_hi
test_field_lo[i] = 0.0
def _iter_one_cov_element(self, offset):
test_field_lo = np.zeros(self._underlying.nP)
test_field_hi = np.zeros(self._underlying.nW)
if offset>=0:
# place test delta function in high-res region
test_field_lo[self.offset*self.pixel_size_ratio + self.nW // 2 + self.pixel_size_ratio//2 + offset] = 1.0
else:
# in low-res region
test_field_lo[(self.offset+offset)*self.pixel_size_ratio:(self.offset+offset+1)*self.pixel_size_ratio] = 1.0
yield FFTArray(test_field_lo).in_fourier_space(), test_field_hi
def realization(self, *args, **kwargs):
if 'underlying_realization' in kwargs:
underlying = kwargs.pop('underlying_realization')
else:
underlying,_ = self._underlying.realization(*args, **kwargs)
underlying_lores = copy.copy(underlying)
lores = self.downsample(underlying_lores, input_unpadded=False)
hires = self._extract_window(underlying)
return lores, hires
@property
def _window_slice(self):
return slice(self.offset * self.pixel_size_ratio, self.offset * self.pixel_size_ratio + self.nW)
def _extract_window(self, vec):
return vec[self._window_slice]
def _place_window(self, W_vec):
vec = np.zeros(self.nP*self.pixel_size_ratio)
vec[self._window_slice] = W_vec
return FFTArray(vec)
def add_constraint(self, val=0.0, hr_covec=None, potential=False):
if hr_covec is None:
hr_covec = self._default_constraint_hr_vec()
hr_covec_full_box = self._place_window(hr_covec)
self.constraints.append(hr_covec) # Do this just so we remember we have a constraint - not actually used in implementation
self._underlying.add_constraint(val, hr_covec_full_box, potential)
def _apply_constraints(self, delta_low_k, delta_high_k):
return delta_low_k, delta_high_k # constraints are applied in underlying object
def _modify_whitenoise(self, noiseP, noiseW):
return noiseP, noiseW
class FastIdealizedZoomConstrained(IdealizedZoomConstrained):
def get_cov(self, one_element=False):
# the base class implementation does work, but this is optimized for the idealized case
if len(self.constraints)>0 or one_element:
# idealized implementation does not work, as it assumes translational invariance
return super().get_cov(one_element)
test_field_lo : FFTArray = np.zeros(self._underlying.nP).view(FFTArray)
test_field_hi = np.zeros(self._underlying.nW)
# in the underlying white noise, just put a single spike at coordinate zero. We will then cycle the result
test_field_lo[0] = np.sqrt(2.0)
test_field_lo.in_fourier_space()
test_field_lo[0]/=np.sqrt(2.0) # constant mode
if len(test_field_lo)%2==0:
test_field_lo[-1]/=np.sqrt(2.0) # nyquist mode
underlying_out_0, _ = self._underlying.realization(noiseP=test_field_lo, noiseW= test_field_hi)
cov = np.zeros((self.nP + self.nW, self.nP + self.nW))
for i in range(self._underlying.nP):
underlying_out_i = np.roll(underlying_out_0,i)
out_lo, out_hi = self.realization(underlying_realization=underlying_out_i)
cov[:self.nP, :self.nP] += np.outer(out_lo, out_lo)
cov[self.nP:, self.nP:] += np.outer(out_hi, out_hi)
cov[:self.nP, self.nP:] += np.outer(out_lo, out_hi)
cov[self.nP:, :self.nP] = cov[:self.nP, self.nP:].T
return cov
|
pynbodyREPO_NAMEgenetICPATH_START.@genetIC_extracted@genetIC-master@tools@toy_implementation@constrainedzoom@methods@idealized.py@.PATH_END.py
|
{
"filename": "filter.py",
"repo_name": "lpsinger/ligo.skymap",
"repo_path": "ligo.skymap_extracted/ligo.skymap-main/ligo/skymap/bayestar/filter.py",
"type": "Python"
}
|
#
# Copyright (C) 2013-2024 Leo Singer
#
# 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 <https://www.gnu.org/licenses/>.
#
"""Utility functions for BAYESTAR that are related to matched filtering."""
from contextlib import contextmanager
import logging
import math
import lal
import lalsimulation
import numpy as np
from scipy import interpolate
from scipy import fftpack as fft
from scipy import linalg
try:
from numpy import trapezoid
except ImportError:
# FIXME: Remove after we require Numpy >=2.0.0.
from numpy import trapz as trapezoid
log = logging.getLogger('BAYESTAR')
@contextmanager
def lal_ndebug():
"""Temporarily disable lal error messages, except for memory errors."""
mask = ~(lal.LALERRORBIT |
lal.LALWARNINGBIT |
lal.LALINFOBIT |
lal.LALTRACEBIT)
old_level = lal.GetDebugLevel()
lal.ClobberDebugLevel(old_level & mask)
try:
yield
finally:
lal.ClobberDebugLevel(old_level)
def unwrap(y, *args, **kwargs):
"""Unwrap phases while skipping NaN or infinite values.
This is a simple wrapper around :meth:`numpy.unwrap` that can handle
invalid values.
Examples
--------
>>> t = np.arange(0, 2 * np.pi, 0.5)
>>> y = np.exp(1j * t)
>>> unwrap(np.angle(y))
array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ])
>>> y[3] = y[4] = y[7] = np.nan
>>> unwrap(np.angle(y))
array([0. , 0.5, 1. , nan, nan, 2.5, 3. , nan, 4. , 4.5, 5. , 5.5, 6. ])
"""
good = np.isfinite(y)
result = np.empty_like(y)
result[~good] = y[~good]
result[good] = np.unwrap(y[good], *args, **kwargs)
return result
def ceil_pow_2(n):
"""Return the least integer power of 2 that is greater than or equal to n.
Examples
--------
>>> ceil_pow_2(128.0)
128.0
>>> ceil_pow_2(0.125)
0.125
>>> ceil_pow_2(129.0)
256.0
>>> ceil_pow_2(0.126)
0.25
>>> ceil_pow_2(1.0)
1.0
"""
# frexp splits floats into mantissa and exponent, ldexp does the opposite.
# For positive numbers, mantissa is in [0.5, 1.).
mantissa, exponent = math.frexp(n)
return math.ldexp(
1 if mantissa >= 0 else float('nan'),
exponent - 1 if mantissa == 0.5 else exponent
)
def abscissa(series):
"""Produce the independent variable for a lal TimeSeries or
FrequencySeries.
"""
try:
delta = series.deltaT
x0 = float(series.epoch)
except AttributeError:
delta = series.deltaF
x0 = series.f0
return x0 + delta * np.arange(len(series.data.data))
def exp_i(phi):
return np.cos(phi) + np.sin(phi) * 1j
def truncated_ifft(y, nsamples_out=None):
r"""Truncated inverse FFT.
See http://www.fftw.org/pruned.html for a discussion of related algorithms.
Perform inverse FFT to obtain truncated autocorrelation time series.
This makes use of a folded DFT for a speedup of::
log(nsamples)/log(nsamples_out)
over directly computing the inverse FFT and truncating. Here is how it
works. Say we have a frequency-domain signal X[k], for 0 ≤ k ≤ N - 1. We
want to compute its DFT x[n], for 0 ≤ n ≤ M, where N is divisible by M:
N = cM, for some integer c. The DFT is::
N - 1
______
\ 2 π i k n
x[n] = \ exp[-----------] Y[k]
/ N
/------
k = 0
c - 1 M - 1
______ ______
\ \ 2 π i n (m c + j)
= \ \ exp[------------------] Y[m c + j]
/ / c M
/------ /------
j = 0 m = 0
c - 1 M - 1
______ ______
\ 2 π i n j \ 2 π i n m
= \ exp[-----------] \ exp[-----------] Y[m c + j]
/ N / M
/------ /------
j = 0 m = 0
So: we split the frequency series into c deinterlaced sub-signals, each of
length M, compute the DFT of each sub-signal, and add them back together
with complex weights.
Parameters
----------
y : `numpy.ndarray`
Complex input vector.
nsamples_out : int, optional
Length of output vector. By default, same as length of input vector.
Returns
-------
x : `numpy.ndarray`
The first nsamples_out samples of the IFFT of x, zero-padded if
Examples
--------
First generate the IFFT of a random signal:
>>> nsamples_out = 1024
>>> y = np.random.randn(nsamples_out) + np.random.randn(nsamples_out) * 1j
>>> x = fft.ifft(y)
Now check that the truncated IFFT agrees:
>>> np.allclose(x, truncated_ifft(y), rtol=1e-15)
True
>>> np.allclose(x, truncated_ifft(y, 1024), rtol=1e-15)
True
>>> np.allclose(x[:128], truncated_ifft(y, 128), rtol=1e-15)
True
>>> np.allclose(x[:1], truncated_ifft(y, 1), rtol=1e-15)
True
>>> np.allclose(x[:32], truncated_ifft(y, 32), rtol=1e-15)
True
>>> np.allclose(x[:63], truncated_ifft(y, 63), rtol=1e-15)
True
>>> np.allclose(x[:25], truncated_ifft(y, 25), rtol=1e-15)
True
>>> truncated_ifft(y, 1025)
Traceback (most recent call last):
...
ValueError: Input is too short: you gave me an input of length 1024, but you asked for an IFFT of length 1025.
""" # noqa: E501
nsamples = len(y)
if nsamples_out is None:
nsamples_out = nsamples
elif nsamples_out > nsamples:
raise ValueError(
'Input is too short: you gave me an input of length {0}, '
'but you asked for an IFFT of length {1}.'.format(
nsamples, nsamples_out))
elif nsamples & (nsamples - 1):
raise NotImplementedError(
'I am too lazy to implement for nsamples that is '
'not a power of 2.')
# Find number of FFTs.
# FIXME: only works if nsamples is a power of 2.
# Would be better to find the smallest divisor of nsamples that is
# greater than or equal to nsamples_out.
nsamples_batch = int(ceil_pow_2(nsamples_out))
c = nsamples // nsamples_batch
# FIXME: Implement for real-to-complex FFTs as well.
twiddle = exp_i(2 * np.pi * np.arange(nsamples_batch) / nsamples)
x = fft.ifft(y.reshape(nsamples_batch, c).T)
result = x[-1]
for row in x[-2::-1]:
result *= twiddle # FIXME: check stability of this recurrence relation
result += row
# Now need to truncate remaining samples.
if nsamples_out < nsamples_batch:
result = result[:nsamples_out]
return result / c
@lal_ndebug()
def get_approximant_and_orders_from_string(s):
"""Determine the approximant, amplitude order, and phase order for a string
of the form "TaylorT4threePointFivePN". In this example, the waveform is
"TaylorT4" and the phase order is 7 (twice 3.5). If the input contains the
substring "restricted" or "Restricted", then the amplitude order is taken
to be 0. Otherwise, the amplitude order is the same as the phase order.
"""
# SWIG-wrapped functions apparently do not understand Unicode, but
# often the input argument will come from a Unicode XML file.
s = str(s)
approximant = lalsimulation.GetApproximantFromString(s)
try:
phase_order = lalsimulation.GetOrderFromString(s)
except RuntimeError:
phase_order = -1
if 'restricted' in s or 'Restricted' in s:
amplitude_order = 0
else:
amplitude_order = phase_order
return approximant, amplitude_order, phase_order
def get_f_lso(mass1, mass2):
"""Calculate the GW frequency during the last stable orbit of a compact
binary.
"""
return 1 / (6 ** 1.5 * np.pi * (mass1 + mass2) * lal.MTSUN_SI)
def sngl_inspiral_psd(waveform, mass1, mass2,
f_min=10, f_final=None, f_ref=None, **kwargs):
# FIXME: uberbank mass criterion. Should find a way to get this from
# pipeline output metadata.
if waveform == 'o1-uberbank':
log.warning('Template is unspecified; '
'using ER8/O1 uberbank criterion')
if mass1 + mass2 < 4:
waveform = 'TaylorF2threePointFivePN'
else:
waveform = 'SEOBNRv2_ROM_DoubleSpin'
elif waveform == 'o2-uberbank':
log.warning('Template is unspecified; '
'using ER10/O2 uberbank criterion')
if mass1 + mass2 < 4:
waveform = 'TaylorF2threePointFivePN'
else:
waveform = 'SEOBNRv4_ROM'
approx, ampo, phaseo = get_approximant_and_orders_from_string(waveform)
log.info('Selected template: %s', waveform)
# Generate conditioned template.
params = lal.CreateDict()
lalsimulation.SimInspiralWaveformParamsInsertPNPhaseOrder(params, phaseo)
lalsimulation.SimInspiralWaveformParamsInsertPNAmplitudeOrder(params, ampo)
hplus, hcross = lalsimulation.SimInspiralFD(
m1=float(mass1) * lal.MSUN_SI, m2=float(mass2) * lal.MSUN_SI,
S1x=float(kwargs.get('spin1x') or 0),
S1y=float(kwargs.get('spin1y') or 0),
S1z=float(kwargs.get('spin1z') or 0),
S2x=float(kwargs.get('spin2x') or 0),
S2y=float(kwargs.get('spin2y') or 0),
S2z=float(kwargs.get('spin2z') or 0),
distance=1e6 * lal.PC_SI, inclination=0, phiRef=0,
longAscNodes=0, eccentricity=0, meanPerAno=0,
deltaF=0, f_min=f_min,
# Note: code elsewhere ensures that the sample rate is at least two
# times f_final; the factor of 2 below is just a safety factor to make
# sure that the sample rate is 2-4 times f_final.
f_max=ceil_pow_2(2 * (f_final or 2048)),
f_ref=float(f_ref or 0),
LALparams=params, approximant=approx)
# Force `plus' and `cross' waveform to be in quadrature.
h = 0.5 * (hplus.data.data + 1j * hcross.data.data)
# For inspiral-only waveforms, nullify frequencies beyond ISCO.
# FIXME: the waveform generation functions pick the end frequency
# automatically. Shouldn't SimInspiralFD?
inspiral_only_waveforms = (
lalsimulation.TaylorF2,
lalsimulation.SpinTaylorF2,
lalsimulation.TaylorF2RedSpin,
lalsimulation.TaylorF2RedSpinTidal,
lalsimulation.SpinTaylorT4Fourier)
if approx in inspiral_only_waveforms:
h[abscissa(hplus) >= get_f_lso(mass1, mass2)] = 0
# Throw away any frequencies above high frequency cutoff
h[abscissa(hplus) >= (f_final or 2048)] = 0
# Drop Nyquist frequency.
if len(h) % 2:
h = h[:-1]
# Create output frequency series.
psd = lal.CreateREAL8FrequencySeries(
'signal PSD', 0, hplus.f0, hcross.deltaF, hplus.sampleUnits**2, len(h))
psd.data.data = abs2(h)
# Done!
return psd
def signal_psd_series(H, S):
n = H.data.data.size
f = H.f0 + np.arange(1, n) * H.deltaF
ret = lal.CreateREAL8FrequencySeries(
'signal PSD / noise PSD', 0, H.f0, H.deltaF, lal.DimensionlessUnit, n)
ret.data.data[0] = 0
ret.data.data[1:] = H.data.data[1:] / S(f)
return ret
def autocorrelation(H, out_duration, normalize=True):
"""Calculate the complex autocorrelation sequence a(t), for t >= 0, of an
inspiral signal.
Parameters
----------
H : lal.REAL8FrequencySeries
Signal PSD series.
S : callable
Noise power spectral density function.
Returns
-------
acor : `numpy.ndarray`
The complex-valued autocorrelation sequence.
sample_rate : float
The sample rate.
"""
# Compute duration of template, rounded up to a power of 2.
H_len = H.data.data.size
nsamples = 2 * H_len
sample_rate = nsamples * H.deltaF
# Compute autopower spectral density.
power = np.empty(nsamples, H.data.data.dtype)
power[:H_len] = H.data.data
power[H_len:] = 0
# Determine length of output FFT.
nsamples_out = int(np.ceil(out_duration * sample_rate))
acor = truncated_ifft(power, nsamples_out)
if normalize:
acor /= np.abs(acor[0])
# If we have done this right, then the zeroth sample represents lag 0
if np.all(np.isreal(H.data.data)):
assert np.argmax(np.abs(acor)) == 0
assert np.isreal(acor[0])
# Done!
return acor, float(sample_rate)
def abs2(y):
"""Return the absolute value squared, :math:`|z|^2` ,for a complex number
:math:`z`, without performing a square root.
"""
return np.square(y.real) + np.square(y.imag)
class vectorize_swig_psd_func: # noqa: N801
"""Create a vectorized Numpy function from a SWIG-wrapped PSD function.
SWIG does not provide enough information for Numpy to determine the number
of input arguments, so we can't just use np.vectorize.
"""
def __init__(self, str):
self.__func = getattr(lalsimulation, str + 'Ptr')
self.__npyfunc = np.frompyfunc(getattr(lalsimulation, str), 1, 1)
def __call__(self, f):
fa = np.asarray(f)
df = np.diff(fa)
if fa.ndim == 1 and df.size > 1 and np.all(df[0] == df[1:]):
fa = np.concatenate((fa, [fa[-1] + df[0]]))
ret = lal.CreateREAL8FrequencySeries(
None, 0, fa[0], df[0], lal.DimensionlessUnit, fa.size)
lalsimulation.SimNoisePSD(ret, 0, self.__func)
ret = ret.data.data[:-1]
else:
ret = self.__npyfunc(f)
if not np.isscalar(ret):
ret = ret.astype(float)
return ret
class InterpolatedPSD(interpolate.interp1d):
"""Create a (linear in log-log) interpolating function for a discretely
sampled power spectrum S(f).
"""
def __init__(self, f, S, f_high_truncate=1.0, fill_value=np.inf):
assert f_high_truncate <= 1.0
f = np.asarray(f)
S = np.asarray(S)
# Exclude DC if present
if f[0] == 0:
f = f[1:]
S = S[1:]
# FIXME: This is a hack to fix an issue with the detection pipeline's
# PSD conditioning. Remove this when the issue is fixed upstream.
if f_high_truncate < 1.0:
log.warning(
'Truncating PSD at %g of maximum frequency to suppress '
'rolloff artifacts. This option may be removed in the future.',
f_high_truncate)
keep = (f <= f_high_truncate * max(f))
f = f[keep]
S = S[keep]
super().__init__(
np.log(f), np.log(S),
kind='linear', bounds_error=False, fill_value=np.log(fill_value))
self._f_min = min(f)
self._f_max = max(f)
@property
def f_min(self):
return self._f_min
@property
def f_max(self):
return self._f_max
def __call__(self, f):
f_min = np.min(f)
f_max = np.max(f)
if f_min < self._f_min:
log.warning('Assuming PSD is infinite at %g Hz because PSD is '
'only sampled down to %g Hz', f_min, self._f_min)
if f_max > self._f_max:
log.warning('Assuming PSD is infinite at %g Hz because PSD is '
'only sampled up to %g Hz', f_max, self._f_max)
return np.where(
(f >= self._f_min) & (f <= self._f_max),
np.exp(super().__call__(np.log(f))),
np.exp(self.fill_value))
class SignalModel:
"""Class to speed up computation of signal/noise-weighted integrals and
Barankin and Cramér-Rao lower bounds on time and phase estimation.
Note that the autocorrelation series and the moments are related,
as shown below.
Examples
--------
Create signal model:
>>> from . import filter
>>> sngl = lambda: None
>>> H = filter.sngl_inspiral_psd(
... 'TaylorF2threePointFivePN', mass1=1.4, mass2=1.4)
>>> S = vectorize_swig_psd_func('SimNoisePSDaLIGOZeroDetHighPower')
>>> W = filter.signal_psd_series(H, S)
>>> sm = SignalModel(W)
Compute one-sided autocorrelation function:
>>> out_duration = 0.1
>>> a, sample_rate = filter.autocorrelation(W, out_duration)
Restore negative time lags using symmetry:
>>> a = np.concatenate((a[:0:-1].conj(), a))
Compute the first 2 frequency moments by taking derivatives of the
autocorrelation sequence using centered finite differences.
The nth frequency moment should be given by (-1j)^n a^(n)(t).
>>> acor_moments = []
>>> for i in range(2):
... acor_moments.append(a[len(a) // 2])
... a = -0.5j * sample_rate * (a[2:] - a[:-2])
>>> assert np.all(np.isreal(acor_moments))
>>> acor_moments = np.real(acor_moments)
Compute the first 2 frequency moments using this class.
>>> quad_moments = [sm.get_sn_moment(i) for i in range(2)]
Compare them.
>>> for i, (am, qm) in enumerate(zip(acor_moments, quad_moments)):
... assert np.allclose(am, qm, rtol=0.05)
"""
def __init__(self, h):
"""Create a TaylorF2 signal model with the given masses, PSD function
S(f), PN amplitude order, and low-frequency cutoff.
"""
# Find indices of first and last nonzero samples.
nonzero = np.flatnonzero(h.data.data)
first_nonzero = nonzero[0]
last_nonzero = nonzero[-1]
# Frequency sample points
self.dw = 2 * np.pi * h.deltaF
f = h.f0 + h.deltaF * np.arange(first_nonzero, last_nonzero + 1)
self.w = 2 * np.pi * f
# Throw away leading and trailing zeros.
h = h.data.data[first_nonzero:last_nonzero + 1]
self.denom_integrand = 4 / (2 * np.pi) * h
self.den = trapezoid(self.denom_integrand, dx=self.dw)
def get_horizon_distance(self, snr_thresh=1):
return np.sqrt(self.den) / snr_thresh
def get_sn_average(self, func):
"""Get the average of a function of angular frequency, weighted by the
signal to noise per unit angular frequency.
"""
num = trapezoid(func(self.w) * self.denom_integrand, dx=self.dw)
return num / self.den
def get_sn_moment(self, power):
"""Get the average of angular frequency to the given power, weighted by
the signal to noise per unit frequency.
"""
return self.get_sn_average(lambda w: w**power)
def get_crb(self, snr):
"""Get the Cramér-Rao bound, or inverse Fisher information matrix,
describing the phase and time estimation covariance.
"""
w1 = self.get_sn_moment(1)
w2 = self.get_sn_moment(2)
fisher = np.asarray(((1, -w1), (-w1, w2)))
return linalg.inv(fisher) / np.square(snr)
# FIXME: np.vectorize doesn't work on unbound instance methods. The
# excluded keyword, added in Numpy 1.7, could be used here to exclude the
# zeroth argument, self.
def __get_crb_toa_uncert(self, snr):
return np.sqrt(self.get_crb(snr)[1, 1])
def get_crb_toa_uncert(self, snr):
return np.frompyfunc(self.__get_crb_toa_uncert, 1, 1)(snr)
|
lpsingerREPO_NAMEligo.skymapPATH_START.@ligo.skymap_extracted@ligo.skymap-main@ligo@skymap@bayestar@filter.py@.PATH_END.py
|
{
"filename": "cube.py",
"repo_name": "macrocosme/shwirl",
"repo_path": "shwirl_extracted/shwirl-master/shwirl/extern/vispy/visuals/cube.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2015, Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
from ..geometry import create_cube
from .mesh import MeshVisual
from .visual import CompoundVisual
class CubeVisual(CompoundVisual):
"""Visual that displays a cube or cuboid
Parameters
----------
size : float or tuple
The size of the cuboid. A float gives a cube, whereas tuples may
specify the size of each axis (x, y, z) independently.
vertex_colors : ndarray
Same as for `MeshVisual` class. See `create_cube` for vertex ordering.
face_colors : ndarray
Same as for `MeshVisual` class. See `create_cube` for vertex ordering.
color : Color
The `Color` to use when drawing the cube faces.
edge_color : tuple or Color
The `Color` to use when drawing the cube edges. If `None`, then no
cube edges are drawn.
"""
def __init__(self, size=1.0, vertex_colors=None, face_colors=None,
color=(0.5, 0.5, 1, 1), edge_color=None, **kwargs):
vertices, filled_indices, outline_indices = create_cube()
vertices['position'] *= size
self._mesh = MeshVisual(vertices=vertices['position'],
faces=filled_indices,
vertex_colors=vertex_colors,
face_colors=face_colors, color=color)
if edge_color:
self._border = MeshVisual(vertices=vertices['position'],
faces=outline_indices,
color=edge_color, mode='lines')
else:
self._border = MeshVisual()
CompoundVisual.__init__(self, [self._mesh, self._border], **kwargs)
self.mesh.set_gl_state(polygon_offset_fill=True,
polygon_offset=(1, 1), depth_test=True)
@property
def mesh(self):
"""The vispy.visuals.MeshVisual that used to fill in.
"""
return self._mesh
@mesh.setter
def mesh(self, mesh):
self._mesh = mesh
@property
def border(self):
"""The vispy.visuals.MeshVisual that used to draw the border.
"""
return self._border
@border.setter
def border(self, border):
self._border = border
|
macrocosmeREPO_NAMEshwirlPATH_START.@shwirl_extracted@shwirl-master@shwirl@extern@vispy@visuals@cube.py@.PATH_END.py
|
{
"filename": "get_data.py",
"repo_name": "multipass-black-holes/multipass",
"repo_path": "multipass_extracted/multipass-main/get_data.py",
"type": "Python"
}
|
import requests
import re
import time
import subprocess
import tarfile
import os
def list_catalog(name):
print(f"Fetching catalog {name}...", end="")
alias = {
'gwtc-1': 'GWTC-1-confident',
'gwtc-2': 'GWTC-2',
'gwtc-2.1': 'GWTC-2.1-confident',
'gwtc-3': 'GWTC-3-confident',
'all': 'GWTC'
}
url = f"https://www.gw-openscience.org/eventapi/jsonfull/{alias[name]}"
catalog = requests.get(url).json()['events']
print(f" found {len(catalog)} events")
for k, v in catalog.items():
p = re.match(r"(GW[\d_]*)-v\d*", k)
if p:
if v['far'] > 1:
print(p.group(1), v['far'])
yield (p.group(1), v['jsonurl'])
def parse_date(date):
return time.mktime(time.strptime(date, "%Y-%m-%d"))
def get_download(url):
m = requests.get(url).json()['events']
assert len(m) == 1
m = next(iter(m.values()))
parameters = [
(k, parse_date(v['date_added']))
for k,v in m['parameters'].items()
if 'pe' in k
]
assert len(parameters) > 0
k = sorted(parameters, key=lambda a: a[1], reverse=True)[0][0]
return m['parameters'][k]['data_url']
def download_file(url):
subprocess.Popen(['wget', url], cwd='tmp/').wait()
def untar(fn):
t = tarfile.open(fn)
pat = re.compile(r'.*/(GW[\d_]*_comoving.h5)')
for i in t:
p = pat.match(i.name)
if p:
with open("tmp/" + p.group(1), "wb") as fp:
fp.write(t.extractfile(i).read())
return
raise KeyError
if __name__ == "__main__":
urls = []
for k, url in list_catalog('all'):
try:
print(f"Getting metadata for {k}...", end="")
url = get_download(url)
print(f" done")
urls.append(url)
download_file(url)
except:
print(" failed!")
download_file("https://dcc.ligo.org/public/0169/P2000223/007/GW190424_180648.tar")
download_file("https://dcc.ligo.org/public/0169/P2000223/007/GW190909_114149.tar")
for i in os.listdir("tmp/"):
if i.endswith(".tar"):
print(f"Untarring {i}...", end="")
untar("tmp/" + i)
print(" done")
|
multipass-black-holesREPO_NAMEmultipassPATH_START.@multipass_extracted@multipass-main@get_data.py@.PATH_END.py
|
{
"filename": "_ticktext.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/volume/colorbar/_ticktext.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TicktextValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(self, plotly_name="ticktext", parent_name="volume.colorbar", **kwargs):
super(TicktextValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
role=kwargs.pop("role", "data"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@volume@colorbar@_ticktext.py@.PATH_END.py
|
{
"filename": "python-reference_catboostregressor_plot_predictions.md",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/catboost/docs/en/concepts/python-reference_catboostregressor_plot_predictions.md",
"type": "Markdown"
}
|
# plot_predictions
{% include [plot_predictions-plot_predictions__desc__full](../_includes/work_src/reusage-python/plot_predictions__desc__full.md) %}
## {{ dl--parameters }} {#parameters}
### data
#### Description
The data to plot predictions for.
For example, use a two-document slice of the original dataset (refer to the example below).
**Possible types:**
- {{ python-type--numpy-ndarray }}
- {{ python-type--pandasDataFrame }}
- {{ python_type__pandas-SparseDataFrame }}
- {{ python_type__scipy-sparse-spmatrix }} (all subclasses except `dia_matrix`)
- {{ python-type--pool }}
**Default value**
{{ python--required }}
### features_to_change
#### Description
The list of numerical features to vary the prediction value for.
For example, chose the required features by selecting top N most important features that impact the prediction results for a pair of objects according to [PredictionDiff](fstr.md#fstr__prediction-diff) (refer to the example below).
**Possible types**
- list of int
- string
- combination of list of int & string
**Default value**
{{ python--required }}
### plot
#### Description
Plot a Jupyter Notebook chart based on the calculated predictions.
**Possible types**
{{ python-type--bool }}
**Default value**
True
### plot_file
#### Description
The name of the output HTML-file to save the chart to.
**Possible types**
{{ python-type--string }}
**Default value**
{{ fit--staged-predict-eval-period }}
## {{ dl__return-value }} {#output-format}
Dict with two fields:
- `params` — `dict` of best-found parameters.
- `cv_results` — `dict` or {{ python-type--pandascoreframeDataFrame }} with cross-validation results. Сolumns are: `test-error-mean`, `test-error-std`, `train-error-mean`, `train-error-std`.
## {{ dl--example }} {#example}
```python
import numpy as np
from catboost import Pool, CatBoostRegressor
train_data = np.random.randint(0, 100, size=(100, 10))
train_label = np.random.randint(0, 1000, size=(100))
train_pool = Pool(train_data, train_label)
train_pool_slice = train_pool.slice([2, 3])
model = CatBoostRegressor()
model.fit(train_pool)
prediction_diff = model.get_feature_importance(train_pool_slice,
type='PredictionDiff',
prettified=True)
model.plot_predictions(data=train_pool_slice,
features_to_change=prediction_diff["Feature Id"][:2],
plot=True,
plot_file="plot_predictions_file.html")
```
An example of the first plotted chart:
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@catboost@docs@en@concepts@python-reference_catboostregressor_plot_predictions.md@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "GeminiDRSoftware/DRAGONS",
"repo_path": "DRAGONS_extracted/DRAGONS-master/gemini_instruments/oscir/tests/__init__.py",
"type": "Python"
}
|
GeminiDRSoftwareREPO_NAMEDRAGONSPATH_START.@DRAGONS_extracted@DRAGONS-master@gemini_instruments@oscir@tests@__init__.py@.PATH_END.py
|
|
{
"filename": "test_memory_astradb.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/tests/integration_tests/memory/test_memory_astradb.py",
"type": "Python"
}
|
import os
from typing import AsyncIterable, Iterable
import pytest
from langchain.memory import ConversationBufferMemory
from langchain_core.messages import AIMessage, HumanMessage
from langchain_community.chat_message_histories.astradb import (
AstraDBChatMessageHistory,
)
from langchain_community.utilities.astradb import SetupMode
def _has_env_vars() -> bool:
return all(
[
"ASTRA_DB_APPLICATION_TOKEN" in os.environ,
"ASTRA_DB_API_ENDPOINT" in os.environ,
]
)
@pytest.fixture(scope="function")
def history1() -> Iterable[AstraDBChatMessageHistory]:
history1 = AstraDBChatMessageHistory(
session_id="session-test-1",
collection_name="langchain_cmh_test",
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
namespace=os.environ.get("ASTRA_DB_KEYSPACE"),
)
yield history1
history1.collection.astra_db.delete_collection("langchain_cmh_test")
@pytest.fixture(scope="function")
def history2() -> Iterable[AstraDBChatMessageHistory]:
history2 = AstraDBChatMessageHistory(
session_id="session-test-2",
collection_name="langchain_cmh_test",
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
namespace=os.environ.get("ASTRA_DB_KEYSPACE"),
)
yield history2
history2.collection.astra_db.delete_collection("langchain_cmh_test")
@pytest.fixture
async def async_history1() -> AsyncIterable[AstraDBChatMessageHistory]:
history1 = AstraDBChatMessageHistory(
session_id="async-session-test-1",
collection_name="langchain_cmh_test",
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
namespace=os.environ.get("ASTRA_DB_KEYSPACE"),
setup_mode=SetupMode.ASYNC,
)
yield history1
await history1.async_collection.astra_db.delete_collection("langchain_cmh_test")
@pytest.fixture(scope="function")
async def async_history2() -> AsyncIterable[AstraDBChatMessageHistory]:
history2 = AstraDBChatMessageHistory(
session_id="async-session-test-2",
collection_name="langchain_cmh_test",
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
namespace=os.environ.get("ASTRA_DB_KEYSPACE"),
setup_mode=SetupMode.ASYNC,
)
yield history2
await history2.async_collection.astra_db.delete_collection("langchain_cmh_test")
@pytest.mark.requires("astrapy")
@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars")
def test_memory_with_message_store(history1: AstraDBChatMessageHistory) -> None:
"""Test the memory with a message store."""
memory = ConversationBufferMemory(
memory_key="baz",
chat_memory=history1,
return_messages=True,
)
assert memory.chat_memory.messages == []
# add some messages
memory.chat_memory.add_messages(
[
AIMessage(content="This is me, the AI"),
HumanMessage(content="This is me, the human"),
]
)
messages = memory.chat_memory.messages
expected = [
AIMessage(content="This is me, the AI"),
HumanMessage(content="This is me, the human"),
]
assert messages == expected
# clear the store
memory.chat_memory.clear()
assert memory.chat_memory.messages == []
@pytest.mark.requires("astrapy")
@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars")
async def test_memory_with_message_store_async(
async_history1: AstraDBChatMessageHistory,
) -> None:
"""Test the memory with a message store."""
memory = ConversationBufferMemory(
memory_key="baz",
chat_memory=async_history1,
return_messages=True,
)
assert await memory.chat_memory.aget_messages() == []
# add some messages
await memory.chat_memory.aadd_messages(
[
AIMessage(content="This is me, the AI"),
HumanMessage(content="This is me, the human"),
]
)
messages = await memory.chat_memory.aget_messages()
expected = [
AIMessage(content="This is me, the AI"),
HumanMessage(content="This is me, the human"),
]
assert messages == expected
# clear the store
await memory.chat_memory.aclear()
assert await memory.chat_memory.aget_messages() == []
@pytest.mark.requires("astrapy")
@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars")
def test_memory_separate_session_ids(
history1: AstraDBChatMessageHistory, history2: AstraDBChatMessageHistory
) -> None:
"""Test that separate session IDs do not share entries."""
memory1 = ConversationBufferMemory(
memory_key="mk1",
chat_memory=history1,
return_messages=True,
)
memory2 = ConversationBufferMemory(
memory_key="mk2",
chat_memory=history2,
return_messages=True,
)
memory1.chat_memory.add_messages([AIMessage(content="Just saying.")])
assert memory2.chat_memory.messages == []
memory2.chat_memory.clear()
assert memory1.chat_memory.messages != []
memory1.chat_memory.clear()
assert memory1.chat_memory.messages == []
@pytest.mark.requires("astrapy")
@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars")
async def test_memory_separate_session_ids_async(
async_history1: AstraDBChatMessageHistory, async_history2: AstraDBChatMessageHistory
) -> None:
"""Test that separate session IDs do not share entries."""
memory1 = ConversationBufferMemory(
memory_key="mk1",
chat_memory=async_history1,
return_messages=True,
)
memory2 = ConversationBufferMemory(
memory_key="mk2",
chat_memory=async_history2,
return_messages=True,
)
await memory1.chat_memory.aadd_messages([AIMessage(content="Just saying.")])
assert await memory2.chat_memory.aget_messages() == []
await memory2.chat_memory.aclear()
assert await memory1.chat_memory.aget_messages() != []
await memory1.chat_memory.aclear()
assert await memory1.chat_memory.aget_messages() == []
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@tests@integration_tests@memory@test_memory_astradb.py@.PATH_END.py
|
{
"filename": "yt_gadget_owls_analysis.ipynb",
"repo_name": "yt-project/yt",
"repo_path": "yt_extracted/yt-main/doc/source/cookbook/yt_gadget_owls_analysis.ipynb",
"type": "Jupyter Notebook"
}
|
# Loading Gadget OWLS Data
## Setup
The first thing you will need to run these examples is a working installation of yt. The author or these examples followed the instructions under "Get yt: from source" at https://yt-project.org/ to install an up to date development version of yt.
We will be working with an OWLS snapshot: snapshot_033
Now we will tell the notebook that we want figures produced inline.
```python
%matplotlib inline
```
## Loading
```python
import yt
```
Now we will load the snapshot.
```python
ds = yt.load_sample("snapshot_033")
```
Set a ``YTRegion`` that contains all the data.
```python
ad = ds.all_data()
```
## Inspecting
The dataset can tell us what fields it knows about,
```python
ds.field_list
```
```python
ds.derived_field_list
```
Note that the ion fields follow the naming convention described in YTEP-0003 http://ytep.readthedocs.org/en/latest/YTEPs/YTEP-0003.html#molecular-and-atomic-species-names
## Accessing Particle Data
The raw particle data can be accessed using the particle types. This corresponds directly with what is in the hdf5 snapshots.
```python
ad["PartType0", "Coordinates"]
```
```python
ad["PartType4", "IronFromSNIa"]
```
```python
ad["PartType1", "ParticleIDs"]
```
```python
ad["PartType0", "Hydrogen"]
```
## Projection Plots
The projection plots make use of derived fields that store the smoothed particle data (particles smoothed onto an oct-tree). Below we make a projection of all hydrogen gas followed by only the neutral hydrogen gas.
```python
pz = yt.ProjectionPlot(ds, "z", ("gas", "H_density"))
```
```python
pz.show()
```
```python
pz = yt.ProjectionPlot(ds, "z", ("gas", "H_p0_density"))
```
```python
pz.show()
```
|
yt-projectREPO_NAMEytPATH_START.@yt_extracted@yt-main@doc@source@cookbook@yt_gadget_owls_analysis.ipynb@.PATH_END.py
|
{
"filename": "plot_adaboost_regression.py",
"repo_name": "scikit-learn/scikit-learn",
"repo_path": "scikit-learn_extracted/scikit-learn-main/examples/ensemble/plot_adaboost_regression.py",
"type": "Python"
}
|
"""
======================================
Decision Tree Regression with AdaBoost
======================================
A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tree
regressor. As the number of boosts is increased the regressor can fit more
detail.
See :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an
example showcasing the benefits of using more efficient regression models such
as :class:`~ensemble.HistGradientBoostingRegressor`.
.. [1] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
<https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3>`_
"""
# %%
# Preparing the data
# ------------------
# First, we prepare dummy data with a sinusoidal relationship and some gaussian noise.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
rng = np.random.RandomState(1)
X = np.linspace(0, 6, 100)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])
# %%
# Training and prediction with DecisionTree and AdaBoost Regressors
# -----------------------------------------------------------------
# Now, we define the classifiers and fit them to the data.
# Then we predict on that same data to see how well they could fit it.
# The first regressor is a `DecisionTreeRegressor` with `max_depth=4`.
# The second regressor is an `AdaBoostRegressor` with a `DecisionTreeRegressor`
# of `max_depth=4` as base learner and will be built with `n_estimators=300`
# of those base learners.
from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor
regr_1 = DecisionTreeRegressor(max_depth=4)
regr_2 = AdaBoostRegressor(
DecisionTreeRegressor(max_depth=4), n_estimators=300, random_state=rng
)
regr_1.fit(X, y)
regr_2.fit(X, y)
y_1 = regr_1.predict(X)
y_2 = regr_2.predict(X)
# %%
# Plotting the results
# --------------------
# Finally, we plot how well our two regressors,
# single decision tree regressor and AdaBoost regressor, could fit the data.
import matplotlib.pyplot as plt
import seaborn as sns
colors = sns.color_palette("colorblind")
plt.figure()
plt.scatter(X, y, color=colors[0], label="training samples")
plt.plot(X, y_1, color=colors[1], label="n_estimators=1", linewidth=2)
plt.plot(X, y_2, color=colors[2], label="n_estimators=300", linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Boosted Decision Tree Regression")
plt.legend()
plt.show()
|
scikit-learnREPO_NAMEscikit-learnPATH_START.@scikit-learn_extracted@scikit-learn-main@examples@ensemble@plot_adaboost_regression.py@.PATH_END.py
|
{
"filename": "base.py",
"repo_name": "LSSTDESC/descqa",
"repo_path": "descqa_extracted/descqa-master/descqa/base.py",
"type": "Python"
}
|
from __future__ import division, unicode_literals, absolute_import
import os
__all__ = ['BaseValidationTest', 'TestResult']
class TestResult(object):
"""
class for passing back test result
"""
def __init__(self, score=None, summary=None, passed=False, skipped=False, inspect_only=False):
"""
Parameters
----------
score : float
a float number to represent the test score
summary : str
short summary string
passed : bool
if the test is passed
skipped : bool
if the test is skipped, overwrites all other arguments
inspect_only : bool
if the test is only for inspection (i.e., no passing criteria)
"""
self.passed = bool(passed)
self.skipped = bool(skipped)
self.inspect_only = bool(inspect_only)
self.summary = summary or ''
if sum((self.passed, self.skipped, self.inspect_only)) > 1:
raise ValueError('Only *one* of `passed`, `skipped`, and `inspect_only` can be set to True.')
# set score
if not (self.skipped or self.inspect_only):
try:
self.score = float(score)
except (TypeError, ValueError):
raise ValueError('Must set a float value for `score`')
@property
def status_code(self):
"""
get status code (e.g. VALIDATION_TEST_PASSED)
"""
if self.passed:
return 'VALIDATION_TEST_PASSED'
if self.skipped:
return 'VALIDATION_TEST_SKIPPED'
if self.inspect_only:
return 'VALIDATION_TEST_INSPECT'
return 'VALIDATION_TEST_FAILED'
@property
def status_full(self):
"""
get full status (3 lines of string: status code, summary, score)
"""
output = [self.status_code, self.summary]
if self.score:
output.append('{:.3g}'.format(self.score))
return '\n'.join(output)
class BaseValidationTest(object):
"""
very abstract class for validation test class
"""
data_dir = os.path.join(os.path.dirname(__file__), 'data')
external_data_dir = "/global/cfs/cdirs/lsst/groups/CS/descqa/data"
def __init__(self, **kwargs):
pass
def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
"""
Run the validation test on a single catalog.
Return an instance of TestResult.
This method will be called once for each catalog.
Parameters
----------
catalog_instance : instance of BaseGenericCatalog
instance of the galaxy catalog
catalog_name : str
name of the galaxy catalog
output_dir : str
output directory (all output must be under this directory)
Returns
-------
test_result : instance of TestResult
use the TestResult object to return test result
"""
raise NotImplementedError
def conclude_test(self, output_dir):
"""
Conclude the test.
One can make summary plots for all catalogs here.
Return None.
This method will be called once when all catalogs are done.
Parameters
----------
output_dir: str
output directory (all output must be under this directory)
"""
|
LSSTDESCREPO_NAMEdescqaPATH_START.@descqa_extracted@descqa-master@descqa@base.py@.PATH_END.py
|
{
"filename": "binary.py",
"repo_name": "laristra/flecsph",
"repo_path": "flecsph_extracted/flecsph-master/tools/starID_tools/nsID_tov/binary.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# Copyright (c) 2017 Triad National Security, LLC
# All rights reserved.
# Author : Hyun Lim
# Date : Jun3.27.2017, Updated : Oct.8.2019
# This is a python script that generates binary system in Keperlian orbit
# Usage : binary.py <separation> <input1> <input2> <output_name>
from __future__ import print_function
import numpy as np
import h5py
import sys
USE_PN=True
USE_TIDALLY_LOCKED=True
FLIP_Z_1=True
FLIP_Z_2=True
# code units
p_cgs_to_cu = 1.0e-22
p_cu_to_cgs = 1.0e-22
rho_cgs_to_cu = 1.0e-12
rho_cu_to_cgs = 1.0e12
u_cgs_to_cu = 1.0e-10
u_cu_to_cgs = 1.0e10
distance_cu_to_cgs = 1.0e5
distance_cgs_to_cu = 1.0e-5
v_cu_to_cgs = 1.0e5
v_cgs_to_cu = 1.0e-5
mass_cu_to_cgs = 1.0e27
mass_cgs_to_cu = 1.0e-27
# Important constants
G_newt = 6.67259e-8
c_light = 2.99792458e10
def get_reduced_mass(m1,m2):
# Reduced mass
return (m1/(m1+m2))*m2
def get_kepplerian_velocity(m_mine,m_other,r_mine,separation):
# The tangential velocity for stable circular orbit
angular_velocity = np.sqrt(G_newt*(m_mine+m_other)/separation**3)
return angular_velocity*r_mine
def get_center_of_mass(m,x,y,z):
# Find the center of mass of a star
mtot = np.sum(m)
x_com = np.sum(x*m)/mtot
y_com = np.sum(y*m)/mtot
z_com = np.sum(z*m)/mtot
return x_com,y_com,z_com
def center_star(m,x,y,z):
# Re-centers star so it sits at the origin
x_com,y_com,z_com = get_center_of_mass(m,x,y,z)
xnew = x - x_com
ynew = y - y_com
znew = z - z_com
return xnew,ynew,znew
def get_pn_velocity_angular(m_mine,m_other,r_mine,separation):
# The tangential velocity for initial data for PN2.5
mtot = m_mine+m_other
eta = (m_mine/mtot)*(m_other/mtot)
factor=G_newt*mtot/(separation*c_light**2)
omega_kepp = np.sqrt(G_newt*mtot/separation**3)
omega = omega_kepp*(1 - 0.5*factor*(3-eta)
+ (factor**2)*(15.+47.*eta+3.*(eta**2))/8.)
vtan = omega*r_mine
#print("\tvtan = ",vtan[0])
return vtan
def get_pn_rdot(m_mine,m_other,separation):
mtot=m_mine+m_other
eta = (m_mine/mtot)*(m_other/mtot)
rdot = -(64./5.)*(G_newt**3)*(mtot**2)*m_other*eta/((separation**3)*(c_light**5))
#print("\trdot = ",rdot)
return rdot
# DO NOT USE. Should use polar coordinates, not spherical
def get_radius(x,y,z):
# Radius of a particle at coordinates x,y,z
return np.sqrt(x**2+y**2+z**2)
def get_offsets(m1,m2,separation):
# Returns radii of m1 and m2 in binary
# r1,r2 are DISPLACEMENTS, not magnitudes
r2 = separation*m1/(m1+m2)
r1 = r2 - separation
return r1,r2
def separate(r1,r2,x1,y1,z1,x2,y2,z2):
#Separate stars by radius and return new position arrays.
#By default we separate along x-axis.
x1new = x1 + r1
y1new = y1
z1new = z1
x2new = x2 + r2
y2new = y2
z2new = z2
return x1new,y1new,z1new,x2new,y2new,z2new
def get_velocities(m_mine,m_other,separation,x,y,z,rstar):
# Get the velocities of a particle.
# Tangential velocity is along y-axis.
# For counterclockwise rotation, vy > 0 iff x > 0
if USE_TIDALLY_LOCKED:
r = np.sqrt(x**2+y**2)
theta = np.arctan2(y,x)
else:
r = np.abs(rstar)
theta = 0 if np.sign(rstar) > 0 else np.pi
if USE_PN:
vtan = get_pn_velocity_angular(m_mine,m_other,r,separation)
vrad = get_pn_rdot(m_mine,m_other,separation)
else:
vtan = get_kepplerian_velocity(m_mine,m_other,r,separation)
vrad = 0
vx = vrad*np.cos(theta) - vtan*np.sin(theta)
vy = vrad*np.sin(theta) + vtan*np.cos(theta)
vz = 0
return vx,vy,vz
def get_initial_data(separation,file1,file2,output_name):
# when reading in data, assume particles roughly at COM
# this may need to change
print("Reading in files:\n\t{}\n\t{}".format(file1,file2))
f1 = h5py.File(file1,'r')
f2 = h5py.File(file2,'r')
# NOTE: Assumes center of mass is origin for each file.
# TODO: Fix this if need be.
# Grab each data set from h5part file
# Extract the data from h5part Step#0 group
# We use this format because paraview requires that format
group_data1=f1.get('Step#0')
group_data2=f2.get('Step#0')
x1=group_data1.get('x')
x1=np.array(x1)
x2=group_data2.get('x')
x2=np.array(x2)
y1=group_data1.get('y')
y1=np.array(y1)
y2=group_data2.get('y')
y2=np.array(y2)
z1=group_data1.get('z')
z1=np.array(z1)
z2=group_data2.get('z')
z2=np.array(z2)
h1=group_data1.get('h')
h1=np.array(h1)
h2=group_data2.get('h')
h2=np.array(h2)
u1=group_data1.get('u')
u1=np.array(u1)
u2=group_data2.get('u')
u2=np.array(u2)
m1=group_data1.get('m')
m1=np.array(m1)
m2=group_data2.get('m')
m2=np.array(m2)
# HL : Unit conversion part
# Convert to cgs
# currently everything goes into memory
# TODO: change this if need be
x1 = x1*distance_cu_to_cgs
x2 = x2*distance_cu_to_cgs
y1 = y1*distance_cu_to_cgs
y2 = y2*distance_cu_to_cgs
z1 = z1*distance_cu_to_cgs
z2 = z2*distance_cu_to_cgs
h1 = h1*distance_cu_to_cgs
h2 = h2*distance_cu_to_cgs
u1 = u1*u_cu_to_cgs
u2 = u2*u_cu_to_cgs
m1 = m1*mass_cu_to_cgs
m2 = m2*mass_cu_to_cgs
# HL : Need to check this
try:
ye1 = group_data1.get('ye')
ye1 = np.array(ye1)
except:
ye1 = 0.5*np.ones_like(x1)
print("Warning. No electron fraction for star 1. "
+"Using a value of ye = 0.5")
try:
ye2 = group_data2.get('ye')
ye2 = np.array(ye2)
except:
ye2 = 0.5*np.ones_like(x2)
print("Warning. No electron fraction for star 2. "
+"Using a value of ye = 0.5")
try:
parttype1 = group_data1.get('parttype')
parttype1 = np.array(parttype1)
except:
parttype1 = np.ones(len(x1),dtype=int)
print("Warning. No particle type for star 1. "
+"Using a value of 1.")
try:
parttype2 = group_data2.get('parttype')
parttype2 = np.array(parttype2)
except:
parttype2 = np.ones(len(x2),dtype=int)
print("Warning. No particle type for star 2. "
+"Using a value of 1.")
# recenter both stars so the are centered exactly at the origin
# of their respective coordinate systems
x1,y1,z1 = center_star(m1,x1,y1,z1)
x2,y2,z2 = center_star(m2,x2,y2,z2)
# Reflect star 1 about its origin so the stars
# are perfectly symmetric about their
# center of mass
x1 *= -1
y1 *= -1
if FLIP_Z_1:
z1 *= -1
if FLIP_Z_2:
z2 *= -1
# total masses and reduced mass
mtot1,mtot2 = np.sum(m1),np.sum(m2)
print("Total masses are {} and {} g".format(mtot1,mtot2))
reduced_mass = get_reduced_mass(mtot1,mtot2)
print("Reduced mass is {} g".format(reduced_mass))
# separate the stars
r1,r2 = get_offsets(m1,m2,separation)
x1,y1,z1,x2,y2,z2 = separate(r1,r2,x1,y1,z1,x2,y2,z2)
# get velocities
print ("Getting Kepplerian velocities.")
vx1,vy1,vz1 = get_velocities(mtot1,mtot2,separation,x1,y1,z1,r1)
vx2,vy2,vz2 = get_velocities(mtot2,mtot1,separation,x2,y2,z2,r2)
vnorm1 = np.sqrt(vx1**2+vy1**2+vz1**2)
vnorm2 = np.sqrt(vx2**2+vy2**2+vz2**2)
print("Average velocity per star is {}".format(np.abs(np.mean(vnorm1))))
print("min velocity is {} and max is {}".format(np.min(np.hstack((vnorm1,vnorm2))),
np.max(np.hstack((vnorm1,vnorm2)))))
# get new variables for combined particles
x = np.hstack((x1,x2))
y = np.hstack((y1,y2))
z = np.hstack((z1,z2))
vx = np.hstack((vx1,vx2))
vy = np.hstack((vy1,vy2))
vz = np.hstack((vz1,vz2))
h = np.hstack((h1,h2))
m = np.hstack((m1,m2))
u = np.hstack((u1,u2))
ye = np.hstack((ye1,ye2))
parttype = np.hstack((parttype1,parttype2))
state = np.hstack((np.full(x1.shape,1,dtype=int),np.full(x2.shape,2,dtype=int)))
#output to file
print("Writing data")
with h5py.File(output_name,'w') as g:
f = g.create_group("Step#0")
xdset = f.create_dataset('x', data = x)
ydset = f.create_dataset('y', data = y)
zdset = f.create_dataset('z', data = z)
vxdset = f.create_dataset('vx', data = vx)
vydset = f.create_dataset('vy', data = vy)
vzdset = f.create_dataset('vz', data = vz)
udset = f.create_dataset('u', data = u)
mdset = f.create_dataset('m', data = m)
hdset = f.create_dataset('h', data = h)
statedset = f.create_dataset('state',data=state)
#yedset = f.create_dataset('ye', data = ye)
#partdset = f.create_dataset('particel-type', data = parttype)
if __name__ == "__main__":
separation = float(sys.argv[1])*distance_cu_to_cgs
file1 = sys.argv[2]
file2 = sys.argv[3]
output_name = sys.argv[4]
print("Making binary initial data...")
get_initial_data(separation,file1,file2,output_name)
print("Finished: have fun with your simulations using these data!")
|
laristraREPO_NAMEflecsphPATH_START.@flecsph_extracted@flecsph-master@tools@starID_tools@nsID_tov@binary.py@.PATH_END.py
|
{
"filename": "blast_sph.py",
"repo_name": "PrincetonUniversity/athena",
"repo_path": "athena_extracted/athena-master/tst/regression/scripts/tests/curvilinear/blast_sph.py",
"type": "Python"
}
|
# Regression test to check whether blast wave remains spherical in spherical_polar coords
# Modules
import logging
import scripts.utils.athena as athena
import sys
sys.path.insert(0, '../../vis/python')
import athena_read # noqa
athena_read.check_nan_flag = True
logger = logging.getLogger('athena' + __name__[7:]) # set logger name based on module
# Prepare Athena++
def prepare(**kwargs):
logger.debug('Running test ' + __name__)
athena.configure(
prob='blast',
coord='spherical_polar', **kwargs)
athena.make()
# Run Athena++
def run(**kwargs):
arguments = ['time/ncycle_out=0', 'problem/compute_error=true']
athena.run('hydro/athinput.blast_sph', arguments)
# Analyze output
def analyze():
analyze_status = True
# read data from error file
filename = 'bin/blastwave-shape.dat'
data = athena_read.error_dat(filename)
# check blast is spherical
if data[0][3] > 1.0:
logger.warning("Distortion of blast wave in spherical coords too large %g",
data[0][3])
analyze_status = False
return analyze_status
|
PrincetonUniversityREPO_NAMEathenaPATH_START.@athena_extracted@athena-master@tst@regression@scripts@tests@curvilinear@blast_sph.py@.PATH_END.py
|
{
"filename": "_ticklabelposition.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/cone/colorbar/_ticklabelposition.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TicklabelpositionValidator(_plotly_utils.basevalidators.EnumeratedValidator):
def __init__(
self, plotly_name="ticklabelposition", parent_name="cone.colorbar", **kwargs
):
super(TicklabelpositionValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
values=kwargs.pop(
"values",
[
"outside",
"inside",
"outside top",
"inside top",
"outside left",
"inside left",
"outside right",
"inside right",
"outside bottom",
"inside bottom",
],
),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@cone@colorbar@_ticklabelposition.py@.PATH_END.py
|
{
"filename": "definitions.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/yt/frontends/tipsy/definitions.py",
"type": "Python"
}
|
npart_mapping = {"Gas": "nsph", "DarkMatter": "ndark", "Stars": "nstar"}
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@yt@frontends@tipsy@definitions.py@.PATH_END.py
|
{
"filename": "_y.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/bar/_y.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class YValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(self, plotly_name="y", parent_name="bar", **kwargs):
super(YValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
anim=kwargs.pop("anim", True),
edit_type=kwargs.pop("edit_type", "calc+clearAxisTypes"),
role=kwargs.pop("role", "data"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@bar@_y.py@.PATH_END.py
|
{
"filename": "io.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/yt/frontends/enzo_e/io.py",
"type": "Python"
}
|
import numpy as np
from yt.frontends.enzo_e.misc import get_particle_mass_correction, nested_dict_get
from yt.utilities.exceptions import YTException
from yt.utilities.io_handler import BaseIOHandler
from yt.utilities.on_demand_imports import _h5py as h5py
class EnzoEIOHandler(BaseIOHandler):
_dataset_type = "enzo_e"
_base = slice(None)
_field_dtype = "float64"
_sep = "_"
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._base = self.ds.dimensionality * (
slice(self.ds.ghost_zones, -self.ds.ghost_zones),
)
# Determine if particle masses are actually masses or densities.
if self.ds.parameters["version"] is not None:
# they're masses for enzo-e versions that record a version string
mass_flag = True
else:
# in earlier versions: query the existence of the "mass_is_mass"
# particle parameter
mass_flag = nested_dict_get(
self.ds.parameters, ("Particle", "mass_is_mass"), default=None
)
# the historic approach for initializing the value of "mass_is_mass"
# was unsound (and could yield a random value). Thus we should only
# check for the parameter's existence and not its value
self._particle_mass_is_mass = mass_flag is not None
def _read_field_names(self, grid):
if grid.filename is None:
return []
f = h5py.File(grid.filename, mode="r")
try:
group = f[grid.block_name]
except KeyError as e:
raise YTException(
message=f"Grid {grid.block_name} is missing from data file {grid.filename}.",
ds=self.ds,
) from e
fields = []
ptypes = set()
dtypes = set()
# keep one field for each particle type so we can count later
sample_pfields = {}
for name, v in group.items():
if not hasattr(v, "shape") or v.dtype == "O":
continue
# mesh fields are "field <name>"
if name.startswith("field"):
_, fname = name.split(self._sep, 1)
fields.append(("enzoe", fname))
dtypes.add(v.dtype)
# particle fields are "particle <type> <name>"
else:
_, ftype, fname = name.split(self._sep, 2)
fields.append((ftype, fname))
ptypes.add(ftype)
dtypes.add(v.dtype)
if ftype not in sample_pfields:
sample_pfields[ftype] = fname
self.sample_pfields = sample_pfields
if len(dtypes) == 1:
# Now, if everything we saw was the same dtype, we can go ahead and
# set it here. We do this because it is a HUGE savings for 32 bit
# floats, since our numpy copying/casting is way faster than
# h5py's, for some reason I don't understand. This does *not* need
# to be correct -- it will get fixed later -- it just needs to be
# okay for now.
self._field_dtype = list(dtypes)[0]
f.close()
return fields, ptypes
def _read_particle_coords(self, chunks, ptf):
yield from (
(ptype, xyz, 0.0)
for ptype, xyz in self._read_particle_fields(chunks, ptf, None)
)
def _read_particle_fields(self, chunks, ptf, selector):
chunks = list(chunks)
dc = self.ds.domain_center.in_units("code_length").d
for chunk in chunks: # These should be organized by grid filename
f = None
for g in chunk.objs:
if g.filename is None:
continue
if f is None:
f = h5py.File(g.filename, mode="r")
if g.particle_count is None:
fnstr = "{}/{}".format(
g.block_name,
self._sep.join(["particle", "%s", "%s"]),
)
g.particle_count = {
ptype: f.get(fnstr % (ptype, self.sample_pfields[ptype])).size
for ptype in self.sample_pfields
}
g.total_particles = sum(g.particle_count.values())
if g.total_particles == 0:
continue
group = f.get(g.block_name)
for ptype, field_list in sorted(ptf.items()):
pn = self._sep.join(["particle", ptype, "%s"])
if g.particle_count[ptype] == 0:
continue
coords = tuple(
np.asarray(group.get(pn % ax)[()], dtype="=f8")
for ax in "xyz"[: self.ds.dimensionality]
)
for i in range(self.ds.dimensionality, 3):
coords += (
dc[i] * np.ones(g.particle_count[ptype], dtype="f8"),
)
if selector is None:
# This only ever happens if the call is made from
# _read_particle_coords.
yield ptype, coords
continue
coords += (0.0,)
mask = selector.select_points(*coords)
if mask is None:
continue
for field in field_list:
data = np.asarray(group.get(pn % field)[()], "=f8")
if field == "mass" and not self._particle_mass_is_mass:
data[mask] *= get_particle_mass_correction(self.ds)
yield (ptype, field), data[mask]
if f:
f.close()
def io_iter(self, chunks, fields):
for chunk in chunks:
fid = None
filename = -1
for obj in chunk.objs:
if obj.filename is None:
continue
if obj.filename != filename:
# Note one really important thing here: even if we do
# implement LRU caching in the _read_obj_field function,
# we'll still be doing file opening and whatnot. This is a
# problem, but one we can return to.
if fid is not None:
fid.close()
fid = h5py.h5f.open(
obj.filename.encode("latin-1"), h5py.h5f.ACC_RDONLY
)
filename = obj.filename
for field in fields:
grid_dim = self.ds.grid_dimensions
nodal_flag = self.ds.field_info[field].nodal_flag
dims = (
grid_dim[: self.ds.dimensionality][::-1]
+ nodal_flag[: self.ds.dimensionality][::-1]
)
data = np.empty(dims, dtype=self._field_dtype)
yield field, obj, self._read_obj_field(obj, field, (fid, data))
if fid is not None:
fid.close()
def _read_obj_field(self, obj, field, fid_data):
if fid_data is None:
fid_data = (None, None)
fid, rdata = fid_data
if fid is None:
close = True
fid = h5py.h5f.open(obj.filename.encode("latin-1"), h5py.h5f.ACC_RDONLY)
else:
close = False
ftype, fname = field
node = f"/{obj.block_name}/field{self._sep}{fname}"
dg = h5py.h5d.open(fid, node.encode("latin-1"))
if rdata is None:
rdata = np.empty(
self.ds.grid_dimensions[: self.ds.dimensionality][::-1],
dtype=self._field_dtype,
)
dg.read(h5py.h5s.ALL, h5py.h5s.ALL, rdata)
if close:
fid.close()
data = rdata[self._base].T
if self.ds.dimensionality < 3:
nshape = data.shape + (1,) * (3 - self.ds.dimensionality)
data = np.reshape(data, nshape)
return data
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@yt@frontends@enzo_e@io.py@.PATH_END.py
|
{
"filename": "spec_mode_stage_2.ipynb",
"repo_name": "spacetelescope/jwebbinar_prep",
"repo_path": "jwebbinar_prep_extracted/jwebbinar_prep-main/spec_mode/spec_mode_stage_2.ipynb",
"type": "Jupyter Notebook"
}
|
<a id='top'></a>
# JWST Spectroscopic Data Calibration: Pipeline Stage 2
---
**Authors**:
Maria Pena-Guerrero (pena@stsci.edu), Jo Taylor (jotaylor@stsci.edu)
Based on original notebooks by Bryan Hilbert (hilbert@stsci.edu)
**Latest Update**: Dec. 8, 2022
<div class="alert alert-block alert-warning">
<h3><u><b>Notebook Goals</b></u></h3>
<br>
Working with the Spectroscopic Stage 2 Calibration Pipeline, we will:
<ul>
<li>Look at the different ways to call the pipeline</li>
<li>Examine exactly what each pipeline step does to the science data</li>
<li>Look in detail at the steps in common to all spectroscopic modes</li>
</ul>
<br>
<b><i>IMPORTANT</b></i>: The goal of this notebook is not to actually run the pipeline, since each step is only applied to specific JWST modes, but rather to illustrate <i>what</i> each step is doing and <i>how</i> you would run it if you need to. Any code below is provided to showcase how to run the pipeline or any steps, and is not intended to actually be executed.
</div>
## Table of Contents
* [Introduction](#intro)
* [Spectroscopy Modes](#spec_modes)
* [Pipeline Resources and Documentation](#resources)
* [Installation](#installation)
* [Reference Files](#reference_files)
* [Imports](#imports)
* [Methods for calling steps/pipelines](#calling_methods)
* [Parameter reference files](#parameter_reffiles)
* [calwebb_spec2](#spec2)
* [Run the entire pipeline](#spec2_at_once)
* [run() method](#run_method)
* [call() method](#call_method)
* [command line](#command_line)
* [Run the individual pipeline steps](#spec2_step_by_step)
* [The `Assign WCS` step](#assign_wcs)
* [The `Background Subtraction` step](#background)
* [The `Imprint Subtraction` step](#imprint)
* [The `MSA Flagging` step](#msaflagging)
* [The `2D Extraction` step](#extract_2d)
* [The `Source Type` step](#srctype)
* [The `Master Background` step](#masterbg)
* [The `Wavecorr` step](#wavecorr)
* [The `Flat Field` step](#flat_field)
* [The `Straylight` step](#straylight)
* [The `Fringe Correction` step](#fringe)
* [The `Pathloss` step](#pathloss)
* [The `Barshadow` step](#barshadow)
* [The `Photom` step](#photom)
* [The `Resample Spec` step](#resample)
* [The `Cube Build` step](#cube_build)
* [The `1D Extraction` step](#extract_1d)
<a id='intro'></a>
## Introduction
This notebook covers the Stage 2 processing of the JWST spectroscopic data, also known as *calwebb_spec2*. This material should serve as a reference guide when you need to run *calwebb_spec2*, or any of its constituent steps, in the future.
**_IMPORTANT_**: The goal of this notebook is not to actually run the pipeline, since each step is only applied to specific JWST modes, but rather to illustrate *what* each step is doing and *how* you would run it if you need to. Any code below is provided to showcase how to run the pipeline or any steps, and is not intended to actually be run.
*Calwebb_spec2* is the “Swiss army knife” of JWST pipelines. It contains corrections that are only applied to certain instruments and/or instrument modes. The pipeline is a wrapper which will string together the appropriate steps for each exposure in the proper order.
The [*calwebb_spec2* pipeline](https://jwst-pipeline.readthedocs.io/en/stable/jwst/pipeline/calwebb_spec2.html#calwebb-spec2) applies corrections to slope images (countrate files) which are the output files of the [Stage 1 pipeline](https://jwst-pipeline.readthedocs.io/en/stable/jwst/pipeline/calwebb_detector1.html#calwebb-detector1). These slope images will be in the form of either *_rate* files (mean countrate across all integrations) or *_rateints* files (one countrate image per integration). A single input file can be processed or an association file listing multiple inputs can be supplied, in which case the processing steps will be applied to each input exposure one at a time. If *_rateints* products are used as input, each step applies its algorithm to each integration in the exposure, where appropriate.
The main output from *calwebb_spec2* is a fully calibrated, but unrectified, exposure with suffix *_cal*, in units of MJy/str (or MJy for NIRSpec or NIRISS SOSS point sources). The exact datamodel type of this file will depend on the spectroscopic mode in use, see the [documentation](https://jwst-pipeline.readthedocs.io/en/stable/jwst/pipeline/calwebb_spec2.html#d-or-3d-calibrated-data) for complete details.
Additionally, 1D extracted spectral products are also created. For non-TSO observations, these are *_x1d* files containing extracted spectra for one or more slits/sources. For TSO observations, these are 3D *_x1dints* files with extracted spectra for each integration. Finally, 2D or 3D rectified products (*_s2d* and *s3d*) may also be produced depending on the mode in use. However, the *_x1d*, *_x1dints*, *_s2d*, and *_s3d* products from *calwebb_spec2* are meant only for user examination of the data. It is the *_cal.fits* files that are passed on to Stage 3 of the pipeline and used for all further processing.
<a id='spec_modes'></a>
## Spectroscopy Modes
The subset, and order, of steps run by *calwebb_spec2* depends on the mode of the input JWST exposure. There are eight different spectroscopic instrument mode combinations, listed below:
* NIRSpec FS = Fixed Slit
* NIRSpec MOS = Multi-Object Spectroscopy (also called MSA or micro-shutter assembly)
* NIRSpec IFU = Integral Field Unit
* MIRI FS = LRS (Low Resolution Spectroscopy) Fixed Slit
* MIRI SL = LRS Slitless
* MIRI MRS = Medium Resolution Spectroscopy (IFU)
* NIRISS SOSS = Single Object Slitless Spectroscopy
* NIRISS and NIRCam WFSS = Wide-Field Slitless Spectroscopy
The list of all *calwebb_spec2* steps and which modes they apply to can be found [here](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec2.html#calwebb-spec2).
<a id='resources'></a>
## Pipeline Resources and Documentation
There are several different places to find information on installing and running the pipeline. This notebook will summarize each Stage 2 spectroscopic pipeline step. To find more in-depth instructions use the links below.
* [JWST Documentation (JDox) for the Stage 2 spectroscopic pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline/stages-of-processing/calwebb_spec2) including short a short summary of what each step does.
* [High-level description of all pipeline stages and steps](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/main.html) from the pipeline software documentation website.
* [`jwst` package documentation](https://jwst-pipeline.readthedocs.io/en/latest/jwst/introduction.html) including how to run the pipeline, input/output files, etc.
* [`jwst` package GitHub repository, with installation instructions](https://github.com/spacetelescope/jwst/blob/master/README.md)
* [**Help Desk**](https://stsci.service-now.com/jwst?id=sc_cat_item&sys_id=27a8af2fdbf2220033b55dd5ce9619cd&sysparm_category=e15706fc0a0a0aa7007fc21e1ab70c2f): **If you have any questions or problems regarding the pipeline, submit a ticket to the Help Desk**
[Top of Notebook](#top)
<a id='installation'></a>
### Installation
<div class="alert alert-block alert-info">
During the JWebbinar, we will be working in a pre-existing environment where the <b>jwst</b> package has already been installed, so you won't need to install it yourself.
</div>
<div class="alert alert-block alert-warning">
If you wish to run this notebook outside of this JWebbinar, you will have to first install the <b>jwst</b> package.<br>
For more detailed instructions on the various ways to install the package, see the [installation instructions](https://github.com/spacetelescope/jwst/blob/master/README.md) on GitHub.
The easiest way to install the pipeline is via `pip`. Below we show how to create a new conda environment, activate that environment, and then install the latest released version of the pipeline. You can name your environment anything you like. In the lines below, replace `<env_name>` with your chosen environment name.
>`conda create -n <env_name> python`<br>
>`conda activate <env_name>`<br>
>`pip install jwst`
If you wish to install the development version of the pipeline, which is more recent than (but not as well tested compared to) the latest released version:
>`conda create -n <env_name> python`<br>
>`conda activate <env_name>`<br>
>`pip install git+https://github.com/spacetelescope/jwst`
</div>
<a id='reference_files'></a>
### Reference Files
[Calibration reference files](https://jwst-docs.stsci.edu/data-processing-and-calibration-files/calibration-reference-files) are a collection of FITS and ASDF files that are used to remove instrumental signatures and calibrate JWST data. For example, the dark current reference file contains a multiaccum ramp of dark current signal to be subtracted from the data during the dark current subtraction step.
When running a pipeline or pipeline step, the pipeline will automatically look for any required reference files in a pre-defined local directory. If the required reference files are not present, they will automatically be downloaded from the Calibration Reference Data System (CRDS) at STScI.
<div class="alert alert-block alert-info">
During the JWebbinar, our pre-existing existing environment is set up to correctly use and store calibration reference files, and you do not need to set the environment variables below.
</div>
<div class="alert alert-block alert-warning">
If you wish to run this notebook outside of this JWebbinar, you will have to specify a local directory in which to store reference files, along with the server to use to download the reference files from CRDS. To accomplish this, there are two environment variables that should be set prior to calling the pipeline. These are the CRDS_PATH and CRDS_SERVER_URL variables. In the example below, reference files will be downloaded to the "crds_cache" directory under the home directory.
>`$ export CRDS_PATH=$HOME/crds_cache`<br>
>`$ export CRDS_SERVER_URL=https://jwst-crds.stsci.edu`<br>
OR:<br>
`os.environ["CRDS_PATH"] = "/user/myself/crds_cache"`<br>
`os.environ["CRDS_SERVER_URL"] = "https://jwst-crds.stsci.edu"`<br>
The first time you run the pipeline, the [CRDS server](https://jwst-pipeline.readthedocs.io/en/latest/jwst/introduction.html#crds) should download all of the context and reference files that are needed for that pipeline run, and dump them into the CRDS_PATH directory. Subsequent executions of the pipeline will first look to see if it has what it needs in CRDS_PATH and anything it doesn't have will be downloaded from the STScI cache.
</div>
<div class="alert alert-block alert-warning">NOTE: Users at STScI should automatically have access to the Calibration Reference Data System (CRDS) cache for running the pipeline, and can skip setting these environment variables.</div>
[Top of Notebook](#top)
<a id='imports'></a>
## Imports
Import packages to illustrate what is needed to run the pipeline and each of its steps.
```python
# Packages that allow us to get information about objects:
import asdf
import os
# Update to a path in your system (see details below at "Reference files")
os.environ["CRDS_PATH"] = "/path/to/my/folder/"
os.environ["CRDS_SERVER_URL"] = "https://jwst-crds.stsci.edu"
# Astropy tools:
from astropy.io import fits
# JWST data models
from jwst import datamodels
# The entire calwebb_spec2 pipeline
from jwst.pipeline.calwebb_spec2 import Spec2Pipeline
# Individual steps that make up calwebb_spec2 and datamodels
from jwst.assign_wcs.assign_wcs_step import AssignWcsStep
from jwst.background.background_step import BackgroundStep
from jwst.imprint.imprint_step import ImprintStep
from jwst.msaflagopen.msaflagopen_step import MSAFlagOpenStep
from jwst.extract_2d.extract_2d_step import Extract2dStep
from jwst.srctype.srctype_step import SourceTypeStep
from jwst.master_background.master_background_step import MasterBackgroundStep
from jwst.wavecorr.wavecorr_step import WavecorrStep
from jwst.flatfield.flat_field_step import FlatFieldStep
from jwst.straylight.straylight_step import StraylightStep
from jwst.fringe.fringe_step import FringeStep
from jwst.pathloss.pathloss_step import PathLossStep
from jwst.barshadow.barshadow_step import BarShadowStep
from jwst.barshadow.wfss_contam import WfssContamStep
from jwst.photom.photom_step import PhotomStep
from jwst.resample import ResampleSpecStep
from jwst.cube_build.cube_build_step import CubeBuildStep
from jwst.extract_1d.extract_1d_step import Extract1dStep
```
Check which version of the pipeline we are running:
```python
import jwst
print(jwst.__version__)
```
Define the output directory.
```python
# To make everything easier, all files saved by the pipeline
# and pipeline steps will be saved to the working directory.
output_dir = './'
# Make sure the output directory exists before downloading any data
if not os.path.exists(output_dir):
os.makedirs(output_dir)
```
[Top of Notebook](#top)
<a id='calling_methods'></a>
## Methods for calling steps/pipelines
There are three common methods by which the pipeline or pipeline steps can be called. From within python, the `run()` and `call()` methods of the pipeline or step classes can be used. Alternatively, the `strun` command can be used from the command line. Examples of each method are shown below.
When using the `call()` method or `strun`, optional input parameters can be specified via [parameter reference files](#parameter_reffiles). When using the `run()` method, these parameters are instead specified within python.
<a id='parameter_reffiles'></a>
## Parameter Reference Files
When calling a pipeline or pipeline step using the `call()` method or the command line, [parameter reference files](https://jwst-pipeline.readthedocs.io/en/stable/jwst/stpipe/config_asdf.html#config-asdf-files) can be used to specify values for input parameters. These reference files are in [asdf](https://asdf.readthedocs.io/en/stable/) format and appear somewhat similar to json files when examined in a text editor.
Versions of parameter reference files containing default parameter values for each step and pipeline are available in CRDS. When using the `call()` method, if you do not specify a parameter reference file name in the call, the pipeline or step will retrieve and use the appropriate file from CRDS, which will then run the pipeline or step with the parameter values in that file. If you provide the name of a parameter reference file, then the parameter values in that file will take precedence. For any parameter not specified in your parameter reference file, the pipeline will use the default value.
When using `strun`, the parameter reference file is a required input.
Let's take a look at the contents of a parameter reference file. We'll open it using the asdf package, and use the `tree` attribute to see what's inside:
```python
step = Spec2Pipeline()
spec2_param_reffile = os.path.join(output_dir, 'calwebb_spec2.asdf')
step.export_config(spec2_param_reffile)
spec2_reffile = asdf.open(spec2_param_reffile)
```
```python
spec2_reffile.info(max_rows=None)
# or
# spec2_reffile.tree
```
The top part of the file contains various metadata entries about the file itself. Below that, you'll see a `'name'` entry, which lists `Spec2Pipeline` as the class to which these parameters apply. The next line contains the `parameters` entry, which lists parameters and values attached to the pipeline itself. Below this is the `steps` entry, which contains a list of dictionaries. Each dictionary refers to one step within the pipeline, and specifies parameters and values that apply to that step. If you look through these entries, you'll see the same parameters and values that we specified manually when using the `run()` method above.
Here's one way to programmatically edit a parameter reference file- it can be a bit tedious. It is often easier to open the ASDF file in a text editor and directly edit parameters.
```python
for i in range(len(spec2_reffile["steps"])):
if spec2_reffile["steps"][i]["name"] == "extract_2d":
# Set minimum magnitude of objects to extract for WFSS data
spec2_reffile["steps"][i]["parameters"]["mmag_extract"] = 20
elif spec2_reffile["steps"][i]["name"] == "extract_1d":
# Fit a polynomial to the background values for each column or row
spec2_reffile["steps"][i]["parameters"]["bkg_fit"] = "poly"
elif spec2_reffile["steps"][i]["name"] == "bkg_subtract":
# Skip the default image-from-image background subtraction method
spec2_reffile["steps"][i]["parameters"]["skip"] = True
```
```python
# Don't forget to close the file
spec2_reffile.close()
```
[Top of Notebook](#top)
---
<a id='spec2'></a>
## The calwebb_spec2 pipeline: spectroscopic processing
In the sections below, we will illustrate how to run the Stage 2 pipeline on a single input rate file. We will first show how to call the entire [*calwebb_spec2* pipeline](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pipeline/calwebb_spec2.html#calwebb-spec2) on the file. The pipeline is a wrapper which wil']l string together all of the appropriate steps in the proper order.
The final output from this call is a flux-calibrated exposure image which is ready to go into the Stage 3 pipeline.
See [Figure 1](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline/stages-of-processing/calwebb_spec2) on the calwebb_spec2 algorithm page for a map of which steps are performed depending on spectroscopic mode.
<a id='spec2_at_once'></a>
### Run the entire `calwebb_spec2` pipeline
In this section we show how to run the entire calwebb_spec2 pipeline with a single call using the `run()`, `call()`, and `strun` methods. In this case the pipeline code can determine which instrument was used to collect the data and runs the appropriate steps in the proper order.
You may set parameter values for some of the individual steps, save some outputs, etc, and then call the pipeline.
<a id='run_method'></a>
#### Call the pipeline using the run() method
When using the `run()` method to execute a pipeline (or step), the pipeline class is first instantiated without the data to be processed. Optional input parameters are specified using attributes of the class instance. Finally, the call to the `run()` method is made and the data are supplied. See here for [more examples of the run() method](https://jwst-pipeline.readthedocs.io/en/stable/jwst/stpipe/call_via_run.html).
The `run()` method does not take any kind of parameter reference file as input. If you wish to set values for various parameters, you must do that manually. Below, we set several parameters in order to show how it's done.
How do you know what parameters are available to be set and what their default values are? The `spec` property for individual steps will list them. The property is less useful for the pipelines themselves, as it does not show the parameters for the steps comprising the pipeline.
All steps and pipelines have several common parameters that can be set.
* `save_results` specifies whether or not to save the output of that step/pipeline to a file. The default is False.
* `output_dir` is the directory into which the output files will be saved.
* `output_file` is the base filename to use for the saved result. Note that each step/pipeline will add a custom suffix onto output_file.
<a id='spec2_using_run'></a>
<div class="alert alert-block alert-info">
Here's how you would run the entire <i>calwebb_spec2</i> pipeline. When you run it, the output can be a little overwhelming. There will be multiple log entries printed to the screen for each step.
</div>
```python
# Create an instance of the pipeline class
spec2 = Spec2Pipeline()
# Set some parameters that pertain to the entire pipeline
spec2.save_results = True
spec2.output_dir = output_dir
# Here you could some parameters that pertain to some of the individual steps
spec2.extract_2d.mmag_extract = 20 # Minimum magnitude of objects to extract for WFSS data
spec2.extract_1d.bkg_fit = 'poly' # Fit a polynomial to the background values for each column or row
spec2.bkg_subtract.skip = True # Skip the default image-from-image background subtraction method
# Use the run() method, using an input rate file.
# The line below is commented since we don't actually have a file to calibrate
# result = spec2.run(rate_file)
```
<a id='call_method'></a>
#### Call the pipeline using the call() method
When using the `call()` method, a single command will instantiate and run the pipeline (or step). The input data and optional parameter reference files are supplied in this single command. In this case, any desired input parameters cannot be set after instantiation, as with the `run()` method. See here for [example usage of call() method](https://jwst-pipeline.readthedocs.io/en/stable/jwst/stpipe/call_via_call.html).
The commands below will call the pipeline using the `call()` method and will supply the parameter reference file.
<div class="alert alert-block alert-info">
<b>Method #1:</b>
<br>
Provide the name of the observation file, the pipeline-specific input paramters, and the name of the parameter reference file that specifies step-specific parameters
</div>
# To run this cell, you would need to change the cell type to "Code"
call_output = Spec2Pipeline.call(rate_file,
output_dir=output_dir,
save_results=True,
config_file=spec2_param_reffile)
<div class="alert alert-block alert-info">
<b>Method #2:</b>
<br>
In this case, build a nested dictionary that specifies parameter values for various steps, and provide it in the call to call().
</div>
# To run this cell, you would need to change the cell type to "Code"
parameter_dict = {"extract_2d": {"mmag_extract": 20},
"extract_1d": {"bkg_fit": "poly"},
"bkg_subtract": {"skip": True}
}
call_output = Spec2Pipeline.call(rate_file, output_dir=output_dir, save_results=True, steps=parameter_dict)
<a id='command_line'></a>
#### Call the pipeline using the command line
Calling a pipeline or step from the command line is similar to using the `call()` method. The data file to be processed, along with an optional parameter reference file and optional parameter/value pairs can be provided to the `strun` command. See here for [additional examples of command line calls](https://jwst-pipeline.readthedocs.io/en/stable/jwst/introduction.html?highlight=%22command%20line%22#running-from-the-command-line).
The cells below contain two different command line commands that use `strun` to call the calwebb_spec2 pipeline.
<div class="alert alert-block alert-info">
<b>Method #1:</b>
<br>
We provide the name of the pipeline class, the observation file, and explicitly set pipeline- and step-specific parameters. You can see that the command quickly becomes quite large with the added parameter settings.
```
strun jwst.pipeline.Spec2Pipeline myinput_rate.fits --save_results=True --output_dir='./' --steps.extract_2d.mmag_extract=20 --steps.extract_1d.bkg_fit='poly' --steps.bkg_subtract.skip=True
```
</div>
<div class="alert alert-block alert-info">
<b>Method #2:</b>
<br>
This version of the command is much more succinct, as the parameter values to be set are all contained within the parameter reference file. The pipeline class is also contained in the parameter reference file, so there is no need to specify it in the command itself.
```
strun spec2_modified_paramfile.asdf myinput_rate.fits
```
</div>
[Top of Notebook](#top)
<a id='spec2_step_by_step'></a>
## Run the individual pipeline steps
In the sections below we explain what each step does and illustrate how to run it. We show only the `run()` method of executing steps, but the `call()` or `strun` methods could be used with the same syntax as shown above.
<a id='assign_wcs'></a>
### The `Assign WCS` step
#### Summary
This step transforms positions in the detector frame to positions in a world coordinate system (WCS) - ICRS and wavelength. There may be intermediate coordinate frames depending on the instrument (e.g. from detector to slit frame). The WCS is saved in the ASDF extension of the FITS file, and it can be accessed as an attribute of the meta object when the fits file is opened as a datamodel. More information can be found at [assign_wcs](https://jwst-pipeline.readthedocs.io/en/stable/jwst/assign_wcs/index.html).
#### Affected Modes
This step is run for the following modes:
- NIRSpec: all
- MIRI: all
- NIRISS: all
- NIRCam: all
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/assign_wcs/main.html) of the step.
#### Arguments
There are no optional arguments for this step.
#### Reference files used
This step uses various reference files, see [`assign_wcs` reference files](https://jwst-pipeline.readthedocs.io/en/stable/jwst/assign_wcs/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Using the run() method. Instantiate and set parameters
step = AssignWcsStep()
step.save_results = True
step.output_dir = output_dir
# Use the run() method
result = step.run(rate_file)
[Top of Notebook](#top)
<a id='background'></a>
## The `Background Subtraction` step
#### Summary
The background subtraction step performs image-from-image subtraction to remove the background signal. For non-WFSS modes, the step takes as input one target exposure and one or more background exposures, which are then subtracted from the target exposure. If more than one background exposure is provided, they will be averaged together first. The single or averaged background exposure is then subtracted from the SCI array of the target exposure, and the DQ arrays of the background and target exposures are combined using a bitwise OR operation.
For WFSS exposures, a background reference image is scaled and subtracted from the target exposure.
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/background/description.html) of the step.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: all
- MIRI: FS, MRS
- NIRISS: all
- NIRCam: all
#### Arguments
There are two optional arguments for this step that are only used for non-WFSS exposures. They are used in the process of creating an average background image, to control the sigma clipping. Please see the documentation for further details: [optional arguments](https://jwst-pipeline.readthedocs.io/en/latest/jwst/background_step/arguments.html).
#### Reference files used
This step uses reference files (`WFSSBKG` and `WAVELENGTHRANGE`) only when processing WFSS exposures, see [WFSS background reference files](https://jwst-pipeline.readthedocs.io/en/stable/jwst/background/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Using the run() method. Instantiate and set parameters
step = BackgroundStep()
step.save_results = True
step.output_dir = output_dir
bkg_list = [background_img1, background_img2]
# Use the run() method
result = step.run(previous_result, bkg_list)
[Top of Notebook](#top)
<a id='imprint'> </a>
## The `Imprint Subtraction` step
#### Summary
This is a NIRSpec-specific step, applied only to the MSA and IFU modes. The MSA metal grid of shutters is not completely opaque, allowing a small amount of light to leak through and create light patterns. Pattern removal is done by subtracting a dedicated exposure taken with all MSA shutters closed and the IFU entrance aperture blocked. This step requires two input parameters, the target exposure and the imprint exposure.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: MOS, IFU
- MIRI: none
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/imprint/description.html) of the step.
#### Arguments
There are no optional arguments for this step.
#### Reference files used
This step uses no specific reference files.
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Using the run() method. Instantiate and set parameters
step = ImprintStep()
step.save_results = True
step.output_dir = output_dir
msa_imprint = 'myinput_msa_imprint.fits'
# Use the run() method
result = step.run(previous_result, msa_imprint)
[Top of Notebook](#top)
<a id='msaflagging'></a>
## The `MSA Flagging` step
#### Summary
This is a NIRSpec-specific step, applied only to the MSA and IFU modes. It flags pixels that are affected by MSA shutters that are stuck in the open position.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: MOS, IFU
- MIRI: none
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/msaflagopen/description.html) of the step.
#### Arguments
There are no optional arguments for this step.
#### Reference files used
This step uses the [`MSAOPER` reference file](https://jwst-pipeline.readthedocs.io/en/stable/jwst/msaflagopen/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = MSAFlagOpenStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='extract_2d'></a>
## The `2D Extraction` step
#### Summary
This step extracts 2D arrays from spectral images. The extractions are performed on all of the SCI, ERR, and DQ arrays of the input dataset, as well as any variance arrays that may be present.
It also computes an array of wavelengths to attach to the extracted data. The extracted arrays are stored as one or more slit objects in an output `MultiSlitModel` and saved as separate tuples of extensions in the output FITS file.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: FS, MOS
- MIRI: none
- NIRISS: WFSS
- NIRCam: all
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/extract_2d/main.html) of the step.
#### Arguments
This step has several [optional arguments](https://jwst-pipeline.readthedocs.io/en/stable/jwst/extract_2d/main.html#step-arguments).
#### Reference files used
For NIRISS and NIRCam WFSS modes, this step uses the [`WAVELENGTHRANGE` reference file](https://jwst-pipeline.readthedocs.io/en/stable/jwst/extract_2d/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = Extract2dStep()
step.output_dir = output_dir
step.save_results = True
# Call using the saturation instance from the previously-run step
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='srctype'></a>
## The `Source Type Determination` step
#### Summary
This step estimates if a spectroscopic source should be considered to be a point or extended object, and will populate each SCI extension's "SRCTYPE" keyword with "POINT", "EXTENDED", or "UNKNOWN". For single source observations, the pipeline will use information provided by the observer in their APT program. If the observer does not specify source type, the pipeline will attempt to determine it based on the observing configuration.
For multi-source NIRSpec MOS observations, each source's stellarity can be supplied by the observer in the MSA Planning Tool. For multi-source WFSS observations, the source type keyword is always set to "UNKNOWN", and the actual source sizes are derived from the direct image's source catalog.
The source type is used in some subsequent processing steps to apply source-dependent corrections.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: all
- MIRI: all
- NIRISS: all
- NIRCam: all
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/srctype/description.html)
#### Arguments
There are no optional arguments for this step.
#### Reference files used
This step uses no specific reference files.
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = SourceTypeStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='masterbg'></a>
## The `Master Background Subtraction` step
#### Summary
This step is typically run during Stage 3 spectroscopic processing (*calwebb_spec3*) for all modes **except** NIRSpec MOS, for which the step is instead run in *calwebb_spec2*.
In general, the master background subtraction method works by taking a 1D background spectrum, interpolating it back into the 2D space of a science image, and then subtracting it. This allows for higher SNR background data to be used than what might be obtainable by doing direct image-from-image subtraction using only one or a few background images. The 1D master background spectrum can either be constructed on-the-fly by the calibration pipeline using available background data or supplied by the user.
For NIRSpec *calwebb_spec2* processing, this step uses data from background slitlets or a user-supplied spectrum.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: MOS
- MIRI: none
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/master_background/description.html) of the step.
#### Arguments
This steps has multiple optional arguments- for further details please see the [documentation](https://jwst-pipeline.readthedocs.io/en/stable/jwst/master_background/arguments.html).
#### Reference files used
This step uses no specific reference files.
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = MasterBackgroundStep()
step.output_dir = output_dir
step.save_results = True
user_bkg = master_bkg_file
# Use the run() method
result = step.run(result, user_background=user_bkg)
[Top of Notebook](#top)
<a id='wavecorr'></a>
## The `Wavelength Correction` step
#### Summary
This step is only applied to NIRSpec FS and MOS modes. It updates the wavelength assignments for point sources that are known to be off center (in the dispersion direction) in their slit.
For NIRSpec MOS exposures, wavelength assignments created during extract_2d step are based on a source that’s perfectly centered in a slitlet. Most sources, however, are not centered in every slitlet in a real observation. The wavecorr step loops over all slit instances in the input science product and applies a wavelength correction to slits that contain a point source. The correction is based on each source's estimated X and Y position within a slitlet.
For NIRSpec FS exposures, fixed slit data do not have an a priori estimate of the source location within a given slit. For each source, the target coordinates and aperture reference point are used to estimate the location within the slit. Any offsets to this estimation are then computed using the same method as for MOS slitlets.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: FS, MOS
- MIRI: none
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/wavecorr/description.html) of the step.
#### Arguments
This step has no step-specific arguments.
#### Reference files used
This step uses the [`WAVECORR` reference file](https://jwst-pipeline.readthedocs.io/en/stable/jwst/wavecorr/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = WavecorrStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='flat_field'></a>
## The `Flat Field Correction` step
#### Summary
This step flat-fields an input science dataset by dividing by a flat-field reference image. The SCI array from the flat-field reference file is divided into both the SCI and ERR arrays of the science dataset, and the flat-field DQ array is combined with the science DQ array using a bitwise OR operation.
Flat field correction for NIRSpec modes does not use one set reference image. The NIRSpec flat-field varies with wavelength, and the wavelength of light that falls on any given pixel depends on the mode in use and which slits are open. Instead, the flat-field is constructed on-the-fly by extracting the relevant regions from reference files, and then- for each pixel- interpolating the appropriate wavelength value for that pixel.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: all
- MIRI: all
- NIRISS: all
- NIRCam: all
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/flatfield/main.html) of the step.
#### Arguments
This step has only one optional argument, used for NIRSpec modes: [save_interpolated_flat](https://jwst-pipeline.readthedocs.io/en/stable/jwst/flatfield/arguments.html).
#### Reference files used
This step uses several reference files: [non-NIRSpec flats](https://jwst-pipeline.readthedocs.io/en/stable/jwst/flatfield/reference_files.html) and [NIRSpec flats](https://jwst-pipeline.readthedocs.io/en/stable/jwst/flatfield/reference_files.html#reference-files-for-nirspec-spectroscopy).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = FlatFieldStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='straylight'></a>
## The `Stray Light Correction` step
#### Summary
This step is only applied to MIRI MRS channel 1 and 2 data. The straylight correction uses information in the `REGIONS` reference file to determine which pixels belong to a slice and which pixels are located in the slice gaps. It removes stray light that may contaminate spectra by interpolating the measured signal in the inter-slice regions of the detector. The inter-slice regions nominally should not receive light from the sky and therefore should serve as a good measure of the amount of stray light within the exposure.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: none
- MIRI: MRS
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/straylight/main.html) of the step.
#### Arguments
This step has three [optional arguments](https://jwst-pipeline.readthedocs.io/en/stable/jwst/straylight/arguments.html), depending on which algorithm is used to correct for stray light.
#### Reference files used
This step uses the [`REGIONS` reference file](https://jwst-pipeline.readthedocs.io/en/stable/jwst/straylight/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = StraylightStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='fringe'></a>
## The `Fringe Correction` step
#### Summary
This step applies a fringe correction to MIRI MRS images. There are significant fringes in MIRI IFU data. To correct them, the SCI array from a fringe reference file is divided into the SCI and ERR arrays of the science dataset. Only pixels that have valid (non-NaN) values in the SCI array of the reference file will be corrected. The DQ and variance arrays of the science exposure are not currently modified by this step.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: none
- MIRI: MRS
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/latest/jwst/fringe/main.html) of the step.
#### Arguments
This step has no step-specific arguments.
#### Reference files used
This step uses the [`FRINGE` reference file](https://jwst-pipeline.readthedocs.io/en/latest/jwst/fringe/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = FringeStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='pathloss'></a>
## The `Pathloss Correction` step
#### Summary
The pathloss step calculates and applies corrections that are needed to account for various types of signal loss in spectroscopic data. For NIRSpec, this correction accounts for losses in the optical system due to light being scattered outside the grating, and to light not passing through the aperture. For NIRISS SOSS data it corrects for the flux that falls outside the subarray. There is currently no correction for MIRI and NIRCam.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: all
- MIRI: none
- NIRISS: SOSS
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pathloss/description.html) of the step.
#### Arguments
This step has no step-specific arguments.
#### Reference files used
This step uses the [`PATHLOSS` reference file](https://jwst-pipeline.readthedocs.io/en/latest/jwst/pathloss/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = PathLossStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='barshadow'></a>
## The `Barshadow` step
#### Summary
This step is only applied to NIRSpec MOS data, specifically to extended sources only. It calculates the correction due to the bar that separates adjacent microshutters. The correction values are divided into the SCI and ERR arrays, and the square of the correction values are divided into the variance arrays.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: MOS
- MIRI: none
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/barshadow/description.html) of the step.
#### Arguments
This step has no specific arguments.
#### Reference files used
This step uses the [`BARSHADOW` reference file](https://jwst-pipeline.readthedocs.io/en/stable/jwst/barshadow/reference_files.html)
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = BarShadowStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='barshadow'></a>
## The `WFSS Contamination Correction` step
#### Summary
This step is only applied to NIRISS and NIRCam WFSS data, and corrects effects from overlapping spectral traces. Source fluxes from the direct image of a dispersed field are used to simulate corresponding grism spectra. Source spectra are corrected for contamination by subtracting simulated spectra of nearby sources. **NOTE**: By default, this step is not run by the pipeline in routine processing. It must be manually toggled or run.
This step should be run **after** `extract_2d` and `srctype`, but **before** `photom`.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: none
- MIRI: none
- NIRISS: WFSS
- NIRCam: WFSS
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/wfss_contam/description.html) of the step.
#### Arguments
There are also 3 [optional arguments](https://jwst-pipeline.readthedocs.io/en/stable/jwst/wfss_contam/arguments.html), two that specify if the simulated images should be saved, and a third which can enable multi-processing.
#### Reference files used
This step uses the [`WAVELENGTHRANGE` reference file](https://jwst-pipeline.readthedocs.io/en/latest/jwst/references_general/wavelengthrange_reffile.html#wavelengthrange-reffile) and the [`PHOTOM` reference file](https://jwst-pipeline.readthedocs.io/en/latest/jwst/photom/reference_files.html#photom-reffile).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = WfssContamStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='photom'></a>
## The `Photom` step
#### Summary
The photom step applies flux (photometric) calibration to convert data from units of countrate to surface brightness (MJy/Sr) or flux density (MJy). For NIRSpec and NIRISS SOSS observations of point sources, the output unit will be flux density, while all other observations will yield units of surface brightness. Pixels with wavelengths outside the range specified in each mode's reference file are set to zero and flagged as "DO_NOT_USE" in the DQ array.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: all
- MIRI: all
- NIRISS: all
- NIRCam: all
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/latest/jwst/photom/main.html) of the step.
#### Arguments
This step has no specific arguments.
#### Reference files used
The photom step uses the [`PHOTOM` and `AREA` reference files](https://jwst-pipeline.readthedocs.io/en/latest/jwst/photom/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = PhotomStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='resample'></a>
## The `Resample Spec` step
#### Summary
This step resamples each input 2D image based on the WCS and distortion information.
The output from this step is the *_s2d* file. A reminder, these are not final rectified 2D products- those will be created during the Stage 3 iteration of the `resample_spec` step. These products are meant for user examination only.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: FS, MOS
- MIRI: FS
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/resample/main.html) of the step.
#### Arguments
There is a list of [optional Astrodrizzle-style](https://jwst-pipeline.readthedocs.io/en/stable/jwst/resample/arguments.html) input parameters that can be used to customize the resampling process.
#### Reference files used
This step uses the [`DRIZPARS`](https://jwst-pipeline.readthedocs.io/en/stable/jwst/resample/reference_files.html) reference file. This file contains Astrodrizzle-style keywords that can be used to control the details of the resampling.
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = ResampleSpecStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='cube_build'></a>
## The `Cube Building` step
#### Summary
This step is applied to MIRI or NIRSpec IFU 2D images to produce 3D spectral cubes. The 2D disjointed IFU slice spectra are corrected for distortion and assembled into a rectangular cube with three orthogonal axes: two spatial and one spectral.
This step is run in both the *calwebb_spec2* and *calwebb_spec3* pipelines. In *calwebb_spec2*, where the input is a single MIRI or NIRSpec IFU exposure, the default output cube will be built from all the data in that single exposure. For MIRI this means using the data from different channels (e.g. 1A and 2A) that are recorded in a single exposure and the output IFU cube will have a non-linear wavelength dimension. For NIRSpec the data is from the single grating and filter combination contained in the exposure and will have a linear wavelength dimension. The *calwebb_spec2* pipeline calls cube_build with `output_type=multi`.
The output from this step is the *_s3d* file. A reminder, these are not final rectified 3D products- those will be created during the Stage 3 iteration of the `cube_build` step. These products are meant for user examination only.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: IFU
- MIRI: MRS
- NIRISS: none
- NIRCam: none
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/cube_build/main.html) of the step.
#### Arguments
There are a number of arguments that control the sampling size of the cube, as well as the type of data that is combined to create the cube. See all the [optional arguments](https://jwst-pipeline.readthedocs.io/en/stable/jwst/cube_build/arguments.html).
#### Reference files used
This step uses the [`CUBEPAR` reference file](https://jwst-pipeline.readthedocs.io/en/stable/jwst/cube_build/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = CubeBuildStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<a id='extract_1d'></a>
## The `1D Extraction` step
#### Summary
This step extracts a 1D signal from a 2D or 3D dataset and writes spectral data to an output 1D extracted spectral data product. For all non-TSO modes, this is a file with suffix *_x1d*, and for TSO modes the suffix is *_x1dints*. A background may optionally be subtracted during this step if not already done.
For non-WFSS modes, a reference file is used to specify the location and size of the target and background extraction regions. For WFSS modes, the extraction regions are defined as the 2D subarray/cutout for each source.
For non-IFU data, rectangular apertures are used. For IFU point sources, the spectrum is extracted using circular aperture photometry with an optional background annulus. For IFU extended sources, rectangular aperture photometry is used over the entire image and no background is estimated. (Note that photometry is performed using the Astropy affiliated package [photutils](https://photutils.readthedocs.io/en/latest/).)
For most spectral modes, an aperture correction is applied to the 1D data in order to put the results onto an infinite aperture scale.
A reminder, these are not final 1D spectral products- those will be created during the Stage 3 iteration of the `extract_1d` step. These products are meant for user examination only.
#### Affected Modes
This step is run for the following modes:
- NIRSpec: all
- MIRI: all
- NIRISS: all
- NIRCam: all
#### Documentation
[Full description](https://jwst-pipeline.readthedocs.io/en/stable/jwst/extract_1d/description.html) of the step.
#### Arguments
This step has multiple [optional arguments](https://jwst-pipeline.readthedocs.io/en/stable/jwst/extract_1d/arguments.html).
#### Reference files used
This step uses the [`EXTRACT1D` and `APCORR` reference files](https://jwst-pipeline.readthedocs.io/en/stable/jwst/extract_1d/reference_files.html).
#### Run the step
# To run this cell, you would need to change the cell type to "Code"
# Instantiate and set parameters
step = Extract1dStep()
step.output_dir = output_dir
step.save_results = True
# Use the run() method
result = step.run(previous_result)
[Top of Notebook](#top)
<img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/>
|
spacetelescopeREPO_NAMEjwebbinar_prepPATH_START.@jwebbinar_prep_extracted@jwebbinar_prep-main@spec_mode@spec_mode_stage_2.ipynb@.PATH_END.py
|
{
"filename": "stats_dist_funnel.ipynb",
"repo_name": "NumCosmo/NumCosmo",
"repo_path": "NumCosmo_extracted/NumCosmo-master/notebooks/stats_dist_tests/stats_dist_funnel.ipynb",
"type": "Jupyter Notebook"
}
|
---
**License**
stats_dist_funnel
Mon Jan 25 20:56:00 2020\
Copyright 2021\
Sandro Dias Pinto Vitenti <vitenti@uel.br>
---
---
stats_dist_funnel\
Copyright (C) 2021 Sandro Dias Pinto Vitenti <vitenti@uel.br>
numcosmo 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.
numcosmo 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/>.
---
```python
import sys
import math
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import LogFormatterMathtext
import numpy as np
from scipy.stats import binned_statistic
import matplotlib.ticker as mtick
from IPython.display import HTML, display
import tabulate
from numcosmo_py import Ncm
from numcosmo_py.plotting.tools import confidence_ellipse
from numcosmo_py.plotting.tools import plot_m2lnp
from numcosmo_py.plotting.tools import set_rc_params_article
%matplotlib inline
```
```python
Ncm.cfg_init()
Ncm.cfg_set_log_handler(lambda msg: sys.stdout.write(msg) and sys.stdout.flush())
```
```python
dim = 25
nps = 4000
nps_test = 10000
split_fraction = 0.6
rng = Ncm.RNG.seeded_new(None, 123)
sigma1 = math.sqrt(10.0)
sigma2 = 1.0 / sigma1
mu1 = 1.0
def m2lnL_val(va):
n = len(va) - 1
nu = va[0]
return n * nu + nu**2 / 9.0 + np.sum(va[1:] ** 2) / np.exp(nu)
```
```python
interps = []
interps_desc = []
kernel0 = Ncm.StatsDistKernelST.new(dim, 1.0)
interp0 = Ncm.StatsDistKDE.new(kernel0, Ncm.StatsDistCV.NONE)
interps.append(interp0)
interps_desc.append("Interp-KDE:Cauchy")
kernel1 = Ncm.StatsDistKernelST.new(dim, 3.0)
interp1 = Ncm.StatsDistKDE.new(kernel1, Ncm.StatsDistCV.NONE)
interps.append(interp1)
interps_desc.append("Interp-KDE:ST3")
kernel2 = Ncm.StatsDistKernelGauss.new(dim)
interp2 = Ncm.StatsDistKDE.new(kernel2, Ncm.StatsDistCV.NONE)
interps.append(interp2)
interps_desc.append("Interp-KDE:Gauss")
kernel3 = Ncm.StatsDistKernelST.new(dim, 1.0)
interp3 = Ncm.StatsDistVKDE.new(kernel3, Ncm.StatsDistCV.NONE)
interps.append(interp3)
interps_desc.append("Interp-VKDE:Cauchy")
kernel4 = Ncm.StatsDistKernelST.new(dim, 3.0)
interp4 = Ncm.StatsDistVKDE.new(kernel4, Ncm.StatsDistCV.NONE)
interps.append(interp4)
interps_desc.append("Interp-VKDE:ST3")
kernel5 = Ncm.StatsDistKernelGauss.new(dim)
interp5 = Ncm.StatsDistVKDE.new(kernel5, Ncm.StatsDistCV.NONE)
interps.append(interp5)
interps_desc.append("Interp-VKDE:Gauss")
```
```python
for interp in interps:
interp.reset()
theta_train = [] # Training set variables
m2lnp_train = [] # Training set m2lnp
for i in range(nps):
nu = rng.gaussian_gen(0.0, 3.0)
sigma = np.exp(0.5 * nu)
theta_i = [rng.gaussian_gen(0.0, sigma) for l in range(1, dim)]
theta_i.insert(0, nu)
theta_i = np.array(theta_i)
m2lnp_i = m2lnL_val(theta_i)
theta_train.append(theta_i)
m2lnp_train.append(m2lnp_i)
theta_v_i = Ncm.Vector.new_array(theta_i)
for interp in interps:
interp.add_obs(theta_v_i)
theta_train = np.array(theta_train)
m2lnp_train = np.array(m2lnp_train)
m2lnp_train_v = Ncm.Vector.new_array(m2lnp_train)
theta_test = [] # Test set variables
m2lnp_test = [] # Test set m2lnp
for i in range(nps_test):
nu = rng.gaussian_gen(0.0, 3.0)
sigma = np.exp(0.5 * nu)
theta_i = [rng.gaussian_gen(0.0, sigma) for l in range(1, dim)]
theta_i.insert(0, nu)
theta_i = np.array(theta_i)
m2lnp_i = m2lnL_val(theta_i)
theta_test.append(theta_i)
m2lnp_test.append(m2lnp_i)
theta_v_i = Ncm.Vector.new_array(theta_i)
theta_test = np.array(theta_test)
m2lnp_test = np.array(m2lnp_test)
```
```python
ml = max([len(interp_desc) for interp_desc in interps_desc])
for interp, interp_desc in zip(interps, interps_desc):
interp.set_cv_type(Ncm.StatsDistCV.SPLIT_NOFIT)
interp.set_split_frac(split_fraction)
interp.set_print_fit(False)
# interp.prepare_interp(m2lnp_train_v)
interp.prepare()
calib_os = interp.get_over_smooth()
print(
f"Calibrate {interp_desc:<{ml}} interpolation "
f"with os = {calib_os:15.8g} and rnorm = {interp.get_rnorm():15.8g}"
)
```
```python
m2lnp_interps = []
for interp in interps:
m2lnp_interp = []
for theta in theta_test:
m2lnp_interp.append(interp.eval_m2lnp(Ncm.Vector.new_array(theta)))
m2lnp_interp = np.array(m2lnp_interp)
m2lnp_interps.append(m2lnp_interp)
```
```python
set_rc_params_article(ncol=2, nrows=2)
plotn = 250
vmin = 1.0e-16
gs = mpl.gridspec.GridSpec(3, 3, wspace=0.0, hspace=0.4, height_ratios=[1, 1, 2])
fig = plt.figure()
axa = []
axa.append(fig.add_subplot(gs[0, 0]))
axa.append(fig.add_subplot(gs[0, 1], sharex=axa[0], sharey=axa[0]))
axa.append(fig.add_subplot(gs[0, 2], sharex=axa[0], sharey=axa[0]))
axa.append(fig.add_subplot(gs[1, 0], sharex=axa[0], sharey=axa[0]))
axa.append(fig.add_subplot(gs[1, 1], sharex=axa[0], sharey=axa[0]))
axa.append(fig.add_subplot(gs[1, 2], sharex=axa[0], sharey=axa[0]))
axT = plt.subplot(gs[2, :], sharex=axa[0], sharey=axa[0])
nu_a = np.linspace(-17.0, -5.0, plotn)
x1_a = np.linspace(-0.5, 0.5, plotn)
for interp, interp_desc, ax0 in zip(interps, interps_desc, axa):
z = np.array(
[
interp.eval_m2lnp(
Ncm.Vector.new_array(
[0.0 if i > 2 else nu if i == 0 else x1 for i in range(dim)]
)
)
for x1 in x1_a
for nu in nu_a
]
)
plot_m2lnp(nu_a, x1_a, z, ax0, plotn=plotn, vmin=vmin)
for i in range(0, interp.get_n_kernels() // 10):
y_i, cov_i, n_i, w_i = interp.get_Ki(i)
mu = y_i.dup_array()
cov = np.array([[cov_i.get(i, j) for j in range(2)] for i in range(2)])
confidence_ellipse(mu, cov, ax0, n_std=4, edgecolor="red", lw=0.1)
ax0.set_title(interp_desc)
ax0.set_xlabel("$\\nu$")
for i, ax in enumerate(axa):
if i == 0 or i == 3:
ax.set_ylabel("$x_1$")
else:
plt.setp(ax.get_yticklabels(), visible=False)
zT = np.array(
[
m2lnL_val(
np.array([0.0 if i > 2 else nu if i == 0 else x1 for i in range(dim)])
)
for x1 in x1_a
for nu in nu_a
]
)
img = plot_m2lnp(nu_a, x1_a, zT, axT, plotn=plotn, vmin=vmin)
axT.set_title(f"True Funnel{dim}")
axT.set_xlabel("$\\nu$")
axT.set_ylabel("$x_1$")
fig.colorbar(img, format=LogFormatterMathtext())
plt.savefig(f"funnel{dim}_kernel_interp.pdf", bbox_inches="tight")
pass
```
```python
table = [
[
"Interpolation",
r"|\tilde{\pi}/\pi-1| < 20%",
r"mean $\alpha$",
"Split Fraction",
"Over-Smooth",
]
]
log_diff_array = []
log_prob_array = []
for interp, interp_desc, m2lnp_interp in zip(interps, interps_desc, m2lnp_interps):
p_test = np.exp(-0.5 * m2lnp_test)
p_interp = np.exp(-0.5 * m2lnp_interp)
log_diff = -0.5 * (m2lnp_interp - m2lnp_test)
rel_diff = np.abs(np.expm1(log_diff))
log_prob = -0.5 * (
(m2lnp_interp[0::2] - m2lnp_test[0::2])
- (m2lnp_interp[1::2] - m2lnp_test[1::2])
)
log_prob = np.clip(log_prob, np.log(1.0e-14), 0.0)
prob = np.exp(log_prob)
log_diff_array.append(np.clip(log_diff, np.log(1.0e-3), np.log(1.0e3)))
log_prob_array.append(log_prob)
qp = [0.05, 0.5]
q_rel_diff = np.quantile(rel_diff, qp) * 100.0
q_prob = np.quantile(prob, qp) * 100.0
line = [interp_desc]
line.append(f"{sum(rel_diff<0.3)*100.0/len(rel_diff):.0f}%")
line.append(f"{100.0*np.mean(prob):.0f}%")
line.append(f"{split_fraction * 100.0:.0f}%")
line.append(f"{interp.get_over_smooth():.2f}")
table.append(line)
display(HTML(tabulate.tabulate(table, tablefmt="html")))
```
```python
set_rc_params_article(ncol=1, nrows=2)
gs = mpl.gridspec.GridSpec(2, 1, hspace=0.0)
fig = plt.figure()
axa = []
axa.append(fig.add_subplot(gs[0, 0]))
axa.append(fig.add_subplot(gs[1, 0], sharex=axa[0]))
p_test_2 = p_test[::2]
index_array_prob = np.argsort(p_test_2)
x_prob = np.log(p_test_2[index_array_prob])
index_array_rel_diff = np.argsort(p_test)
x_rel_diff = np.log(p_test[index_array_rel_diff])
lstyles = [
("k", "-", "o"),
("k", "--", "v"),
("k", ":", "x"),
("b", "-", "o"),
("b", "--", "v"),
("b", ":", "x"),
]
bins = np.logspace(-4, 0, 7)
for desc, log_diff, log_prob, lstyle in zip(
interps_desc, log_diff_array, log_prob_array, lstyles
):
s, edges, _ = binned_statistic(
np.exp(x_rel_diff),
np.abs(np.expm1(log_diff[index_array_rel_diff])),
statistic=lambda x: 100.0 * sum(x < 0.20) / len(x),
bins=bins,
)
axa[0].hlines(
s,
edges[:-1],
edges[1:],
color=lstyle[0],
linestyle=lstyle[1],
label=desc,
lw=0.5,
)
s, edges, _ = binned_statistic(
np.exp(x_prob),
np.exp(log_prob[index_array_prob]),
statistic=lambda x: 100.0 * sum(x) / len(x),
bins=bins,
)
axa[1].hlines(
s,
edges[:-1],
edges[1:],
color=lstyle[0],
linestyle=lstyle[1],
label=desc,
lw=0.5,
)
plt.setp(axa[0].get_xticklabels(), visible=False)
axa[0].yaxis.set_major_formatter(mtick.PercentFormatter())
axa[1].yaxis.set_major_formatter(mtick.PercentFormatter())
axa[0].set_xscale("log")
axa[1].set_xscale("log")
axa[0].legend(loc="upper left")
axa[0].set_ylabel("$|\\tilde\\pi/\\pi-1| < 0.2$")
axa[1].set_ylabel("mean $\\alpha$")
axa[1].set_xlabel("$\\pi$")
plt.savefig(f"funnel{dim}_kernel_err_acpr.pdf", bbox_inches="tight")
pass
```
|
NumCosmoREPO_NAMENumCosmoPATH_START.@NumCosmo_extracted@NumCosmo-master@notebooks@stats_dist_tests@stats_dist_funnel.ipynb@.PATH_END.py
|
{
"filename": "2023_01_26_045501_2882cd2df464_create_migration_index.py",
"repo_name": "PrefectHQ/prefect",
"repo_path": "prefect_extracted/prefect-main/src/prefect/server/database/_migrations/versions/postgresql/2023_01_26_045501_2882cd2df464_create_migration_index.py",
"type": "Python"
}
|
"""Adds a helper index for the artifact data migration
Revision ID: 2882cd2df464
Revises: 2882cd2df463
Create Date: 2023-01-26 04:55:01.358638
"""
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision = "2882cd2df464"
down_revision = "2882cd2df463"
branch_labels = None
depends_on = None
def upgrade():
with op.batch_alter_table("flow_run_state", schema=None) as batch_op:
batch_op.add_column(sa.Column("has_data", sa.Boolean))
batch_op.create_index(
batch_op.f("ix_flow_run_state__has_data"),
["has_data"],
unique=False,
)
with op.batch_alter_table("task_run_state", schema=None) as batch_op:
batch_op.add_column(sa.Column("has_data", sa.Boolean))
batch_op.create_index(
batch_op.f("ix_task_run_state__has_data"),
["has_data"],
unique=False,
)
def populate_flow_has_data_in_batches(batch_size):
return f"""
UPDATE flow_run_state
SET has_data = (data IS NOT NULL AND data != 'null')
WHERE flow_run_state.id in (SELECT id FROM flow_run_state WHERE (has_data IS NULL) LIMIT {batch_size});
"""
def populate_task_has_data_in_batches(batch_size):
return f"""
UPDATE task_run_state
SET has_data = (data IS NOT NULL AND data != 'null')
WHERE task_run_state.id in (SELECT id FROM task_run_state WHERE (has_data IS NULL) LIMIT {batch_size});
"""
migration_statements = [
populate_flow_has_data_in_batches,
populate_task_has_data_in_batches,
]
with op.get_context().autocommit_block():
conn = op.get_bind()
for query in migration_statements:
batch_size = 500
while True:
# execute until we've updated task_run_state_id and artifact_data
# autocommit mode will commit each time `execute` is called
sql_stmt = sa.text(query(batch_size))
result = conn.execute(sql_stmt)
if result.rowcount < batch_size:
break
def downgrade():
with op.batch_alter_table("task_run_state", schema=None) as batch_op:
batch_op.drop_index(batch_op.f("ix_task_run_state__has_data"))
batch_op.drop_column("has_data")
with op.batch_alter_table("flow_run_state", schema=None) as batch_op:
batch_op.drop_index(batch_op.f("ix_flow_run_state__has_data"))
batch_op.drop_column("has_data")
|
PrefectHQREPO_NAMEprefectPATH_START.@prefect_extracted@prefect-main@src@prefect@server@database@_migrations@versions@postgresql@2023_01_26_045501_2882cd2df464_create_migration_index.py@.PATH_END.py
|
{
"filename": "array_test.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/ops/parallel_for/array_test.py",
"type": "Python"
}
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for vectorization of array kernels."""
from tensorflow.python.eager import backprop
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import array_ops_stack
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import tensor_array_grad # pylint: disable=unused-import
from tensorflow.python.ops.parallel_for import control_flow_ops as pfor_control_flow_ops
from tensorflow.python.ops.parallel_for.test_util import PForTestCase
from tensorflow.python.platform import test
@test_util.with_eager_op_as_function
@test_util.run_all_in_graph_and_eager_modes
class ArrayTest(PForTestCase):
def test_gather(self):
x = random_ops.random_uniform([3, 3, 3, 3])
x2 = array_ops.placeholder_with_default(x, shape=None) # Has dynamic shape.
def loop_fn(i):
outputs = []
x_i = array_ops.gather(x, i)
for y in [x, x2, x_i]:
for axis in [0, 2, -1]:
outputs.append(array_ops.gather(y, 2, axis=axis))
outputs.append(
array_ops.gather(y, math_ops.cast(2, dtypes.int64), axis=axis))
outputs.append(
array_ops.gather(y, 2, axis=math_ops.cast(axis, dtypes.int64)))
outputs.append(
array_ops.gather(y, math_ops.cast(i, dtypes.int64), axis=axis))
outputs.append(array_ops.gather(y, [i], axis=axis))
outputs.append(array_ops.gather(y, [i, 2], axis=axis))
outputs.append(array_ops.gather(y, [[2, i], [i, 1]], axis=axis))
outputs.append(array_ops.gather(y, [0, 1, 2], axis=1, batch_dims=1))
outputs.append(array_ops.gather(y, [i, 1, 2], axis=2, batch_dims=1))
outputs.append(array_ops.gather(y, [[2, i], [i, 1], [2, 1]],
axis=-1, batch_dims=1))
outputs.append(
array_ops.gather(y, [[0, 1, 2]] * 3, axis=2, batch_dims=2))
outputs.append(array_ops.gather(y, [0, 1, 2], axis=1, batch_dims=-1))
outputs.append(
array_ops.gather(y, [[0, 1, 2]] * 3, axis=2, batch_dims=-2))
return outputs
self._test_loop_fn(loop_fn, 3)
def test_gather_nd(self):
x = random_ops.random_uniform([3, 3, 3])
def loop_fn(i):
outputs = []
x_i = array_ops.gather(x, i)
outputs.append(array_ops.gather_nd(x_i, [0], batch_dims=0))
outputs.append(array_ops.gather_nd(x_i, [i], batch_dims=0))
outputs.append(array_ops.gather_nd(x_i, [[i], [i], [i]], batch_dims=1))
return outputs
self._test_loop_fn(loop_fn, 3)
@test_util.run_v2_only
def test_gather_pfor_grad(self):
x = array_ops.zeros([1, 2])
with backprop.GradientTape() as tape:
tape.watch(x)
r = pfor_control_flow_ops.vectorized_map(
lambda t: array_ops.gather(x, t, axis=-1), math_ops.range(2))
self.assertAllClose([[1., 1.]], tape.gradient(r, x))
def test_shape(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x_i = array_ops.gather(x, i)
return array_ops.shape(x_i), array_ops.shape(x_i, out_type=dtypes.int64)
self._test_loop_fn(loop_fn, 3)
def test_size(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x_i = array_ops.gather(x, i)
return array_ops.size(x_i), array_ops.size(x_i, out_type=dtypes.int64)
self._test_loop_fn(loop_fn, 3)
def test_rank(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x_i = array_ops.gather(x, i)
return array_ops.rank(x_i)
self._test_loop_fn(loop_fn, 3)
def test_shape_n(self):
x = random_ops.random_uniform([3, 2, 3])
y = random_ops.random_uniform([3])
def loop_fn(i):
x_i = array_ops.gather(x, i)
y_i = array_ops.gather(y, i)
return array_ops.shape_n([x_i, x, y,
y_i]), array_ops.shape_n([x_i, x, y, y_i],
out_type=dtypes.int64)
self._test_loop_fn(loop_fn, 3)
def test_reshape(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.reshape(x1, [-1]), array_ops.reshape(x1, [1, 3, 1, -1])
self._test_loop_fn(loop_fn, 3)
def test_fill(self):
def loop_fn(i):
return array_ops.fill((2, 3), i)
self._test_loop_fn(loop_fn, 3)
def test_broadcast_to(self):
x = random_ops.random_uniform([3, 2, 1, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return (array_ops.broadcast_to(x1, [2, 2, 3]),
array_ops.broadcast_to(x1, [1, 2, 1, 3]))
self._test_loop_fn(loop_fn, 3)
def test_expand_dims(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return [
array_ops.expand_dims(x1, axis=-1),
array_ops.expand_dims(x1, axis=1),
array_ops.expand_dims(
x1, axis=constant_op.constant(1, dtype=dtypes.int64))
]
self._test_loop_fn(loop_fn, 3)
def test_one_hot(self):
indices = random_ops.random_uniform([3, 2, 3],
minval=0,
maxval=4,
dtype=dtypes.int32)
def loop_fn(i):
indices_i = array_ops.gather(indices, i)
return (array_ops.one_hot(indices_i, depth=4, on_value=2., off_value=-2.),
array_ops.one_hot(indices_i, depth=4, axis=1))
self._test_loop_fn(loop_fn, 3)
def test_searchsorted(self):
sorted_inputs = math_ops.cumsum(
random_ops.random_uniform([3, 2, 4]), axis=-1)
values = random_ops.random_uniform([2, 3], minval=-1, maxval=4.5)
def loop_fn(i):
inputs_i = array_ops.gather(sorted_inputs, i)
return [
array_ops.searchsorted(
inputs_i, values, out_type=dtypes.int32,
side="left"), # creates LowerBound op.
array_ops.searchsorted(
inputs_i, values, out_type=dtypes.int64, side="right")
] # creates UpperBound op.
self._test_loop_fn(loop_fn, 3)
def test_slice(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.slice(x1, begin=(0, 1), size=(2, 1))
self._test_loop_fn(loop_fn, 3)
def test_slice_loop_variant_begin(self):
x = random_ops.random_uniform([3, 2, 5, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.slice(x1, begin=(0, 2 - i, i), size=(-1, 2, 1))
self._test_loop_fn(loop_fn, 3)
def test_tile(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.tile(x1, [2, 1])
self._test_loop_fn(loop_fn, 3)
def test_tile_loop_dependent(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.tile(x1, [i, 1])
with self.assertRaisesRegex(ValueError, "expected to be loop invariant"):
pfor_control_flow_ops.pfor(loop_fn, 2, fallback_to_while_loop=False)
def test_pack(self):
x = random_ops.random_uniform([3, 2, 3])
y = random_ops.random_uniform([2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops_stack.stack([x1, y], axis=-1)
self._test_loop_fn(loop_fn, 1)
def test_unpack(self):
x = random_ops.random_uniform([3, 2, 3, 4])
def loop_fn(i):
x_i = array_ops.gather(x, i)
return array_ops_stack.unstack(
x_i, 4, axis=-1), array_ops_stack.unstack(
x_i, 3, axis=1)
self._test_loop_fn(loop_fn, 3)
def test_pad(self):
x = random_ops.random_uniform([3, 2, 3])
padding = constant_op.constant([[1, 2], [3, 4]])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.pad(x1, padding, mode="CONSTANT")
self._test_loop_fn(loop_fn, 3)
def test_pad_v2(self):
x = random_ops.random_uniform([3, 2, 3])
padding = constant_op.constant([[1, 2], [3, 4]])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.pad_v2(x1, padding, mode="CONSTANT")
self._test_loop_fn(loop_fn, 3)
def test_split(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.split(x1, 2, axis=0), array_ops.split(x1, 3, axis=-1)
self._test_loop_fn(loop_fn, 3)
def test_split_v(self):
x = random_ops.random_uniform([3, 6, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return (array_ops.split(x1, [2, 1, 3],
axis=0), array_ops.split(x1, [3], axis=-1))
self._test_loop_fn(loop_fn, 3)
def test_squeeze(self):
x = random_ops.random_uniform([5, 1, 2, 1])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return (array_ops.squeeze(x1, axis=0), array_ops.squeeze(x1, axis=-1),
array_ops.squeeze(x1))
self._test_loop_fn(loop_fn, 3)
def test_reverse(self):
x = random_ops.random_uniform([3, 4, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return (array_ops.reverse(x1, axis=[0]),
array_ops.reverse(x1, axis=[-1]),
array_ops.reverse(x1, axis=[1, -1]))
self._test_loop_fn(loop_fn, 3)
def test_transpose(self):
x = random_ops.random_uniform([3, 2, 3, 4])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.transpose(x1, [2, 1, 0])
self._test_loop_fn(loop_fn, 3)
def test_top_k(self):
x = random_ops.random_uniform([3, 2, 3, 4])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return nn.top_k(x1, k=2)
self._test_loop_fn(loop_fn, 3)
def test_conjugate_transpose(self):
x = math_ops.complex(
random_ops.random_uniform([3, 2, 3, 4]),
random_ops.random_uniform([3, 2, 3, 4]))
def loop_fn(i):
x_i = array_ops.gather(x, i)
return array_ops.conjugate_transpose(x_i, [2, 1, 0])
self._test_loop_fn(loop_fn, 3)
def test_zeros_like(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
z = array_ops.zeros_like(x1),
return z, z + x1
self._test_loop_fn(loop_fn, 3)
def test_ones_like(self):
x = random_ops.random_uniform([3, 2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
z = array_ops.ones_like(x1),
return z, z + x1
self._test_loop_fn(loop_fn, 3)
def test_concat_v2(self):
x = random_ops.random_uniform([3, 2, 3])
y = random_ops.random_uniform([2, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return [
array_ops.concat([x1, x1, y], axis=0),
array_ops.concat([x1, x1, y], axis=-1),
array_ops.concat([x1, x1, y],
axis=constant_op.constant(0, dtype=dtypes.int64))
]
self._test_loop_fn(loop_fn, 3)
def test_unary_cwise_ops(self):
for op in [array_ops.identity, array_ops.stop_gradient]:
with backprop.GradientTape(persistent=True) as g:
x = random_ops.random_uniform([3, 5])
g.watch(x)
# pylint: disable=cell-var-from-loop
def loop_fn(i):
with g:
x1 = array_ops.gather(x, i)
y = op(x1) + x1
loss = nn.l2_loss(y)
return op(x), y, g.gradient(loss, x1)
# pylint: enable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3)
def test_identity_n(self):
x = random_ops.random_uniform([3, 4])
def loop_fn(i):
return array_ops.identity_n([x, array_ops.gather(x, i)])
self._test_loop_fn(loop_fn, 3)
def test_matrix_band_part(self):
x = random_ops.random_uniform([3, 4, 2, 2])
for num_lower, num_upper in ((0, -1), (-1, 0), (1, 1)):
# pylint: disable=cell-var-from-loop
def loop_fn(i):
return array_ops.matrix_band_part(
array_ops.gather(x, i), num_lower=num_lower, num_upper=num_upper)
# pylint: enable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3)
def test_matrix_diag(self):
x = random_ops.random_uniform([3, 2, 4])
def loop_fn(i):
diagonal = array_ops.gather(x, i)
return array_ops.matrix_diag(
diagonal, k=(0, 1), num_rows=4, num_cols=5, align="RIGHT_LEFT")
self._test_loop_fn(loop_fn, 3)
def test_matrix_diag_part(self):
x = random_ops.random_uniform([3, 4, 6])
def loop_fn(i):
input = array_ops.gather(x, i) # pylint: disable=redefined-builtin
return array_ops.matrix_diag_part(
input, k=(-2, 0), padding_value=3, align="RIGHT_LEFT")
self._test_loop_fn(loop_fn, 3)
def test_diag(self):
for x in (random_ops.random_uniform([3, 4]),
random_ops.random_uniform([3, 4, 2])):
# pylint: disable=cell-var-from-loop
def loop_fn(i):
inp = array_ops.gather(x, i)
return array_ops.diag(inp)
# pylint: disable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3)
def test_diag_part(self):
for x in (random_ops.random_uniform([3, 2, 2]),
random_ops.random_uniform([3, 4, 2, 4, 2])):
# pylint: disable=cell-var-from-loop
def loop_fn(i):
inp = array_ops.gather(x, i) # pylint: disable=redefined-builtin
return array_ops.diag_part(inp)
# pylint: disable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3)
def test_matrix_set_diag(self):
matrices = random_ops.random_uniform([3, 4, 4])
diags = random_ops.random_uniform([3, 4])
bands = random_ops.random_uniform([3, 3, 4])
def loop_fn(i):
matrix_i = array_ops.gather(matrices, i)
diag_i = array_ops.gather(diags, i)
results = [
array_ops.matrix_set_diag(matrix_i, diag_i),
array_ops.matrix_set_diag(matrices[0, ...], diag_i),
array_ops.matrix_set_diag(matrix_i, diags[0, ...]),
]
k = (-1, 1)
band_i = array_ops.gather(bands, i)
for align in ["RIGHT_LEFT", "LEFT_RIGHT"]:
results.extend([
array_ops.matrix_set_diag(matrix_i, band_i, k=k, align=align),
array_ops.matrix_set_diag(
matrices[0, ...], band_i, k=k, align=align),
array_ops.matrix_set_diag(
matrix_i, bands[0, ...], k=k, align=align)
])
return results
self._test_loop_fn(loop_fn, 3)
def test_strided_slice(self):
with backprop.GradientTape(persistent=True) as g:
x = random_ops.random_uniform([3, 3, 4, 4, 2, 2, 2])
g.watch(x)
def loop_fn(i):
with g:
x_i = array_ops.gather(x, i)
y = x_i[:2, ::2, 1::3, ..., array_ops.newaxis, 1]
loss = nn.l2_loss(y)
return y, g.gradient(loss, x_i)
self._test_loop_fn(loop_fn, 3)
def test_strided_slice_loop_variant(self):
x = random_ops.random_uniform([3, 3, 4, 4, 2, 2, 2])
def loop_fn(i):
x_i = array_ops.gather(x, i)
return x_i[i:i+1, ...]
# Test the fallback to while loop for a ConversionNotImplementedError is
# handled.
self._test_loop_fn(loop_fn, 3, fallback_to_while_loop=True)
# Without fallback, ValueError is thrown.
with self.assertRaisesRegex(ValueError, "expected to be loop invariant"):
self._test_loop_fn(loop_fn, 3, fallback_to_while_loop=False)
def test_depth_to_space(self):
x = random_ops.random_uniform([2, 3, 2, 2, 12])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.depth_to_space(x1, 2, data_format="NHWC")
self._test_loop_fn(loop_fn, 2)
def test_space_to_depth(self):
x = random_ops.random_uniform([2, 3, 12, 12, 3])
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.space_to_depth(x1, 2, data_format="NHWC")
self._test_loop_fn(loop_fn, 2)
def test_batch_to_space_nd(self):
x = random_ops.random_uniform([7, 5 * 2 * 3, 2, 2, 3, 2])
block_shapes = [2, 3]
crops = [[1, 2], [1, 0]]
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.batch_to_space_nd(x1, block_shapes, crops)
self._test_loop_fn(loop_fn, 7)
def test_space_to_batch_nd(self):
x = random_ops.random_uniform([7, 5, 2 * 2 - 3, 2 * 3 - 1, 3, 2])
block_shapes = [2, 3]
paddings = [[1, 2], [1, 0]]
def loop_fn(i):
x1 = array_ops.gather(x, i)
return array_ops.space_to_batch_nd(x1, block_shapes, paddings)
self._test_loop_fn(loop_fn, 7)
def test_check_numerics(self):
x = random_ops.random_uniform([2, 3, 4])
def loop_fn(i):
x_i = array_ops.gather(x, i)
return array_ops.check_numerics(x_i, "test_message")
self._test_loop_fn(loop_fn, 2)
if __name__ == "__main__":
test.main()
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@ops@parallel_for@array_test.py@.PATH_END.py
|
{
"filename": "mockup.py",
"repo_name": "pyDANDIA/pyDANDIA",
"repo_path": "pyDANDIA_extracted/pyDANDIA-main/trials/db/mockup.py",
"type": "Python"
}
|
"""
Produce some mockup data to fill the database with test data.
"""
import random
import os
from os import getcwd, path
from sys import path as systempath
cwd = getcwd()
systempath.append(path.join(cwd, '../'))
from phot_db import *
def fill_with_fake_entries():
conn = get_connection(database_file_path)
ensure_tables(conn)
create_stars_if_necessary(conn, n=5)
create_reference_images_if_necessary(conn, n=2)
create_exposures_if_necessary(conn, n=10)
create_phot_if_necessary(conn, n=10)
conn.commit()
conn.close()
def create_stars_if_necessary(conn, n):
"""stuffs n random stars into the stars table if there are fewer than n rows in
there.
(warning: minimum star id is 1!)
!!!WARNING!!! Only use this for testing purposes.
In normal operations you want to populate the star list from the star catalog
returned from starfinder.
"""
if list(conn.execute("SELECT COUNT(*) FROM stars"))[0][0]<n:
for i in range(n):
feed_to_table(conn, "stars", [
"ra",
"dec",
], [
random.normalvariate(266.4, 15),
random.normalvariate(-29, 15),
])
def create_reference_images_if_necessary(conn, n):
"""stuffs n random reference images into the reference_images table if there are fewer than n rows in
there.
(warning: minimum exposure id is 1!)
!!!WARNING!!! Only use this for testing purposes.
In normal operations the pipeline will enter the reference_images.
"""
if list(conn.execute("SELECT COUNT(*) FROM reference_images"))[0][0]<n:
for i in range(n):
feed_to_table(conn, "reference_images", [
"telescope_id",
"instrument_id",
"filter_id",
"refimg_ellipticity",
"refimg_ellipticity_err",
"refimg_fwhm",
"refimg_fwhm_err",
"slope",
"slope_err",
"intercept",
"intercept_err",
"refimg_name",
], [
random.choice(telescopes.keys()),
random.choice(instruments.keys()),
random.choice(filters.keys()),
random.uniform(0.0,0.2),
random.uniform(0.005,0.01),
random.uniform(0.8,1.1),
random.uniform(0.005,0.01),
random.uniform(0.1,1.0),
random.uniform(0.01,0.09),
random.uniform(1,10),
random.uniform(0.01,0.8),
"ref_image_"+str(i),
])
def create_exposures_if_necessary(conn, n):
"""stuffs n random exposures into the exposures table if there are fewer than n rows in
there.
(warning: minimum exposure id is 1!)
!!!WARNING!!! Only use this for testing purposes.
In normal operations the pipeline will enter the exposures.
"""
if list(conn.execute("SELECT COUNT(*) FROM exposures"))[0][0]<n:
jd_start = 2458100.5
for i in range(n):
feed_to_table(conn, "exposures", [
"reference_image",
"jd",
"exposure_ellipticity",
"exposure_ellipticity_err",
"exposure_fwhm",
"exposure_fwhm_err",
"airmass",
"exposure_time",
"moon_phase",
"moon_separation",
"delta_x",
"delta_y",
"exposure_name",
], [
random.randint(1,2),
jd_start+1.0+random.uniform(0.1,0.6),
random.uniform(0.0,0.5),
random.uniform(0.01,0.05),
random.uniform(0.8,5.0),
random.uniform(0.01,0.05),
random.uniform(1.0,4.0),
random.randint(100,300),
random.uniform(0.0,1.0),
random.uniform(1,60),
random.uniform(0.01,1.0),
random.uniform(0.01,1.0),
"my_image_"+str(i),
])
def create_phot_if_necessary(conn, n):
"""stuffs n random photometry points into the phot table if there are fewer than n rows in
there.
(warning: minimum phot id is 1!)
!!!WARNING!!! Only use this for testing purposes.
In normal operations the pipeline will enter these points.
"""
if list(conn.execute("SELECT COUNT(*) FROM phot"))[0][0]<n:
for i in range(n):
feed_to_table(conn, "phot", [
"exposure_id",
"star_id",
"reference_flux",
"reference_flux_err",
"reference_mag",
"reference_mag_err",
"diff_flux",
"diff_flux_err",
"magnitude",
"magnitude_err",
"phot_scale_factor",
"phot_scale_factor_err",
"local_background",
"local_background_err",
"residual_x",
"residual_y",
], [
random.randint(1,10),
random.randint(1,5),
random.uniform(5000,60000),
random.uniform(200,8000),
random.uniform(14,19),
random.uniform(0.01,0.05),
random.uniform(-100,100),
random.uniform(0.01,0.05),
random.uniform(14,19),
random.uniform(0.01,0.05),
random.uniform(0.1,1.0),
random.uniform(0.01,0.05),
random.uniform(500,2000),
random.uniform(100,500),
random.uniform(0.01,0.05),
random.uniform(0.01,0.05),
])
#def get_test_data():
# """returns fake data for test and demo purposes.
#
# It's an exposure row and a sequence of photometry records.
# """
# return {
# 'jd': 2540000+random.random()*500,
# 'telescope_id': ['fred', 'joe'][random.randint(0, 1)],
# 'instrument_id': ['cam1','cam2'][random.randint(0, 1)],
# 'filter_id': ['filt1','filt2'][random.randint(0, 1)],
# 'airmass': random.random()+0.1,
# 'name': '/foo/bar/baz%0d.fz'%random.randint(0, 100000000),
# 'fwhm' : random.random()+0.1,
# 'fwhm_err' : random.random()+0.1,
# 'ellipticity' : random.random()+0.1,
# 'ellipticity_err' : random.random()+0.1,
# 'exposure_time' : random.randint(0, 1),
# 'moon_phase' : random.random()+0.1,
# 'moon_separation' : random.random()*60+0.1
# }, [{
# 'star_id': i+1,
# 'diff_flux': random.random()*10,
# 'diff_flux_err': random.random()*0.1,
# 'phot_scale_factor': 0.14+random.random()*0.1,
# 'phot_scale_factor_error': 0.014+random.random()*0.01,
# 'magnitude': random.uniform(14,19),
# 'magnitude_err': 0.014+random.random()*0.01,
# 'local_background': random.random(),
# 'local_background_error': random.random()/100,
# 'delta_x': 0.1*random.random(),
# 'delta_y': 0.1*random.random(),
# 'residual_x': 0.01*random.random(),
# 'residual_y': 0.01*random.random(),
# } for i in range(random.randint(5, 15))]
|
pyDANDIAREPO_NAMEpyDANDIAPATH_START.@pyDANDIA_extracted@pyDANDIA-main@trials@db@mockup.py@.PATH_END.py
|
{
"filename": "testsim.py",
"repo_name": "GeminiDRSoftware/GHOSTDR",
"repo_path": "GHOSTDR_extracted/GHOSTDR-master/simulator/testsim.py",
"type": "Python"
}
|
import pyghost.tests
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-cosmics', action='store_false', help="*don't* add in"
" cosmic rays to the generated frames")
parser.add_argument('-crplane', action='store_true', help='output cosmic'
' ray locations as separate fits files')
parser.add_argument('-hpplane', action='store_true', help='output hot pixel'
' locations as separate fits files')
parser.add_argument('-split', action='store_true', help='output frames as'
' individual files instead of bundled together (as a'
' MEF)')
parser.add_argument('-check', action='store_true', help='output additional'
' check files that contain the input spectrum, interpolated'
' onto the pixel grid for each order')
parser.add_argument('-dual_target', action='store_true', help='simulates dual'
' targets in STD res mode (ignored for HIGH res mode)')
# will raise if too few/many args or unsupported options used
args = parser.parse_args()
pyghost.tests.run(**vars(args)) # pylint: disable=star-args
|
GeminiDRSoftwareREPO_NAMEGHOSTDRPATH_START.@GHOSTDR_extracted@GHOSTDR-master@simulator@testsim.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "QEF/q-e",
"repo_path": "q-e_extracted/q-e-master/KCW/examples/example05.1/nspin1/0_dft/README.md",
"type": "Markdown"
}
|
# Run the calculations in the following way:
pw.x < scf.pwi | tee scf.pwo
pw.x < bands.pwi | tee bands.pwo
bands.x < bands.in | tee bands.out
gnuplot plot_bands.gnu
|
QEFREPO_NAMEq-ePATH_START.@q-e_extracted@q-e-master@KCW@examples@example05.1@nspin1@0_dft@README.md@.PATH_END.py
|
{
"filename": "frequencyseries.py",
"repo_name": "gwastro/pycbc",
"repo_path": "pycbc_extracted/pycbc-master/pycbc/types/frequencyseries.py",
"type": "Python"
}
|
# Copyright (C) 2012 Tito Dal Canton, Josh Willis
#
# 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, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""
Provides a class representing a frequency series.
"""
import os as _os
import h5py
from pycbc.types.array import Array, _convert, zeros, _noreal
import lal as _lal
import numpy as _numpy
class FrequencySeries(Array):
"""Models a frequency series consisting of uniformly sampled scalar values.
Parameters
----------
initial_array : array-like
Array containing sampled data.
delta_f : float
Frequency between consecutive samples in Hertz.
epoch : {None, lal.LIGOTimeGPS}, optional
Start time of the associated time domain data in seconds.
dtype : {None, data-type}, optional
Sample data type.
copy : boolean, optional
If True, samples are copied to a new array.
"""
def __init__(self, initial_array, delta_f=None, epoch="", dtype=None, copy=True):
if len(initial_array) < 1:
raise ValueError('initial_array must contain at least one sample.')
if delta_f is None:
try:
delta_f = initial_array.delta_f
except AttributeError:
raise TypeError('must provide either an initial_array with a delta_f attribute, or a value for delta_f')
if not delta_f > 0:
raise ValueError('delta_f must be a positive number')
# We gave a nonsensical default value to epoch so we can test if it's been set.
# If the user passes in an initial_array that has an 'epoch' attribute and doesn't
# pass in a value of epoch, then our new object's epoch comes from initial_array.
# But if the user passed in a value---even 'None'---that will take precedence over
# anything set in initial_array. Finally, if the user passes in something without
# an epoch attribute *and* doesn't pass in a value of epoch, it becomes 'None'
if not isinstance(epoch,_lal.LIGOTimeGPS):
if epoch == "":
if isinstance(initial_array,FrequencySeries):
epoch = initial_array._epoch
else:
epoch = _lal.LIGOTimeGPS(0)
elif epoch is not None:
try:
if isinstance(epoch, _numpy.generic):
# In python3 lal LIGOTimeGPS will not work on numpy
# types as input. A quick google on how to generically
# convert numpy floats/ints to the python equivalent
# https://stackoverflow.com/questions/9452775/
epoch = _lal.LIGOTimeGPS(epoch.item())
else:
epoch = _lal.LIGOTimeGPS(epoch)
except:
raise TypeError('epoch must be either None or a lal.LIGOTimeGPS')
Array.__init__(self, initial_array, dtype=dtype, copy=copy)
self._delta_f = delta_f
self._epoch = epoch
def _return(self, ary):
return FrequencySeries(ary, self._delta_f, epoch=self._epoch, copy=False)
def _typecheck(self, other):
if isinstance(other, FrequencySeries):
try:
_numpy.testing.assert_almost_equal(other._delta_f,
self._delta_f)
except:
raise ValueError('different delta_f')
# consistency of _epoch is not required because we may want
# to combine frequency series estimated at different times
# (e.g. PSD estimation)
def get_delta_f(self):
"""Return frequency between consecutive samples in Hertz.
"""
return self._delta_f
delta_f = property(get_delta_f,
doc="Frequency between consecutive samples in Hertz.")
def get_epoch(self):
"""Return frequency series epoch as a LIGOTimeGPS.
"""
return self._epoch
epoch = property(get_epoch,
doc="Frequency series epoch as a LIGOTimeGPS.")
def get_sample_frequencies(self):
"""Return an Array containing the sample frequencies.
"""
return Array(range(len(self))) * self._delta_f
sample_frequencies = property(get_sample_frequencies,
doc="Array of the sample frequencies.")
def _getslice(self, index):
if index.step is not None:
new_delta_f = self._delta_f * index.step
else:
new_delta_f = self._delta_f
return FrequencySeries(Array._getslice(self, index),
delta_f=new_delta_f,
epoch=self._epoch,
copy=False)
def at_frequency(self, freq):
""" Return the value at the specified frequency
"""
return self[int(freq / self.delta_f)]
@property
def start_time(self):
"""Return the start time of this vector
"""
return self.epoch
@start_time.setter
def start_time(self, time):
""" Set the start time
"""
self._epoch = _lal.LIGOTimeGPS(time)
@property
def end_time(self):
"""Return the end time of this vector
"""
return self.start_time + self.duration
@property
def duration(self):
"""Return the time duration of this vector
"""
return 1.0 / self.delta_f
@property
def delta_t(self):
"""Return the time between samples if this were a time series.
This assume the time series is even in length!
"""
return 1.0 / self.sample_rate
@property
def sample_rate(self):
"""Return the sample rate this would have in the time domain. This
assumes even length time series!
"""
return (len(self) - 1) * self.delta_f * 2.0
def __eq__(self,other):
"""
This is the Python special method invoked whenever the '=='
comparison is used. It will return true if the data of two
frequency series are identical, and all of the numeric meta-data
are identical, irrespective of whether or not the two
instances live in the same memory (for that comparison, the
Python statement 'a is b' should be used instead).
Thus, this method returns 'True' if the types of both 'self'
and 'other' are identical, as well as their lengths, dtypes,
epochs, delta_fs and the data in the arrays, element by element.
It will always do the comparison on the CPU, but will *not* move
either object to the CPU if it is not already there, nor change
the scheme of either object. It is possible to compare a CPU
object to a GPU object, and the comparison should be true if the
data and meta-data of the two objects are the same.
Note in particular that this function returns a single boolean,
and not an array of booleans as Numpy does. If the numpy
behavior is instead desired it can be obtained using the numpy()
method of the PyCBC type to get a numpy instance from each
object, and invoking '==' on those two instances.
Parameters
----------
other: another Python object, that should be tested for equality
with 'self'.
Returns
-------
boolean: 'True' if the types, dtypes, lengths, epochs, delta_fs
and data of the two objects are each identical.
"""
if super(FrequencySeries,self).__eq__(other):
return (self._epoch == other._epoch and self._delta_f == other._delta_f)
else:
return False
def almost_equal_elem(self,other,tol,relative=True,dtol=0.0):
"""
Compare whether two frequency series are almost equal, element
by element.
If the 'relative' parameter is 'True' (the default) then the
'tol' parameter (which must be positive) is interpreted as a
relative tolerance, and the comparison returns 'True' only if
abs(self[i]-other[i]) <= tol*abs(self[i])
for all elements of the series.
If 'relative' is 'False', then 'tol' is an absolute tolerance,
and the comparison is true only if
abs(self[i]-other[i]) <= tol
for all elements of the series.
The method also checks that self.delta_f is within 'dtol' of
other.delta_f; if 'dtol' has its default value of 0 then exact
equality between the two is required.
Other meta-data (type, dtype, length, and epoch) must be exactly
equal. If either object's memory lives on the GPU it will be
copied to the CPU for the comparison, which may be slow. But the
original object itself will not have its memory relocated nor
scheme changed.
Parameters
----------
other: another Python object, that should be tested for
almost-equality with 'self', element-by-element.
tol: a non-negative number, the tolerance, which is interpreted
as either a relative tolerance (the default) or an absolute
tolerance.
relative: A boolean, indicating whether 'tol' should be interpreted
as a relative tolerance (if True, the default if this argument
is omitted) or as an absolute tolerance (if tol is False).
dtol: a non-negative number, the tolerance for delta_f. Like 'tol',
it is interpreted as relative or absolute based on the value of
'relative'. This parameter defaults to zero, enforcing exact
equality between the delta_f values of the two FrequencySeries.
Returns
-------
boolean: 'True' if the data and delta_fs agree within the tolerance,
as interpreted by the 'relative' keyword, and if the types,
lengths, dtypes, and epochs are exactly the same.
"""
# Check that the delta_f tolerance is non-negative; raise an exception
# if needed.
if (dtol < 0.0):
raise ValueError("Tolerance in delta_f cannot be negative")
if super(FrequencySeries,self).almost_equal_elem(other,tol=tol,relative=relative):
if relative:
return (self._epoch == other._epoch and
abs(self._delta_f-other._delta_f) <= dtol*self._delta_f)
else:
return (self._epoch == other._epoch and
abs(self._delta_f-other._delta_f) <= dtol)
else:
return False
def almost_equal_norm(self,other,tol,relative=True,dtol=0.0):
"""
Compare whether two frequency series are almost equal, normwise.
If the 'relative' parameter is 'True' (the default) then the
'tol' parameter (which must be positive) is interpreted as a
relative tolerance, and the comparison returns 'True' only if
abs(norm(self-other)) <= tol*abs(norm(self)).
If 'relative' is 'False', then 'tol' is an absolute tolerance,
and the comparison is true only if
abs(norm(self-other)) <= tol
The method also checks that self.delta_f is within 'dtol' of
other.delta_f; if 'dtol' has its default value of 0 then exact
equality between the two is required.
Other meta-data (type, dtype, length, and epoch) must be exactly
equal. If either object's memory lives on the GPU it will be
copied to the CPU for the comparison, which may be slow. But the
original object itself will not have its memory relocated nor
scheme changed.
Parameters
----------
other: another Python object, that should be tested for
almost-equality with 'self', based on their norms.
tol: a non-negative number, the tolerance, which is interpreted
as either a relative tolerance (the default) or an absolute
tolerance.
relative: A boolean, indicating whether 'tol' should be interpreted
as a relative tolerance (if True, the default if this argument
is omitted) or as an absolute tolerance (if tol is False).
dtol: a non-negative number, the tolerance for delta_f. Like 'tol',
it is interpreted as relative or absolute based on the value of
'relative'. This parameter defaults to zero, enforcing exact
equality between the delta_f values of the two FrequencySeries.
Returns
-------
boolean: 'True' if the data and delta_fs agree within the tolerance,
as interpreted by the 'relative' keyword, and if the types,
lengths, dtypes, and epochs are exactly the same.
"""
# Check that the delta_f tolerance is non-negative; raise an exception
# if needed.
if (dtol < 0.0):
raise ValueError("Tolerance in delta_f cannot be negative")
if super(FrequencySeries,self).almost_equal_norm(other,tol=tol,relative=relative):
if relative:
return (self._epoch == other._epoch and
abs(self._delta_f-other._delta_f) <= dtol*self._delta_f)
else:
return (self._epoch == other._epoch and
abs(self._delta_f-other._delta_f) <= dtol)
else:
return False
@_convert
def lal(self):
"""Produces a LAL frequency series object equivalent to self.
Returns
-------
lal_data : {lal.*FrequencySeries}
LAL frequency series object containing the same data as self.
The actual type depends on the sample's dtype. If the epoch of
self was 'None', the epoch of the returned LAL object will be
LIGOTimeGPS(0,0); otherwise, the same as that of self.
Raises
------
TypeError
If frequency series is stored in GPU memory.
"""
lal_data = None
if self._epoch is None:
ep = _lal.LIGOTimeGPS(0,0)
else:
ep = self._epoch
if self._data.dtype == _numpy.float32:
lal_data = _lal.CreateREAL4FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
elif self._data.dtype == _numpy.float64:
lal_data = _lal.CreateREAL8FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
elif self._data.dtype == _numpy.complex64:
lal_data = _lal.CreateCOMPLEX8FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
elif self._data.dtype == _numpy.complex128:
lal_data = _lal.CreateCOMPLEX16FrequencySeries("",ep,0,self.delta_f,_lal.SecondUnit,len(self))
lal_data.data.data[:] = self.numpy()
return lal_data
def save(self, path, group=None, ifo='P1'):
"""
Save frequency series to a Numpy .npy, hdf, or text file. The first column
contains the sample frequencies, the second contains the values.
In the case of a complex frequency series saved as text, the imaginary
part is written as a third column. When using hdf format, the data is stored
as a single vector, along with relevant attributes.
Parameters
----------
path: string
Destination file path. Must end with either .hdf, .npy or .txt.
group: string
Additional name for internal storage use. Ex. hdf storage uses
this as the key value.
Raises
------
ValueError
If path does not end in .npy or .txt.
"""
ext = _os.path.splitext(path)[1]
if ext == '.npy':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy())).T
_numpy.save(path, output)
elif ext == '.txt':
if self.kind == 'real':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy())).T
elif self.kind == 'complex':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy().real,
self.numpy().imag)).T
_numpy.savetxt(path, output)
elif ext == '.xml' or path.endswith('.xml.gz'):
from pycbc.io.ligolw import make_psd_xmldoc
from ligo.lw import utils
if self.kind != 'real':
raise ValueError('XML only supports real frequency series')
output = self.lal()
output.name = 'psd'
# When writing in this format we must *not* have the 0 values at
# frequencies less than flow. To resolve this we set the first
# non-zero value < flow.
data_lal = output.data.data
first_idx = _numpy.argmax(data_lal>0)
if not first_idx == 0:
data_lal[:first_idx] = data_lal[first_idx]
psddict = {ifo: output}
utils.write_filename(
make_psd_xmldoc(psddict),
path,
compress='auto'
)
elif ext == '.hdf':
key = 'data' if group is None else group
with h5py.File(path, 'a') as f:
ds = f.create_dataset(key, data=self.numpy(),
compression='gzip',
compression_opts=9, shuffle=True)
if self.epoch is not None:
ds.attrs['epoch'] = float(self.epoch)
ds.attrs['delta_f'] = float(self.delta_f)
else:
raise ValueError('Path must end with .npy, .txt, .xml, .xml.gz '
'or .hdf')
def to_frequencyseries(self):
""" Return frequency series """
return self
@_noreal
def to_timeseries(self, delta_t=None):
""" Return the Fourier transform of this time series.
Note that this assumes even length time series!
Parameters
----------
delta_t : {None, float}, optional
The time resolution of the returned series. By default the
resolution is determined by length and delta_f of this frequency
series.
Returns
-------
TimeSeries:
The inverse fourier transform of this frequency series.
"""
from pycbc.fft import ifft
from pycbc.types import TimeSeries, real_same_precision_as
nat_delta_t = 1.0 / ((len(self)-1)*2) / self.delta_f
if not delta_t:
delta_t = nat_delta_t
# add 0.5 to round integer
tlen = int(1.0 / self.delta_f / delta_t + 0.5)
flen = int(tlen / 2 + 1)
if flen < len(self):
raise ValueError("The value of delta_t (%s) would be "
"undersampled. Maximum delta_t "
"is %s." % (delta_t, nat_delta_t))
if not delta_t:
tmp = self
else:
tmp = FrequencySeries(zeros(flen, dtype=self.dtype),
delta_f=self.delta_f, epoch=self.epoch,
copy=False)
tmp[:len(self)] = self[:]
f = TimeSeries(zeros(tlen,
dtype=real_same_precision_as(self)),
delta_t=delta_t, copy=False)
ifft(tmp, f)
f._delta_t = delta_t
return f
@_noreal
def cyclic_time_shift(self, dt):
"""Shift the data and timestamps by a given number of seconds
Shift the data and timestamps in the time domain a given number of
seconds. To just change the time stamps, do ts.start_time += dt.
The time shift may be smaller than the intrinsic sample rate of the data.
Note that data will be cycliclly rotated, so if you shift by 2
seconds, the final 2 seconds of your data will now be at the
beginning of the data set.
Parameters
----------
dt : float
Amount of time to shift the vector.
Returns
-------
data : pycbc.types.FrequencySeries
The time shifted frequency series.
"""
from pycbc.waveform import apply_fseries_time_shift
data = apply_fseries_time_shift(self, dt)
data.start_time = self.start_time - dt
return data
def match(self, other, psd=None,
low_frequency_cutoff=None, high_frequency_cutoff=None):
""" Return the match between the two TimeSeries or FrequencySeries.
Return the match between two waveforms. This is equivalent to the overlap
maximized over time and phase. By default, the other vector will be
resized to match self. Beware, this may remove high frequency content or the
end of the vector.
Parameters
----------
other : TimeSeries or FrequencySeries
The input vector containing a waveform.
psd : Frequency Series
A power spectral density to weight the overlap.
low_frequency_cutoff : {None, float}, optional
The frequency to begin the match.
high_frequency_cutoff : {None, float}, optional
The frequency to stop the match.
index: int
The number of samples to shift to get the match.
Returns
-------
match: float
index: int
The number of samples to shift to get the match.
"""
from pycbc.types import TimeSeries
from pycbc.filter import match
if isinstance(other, TimeSeries):
if other.duration != self.duration:
other = other.copy()
other.resize(int(other.sample_rate * self.duration))
other = other.to_frequencyseries()
if len(other) != len(self):
other = other.copy()
other.resize(len(self))
if psd is not None and len(psd) > len(self):
psd = psd.copy()
psd.resize(len(self))
return match(self, other, psd=psd,
low_frequency_cutoff=low_frequency_cutoff,
high_frequency_cutoff=high_frequency_cutoff)
def plot(self, **kwds):
""" Basic plot of this frequency series
"""
from matplotlib import pyplot
if self.kind == 'real':
plot = pyplot.plot(self.sample_frequencies, self, **kwds)
return plot
elif self.kind == 'complex':
plot1 = pyplot.plot(self.sample_frequencies, self.real(), **kwds)
plot2 = pyplot.plot(self.sample_frequencies, self.imag(), **kwds)
return plot1, plot2
def load_frequencyseries(path, group=None):
"""Load a FrequencySeries from an HDF5, ASCII or Numpy file. The file type
is inferred from the file extension, which must be `.hdf`, `.txt` or
`.npy`.
For ASCII and Numpy files, the first column of the array is assumed to
contain the frequency. If the array has two columns, a real frequency
series is returned. If the array has three columns, the second and third
ones are assumed to contain the real and imaginary parts of a complex
frequency series.
For HDF files, the dataset is assumed to contain the attribute `delta_f`
giving the frequency resolution in Hz. The attribute `epoch`, if present,
is taken as the start GPS time (epoch) of the data in the series.
The default data types will be double precision floating point.
Parameters
----------
path: string
Input file path. Must end with either `.npy`, `.txt` or `.hdf`.
group: string
Additional name for internal storage use. When reading HDF files, this
is the path to the HDF dataset to read.
Raises
------
ValueError
If the path does not end in a supported extension.
For Numpy and ASCII input files, this is also raised if the array
does not have 2 or 3 dimensions.
"""
ext = _os.path.splitext(path)[1]
if ext == '.npy':
data = _numpy.load(path)
elif ext == '.txt':
data = _numpy.loadtxt(path)
elif ext == '.hdf':
key = 'data' if group is None else group
with h5py.File(path, 'r') as f:
data = f[key][:]
delta_f = f[key].attrs['delta_f']
epoch = f[key].attrs['epoch'] if 'epoch' in f[key].attrs else None
series = FrequencySeries(data, delta_f=delta_f, epoch=epoch)
return series
else:
raise ValueError('Path must end with .npy, .hdf, or .txt')
delta_f = (data[-1][0] - data[0][0]) / (len(data) - 1)
if data.ndim == 2:
return FrequencySeries(data[:,1], delta_f=delta_f, epoch=None)
elif data.ndim == 3:
return FrequencySeries(data[:,1] + 1j*data[:,2], delta_f=delta_f,
epoch=None)
raise ValueError('File has %s dimensions, cannot convert to FrequencySeries, \
must be 2 (real) or 3 (complex)' % data.ndim)
|
gwastroREPO_NAMEpycbcPATH_START.@pycbc_extracted@pycbc-master@pycbc@types@frequencyseries.py@.PATH_END.py
|
{
"filename": "_tickvals.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scatterpolargl/marker/colorbar/_tickvals.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickvalsValidator(_plotly_utils.basevalidators.DataArrayValidator):
def __init__(
self,
plotly_name="tickvals",
parent_name="scatterpolargl.marker.colorbar",
**kwargs,
):
super(TickvalsValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scatterpolargl@marker@colorbar@_tickvals.py@.PATH_END.py
|
{
"filename": "_tickformat.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/parcoords/dimension/_tickformat.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TickformatValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="tickformat", parent_name="parcoords.dimension", **kwargs
):
super(TickformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@parcoords@dimension@_tickformat.py@.PATH_END.py
|
{
"filename": "_scattermap.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/template/data/_scattermap.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ScattermapValidator(_plotly_utils.basevalidators.CompoundArrayValidator):
def __init__(
self, plotly_name="scattermap", parent_name="layout.template.data", **kwargs
):
super(ScattermapValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Scattermap"),
data_docs=kwargs.pop(
"data_docs",
"""
""",
),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@template@data@_scattermap.py@.PATH_END.py
|
{
"filename": "tracker.py",
"repo_name": "dmlc/xgboost",
"repo_path": "xgboost_extracted/xgboost-master/python-package/xgboost/tracker.py",
"type": "Python"
}
|
"""Tracker for XGBoost collective."""
import ctypes
import json
import socket
from enum import IntEnum, unique
from typing import Dict, Optional, Union
from .core import _LIB, _check_call, _deprecate_positional_args, make_jcargs
def get_family(addr: str) -> int:
"""Get network family from address."""
return socket.getaddrinfo(addr, None)[0][0]
class RabitTracker:
"""Tracker for the collective used in XGBoost, acting as a coordinator between
workers.
Parameters
----------
n_workers:
The total number of workers in the communication group.
host_ip:
The IP address of the tracker node. XGBoost can try to guess one by probing with
sockets. But it's best to explicitly pass an address.
port:
The port this tracker should listen to. XGBoost can query an available port from
the OS, this configuration is useful for restricted network environments.
sortby:
How to sort the workers for rank assignment. The default is host, but users can
set the `DMLC_TASK_ID` via arguments of :py:meth:`~xgboost.collective.init` and
obtain deterministic rank assignment through sorting by task name. Available
options are:
- host
- task
timeout :
Timeout for constructing (bootstrap) and shutting down the communication group,
doesn't apply to communication when the group is up and running.
The timeout value should take the time of data loading and pre-processing into
account, due to potential lazy execution. By default the Tracker doesn't have
any timeout to avoid pre-mature aborting.
The :py:meth:`.wait_for` method has a different timeout parameter that can stop
the tracker even if the tracker is still being used. A value error is raised
when timeout is reached.
Examples
--------
.. code-block:: python
from xgboost.tracker import RabitTracker
from xgboost import collective as coll
tracker = RabitTracker(host_ip="127.0.0.1", n_workers=2)
tracker.start()
with coll.CommunicatorContext(**tracker.worker_args()):
ret = coll.broadcast("msg", 0)
assert str(ret) == "msg"
"""
@unique
class _SortBy(IntEnum):
HOST = 0
TASK = 1
@_deprecate_positional_args
def __init__( # pylint: disable=too-many-arguments
self,
n_workers: int,
host_ip: Optional[str],
port: int = 0,
*,
sortby: str = "host",
timeout: int = 0,
) -> None:
handle = ctypes.c_void_p()
if sortby not in ("host", "task"):
raise ValueError("Expecting either 'host' or 'task' for sortby.")
if host_ip is not None:
get_family(host_ip) # use python socket to stop early for invalid address
args = make_jcargs(
host=host_ip,
n_workers=n_workers,
port=port,
dmlc_communicator="rabit",
sortby=self._SortBy.HOST if sortby == "host" else self._SortBy.TASK,
timeout=int(timeout),
)
_check_call(_LIB.XGTrackerCreate(args, ctypes.byref(handle)))
self.handle = handle
def free(self) -> None:
"""Internal function for testing."""
if hasattr(self, "handle"):
handle = self.handle
del self.handle
_check_call(_LIB.XGTrackerFree(handle))
def __del__(self) -> None:
self.free()
def start(self) -> None:
"""Start the tracker. Once started, the client still need to call the
:py:meth:`wait_for` method in order to wait for it to finish (think of it as a
thread).
"""
_check_call(_LIB.XGTrackerRun(self.handle, make_jcargs()))
def wait_for(self, timeout: Optional[int] = None) -> None:
"""Wait for the tracker to finish all the work and shutdown. When timeout is
reached, a value error is raised. By default we don't have timeout since we
don't know how long it takes for the model to finish training.
"""
_check_call(_LIB.XGTrackerWaitFor(self.handle, make_jcargs(timeout=timeout)))
def worker_args(self) -> Dict[str, Union[str, int]]:
"""Get arguments for workers."""
c_env = ctypes.c_char_p()
_check_call(_LIB.XGTrackerWorkerArgs(self.handle, ctypes.byref(c_env)))
assert c_env.value is not None
env = json.loads(c_env.value)
return env
|
dmlcREPO_NAMExgboostPATH_START.@xgboost_extracted@xgboost-master@python-package@xgboost@tracker.py@.PATH_END.py
|
{
"filename": "losses.py",
"repo_name": "henrysky/astroNN",
"repo_path": "astroNN_extracted/astroNN-master/src/astroNN/nn/losses.py",
"type": "Python"
}
|
# ---------------------------------------------------------------#
# astroNN.nn.losses: losses
# ---------------------------------------------------------------#
import keras
from astroNN.config import MAGIC_NUMBER
from astroNN.nn import nn_obj_lookup
epsilon = keras.backend.epsilon
Model = keras.Model
def magic_num_check(x):
# check for magic num and nan
return keras.ops.logical_or(keras.ops.equal(x, MAGIC_NUMBER), keras.ops.isnan(x))
def magic_correction_term(y_true):
"""
Calculate a correction term to prevent the loss being "lowered" by magic_num or NaN
:param y_true: Ground Truth
:type y_true: keras.ops.Tensor
:return: Correction Term
:rtype: keras.ops.Tensor
:History:
| 2018-Jan-30 - Written - Henry Leung (University of Toronto)
| 2018-Feb-17 - Updated - Henry Leung (University of Toronto)
"""
num_nonmagic = keras.ops.sum(
keras.ops.cast(
keras.ops.logical_not(magic_num_check(y_true)),
"float32",
),
axis=-1,
)
num_magic = keras.ops.sum(
keras.ops.cast(magic_num_check(y_true), "float32"), axis=-1
)
# If no magic number, then num_zero=0 and whole expression is just 1 and get back our good old loss
# If num_nonzero is 0, that means we don't have any information, then set the correction term to ones
return (num_nonmagic + num_magic) / num_nonmagic
def weighted_loss(losses, sample_weight=None):
"""
Calculate sample-weighted losses from losses
:param losses: Losses
:type losses: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Weighted loss
:rtype: keras.ops.Tensor
:History: 2021-Feb-02 - Written - Henry Leung (University of Toronto)
"""
if sample_weight is None:
return losses
else:
return keras.ops.multiply(losses, sample_weight)
def median(x, axis=None):
"""
Calculate median
:param x: Data
:type x: keras.ops.Tensor
:param axis: Axis
:type axis: int
:return: Variance
:rtype: keras.ops.Tensor
:History: 2021-Aug-13 - Written - Henry Leung (University of Toronto)
"""
def median_internal(_x):
shape = keras.ops.shape(_x)[0]
if shape % 2 == 1:
_median = keras.ops.top_k(_x, shape // 2 + 1).values[-1]
else:
_median = (
keras.ops.top_k(_x, shape // 2).values[-1]
+ keras.ops.top_k(_x, shape // 2 + 1).values[-1]
) / 2
return _median
if axis is None:
x_flattened = keras.ops.reshape(x, [-1])
median = median_internal(x_flattened)
return median
else:
x_unstacked = keras.ops.unstack(keras.ops.transpose(x), axis=axis)
median = keras.ops.stack([median_internal(_x) for _x in x_unstacked])
return median
def mean_squared_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean square error losses
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Mean Squared Error
:rtype: keras.ops.Tensor
:History: 2017-Nov-16 - Written - Henry Leung (University of Toronto)
"""
losses = keras.ops.mean(
keras.ops.where(
magic_num_check(y_true),
keras.ops.zeros_like(y_true),
keras.ops.square(y_true - y_pred),
),
axis=-1,
) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_squared_reconstruction_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean square reconstruction error losses
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Mean Squared Error
:rtype: keras.ops.Tensor
:History: 2022-May-05 - Written - Henry Leung (University of Toronto)
"""
raw_loss = keras.ops.square(y_true - y_pred)
fixed_loss = keras.ops.where(
magic_num_check(y_true),
keras.ops.zeros_like(y_true),
raw_loss,
)
losses = weighted_loss(keras.ops.mean(fixed_loss, axis=-1), sample_weight)
return keras.ops.mean(keras.ops.sum(losses, axis=-1))
def mse_lin_wrapper(var, labels_err):
"""
Calculate predictive variance, and takes account of labels error in Bayesian Neural Network
:param var: Predictive Variance
:type var: Union(keras.ops.Tensor, keras.ops.Variable)
:param labels_err: Known labels error, give zeros if unknown/unavailable
:type labels_err: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Robust MSE function for labels prediction neurones, which matches Keras losses API
:rtype: function
:Returned Funtion Parameter:
| **function(y_true, y_pred)**
| - **y_true** (*keras.ops.Tensor*): Ground Truth
| - **y_pred** (*keras.ops.Tensor*): Prediction
| Return (*keras.ops.Tensor*): Robust Mean Squared Error
:History: 2017-Nov-16 - Written - Henry Leung (University of Toronto)
"""
def mse_lin(y_true, y_pred, sample_weight=None):
return robust_mse(y_true, y_pred, var, labels_err, sample_weight)
mse_lin.__name__ = (
"mse_lin_wrapper" # set the name to be the same as parent so it can be found
)
return mse_lin
def mse_var_wrapper(lin, labels_err):
"""
Calculate predictive variance, and takes account of labels error in Bayesian Neural Network
:param lin: Prediction
:type lin: Union(keras.ops.Tensor, keras.ops.Variable)
:param labels_err: Known labels error, give zeros if unknown/unavailable
:type labels_err: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Robust MSE function for predictive variance neurones which matches Keras losses API
:rtype: function
:Returned Funtion Parameter:
| **function(y_true, y_pred)**
| - **y_true** (*keras.ops.Tensor*): Ground Truth
| - **y_pred** (*keras.ops.Tensor*): Predictive Variance
| Return (*keras.ops.Tensor*): Robust Mean Squared Error
:History: 2017-Nov-16 - Written - Henry Leung (University of Toronto)
"""
def mse_var(y_true, y_pred, sample_weight=None):
return robust_mse(y_true, lin, y_pred, labels_err, sample_weight)
mse_var.__name__ = (
"mse_var_wrapper" # set the name to be the same as parent so it can be found
)
return mse_var
def robust_mse(y_true, y_pred, logvar, labels_err, sample_weight=None):
"""
Calculate predictive variance, and takes account of labels error in Bayesian Neural Network
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param logvar: Log Predictive Variance
:type logvar: Union(keras.ops.Tensor, keras.ops.Variable)
:param labels_err: Known labels error, give zeros if unknown/unavailable
:type labels_err: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Robust Mean Squared Error
:rtype: keras.ops.Tensor
:History: 2018-April-07 - Written - Henry Leung (University of Toronto)
"""
# labels_err still contains magic_number
labels_err = keras.ops.cast(labels_err, "float32")
labels_err_y = keras.ops.where(
magic_num_check(y_true),
keras.ops.zeros_like(y_true),
labels_err,
)
# Neural Net is predicting log(var), so take exp, takes account the target variance, and take log back
var_corrected = keras.ops.exp(logvar) + keras.ops.square(labels_err_y)
raw_output = 0.5 * (
(keras.ops.square(y_true - y_pred) / var_corrected)
+ keras.ops.log(var_corrected)
)
wrapper_output = keras.ops.where(
magic_num_check(y_true), keras.ops.zeros_like(y_true), raw_output
)
losses = keras.ops.mean(wrapper_output, axis=-1) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_absolute_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean absolute error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Mean Absolute Error
:rtype: keras.ops.Tensor
:History: 2018-Jan-14 - Written - Henry Leung (University of Toronto)
"""
raw_loss = keras.ops.abs(y_true - y_pred)
losses = keras.ops.mean(
keras.ops.where(
magic_num_check(y_true),
keras.ops.zeros_like(y_true),
raw_loss,
),
axis=-1,
) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_absolute_percentage_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean absolute percentage error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Mean Absolute Percentage Error
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:rtype: keras.ops.Tensor
:History: 2018-Feb-17 - Written - Henry Leung (University of Toronto)
"""
keras.ops_inf = keras.ops.cast(
keras.ops.array(1) / keras.ops.array(0),
"float32",
)
epsilon_tensor = keras.ops.cast(
keras.ops.array(keras.backend.epsilon()),
"float32",
)
diff = keras.ops.abs(
(y_true - y_pred)
/ keras.ops.clip(keras.ops.abs(y_true), epsilon_tensor, keras.ops_inf)
)
diff_corrected = keras.ops.where(
magic_num_check(y_true), keras.ops.zeros_like(y_true), diff
)
losses = (
100.0 * keras.ops.mean(diff_corrected, axis=-1) * magic_correction_term(y_true)
)
return weighted_loss(losses, sample_weight)
def median_absolute_percentage_error(y_true, y_pred, sample_weight=None):
"""
Calculate median absolute percentage error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Median Absolute Percentage Error
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:rtype: keras.ops.Tensor
:History: 2020-Aug-13 - Written - Henry Leung (University of Toronto)
"""
keras.ops_inf = keras.ops.cast(
keras.ops.array(1) / keras.ops.array(0),
"float32",
)
epsilon_tensor = keras.ops.cast(
keras.ops.array(keras.backend.epsilon()),
"float32",
)
diff = keras.ops.abs(
(y_true - y_pred)
/ keras.ops.clip(keras.ops.abs(y_true), epsilon_tensor, keras.ops_inf)
)
diff_corrected = keras.ops.where(
magic_num_check(y_true), keras.ops.zeros_like(y_true), diff
)
losses = 100.0 * median(diff_corrected, axis=None) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_squared_logarithmic_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean squared logarithmic error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Mean Squared Logarithmic Error
:rtype: keras.ops.Tensor
:History: 2018-Feb-17 - Written - Henry Leung (University of Toronto)
"""
keras.ops_inf = keras.ops.cast(
keras.ops.array(1) / keras.ops.array(0),
"float32",
)
epsilon_tensor = keras.ops.cast(
keras.ops.array(keras.backend.epsilon()),
"float32",
)
first_log = keras.ops.log(
keras.ops.clip(y_pred, epsilon_tensor, keras.ops_inf) + 1.0
)
second_log = keras.ops.log(
keras.ops.clip(y_true, epsilon_tensor, keras.ops_inf) + 1.0
)
raw_log_diff = keras.ops.square(first_log - second_log)
log_diff = keras.ops.where(
magic_num_check(y_true),
keras.ops.zeros_like(y_true),
raw_log_diff,
)
losses = keras.ops.mean(log_diff, axis=-1) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean error as a way to get the bias in prediction, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Mean Error
:rtype: keras.ops.Tensor
:History: 2018-May-22 - Written - Henry Leung (University of Toronto)
"""
raw_loss = y_true - y_pred
losses = keras.ops.mean(
keras.ops.where(
magic_num_check(y_true),
keras.ops.zeros_like(y_true),
raw_loss,
),
axis=-1,
) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def mean_percentage_error(y_true, y_pred, sample_weight=None):
"""
Calculate mean percentage error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Mean Percentage Error
:rtype: keras.ops.Tensor
:History: 2018-Jun-06 - Written - Henry Leung (University of Toronto)
"""
keras.ops_inf = keras.ops.cast(
keras.ops.array(1) / keras.ops.array(0),
"float32",
)
epsilon_tensor = keras.ops.cast(
keras.ops.array(keras.backend.epsilon()),
"float32",
)
diff = y_true - y_pred / keras.ops.clip(y_true, epsilon_tensor, keras.ops_inf)
diff_corrected = keras.ops.where(
magic_num_check(y_true), keras.ops.zeros_like(y_true), diff
)
losses = (
100.0 * keras.ops.mean(diff_corrected, axis=-1) * magic_correction_term(y_true)
)
return weighted_loss(losses, sample_weight)
def median_percentage_error(y_true, y_pred, sample_weight=None):
"""
Calculate median percentage error, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Median Percentage Error
:rtype: keras.ops.Tensor
:History: 2020-Aug-13 - Written - Henry Leung (University of Toronto)
"""
keras.ops_inf = keras.ops.cast(
keras.ops.array(1) / keras.ops.array(0),
"float32",
)
epsilon_tensor = keras.ops.cast(
keras.ops.array(keras.backend.epsilon()),
"float32",
)
diff = y_true - y_pred / keras.ops.clip(y_true, epsilon_tensor, keras.ops_inf)
diff_corrected = keras.ops.where(
magic_num_check(y_true), keras.ops.zeros_like(y_true), diff
)
losses = 100.0 * median(diff_corrected, axis=None) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def categorical_crossentropy(y_true, y_pred, sample_weight=None, from_logits=False):
"""
Categorical cross-entropy between an output tensor and a target tensor, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:param from_logits: From logits space or not. If you want to use logits, please use from_logits=True
:type from_logits: boolean
:return: Categorical Cross-Entropy
:rtype: keras.ops.Tensor
:History: 2018-Jan-14 - Written - Henry Leung (University of Toronto)
"""
# calculate correction term first
correction = magic_correction_term(y_true)
# Deal with magic number
y_true = keras.ops.where(
magic_num_check(y_true), keras.ops.zeros_like(y_true), y_true
)
# Note: keras.ops.softmax_cross_entropy_with_logits expects logits, we expects probabilities by default.
if not from_logits:
epsilon_tensor = keras.ops.cast(
keras.ops.array(keras.backend.epsilon()),
"float32",
)
# scale preds so that the class probas of each sample sum to 1
y_pred = y_pred / keras.ops.sum(y_pred, len(keras.ops.shape(y_pred)) - 1, True)
# manual computation of crossentropy
y_pred = keras.ops.clip(y_pred, epsilon_tensor, 1.0 - epsilon_tensor)
losses = (
-keras.ops.sum(
y_true * keras.ops.log(y_pred), len(keras.ops.shape(y_pred)) - 1
)
* correction
)
return weighted_loss(losses, sample_weight)
else:
losses = (
keras.ops.categorical_crossentropy(
target=y_true, output=y_pred, from_logits=True
)
* correction
)
return weighted_loss(losses, sample_weight)
def binary_crossentropy(y_true, y_pred, sample_weight=None, from_logits=False):
"""
Binary cross-entropy between an output tensor and a target tensor, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param from_logits: From logits space or not. If you want to use logits, please use from_logits=True
:type from_logits: boolean
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Binary Cross-Entropy
:rtype: keras.ops.Tensor
:History: 2018-Jan-14 - Written - Henry Leung (University of Toronto)
"""
# Note: keras.ops.sigmoid_cross_entropy_with_logits expects logits, we expects probabilities by default.
if not from_logits:
epsilon_tensor = keras.ops.cast(
keras.ops.array(keras.backend.epsilon()),
"float32",
)
# transform back to logits
y_pred = keras.ops.clip(y_pred, epsilon_tensor, 1.0 - epsilon_tensor)
y_pred = keras.ops.log(y_pred / (1.0 - y_pred))
cross_entropy = keras.ops.binary_crossentropy(
target=y_true, output=y_pred, from_logits=True
)
corrected_cross_entropy = keras.ops.where(
magic_num_check(y_true),
keras.ops.zeros_like(cross_entropy),
cross_entropy,
)
losses = keras.ops.mean(corrected_cross_entropy, axis=-1) * magic_correction_term(
y_true
)
return weighted_loss(losses, sample_weight)
def bayesian_categorical_crossentropy_wrapper(logit_var, *args, **kwargs):
"""
| Categorical crossentropy between an output tensor and a target tensor for Bayesian Neural Network
| equation (12) of arxiv:1703.04977
:param logit_var: Predictive variance
:type logit_var: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Robust categorical_crossentropy function for predictive variance neurones which matches Keras losses API
:rtype: function
:Returned Function Parameter:
| **function(y_true, y_pred)**
| - **y_true** (*keras.ops.Tensor*): Ground Truth
| - **y_pred** (*keras.ops.Tensor*): Prediction in logits space
| Return (*keras.ops.Tensor*): Robust categorical crossentropy
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
# y_pred is logits
def bayesian_crossentropy(y_true, y_pred, sample_weight=None):
return robust_categorical_crossentropy(y_true, y_pred, logit_var, sample_weight)
# set the name to be the same as parent so it can be found
bayesian_crossentropy.__name__ = "bayesian_categorical_crossentropy_wrapper"
return bayesian_crossentropy
def bayesian_categorical_crossentropy_var_wrapper(logits):
"""
| Categorical crossentropy between an output tensor and a target tensor for Bayesian Neural Network
| equation (12) of arxiv:1703.04977
:param logits: Prediction in logits space
:type logits: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Robust categorical_crossentropy function for predictive variance neurones which matches Keras losses API
:rtype: function
:Returned Function Parameter:
| **function(y_true, y_pred)**
| - **y_true** (*keras.ops.Tensor*): Ground Truth
| - **y_pred** (*keras.ops.Tensor*): Predictive variance in logits space
| Return (*keras.ops.Tensor*): Robust categorical crossentropy
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
# y_pred is predictive entropy
def bayesian_crossentropy(y_true, y_pred, sample_weight=None):
return robust_categorical_crossentropy(y_true, logits, y_pred, sample_weight)
# set the name to be the same as parent so it can be found
bayesian_crossentropy.__name__ = "bayesian_categorical_crossentropy_var_wrapper"
return bayesian_crossentropy
def robust_categorical_crossentropy(y_true, y_pred, logit_var, sample_weight):
"""
Calculate categorical accuracy, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction in logits space
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param logit_var: Predictive variance in logits space
:type logit_var: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: categorical cross-entropy
:rtype: keras.ops.Tensor
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
variance_depressor = keras.ops.mean(
keras.ops.exp(logit_var) - keras.ops.ones_like(logit_var)
)
undistorted_loss = categorical_crossentropy(
y_true, y_pred, sample_weight, from_logits=True
)
mc_num = 25
batch_size = keras.ops.shape(y_pred)[0]
label_size = keras.ops.shape(y_pred)[-1]
noise = keras.random.normal(shape=[mc_num, batch_size, label_size])
dist = y_pred + noise * logit_var
mc_result = -keras.ops.elu(
keras.ops.tile(undistorted_loss, [mc_num])
- categorical_crossentropy(
keras.ops.tile(y_true, [mc_num, 1]),
keras.ops.reshape(dist, (batch_size * mc_num, label_size)),
from_logits=True,
)
)
variance_loss = (
keras.ops.mean(keras.ops.reshape(mc_result, (mc_num, batch_size)), axis=0)
* undistorted_loss
)
losses = (
variance_loss + undistorted_loss + variance_depressor
) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def bayesian_binary_crossentropy_wrapper(logit_var, *args, **kwargs):
"""
| Binary crossentropy between an output tensor and a target tensor for Bayesian Neural Network
| equation (12) of arxiv:1703.04977
:param logit_var: Predictive variance
:type logit_var: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Robust binary_crossentropy function for predictive variance neurones which matches Keras losses API
:rtype: function
:Returned Function Parameter:
| **function(y_true, y_pred)**
| - **y_true** (*keras.ops.Tensor*): Ground Truth
| - **y_pred** (*keras.ops.Tensor*): Prediction in logits space
| Return (*keras.ops.Tensor*): Robust binary crossentropy
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
# y_pred is logits
def bayesian_crossentropy(y_true, y_pred, sample_weight=None):
return robust_binary_crossentropy(y_true, y_pred, logit_var, sample_weight)
# set the name to be the same as parent so it can be found
bayesian_crossentropy.__name__ = "bayesian_binary_crossentropy_wrapper"
return bayesian_crossentropy
def bayesian_binary_crossentropy_var_wrapper(logits):
"""
| Binary crossentropy between an output tensor and a target tensor for Bayesian Neural Network
| equation (12) of arxiv:1703.04977
:param logits: Prediction in logits space
:type logits: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Robust binary_crossentropy function for predictive variance neurones which matches Keras losses API
:rtype: function
:Returned Function Parameter:
| **function(y_true, y_pred)**
| - **y_true** (*keras.ops.Tensor*): Ground Truth
| - **y_pred** (*keras.ops.Tensor*): Predictive variance in logits space
| Return (*keras.ops.Tensor*): Robust binary crossentropy
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
# y_pred is predictive entropy
def bayesian_crossentropy(y_true, y_pred, sample_weight=None):
return robust_binary_crossentropy(y_true, logits, y_pred, sample_weight)
# set the name to be the same as parent so it can be found
bayesian_crossentropy.__name__ = "bayesian_binary_crossentropy_var_wrapper"
return bayesian_crossentropy
def robust_binary_crossentropy(y_true, y_pred, logit_var, sample_weight):
"""
Calculate binary accuracy, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction in logits space
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param logit_var: Predictive variance in logits space
:type logit_var: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: categorical cross-entropy
:rtype: keras.ops.Tensor
:History: 2018-Mar-15 - Written - Henry Leung (University of Toronto)
"""
variance_depressor = keras.ops.mean(
keras.ops.exp(logit_var) - keras.ops.ones_like(logit_var)
)
undistorted_loss = binary_crossentropy(y_true, y_pred, from_logits=True)
mc_num = 25
batch_size = keras.ops.shape(y_pred)[0]
label_size = keras.ops.shape(y_pred)[-1]
noise = keras.random.normal(shape=[mc_num, batch_size, label_size])
dist = y_pred + noise * logit_var
mc_result = -keras.ops.elu(
keras.ops.tile(undistorted_loss, [mc_num])
- binary_crossentropy(
keras.ops.tile(y_true, [mc_num, 1]),
keras.ops.reshape(dist, (batch_size * mc_num, label_size)),
from_logits=True,
)
)
variance_loss = (
keras.ops.mean(keras.ops.reshape(mc_result, (mc_num, batch_size)), axis=0)
* undistorted_loss
)
losses = (
variance_loss + undistorted_loss + variance_depressor
) * magic_correction_term(y_true)
return weighted_loss(losses, sample_weight)
def nll(y_true, y_pred, sample_weight=None):
"""
Calculate negative log likelihood
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Negative log likelihood
:rtype: keras.ops.Tensor
:History: 2018-Jan-30 - Written - Henry Leung (University of Toronto)
"""
# astroNN binary_cross_entropy gives the mean over the last axis. we require the sum
losses = keras.ops.sum(binary_crossentropy(y_true, y_pred), axis=-1)
return weighted_loss(losses, sample_weight)
def categorical_accuracy(y_true, y_pred):
"""
Calculate categorical accuracy, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Categorical Classification Accuracy
:rtype: keras.ops.Tensor
:History: 2018-Jan-21 - Written - Henry Leung (University of Toronto)
"""
y_true = keras.ops.where(
magic_num_check(y_true), keras.ops.zeros_like(y_true), y_true
)
return keras.ops.cast(
keras.ops.equal(
keras.ops.argmax(y_true, axis=-1),
keras.ops.argmax(y_pred, axis=-1),
),
"float32",
) * magic_correction_term(y_true)
def __binary_accuracy(from_logits=False):
"""
Calculate binary accuracy, ignoring the magic number
:param from_logits: From logits space or not. If you want to use logits, please use from_logits=True
:type from_logits: boolean
:return: Function for Binary classification accuracy which matches Keras losses API
:rtype: function
:Returned Funtion Parameter:
| **function(y_true, y_pred)**
| - **y_true** (*keras.ops.Tensor*): Ground Truth
| - **y_pred** (*keras.ops.Tensor*): Prediction
| Return (*keras.ops.Tensor*): Binary Classification Accuracy
:History: 2018-Jan-31 - Written - Henry Leung (University of Toronto)
"""
# DO NOT correct y_true for magic number, just let it goes wrong and then times a correction terms
def binary_accuracy_internal(y_true, y_pred):
if from_logits:
y_pred = keras.ops.sigmoid(y_pred)
return keras.ops.mean(
keras.ops.cast(
keras.ops.equal(y_true, keras.ops.round(y_pred)),
"float32",
),
axis=-1,
) * magic_correction_term(y_true)
if not from_logits:
binary_accuracy_internal.__name__ = (
"binary_accuracy" # set the name to be displayed in keras.ops/Keras log
)
else:
binary_accuracy_internal.__name__ = "binary_accuracy_from_logits" # set the name to be displayed in keras.ops/Keras log
return binary_accuracy_internal
def binary_accuracy(*args, **kwargs):
"""
Calculate binary accuracy, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Binary accuracy
:rtype: keras.ops.Tensor
:History: 2018-Jan-31 - Written - Henry Leung (University of Toronto)
"""
return __binary_accuracy(from_logits=False)(*args, **kwargs)
def binary_accuracy_from_logits(*args, **kwargs):
"""
Calculate binary accuracy from logits, ignoring the magic number
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:return: Binary accuracy
:rtype: keras.ops.Tensor
:History: 2018-Jan-31 - Written - Henry Leung (University of Toronto)
"""
return __binary_accuracy(from_logits=True)(*args, **kwargs)
def zeros_loss(y_true, y_pred, sample_weight=None):
"""
Always return zeros
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param sample_weight: Sample weights
:type sample_weight: Union(keras.ops.Tensor, keras.ops.Variable, list)
:return: Zeros
:rtype: keras.ops.Tensor
:History: 2018-May-24 - Written - Henry Leung (University of Toronto)
"""
losses = keras.ops.mean(
keras.ops.where(magic_num_check(y_true), keras.ops.zeros_like(y_true), y_true)
* 0.0
+ 0.0 * y_pred,
axis=-1,
)
return weighted_loss(losses, sample_weight)
def median_error(y_true, y_pred, sample_weight=None, axis=-1):
"""
Calculate median difference
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param axis: Axis
:type axis: int
:return: Variance
:rtype: keras.ops.Tensor
:History: 2021-Aug-13 - Written - Henry Leung (University of Toronto)
"""
# keras.ops.boolean_mask(keras.ops.logical_not(magic_num_check(y_true))
return weighted_loss(median(y_true - y_pred, axis=axis), sample_weight)
def median_absolute_deviation(y_true, y_pred, sample_weight=None, axis=-1):
"""
Calculate median absilute difference
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param axis: Axis
:type axis: int
:return: Variance
:rtype: keras.ops.Tensor
:History: 2021-Aug-13 - Written - Henry Leung (University of Toronto)
"""
return weighted_loss(
median(keras.ops.abs(y_true - y_pred), axis=axis), sample_weight
)
def mad_std(y_true, y_pred, sample_weight=None, axis=-1):
"""
Calculate 1.4826 * median absilute difference
:param y_true: Ground Truth
:type y_true: Union(keras.ops.Tensor, keras.ops.Variable)
:param y_pred: Prediction
:type y_pred: Union(keras.ops.Tensor, keras.ops.Variable)
:param axis: Axis
:type axis: int
:return: Variance
:rtype: keras.ops.Tensor
:History: 2021-Aug-13 - Written - Henry Leung (University of Toronto)
"""
return weighted_loss(
1.4826 * median_absolute_deviation(y_true, y_pred, axis=axis), sample_weight
)
# Just alias functions
mse = mean_squared_error
mae = mean_absolute_error
mape = mean_absolute_percentage_error
msle = mean_squared_logarithmic_error
me = mean_error
mpe = mean_percentage_error
mad = median_absolute_deviation
# legacy support
mse_lin = mse_lin_wrapper
mse_var = mse_var_wrapper
def losses_lookup(identifier):
"""
Lookup astroNN.nn.losses function by name
:param identifier: identifier
:type identifier: str
:return: Looked up function
:rtype: function
:History: 2018-Apr-28 - Written - Henry Leung (University of Toronto)
"""
return nn_obj_lookup(identifier, module_obj=globals(), module_name=__name__)
|
henryskyREPO_NAMEastroNNPATH_START.@astroNN_extracted@astroNN-master@src@astroNN@nn@losses.py@.PATH_END.py
|
{
"filename": "filtering.py",
"repo_name": "RuthAngus/gprotation",
"repo_path": "gprotation_extracted/gprotation-master/gprotation/filtering.py",
"type": "Python"
}
|
from scipy.signal import butter, lfilter
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import freqz
def butter_bandpass(lowcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
b, a = butter(order, low, btype='highpass')
return b, a
def butter_bandpass_filter(data, lowcut, fs, order=5, plot=False):
b, a = butter_bandpass(lowcut, fs, order=order)
y = lfilter(b, a, data)
if plot:
plot_bandpass(lowcut, fs, order)
return y
def plot_bandpass(lowcut, fs, order):
# Plot the frequency response for a few different orders.
plt.clf()
b, a = butter_bandpass(lowcut, fs, order=3)
w, h = freqz(b, a, worN=5000)
plt.plot((fs * 0.5 / np.pi) * w, abs(h), label="order = %d" % order)
plt.plot([0, 0.5 * fs], [np.sqrt(0.5), np.sqrt(0.5)],
'--', label='sqrt(0.5)')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Gain')
plt.grid(True)
plt.legend(loc='best')
plt.xlim(0, 1)
plt.savefig("butter_bandpass")
if __name__ == "__main__":
# Sample rate and desired cutoff frequencies (in Hz).
fs = 5000.0
lowcut = 500.0
# Filter a noisy signal.
T = 0.05
nsamples = T * fs
t = np.linspace(0, T, nsamples, endpoint=False)
a = 0.02
f0 = 600.0
x = 0.1 * np.sin(2 * np.pi * 1.2 * np.sqrt(t))
x += 0.01 * np.cos(2 * np.pi * 312 * t + 0.1)
x += a * np.cos(2 * np.pi * f0 * t + .11)
x += 0.03 * np.cos(2 * np.pi * 2000 * t)
plt.figure(2)
plt.clf()
plt.plot(t, x, label='Noisy signal')
y = butter_bandpass_filter(x, lowcut, fs, order=6)
plt.plot(t, y, label='Filtered signal (%g Hz)' % f0)
plt.xlabel('time (seconds)')
plt.hlines([-a, a], 0, T, linestyles='--')
plt.grid(True)
plt.axis('tight')
plt.legend(loc='upper left')
plt.savefig("butter_filtered")
|
RuthAngusREPO_NAMEgprotationPATH_START.@gprotation_extracted@gprotation-master@gprotation@filtering.py@.PATH_END.py
|
{
"filename": "sequential.py",
"repo_name": "jiffyclub/palettable",
"repo_path": "palettable_extracted/palettable-master/palettable/colorbrewer/sequential.py",
"type": "Python"
}
|
from __future__ import absolute_import
from .colorbrewer import _load_maps_by_type
globals().update(_load_maps_by_type('sequential'))
|
jiffyclubREPO_NAMEpalettablePATH_START.@palettable_extracted@palettable-master@palettable@colorbrewer@sequential.py@.PATH_END.py
|
{
"filename": "Pick.py",
"repo_name": "wmpg/Supracenter",
"repo_path": "Supracenter_extracted/Supracenter-master/supra/Stations/Pick.py",
"type": "Python"
}
|
""" Inputs a single-component waveform (trace or stream not certain yet) and
outputs the times of the signals.
Pick objects might be overkill for this project, but BAM will use them,
so I've put them in
Author: Luke McFadden
"""
import numpy as np
class Pick:
def __init__(self, first_pt, last_pt):
""" Make a pick object defined as the outer limits of the region
Example Usage:
A = Pick(1.0, 2.0)
print(A)
>>> Pick object defined from 1.00 - 2.00 s
Arguments:
first_pt [float] - the initial time of the pick
last_pt [float] - the ending time of the pick
"""
try:
first_pt = float(first_pt)
last_pt = float(last_pt)
except ValueError:
print("[ERROR] Pick must be defined with two floats!")
first_pt = np.nan
last_pt = np.nan
if first_pt > last_pt:
first_pt, last_pt = last_pt, first_pt
self.first_pt = first_pt
self.last_pt = last_pt
def __str__(self):
""" Prints the pick boundaries for testing and display
"""
return "Pick object defined from {:.2f} - {:.2f} s".format(self.first_pt, self.last_pt)
def __add__(self, other):
'''
Returns a pick object which envelopes the outer maxima of both picks
Example Usage:
A = Pick(1, 2)
B = Pick(2, 3)
A + B -> Pick(1, 3)
Arguments:
self [Pick Obj] - the first pick object to be added to the range
other [Pick Obj] - the second pick object to be added to the range
Returns [Pick Obj] - the first_pt is the minimum of the two first_pt inputs and the
last_pt is the maximum of the two last_pt inputs
'''
first_pt = min(self.first_pt, other.first_pt)
last_pt = max(self.last_pt, other.last_pt)
return Pick(first_pt, last_pt)
def __sub__(self, other):
''' Returns the difference between the two first_pt of the inputs
Example Usage:
A = Pick(1, 2)
B = Pick(2, 3)
B - A -> 1
Arguments:
self [Pick Obj] - the first pick object to be subtracted
other [Pick Obj] - the second pick object to be subtracted
Returns [float] - the difference between the first_pts
'''
return self.first_pt - other.first_pt
def getLen(self):
'''
Returns the length of the pick (difference between first and last point)
Example Usage:
A = Pick(1, 3)
A.getLen()
>>> 2
'''
return self.last_pt - self.first_pt
if __name__ == "__main__":
pass
A = Pick(1, 2)
B = Pick(2, 3)
print("For testing: Error message -> pass, crash -> fail")
C = Pick("A", "B")
D = A + B
E = A - B
assert (D.first_pt == min(A.first_pt, B.first_pt))
assert (D.last_pt == max(A.last_pt, B.last_pt))
assert (E == A.first_pt - B.first_pt)
assert np.isnan(C.first_pt)
assert np.isnan(C.last_pt)
F = Pick(2, 1)
assert F.first_pt == 1 and F.last_pt == 2
assert A.getLen() == 1
assert D.getLen() == 2
print("Pick testing done!")
|
wmpgREPO_NAMESupracenterPATH_START.@Supracenter_extracted@Supracenter-master@supra@Stations@Pick.py@.PATH_END.py
|
{
"filename": "test_structured_chat.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/tests/unit_tests/agents/test_structured_chat.py",
"type": "Python"
}
|
"""Unittests for langchain.agents.chat package."""
from textwrap import dedent
from typing import Any, Tuple
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_core.tools import Tool
from langchain.agents.structured_chat.base import StructuredChatAgent
from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser
output_parser = StructuredChatOutputParser()
def get_action_and_input(text: str) -> Tuple[str, str]:
output = output_parser.parse(text)
if isinstance(output, AgentAction):
return output.tool, str(output.tool_input)
elif isinstance(output, AgentFinish):
return output.return_values["output"], output.log
else:
raise ValueError("Unexpected output type")
def test_parse_with_language() -> None:
llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```json
{
"action": "foo",
"action_input": "bar"
}
```
"""
action, action_input = get_action_and_input(llm_output)
assert action == "foo"
assert action_input == "bar"
def test_parse_without_language() -> None:
llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```
{
"action": "foo",
"action_input": "bar"
}
```
"""
action, action_input = get_action_and_input(llm_output)
assert action == "foo"
assert action_input == "bar"
def test_parse_with_language_and_spaces() -> None:
llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```json
{
"action": "foo",
"action_input": "bar"
}
```
"""
action, action_input = get_action_and_input(llm_output)
assert action == "foo"
assert action_input == "bar"
def test_parse_without_language_without_a_new_line() -> None:
llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```{"action": "foo", "action_input": "bar"}```
"""
action, action_input = get_action_and_input(llm_output)
assert action == "foo"
assert action_input == "bar"
def test_parse_with_language_without_a_new_line() -> None:
llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```json{"action": "foo", "action_input": "bar"}```
"""
# TODO: How should this be handled?
output, log = get_action_and_input(llm_output)
assert output == llm_output
assert log == llm_output
def test_parse_case_matched_and_final_answer() -> None:
llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```json
{
"action": "Final Answer",
"action_input": "This is the final answer"
}
```
"""
output, log = get_action_and_input(llm_output)
assert output == "This is the final answer"
assert log == llm_output
# TODO: add more tests.
# Test: StructuredChatAgent.create_prompt() method.
class TestCreatePrompt:
# Test: Output should be a ChatPromptTemplate with sys and human messages.
def test_create_prompt_output(self) -> None:
prompt = StructuredChatAgent.create_prompt(
[Tool(name="foo", description="Test tool FOO", func=lambda x: x)]
)
assert isinstance(prompt, ChatPromptTemplate)
assert len(prompt.messages) == 2
assert isinstance(prompt.messages[0], SystemMessagePromptTemplate)
assert isinstance(prompt.messages[1], HumanMessagePromptTemplate)
# Test: Format with a single tool.
def test_system_message_single_tool(self) -> None:
prompt: Any = StructuredChatAgent.create_prompt(
[Tool(name="foo", description="Test tool FOO", func=lambda x: x)]
)
actual = prompt.messages[0].prompt.format()
expected = dedent(
"""
Respond to the human as helpfully and accurately as possible. You have access to the following tools:
foo: Test tool FOO, args: {'tool_input': {'type': 'string'}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or foo
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Thought:
""" # noqa: E501
).strip()
assert actual == expected
# Test: Format with multiple tools.
#
# Check:
#
# You have access to the following tools:
# ...
#
# and
#
# Valid "action" values: "Final Answer" or ...
#
def test_system_message_multiple_tools(self) -> None:
prompt: Any = StructuredChatAgent.create_prompt(
[
Tool(name="foo", description="Test tool FOO", func=lambda x: x),
Tool(name="bar", description="Test tool BAR", func=lambda x: x),
]
)
actual = prompt.messages[0].prompt.format()
expected = dedent(
"""
Respond to the human as helpfully and accurately as possible. You have access to the following tools:
foo: Test tool FOO, args: {'tool_input': {'type': 'string'}}
bar: Test tool BAR, args: {'tool_input': {'type': 'string'}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or foo, bar
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Thought:
""" # noqa: E501
).strip()
assert actual == expected
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@tests@unit_tests@agents@test_structured_chat.py@.PATH_END.py
|
{
"filename": "launch.py",
"repo_name": "davidharvey1986/pyRRG",
"repo_path": "pyRRG_extracted/pyRRG-master/unittests/bugFixPyRRG/lib/python3.7/site-packages/setuptools/launch.py",
"type": "Python"
}
|
"""
Launch the Python script on the command line after
setuptools is bootstrapped via import.
"""
# Note that setuptools gets imported implicitly by the
# invocation of this script using python -m setuptools.launch
import tokenize
import sys
def run():
"""
Run the script in sys.argv[1] as if it had
been invoked naturally.
"""
__builtins__
script_name = sys.argv[1]
namespace = dict(
__file__=script_name,
__name__='__main__',
__doc__=None,
)
sys.argv[:] = sys.argv[1:]
open_ = getattr(tokenize, 'open', open)
script = open_(script_name).read()
norm_script = script.replace('\\r\\n', '\\n')
code = compile(norm_script, script_name, 'exec')
exec(code, namespace)
if __name__ == '__main__':
run()
|
davidharvey1986REPO_NAMEpyRRGPATH_START.@pyRRG_extracted@pyRRG-master@unittests@bugFixPyRRG@lib@python3.7@site-packages@setuptools@launch.py@.PATH_END.py
|
{
"filename": "agent_fireworks_ai_langchain_mongodb.ipynb",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/cookbook/agent_fireworks_ai_langchain_mongodb.ipynb",
"type": "Jupyter Notebook"
}
|
[](https://colab.research.google.com/github/mongodb-developer/GenAI-Showcase/blob/main/notebooks/agents/agent_fireworks_ai_langchain_mongodb.ipynb)
[](https://www.mongodb.com/developer/products/atlas/agent-fireworksai-mongodb-langchain/)
## Install Libraries
```python
!pip install langchain langchain_openai langchain-fireworks langchain-mongodb arxiv pymupdf datasets pymongo
```
## Set Evironment Variables
```python
import os
os.environ["OPENAI_API_KEY"] = ""
os.environ["FIREWORKS_API_KEY"] = ""
os.environ["MONGO_URI"] = ""
FIREWORKS_API_KEY = os.environ.get("FIREWORKS_API_KEY")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
MONGO_URI = os.environ.get("MONGO_URI")
```
## Data Ingestion into MongoDB Vector Database
```python
import pandas as pd
from datasets import load_dataset
data = load_dataset("MongoDB/subset_arxiv_papers_with_emebeddings")
dataset_df = pd.DataFrame(data["train"])
```
/Users/richmondalake/miniconda3/envs/langchain_workarea/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Downloading readme: 100%|██████████| 701/701 [00:00<00:00, 2.04MB/s]
Repo card metadata block was not found. Setting CardData to empty.
Downloading data: 100%|██████████| 102M/102M [00:15<00:00, 6.41MB/s]
Generating train split: 50000 examples [00:01, 38699.64 examples/s]
```python
print(len(dataset_df))
dataset_df.head()
```
50000
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>submitter</th>
<th>authors</th>
<th>title</th>
<th>comments</th>
<th>journal-ref</th>
<th>doi</th>
<th>report-no</th>
<th>categories</th>
<th>license</th>
<th>abstract</th>
<th>versions</th>
<th>update_date</th>
<th>authors_parsed</th>
<th>embedding</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>704.0001</td>
<td>Pavel Nadolsky</td>
<td>C. Bal\'azs, E. L. Berger, P. M. Nadolsky, C.-...</td>
<td>Calculation of prompt diphoton production cros...</td>
<td>37 pages, 15 figures; published version</td>
<td>Phys.Rev.D76:013009,2007</td>
<td>10.1103/PhysRevD.76.013009</td>
<td>ANL-HEP-PR-07-12</td>
<td>hep-ph</td>
<td>None</td>
<td>A fully differential calculation in perturba...</td>
<td>[{'version': 'v1', 'created': 'Mon, 2 Apr 2007...</td>
<td>2008-11-26</td>
<td>[[Balázs, C., ], [Berger, E. L., ], [Nadolsky,...</td>
<td>[0.0594153292, -0.0440569334, -0.0487333685, -...</td>
</tr>
<tr>
<th>1</th>
<td>704.0002</td>
<td>Louis Theran</td>
<td>Ileana Streinu and Louis Theran</td>
<td>Sparsity-certifying Graph Decompositions</td>
<td>To appear in Graphs and Combinatorics</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>math.CO cs.CG</td>
<td>http://arxiv.org/licenses/nonexclusive-distrib...</td>
<td>We describe a new algorithm, the $(k,\ell)$-...</td>
<td>[{'version': 'v1', 'created': 'Sat, 31 Mar 200...</td>
<td>2008-12-13</td>
<td>[[Streinu, Ileana, ], [Theran, Louis, ]]</td>
<td>[0.0247399714, -0.065658465, 0.0201423876, -0....</td>
</tr>
<tr>
<th>2</th>
<td>704.0003</td>
<td>Hongjun Pan</td>
<td>Hongjun Pan</td>
<td>The evolution of the Earth-Moon system based o...</td>
<td>23 pages, 3 figures</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>physics.gen-ph</td>
<td>None</td>
<td>The evolution of Earth-Moon system is descri...</td>
<td>[{'version': 'v1', 'created': 'Sun, 1 Apr 2007...</td>
<td>2008-01-13</td>
<td>[[Pan, Hongjun, ]]</td>
<td>[0.0491479263, 0.0728017688, 0.0604138002, 0.0...</td>
</tr>
<tr>
<th>3</th>
<td>704.0004</td>
<td>David Callan</td>
<td>David Callan</td>
<td>A determinant of Stirling cycle numbers counts...</td>
<td>11 pages</td>
<td>None</td>
<td>None</td>
<td>None</td>
<td>math.CO</td>
<td>None</td>
<td>We show that a determinant of Stirling cycle...</td>
<td>[{'version': 'v1', 'created': 'Sat, 31 Mar 200...</td>
<td>2007-05-23</td>
<td>[[Callan, David, ]]</td>
<td>[0.0389556214, -0.0410280302, 0.0410280302, -0...</td>
</tr>
<tr>
<th>4</th>
<td>704.0005</td>
<td>Alberto Torchinsky</td>
<td>Wael Abu-Shammala and Alberto Torchinsky</td>
<td>From dyadic $\Lambda_{\alpha}$ to $\Lambda_{\a...</td>
<td>None</td>
<td>Illinois J. Math. 52 (2008) no.2, 681-689</td>
<td>None</td>
<td>None</td>
<td>math.CA math.FA</td>
<td>None</td>
<td>In this paper we show how to compute the $\L...</td>
<td>[{'version': 'v1', 'created': 'Mon, 2 Apr 2007...</td>
<td>2013-10-15</td>
<td>[[Abu-Shammala, Wael, ], [Torchinsky, Alberto, ]]</td>
<td>[0.118412666, -0.0127423415, 0.1185125113, 0.0...</td>
</tr>
</tbody>
</table>
</div>
```python
from pymongo import MongoClient
# Initialize MongoDB python client
client = MongoClient(MONGO_URI, appname="devrel.content.ai_agent_firechain.python")
DB_NAME = "agent_demo"
COLLECTION_NAME = "knowledge"
ATLAS_VECTOR_SEARCH_INDEX_NAME = "vector_index"
collection = client[DB_NAME][COLLECTION_NAME]
```
```python
# Delete any existing records in the collection
collection.delete_many({})
# Data Ingestion
records = dataset_df.to_dict("records")
collection.insert_many(records)
print("Data ingestion into MongoDB completed")
```
Data ingestion into MongoDB completed
## Create Vector Search Index Defintion
```
{
"fields": [
{
"type": "vector",
"path": "embedding",
"numDimensions": 256,
"similarity": "cosine"
}
]
}
```
## Create LangChain Retriever (MongoDB)
```python
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain_openai import OpenAIEmbeddings
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small", dimensions=256)
# Vector Store Creation
vector_store = MongoDBAtlasVectorSearch.from_connection_string(
connection_string=MONGO_URI,
namespace=DB_NAME + "." + COLLECTION_NAME,
embedding=embedding_model,
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
text_key="abstract",
)
retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
```
### Optional: Creating a retrevier with compression capabilities using LLMLingua
```python
!pip install langchain_community llmlingua
```
```python
from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.document_compressors import LLMLinguaCompressor
```
```python
compressor = LLMLinguaCompressor(model_name="openai-community/gpt2", device_map="cpu")
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
)
```
/Users/richmondalake/miniconda3/envs/langchain_workarea/lib/python3.12/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
warnings.warn(
## Configure LLM Using Fireworks AI
```python
from langchain_fireworks import ChatFireworks
llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", max_tokens=256)
```
## Agent Tools Creation
```python
from langchain.agents import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_community.document_loaders import ArxivLoader
# Custom Tool Definiton
@tool
def get_metadata_information_from_arxiv(word: str) -> list:
"""
Fetches and returns metadata for a maximum of ten documents from arXiv matching the given query word.
Args:
word (str): The search query to find relevant documents on arXiv.
Returns:
list: Metadata about the documents matching the query.
"""
docs = ArxivLoader(query=word, load_max_docs=10).load()
# Extract just the metadata from each document
metadata_list = [doc.metadata for doc in docs]
return metadata_list
@tool
def get_information_from_arxiv(word: str) -> list:
"""
Fetches and returns metadata for a single research paper from arXiv matching the given query word, which is the ID of the paper, for example: 704.0001.
Args:
word (str): The search query to find the relevant paper on arXiv using the ID.
Returns:
list: Data about the paper matching the query.
"""
doc = ArxivLoader(query=word, load_max_docs=1).load()
return doc
# If you created a retriever with compression capaitilies in the optional cell in an earlier cell, you can replace 'retriever' with 'compression_retriever'
# Otherwise you can also create a compression procedure as a tool for the agent as shown in the `compress_prompt_using_llmlingua` tool definition function
retriever_tool = create_retriever_tool(
retriever=retriever,
name="knowledge_base",
description="This serves as the base knowledge source of the agent and contains some records of research papers from Arxiv. This tool is used as the first step for exploration and reseach efforts.",
)
```
```python
from langchain_community.document_compressors import LLMLinguaCompressor
compressor = LLMLinguaCompressor(model_name="openai-community/gpt2", device_map="cpu")
@tool
def compress_prompt_using_llmlingua(prompt: str, compression_rate: float = 0.5) -> str:
"""
Compresses a long data or prompt using the LLMLinguaCompressor.
Args:
data (str): The data or prompt to be compressed.
compression_rate (float): The rate at which to compress the data (default is 0.5).
Returns:
str: The compressed data or prompt.
"""
compressed_data = compressor.compress_prompt(
prompt,
rate=compression_rate,
force_tokens=["!", ".", "?", "\n"],
drop_consecutive=True,
)
return compressed_data
```
```python
tools = [
retriever_tool,
get_metadata_information_from_arxiv,
get_information_from_arxiv,
compress_prompt_using_llmlingua,
]
```
## Agent Prompt Creation
```python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
agent_purpose = """
You are a helpful research assistant equipped with various tools to assist with your tasks efficiently.
You have access to conversational history stored in your inpout as chat_history.
You are cost-effective and utilize the compress_prompt_using_llmlingua tool whenever you determine that a prompt or conversational history is too long.
Below are instructions on when and how to use each tool in your operations.
1. get_metadata_information_from_arxiv
Purpose: To fetch and return metadata for up to ten documents from arXiv that match a given query word.
When to Use: Use this tool when you need to gather metadata about multiple research papers related to a specific topic.
Example: If you are asked to provide an overview of recent papers on "machine learning," use this tool to fetch metadata for relevant documents.
2. get_information_from_arxiv
Purpose: To fetch and return metadata for a single research paper from arXiv using the paper's ID.
When to Use: Use this tool when you need detailed information about a specific research paper identified by its arXiv ID.
Example: If you are asked to retrieve detailed information about the paper with the ID "704.0001," use this tool.
3. retriever_tool
Purpose: To serve as your base knowledge, containing records of research papers from arXiv.
When to Use: Use this tool as the first step for exploration and research efforts when dealing with topics covered by the documents in the knowledge base.
Example: When beginning research on a new topic that is well-documented in the arXiv repository, use this tool to access the relevant papers.
4. compress_prompt_using_llmlingua
Purpose: To compress long prompts or conversational histories using the LLMLinguaCompressor.
When to Use: Use this tool whenever you determine that a prompt or conversational history is too long to be efficiently processed.
Example: If you receive a very lengthy query or conversation context that exceeds the typical token limits, compress it using this tool before proceeding with further processing.
"""
prompt = ChatPromptTemplate.from_messages(
[
("system", agent_purpose),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
```
## Agent Memory Using MongoDB
```python
from langchain.memory import ConversationBufferMemory
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
def get_session_history(session_id: str) -> MongoDBChatMessageHistory:
return MongoDBChatMessageHistory(
MONGO_URI, session_id, database_name=DB_NAME, collection_name="history"
)
memory = ConversationBufferMemory(
memory_key="chat_history", chat_memory=get_session_history("latest_agent_session")
)
```
## Agent Creation
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True,
handle_parsing_errors=True,
memory=memory,
)
```
## Agent Exectution
```python
agent_executor.invoke(
{
"input": "Get me a list of research papers on the topic Prompt Compression in LLM Applications."
}
)
```
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m
Invoking: `get_metadata_information_from_arxiv` with `{'word': 'Prompt Compression in LLM Applications'}`
[0m[33;1m[1;3m[{'Published': '2024-05-27', 'Title': 'SelfCP: Compressing Long Prompt to 1/12 Using the Frozen Large Language Model Itself', 'Authors': 'Jun Gao', 'Summary': 'Long prompt leads to huge hardware costs when using Large Language Models\n(LLMs). Unfortunately, many tasks, such as summarization, inevitably introduce\nlong task-inputs, and the wide application of in-context learning easily makes\nthe prompt length explode. Inspired by the language understanding ability of\nLLMs, this paper proposes SelfCP, which uses the LLM \\textbf{itself} to\n\\textbf{C}ompress long \\textbf{P}rompt into compact virtual tokens. SelfCP\napplies a general frozen LLM twice, first as an encoder to compress the prompt\nand then as a decoder to generate responses. Specifically, given a long prompt,\nwe place special tokens within the lengthy segment for compression and signal\nthe LLM to generate $k$ virtual tokens. Afterward, the virtual tokens\nconcatenate with the uncompressed prompt and are fed into the same LLM to\ngenerate the response. In general, SelfCP facilitates the unconditional and\nconditional compression of prompts, fitting both standard tasks and those with\nspecific objectives. Since the encoder and decoder are frozen, SelfCP only\ncontains 17M trainable parameters and allows for convenient adaptation across\nvarious backbones. We implement SelfCP with two LLM backbones and evaluate it\nin both in- and out-domain tasks. Results show that the compressed virtual\ntokens can substitute $12 \\times$ larger original prompts effectively'}, {'Published': '2024-04-18', 'Title': 'Adapting LLMs for Efficient Context Processing through Soft Prompt Compression', 'Authors': 'Cangqing Wang, Yutian Yang, Ruisi Li, Dan Sun, Ruicong Cai, Yuzhu Zhang, Chengqian Fu, Lillian Floyd', 'Summary': "The rapid advancement of Large Language Models (LLMs) has inaugurated a\ntransformative epoch in natural language processing, fostering unprecedented\nproficiency in text generation, comprehension, and contextual scrutiny.\nNevertheless, effectively handling extensive contexts, crucial for myriad\napplications, poses a formidable obstacle owing to the intrinsic constraints of\nthe models' context window sizes and the computational burdens entailed by\ntheir operations. This investigation presents an innovative framework that\nstrategically tailors LLMs for streamlined context processing by harnessing the\nsynergies among natural language summarization, soft prompt compression, and\naugmented utility preservation mechanisms. Our methodology, dubbed\nSoftPromptComp, amalgamates natural language prompts extracted from\nsummarization methodologies with dynamically generated soft prompts to forge a\nconcise yet semantically robust depiction of protracted contexts. This\ndepiction undergoes further refinement via a weighting mechanism optimizing\ninformation retention and utility for subsequent tasks. We substantiate that\nour framework markedly diminishes computational overhead and enhances LLMs'\nefficacy across various benchmarks, while upholding or even augmenting the\ncaliber of the produced content. By amalgamating soft prompt compression with\nsophisticated summarization, SoftPromptComp confronts the dual challenges of\nmanaging lengthy contexts and ensuring model scalability. Our findings point\ntowards a propitious trajectory for augmenting LLMs' applicability and\nefficiency, rendering them more versatile and pragmatic for real-world\napplications. This research enriches the ongoing discourse on optimizing\nlanguage models, providing insights into the potency of soft prompts and\nsummarization techniques as pivotal instruments for the forthcoming generation\nof NLP solutions."}, {'Published': '2023-12-06', 'Title': 'LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models', 'Authors': 'Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu', 'Summary': 'Large language models (LLMs) have been applied in various applications due to\ntheir astonishing capabilities. With advancements in technologies such as\nchain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed\nto LLMs are becoming increasingly lengthy, even exceeding tens of thousands of\ntokens. To accelerate model inference and reduce cost, this paper presents\nLLMLingua, a coarse-to-fine prompt compression method that involves a budget\ncontroller to maintain semantic integrity under high compression ratios, a\ntoken-level iterative compression algorithm to better model the interdependence\nbetween compressed contents, and an instruction tuning based method for\ndistribution alignment between language models. We conduct experiments and\nanalysis over four datasets from different scenarios, i.e., GSM8K, BBH,\nShareGPT, and Arxiv-March23; showing that the proposed approach yields\nstate-of-the-art performance and allows for up to 20x compression with little\nperformance loss. Our code is available at https://aka.ms/LLMLingua.'}, {'Published': '2024-04-02', 'Title': 'Learning to Compress Prompt in Natural Language Formats', 'Authors': 'Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, Xia Hu', 'Summary': 'Large language models (LLMs) are great at processing multiple natural\nlanguage processing tasks, but their abilities are constrained by inferior\nperformance with long context, slow inference speed, and the high cost of\ncomputing the results. Deploying LLMs with precise and informative context\nhelps users process large-scale datasets more effectively and cost-efficiently.\nExisting works rely on compressing long prompt contexts into soft prompts.\nHowever, soft prompt compression encounters limitations in transferability\nacross different LLMs, especially API-based LLMs. To this end, this work aims\nto compress lengthy prompts in the form of natural language with LLM\ntransferability. This poses two challenges: (i) Natural Language (NL) prompts\nare incompatible with back-propagation, and (ii) NL prompts lack flexibility in\nimposing length constraints. In this work, we propose a Natural Language Prompt\nEncapsulation (Nano-Capsulator) framework compressing original prompts into NL\nformatted Capsule Prompt while maintaining the prompt utility and\ntransferability. Specifically, to tackle the first challenge, the\nNano-Capsulator is optimized by a reward function that interacts with the\nproposed semantics preserving loss. To address the second question, the\nNano-Capsulator is optimized by a reward function featuring length constraints.\nExperimental results demonstrate that the Capsule Prompt can reduce 81.4% of\nthe original length, decrease inference latency up to 4.5x, and save 80.1% of\nbudget overheads while providing transferability across diverse LLMs and\ndifferent datasets.'}, {'Published': '2024-03-30', 'Title': 'PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression', 'Authors': 'Muhammad Asif Ali, Zhengping Li, Shu Yang, Keyuan Cheng, Yang Cao, Tianhao Huang, Lijie Hu, Lu Yu, Di Wang', 'Summary': "Large language models (LLMs) have shown exceptional abilities for multiple\ndifferent natural language processing tasks. While prompting is a crucial tool\nfor LLM inference, we observe that there is a significant cost associated with\nexceedingly lengthy prompts. Existing attempts to compress lengthy prompts lead\nto sub-standard results in terms of readability and interpretability of the\ncompressed prompt, with a detrimental impact on prompt utility. To address\nthis, we propose PROMPT-SAW: Prompt compresSion via Relation AWare graphs, an\neffective strategy for prompt compression over task-agnostic and task-aware\nprompts. PROMPT-SAW uses the prompt's textual information to build a graph,\nlater extracts key information elements in the graph to come up with the\ncompressed prompt. We also propose GSM8K-AUG, i.e., an extended version of the\nexisting GSM8k benchmark for task-agnostic prompts in order to provide a\ncomprehensive evaluation platform. Experimental evaluation using benchmark\ndatasets shows that prompts compressed by PROMPT-SAW are not only better in\nterms of readability, but they also outperform the best-performing baseline\nmodels by up to 14.3 and 13.7 respectively for task-aware and task-agnostic\nsettings while compressing the original prompt text by 33.0 and 56.7."}, {'Published': '2024-02-25', 'Title': 'Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression', 'Authors': 'Xinze Li, Zhenghao Liu, Chenyan Xiong, Shi Yu, Yukun Yan, Shuo Wang, Ge Yu', 'Summary': 'Large language models (LLMs) require lengthy prompts as the input context to\nproduce output aligned with user intentions, a process that incurs extra costs\nduring inference. In this paper, we propose the Gist COnditioned deCOding\n(Gist-COCO) model, introducing a novel method for compressing prompts which\nalso can assist the prompt interpretation and engineering. Gist-COCO employs an\nencoder-decoder based language model and then incorporates an additional\nencoder as a plugin module to compress prompts with inputs using gist tokens.\nIt finetunes the compression plugin module and uses the representations of gist\ntokens to emulate the raw prompts in the vanilla language model. By verbalizing\nthe representations of gist tokens into gist prompts, the compression ability\nof Gist-COCO can be generalized to different LLMs with high compression rates.\nOur experiments demonstrate that Gist-COCO outperforms previous prompt\ncompression models in both passage and instruction compression tasks. Further\nanalysis on gist verbalization results suggests that our gist prompts serve\ndifferent functions in aiding language models. They may directly provide\npotential answers, generate the chain-of-thought, or simply repeat the inputs.\nAll data and codes are available at https://github.com/OpenMatch/Gist-COCO .'}, {'Published': '2023-10-10', 'Title': 'Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt', 'Authors': 'Zhaozhuo Xu, Zirui Liu, Beidi Chen, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu, Anshumali Shrivastava', 'Summary': "While the numerous parameters in Large Language Models (LLMs) contribute to\ntheir superior performance, this massive scale makes them inefficient and\nmemory-hungry. Thus, they are hard to deploy on commodity hardware, such as one\nsingle GPU. Given the memory and power constraints of such devices, model\ncompression methods are widely employed to reduce both the model size and\ninference latency, which essentially trades off model quality in return for\nimproved efficiency. Thus, optimizing this accuracy-efficiency trade-off is\ncrucial for the LLM deployment on commodity hardware. In this paper, we\nintroduce a new perspective to optimize this trade-off by prompting compressed\nmodels. Specifically, we first observe that for certain questions, the\ngeneration quality of a compressed LLM can be significantly improved by adding\ncarefully designed hard prompts, though this isn't the case for all questions.\nBased on this observation, we propose a soft prompt learning method where we\nexpose the compressed model to the prompt learning process, aiming to enhance\nthe performance of prompts. Our experimental analysis suggests our soft prompt\nstrategy greatly improves the performance of the 8x compressed LLaMA-7B model\n(with a joint 4-bit quantization and 50% weight pruning compression), allowing\nthem to match their uncompressed counterparts on popular benchmarks. Also, we\ndemonstrate that these learned prompts can be transferred across various\ndatasets, tasks, and compression levels. Hence with this transferability, we\ncan stitch the soft prompt to a newly compressed model to improve the test-time\naccuracy in an ``in-situ'' way."}, {'Published': '2024-04-01', 'Title': 'Efficient Prompting Methods for Large Language Models: A Survey', 'Authors': 'Kaiyan Chang, Songcheng Xu, Chenglong Wang, Yingfeng Luo, Tong Xiao, Jingbo Zhu', 'Summary': 'Prompting has become a mainstream paradigm for adapting large language models\n(LLMs) to specific natural language processing tasks. While this approach opens\nthe door to in-context learning of LLMs, it brings the additional computational\nburden of model inference and human effort of manual-designed prompts,\nparticularly when using lengthy and complex prompts to guide and control the\nbehavior of LLMs. As a result, the LLM field has seen a remarkable surge in\nefficient prompting methods. In this paper, we present a comprehensive overview\nof these methods. At a high level, efficient prompting methods can broadly be\ncategorized into two approaches: prompting with efficient computation and\nprompting with efficient design. The former involves various ways of\ncompressing prompts, and the latter employs techniques for automatic prompt\noptimization. We present the basic concepts of prompting, review the advances\nfor efficient prompting, and highlight future research directions.'}, {'Published': '2023-10-10', 'Title': 'Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction', 'Authors': 'Cheng Peng, Xi Yang, Kaleb E Smith, Zehao Yu, Aokun Chen, Jiang Bian, Yonghui Wu', 'Summary': 'Objective To develop soft prompt-based learning algorithms for large language\nmodels (LLMs), examine the shape of prompts, prompt-tuning using\nfrozen/unfrozen LLMs, transfer learning, and few-shot learning abilities.\nMethods We developed a soft prompt-based LLM model and compared 4 training\nstrategies including (1) fine-tuning without prompts; (2) hard-prompt with\nunfrozen LLMs; (3) soft-prompt with unfrozen LLMs; and (4) soft-prompt with\nfrozen LLMs. We evaluated 7 pretrained LLMs using the 4 training strategies for\nclinical concept and relation extraction on two benchmark datasets. We\nevaluated the transfer learning ability of the prompt-based learning algorithms\nin a cross-institution setting. We also assessed the few-shot learning ability.\nResults and Conclusion When LLMs are unfrozen, GatorTron-3.9B with soft\nprompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept\nextraction, outperforming the traditional fine-tuning and hard prompt-based\nmodels by 0.6~3.1% and 1.2~2.9%, respectively; GatorTron-345M with soft\nprompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end\nrelation extraction, outperforming the other two models by 0.2~2% and\n0.6~11.7%, respectively. When LLMs are frozen, small (i.e., 345 million\nparameters) LLMs have a big gap to be competitive with unfrozen models; scaling\nLLMs up to billions of parameters makes frozen LLMs competitive with unfrozen\nLLMs. For cross-institute evaluation, soft prompting with a frozen\nGatorTron-8.9B model achieved the best performance. This study demonstrates\nthat (1) machines can learn soft prompts better than humans, (2) frozen LLMs\nhave better few-shot learning ability and transfer learning ability to\nfacilitate muti-institution applications, and (3) frozen LLMs require large\nmodels.'}, {'Published': '2024-02-16', 'Title': 'Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications', 'Authors': 'Duc N. M Hoang, Minsik Cho, Thomas Merth, Mohammad Rastegari, Zhangyang Wang', 'Summary': 'Compressing Large Language Models (LLMs) often leads to reduced performance,\nespecially for knowledge-intensive tasks. In this work, we dive into how\ncompression damages LLMs\' inherent knowledge and the possible remedies. We\nstart by proposing two conjectures on the nature of the damage: one is certain\nknowledge being forgotten (or erased) after LLM compression, hence\nnecessitating the compressed model to (re)learn from data with additional\nparameters; the other presumes that knowledge is internally displaced and hence\none requires merely "inference re-direction" with input-side augmentation such\nas prompting, to recover the knowledge-related performance. Extensive\nexperiments are then designed to (in)validate the two conjectures. We observe\nthe promise of prompting in comparison to model tuning; we further unlock\nprompting\'s potential by introducing a variant called Inference-time Dynamic\nPrompting (IDP), that can effectively increase prompt diversity without\nincurring any inference overhead. Our experiments consistently suggest that\ncompared to the classical re-training alternatives such as LoRA, prompting with\nIDP leads to better or comparable post-compression performance recovery, while\nsaving the extra parameter size by 21x and reducing inference latency by 60%.\nOur experiments hence strongly endorse the conjecture of "knowledge displaced"\nover "knowledge forgotten", and shed light on a new efficient mechanism to\nrestore compressed LLM performance. We additionally visualize and analyze the\ndifferent attention and activation patterns between prompted and re-trained\nmodels, demonstrating they achieve performance recovery in two different\nregimes.'}][0m[32;1m[1;3mHere are some research papers on the topic Prompt Compression in LLM Applications:
1. "SelfCP: Compressing Long Prompt to 1/12 Using the Frozen Large Language Model Itself" by Jun Gao
2. "Adapting LLMs for Efficient Context Processing through Soft Prompt Compression" by Cangqing Wang, Yutian Yang, Ruisi Li, Dan Sun, Ruicong Cai, Yuzhu Zhang, Chengqian Fu, Lillian Floyd
3. "LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models" by Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu
4. "Learning to Compress Prompt in Natural Language Formats" by Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, Xia Hu
5. "PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression"[0m
[1m> Finished chain.[0m
{'input': 'Get me a list of research papers on the topic Prompt Compression in LLM Applications.',
'chat_history': '',
'output': 'Here are some research papers on the topic Prompt Compression in LLM Applications:\n\n1. "SelfCP: Compressing Long Prompt to 1/12 Using the Frozen Large Language Model Itself" by Jun Gao\n2. "Adapting LLMs for Efficient Context Processing through Soft Prompt Compression" by Cangqing Wang, Yutian Yang, Ruisi Li, Dan Sun, Ruicong Cai, Yuzhu Zhang, Chengqian Fu, Lillian Floyd\n3. "LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models" by Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu\n4. "Learning to Compress Prompt in Natural Language Formats" by Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, Xia Hu\n5. "PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression"'}
```python
agent_executor.invoke({"input": "What paper did we speak about from our chat history?"})
```
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m
Invoking: `get_metadata_information_from_arxiv` with `{'word': 'chat history'}`
responded: I need to access the chat history to answer this question.
[0m[33;1m[1;3m[{'Published': '2023-10-20', 'Title': 'Towards Detecting Contextual Real-Time Toxicity for In-Game Chat', 'Authors': 'Zachary Yang, Nicolas Grenan-Godbout, Reihaneh Rabbany', 'Summary': "Real-time toxicity detection in online environments poses a significant\nchallenge, due to the increasing prevalence of social media and gaming\nplatforms. We introduce ToxBuster, a simple and scalable model that reliably\ndetects toxic content in real-time for a line of chat by including chat history\nand metadata. ToxBuster consistently outperforms conventional toxicity models\nacross popular multiplayer games, including Rainbow Six Siege, For Honor, and\nDOTA 2. We conduct an ablation study to assess the importance of each model\ncomponent and explore ToxBuster's transferability across the datasets.\nFurthermore, we showcase ToxBuster's efficacy in post-game moderation,\nsuccessfully flagging 82.1% of chat-reported players at a precision level of\n90.0%. Additionally, we show how an additional 6% of unreported toxic players\ncan be proactively moderated."}, {'Published': '2021-07-13', 'Title': "A First Look at Developers' Live Chat on Gitter", 'Authors': 'Lin Shi, Xiao Chen, Ye Yang, Hanzhi Jiang, Ziyou Jiang, Nan Niu, Qing Wang', 'Summary': "Modern communication platforms such as Gitter and Slack play an increasingly\ncritical role in supporting software teamwork, especially in open source\ndevelopment.Conversations on such platforms often contain intensive, valuable\ninformation that may be used for better understanding OSS developer\ncommunication and collaboration. However, little work has been done in this\nregard. To bridge the gap, this paper reports a first comprehensive empirical\nstudy on developers' live chat, investigating when they interact, what\ncommunity structures look like, which topics are discussed, and how they\ninteract. We manually analyze 749 dialogs in the first phase, followed by an\nautomated analysis of over 173K dialogs in the second phase. We find that\ndevelopers tend to converse more often on weekdays, especially on Wednesdays\nand Thursdays (UTC), that there are three common community structures observed,\nthat developers tend to discuss topics such as API usages and errors, and that\nsix dialog interaction patterns are identified in the live chat communities.\nBased on the findings, we provide recommendations for individual developers and\nOSS communities, highlight desired features for platform vendors, and shed\nlight on future research directions. We believe that the findings and insights\nwill enable a better understanding of developers' live chat, pave the way for\nother researchers, as well as a better utilization and mining of knowledge\nembedded in the massive chat history."}, {'Published': '2022-02-28', 'Title': 'MSCTD: A Multimodal Sentiment Chat Translation Dataset', 'Authors': 'Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, Jie Zhou', 'Summary': 'Multimodal machine translation and textual chat translation have received\nconsiderable attention in recent years. Although the conversation in its\nnatural form is usually multimodal, there still lacks work on multimodal\nmachine translation in conversations. In this work, we introduce a new task\nnamed Multimodal Chat Translation (MCT), aiming to generate more accurate\ntranslations with the help of the associated dialogue history and visual\ncontext. To this end, we firstly construct a Multimodal Sentiment Chat\nTranslation Dataset (MSCTD) containing 142,871 English-Chinese utterance pairs\nin 14,762 bilingual dialogues and 30,370 English-German utterance pairs in\n3,079 bilingual dialogues. Each utterance pair, corresponding to the visual\ncontext that reflects the current conversational scene, is annotated with a\nsentiment label. Then, we benchmark the task by establishing multiple baseline\nsystems that incorporate multimodal and sentiment features for MCT. Preliminary\nexperiments on four language directions (English-Chinese and English-German)\nverify the potential of contextual and multimodal information fusion and the\npositive impact of sentiment on the MCT task. Additionally, as a by-product of\nthe MSCTD, it also provides two new benchmarks on multimodal dialogue sentiment\nanalysis. Our work can facilitate research on both multimodal chat translation\nand multimodal dialogue sentiment analysis.'}, {'Published': '2021-09-15', 'Title': 'ISPY: Automatic Issue-Solution Pair Extraction from Community Live Chats', 'Authors': 'Lin Shi, Ziyou Jiang, Ye Yang, Xiao Chen, Yumin Zhang, Fangwen Mu, Hanzhi Jiang, Qing Wang', 'Summary': 'Collaborative live chats are gaining popularity as a development\ncommunication tool. In community live chatting, developers are likely to post\nissues they encountered (e.g., setup issues and compile issues), and other\ndevelopers respond with possible solutions. Therefore, community live chats\ncontain rich sets of information for reported issues and their corresponding\nsolutions, which can be quite useful for knowledge sharing and future reuse if\nextracted and restored in time. However, it remains challenging to accurately\nmine such knowledge due to the noisy nature of interleaved dialogs in live chat\ndata. In this paper, we first formulate the problem of issue-solution pair\nextraction from developer live chat data, and propose an automated approach,\nnamed ISPY, based on natural language processing and deep learning techniques\nwith customized enhancements, to address the problem. Specifically, ISPY\nautomates three tasks: 1) Disentangle live chat logs, employing a feedforward\nneural network to disentangle a conversation history into separate dialogs\nautomatically; 2) Detect dialogs discussing issues, using a novel convolutional\nneural network (CNN), which consists of a BERT-based utterance embedding layer,\na context-aware dialog embedding layer, and an output layer; 3) Extract\nappropriate utterances and combine them as corresponding solutions, based on\nthe same CNN structure but with different feeding inputs. To evaluate ISPY, we\ncompare it with six baselines, utilizing a dataset with 750 dialogs including\n171 issue-solution pairs and evaluate ISPY from eight open source communities.\nThe results show that, for issue-detection, our approach achieves the F1 of\n76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63%\nfor solution-extraction and outperforms the baselines by 20%.'}, {'Published': '2023-05-23', 'Title': 'ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings', 'Authors': 'Yuki Saito, Shinnosuke Takamichi, Eiji Iimori, Kentaro Tachibana, Hiroshi Saruwatari', 'Summary': "We propose ChatGPT-EDSS, an empathetic dialogue speech synthesis (EDSS)\nmethod using ChatGPT for extracting dialogue context. ChatGPT is a chatbot that\ncan deeply understand the content and purpose of an input prompt and\nappropriately respond to the user's request. We focus on ChatGPT's reading\ncomprehension and introduce it to EDSS, a task of synthesizing speech that can\nempathize with the interlocutor's emotion. Our method first gives chat history\nto ChatGPT and asks it to generate three words representing the intention,\nemotion, and speaking style for each line in the chat. Then, it trains an EDSS\nmodel using the embeddings of ChatGPT-derived context words as the conditioning\nfeatures. The experimental results demonstrate that our method performs\ncomparably to ones using emotion labels or neural network-derived context\nembeddings learned from chat histories. The collected ChatGPT-derived context\ninformation is available at\nhttps://sarulab-speech.github.io/demo_ChatGPT_EDSS/."}, {'Published': '2019-06-04', 'Title': 'Joint Effects of Context and User History for Predicting Online Conversation Re-entries', 'Authors': 'Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong', 'Summary': "As the online world continues its exponential growth, interpersonal\ncommunication has come to play an increasingly central role in opinion\nformation and change. In order to help users better engage with each other\nonline, we study a challenging problem of re-entry prediction foreseeing\nwhether a user will come back to a conversation they once participated in. We\nhypothesize that both the context of the ongoing conversations and the users'\nprevious chatting history will affect their continued interests in future\nengagement. Specifically, we propose a neural framework with three main layers,\neach modeling context, user history, and interactions between them, to explore\nhow the conversation context and user chatting history jointly result in their\nre-entry behavior. We experiment with two large-scale datasets collected from\nTwitter and Reddit. Results show that our proposed framework with bi-attention\nachieves an F1 score of 61.1 on Twitter conversations, outperforming the\nstate-of-the-art methods from previous work."}, {'Published': '2022-01-27', 'Title': 'Group Chat Ecology in Enterprise Instant Messaging: How Employees Collaborate Through Multi-User Chat Channels on Slack', 'Authors': 'Dakuo Wang, Haoyu Wang, Mo Yu, Zahra Ashktorab, Ming Tan', 'Summary': "Despite the long history of studying instant messaging usage, we know very\nlittle about how today's people participate in group chat channels and interact\nwith others inside a real-world organization. In this short paper, we aim to\nupdate the existing knowledge on how group chat is used in the context of\ntoday's organizations. The knowledge is particularly important for the new norm\nof remote works under the COVID-19 pandemic. We have the privilege of\ncollecting two valuable datasets: a total of 4,300 group chat channels in Slack\nfrom an R&D department in a multinational IT company; and a total of 117\ngroups' performance data. Through qualitative coding of 100 randomly sampled\ngroup channels from the 4,300 channels dataset, we identified and reported 9\ncategories such as Project channels, IT-Support channels, and Event channels.\nWe further defined a feature metric with 21 meta features (and their derived\nfeatures) without looking at the message content to depict the group\ncommunication style for these group chat channels, with which we successfully\ntrained a machine learning model that can automatically classify a given group\nchannel into one of the 9 categories. In addition to the descriptive data\nanalysis, we illustrated how these communication metrics can be used to analyze\nteam performance. We cross-referenced 117 project teams and their team-based\nSlack channels and identified 57 teams that appeared in both datasets, then we\nbuilt a regression model to reveal the relationship between these group\ncommunication styles and the project team performance. This work contributes an\nupdated empirical understanding of human-human communication practices within\nthe enterprise setting, and suggests design opportunities for the future of\nhuman-AI communication experience."}, {'Published': '2023-05-21', 'Title': 'ToxBuster: In-game Chat Toxicity Buster with BERT', 'Authors': 'Zachary Yang, Yasmine Maricar, MohammadReza Davari, Nicolas Grenon-Godbout, Reihaneh Rabbany', 'Summary': 'Detecting toxicity in online spaces is challenging and an ever more pressing\nproblem given the increase in social media and gaming consumption. We introduce\nToxBuster, a simple and scalable model trained on a relatively large dataset of\n194k lines of game chat from Rainbow Six Siege and For Honor, carefully\nannotated for different kinds of toxicity. Compared to the existing\nstate-of-the-art, ToxBuster achieves 82.95% (+7) in precision and 83.56% (+57)\nin recall. This improvement is obtained by leveraging past chat history and\nmetadata. We also study the implication towards real-time and post-game\nmoderation as well as the model transferability from one game to another.'}, {'Published': '2023-07-30', 'Title': 'ChatGPT is Good but Bing Chat is Better for Vietnamese Students', 'Authors': 'Xuan-Quy Dao, Ngoc-Bich Le', 'Summary': 'This study examines the efficacy of two SOTA large language models (LLMs),\nnamely ChatGPT and Microsoft Bing Chat (BingChat), in catering to the needs of\nVietnamese students. Although ChatGPT exhibits proficiency in multiple\ndisciplines, Bing Chat emerges as the more advantageous option. We conduct a\ncomparative analysis of their academic achievements in various disciplines,\nencompassing mathematics, literature, English language, physics, chemistry,\nbiology, history, geography, and civic education. The results of our study\nsuggest that BingChat demonstrates superior performance compared to ChatGPT\nacross a wide range of subjects, with the exception of literature, where\nChatGPT exhibits better performance. Additionally, BingChat utilizes the more\nadvanced GPT-4 technology in contrast to ChatGPT, which is built upon GPT-3.5.\nThis allows BingChat to improve to comprehension, reasoning and generation of\ncreative and informative text. Moreover, the fact that BingChat is accessible\nin Vietnam and its integration of hyperlinks and citations within responses\nserve to reinforce its superiority. In our analysis, it is evident that while\nChatGPT exhibits praiseworthy qualities, BingChat presents a more apdated\nsolutions for Vietnamese students.'}, {'Published': '2020-04-23', 'Title': 'Distilling Knowledge for Fast Retrieval-based Chat-bots', 'Authors': 'Amir Vakili Tahami, Kamyar Ghajar, Azadeh Shakery', 'Summary': 'Response retrieval is a subset of neural ranking in which a model selects a\nsuitable response from a set of candidates given a conversation history.\nRetrieval-based chat-bots are typically employed in information seeking\nconversational systems such as customer support agents. In order to make\npairwise comparisons between a conversation history and a candidate response,\ntwo approaches are common: cross-encoders performing full self-attention over\nthe pair and bi-encoders encoding the pair separately. The former gives better\nprediction quality but is too slow for practical use. In this paper, we propose\na new cross-encoder architecture and transfer knowledge from this model to a\nbi-encoder model using distillation. This effectively boosts bi-encoder\nperformance at no cost during inference time. We perform a detailed analysis of\nthis approach on three response retrieval datasets.'}][0m[32;1m[1;3mThe paper we spoke about from our chat history is "ToxBuster: In-game Chat Toxicity Buster with BERT" by Zachary Yang, Yasmine Maricar, MohammadReza Davari, Nicolas Grenon-Godbout, and Reihaneh Rabbany.[0m
[1m> Finished chain.[0m
{'input': 'What paper did we speak about from our chat history?',
'chat_history': 'Human: Get me a list of research papers on the topic Prompt Compression in LLM Applications.\nAI: Here are some research papers on the topic Prompt Compression in LLM Applications:\n\n1. "SelfCP: Compressing Long Prompt to 1/12 Using the Frozen Large Language Model Itself" by Jun Gao\n2. "Adapting LLMs for Efficient Context Processing through Soft Prompt Compression" by Cangqing Wang, Yutian Yang, Ruisi Li, Dan Sun, Ruicong Cai, Yuzhu Zhang, Chengqian Fu, Lillian Floyd\n3. "LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models" by Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu\n4. "Learning to Compress Prompt in Natural Language Formats" by Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, Xia Hu\n5. "PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression"',
'output': 'The paper we spoke about from our chat history is "ToxBuster: In-game Chat Toxicity Buster with BERT" by Zachary Yang, Yasmine Maricar, MohammadReza Davari, Nicolas Grenon-Godbout, and Reihaneh Rabbany.'}
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@cookbook@agent_fireworks_ai_langchain_mongodb.ipynb@.PATH_END.py
|
{
"filename": "_font.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/layout/title/subtitle/_font.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Font(_BaseLayoutHierarchyType):
# class properties
# --------------------
_parent_path_str = "layout.title.subtitle"
_path_str = "layout.title.subtitle.font"
_valid_props = {
"color",
"family",
"lineposition",
"shadow",
"size",
"style",
"textcase",
"variant",
"weight",
}
# color
# -----
@property
def color(self):
"""
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# family
# ------
@property
def family(self):
"""
HTML font family - the typeface that will be applied by the web
browser. The web browser will only be able to apply a font if
it is available on the system which it operates. Provide
multiple font families, separated by commas, to indicate the
preference in which to apply fonts if they aren't available on
the system. The Chart Studio Cloud (at https://chart-
studio.plotly.com or on-premise) generates images on a server,
where only a select number of fonts are installed and
supported. These include "Arial", "Balto", "Courier New",
"Droid Sans", "Droid Serif", "Droid Sans Mono", "Gravitas One",
"Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow",
"Raleway", "Times New Roman".
The 'family' property is a string and must be specified as:
- A non-empty string
Returns
-------
str
"""
return self["family"]
@family.setter
def family(self, val):
self["family"] = val
# lineposition
# ------------
@property
def lineposition(self):
"""
Sets the kind of decoration line(s) with text, such as an
"under", "over" or "through" as well as combinations e.g.
"under+over", etc.
The 'lineposition' property is a flaglist and may be specified
as a string containing:
- Any combination of ['under', 'over', 'through'] joined with '+' characters
(e.g. 'under+over')
OR exactly one of ['none'] (e.g. 'none')
Returns
-------
Any
"""
return self["lineposition"]
@lineposition.setter
def lineposition(self, val):
self["lineposition"] = val
# shadow
# ------
@property
def shadow(self):
"""
Sets the shape and color of the shadow behind text. "auto"
places minimal shadow and applies contrast text font color. See
https://developer.mozilla.org/en-US/docs/Web/CSS/text-shadow
for additional options.
The 'shadow' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["shadow"]
@shadow.setter
def shadow(self, val):
self["shadow"] = val
# size
# ----
@property
def size(self):
"""
The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
Returns
-------
int|float
"""
return self["size"]
@size.setter
def size(self, val):
self["size"] = val
# style
# -----
@property
def style(self):
"""
Sets whether a font should be styled with a normal or italic
face from its family.
The 'style' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'italic']
Returns
-------
Any
"""
return self["style"]
@style.setter
def style(self, val):
self["style"] = val
# textcase
# --------
@property
def textcase(self):
"""
Sets capitalization of text. It can be used to make text appear
in all-uppercase or all-lowercase, or with each word
capitalized.
The 'textcase' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'word caps', 'upper', 'lower']
Returns
-------
Any
"""
return self["textcase"]
@textcase.setter
def textcase(self, val):
self["textcase"] = val
# variant
# -------
@property
def variant(self):
"""
Sets the variant of the font.
The 'variant' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'small-caps', 'all-small-caps',
'all-petite-caps', 'petite-caps', 'unicase']
Returns
-------
Any
"""
return self["variant"]
@variant.setter
def variant(self, val):
self["variant"] = val
# weight
# ------
@property
def weight(self):
"""
Sets the weight (or boldness) of the font.
The 'weight' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [1, 1000]
OR exactly one of ['normal', 'bold'] (e.g. 'bold')
Returns
-------
int
"""
return self["weight"]
@weight.setter
def weight(self, val):
self["weight"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
"""
def __init__(
self,
arg=None,
color=None,
family=None,
lineposition=None,
shadow=None,
size=None,
style=None,
textcase=None,
variant=None,
weight=None,
**kwargs,
):
"""
Construct a new Font object
Sets the subtitle font.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.layout.title.subtitle.Font`
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
Returns
-------
Font
"""
super(Font, self).__init__("font")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.layout.title.subtitle.Font
constructor must be a dict or
an instance of :class:`plotly.graph_objs.layout.title.subtitle.Font`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("family", None)
_v = family if family is not None else _v
if _v is not None:
self["family"] = _v
_v = arg.pop("lineposition", None)
_v = lineposition if lineposition is not None else _v
if _v is not None:
self["lineposition"] = _v
_v = arg.pop("shadow", None)
_v = shadow if shadow is not None else _v
if _v is not None:
self["shadow"] = _v
_v = arg.pop("size", None)
_v = size if size is not None else _v
if _v is not None:
self["size"] = _v
_v = arg.pop("style", None)
_v = style if style is not None else _v
if _v is not None:
self["style"] = _v
_v = arg.pop("textcase", None)
_v = textcase if textcase is not None else _v
if _v is not None:
self["textcase"] = _v
_v = arg.pop("variant", None)
_v = variant if variant is not None else _v
if _v is not None:
self["variant"] = _v
_v = arg.pop("weight", None)
_v = weight if weight is not None else _v
if _v is not None:
self["weight"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@layout@title@subtitle@_font.py@.PATH_END.py
|
{
"filename": "_version.py",
"repo_name": "simonsobs/nemo",
"repo_path": "nemo_extracted/nemo-main/nemo/_version.py",
"type": "Python"
}
|
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains the computed version number.
# This file is released into the public domain. Generated by
# versioneer-0.19 (https://github.com/python-versioneer/python-versioneer)
"""Git implementation of _version.py."""
import errno
import os
import re
import subprocess
import sys
def get_keywords():
"""Get the keywords needed to look up the version information."""
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = " (HEAD -> main)"
git_full = "bd5107729ca0554317d506ce9129c0ed234b3985"
git_date = "2024-07-02 22:58:11 +0200"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
class VersioneerConfig:
"""Container for Versioneer configuration parameters."""
def get_config():
"""Create, populate and return the VersioneerConfig() object."""
# these strings are filled in when 'setup.py versioneer' creates
# _version.py
cfg = VersioneerConfig()
cfg.VCS = "git"
cfg.style = "pep440"
cfg.tag_prefix = "v"
cfg.parentdir_prefix = "nemo-"
cfg.versionfile_source = "nemo/_version.py"
cfg.verbose = False
return cfg
class NotThisMethod(Exception):
"""Exception raised if a method is not valid for the current scenario."""
LONG_VERSION_PY = {}
HANDLERS = {}
def register_vcs_handler(vcs, method): # decorator
"""Create decorator to mark a method as the handler of a VCS."""
def decorate(f):
"""Store f in HANDLERS[vcs][method]."""
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
env=None):
"""Call the given command(s)."""
assert isinstance(commands, list)
p = None
for c in commands:
try:
dispcmd = str([c] + args)
# remember shell=False, so use git.cmd on windows, not just git
p = subprocess.Popen([c] + args, cwd=cwd, env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr
else None))
break
except EnvironmentError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run %s" % dispcmd)
print(e)
return None, None
else:
if verbose:
print("unable to find command, tried %s" % (commands,))
return None, None
stdout = p.communicate()[0].strip().decode()
if p.returncode != 0:
if verbose:
print("unable to run %s (error)" % dispcmd)
print("stdout was %s" % stdout)
return None, p.returncode
return stdout, p.returncode
def versions_from_parentdir(parentdir_prefix, root, verbose):
"""Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
"""
rootdirs = []
for i in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
else:
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %s but none started with prefix %s" %
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
@register_vcs_handler("git", "get_keywords")
def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
f = open(versionfile_abs, "r")
for line in f.readlines():
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
f.close()
except EnvironmentError:
pass
return keywords
@register_vcs_handler("git", "keywords")
def git_versions_from_keywords(keywords, tag_prefix, verbose):
"""Get version information from git keywords."""
if not keywords:
raise NotThisMethod("no keywords at all, weird")
date = keywords.get("date")
if date is not None:
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
# git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
# datestamp. However we prefer "%ci" (which expands to an "ISO-8601
# -like" string, which we must then edit to make compliant), because
# it's been around since git-1.5.3, and it's too difficult to
# discover which version we're using, or to work around using an
# older one.
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = set([r.strip() for r in refnames.strip("()").split(",")])
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = set([r for r in refs if re.search(r'\d', r)])
if verbose:
print("discarding '%s', no digits" % ",".join(refs - tags))
if verbose:
print("likely tags: %s" % ",".join(sorted(tags)))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix):]
if verbose:
print("picking %s" % r)
return {"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": None,
"date": date}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": "no suitable tags", "date": None}
@register_vcs_handler("git", "pieces_from_vcs")
def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
"""Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
hide_stderr=True)
if rc != 0:
if verbose:
print("Directory %s not under git control" % root)
raise NotThisMethod("'git rev-parse --git-dir' returned error")
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
# if there isn't one, this yields HEX[-dirty] (no NUM)
describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
"--always", "--long",
"--match", "%s*" % tag_prefix],
cwd=root)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[:git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
if not mo:
# unparseable. Maybe git-describe is misbehaving?
pieces["error"] = ("unable to parse git-describe output: '%s'"
% describe_out)
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '%s' doesn't start with prefix '%s'"
print(fmt % (full_tag, tag_prefix))
pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
% (full_tag, tag_prefix))
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix):]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
cwd=root)
pieces["distance"] = int(count_out) # total number of commits
# commit date: see ISO-8601 comment in git_versions_from_keywords()
date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"],
cwd=root)[0].strip()
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
return pieces
def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
def render_pep440(pieces):
"""Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.%d.g%s" % (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_pre(pieces):
"""TAG[.post0.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post0.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post0.dev%d" % pieces["distance"]
else:
# exception #1
rendered = "0.post0.dev%d" % pieces["distance"]
return rendered
def render_pep440_post(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%s" % pieces["short"]
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g%s" % pieces["short"]
return rendered
def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered
def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render_git_describe_long(pieces):
"""TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render(pieces, style):
"""Render the given version pieces into the requested style."""
if pieces["error"]:
return {"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"],
"date": None}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '%s'" % style)
return {"version": rendered, "full-revisionid": pieces["long"],
"dirty": pieces["dirty"], "error": None,
"date": pieces.get("date")}
def get_versions():
"""Get version information or return default if unable to do so."""
# I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
# __file__, we can work backwards from there to the root. Some
# py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
# case we can only use expanded keywords.
cfg = get_config()
verbose = cfg.verbose
try:
return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
verbose)
except NotThisMethod:
pass
try:
root = os.path.realpath(__file__)
# versionfile_source is the relative path from the top of the source
# tree (where the .git directory might live) to this file. Invert
# this to find the root from __file__.
for i in cfg.versionfile_source.split('/'):
root = os.path.dirname(root)
except NameError:
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to find root of source tree",
"date": None}
try:
pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
return render(pieces, cfg.style)
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
except NotThisMethod:
pass
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to compute version", "date": None}
|
simonsobsREPO_NAMEnemoPATH_START.@nemo_extracted@nemo-main@nemo@_version.py@.PATH_END.py
|
{
"filename": "agent.py",
"repo_name": "simonsobs/socs",
"repo_path": "socs_extracted/socs-main/socs/agents/lakeshore372/agent.py",
"type": "Python"
}
|
import argparse
import os
import time
import numpy as np
import txaio
import yaml
from ocs import ocs_agent, site_config
from ocs.ocs_twisted import Pacemaker, TimeoutLock
from twisted.internet import reactor
from socs.Lakeshore.Lakeshore372 import LS372
def still_power_to_perc(power, res, lead, max_volts):
cur = np.sqrt(power / res)
volt = (lead + res) * cur
return 100 * volt / max_volts
class LS372_Agent:
"""Agent to connect to a single Lakeshore 372 device.
Args:
name (ApplicationSession): ApplicationSession for the Agent.
ip (str): IP Address for the 372 device.
dwell_time_delay (int, optional): Amount of time, in seconds, to
delay data collection after switching channels. Note this time
should not include the change pause time, which is automatically
accounted for. Will automatically be reduced to dwell_time - 1
second if it is set longer than a channel's dwell time. This
ensures at least one second of data collection at the end of a scan.
enable_control_chan (bool, optional):
If True, will read data from the control channel each iteration of
the acq loop. Defaults to False.
configfile (str, optional):
Path to a LS372 config file. This will be loaded by default by
input_configfile by default
"""
def __init__(self, agent, name, ip, dwell_time_delay=0,
enable_control_chan=False, configfile=None):
# self._acq_proc_lock is held for the duration of the acq Process.
# Tasks that require acq to not be running, at all, should use
# this lock.
self._acq_proc_lock = TimeoutLock()
# self._lock is held by the acq Process only when accessing
# the hardware but released occasionally so that (short) Tasks
# may run. Use a TimeoutLock to guarantee that a waiting
# Task gets activated preferentially, even if the acq thread
# immediately tries to reacquire.
self._lock = TimeoutLock(default_timeout=5)
self.name = name
self.ip = ip
self.dwell_time_delay = dwell_time_delay
self.module = None
self.thermometers = []
self.log = agent.log
self.initialized = False
self.take_data = False
self.control_chan_enabled = enable_control_chan
self.configfile = configfile
self.agent = agent
# Registers temperature feeds
agg_params = {
'frame_length': 10 * 60 # [sec]
}
self.agent.register_feed('temperatures',
record=True,
agg_params=agg_params,
buffer_time=1)
@ocs_agent.param('_')
def enable_control_chan(self, session, params=None):
"""enable_control_chan()
**Task** - Enables readout on the control channel (Channel A).
"""
self.control_chan_enabled = True
return True, 'Enabled control channel'
@ocs_agent.param('_')
def disable_control_chan(self, session, params=None):
"""disable_control_chan()
**Task** - Disables readout on the control channel (Channel A).
"""
self.control_chan_enabled = False
return True, 'Disabled control channel'
@ocs_agent.param('auto_acquire', default=False, type=bool)
@ocs_agent.param('acq_params', type=dict, default=None)
@ocs_agent.param('force', default=False, type=bool)
@ocs_agent.param('configfile', type=str, default=None)
def init_lakeshore(self, session, params=None):
"""init_lakeshore(auto_acquire=False, acq_params=None, force=False, \
configfile=None)
**Task** - Perform first time setup of the Lakeshore 372 communication.
Parameters:
auto_acquire (bool, optional): Default is False. Starts data
acquisition after initialization if True.
acq_params (dict, optional): Params to pass to acq process if
auto_acquire is True.
force (bool, optional): Force initialization, even if already
initialized. Defaults to False.
configfile (str, optional): .yaml file for initializing 372 channel
settings
"""
if params is None:
params = {}
if self.initialized and not params.get('force', False):
self.log.info("Lakeshore already initialized. Returning...")
return True, "Already initialized"
with self._lock.acquire_timeout(job='init') as acquired1, \
self._acq_proc_lock.acquire_timeout(timeout=0., job='init') \
as acquired2:
if not acquired1:
self.log.warn(f"Could not start init because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
if not acquired2:
self.log.warn(f"Could not start init because "
f"{self._acq_proc_lock.job} is already running")
return False, "Could not acquire lock"
try:
self.module = LS372(self.ip)
except ConnectionError:
self.log.error("Could not connect to the LS372. Exiting.")
reactor.callFromThread(reactor.stop)
return False, 'Lakeshore initialization failed'
except Exception as e:
self.log.error(f"Unhandled exception encountered: {e}")
reactor.callFromThread(reactor.stop)
return False, 'Lakeshore initialization failed'
print("Initialized Lakeshore module: {!s}".format(self.module))
session.add_message("Lakeshore initilized with ID: %s" % self.module.id)
self.thermometers = [channel.name for channel in self.module.channels]
self.initialized = True
if params.get('configfile') is not None:
self.input_configfile(session, params)
session.add_message("Lakeshore initial configurations uploaded using: %s" % params['configfile'])
# Start data acquisition if requested
if params.get('auto_acquire', False):
self.agent.start('acq', params.get('acq_params', None))
return True, 'Lakeshore module initialized.'
@ocs_agent.param('sample_heater', default=False, type=bool)
@ocs_agent.param('run_once', default=False, type=bool)
def acq(self, session, params=None):
"""acq(sample_heater=False, run_once=False)
**Process** - Acquire data from the Lakeshore 372.
Parameters:
sample_heater (bool, optional): Default is False. Will record
values from the sample heater, typically used to servo a DR if
True.
Notes:
The most recent data collected is stored in session data in the
structure::
>>> response.session['data']
{"fields":
{"Channel_05": {"T": 293.644, "R": 33.752, "timestamp": 1601924482.722671},
"Channel_06": {"T": 0, "R": 1022.44, "timestamp": 1601924499.5258765},
"Channel_08": {"T": 0, "R": 1026.98, "timestamp": 1601924494.8172355},
"Channel_01": {"T": 293.41, "R": 108.093, "timestamp": 1601924450.9315426},
"Channel_02": {"T": 293.701, "R": 30.7398, "timestamp": 1601924466.6130798},
"control": {"T": 293.701, "R": 30.7398, "timestamp": 1601924466.6130798}
}
}
"""
pm = Pacemaker(10, quantize=True)
with self._acq_proc_lock.acquire_timeout(timeout=0, job='acq') \
as acq_acquired, \
self._lock.acquire_timeout(job='acq') as acquired:
if not acq_acquired:
self.log.warn(f"Could not start Process because "
f"{self._acq_proc_lock.job} is already running")
return False, "Could not acquire lock"
if not acquired:
self.log.warn(f"Could not start Process because "
f"{self._lock.job} is holding the lock")
return False, "Could not acquire lock"
self.log.info("Starting data acquisition for {}".format(self.agent.agent_address))
previous_channel = None
last_release = time.time()
session.data = {"fields": {}}
self.take_data = True
while self.take_data:
pm.sleep()
# Relinquish sampling lock occasionally.
if time.time() - last_release > 1.:
last_release = time.time()
if not self._lock.release_and_acquire(timeout=10):
self.log.warn(f"Failed to re-acquire sampling lock, "
f"currently held by {self._lock.job}.")
continue
active_channel = self.module.get_active_channel()
# The 372 reports the last updated measurement repeatedly
# during the "pause change time", this results in several
# stale datapoints being recorded. To get around this we
# query the pause time and skip data collection during it
# if the channel has changed (as it would if autoscan is
# enabled.)
if previous_channel != active_channel:
if previous_channel is not None:
pause_time = active_channel.get_pause()
self.log.debug("Pause time for {c}: {p}",
c=active_channel.channel_num,
p=pause_time)
dwell_time = active_channel.get_dwell()
self.log.debug("User set dwell_time_delay: {p}",
p=self.dwell_time_delay)
# Check user set dwell time isn't too long
if self.dwell_time_delay > dwell_time:
self.log.warn("WARNING: User set dwell_time_delay of "
+ "{delay} s is larger than channel "
+ "dwell time of {chan_time} s. If "
+ "you are autoscanning this will "
+ "cause no data to be collected. "
+ "Reducing dwell time delay to {s} s.",
delay=self.dwell_time_delay,
chan_time=dwell_time,
s=dwell_time - 1)
total_time = pause_time + dwell_time - 1
else:
total_time = pause_time + self.dwell_time_delay
for i in range(total_time):
self.log.debug("Sleeping for {t} more seconds...",
t=total_time - i)
time.sleep(1)
# Track the last channel we measured
previous_channel = self.module.get_active_channel()
current_time = time.time()
data = {
'timestamp': current_time,
'block_name': active_channel.name,
'data': {}
}
# Collect both temperature and resistance values from each Channel
channel_str = active_channel.name.replace(' ', '_')
temp_reading = self.module.get_temp(unit='kelvin',
chan=active_channel.channel_num)
res_reading = self.module.get_temp(unit='ohms',
chan=active_channel.channel_num)
# For data feed
data['data'][channel_str + '_T'] = temp_reading
data['data'][channel_str + '_R'] = res_reading
session.app.publish_to_feed('temperatures', data)
self.log.debug("{data}", data=session.data)
# For session.data
field_dict = {channel_str: {"T": temp_reading,
"R": res_reading,
"timestamp": current_time}}
session.data['fields'].update(field_dict)
# Also queries control channel if enabled
if self.control_chan_enabled:
temp = self.module.get_temp(unit='kelvin', chan=0)
res = self.module.get_temp(unit='ohms', chan=0)
cur_time = time.time()
data = {
'timestamp': time.time(),
'block_name': 'control_chan',
'data': {
'control_T': temp,
'control_R': res
}
}
session.app.publish_to_feed('temperatures', data)
self.log.debug("{data}", data=session.data)
# Updates session data w/ control field
session.data['fields'].update({
'control': {
'T': temp, 'R': res, 'timestamp': cur_time
}
})
if params.get("sample_heater", False):
# Sample Heater
heater = self.module.sample_heater
hout = heater.get_sample_heater_output()
current_time = time.time()
htr_data = {
'timestamp': current_time,
'block_name': "heaters",
'data': {}
}
htr_data['data']['sample_heater_output'] = hout
session.app.publish_to_feed('temperatures', htr_data)
if params['run_once']:
break
return True, 'Acquisition exited cleanly.'
def _stop_acq(self, session, params=None):
"""
Stops acq process.
"""
if self.take_data:
session.set_status('stopping')
self.take_data = False
return True, 'requested to stop taking data.'
else:
return False, 'acq is not currently running'
@ocs_agent.param('heater', type=str)
@ocs_agent.param('range')
@ocs_agent.param('wait', type=float, default=0)
def set_heater_range(self, session, params):
"""set_heater_range(heater, range, wait=0)
**Task** - Adjust the heater range for servoing cryostat. Wait for a
specified amount of time after the change.
Parameters:
heater (str): Name of heater to set range for, either 'sample' or
'still'.
range (str, float): see arguments in
:func:`socs.Lakeshore.Lakeshore372.Heater.set_heater_range`
wait (float, optional): Amount of time to wait after setting the
heater range. This allows the servo time to adjust to the new range.
"""
with self._lock.acquire_timeout(job='set_heater_range') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
heater_string = params.get('heater', 'sample')
if heater_string.lower() == 'sample':
heater = self.module.sample_heater
elif heater_string.lower() == 'still':
heater = self.module.still_heater
current_range = heater.get_heater_range()
self.log.debug(f"Current heater range: {current_range}")
if params['range'] == current_range:
print("Current heater range matches commanded value. Proceeding unchanged.")
else:
heater.set_heater_range(params['range'])
time.sleep(params.get('wait', 0))
return True, f'Set {heater_string} heater range to {params["range"]}'
@ocs_agent.param('channel', type=int, check=lambda x: 1 <= x <= 16)
@ocs_agent.param('mode', type=str, choices=['current', 'voltage'])
def set_excitation_mode(self, session, params):
"""set_excitation_mode(channel, mode)
**Task** - Set the excitation mode of a specified channel.
Parameters:
channel (int): Channel to set the excitation mode for. Valid values
are 1-16.
mode (str): Excitation mode. Possible modes are 'current' or
'voltage'.
"""
with self._lock.acquire_timeout(job='set_excitation_mode') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
self.module.channels[params['channel']].set_excitation_mode(params['mode'])
session.add_message(f'post message in agent for Set channel {params["channel"]} excitation mode to {params["mode"]}')
print(f'print statement in agent for Set channel {params["channel"]} excitation mode to {params["mode"]}')
return True, f'return text for Set channel {params["channel"]} excitation mode to {params["mode"]}'
@ocs_agent.param('channel', type=int, check=lambda x: 1 <= x <= 16)
@ocs_agent.param('value', type=float)
def set_excitation(self, session, params):
"""set_excitation(channel, value)
**Task** - Set the excitation voltage/current value of a specified
channel.
Parameters:
channel (int): Channel to set the excitation for. Valid values
are 1-16.
value (float): Excitation value in volts or amps depending on set
excitation mode. See
:func:`socs.Lakeshore.Lakeshore372.Channel.set_excitation`
"""
with self._lock.acquire_timeout(job='set_excitation') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
current_excitation = self.module.channels[params['channel']].get_excitation()
mode = self.module.channels[params["channel"]].get_excitation_mode()
units = 'amps' if mode == 'current' else 'volts'
if params['value'] == current_excitation:
session.add_message(f'Channel {params["channel"]} excitation {mode} already set to {params["value"]} {units}')
else:
self.module.channels[params['channel']].set_excitation(params['value'])
session.add_message(f'Set channel {params["channel"]} excitation {mode} to {params["value"]} {units}')
return True, f'Set channel {params["channel"]} excitation to {params["value"]} {units}'
@ocs_agent.param('channel', type=int, check=lambda x: 1 <= x <= 16)
def get_excitation(self, session, params):
"""get_excitation(channel)
**Task** - Get the excitation voltage/current value of a specified
channel.
Parameters:
channel (int): Channel to get the excitation for. Valid values
are 1-16.
"""
with self._lock.acquire_timeout(job='get_excitation') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
current_excitation = self.module.channels[params["channel"]].get_excitation()
mode = self.module.channels[params["channel"]].get_excitation_mode()
units = 'amps' if mode == 'current' else 'volts'
session.add_message(f'Channel {params["channel"]} excitation {mode} is {current_excitation} {units}')
session.data = {"excitation": current_excitation}
return True, f'Channel {params["channel"]} excitation {mode} is {current_excitation} {units}'
@ocs_agent.param('channel', type=int, check=lambda x: 1 <= x <= 16)
@ocs_agent.param('resistance_range', type=float)
def set_resistance_range(self, session, params):
"""set_resistance_range(channel, resistance_range)
**Task** - Set the resistance range for a specified channel.
Parameters:
channel (int): Channel to set the resistance range for. Valid values
are 1-16.
resistance_range (float): range in ohms we want to measure. Doesn't
need to be exactly one of the options on the lakeshore, will select
closest valid range, though note these are in increments of 2, 6.32,
20, 63.2, etc.
Notes:
If autorange is on when you change the resistance range, it may try to change
it to another value.
"""
with self._lock.acquire_timeout(job='set_resistance_range') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
current_resistance_range = self.module.channels[params['channel']].get_resistance_range()
if params['resistance_range'] == current_resistance_range:
session.add_message(f'Channel {params["channel"]} resistance_range already set to {params["resistance_range"]}')
else:
self.module.channels[params['channel']].set_resistance_range(params['resistance_range'])
session.add_message(f'Set channel {params["channel"]} resistance range to {params["resistance_range"]}')
return True, f'Set channel {params["channel"]} resistance range to {params["resistance_range"]}'
@ocs_agent.param('channel', type=int, check=lambda x: 1 <= x <= 16)
def get_resistance_range(self, session, params):
"""get_resistance_range(channel)
**Task** - Get the resistance range for a specified channel.
Parameters:
channel (int): Channel to get the resistance range for. Valid values
are 1-16.
"""
with self._lock.acquire_timeout(job='get_resistance_range') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
current_resistance_range = self.module.channels[params['channel']].get_resistance_range()
session.add_message(f'Channel {params["channel"]} resistance range is {current_resistance_range}')
session.data = {"resistance_range": current_resistance_range}
return True, f'Channel {params["channel"]} resistance range is {current_resistance_range}'
@ocs_agent.param('channel', type=int, check=lambda x: 1 <= x <= 16)
@ocs_agent.param('dwell', type=int, check=lambda x: 1 <= x <= 200)
def set_dwell(self, session, params):
"""set_dwell(channel, dwell)
**Task** - Set the autoscanning dwell time for a particular channel.
Parameters:
channel (int): Channel to set the dwell time for. Valid values
are 1-16.
dwell (int): Dwell time in seconds, type is int and must be in the
range 1-200 inclusive.
"""
with self._lock.acquire_timeout(job='set_dwell') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
self.module.channels[params["channel"]].set_dwell(params["dwell"])
session.add_message(f'Set dwell to {params["dwell"]}')
return True, f'Set channel {params["channel"]} dwell time to {params["dwell"]}'
@ocs_agent.param('channel', type=int, check=lambda x: 1 <= x <= 16)
def get_dwell(self, session, params):
"""get_dwell(channel)
**Task** - Get the autoscanning dwell time for a particular channel.
Parameters:
channel (int): Channel to get the dwell time for. Valid values
are 1-16.
"""
with self._lock.acquire_timeout(job='set_dwell') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
current_dwell = self.module.channels[params["channel"]].get_dwell()
session.add_message(f'Dwell time for channel {params["channel"]} is {current_dwell}')
session.data = {"dwell_time": current_dwell}
return True, f'Channel {params["channel"]} dwell time is {current_dwell}'
@ocs_agent.param('P', type=int)
@ocs_agent.param('I', type=int)
@ocs_agent.param('D', type=int)
def set_pid(self, session, params):
"""set_pid(P, I, D)
**Task** - Set the PID parameters for servo control of fridge.
Parameters:
P (int): Proportional term for PID loop
I (int): Integral term for the PID loop
D (int): Derivative term for the PID loop
Notes:
Makes a call to :func:`socs.Lakeshore.Lakeshore372.Heater.set_pid`.
"""
with self._lock.acquire_timeout(job='set_pid') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
self.module.sample_heater.set_pid(params["P"], params["I"], params["D"])
session.add_message(f'post message text for Set PID to {params["P"]}, {params["I"]}, {params["D"]}')
print(f'print text for Set PID to {params["P"]}, {params["I"]}, {params["D"]}')
return True, f'return text for Set PID to {params["P"]}, {params["I"]}, {params["D"]}'
@ocs_agent.param('channel', type=int)
def set_active_channel(self, session, params):
"""set_active_channel(channel)
**Task** - Set the active channel on the LS372.
Parameters:
channel (int): Channel to switch readout to. Valid values are 1-16.
"""
with self._lock.acquire_timeout(job='set_active_channel') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
self.module.set_active_channel(params["channel"])
session.add_message(f'post message text for set channel to {params["channel"]}')
print(f'print text for set channel to {params["channel"]}')
return True, f'return text for set channel to {params["channel"]}'
@ocs_agent.param('autoscan', type=bool)
def set_autoscan(self, session, params):
"""set_autoscan(autoscan)
**Task** - Sets autoscan on the LS372.
Parameters:
autoscan (bool): True to enable autoscan, False to disable.
"""
with self._lock.acquire_timeout(job='set_autoscan') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
if params['autoscan']:
self.module.enable_autoscan()
self.log.info('enabled autoscan')
else:
self.module.disable_autoscan()
self.log.info('disabled autoscan')
return True, 'Set autoscan to {}'.format(params['autoscan'])
@ocs_agent.param('temperature', type=float, check=lambda x: x < 1)
def servo_to_temperature(self, session, params):
"""servo_to_temperature(temperature)
**Task** - Servo to a given temperature using a closed loop PID on a
fixed channel. This will automatically disable autoscan if enabled.
Parameters:
temperature (float): Temperature to servo to in units of Kelvin.
"""
with self._lock.acquire_timeout(job='servo_to_temperature') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
# Check we're in correct control mode for servo.
if self.module.sample_heater.mode != 'Closed Loop':
session.add_message('Changing control to Closed Loop mode for servo.')
self.module.sample_heater.set_mode("Closed Loop")
# Check we aren't autoscanning.
if self.module.get_autoscan() is True:
session.add_message('Autoscan is enabled, disabling for PID control on dedicated channel.')
self.module.disable_autoscan()
# Check we're scanning same channel expected by heater for control.
if self.module.get_active_channel().channel_num != int(self.module.sample_heater.input):
session.add_message('Changing active channel to expected heater control input')
self.module.set_active_channel(int(self.module.sample_heater.input))
# Check we're setup to take correct units.
if self.module.get_active_channel().units != 'kelvin':
session.add_message('Setting preferred units to Kelvin on heater control input.')
self.module.get_active_channel().set_units('kelvin')
# Make sure we aren't servoing too high in temperature.
if params["temperature"] > 1:
return False, 'Servo temperature is set above 1K. Aborting.'
self.module.sample_heater.set_setpoint(params["temperature"])
return True, f'Setpoint now set to {params["temperature"]} K'
@ocs_agent.param('channel', type=int)
@ocs_agent.param('state', type=str, choices=['on', 'off'])
def engage_channel(self, session, params):
"""engage_channel(channel, state)
**Task** - Enables/disables a channel on the LS372
Parameters:
channel (int): Channel number to enable
state (str): Desired power state of channel; 'on' or 'off'
"""
with self._lock.acquire_timeout(job='engage_channel') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
channel = params['channel']
state = params['state']
if state == 'on':
self.module.channels[channel].enable_channel()
else:
self.module.channels[channel].disable_channel()
return True, "Channel {} powered {}".format(channel, state)
@ocs_agent.param('channel', type=int)
@ocs_agent.param('state', type=str, choices=['on', 'off'])
def engage_autorange(self, session, params):
"""engage_autorange(channel, state)
**Task** - Enables/disables autorange for a channel on the LS372
Parameters:
channel (int): Channel number for enabling autorange
state (str): Desired autorange state of channel: 'on' or 'off'
"""
with self._lock.acquire_timeout(job='engage_channel') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
channel = params['channel']
state = params['state']
if state == 'on':
self.module.channels[channel].enable_autorange()
else:
self.module.channels[channel].disable_autorange()
return True, "Channel {} autorange status is {}".format(channel, state)
@ocs_agent.param('channel', type=int, check=lambda x: 0 <= x <= 16)
@ocs_agent.param('curve_number', type=int, check=lambda x: 21 <= x <= 59)
def set_calibration_curve(self, session, params):
"""set_calibration_curve(channel, curve_number)
**Task** - Sets the calibration curve number for a particular channel.
Parameters:
channel (int): Channel number to set calibration curve to. Channel
ranges are 1 to 16, and 0 for control channel A.
curve_number (int): Curve number of calibration curve uploaded to
the LS372. Curve number ranges are 21 to 59.
"""
with self._lock.acquire_timeout(job='set_calibration_curve') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
channel = params['channel']
curve_number = params['curve_number']
self.module.channels[channel].set_calibration_curve(curve_number)
return True, f"Assigned channel {channel} to curve number {curve_number}."
@ocs_agent.param('channel', type=int)
def get_input_setup(self, session, params):
"""get_input_setup(channel)
**Task** - Gets measurement inputs for a specific channel on the LS372
Parameters:
channel (int): Channel number to get input setup
Notes:
The most recent data collected is stored in session data in the
structure::
>>> response.session['data']
{"mode": 'voltage',
"excitation": 6.32e-05,
"excitation_units": 'volts',
"autorange": 'on',
"range": 2000.0,
"csshunt": 'on',
"units": 'kelvin'}
"""
with self._lock.acquire_timeout(job='get_input_setup') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
channel = params['channel']
ls_chann_setting = self.module.channels[channel]
input_setup = ls_chann_setting.get_input_setup()
session.data = {"mode": ls_chann_setting.mode,
"excitation": ls_chann_setting.excitation,
"excitation_units": ls_chann_setting.excitation_units,
"autorange": ls_chann_setting.autorange,
"range": ls_chann_setting.range,
"csshunt": ls_chann_setting.csshunt,
"units": ls_chann_setting.units}
return True, f"Channel {channel} has measurement inputs {input_setup} = [mode," \
"excitation, auto range, range, cs_shunt, units]"
@ocs_agent.param('setpoint', type=float)
@ocs_agent.param('heater', type=str)
@ocs_agent.param('channel', type=int)
@ocs_agent.param('P', type=float)
@ocs_agent.param('I', type=float)
@ocs_agent.param('update_time', type=float)
@ocs_agent.param('query_rate', type=float, default=10.)
@ocs_agent.param('sample_heater_range', type=float, default=10e-3)
@ocs_agent.param('test_mode', type=bool, default=False)
def custom_pid(self, session, params):
"""custom_pid(setpoint, heater, channel, P, \
I, update_time, query_rate=10., sample_heater_range=10e-3, \
test_mode=False)
**Process** - Set custom software PID parameters for servo control of fridge
using still or sample heater. Currently only P and I implemented.
Parameters:
setpoint (float): Setpoint in Kelvin
heater (str): 'still' or 'sample'
channel (int): LS372 Channel to PID off of
P (float): Proportional value in Watts/Kelvin
I (float): Integral Value in Hz
update_time (float): Time between PID updates in seconds
query_rate (float): Rate at which to query temp data in Hz.
sample_heater_range (float): Range for sample heater in Amps.
Default is 10e-3.
test_mode (bool, optional): Run the Process loop only once.
This is meant only for testing.
Default is False.
Notes:
The most recent data collected is stored in session data in the structure.
The channel number noted below depends upon which channel is being used
to servo::
>>> response.session['data']
{"fields":
{"Channel_02": {"T": 293.644, "R": 33.752, "timestamps": 1601924482.722671}}
}
"""
pm = Pacemaker(params['query_rate'])
with self._acq_proc_lock.acquire_timeout(timeout=0, job='custom_pid') \
as acq_acquired, \
self._lock.acquire_timeout(job='custom_pid') as acquired:
if not acq_acquired:
self.log.warn(f"Could not start Process because "
f"{self._acq_proc_lock.job} is already running")
return False, "Could not acquire lock"
if not acquired:
self.log.warn(f"Could not start Process because "
f"{self._lock.job} is holding the lock")
return False, "Could not acquire lock"
session.data = {"fields": {}}
setpoint = params['setpoint']
heater = params['heater']
ch = params['channel']
P_val = params['P']
I_val = params['I']
update_time = params['update_time']
sample_heater_range = params['sample_heater_range']
# Constants
still_heater_R = 120 # [ohms]
still_lead_R = 14.679 # [ohms]
delta_t = 0.32 # rough intrinsic sampling period of the LS372 (s)
max_voltage = 10 # [V]
# Get heaters in the correct configuration
if heater == 'sample':
# Set heater range
if sample_heater_range == self.module.sample_heater.get_heater_range():
self.log.info(f"Heater range already set to {sample_heater_range} amps")
else:
self.module.sample_heater.set_heater_range(sample_heater_range)
# Check we're in correct control mode for servo.
if self.module.sample_heater.mode != 'Open Loop':
session.add_message('Changing control to Open Loop mode for sample PID.')
self.module.sample_heater.set_mode("Open Loop")
# Check we're in the correct display mode for servo.
# The custom PID expects that the sample heater is in 'current' mode
if self.module.sample_heater.display != "current":
session.add_message("Changing display mode to \'current\' for sample PID.")
self.module.sample_heater.set_heater_display("current")
if heater == 'still':
# Check we're in correct control mode for servo.
if self.module.still_heater.mode != 'Open Loop':
session.add_message('Changing control to Open Loop mode for still PID.')
self.module.still_heater.set_mode("Open Loop")
# Check we aren't autoscanning.
if self.module.get_autoscan() is True:
session.add_message('Autoscan is enabled, disabling for still PID control on dedicated channel.')
self.module.disable_autoscan()
# Check we're scanning same channel expected by heater for control.
if self.module.get_active_channel().channel_num != ch:
session.add_message(f'Changing active channel to {ch} for still PID')
self.module.set_active_channel(ch)
# Start the PID
self.custom_pid = True
active_channel = self.module.get_active_channel()
channel_str = active_channel.name.replace(' ', '_')
last_pid = time.time()
heater_I = 0
temps, resistances, times = [], [], []
self.log.info(f"Starting PID on heater {heater}, ch {ch} to setpoint {setpoint} K")
while self.custom_pid:
pm.sleep()
# Get a list of T and R at the maximum sample frequency
temps.append(self.module.get_temp(unit='kelvin', chan=ch))
resistances.append(self.module.get_temp(unit='ohms', chan=ch))
times.append(time.time())
attempted_heater_pow = 0
# Calculate and apply the PID based on the most recent temperature set
if times[-1] - last_pid > update_time:
heater_P = P_val * (setpoint - np.mean(np.array(temps)))
# Update the I_val of the PID
# The I_val should not become negative because negative heater values are unphysical
d_heater_I = P_val * I_val * (delta_t * np.sum(setpoint - np.array(temps)))
heater_I = max(0.0, heater_I + d_heater_I)
attempted_heater_pow = heater_P + heater_I
self.log.info(f"Attempted Power: {attempted_heater_pow}, Heater_I: {heater_I}")
heater_pow = max(0.0, attempted_heater_pow)
if heater == 'still':
heater_frac = still_power_to_perc(heater_pow, still_heater_R, still_lead_R, max_voltage)
self.module.still_heater.set_heater_output(heater_frac)
if heater == 'sample':
self.module.sample_heater.set_heater_output(heater_pow)
# Publish heater values
if heater == 'still':
heater_data = {
'timestamp': last_pid,
'block_name': 'still_heater_out',
'data': {'still_heater_out': heater_frac}
}
session.app.publish_to_feed('temperatures', heater_data)
if heater == 'sample':
heater_data = {
'timestamp': last_pid,
'block_name': 'sample_heater_out',
'data': {'sample_heater_out': heater_pow}
}
session.app.publish_to_feed('temperatures', heater_data)
# Publish T and R values
temp_data = {
'timestamps': times,
'block_name': active_channel.name,
'data': {channel_str + '_T': temps,
channel_str + '_R': resistances}
}
session.app.publish_to_feed('temperatures', temp_data)
# For session.data
field_dict = {channel_str: {"T": temps,
"R": resistances,
"timestamps": times}}
session.data['fields'].update(field_dict)
self.log.debug("{data}", data=session.data)
# Reset for the next PID iteration
temps, resistances, times = [], [], []
last_pid = time.time()
# Relinquish sampling lock temporarily
if not self._lock.release_and_acquire(timeout=10):
self.log.warn(f"Failed to re-acquire sampling lock, "
f"currently held by {self._lock.job}.")
continue
if params['test_mode']:
break
return True, 'PID exited cleanly.'
def _stop_custom_pid(self, session, params=None):
"""
Stops acq process.
"""
if self.custom_pid:
self.custom_pid = False
return True, 'Stopping the custom PID'
else:
return False, 'PID is not currently running'
@ocs_agent.param('measurements', type=int)
@ocs_agent.param('threshold', type=float)
def check_temperature_stability(self, session, params):
"""check_temperature_stability(measurements, threshold)
Check servo temperature stability is within threshold.
Parameters:
measurements (int): number of measurements to average for stability
check
threshold (float): amount within which the average needs to be to
the setpoint for stability
"""
with self._lock.acquire_timeout(job='check_temp_stability') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
setpoint = float(self.module.sample_heater.get_setpoint())
if params is None:
params = {'measurements': 10, 'threshold': 0.5e-3}
test_temps = []
for i in range(params['measurements']):
test_temps.append(self.module.get_temp())
time.sleep(.1) # sampling rate is 10 readings/sec, so wait 0.1 s for a new reading
mean = np.mean(test_temps)
session.add_message(f'Average of {params["measurements"]} measurements is {mean} K.')
print(f'Average of {params["measurements"]} measurements is {mean} K.')
if np.abs(mean - setpoint) < params['threshold']:
print("passed threshold")
session.add_message('Setpoint Difference: ' + str(mean - setpoint))
session.add_message(f'Average is within {params["threshold"]} K threshold. Proceeding with calibration.')
return True, f"Servo temperature is stable within {params['threshold']} K"
else:
print("we're in the else")
# adjust_heater(t,rest)
return False, f"Temperature not stable within {params['threshold']}."
@ocs_agent.param('heater', type=str, choices=['sample', 'still'])
@ocs_agent.param('mode', type=str, choices=['Off', 'Monitor Out', 'Open Loop', 'Zone', 'Still', 'Closed Loop', 'Warm up'])
def set_output_mode(self, session, params=None):
"""set_output_mode(heater, mode)
**Task** - Set output mode of the heater.
Parameters:
heater (str): Name of heater to set range for, either 'sample' or
'still'.
mode (str): Specifies mode of heater. Can be "Off", "Monitor Out",
"Open Loop", "Zone", "Still", "Closed Loop", or "Warm up"
"""
with self._lock.acquire_timeout(job='set_output_mode') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
if params['heater'].lower() == 'still':
self.module.still_heater.set_mode(params['mode'])
if params['heater'].lower() == 'sample':
self.module.sample_heater.set_mode(params['mode'])
self.log.info("Set {} output mode to {}".format(params['heater'], params['mode']))
return True, "Set {} output mode to {}".format(params['heater'], params['mode'])
@ocs_agent.param('heater', type=str, choices=['sample', 'still'])
@ocs_agent.param('output', type=float)
@ocs_agent.param('display', type=str, choices=['current', 'power'], default=None)
def set_heater_output(self, session, params=None):
"""set_heater_output(heater, output, display=None)
**Task** - Set display type and output of the heater.
Parameters:
heater (str): Name of heater to set range for, either 'sample' or
'still'.
output (float): Specifies heater output value. For possible values see
:func:`socs.Lakeshore.Lakeshore372.Heater.set_heater_output`
display (str, optional): Specifies heater display type. Can be
"current" or "power". If None, heater display is not reset
before setting output.
Notes:
For the still heater this sets the still heater manual output, *not*
the still heater still output. Use
:func:`LS372_Agent.set_still_output()`
instead to set the still output.
"""
with self._lock.acquire_timeout(job='set_heater_output') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
heater = params['heater'].lower()
output = params['output']
display = params.get('display', None)
if heater == 'still':
self.module.still_heater.set_heater_output(output, display_type=display)
if heater.lower() == 'sample':
self.log.info("display: {}\toutput: {}".format(display, output))
self.module.sample_heater.set_heater_output(output, display_type=display)
self.log.info("Set {} heater display to {}, output to {}".format(heater, display, output))
data = {'timestamp': time.time(),
'block_name': '{}_heater_out'.format(heater),
'data': {'{}_heater_out'.format(heater): output}
}
session.app.publish_to_feed('temperatures', data)
return True, "Set {} display to {}, output to {}".format(heater, display, output)
@ocs_agent.param('output', type=float, check=lambda x: 0 <= x <= 100)
def set_still_output(self, session, params=None):
"""set_still_output(output)
**Task** - Set the still output on the still heater. This is different
than the manual output on the still heater. Use
:func:`LS372_Agent.set_heater_output()` for that.
Parameters:
output (float): Specifies still heater output value as a percentage. Can be any
number between 0 and 100.
"""
with self._lock.acquire_timeout(job='set_still_output') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
output = params['output']
self.module.still_heater.set_still_output(output)
self.log.info("Set still output to {}".format(output))
data = {'timestamp': time.time(),
'block_name': 'still_heater_still_out',
'data': {'still_heater_still_out': output}
}
session.app.publish_to_feed('temperatures', data)
return True, "Set still output to {}".format(output)
@ocs_agent.param('_')
def get_still_output(self, session, params=None):
"""get_still_output()
**Task** - Gets the current still output on the still heater.
Notes:
The still heater output is stored in the session data
object in the format::
>>> response.session['data']
{"still_heater_still_out": 9.628}
"""
with self._lock.acquire_timeout(job='get_still_output') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
still_output = self.module.still_heater.get_still_output()
self.log.info("Current still output is {}".format(still_output))
session.data = {"still_heater_still_out": still_output}
return True, "Current still output is {}".format(still_output)
@ocs_agent.param('configfile', type=str, default=None)
def input_configfile(self, session, params=None):
"""input_configfile(configfile=None)
**Task** - Upload 372 configuration file to initialize channel/device
settings.
Parameters:
configfile (str, optional):
name of .yaml config file. Defaults to the file set in the
site config
"""
configfile = params['configfile']
if configfile is None:
configfile = self.configfile
if configfile is None:
raise ValueError("No configfile specified")
configfile = os.path.join(os.environ['OCS_CONFIG_DIR'], configfile)
with self._lock.acquire_timeout(job='input_configfile') as acquired:
if not acquired:
self.log.warn(f"Could not start Task because "
f"{self._lock.job} is already running")
return False, "Could not acquire lock"
with open(configfile) as f:
config = yaml.safe_load(f)
ls = self.module
ls_serial = ls.id.split(',')[2]
device_config = config[ls_serial]['device_settings']
ls_chann_settings = config[ls_serial]['channel']
# enable/disable autoscan
if device_config['autoscan'] == 'on':
ls.enable_autoscan()
self.log.info("autoscan enabled")
elif device_config['autoscan'] == 'off':
ls.disable_autoscan()
self.log.info("autoscan disabled")
for i in ls_chann_settings:
# enable/disable channel
if ls_chann_settings[i]['enable'] == 'on':
ls.channels[i].enable_channel()
self.log.info("CH.{channel} enabled".format(channel=i))
elif ls_chann_settings[i]['enable'] == 'off':
ls.channels[i].disable_channel()
self.log.info("CH.{channel} disabled".format(channel=i))
# autorange
if ls_chann_settings[i]['autorange'] == 'on':
ls.channels[i].enable_autorange()
self.log.info("autorange on")
elif ls_chann_settings[i]['autorange'] == 'off':
ls.channels[i].disable_autorange()
self.log.info("autorange off")
# set to desired resistance range after disabling autorange
resistance_range = ls_chann_settings[i]['resistance_range']
ls.channels[i].set_resistance_range(resistance_range)
self.log.info("resistance range for CH.{channel} set to {get_res}, the closest valid range to {res}".format(channel=i, get_res=ls.channels[i].get_resistance_range(), res=resistance_range))
excitation_mode = ls_chann_settings[i]['excitation_mode']
ls.channels[i].set_excitation_mode(excitation_mode)
self.log.info("excitation mode for CH.{channel} set to {exc_mode}".format(channel=i, exc_mode=excitation_mode))
excitation_value = ls_chann_settings[i]['excitation_value']
ls.channels[i].set_excitation(excitation_value)
self.log.info("excitation for CH.{channel} set to {exc}".format(channel=i, exc=excitation_value))
dwell = ls_chann_settings[i]['dwell']
ls.channels[i].set_dwell(dwell)
self.log.info("dwell for CH.{channel} is set to {dwell}".format(channel=i, dwell=dwell))
pause = ls_chann_settings[i]['pause']
ls.channels[i].set_pause(pause)
self.log.info("pause for CH.{channel} is set to {pause}".format(channel=i, pause=pause))
calibration_curvenum = ls_chann_settings[i]['calibration_curve_num']
ls.channels[i].set_calibration_curve(calibration_curvenum)
self.log.info("calibration curve for CH.{channel} set to {cal_curve}".format(channel=i, cal_curve=calibration_curvenum))
tempco = ls_chann_settings[i]['temperature_coeff']
ls.channels[i].set_temperature_coefficient(tempco)
self.log.info("temperature coeff. for CH.{channel} set to {tempco}".format(channel=i, tempco=tempco))
return True, "Uploaded {}".format(configfile)
def make_parser(parser=None):
"""Build the argument parser for the Agent. Allows sphinx to automatically
build documentation based on this function.
"""
if parser is None:
parser = argparse.ArgumentParser()
# Add options specific to this agent.
pgroup = parser.add_argument_group('Agent Options')
pgroup.add_argument('--ip-address')
pgroup.add_argument('--serial-number')
pgroup.add_argument('--fake-data', type=int, default=0,
help='Set non-zero to fake data, without hardware.')
pgroup.add_argument('--dwell-time-delay', type=int, default=0,
help="Amount of time, in seconds, to delay data\
collection after switching channels. Note this\
time should not include the change pause time,\
which is automatically accounted for.\
Will automatically be reduced to dwell_time - 1\
second if it is set longer than a channel's dwell\
time. This ensures at least one second of data\
collection at the end of a scan.")
pgroup.add_argument('--mode', type=str, default='acq',
choices=['idle', 'init', 'acq'],
help="Starting action for the Agent.")
pgroup.add_argument('--sample-heater', type=bool, default=False,
help='Record sample heater output during acquisition.')
pgroup.add_argument('--enable-control-chan', action='store_true',
help='Enable reading of the control input each acq cycle')
pgroup.add_argument('--configfile', type=str, help='Yaml file for initializing 372 settings')
return parser
def main(args=None):
# For logging
txaio.use_twisted()
txaio.make_logger()
# Start logging
txaio.start_logging(level=os.environ.get("LOGLEVEL", "info"))
parser = make_parser()
args = site_config.parse_args(agent_class='Lakeshore372Agent',
parser=parser,
args=args)
# Automatically acquire data if requested (default)
init_params = False
if args.mode == 'init':
init_params = {'auto_acquire': False,
'acq_params': {'sample_heater': args.sample_heater},
'configfile': args.configfile}
elif args.mode == 'acq':
init_params = {'auto_acquire': True,
'acq_params': {'sample_heater': args.sample_heater}}
# Interpret options in the context of site_config.
print('I am in charge of device with serial number: %s' % args.serial_number)
agent, runner = ocs_agent.init_site_agent(args)
lake_agent = LS372_Agent(agent, args.serial_number, args.ip_address,
dwell_time_delay=args.dwell_time_delay,
enable_control_chan=args.enable_control_chan,
configfile=args.configfile)
agent.register_task('init_lakeshore', lake_agent.init_lakeshore,
startup=init_params)
agent.register_task('set_heater_range', lake_agent.set_heater_range)
agent.register_task('set_excitation_mode', lake_agent.set_excitation_mode)
agent.register_task('set_excitation', lake_agent.set_excitation)
agent.register_task('get_excitation', lake_agent.get_excitation)
agent.register_task('set_resistance_range', lake_agent.set_resistance_range)
agent.register_task('get_resistance_range', lake_agent.get_resistance_range)
agent.register_task('set_dwell', lake_agent.set_dwell)
agent.register_task('get_dwell', lake_agent.get_dwell)
agent.register_task('engage_channel', lake_agent.engage_channel)
agent.register_task('engage_autorange', lake_agent.engage_autorange)
agent.register_task('get_input_setup', lake_agent.get_input_setup)
agent.register_task('set_calibration_curve', lake_agent.set_calibration_curve)
agent.register_task('set_pid', lake_agent.set_pid)
agent.register_task('set_autoscan', lake_agent.set_autoscan)
agent.register_task('set_active_channel', lake_agent.set_active_channel)
agent.register_task('servo_to_temperature', lake_agent.servo_to_temperature)
agent.register_task('check_temperature_stability', lake_agent.check_temperature_stability)
agent.register_task('set_output_mode', lake_agent.set_output_mode)
agent.register_task('set_heater_output', lake_agent.set_heater_output)
agent.register_task('set_still_output', lake_agent.set_still_output)
agent.register_task('get_still_output', lake_agent.get_still_output)
agent.register_process('acq', lake_agent.acq, lake_agent._stop_acq)
agent.register_process('custom_pid', lake_agent.custom_pid, lake_agent._stop_custom_pid)
agent.register_task('enable_control_chan', lake_agent.enable_control_chan)
agent.register_task('disable_control_chan', lake_agent.disable_control_chan)
agent.register_task('input_configfile', lake_agent.input_configfile)
runner.run(agent, auto_reconnect=True)
if __name__ == '__main__':
main()
|
simonsobsREPO_NAMEsocsPATH_START.@socs_extracted@socs-main@socs@agents@lakeshore372@agent.py@.PATH_END.py
|
{
"filename": "scene_ui_plugin.py",
"repo_name": "enthought/mayavi",
"repo_path": "mayavi_extracted/mayavi-master/tvtk/plugins/scene/ui/scene_ui_plugin.py",
"type": "Python"
}
|
""" A TVTK render window scene UI plugin. """
# Enthought library imports.
from envisage.api import Plugin
from traits.api import List
class SceneUIPlugin(Plugin):
""" A TVTK render window scene UI plugin.
This is the plugin that contributes actions, menus, preferences pages
etc.
"""
# Extension point Ids.
ACTION_SETS = 'envisage.ui.workbench.action_sets'
PREFERENCES_PAGES = 'envisage.ui.workbench.preferences_pages'
#### 'IPlugin' interface ##################################################
# The plugin's name (suitable for displaying to the user).
name = 'TVTK Scene UI Plugin'
# Our ID.
id = 'tvtk.scene_ui'
#### Extension points offered by this plugin ##############################
# None.
#### Contributions to extension points made by this plugin ################
action_sets = List(contributes_to=ACTION_SETS)
def _action_sets_default(self):
""" Trait initializer. """
from tvtk.plugins.scene.ui.scene_ui_action_set import (
SceneUIActionSet
)
return [SceneUIActionSet]
preferences_pages = List(contributes_to=PREFERENCES_PAGES)
def _preferences_pages_default(self):
""" Trait initializer. """
from tvtk.plugins.scene.ui.scene_preferences_page import (
ScenePreferencesPage
)
return [ScenePreferencesPage]
#### EOF ######################################################################
|
enthoughtREPO_NAMEmayaviPATH_START.@mayavi_extracted@mayavi-master@tvtk@plugins@scene@ui@scene_ui_plugin.py@.PATH_END.py
|
{
"filename": "units.py",
"repo_name": "amusecode/amuse",
"repo_path": "amuse_extracted/amuse-main/src/amuse/units/units.py",
"type": "Python"
}
|
"""
Units supported in AMUSE
"""
import numpy
from . import constants
from . import quantities
# The two imports below are to explicitly expose everything directly used in
# this module.
from .si import (named, s, m, kg, none, core, no_unit,)
from .derivedsi import (km, V, W, J, Pa, rad,)
# importing * from si and derivedsi since we do want all of the units in these
# modules, and we don't want to repeat everything here.
from .si import *
from .derivedsi import *
from .stellar_types import stellar_type
# misc every day
minute = named("minute", "min", 60.0 * s)
hour = named("hour", "hr", 60.0 * minute)
day = named("day", "day", 24.0 * hour)
yr = named("year", "yr", 365.242199 * day)
julianyr = named("julian yr", "julianyr", 365.25 * day)
ms = named("meter per seconds", "ms", m / s)
kms = named("kilometer per seconds", "kms", km / s)
# units based on measured quantities
e = named("electron charge", "e", constants.elementary_charge.as_unit())
eV = named("electron volt", "eV", e * V)
MeV = named("mega electron volt", "MeV", 1e6 * eV)
GeV = named("giga electron volt", "GeV", 1e9 * eV)
E_h = named("hartree energy", "E_h", constants.Hartree_energy.as_unit())
amu = named("atomic mass unit", "amu", constants.u.as_unit())
Ry = named(
"rydberg unit",
"Ry",
(constants.Rydberg_constant * constants.h * constants.c)
.as_quantity_in(eV)
.as_unit(),
)
# astronomical units
angstrom = named("angstrom", "angstrom", 1e-10 * m)
au = named("astronomical unit", "au", 149597870691.0 * m)
aud = named("au per day", "aud", 149597870691.0 * m / day)
parsec = named("parsec", "parsec", au / numpy.tan(numpy.pi / (180 * 60 * 60)))
kpc = named("kilo parsec", "kpc", 10**3 * parsec)
Mpc = named("mega parsec", "Mpc", 10**6 * parsec)
Gpc = named("giga parsec", "Gpc", 10**9 * parsec)
lightyear = named("light year", "ly", 9460730472580.8 * km)
LSun = named("solar luminosity", "LSun", 3.839e26 * W, latex=r"L_{\odot}")
MSun = named("solar mass", "MSun", 1.98892e30 * kg, latex=r"M_{\odot}")
RSun = named("solar radius", "RSun", 6.955e8 * m, latex=r"R_{\odot}")
MJupiter = named("jupiter mass", "MJupiter", 1.8987e27 * kg)
RJupiter = named("jupiter radius", "RJupiter", 71492.0 * km)
MEarth = named("earth mass", "MEarth", 5.9722e24 * kg)
REarth = named("earth radius", "REarth", 6371.0088 * km) # IUGG mean radius
kyr = named("kilo year", "kyr", 1000 * yr)
myr = named("million year", "Myr", 1000000 * yr)
gyr = named("giga (billion) year", "Gyr", 1000000000 * yr)
# alternatives, for compatibility
AU = au
AUd = aud
Myr = myr
Gyr = gyr
pc = parsec
Lsun = LSun
Msun = MSun
Rsun = RSun
Mjupiter = MJupiter
Rjupiter = RJupiter
Mearth = MEarth
Rearth = REarth
# cgs units
g = named("gram", "g", 1e-3 * kg)
cm = named("centimeter", "cm", 0.01 * m)
erg = named("erg", "erg", 1e-7 * J)
barye = named("barye", "Ba", 0.1 * Pa)
# imperial distance units
inch = named("inch", "in", 0.0254 * m)
foot = named("foot", "ft", 0.3048 * m)
mile = named("mile", "mi", 1609.344 * m)
percent = named("percent", "%", 0.01 * none)
metallicity = core.none_unit("metallicity", "metallicity")
string = core.string_unit("string", "string")
# special unit for keys of particles
object_key = core.key_unit("object_key", "key")
# angles
pi = numpy.pi | rad
rev = named("revolutions", "rev", (2 * numpy.pi) * rad)
deg = named("degree", "deg", (numpy.pi / 180) * rad)
arcmin = named("arcminutes", "arcmin", (1.0 / 60) * deg)
arcsec = named("arcseconds", "arcsec", (1.0 / 3600) * deg)
|
amusecodeREPO_NAMEamusePATH_START.@amuse_extracted@amuse-main@src@amuse@units@units.py@.PATH_END.py
|
{
"filename": "post_process_tod.py",
"repo_name": "cosmo-ethz/seek",
"repo_path": "seek_extracted/seek-master/seek/plugins/post_process_tod.py",
"type": "Python"
}
|
# SEEK 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.
#
# SEEK 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 SEEK. If not, see <http://www.gnu.org/licenses/>.
'''
Created on Feb 6, 2015
author: jakeret
'''
from __future__ import print_function, division, absolute_import, unicode_literals
from ivy.plugin.base_plugin import BasePlugin
import h5py
import os
class Plugin(BasePlugin):
"""
Writes the data, mask and frequencies of the current iteration to disk. Can
be used for closer analysis of the masking (sum threshold). Output is
written to the current folder using the same filename as the first input
filename (may overwrite the original file if not being careful)
"""
def __call__(self):
if not self.ctx.params.store_intermediate_result:
return
filename = os.path.basename(self.ctx.file_paths[0])
filepath = os.path.join(self.ctx.params.post_processing_prefix,
filename)
with h5py.File(filepath, "w") as fp:
fp["data"] = self.ctx.tod_vx.data
fp["mask"] = self.ctx.tod_vx.mask
fp["frequencies"] = self.ctx.frequencies
fp["time"] = self.ctx.time_axis
fp["ra"] = self.ctx.coords.ra
fp["dec"] = self.ctx.coords.dec
fp["ref_channel"] = self.ctx.ref_channel
def __str__(self):
return "postprocessing TOD"
|
cosmo-ethzREPO_NAMEseekPATH_START.@seek_extracted@seek-master@seek@plugins@post_process_tod.py@.PATH_END.py
|
{
"filename": "pldcorrector.py",
"repo_name": "lightkurve/lightkurve",
"repo_path": "lightkurve_extracted/lightkurve-main/src/lightkurve/correctors/pldcorrector.py",
"type": "Python"
}
|
"""Defines a `PLDCorrector` class which provides a simple way to correct a
light curve by utilizing the pixel time series data contained within the
target's own Target Pixel File.
`PLDCorrector` builds upon `RegressionCorrector` by correlating the light curve
against a design matrix composed of the following elements:
* A background light curve to capture the dominant scattered light systematics.
* Background-corrected pixel time series to capture any residual systematics.
* Splines to capture the target's intrinsic variability
"""
import logging
import warnings
from itertools import combinations_with_replacement as multichoose
import numpy as np
import matplotlib.pyplot as plt
from astropy.utils.decorators import deprecated, deprecated_renamed_argument
from .designmatrix import (
DesignMatrix,
DesignMatrixCollection,
SparseDesignMatrixCollection,
)
from .regressioncorrector import RegressionCorrector
from .designmatrix import create_spline_matrix, create_sparse_spline_matrix
from .. import MPLSTYLE
from ..utils import LightkurveDeprecationWarning
log = logging.getLogger(__name__)
__all__ = ["PLDCorrector", "TessPLDCorrector"]
class PLDCorrector(RegressionCorrector):
r"""Implements the Pixel Level Decorrelation (PLD) systematics removal method.
Special case of `.RegressionCorrector` where the `.DesignMatrix` is
composed of background-corrected pixel time series.
The design matrix also contains columns representing a spline in time
design to capture the intrinsic, long-term variability of the target.
Pixel Level Decorrelation (PLD) was developed by [1]_ to remove
systematic noise caused by spacecraft jitter for the Spitzer
Space Telescope. It was adapted to K2 data by [2]_ and [3]_
for the EVEREST pipeline [4]_.
For a detailed description and implementation of PLD, please refer to
these references. Lightkurve provides a reference implementation
of PLD that is less sophisticated than EVEREST, but is suitable
for quick-look analyses and detrending experiments.
Our simple implementation of PLD is performed by first calculating the
noise model for each cadence in time. This function goes up to arbitrary
order, and is represented by
.. math::
m_i = \sum_l a_l \frac{f_{il}}{\sum_k f_{ik}} + \sum_l \sum_m b_{lm} \frac{f_{il}f_{im}}{\left( \sum_k f_{ik} \right)^2} + ...
where
- :math:`m_i` is the noise model at time :math:`t_i`
- :math:`f_{il}` is the flux in the :math:`l^\text{th}` pixel at time :math:`t_i`
- :math:`a_l` is the first-order PLD coefficient on the linear term
- :math:`b_{lm}` is the second-order PLD coefficient on the :math:`l^\text{th}`,
:math:`m^\text{th}` pixel pair
We perform Principal Component Analysis (PCA) to reduce the number of
vectors in our final model to limit the set to best capture instrumental
noise. With a PCA-reduced set of vectors, we can construct a design matrix
containing fractional pixel fluxes.
To solve for the PLD model, we need to minimize the difference squared
.. math::
\chi^2 = \sum_i \frac{(y_i - m_i)^2}{\sigma_i^2},
where :math:`y_i` is the observed flux value at time :math:`t_i`, by solving
.. math::
\frac{\partial \chi^2}{\partial a_l} = 0.
The design matrix also contains columns representing a spline in time
design to capture the intrinsic, long-term variability of the target.
Examples
--------
Download the pixel data for GJ 9827 and obtain a PLD-corrected light curve:
>>> import lightkurve as lk
>>> tpf = lk.search_targetpixelfile("GJ9827").download() # doctest: +SKIP
>>> corrector = tpf.to_corrector('pld') # doctest: +SKIP
>>> lc = corrector.correct() # doctest: +SKIP
>>> lc.plot() # doctest: +SKIP
However, the above example will over-fit the small transits!
It is necessary to mask the transits using `corrector.correct(cadence_mask=...)`.
References
----------
.. [1] Deming et al. (2015), ads:2015ApJ...805..132D.
(arXiv:1411.7404)
.. [2] Luger et al. (2016), ads:2016AJ....152..100L
(arXiv:1607.00524)
.. [3] Luger et al. (2018), ads:2018AJ....156...99L
(arXiv:1702.05488)
.. [4] EVEREST pipeline webpage, https://rodluger.github.io/everest
"""
def __init__(self, tpf, aperture_mask=None):
if aperture_mask is None:
aperture_mask = tpf.create_threshold_mask(3)
self.aperture_mask = aperture_mask
lc = tpf.to_lightcurve(aperture_mask=aperture_mask)
# Remove cadences that have NaN flux (cf. #874). We don't simply call
# `lc.remove_nans()` here because we need to mask both lc & tpf.
# we also need to remove nans from the flux_err and combine both masks.
nan_mask = np.isnan(lc.flux) | np.isnan(lc.flux_err)
lc = lc[~nan_mask]
self.tpf = tpf[~nan_mask]
super().__init__(lc=lc)
def __repr__(self):
return "PLDCorrector (ID: {})".format(self.lc.label)
def create_design_matrix(
self,
pld_order=3,
pca_components=16,
pld_aperture_mask=None,
background_aperture_mask="background",
spline_n_knots=None,
spline_degree=3,
normalize_background_pixels=None,
sparse=False,
):
"""Returns a `.DesignMatrixCollection` containing a `DesignMatrix` object
for the background regressors, the PLD pixel component regressors, and
the spline regressors.
If the parameters `pld_order` and `pca_components` are None, their
value will be assigned based on the mission. K2 and TESS experience
different dominant sources of noise, and require different defaults.
For information about how the defaults were chosen, see Pull Request #746.
Parameters
----------
pld_order : int
The order of Pixel Level De-correlation to be performed. First order
(`n=1`) uses only the pixel fluxes to construct the design matrix.
Higher order populates the design matrix with columns constructed
from the products of pixel fluxes.
pca_components : int or tuple of int
Number of terms added to the design matrix for each order of PLD
pixel fluxes. Increasing this value may provide higher precision
at the expense of slower speed and/or overfitting.
If performing PLD with `pld_order > 1`, `pca_components` can be
a tuple containing the number of terms for each order of PLD.
If a single int is passed, the same number of terms will be used
for each order. If zero is passed, PCA will not be performed.
Defaults to 16 for K2 and 8 for TESS.
pld_aperture_mask : array-like, 'pipeline', 'all', 'threshold', or None
A boolean array describing the aperture such that `True` means
that the pixel will be used when selecting the PLD basis vectors.
If `None` or `all` are passed in, all pixels will be used.
If 'pipeline' is passed, the mask suggested by the official pipeline
will be returned.
If 'threshold' is passed, all pixels brighter than 3-sigma above
the median flux will be used.
spline_n_knots : int
Number of knots in spline.
spline_degree : int
Polynomial degree of spline.
sparse : bool
Whether to create `SparseDesignMatrix`.
Returns
-------
dm : `.DesignMatrixCollection`
`.DesignMatrixCollection` containing pixel, background, and spline
components.
"""
# Validate the inputs
pld_aperture_mask = self.tpf._parse_aperture_mask(pld_aperture_mask)
self.pld_aperture_mask = pld_aperture_mask
background_aperture_mask = self.tpf._parse_aperture_mask(
background_aperture_mask
)
self.background_aperture_mask = background_aperture_mask
if spline_n_knots is None:
# Default to a spline per 50 data points
spline_n_knots = int(len(self.lc) / 50)
if sparse:
DMC = SparseDesignMatrixCollection
spline = create_sparse_spline_matrix
else:
DMC = DesignMatrixCollection
spline = create_spline_matrix
# We set the width of all coefficient priors to 10 times the standard
# deviation to prevent the fit from going crazy.
prior_sigma = np.nanstd(self.lc.flux.value) * 10
# Flux normalize background components for K2 and not for TESS by default
bkg_pixels = self.tpf.flux[:, background_aperture_mask].reshape(
len(self.tpf.flux), -1
)
if normalize_background_pixels:
bkg_flux = np.nansum(self.tpf.flux[:, background_aperture_mask], -1)
bkg_pixels = np.array([r / f for r, f in zip(bkg_pixels, bkg_flux)])
else:
bkg_pixels = bkg_pixels.value
# Remove NaNs
bkg_pixels = np.array([r[np.isfinite(r)] for r in bkg_pixels])
# Create background design matrix
dm_bkg = DesignMatrix(bkg_pixels, name="background")
# Apply PCA
dm_bkg = dm_bkg.pca(pca_components)
# Set prior sigma to 10 * standard deviation
dm_bkg.prior_sigma = np.ones(dm_bkg.shape[1]) * prior_sigma
# Create a design matric containing splines plus a constant
dm_spline = spline(
self.lc.time.value, n_knots=spline_n_knots, degree=spline_degree
).append_constant()
# Set prior sigma to 10 * standard deviation
dm_spline.prior_sigma = np.ones(dm_spline.shape[1]) * prior_sigma
# Create a PLD matrix if there are pixels in the pld_aperture_mask
if np.sum(pld_aperture_mask) != 0:
# Flux normalize the PLD components
pld_pixels = self.tpf.flux[:, pld_aperture_mask].reshape(
len(self.tpf.flux), -1
)
pld_pixels = np.array(
[r / f for r, f in zip(pld_pixels, self.lc.flux.value)]
)
# Remove NaNs
pld_pixels = np.array([r[np.isfinite(r)] for r in pld_pixels])
# Use the DesignMatrix infrastructure to apply PCA to the regressors.
regressors_dm = DesignMatrix(pld_pixels)
if pca_components > 0:
regressors_dm = regressors_dm.pca(pca_components)
regressors_pld = regressors_dm.values
# Create a DesignMatrix for each PLD order
all_pld = []
for order in range(1, pld_order + 1):
reg_n = np.product(list(multichoose(regressors_pld.T, order)), axis=1).T
pld_n = DesignMatrix(
reg_n,
prior_sigma=np.ones(reg_n.shape[1]) * prior_sigma / reg_n.shape[1],
name=f"pld_order_{order}",
)
# Apply PCA.
if pca_components > 0:
pld_n = pld_n.pca(pca_components)
# Calling pca() resets the priors, so we set them again.
pld_n.prior_sigma = (
np.ones(pld_n.shape[1]) * prior_sigma / pca_components
)
all_pld.append(pld_n)
# Create the collection of DesignMatrix objects.
# DesignMatrix 1 contains the PLD pixel series
dm_pixels = DesignMatrixCollection(all_pld).to_designmatrix(
name="pixel_series"
)
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message=".*Not all matrices are `SparseDesignMatrix` objects..*",
)
dm_collection = DMC([dm_pixels, dm_bkg, dm_spline])
else:
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message=".*Not all matrices are `SparseDesignMatrix` objects..*",
)
dm_collection = DMC([dm_bkg, dm_spline])
return dm_collection
@deprecated_renamed_argument(
"n_pca_terms",
"pca_components",
"2.0",
warning_type=LightkurveDeprecationWarning,
)
@deprecated_renamed_argument(
"use_gp", None, "2.0", warning_type=LightkurveDeprecationWarning
)
@deprecated_renamed_argument(
"gp_timescale", None, "2.0", warning_type=LightkurveDeprecationWarning
)
@deprecated_renamed_argument(
"aperture_mask", None, "2.0", warning_type=LightkurveDeprecationWarning
)
def correct(
self,
pld_order=None,
pca_components=None,
pld_aperture_mask=None,
background_aperture_mask="background",
spline_n_knots=None,
spline_degree=5,
normalize_background_pixels=None,
restore_trend=True,
sparse=False,
cadence_mask=None,
sigma=5,
niters=5,
propagate_errors=False,
use_gp=None,
gp_timescale=None,
aperture_mask=None,
):
"""Returns a systematics-corrected light curve.
If the parameters `pld_order` and `pca_components` are None, their
value will be assigned based on the mission. K2 and TESS experience
different dominant sources of noise, and require different defaults.
For information about how the defaults were chosen, see PR #746 at
https://github.com/lightkurve/lightkurve/pull/746#issuecomment-658458270
Parameters
----------
pld_order : int
The order of Pixel Level De-correlation to be performed. First order
(`n=1`) uses only the pixel fluxes to construct the design matrix.
Higher order populates the design matrix with columns constructed
from the products of pixel fluxes. Default 3 for K2 and 1 for TESS.
pca_components : int
Number of terms added to the design matrix for each order of PLD
pixel fluxes. Increasing this value may provide higher precision
at the expense of slower speed and/or overfitting.
pld_aperture_mask : array-like, 'pipeline', 'all', 'threshold', or None
A boolean array describing the aperture such that `True` means
that the pixel will be used when selecting the PLD basis vectors.
If `None` or `all` are passed in, all pixels will be used.
If 'pipeline' is passed, the mask suggested by the official pipeline
will be returned.
If 'threshold' is passed, all pixels brighter than 3-sigma above
the median flux will be used.
spline_n_knots : int
Number of knots in spline.
spline_degree : int
Polynomial degree of spline.
restore_trend : bool
Whether to restore the long term spline trend to the light curve.
sparse : bool
Whether to create `SparseDesignMatrix`.
cadence_mask : np.ndarray of bools (optional)
Mask, where True indicates a cadence that should be used.
sigma : int (default 5)
Standard deviation at which to remove outliers from fitting
niters : int (default 5)
Number of iterations to fit and remove outliers
propagate_errors : bool (default False)
Whether to propagate the uncertainties from the regression. Default is False.
Setting to True will increase run time, but will sample from multivariate normal
distribution of weights.
use_gp, gp_timescale : DEPRECATED
As of Lightkurve v2.0 PLDCorrector uses splines instead of Gaussian Processes.
aperture_mask : DEPRECATED
As of Lightkurve v2.0 the `aperture_mask` parameter needs to be
passed to the class constructor.
Returns
-------
clc : `.LightCurve`
Noise-corrected `.LightCurve`.
"""
self.restore_trend = restore_trend
# Set mission-specific values for pld_order and pca_components
if pld_order is None:
if self.tpf.meta.get("MISSION") == "K2":
pld_order = 3
else:
pld_order = 1
if pca_components is None:
if self.tpf.meta.get("MISSION") == "K2":
pca_components = 16
else:
pca_components = 3
if pld_aperture_mask is None:
if self.tpf.meta.get("MISSION") == "K2":
# K2 noise is dominated by motion
pld_aperture_mask = "threshold"
else:
# TESS noise is dominated by background
pld_aperture_mask = "empty"
if normalize_background_pixels is None:
if self.tpf.meta.get("MISSION") == "K2":
normalize_background_pixels = True
else:
normalize_background_pixels = False
dm = self.create_design_matrix(
pld_aperture_mask=pld_aperture_mask,
background_aperture_mask=background_aperture_mask,
pld_order=pld_order,
pca_components=pca_components,
spline_n_knots=spline_n_knots,
spline_degree=spline_degree,
normalize_background_pixels=normalize_background_pixels,
sparse=sparse,
)
clc = super().correct(
dm,
cadence_mask=cadence_mask,
sigma=sigma,
niters=niters,
propagate_errors=propagate_errors,
)
if restore_trend:
clc += self.diagnostic_lightcurves["spline"] - np.median(
self.diagnostic_lightcurves["spline"].flux
)
return clc
def diagnose(self):
"""Returns diagnostic plots to assess the most recent call to `correct()`.
If `correct()` has not yet been called, a ``ValueError`` will be raised.
Returns
-------
`~matplotlib.axes.Axes`
The matplotlib axes object.
"""
if not getattr(self, "corrected_lc"):
raise ValueError(
"You need to call the `correct()` method "
"before you can call `diagnose()`."
)
names = self.diagnostic_lightcurves.keys()
# Plot the right version of corrected light curve
if self.restore_trend:
clc = (
self.corrected_lc
+ self.diagnostic_lightcurves["spline"]
- np.median(self.diagnostic_lightcurves["spline"].flux)
)
else:
clc = self.corrected_lc
uncorr_cdpp = self.lc.estimate_cdpp()
corr_cdpp = clc.estimate_cdpp()
# Get y-axis limits
ylim = [
min(min(self.lc.flux.value), min(clc.flux.value)),
max(max(self.lc.flux.value), max(clc.flux.value)),
]
# Use lightkurve plotting style
with plt.style.context(MPLSTYLE):
# Plot background model
_, axs = plt.subplots(3, figsize=(10, 9), sharex=True)
ax = axs[0]
self.lc.plot(
ax=ax,
normalize=False,
clip_outliers=True,
label=f"uncorrected ({uncorr_cdpp:.0f})",
)
ax.set_xlabel("")
ax.set_ylim(ylim) # use same ylim for all plots
# Plot pixel and spline components
ax = axs[1]
clc.plot(
ax=ax, normalize=False, alpha=0.4, label=f"corrected ({corr_cdpp:.0f})"
)
for key, color in zip(names, ["dodgerblue", "r", "C1"]):
if key in ["background", "spline", "pixel_series"]:
tmplc = (
self.diagnostic_lightcurves[key]
- np.median(self.diagnostic_lightcurves[key].flux)
+ np.median(self.lc.flux)
)
tmplc.plot(ax=ax, c=color)
ax.set_xlabel("")
ax.set_ylim(ylim)
# Plot final corrected light curve with outliers marked
ax = axs[2]
self.lc.plot(
ax=ax,
normalize=False,
alpha=0.2,
label=f"uncorrected ({uncorr_cdpp:.0f})",
)
clc[self.outlier_mask].scatter(
normalize=False, c="r", marker="x", s=10, label="outlier_mask", ax=ax
)
clc[~self.cadence_mask].scatter(
normalize=False,
c="dodgerblue",
marker="x",
s=10,
label="~cadence_mask",
ax=ax,
)
clc.plot(
normalize=False, ax=ax, c="k", label=f"corrected ({corr_cdpp:.0f})"
)
ax.set_ylim(ylim)
return axs
def diagnose_masks(self):
"""Show different aperture masks used by PLD in the most recent call to
`correct()`. If `correct()` has not yet been called, a ``ValueError``
will be raised.
Returns
-------
`~matplotlib.axes.Axes`
The matplotlib axes object.
"""
if not hasattr(self, "corrected_lc"):
raise ValueError(
"You need to call the `correct()` method "
"before you can call `diagnose()`."
)
# Use lightkurve plotting style
with plt.style.context(MPLSTYLE):
_, axs = plt.subplots(1, 3, figsize=(12, 4), sharey=True)
# Show light curve aperture mask
ax = axs[0]
self.tpf.plot(
ax=ax,
show_colorbar=False,
aperture_mask=self.aperture_mask,
title="aperture_mask",
)
# Show PLD pixel mask
ax = axs[1]
self.tpf.plot(
ax=ax,
show_colorbar=False,
aperture_mask=self.pld_aperture_mask,
title="pld_aperture_mask",
)
ax = axs[2]
self.tpf.plot(
ax=ax,
show_colorbar=False,
aperture_mask=self.background_aperture_mask,
title="background_aperture_mask",
)
return axs
# `TessPLDCorrector` was briefly introduced in Lightkurve v1.9
# but was removed in v2.0 in favor of a single generic `PLDCorrector`.
@deprecated(
"2.0", alternative="PLDCorrector", warning_type=LightkurveDeprecationWarning
)
class TessPLDCorrector(PLDCorrector):
pass
|
lightkurveREPO_NAMElightkurvePATH_START.@lightkurve_extracted@lightkurve-main@src@lightkurve@correctors@pldcorrector.py@.PATH_END.py
|
{
"filename": "edit_nc_file_to_preserve_monthly_means.py",
"repo_name": "ExeClim/Isca",
"repo_path": "Isca_extracted/Isca-master/src/extra/python/scripts/edit_nc_file_to_preserve_monthly_means.py",
"type": "Python"
}
|
import xarray as xar
import numpy as np
import pdb
import matplotlib.pyplot as plt
import create_timeseries as cts
def adjust_data(input_data):
"""
adjust_data solves the matrix problem Ax=b, where A is a matrix with recurring entries of 0.125, 0.75, 0.125,
x is 12 unknowns, and b is a column vector of 12 monthly average data points. The purpose of solving for x is
that the values for x will produce mid-month values that, when linearly interpolated by a model's interpolator
will reproduce monthly means equal to b. This method is described in detail here:
http://www-pcmdi.llnl.gov/projects/amip/AMIP2EXPDSN/BCS/amip2bcs.php.
"""
multiply_matrix=np.asarray([
[0.75, 0.125, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.125],
[0.125,0.75, 0.125,0., 0., 0., 0., 0., 0., 0., 0., 0. ],
[0., 0.125, 0.75, 0.125,0., 0., 0., 0., 0., 0., 0., 0. ],
[0., 0., 0.125,0.75, 0.125,0., 0., 0., 0., 0., 0., 0. ],
[0., 0., 0., 0.125,0.75, 0.125,0., 0., 0., 0., 0., 0. ],
[0., 0., 0., 0., 0.125,0.75, 0.125,0., 0., 0., 0., 0. ],
[0., 0., 0., 0., 0., 0.125,0.75, 0.125,0., 0., 0., 0. ],
[0., 0., 0., 0., 0., 0., 0.125,0.75, 0.125,0., 0., 0. ],
[0., 0., 0., 0., 0., 0., 0., 0.125,0.75, 0.125,0., 0. ],
[0., 0., 0., 0., 0., 0., 0., 0., 0.125,0.75, 0.125,0. ],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.125,0.75, 0.125],
[0.125,0., 0., 0., 0., 0., 0., 0., 0., 0., 0.125,0.75 ]
])
real_monthly_means = input_data
solution = np.linalg.solve(multiply_matrix, real_monthly_means)
return solution
def perform_adj(data_array):
"""
Function for looping over spatial points in data array.
"""
output_array=np.zeros_like(data_array)
nt, ny, nx = np.shape(data_array)
for j in range(ny):
print(j)
for i in range(nx):
output_array[:,j,i] = adjust_data(data_array[:,j,i])
return output_array
def output_to_file(dataset, output_array, output_filename, variable_name):
"""
Simple function for outputting adjusted data as a netcdf file.
"""
nt, ny, nx = np.shape(output_array)
p_full=None
p_half=None
npfull=None
nphalf=None
#Find grid and time numbers
lats=dataset.lat.values
lons=dataset.lon.values
latbs=dataset.latb.values
lonbs=dataset.lonb.values
time_arr=dataset.time.values
ntime=nt
nlonb=dataset.lonb.values.shape[0]
nlatb=dataset.latb.values.shape[0]
#Output it to a netcdf file.
number_dict={}
number_dict['nlat']=ny
number_dict['nlon']=nx
number_dict['nlatb']=nlatb
number_dict['nlonb']=nlonb
number_dict['npfull']=npfull
number_dict['nphalf']=nphalf
number_dict['ntime']=ntime
time_units='days since 0000-01-01 00:00:00.0'
cts.output_to_file(output_array,lats,lons,latbs,lonbs,p_full,p_half,time_arr,time_units,output_file_name,variable_name,number_dict)
def run_steps(input_file_name, input_variable_name, output_file_name, output_variable_name):
"""
Function for performing the necessary steps to execute the program.
"""
dataset=xar.open_dataset(input_file_name, decode_times=False)
data_array=dataset[input_variable_name].values
adjusted_data_array = perform_adj(data_array)
output_to_file(dataset, adjusted_data_array, output_file_name, output_variable_name)
if __name__=="__main__":
input_file_name='/scratch/sit204/FMS2013/GFDLmoistModel/src/extra/python/scripts/nc_files_11_01_17/ami_qflux_ctrl_ice_4320.nc'
input_variable_name='ami_qflux_ctrl_ice_4320'
output_file_name='ami_qflux_ctrl_ice_4320m.nc'
output_variable_name='ami_qflux_ctrl_ice_4320m'
run_steps(input_file_name, input_variable_name, output_file_name, output_variable_name)
|
ExeClimREPO_NAMEIscaPATH_START.@Isca_extracted@Isca-master@src@extra@python@scripts@edit_nc_file_to_preserve_monthly_means.py@.PATH_END.py
|
{
"filename": "toymodel.py",
"repo_name": "cta-observatory/ctapipe",
"repo_path": "ctapipe_extracted/ctapipe-main/src/ctapipe/image/toymodel.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Utilities to generate toymodel (fake) reconstruction inputs for testing
purposes.
Examples:
.. code-block:: python
>>> from ctapipe.instrument import CameraGeometry
>>> geom = CameraGeometry.make_rectangular(20, 20)
>>> showermodel = Gaussian(
... x=0.25 * u.m, y=0.0 * u.m,
... length=0.1 * u.m, width=0.02 * u.m,
... psi='40d'
... )
>>> image, signal, noise = showermodel.generate_image(geom, intensity=1000)
>>> print(image.shape)
(400,)
"""
from abc import ABCMeta, abstractmethod
import astropy.units as u
import numpy as np
from numpy.random import default_rng
from scipy.ndimage import convolve1d
from scipy.stats import multivariate_normal, norm, skewnorm
from ctapipe.calib.camera.gainselection import GainChannel
from ctapipe.image.hillas import camera_to_shower_coordinates
from ctapipe.utils import linalg
__all__ = [
"WaveformModel",
"Gaussian",
"SkewedGaussian",
"ImageModel",
"obtain_time_image",
]
TOYMODEL_RNG = default_rng(0)
VALID_GAIN_CHANNEL = {"HIGH", "LOW", "ALL"}
def obtain_time_image(x, y, centroid_x, centroid_y, psi, time_gradient, time_intercept):
"""Create a pulse time image for a toymodel shower. Assumes the time development
occurs only along the longitudinal (major) axis of the shower, and scales
linearly with distance along the axis.
Parameters
----------
x : u.Quantity[length]
X camera coordinate to evaluate the time at.
Usually the array of pixel X positions
y : u.Quantity[length]
Y camera coordinate to evaluate the time at.
Usually the array of pixel Y positions
centroid_x : u.Quantity[length]
X camera coordinate for the centroid of the shower
centroid_y : u.Quantity[length]
Y camera coordinate for the centroid of the shower
psi : convertible to `astropy.coordinates.Angle`
rotation angle about the centroid (0=x-axis)
time_gradient : u.Quantity[time/angle]
Rate at which the time changes with distance along the shower axis
time_intercept : u.Quantity[time]
Pulse time at the shower centroid
Returns
-------
float or ndarray
Pulse time in nanoseconds at (x, y)
"""
unit = x.unit
x = x.to_value(unit)
y = y.to_value(unit)
centroid_x = centroid_x.to_value(unit)
centroid_y = centroid_y.to_value(unit)
psi = psi.to_value(u.rad)
time_gradient = time_gradient.to_value(u.ns / unit)
time_intercept = time_intercept.to_value(u.ns)
longitudinal, _ = camera_to_shower_coordinates(x, y, centroid_x, centroid_y, psi)
return longitudinal * time_gradient + time_intercept
class WaveformModel:
@u.quantity_input(reference_pulse_sample_width=u.ns, sample_width=u.ns)
def __init__(self, reference_pulse, reference_pulse_sample_width, sample_width):
"""Generate a toy model waveform for each gain channel using the reference
pulse shape of a camera. Useful for testing image extraction algorithms.
Does not include the electronic noise and the Excess Noise Factor of
the photosensor, and therefore should not be used to make charge
resolution conclusions about a camera.
Parameters
----------
reference_pulse_sample_width : u.Quantity[time]
Sample width of the reference pulse shape
reference_pulse : ndarray
Reference pulse shape for each channel
sample_width : u.Quantity[time]
Sample width of the waveform
"""
self.n_channels = reference_pulse.shape[-2]
self.upsampling = 10
reference_pulse_sample_width = reference_pulse_sample_width.to_value(u.ns)
sample_width_ns = sample_width.to_value(u.ns)
ref_max_sample = reference_pulse[0].size * reference_pulse_sample_width
reference_pulse_x = np.arange(0, ref_max_sample, reference_pulse_sample_width)
self.ref_width_ns = sample_width_ns / self.upsampling
self.ref_interp_x = np.arange(0, reference_pulse_x.max(), self.ref_width_ns)
self.ref_interp_y = np.zeros((self.n_channels, self.ref_interp_x.size))
for channel in range(self.n_channels):
self.ref_interp_y[channel] = np.interp(
self.ref_interp_x, reference_pulse_x, reference_pulse[channel]
)
self.ref_interp_y = (
self.ref_interp_y.T / (self.ref_interp_y.sum(-1) * self.ref_width_ns)
).T
self.origin = self.ref_interp_y.argmax(-1) - self.ref_interp_y[0].size // 2
def get_waveform(self, charge, time, n_samples):
"""Obtain the waveform toy model.
Parameters
----------
charge : ndarray
Amount of charge in each pixel
Shape: (n_pixels)
time : ndarray
The signal time in the waveform in nanoseconds
Shape: (n_pixels)
n_samples : int
Number of samples in the waveform
Returns
-------
waveform : ndarray
Toy model waveform
Shape (n_channels, n_pixels, n_samples)
"""
n_pixels = charge.size
n_upsampled_samples = n_samples * self.upsampling
readout = np.zeros((n_pixels, n_upsampled_samples))
sample = (time / self.ref_width_ns).astype(np.int64)
outofrange = (sample < 0) | (sample >= n_upsampled_samples)
sample[outofrange] = 0
charge[outofrange] = 0
readout[np.arange(n_pixels), sample] = charge
convolved = np.zeros((self.n_channels, n_pixels, n_upsampled_samples))
for channel in range(self.n_channels):
convolved[channel] = convolve1d(
readout,
self.ref_interp_y[channel],
mode="constant",
origin=self.origin[channel],
)
sampled = (
convolved.reshape(
(
self.n_channels,
n_pixels,
convolved.shape[-1] // self.upsampling,
self.upsampling,
)
).sum(-1)
* self.ref_width_ns # Waveform units: p.e.
)
return sampled
@classmethod
def from_camera_readout(cls, readout, gain_channel="ALL"):
"""Create class from a `ctapipe.instrument.CameraReadout`.
Parameters
----------
readout : `ctapipe.instrument.CameraReadout`
gain_channel : str
The reference pulse gain channel to use.
Choose between 'HIGH', 'LOW' and 'ALL'.
Returns
-------
WaveformModel
"""
if gain_channel not in VALID_GAIN_CHANNEL:
raise ValueError(f"gain_channel must be one of {VALID_GAIN_CHANNEL}")
ref_pulse_shape = readout.reference_pulse_shape
if gain_channel != "ALL":
ref_pulse_shape = ref_pulse_shape[np.newaxis, GainChannel[gain_channel]]
return cls(
ref_pulse_shape,
readout.reference_pulse_sample_width,
(1 / readout.sampling_rate).to(u.ns),
)
class ImageModel(metaclass=ABCMeta):
@abstractmethod
def pdf(self, x, y):
"""Probability density function."""
def generate_image(self, camera, intensity=50, nsb_level_pe=20, rng=None):
"""Generate a randomized DL1 shower image.
For the signal, poisson random numbers are drawn from
the expected signal distribution for each pixel.
For the background, for each pixel a poisson random number
if drawn with mean ``nsb_level_pe``.
Parameters
----------
camera : `ctapipe.instrument.CameraGeometry`
camera geometry object
intensity : int
Total number of photo electrons to generate
nsb_level_pe : type
level of NSB/pedestal in photo-electrons
Returns
-------
image: array with length n_pixels containing the image
signal: only the signal part of image
noise: only the noise part of image
"""
if rng is None:
rng = TOYMODEL_RNG
expected_signal = self.expected_signal(camera, intensity)
signal = rng.poisson(expected_signal)
noise = rng.poisson(nsb_level_pe, size=signal.shape)
image = (signal + noise) - np.mean(noise)
return image, signal, noise
def expected_signal(self, camera, intensity):
"""Expected signal in each pixel for the given camera
and total intensity.
Parameters
----------
camera: `ctapipe.instrument.CameraGeometry`
camera geometry object
intensity: int
Total number of expected photo electrons
Returns
-------
image: array with length n_pixels containing the image
"""
pdf = self.pdf(camera.pix_x, camera.pix_y)
return pdf * intensity * camera.pix_area.value
class Gaussian(ImageModel):
def __init__(self, x, y, length, width, psi):
"""Create 2D Gaussian model for a shower image in a camera.
Parameters
----------
centroid : u.Quantity[length, shape=(2, )]
position of the centroid of the shower in camera coordinates
width: u.Quantity[length]
width of shower (minor axis)
length: u.Quantity[length]
length of shower (major axis)
psi : u.Quantity[angle]
rotation angle about the centroid (0=x-axis)
Returns
-------
a `scipy.stats` object
"""
self.unit = x.unit
self.x = x
self.y = y
self.width = width
self.length = length
self.psi = psi
aligned_covariance = np.array(
[
[self.length.to_value(self.unit) ** 2, 0],
[0, self.width.to_value(self.unit) ** 2],
]
)
# rotate by psi angle: C' = R C R+
rotation = linalg.rotation_matrix_2d(self.psi)
rotated_covariance = rotation @ aligned_covariance @ rotation.T
self.dist = multivariate_normal(
mean=u.Quantity([self.x, self.y]).to_value(self.unit),
cov=rotated_covariance,
)
def pdf(self, x, y):
"""2d probability for photon electrons in the camera plane"""
X = np.column_stack([x.to_value(self.unit), y.to_value(self.unit)])
return self.dist.pdf(X)
class SkewedGaussian(ImageModel):
"""A shower image that has a skewness along the major axis."""
def __init__(self, x, y, length, width, psi, skewness):
"""Create 2D skewed Gaussian model for a shower image in a camera.
Skewness is only applied along the main shower axis.
See https://en.wikipedia.org/wiki/Skew_normal_distribution and
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skewnorm.html
for details.
Parameters
----------
centroid : u.Quantity[length, shape=(2, )]
position of the centroid of the shower in camera coordinates
width: u.Quantity[length]
width of shower (minor axis)
length: u.Quantity[length]
length of shower (major axis)
psi : u.Quantity[angle]
rotation angle about the centroid (0=x-axis)
Returns
-------
a `scipy.stats` object
"""
self.unit = x.unit
self.x = x
self.y = y
self.width = width
self.length = length
self.psi = psi
self.skewness = skewness
a, loc, scale = self._moments_to_parameters()
self.long_dist = skewnorm(a=a, loc=loc, scale=scale)
self.trans_dist = norm(loc=0, scale=self.width.to_value(self.unit))
self.rotation = linalg.rotation_matrix_2d(-self.psi)
self.mu = u.Quantity([self.x, self.y]).to_value(self.unit)
def _moments_to_parameters(self):
"""Returns loc and scale from mean, std and skewnewss."""
# see https://en.wikipedia.org/wiki/Skew_normal_distribution#Estimation
skew23 = np.abs(self.skewness) ** (2 / 3)
delta = np.sign(self.skewness) * np.sqrt(
(np.pi / 2 * skew23) / (skew23 + (0.5 * (4 - np.pi)) ** (2 / 3))
)
a = delta / np.sqrt(1 - delta**2)
scale = self.length.to_value(self.unit) / np.sqrt(1 - 2 * delta**2 / np.pi)
loc = -scale * delta * np.sqrt(2 / np.pi)
return a, loc, scale
def pdf(self, x, y):
"""2d probability for photon electrons in the camera plane."""
pos = np.column_stack([x.to_value(self.unit), y.to_value(self.unit)])
long, trans = self.rotation @ (pos - self.mu).T
return self.trans_dist.pdf(trans) * self.long_dist.pdf(long)
class RingGaussian(ImageModel):
"""A shower image consisting of a ring with gaussian radial profile.
Simplified model for a muon ring.
"""
def __init__(self, x, y, radius, sigma):
self.unit = x.unit
self.x = x
self.y = y
self.sigma = sigma
self.radius = radius
self.dist = norm(
self.radius.to_value(self.unit), self.sigma.to_value(self.unit)
)
def pdf(self, x, y):
"""2d probability for photon electrons in the camera plane."""
r = np.sqrt((x - self.x) ** 2 + (y - self.y) ** 2)
return self.dist.pdf(r)
|
cta-observatoryREPO_NAMEctapipePATH_START.@ctapipe_extracted@ctapipe-main@src@ctapipe@image@toymodel.py@.PATH_END.py
|
{
"filename": "custom_partitioning_sharding_rule_test.py",
"repo_name": "jax-ml/jax",
"repo_path": "jax_extracted/jax-main/tests/custom_partitioning_sharding_rule_test.py",
"type": "Python"
}
|
# Copyright 2024 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from absl.testing import absltest
from jax._src import test_util as jtu
from jax._src.lib.mlir import ir
from jax._src.lib.mlir.dialects import sdy
from jax._src.custom_partitioning_sharding_rule import ArrayMapping, BATCHING, CompoundFactor, sdy_sharding_rule_to_mlir, str_to_sdy_sharding_rule, SdyShardingRule
from jax._src.lib.mlir.dialects import hlo as stablehlo
class SdyShardingRuleTest(jtu.JaxTestCase):
def test_compound_factor_not_enough_factors(self):
with self.assertRaisesRegex(ValueError, "A compound factor should contain at least two factors"):
CompoundFactor("i")
def test_compound_factor_batching_now_allowed(self):
with self.assertRaisesRegex(ValueError, "Ellipsis can't be used in a compound factor"):
CompoundFactor(BATCHING, "i")
def test_compound_factor_element_not_a_str(self):
with self.assertRaisesRegex(ValueError, "Each element of CompoundFactor must be a str"):
CompoundFactor("i", 2)
def test_compound_factor_str(self):
c = CompoundFactor("i", "j", "k")
self.assertEqual(str(c), "('i', 'j', 'k')")
def test_value_mapping_element_not_a_str_or_compound_factor(self):
with self.assertRaisesRegex(ValueError, "Each element of ArrayMapping must be a str or CompoundFactor"):
ArrayMapping(CompoundFactor("i", "j"), 3)
def test_value_mapping_factor_name_not_start_with_letter(self):
with self.assertRaisesRegex(ValueError, "Factor names have to start with a letter"):
ArrayMapping("3i", "j")
def test_value_mapping_ellipsis_not_first(self):
with self.assertRaisesRegex(ValueError, "Ellipsis can only be used at the beginning of a dimension"):
ArrayMapping("i_j", BATCHING)
def test_value_mapping_str(self):
v = ArrayMapping(BATCHING, "m", CompoundFactor("i", "j"), "k")
self.assertEqual(str(v), f"('{BATCHING}', 'm', ('i', 'j'), 'k')")
def test_sdy_sharding_rule_factor_size_not_used(self):
with self.assertRaisesRegex(ValueError, "Factor k is not used"):
SdyShardingRule(("i",), ("j",), k=10)
def test_sdy_sharding_rule_factor_sizes_missing(self):
with self.assertRaisesRegex(
ValueError,
"Factor k is only used in compound factors; must specify its size"):
SdyShardingRule((ArrayMapping("i"), ArrayMapping("j")),
(ArrayMapping(CompoundFactor("j", "k")),))
def test_sdy_sharding_rule_factor_size_not_necessary(self):
with self.assertRaisesRegex(
ValueError,
"Factor i represents a whole dimension; do not specify its size"):
SdyShardingRule((ArrayMapping("i"),), (ArrayMapping("j"),), i=10)
def test_sdy_sharding_rule_compound_factor_size_not_necessary(self):
with self.assertRaisesRegex(
ValueError,
"Factor i represents a whole dimension; do not specify its size"):
SdyShardingRule((ArrayMapping(CompoundFactor("i", "j")),),
(ArrayMapping("i"),), i=10, j=20)
def test_sdy_sharding_rule_str(self):
r = SdyShardingRule((ArrayMapping("i"), ArrayMapping("j")),
(ArrayMapping(CompoundFactor("j", "k")),), k=10)
self.assertEqual(str(r), "SdyShardingRule((('i',), ('j',)), ((('j', 'k'),),), {'k': 10})")
class StrToSdyShardingRuleTest(jtu.JaxTestCase):
def test_rule_is_not_a_str(self):
with self.assertRaisesRegex(TypeError, "rule must be a str"):
str_to_sdy_sharding_rule(1)
def test_factor_sizes_is_not_a_proper_dict(self):
with self.assertRaisesRegex(
TypeError, "factor_sizes must be a dict of str to int"):
str_to_sdy_sharding_rule("i->j", i="j")
def test_sharding_rule_ellipsis_not_complete(self):
with self.assertRaisesRegex(
ValueError, "Character '.' must be used inside ellipsis '...'"):
str_to_sdy_sharding_rule(".i -> j")
def test_sharding_rule_invalid_factor_name(self):
with self.assertRaisesRegex(ValueError, "Factor names have to start with a letter"):
str_to_sdy_sharding_rule("2i -> j")
def test_sharding_rule_missing_results(self):
with self.assertRaisesRegex(ValueError, "There is no -> in rule"):
str_to_sdy_sharding_rule("i")
def test_sharding_rule_inbalenced_brackets(self):
with self.assertRaisesRegex(ValueError, "Brackets are not balanced"):
str_to_sdy_sharding_rule("i j, k)->j")
def test_sharding_rule_inbalenced_brackets2(self):
with self.assertRaisesRegex(ValueError, "Brackets are not balanced"):
str_to_sdy_sharding_rule("i (j k->j")
def test_sharding_rule_empty_compound_dim(self):
with self.assertRaisesRegex(
ValueError, "Brackets should contain at least two factors"):
str_to_sdy_sharding_rule("i ( ) j k->j")
def test_sharding_rule_one_factorcompound_dim(self):
with self.assertRaisesRegex(
ValueError, "Brackets should contain at least two factors"):
str_to_sdy_sharding_rule("i (j ) k->j")
def test_sharding_rule_nested_brackets(self):
with self.assertRaisesRegex(
ValueError, "Compound factors should be one level"):
str_to_sdy_sharding_rule("i (j (k))->j")
def test_sharding_rule_unknown_char(self):
with self.assertRaisesRegex(ValueError, "Unknown character"):
str_to_sdy_sharding_rule("i; j->j")
def test_sharding_rule_unknown_single_char_ellipse(self):
with self.assertRaisesRegex(ValueError, "Unknown character"):
str_to_sdy_sharding_rule("…j->…j")
def test_sharding_rule_ellipsis_not_leading_dim(self):
with self.assertRaisesRegex(
ValueError, "Ellipsis can only be used at the beginning of a dimension"):
str_to_sdy_sharding_rule("i ... -> j")
def test_sharding_rule_ellipsis_inside_compound_dim(self):
with self.assertRaisesRegex(
ValueError, "Ellipsis can only be used at the beginning of a dimension"):
str_to_sdy_sharding_rule("i, (..., j) -> j")
def test_sharding_rule_scalar_operand_scalar_result(self):
rule = str_to_sdy_sharding_rule("->")
self.assertEqual(str(rule), "SdyShardingRule(((),), ((),), {})")
def test_sharding_rule_one_scalar_operand(self):
rule = str_to_sdy_sharding_rule("i j, , k->j")
self.assertEqual(
str(rule), "SdyShardingRule((('i', 'j'), (), ('k',)), (('j',),), {})")
def test_sharding_rule_factor_elementwise_add(self):
rule = str_to_sdy_sharding_rule("... i j, ...i j -> ...i j")
self.assertEqual(
str(rule),
"SdyShardingRule((('…', 'i', 'j'), ('…', 'i', 'j')), (('…', 'i',"
" 'j'),), {})")
def test_sharding_rule_factor_vector_scalar_add(self):
rule = str_to_sdy_sharding_rule("...i, -> ...i")
self.assertEqual(
str(rule),
"SdyShardingRule((('…', 'i'), ()), (('…', 'i'),), {})")
def test_sharding_rule_factor_reshape_combining(self):
rule = str_to_sdy_sharding_rule("i j -> (i j)")
self.assertEqual(
str(rule), "SdyShardingRule((('i', 'j'),), ((('i', 'j'),),), {})")
def test_sharding_rule_factor_reshape_reordering(self):
rule = str_to_sdy_sharding_rule("(j i) -> (i j)", i=10, j=20)
self.assertEqual(
str(rule),
"SdyShardingRule(((('j', 'i'),),), ((('i', 'j'),),), {'i': 10, 'j':"
" 20})")
def test_sharding_rule_factor_compound_then_individual(self):
rule = str_to_sdy_sharding_rule("(i j) (j k) i -> j k")
self.assertEqual(
str(rule),
"SdyShardingRule(((('i', 'j'), ('j', 'k'), 'i'),), (('j', 'k'),), {})")
def test_sharding_rule_factor_individual_then_compound(self):
rule = str_to_sdy_sharding_rule("i j k -> (i j) (j k)")
self.assertEqual(
str(rule),
"SdyShardingRule((('i', 'j', 'k'),), ((('i', 'j'), ('j', 'k')),), {})")
def test_sharding_rule_factor_infer_k(self):
rule = str_to_sdy_sharding_rule("i_ (j k)-> j foo (m bar_24)", k=10, m=10, bar_24=20)
self.assertEqual(
str(rule),
"SdyShardingRule((('i_', ('j', 'k')),), (('j', 'foo', ('m', 'bar_24'))"
",), {'k': 10, 'm': 10, 'bar_24': 20})")
class SdyShardingRuleConversionTest(jtu.JaxTestCase):
def run(self, result=None):
with ir.Context() as ctx, ir.Location.unknown(ctx):
sdy.register_dialect(ctx)
stablehlo.register_dialect(ctx)
module = ir.Module.create()
with ir.InsertionPoint(module.body):
super().run(result)
def get_tensor_type(self, shape):
return ir.RankedTensorType.get(shape, ir.F32Type.get())
def create_tensor_value(self, shape):
return ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type(shape)],
attributes=dict(call_target_name=ir.StringAttr.get("dummy_target"))
).result
def test_conversion_rule_op_mismatch_in_operands_num(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")),)
rule = str_to_sdy_sharding_rule("i j-> i j")
with self.assertRaisesRegex(
ValueError,
"Sharding rule has 1 operands, but the operation has 2 operands"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
def test_conversion_rule_op_mismatch_in_operands_rank(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")),)
rule = str_to_sdy_sharding_rule("i j, i j k-> i j")
with self.assertRaisesRegex(
ValueError,
"Sharding rule 1th operand has rank 3, but the operation 1th "
"operand has rank 2"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
def test_conversion_rule_op_mismatch_in_results_num(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0,
opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")),)
rule = str_to_sdy_sharding_rule("i j, i j -> i j, i j")
with self.assertRaisesRegex(
ValueError,
"Sharding rule has 2 results, but the operation has 1 results"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
def test_conversion_rule_op_mismatch_in_results_dim(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("i j, i j -> i j k")
with self.assertRaisesRegex(
ValueError,
"Sharding rule 0th result has rank 3, but the operation 0th "
"result has rank 2"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
def test_conversion_factor_has_two_sizes(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 64))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("i j, i j -> i j")
with self.assertRaisesRegex(
ValueError,
"Factor j corresponds to two sizes: 32 and 64"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
def test_conversion_batching_dim_has_two_sizes(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 64))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("..., ... -> ...")
with self.assertRaisesRegex(
ValueError,
"Batching dimension 1 corresponds to two sizes: 32 and 64"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,],)
def test_conversion_invalid_batching_dim(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")),)
rule = str_to_sdy_sharding_rule("... i j k, ... i j k -> ... i j k")
with self.assertRaisesRegex(
ValueError,
"Sharding rule 0th operand has rank 3, but the operation 0th operand has rank 2"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
def test_conversion_compound_dimension_size_mismatch(self):
opnd = self.create_tensor_value((2, 4))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((9,))],
operands=[opnd,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("i j -> (i j)")
with self.assertRaisesRegex(
ValueError,
"0th result actual size 9 doesn't match the size 8 derived from the"
" compound factors"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type],
[result.result.type,])
def test_conversion_elementwise_rule_mismatching_ellipsis_rank(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16,))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("..., ... -> ...")
with self.assertRaisesRegex(
ValueError,
"Ellipsis represents different number of leading dimensions 2 and 1"):
sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
def test_conversion_compound_then_individual(self):
opnd = self.create_tensor_value((8,))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((2,4))],
operands=[opnd,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("(i j) -> i j")
mlir_rule = sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type],
[result.result.type,])
self.assertEqual(
str(mlir_rule),
"#sdy.op_sharding_rule<([ij])->([i, j]) {i=2, j=4}>")
def test_conversion_elementwise_rule_scalar_instance(self):
opnd0 = self.create_tensor_value(())
opnd1 = self.create_tensor_value(())
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type(())],
operands=[opnd0, opnd1],
attributes=dict(call_target_name=ir.StringAttr.get("foo")),)
rule = str_to_sdy_sharding_rule("..., ... -> ...")
mlir_rule = sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
self.assertEqual(
str(mlir_rule),
"#sdy.op_sharding_rule<([], [])->([])>")
def test_conversion_elementwise_rule_2D_instance(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((16, 32))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")),)
rule = str_to_sdy_sharding_rule("..., ... -> ...")
mlir_rule = sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
self.assertEqual(
str(mlir_rule),
"#sdy.op_sharding_rule<([i, j], [i, j])->([i, j]) {i=16, j=32}>")
def test_conversion_vector_scalar_add_2D_instance(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value(())
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 32))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")),)
rule = str_to_sdy_sharding_rule("..., -> ...")
mlir_rule = sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
self.assertEqual(
str(mlir_rule),
"#sdy.op_sharding_rule<([i, j], [])->([i, j]) {i=16, j=32}>")
def test_conversion_reshape_rule(self):
opnd0 = self.create_tensor_value((2, 4))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((8,))],
operands=[opnd0,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("i j -> (i j)")
mlir_rule = sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type],
[result.result.type,])
self.assertEqual(
str(mlir_rule),
"#sdy.op_sharding_rule<([i, j])->([ij]) {i=2, j=4}>")
def test_conversion_contracting_dim_matmul(self):
opnd0 = self.create_tensor_value((16, 32))
opnd1 = self.create_tensor_value((32, 8))
result = ir.Operation.create(
"stablehlo.custom_call",
results=[self.get_tensor_type((16, 8))],
operands=[opnd0, opnd1,],
attributes=dict(call_target_name=ir.StringAttr.get("foo")))
rule = str_to_sdy_sharding_rule("... contracting_dim, contracting_dim k -> ... k")
mlir_rule = sdy_sharding_rule_to_mlir(rule,
[result.operands[0].type, result.operands[1].type],
[result.result.type,])
self.assertEqual(
str(mlir_rule),
"#sdy.op_sharding_rule<([i, j], [j, k])->([i, k]) {i=16, j=32, k=8}>")
if __name__ == "__main__":
absltest.main(testLoader=jtu.JaxTestLoader())
|
jax-mlREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@tests@custom_partitioning_sharding_rule_test.py@.PATH_END.py
|
{
"filename": "UBVRI.py",
"repo_name": "yt-project/yt",
"repo_path": "yt_extracted/yt-main/yt/visualization/volume_rendering/UBVRI.py",
"type": "Python"
}
|
import numpy as np
johnson_filters = {
"B": {
"wavelen": np.array(
[
3600,
3650,
3700,
3750,
3800,
3850,
3900,
3950,
4000,
4050,
4100,
4150,
4200,
4250,
4300,
4350,
4400,
4450,
4500,
4550,
4600,
4650,
4700,
4750,
4800,
4850,
4900,
4950,
5000,
5050,
5100,
5150,
5200,
5250,
5300,
5350,
5400,
5450,
5500,
5550,
],
dtype="float64",
),
"trans": np.array(
[
0.0,
0.0,
0.02,
0.05,
0.11,
0.18,
0.35,
0.55,
0.92,
0.95,
0.98,
0.99,
1.0,
0.99,
0.98,
0.96,
0.94,
0.91,
0.87,
0.83,
0.79,
0.74,
0.69,
0.63,
0.58,
0.52,
0.46,
0.41,
0.36,
0.3,
0.25,
0.2,
0.15,
0.12,
0.09,
0.06,
0.04,
0.02,
0.01,
0.0,
],
dtype="float64",
),
},
"I": {
"wavelen": np.array(
[
6800,
6850,
6900,
6950,
7000,
7050,
7100,
7150,
7200,
7250,
7300,
7350,
7400,
7450,
7500,
7550,
7600,
7650,
7700,
7750,
7800,
7850,
7900,
7950,
8000,
8050,
8100,
8150,
8200,
8250,
8300,
8350,
8400,
8450,
8500,
8550,
8600,
8650,
8700,
8750,
8800,
8850,
8900,
8950,
9000,
9050,
9100,
9150,
9200,
9250,
9300,
9350,
9400,
9450,
9500,
9550,
9600,
9650,
9700,
9750,
9800,
9850,
9900,
9950,
10000,
10050,
10100,
10150,
10200,
10250,
10300,
10350,
10400,
10450,
10500,
10550,
10600,
10650,
10700,
10750,
10800,
10850,
10900,
10950,
11000,
11050,
11100,
11150,
11200,
11250,
11300,
11350,
11400,
11450,
11500,
11550,
11600,
11650,
11700,
11750,
11800,
11850,
],
dtype="float64",
),
"trans": np.array(
[
0.0,
0.0,
0.01,
0.01,
0.01,
0.04,
0.08,
0.13,
0.17,
0.21,
0.26,
0.3,
0.36,
0.4,
0.44,
0.49,
0.56,
0.6,
0.65,
0.72,
0.76,
0.84,
0.9,
0.93,
0.96,
0.97,
0.97,
0.98,
0.98,
0.99,
0.99,
0.99,
0.99,
1.0,
1.0,
1.0,
1.0,
1.0,
0.99,
0.98,
0.98,
0.97,
0.96,
0.94,
0.93,
0.9,
0.88,
0.86,
0.84,
0.8,
0.76,
0.74,
0.71,
0.68,
0.65,
0.61,
0.58,
0.56,
0.52,
0.5,
0.47,
0.44,
0.42,
0.39,
0.36,
0.34,
0.32,
0.3,
0.28,
0.26,
0.24,
0.22,
0.2,
0.19,
0.17,
0.16,
0.15,
0.13,
0.12,
0.11,
0.1,
0.09,
0.09,
0.08,
0.08,
0.07,
0.06,
0.05,
0.05,
0.04,
0.04,
0.03,
0.03,
0.02,
0.02,
0.02,
0.02,
0.02,
0.01,
0.01,
0.01,
0.0,
],
dtype="float64",
),
},
"R": {
"wavelen": np.array(
[
5200,
5250,
5300,
5350,
5400,
5450,
5500,
5550,
5600,
5650,
5700,
5750,
5800,
5850,
5900,
5950,
6000,
6050,
6100,
6150,
6200,
6250,
6300,
6350,
6400,
6450,
6500,
6550,
6600,
6650,
6700,
6750,
6800,
6850,
6900,
6950,
7000,
7050,
7100,
7150,
7200,
7250,
7300,
7350,
7400,
7450,
7500,
7550,
7600,
7650,
7700,
7750,
7800,
7850,
7900,
7950,
8000,
8050,
8100,
8150,
8200,
8250,
8300,
8350,
8400,
8450,
8500,
8550,
8600,
8650,
8700,
8750,
8800,
8850,
8900,
8950,
9000,
9050,
9100,
9150,
9200,
9250,
9300,
9350,
9400,
9450,
9500,
],
dtype="float64",
),
"trans": np.array(
[
0.0,
0.01,
0.02,
0.04,
0.06,
0.11,
0.18,
0.23,
0.28,
0.34,
0.4,
0.46,
0.5,
0.55,
0.6,
0.64,
0.69,
0.71,
0.74,
0.77,
0.79,
0.81,
0.84,
0.86,
0.88,
0.9,
0.91,
0.92,
0.94,
0.95,
0.96,
0.97,
0.98,
0.99,
0.99,
1.0,
1.0,
0.99,
0.98,
0.96,
0.94,
0.92,
0.9,
0.88,
0.85,
0.83,
0.8,
0.77,
0.73,
0.7,
0.66,
0.62,
0.57,
0.53,
0.49,
0.45,
0.42,
0.39,
0.36,
0.34,
0.31,
0.27,
0.22,
0.19,
0.17,
0.15,
0.13,
0.12,
0.11,
0.1,
0.08,
0.07,
0.06,
0.06,
0.05,
0.04,
0.04,
0.03,
0.03,
0.02,
0.02,
0.02,
0.01,
0.01,
0.01,
0.01,
0.0,
],
dtype="float64",
),
},
"U": {
"wavelen": np.array(
[
3000,
3050,
3100,
3150,
3200,
3250,
3300,
3350,
3400,
3450,
3500,
3550,
3600,
3650,
3700,
3750,
3800,
3850,
3900,
3950,
4000,
4050,
4100,
4150,
],
dtype="float64",
),
"trans": np.array(
[
0.0,
0.04,
0.1,
0.25,
0.61,
0.75,
0.84,
0.88,
0.93,
0.95,
0.97,
0.99,
1.0,
0.99,
0.97,
0.92,
0.73,
0.56,
0.36,
0.23,
0.05,
0.03,
0.01,
0.0,
],
dtype="float64",
),
},
"V": {
"wavelen": np.array(
[
4600,
4650,
4700,
4750,
4800,
4850,
4900,
4950,
5000,
5050,
5100,
5150,
5200,
5250,
5300,
5350,
5400,
5450,
5500,
5550,
5600,
5650,
5700,
5750,
5800,
5850,
5900,
5950,
6000,
6050,
6100,
6150,
6200,
6250,
6300,
6350,
6400,
6450,
6500,
6550,
6600,
6650,
6700,
6750,
6800,
6850,
6900,
6950,
7000,
7050,
7100,
7150,
7200,
7250,
7300,
7350,
],
dtype="float64",
),
"trans": np.array(
[
0.0,
0.0,
0.01,
0.01,
0.02,
0.05,
0.11,
0.2,
0.38,
0.67,
0.78,
0.85,
0.91,
0.94,
0.96,
0.98,
0.98,
0.95,
0.87,
0.79,
0.72,
0.71,
0.69,
0.65,
0.62,
0.58,
0.52,
0.46,
0.4,
0.34,
0.29,
0.24,
0.2,
0.17,
0.14,
0.11,
0.08,
0.06,
0.05,
0.03,
0.02,
0.02,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.01,
0.0,
],
dtype="float64",
),
},
}
for vals in johnson_filters.values():
wavelen = vals["wavelen"]
trans = vals["trans"]
vals["Lchar"] = wavelen[np.argmax(trans)]
|
yt-projectREPO_NAMEytPATH_START.@yt_extracted@yt-main@yt@visualization@volume_rendering@UBVRI.py@.PATH_END.py
|
{
"filename": "conf.py",
"repo_name": "astropy/photutils",
"repo_path": "photutils_extracted/photutils-main/docs/conf.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Documentation build configuration file.
This file is execfile()d with the current directory set to its
containing dir.
Note that not all possible configuration values are present in this
file.
All configuration values have a default. Some values are defined in the
global Astropy configuration which is loaded here before anything else.
See astropy.sphinx.conf for which values are set there.
"""
import os
import sys
import tomllib
from datetime import UTC, datetime
from importlib import metadata
from pathlib import Path
from sphinx.util import logging
logger = logging.getLogger(__name__)
try:
from sphinx_astropy.conf.v2 import * # noqa: F403
from sphinx_astropy.conf.v2 import extensions # noqa: E402
except ImportError:
msg = ('The documentation requires the sphinx-astropy package to be '
'installed. Please install the "docs" requirements.')
logger.error(msg)
sys.exit(1)
# Get configuration information from pyproject.toml
with (Path(__file__).parents[1] / 'pyproject.toml').open('rb') as fh:
project_meta = tomllib.load(fh)['project']
# -- Plot configuration -------------------------------------------------------
plot_rcparams = {
'axes.labelsize': 'large',
'figure.figsize': (6, 6),
'figure.subplot.hspace': 0.5,
'savefig.bbox': 'tight',
'savefig.facecolor': 'none',
}
plot_apply_rcparams = True
plot_html_show_source_link = True
plot_formats = ['png', 'hires.png', 'pdf', 'svg']
# Don't use the default - which includes a numpy and matplotlib import
plot_pre_code = ''
# -- General configuration ----------------------------------------------------
# By default, highlight as Python 3.
highlight_language = 'python3'
# If your documentation needs a minimal Sphinx version, state it here.
needs_sphinx = '3.0'
# Extend astropy intersphinx_mapping with packages we use here
intersphinx_mapping.update( # noqa: F405
{'regions': ('https://astropy-regions.readthedocs.io/en/stable/', None),
'skimage': ('https://scikit-image.org/docs/stable/', None),
'gwcs': ('https://gwcs.readthedocs.io/en/latest/', None)})
# Exclude astropy intersphinx_mapping for unused packages
del intersphinx_mapping['h5py'] # noqa: F405
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# .inc.rst mean *include* files, don't have sphinx process them
# exclude_patterns += ["_templates", "_pkgtemplate.rst", "**/*.inc.rst"]
extensions += [
'sphinx_design',
]
# This is added to the end of RST files - a good place to put
# substitutions to be used globally.
rst_epilog = """
.. _Astropy: https://www.astropy.org/
"""
# -- Project information ------------------------------------------------------
project = project_meta['name']
author = project_meta['authors'][0]['name']
project_copyright = f'2011-{datetime.now(tz=UTC).year}, {author}'
github_project = 'astropy/photutils'
# The version info for the project you're documenting, acts as
# replacement for |version| and |release|, also used in various other
# places throughout the built documents.
# The full version, including alpha/beta/rc tags.
release = metadata.version(project)
# The short X.Y version.
version = '.'.join(release.split('.')[:2])
dev = 'dev' in release
# -- Options for HTML output --------------------------------------------------
html_theme_options.update( # noqa: F405
{
'github_url': 'https://github.com/astropy/photutils',
'header_links_before_dropdown': 6,
'navigation_with_keys': False,
'use_edit_page_button': False,
'logo': {
'image_light': 'photutils_logo_light_plain_path.svg',
'image_dark': 'photutils_logo_dark_plain_path.svg',
},
}
)
html_title = f'{project} {release}'
html_show_sourcelink = False
html_favicon = os.path.join('_static', 'photutils_logo.ico')
html_static_path = ['_static']
html_css_files = ['custom.css'] # path relative to _static
# Output file base name for HTML help builder.
htmlhelp_basename = project + 'doc'
# Set canonical URL from the Read the Docs Domain
html_baseurl = os.environ.get('READTHEDOCS_CANONICAL_URL', '')
# A dictionary of values to pass into the template engine's context for
# all pages.
html_context = {
'default_mode': 'light',
'to_be_indexed': ['stable', 'latest'],
'is_development': dev,
'github_user': 'astropy',
'github_repo': 'photutils',
'github_version': 'main',
'doc_path': 'docs',
# Tell Jinja2 templates the build is running on Read the Docs
'READTHEDOCS': os.environ.get('READTHEDOCS', '') == 'True',
}
# fix size of inheritance diagrams (e.g., PSF diagram was cut off)
inheritance_graph_attrs = {'size': '""'}
# -- Options for LaTeX output -------------------------------------------------
# Grouping the document tree into LaTeX files. List of tuples (source
# start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [('index', project + '.tex', project + ' Documentation',
author, 'manual')]
# latex_logo = '_static/photutils_banner.pdf'
# -- Options for manual page output -------------------------------------------
# One entry per manual page. List of tuples (source start file, name,
# description, authors, manual section).
man_pages = [('index', project.lower(), project + ' Documentation',
[author], 1)]
# -- Resolving issue number to links in changelog -----------------------------
github_issues_url = f'https://github.com/{github_project}/issues/'
# -- Turn on nitpicky mode for sphinx (to warn about references not found) ----
nitpicky = True
# Some warnings are impossible to suppress, and you can list specific
# references that should be ignored in a nitpick-exceptions file which
# should be inside the docs/ directory. The format of the file should be:
#
# <type> <class>
#
# for example:
#
# py:class astropy.io.votable.tree.Element
# py:class astropy.io.votable.tree.SimpleElement
# py:class astropy.io.votable.tree.SimpleElementWithContent
#
# Uncomment the following lines to enable the exceptions:
nitpick_ignore = []
nitpick_filename = 'nitpick-exceptions.txt'
if os.path.isfile(nitpick_filename):
with open(nitpick_filename) as fh:
for line in fh:
if line.strip() == '' or line.startswith('#'):
continue
dtype, target = line.split(None, 1)
target = target.strip()
nitpick_ignore.append((dtype, target))
# -- Options for linkcheck output ---------------------------------------------
linkcheck_retry = 5
linkcheck_ignore = ['http://data.astropy.org',
r'https://iraf.net/*',
r'https://github\.com/astropy/photutils/(?:issues|pull)/\d+']
linkcheck_timeout = 180
|
astropyREPO_NAMEphotutilsPATH_START.@photutils_extracted@photutils-main@docs@conf.py@.PATH_END.py
|
{
"filename": "run.py",
"repo_name": "pmelchior/skylens",
"repo_path": "skylens_extracted/skylens-master/moka_batch/run.py",
"type": "Python"
}
|
#!/bin/env python
from os import system, popen, environ
import fitsio
import numpy as np
from glob import glob
from random import randint
def kappa2alpha(kappafile, alphafile):
fits = fitsio.FITS(kappafile)
kappa = fits[0].read()
h = fits[0].read_header()
fits.close()
sidel = h['SIDEL']
Dl = h['DL']
Ds = h['DS']
Dls = h['DLS']
# smooth roll-off on the edge
L = kappa.shape[0]
roll = 10
window = np.outer(np.sin(np.pi/2 * np.linspace(0,1,roll)), np.ones(L))
kappa[0:roll, :] *= window
kappa[:, 0:roll] *= window.T
window = np.flipud(window)
kappa[L-roll:, :] *= window
kappa[:, L-roll:] *= window.T
# mechanics of making gradient field in FFT
mu = kappa.mean()
kappa_ = np.fft.fft2(kappa)
k = np.fft.fftfreq(L, d=sidel/L) # spacing now in arcsec
Ka = np.add.outer(k, 1j*k)
with np.errstate(divide="ignore", invalid="ignore"):
alpha_ = kappa_ / Ka
alpha_[Ka == 0] = 0
alpha = np.fft.ifft2(alpha_);
# since kappa has no-zero mean, we need to add a linear function
#tilt = np.outer(np.ones(L), np.arange(L) - L/2)*mu
#alpha.real += tilt
#alpha.imag += tilt.T
alpha *= 1. / 3600 / 180 * np.pi / Ds * Dls # now in radians
# write alpha to FITS in SkyLens++ format
fits = fitsio.FITS(alphafile, 'rw')
sidel2 = h['SIDEL2']
h['SIDEL'] = sidel2 # doesn't update comments. Be it...
h['SIDEL2'] = sidel
fits.write(np.array(np.real(alpha), dtype='float32'), header=h)
fits.write(np.array(np.imag(alpha), dtype='float32'))
fits.close()
def getMstar(z):
data = np.loadtxt('mstar_z.cmbf.dat')
return np.interp(z, data[:,0], data[:,1])
def getSeed():
return randint(0, 4294967295) # whole unsiged int range
def getFieldSize(M):
return 60*2*(M/1e14)**0.5 # arcsec
def createMainConfig(outdir):
fp = open(outdir + "/shear_accuracy.conf", "w")
fp.write("PROJECT\tS\tSHEAR_ACC\n")
fp.write("OUTFILE\tS\tSHEAR_ACC.fits\n")
fp.write("TELESCOPE\tS\ttelescope.conf\n")
fp.write("FILTER\tS\ttelescopes/Subaru/I.fits\n")
fp.write("EXPTIME\tI\t2000\n")
fp.write("SKY\tD\t22.7\n")
fp.write("ATMOSPHERE\tD\t0.\n")
fp.write("AIRMASS\tD\t1.0\n")
fp.write("LENSES\tVS\tmoka_lens.conf\n")
fp.write("OVERSAMPLING\tI\t1\n")
fp.write("NOISE\tI\t0\n")
fp.close()
def createLensConfig(zl, mokafile, outdir):
fp = open(outdir + "/moka_lens.conf", "w")
fp.write("REDSHIFT\tD\t%.2f\t# lens redshift\n" % zl)
fp.write("ANGLEFILE\tS\t%s\t# FITS file with deflection angles\n" % mokafile)
fp.close()
def createTelescopeConfig(M, outdir):
fp = open(outdir + "/telescope.conf", "w")
fp.write("DIAMETER\tD\t1.0\n")
fp.write("FLAT-ACC\tD\t0.0\n")
fp.write("GAIN\tD\t1\n")
fp.write("PIXSIZE\tD\t0.05\n")
fp.write("RON\tD\t10.0\n")
fp.write("MIRROR\tD\t1\n")
fp.write("OPTICS\tD\t1\n")
fp.write("CCD\tD\t1\n")
fp.write("FOV_X\tD\t%.1f\n" % getFieldSize(M))
fp.write("FOV_Y\tD\t%.1f\n" % getFieldSize(M))
fp.close()
def createMOKAInput(M, zl, zs, seed, outdir):
fp = open(outdir + "/INPUT", "w")
fp.write("\n!...Omega0\n0.3\n!...OmegaL\n0.7\n!...h0\n0.7\n!...w_dark_energy\n-1\n")
fp.write("!...lens_redshift\n%.2f\n" % zl)
fp.write("!...halo_mass\n%.4e\n" % M)
fp.write("!...source_redshift\n%.2f\n" % zs)
fp.write("!...field_of_view\n%.2f\n" % (getFieldSize(M)/60))
fp.write("!...JAFFE_HERNQUIST_or_DM_for_the_BCG_or_not\nNO\n")
fp.write("!...number_of_haloes\n1\n")
fp.write("!...npixels\n2048\n")
fp.write("!...write_fits_for_what_component\nNO\n")
fp.write("!...write_SkyLens_file_FORTRAN_or_CPP\nCPP\n")
fp.write("!...input_file_mass_concentration\nNO\n")
fp.write("!...scatter_in_concentration_log_normal\n0.25\n")
fp.write("!...mass_resolution_subhalo_mass_function\n1.e10\n")
fp.write("!...SISc_or_NFWc_for_satellitess_trunkated_SIS_and_NFW_not_trunkated\nNFWc\n")
fp.write("!...satellite_distribution_model_NFW_or_GAO\nGAO\n")
fp.write("!...mstar_zl\n%.5e\n" % getMstar(zl))
fp.write("!..elliptical_or_spherical_halo\n1\n")
fp.write("!...n_pix_smooth_fourier\n0\n")
fp.write("!...ADC\nYES\n")
fp.write("!...zero_padding_size\n2\n")
fp.write("!..bcg_velocity_disp_prof_and_lensing_comps_prof\nNO\n")
fp.write("!..RNG_seed\n%d" % seed)
fp.close()
import sys
import tempfile
import sqlite3
if (len(sys.argv) < 4):
print "usage: " + sys.argv[0] + " <Mass> <Redshift> <Repetitions>"
exit(0)
M = float(sys.argv[1])
zl = float(sys.argv[2])
repetition = int(sys.argv[3])
for repeat in xrange(repetition):
# create tempdir
tmpdir = tempfile.mkdtemp(prefix='moka_batch_', dir='.')
# run MOKA for halo with given parameters
seed = getSeed()
createMOKAInput(M, zl, 1.5, seed, tmpdir)
print "running MOKA", M, zl, repeat
popen("cd " + tmpdir + " && " + environ["MOKABIN"])
mokafile = "/".join(glob(tmpdir + "/fits/0SkyLens*.fits")[0].split("/")[2:])
# update SkyLens config files with new lens parameters
createMainConfig(tmpdir)
createLensConfig(zl, mokafile, tmpdir)
createTelescopeConfig(M, tmpdir)
print "running SkyLens++"
# run range of source redshifts per lens
for zs in [0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 2, 2.5, 3, 4]:
if zl < zs:
popen("cd " + tmpdir + " && ../../bin/shear_accuracy -o -c shear_accuracy.conf -N 1000 -z %1.2f -s %d > SHEAR_ACC_%1.2f.log" % (zs, seed, zs))
# gather the outputs and delete tmpdir...
|
pmelchiorREPO_NAMEskylensPATH_START.@skylens_extracted@skylens-master@moka_batch@run.py@.PATH_END.py
|
{
"filename": "mockmaker.py",
"repo_name": "bccp/nbodykit",
"repo_path": "nbodykit_extracted/nbodykit-master/nbodykit/mockmaker.py",
"type": "Python"
}
|
import numpy
import numbers
from mpi4py import MPI
from pmesh.pm import RealField, ComplexField
from nbodykit.meshtools import SlabIterator
from nbodykit.utils import GatherArray, ScatterArray
from nbodykit.mpirng import MPIRandomState
import mpsort
def gaussian_complex_fields(pm, linear_power, seed,
unitary_amplitude=False, inverted_phase=False,
compute_displacement=False, logger=None):
r"""
Make a Gaussian realization of a overdensity field, :math:`\delta(x)`.
If specified, also compute the corresponding 1st order Lagrangian
displacement field (Zel'dovich approximation) :math:`\psi(x)`,
which is related to the linear velocity field via:
.. math::
v(x) = f a H \psi(x)
Notes
-----
This computes the overdensity field using the following steps:
#. Generate complex variates with unity variance
#. Scale the Fourier field by :math:`(P(k) / V)^{1/2}`
After step 2, the complex field has unity variance. This
is equivalent to generating real-space normal variates
with mean and unity variance, calling r2c() and dividing by :math:`N^3`
since the variance of the complex FFT (with no additional normalization)
is :math:`N^3 \times \sigma^2_\mathrm{real}`.
Furthermore, the power spectrum is defined as V * variance.
So a normalization factor of 1 / V shows up in step 2,
cancels this factor such that the power spectrum is P(k).
The linear displacement field is computed as:
.. math::
\psi_i(k) = i \frac{k_i}{k^2} \delta(k)
.. note::
To recover the linear velocity in proper units, i.e., km/s,
from the linear displacement, an additional factor of
:math:`f \times a \times H(a)` is required
Parameters
----------
pm : pmesh.pm.ParticleMesh
the mesh object
linear_power : callable
a function taking wavenumber as its only argument, which returns
the linear power spectrum
seed : int
the random seed used to generate the random field
compute_displacement : bool, optional
if ``True``, also return the linear Zel'dovich displacement field;
default is ``False``
unitary_amplitude : bool, optional
if ``True``, the seed gaussian has unitary_amplitude.
inverted_phase: bool, optional
if ``True``, the phase of the seed gaussian is inverted
Returns
-------
delta_k : ComplexField
the real-space Gaussian overdensity field
disp_k : ComplexField or ``None``
if requested, the Gaussian displacement field
"""
if not isinstance(seed, numbers.Integral):
raise ValueError("the seed used to generate the linear field must be an integer")
if logger and pm.comm.rank == 0:
logger.info("Generating whitenoise")
# use pmesh to generate random complex white noise field (done in parallel)
# variance of complex field is unity
# multiply by P(k)**0.5 to get desired variance
delta_k = pm.generate_whitenoise(seed, type='untransposedcomplex', unitary=unitary_amplitude)
if logger and pm.comm.rank == 0:
logger.info("Write noise generated")
if inverted_phase: delta_k[...] *= -1
# initialize the displacement fields for (x,y,z)
if compute_displacement:
disp_k = [pm.create(type='untransposedcomplex') for i in range(delta_k.ndim)]
for i in range(delta_k.ndim): disp_k[i][:] = 1j
else:
disp_k = None
# volume factor needed for normalization
norm = 1.0 / pm.BoxSize.prod()
# iterate in slabs over fields
slabs = [delta_k.slabs.x, delta_k.slabs]
if compute_displacement:
slabs += [d.slabs for d in disp_k]
# loop over the mesh, slab by slab
for islabs in zip(*slabs):
kslab, delta_slab = islabs[:2] # the k arrays and delta slab
# the square of the norm of k on the mesh
k2 = sum(kk**2 for kk in kslab)
zero_idx = k2 == 0.
k2[zero_idx] = 1. # avoid dividing by zero
# the linear power (function of k)
power = linear_power((k2**0.5).flatten())
# multiply complex field by sqrt of power
delta_slab[...].flat *= (power*norm)**0.5
# set k == 0 to zero (zero config-space mean)
delta_slab[zero_idx] = 0.
# compute the displacement
if compute_displacement:
# ignore division where k==0 and set to 0
with numpy.errstate(invalid='ignore', divide='ignore'):
for i in range(delta_k.ndim):
disp_slab = islabs[2+i]
disp_slab[...] *= kslab[i] / k2 * delta_slab[...]
disp_slab[zero_idx] = 0. # no bulk displacement
if logger and pm.comm.rank == 0:
logger.info("Displacement computed in fourier space")
# return Fourier-space density and displacement (which could be None)
return delta_k, disp_k
def gaussian_real_fields(pm, linear_power, seed,
unitary_amplitude=False,
inverted_phase=False, compute_displacement=False, logger=None):
r"""
Make a Gaussian realization of a overdensity field in
real-space :math:`\delta(x)`.
If specified, also compute the corresponding linear Zel'dovich
displacement field :math:`\psi(x)`, which is related to the
linear velocity field via:
Notes
-----
See the docstring for :func:`gaussian_complex_fields` for the
steps involved in generating the fields.
Parameters
----------
pm : pmesh.pm.ParticleMesh
the mesh object
linear_power : callable
a function taking wavenumber as its only argument, which returns
the linear power spectrum
seed : int
the random seed used to generate the random field
compute_displacement : bool, optional
if ``True``, also return the linear Zel'dovich displacement field;
default is ``False``
unitary_amplitude : bool, optional
if ``True``, the seed gaussian has unitary_amplitude.
inverted_phase: bool, optional
if ``True``, the phase of the seed gaussian is inverted
Returns
-------
delta : RealField
the real-space Gaussian overdensity field
disp : RealField or ``None``
if requested, the Gaussian displacement field
"""
# make fourier fields
delta_k, disp_k = gaussian_complex_fields(pm, linear_power, seed,
inverted_phase=inverted_phase,
unitary_amplitude=unitary_amplitude,
compute_displacement=compute_displacement,
logger=logger)
# FFT the density to real-space
delta = delta_k.c2r()
std = (delta ** 2).cmean() ** 0.5
if logger and pm.comm.rank == 0:
logger.info("Overdensity computed in configuration space: std = %s" % str(std))
# FFT the velocity back to real space
if compute_displacement:
disp = [disp_k[i].c2r() for i in range(delta.ndim)]
std = [(disp[i] ** 2).cmean() ** 0.5 for i in range(delta.ndim)]
if logger and pm.comm.rank == 0:
logger.info("Displacement computed in configuration space: std = %s" % str(std))
else:
disp = None
# return density and displacement (which could be None)
return delta, disp
def lognormal_transform(density, bias=1.):
r"""
Apply a (biased) lognormal transformation of the density
field by computing:
.. math::
F(\delta) = \frac{1}{N} e^{b*\delta}
where :math:`\delta` is the initial overdensity field and the
normalization :math:`N` is chosen such that
:math:`\langle F(\delta) \rangle = 1`
Parameters
----------
density : array_like
the input density field to apply the transformation to
bias : float, optional
optionally apply a linear bias to the density field;
default is unbiased (1.0)
Returns
-------
toret : RealField
the real field holding the transformed density field
"""
toret = density.copy()
toret[:] = numpy.exp(bias * density.value)
toret[:] /= toret.cmean(dtype='f8')
return toret
def poisson_sample_to_points(delta, displacement, pm, nbar, bias=1., seed=None, logger=None):
"""
Poisson sample the linear delta and displacement fields to points.
The steps in this function:
#. Apply a biased, lognormal transformation to the input ``delta`` field
#. Poisson sample the overdensity field to discrete points
#. Disribute the positions of particles uniformly within the mesh cells,
and assign the displacement field at each cell to the particles
Parameters
----------
delta : RealField
the linear overdensity field to sample
displacement : list of RealField (3,)
the linear displacement fields which is used to move the particles
nbar : float
the desired number density of the output catalog of objects
bias : float, optional
apply a linear bias to the overdensity field (default is 1.)
seed : int, optional
the random seed used to Poisson sample the field to points
Returns
-------
pos : array_like, (N, 3)
the Cartesian positions of each of the generated particles
displ : array_like, (N, 3)
the displacement field sampled for each of the generated particles in the
same units as the ``pos`` array
"""
comm = delta.pm.comm
# seed1 used for poisson sampling
# seed2 used for uniform shift within a cell.
seed1, seed2 = numpy.random.RandomState(seed).randint(0, 0xfffffff, size=2)
# apply the lognormal transformation to the initial conditions density
# this creates a positive-definite delta (necessary for Poisson sampling)
lagrangian_bias = bias - 1.
delta = lognormal_transform(delta, bias=lagrangian_bias)
if logger and pm.comm.rank == 0:
logger.info("Lognormal transformation done")
# mean number of objects per cell
H = delta.BoxSize / delta.Nmesh
overallmean = H.prod() * nbar
# number of objects in each cell (per rank, as a RealField)
cellmean = delta * overallmean
# create a random state with the input seed
rng = MPIRandomState(seed=seed1, comm=comm, size=delta.size)
# generate poissons. Note that we use ravel/unravel to
# maintain MPI invariane.
Nravel = rng.poisson(lam=cellmean.ravel())
N = delta.pm.create(type='real')
N.unravel(Nravel)
Ntot = N.csum()
if logger and pm.comm.rank == 0:
logger.info("Poisson sampling done, total number of objects is %d" % Ntot)
pos_mesh = delta.pm.generate_uniform_particle_grid(shift=0.0)
disp_mesh = numpy.empty_like(pos_mesh)
# no need to do decompose because pos_mesh is strictly within the
# local volume of the RealField.
N_per_cell = N.readout(pos_mesh, resampler='nnb')
for i in range(N.ndim):
disp_mesh[:, i] = displacement[i].readout(pos_mesh, resampler='nnb')
# fight round off errors, if any
N_per_cell = numpy.int64(N_per_cell + 0.5)
pos = pos_mesh.repeat(N_per_cell, axis=0)
disp = disp_mesh.repeat(N_per_cell, axis=0)
del pos_mesh
del disp_mesh
if logger and pm.comm.rank == 0:
logger.info("catalog produced. Assigning in cell shift.")
# generate linear ordering of the positions.
# this should have been a method in pmesh, e.g. argument
# to genereate_uniform_particle_grid(return_id=True);
# FIXME: after pmesh update, remove this
orderby = numpy.int64(pos[:, 0] / H[0] + 0.5)
for i in range(1, delta.ndim):
orderby[...] *= delta.Nmesh[i]
orderby[...] += numpy.int64(pos[:, i] / H[i] + 0.5)
# sort by ID to maintain MPI invariance.
pos = mpsort.sort(pos, orderby=orderby, comm=comm)
disp = mpsort.sort(disp, orderby=orderby, comm=comm)
if logger and pm.comm.rank == 0:
logger.info("sorting done")
rng_shift = MPIRandomState(seed=seed2, comm=comm, size=len(pos))
in_cell_shift = rng_shift.uniform(0, H[i], itemshape=(delta.ndim,))
pos[...] += in_cell_shift
pos[...] %= delta.BoxSize
if logger and pm.comm.rank == 0:
logger.info("catalog shifted.")
return pos, disp
|
bccpREPO_NAMEnbodykitPATH_START.@nbodykit_extracted@nbodykit-master@nbodykit@mockmaker.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattermap/cluster/__init__.py",
"type": "Python"
}
|
import sys
from typing import TYPE_CHECKING
if sys.version_info < (3, 7) or TYPE_CHECKING:
from ._stepsrc import StepsrcValidator
from ._step import StepValidator
from ._sizesrc import SizesrcValidator
from ._size import SizeValidator
from ._opacitysrc import OpacitysrcValidator
from ._opacity import OpacityValidator
from ._maxzoom import MaxzoomValidator
from ._enabled import EnabledValidator
from ._colorsrc import ColorsrcValidator
from ._color import ColorValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
[
"._stepsrc.StepsrcValidator",
"._step.StepValidator",
"._sizesrc.SizesrcValidator",
"._size.SizeValidator",
"._opacitysrc.OpacitysrcValidator",
"._opacity.OpacityValidator",
"._maxzoom.MaxzoomValidator",
"._enabled.EnabledValidator",
"._colorsrc.ColorsrcValidator",
"._color.ColorValidator",
],
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattermap@cluster@__init__.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "mit-ll/spacegym-kspdg",
"repo_path": "spacegym-kspdg_extracted/spacegym-kspdg-main/src/kspdg/pe1/__init__.py",
"type": "Python"
}
|
# Copyright (c) 2024, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
# Subject to FAR 52.227-11 – Patent Rights – Ownership by the Contractor (May 2014).
# SPDX-License-Identifier: MIT
|
mit-llREPO_NAMEspacegym-kspdgPATH_START.@spacegym-kspdg_extracted@spacegym-kspdg-main@src@kspdg@pe1@__init__.py@.PATH_END.py
|
{
"filename": "compiled.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/Pygments/py2/pygments/lexers/compiled.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
pygments.lexers.compiled
~~~~~~~~~~~~~~~~~~~~~~~~
Just export lexer classes previously contained in this module.
:copyright: Copyright 2006-2019 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
from pygments.lexers.jvm import JavaLexer, ScalaLexer
from pygments.lexers.c_cpp import CLexer, CppLexer
from pygments.lexers.d import DLexer
from pygments.lexers.objective import ObjectiveCLexer, \
ObjectiveCppLexer, LogosLexer
from pygments.lexers.go import GoLexer
from pygments.lexers.rust import RustLexer
from pygments.lexers.c_like import ECLexer, ValaLexer, CudaLexer
from pygments.lexers.pascal import DelphiLexer, Modula2Lexer, AdaLexer
from pygments.lexers.business import CobolLexer, CobolFreeformatLexer
from pygments.lexers.fortran import FortranLexer
from pygments.lexers.prolog import PrologLexer
from pygments.lexers.python import CythonLexer
from pygments.lexers.graphics import GLShaderLexer
from pygments.lexers.ml import OcamlLexer
from pygments.lexers.basic import BlitzBasicLexer, BlitzMaxLexer, MonkeyLexer
from pygments.lexers.dylan import DylanLexer, DylanLidLexer, DylanConsoleLexer
from pygments.lexers.ooc import OocLexer
from pygments.lexers.felix import FelixLexer
from pygments.lexers.nimrod import NimrodLexer
from pygments.lexers.crystal import CrystalLexer
__all__ = []
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@Pygments@py2@pygments@lexers@compiled.py@.PATH_END.py
|
{
"filename": "_title.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/parcoords/line/colorbar/_title.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TitleValidator(_plotly_utils.basevalidators.TitleValidator):
def __init__(
self, plotly_name="title", parent_name="parcoords.line.colorbar", **kwargs
):
super(TitleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Title"),
data_docs=kwargs.pop(
"data_docs",
"""
font
Sets this color bar's title font. Note that the
title's font used to be set by the now
deprecated `titlefont` attribute.
side
Determines the location of color bar's title
with respect to the color bar. Note that the
title's location used to be set by the now
deprecated `titleside` attribute.
text
Sets the title of the color bar. Note that
before the existence of `title.text`, the
title's contents used to be defined as the
`title` attribute itself. This behavior has
been deprecated.
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@parcoords@line@colorbar@_title.py@.PATH_END.py
|
{
"filename": "chaindesk.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/langchain/langchain/retrievers/chaindesk.py",
"type": "Python"
}
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.retrievers import ChaindeskRetriever
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"ChaindeskRetriever": "langchain_community.retrievers"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"ChaindeskRetriever",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@langchain@langchain@retrievers@chaindesk.py@.PATH_END.py
|
{
"filename": "_ticklabelstep.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scatter/marker/colorbar/_ticklabelstep.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TicklabelstepValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(
self,
plotly_name="ticklabelstep",
parent_name="scatter.marker.colorbar",
**kwargs,
):
super(TicklabelstepValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 1),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scatter@marker@colorbar@_ticklabelstep.py@.PATH_END.py
|
{
"filename": "verification.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/Pygments/py2/pygments/lexers/verification.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
pygments.lexers.verification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Lexer for Intermediate Verification Languages (IVLs).
:copyright: Copyright 2006-2019 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
from pygments.lexer import RegexLexer, include, words
from pygments.token import Comment, Operator, Keyword, Name, Number, \
Punctuation, Whitespace
__all__ = ['BoogieLexer', 'SilverLexer']
class BoogieLexer(RegexLexer):
"""
For `Boogie <https://boogie.codeplex.com/>`_ source code.
.. versionadded:: 2.1
"""
name = 'Boogie'
aliases = ['boogie']
filenames = ['*.bpl']
tokens = {
'root': [
# Whitespace and Comments
(r'\n', Whitespace),
(r'\s+', Whitespace),
(r'//[/!](.*?)\n', Comment.Doc),
(r'//(.*?)\n', Comment.Single),
(r'/\*', Comment.Multiline, 'comment'),
(words((
'axiom', 'break', 'call', 'ensures', 'else', 'exists', 'function',
'forall', 'if', 'invariant', 'modifies', 'procedure', 'requires',
'then', 'var', 'while'),
suffix=r'\b'), Keyword),
(words(('const',), suffix=r'\b'), Keyword.Reserved),
(words(('bool', 'int', 'ref'), suffix=r'\b'), Keyword.Type),
include('numbers'),
(r"(>=|<=|:=|!=|==>|&&|\|\||[+/\-=>*<\[\]])", Operator),
(r"([{}():;,.])", Punctuation),
# Identifier
(r'[a-zA-Z_]\w*', Name),
],
'comment': [
(r'[^*/]+', Comment.Multiline),
(r'/\*', Comment.Multiline, '#push'),
(r'\*/', Comment.Multiline, '#pop'),
(r'[*/]', Comment.Multiline),
],
'numbers': [
(r'[0-9]+', Number.Integer),
],
}
class SilverLexer(RegexLexer):
"""
For `Silver <https://bitbucket.org/viperproject/silver>`_ source code.
.. versionadded:: 2.2
"""
name = 'Silver'
aliases = ['silver']
filenames = ['*.sil', '*.vpr']
tokens = {
'root': [
# Whitespace and Comments
(r'\n', Whitespace),
(r'\s+', Whitespace),
(r'//[/!](.*?)\n', Comment.Doc),
(r'//(.*?)\n', Comment.Single),
(r'/\*', Comment.Multiline, 'comment'),
(words((
'result', 'true', 'false', 'null', 'method', 'function',
'predicate', 'program', 'domain', 'axiom', 'var', 'returns',
'field', 'define', 'requires', 'ensures', 'invariant',
'fold', 'unfold', 'inhale', 'exhale', 'new', 'assert',
'assume', 'goto', 'while', 'if', 'elseif', 'else', 'fresh',
'constraining', 'Seq', 'Set', 'Multiset', 'union', 'intersection',
'setminus', 'subset', 'unfolding', 'in', 'old', 'forall', 'exists',
'acc', 'wildcard', 'write', 'none', 'epsilon', 'perm', 'unique',
'apply', 'package', 'folding', 'label', 'forperm'),
suffix=r'\b'), Keyword),
(words(('Int', 'Perm', 'Bool', 'Ref'), suffix=r'\b'), Keyword.Type),
include('numbers'),
(r'[!%&*+=|?:<>/\-\[\]]', Operator),
(r'([{}():;,.])', Punctuation),
# Identifier
(r'[\w$]\w*', Name),
],
'comment': [
(r'[^*/]+', Comment.Multiline),
(r'/\*', Comment.Multiline, '#push'),
(r'\*/', Comment.Multiline, '#pop'),
(r'[*/]', Comment.Multiline),
],
'numbers': [
(r'[0-9]+', Number.Integer),
],
}
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@Pygments@py2@pygments@lexers@verification.py@.PATH_END.py
|
{
"filename": "stats.py",
"repo_name": "gogrean/PyXel",
"repo_path": "PyXel_extracted/PyXel-master/pyxel/stats.py",
"type": "Python"
}
|
import numpy as np
def cstat_deriv(measured_raw_cts, updated_model, measured_bkg_cts,
t_raw, t_bkg, x):
"""
Calculates the derivatives of the C-statistic.
"""
model_derivs = np.array(updated_model.fit_deriv(x, *updated_model.parameters))
model_vals = updated_model(x)
nbins = len(model_vals)
nparams = len(updated_model.parameters)
d_cash = np.zeros(nparams)
# Some of these calculations are used often, so they are done only once
# here to speed up the code.
tmp1 = t_raw + t_bkg
tmp2 = tmp1 * model_vals
tmp3 = tmp2 - measured_raw_cts - measured_bkg_cts
tmp4 = tmp2 * measured_bkg_cts
tmp5 = tmp1 * model_derivs
tmp6 = tmp5 * measured_bkg_cts
tmp7 = t_raw * model_derivs
tmp8 = t_bkg * model_derivs
d = (tmp3 ** 2 + 4. * tmp4)**0.5
f = (-tmp3 + d) / (2. * tmp1)
d_d = (tmp3 ** 2 + 4. * tmp4)**-0.5 * (2. * tmp6 + tmp3 * tmp5)
d_f = -0.5 * model_derivs + d_d / (2. * tmp1)
for i in range(nbins):
if measured_raw_cts[i] == 0 and measured_bkg_cts[i] > 0:
d_cash += tmp7[:,i]
elif measured_bkg_cts[i] == 0 and measured_raw_cts[i] > 0:
if tmp2[i] < measured_raw_cts[i]:
d_cash -= tmp8[:,i]
else:
d_cash += tmp7[:,i] - \
measured_raw_cts[i] * 1. / model_vals[i] * \
model_derivs[:,i]
else:
d_cash += tmp7[:,i] + tmp1[i] * d_f[:,i] - \
measured_raw_cts[i] * \
1. / (model_vals[i] + f[i]) * \
(model_derivs[:,i] + d_f[:,i]) - \
measured_bkg_cts[i] * 1. / f[i] * d_f[:,i]
return 2. * d_cash
def cstat(measured_raw_cts, updated_model, measured_bkg_cts, t_raw, t_bkg, x):
"""
C-statistic implementation. [1][2]
This algorithm requires total and background counts, as well as the
factors that transform counts to rates.
.. [1] Cash, W. (1979), "Parameter estimation in astronomy through
application of the likelihood ratio", ApJ, 228, p. 939-947
.. [2] Wachter, K., Leach, R., Kellogg, E. (1979), "Parameter estimation
in X-ray astronomy using maximum likelihood", ApJ, 230, p. 274-287
"""
model_vals = updated_model(x)
# Some of these calculations are used often, so they are done only once
# here to speed up the code.
tmp1 = t_raw + t_bkg
tmp2 = tmp1 * model_vals
tmp3 = tmp2 - measured_raw_cts - measured_bkg_cts
tmp4 = tmp2 * measured_bkg_cts
tmp5 = t_raw * model_vals
tmp6 = t_bkg * model_vals
d = (tmp3 ** 2 + 4. * tmp4)**0.5
f = (-tmp3 + d) / (2. * tmp1)
nbins = len(model_vals)
cash = 0.
for i in range(nbins):
if measured_raw_cts[i] == 0 and measured_bkg_cts[i] > 0:
cash += tmp5[i] - measured_bkg_cts[i] * np.log(t_bkg[i] / tmp1[i])
elif measured_bkg_cts[i] == 0 and measured_raw_cts[i] > 0:
if tmp2[i] < measured_raw_cts[i]:
cash -= tmp6[i] + measured_raw_cts[i] * np.log(t_raw[i] / tmp1[i])
else:
cash += tmp5[i] + measured_raw_cts[i] * \
(np.log(measured_raw_cts[i]/tmp5[i]) - 1)
else:
cash += tmp5[i] + tmp1[i] * f[i] - \
measured_raw_cts[i] * np.log(t_raw[i] * (model_vals[i] + f[i])) \
- measured_bkg_cts[i] * np.log(t_bkg[i] * f[i]) - \
measured_raw_cts[i] * (1 - np.log(measured_raw_cts[i])) - \
measured_bkg_cts[i] * (1 - np.log(measured_bkg_cts[i]))
return 2. * cash
|
gogreanREPO_NAMEPyXelPATH_START.@PyXel_extracted@PyXel-master@pyxel@stats.py@.PATH_END.py
|
{
"filename": "_legendgroup.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/area/_legendgroup.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LegendgroupValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(self, plotly_name="legendgroup", parent_name="area", **kwargs):
super(LegendgroupValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
role=kwargs.pop("role", "info"),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@area@_legendgroup.py@.PATH_END.py
|
{
"filename": "_color.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/layout/xaxis/rangeselector/font/_color.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ColorValidator(_plotly_utils.basevalidators.ColorValidator):
def __init__(
self,
plotly_name="color",
parent_name="layout.xaxis.rangeselector.font",
**kwargs,
):
super(ColorValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "plot"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@layout@xaxis@rangeselector@font@_color.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "starkit/wsynphot",
"repo_path": "wsynphot_extracted/wsynphot-master/wsynphot/data/hst/acs/__init__.py",
"type": "Python"
}
|
starkitREPO_NAMEwsynphotPATH_START.@wsynphot_extracted@wsynphot-master@wsynphot@data@hst@acs@__init__.py@.PATH_END.py
|
|
{
"filename": "parameter_server_strategy_test.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/python/distribute/parameter_server_strategy_test.py",
"type": "Python"
}
|
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for ParameterServerStrategy."""
import copy
import threading
from absl.testing import parameterized
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.distribute import central_storage_strategy
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import device_util
from tensorflow.python.distribute import distribute_lib
from tensorflow.python.distribute import distribute_utils
from tensorflow.python.distribute import multi_worker_test_base
from tensorflow.python.distribute import multi_worker_util
from tensorflow.python.distribute import parameter_server_strategy
from tensorflow.python.distribute import ps_values
from tensorflow.python.distribute import reduce_util
from tensorflow.python.distribute import strategy_test_lib
from tensorflow.python.distribute.cluster_resolver import cluster_resolver as cluster_resolver_lib
from tensorflow.python.distribute.v1 import input_lib as input_lib_v1
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import device as tf_device
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import partitioned_variables
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import training_util
CHIEF = 'chief'
WORKER = 'worker'
PS = 'ps'
def _get_replica_id_integer():
replica_id = distribute_lib.get_replica_context().replica_id_in_sync_group
if isinstance(replica_id, tensor.Tensor):
replica_id = tensor_util.constant_value(replica_id)
return replica_id
def create_test_objects(cluster_spec=None,
task_type=None,
task_id=None,
num_gpus=None,
sess_config=None):
sess_config = sess_config or config_pb2.ConfigProto()
if num_gpus is None:
num_gpus = context.num_gpus()
if cluster_spec and task_type and task_id is not None:
cluster_resolver = cluster_resolver_lib.SimpleClusterResolver(
cluster_spec=multi_worker_util.normalize_cluster_spec(cluster_spec),
task_type=task_type,
task_id=task_id,
num_accelerators={'GPU': num_gpus})
distribution = parameter_server_strategy.ParameterServerStrategyV1(
cluster_resolver)
target = 'grpc://' + cluster_spec[WORKER][task_id]
else:
distribution = (
central_storage_strategy.CentralStorageStrategy._from_num_gpus(num_gpus)
)
target = ''
sess_config = copy.deepcopy(sess_config)
sess_config = distribution.update_config_proto(sess_config)
return distribution, target, sess_config
class ParameterServerStrategyTestBase(
multi_worker_test_base.MultiWorkerTestBase):
def setUp(self):
self._result = 0
self._lock = threading.Lock()
self._init_condition = threading.Condition()
self._init_reached = 0
self._finish_condition = threading.Condition()
self._finish_reached = 0
self._sess_config = config_pb2.ConfigProto(allow_soft_placement=True)
super(ParameterServerStrategyTestBase, self).setUp()
def _get_test_objects(self, task_type, task_id, num_gpus):
return create_test_objects(
cluster_spec=self._cluster_spec,
task_type=task_type,
task_id=task_id,
num_gpus=num_gpus,
sess_config=self._sess_config)
def _test_device_assignment_distributed(self, task_type, task_id, num_gpus):
worker_device = '/job:%s/replica:0/task:%d' % (task_type, task_id)
d, _, sess_config = self._get_test_objects(task_type, task_id, num_gpus)
with ops.Graph().as_default(), \
self.cached_session(target=self._default_target,
config=sess_config) as sess, \
d.scope():
# Define a variable outside the call_for_each_replica scope.
n = variable_scope.get_variable('n', initializer=10.0)
self.assertEqual(n.device, '/job:ps/task:0')
def model_fn():
if num_gpus == 0:
last_part_device = 'device:CPU:0'
else:
replica_id = _get_replica_id_integer()
last_part_device = ('device:GPU:%d' % replica_id)
a = constant_op.constant(1.0)
b = constant_op.constant(2.0)
c = a + b
self.assertEqual(a.device, worker_device + '/' + last_part_device)
self.assertEqual(b.device, worker_device + '/' + last_part_device)
self.assertEqual(c.device, worker_device + '/' + last_part_device)
# The device scope is ignored for variables but not for normal ops.
with ops.device('/job:worker/task:0'):
x = variable_scope.get_variable(
'x', initializer=10.0,
aggregation=variable_scope.VariableAggregation.SUM)
x_add = x.assign_add(c)
e = a + c
# The variable x is on the task 1 since the device_function has been
# called once before the model_fn.
self.assertEqual(x.device, '/job:ps/task:1')
self.assertEqual(x_add.device, x.device)
self.assertEqual(e.device,
'/job:worker/replica:0/task:0/%s' % last_part_device)
# The colocate_vars_with can override the distribution's device.
with d.extended.colocate_vars_with(x):
y = variable_scope.get_variable(
'y', initializer=20.0,
aggregation=variable_scope.VariableAggregation.SUM)
# We add an identity here to avoid complaints about summing
# non-distributed values.
y_add = y.assign_add(array_ops.identity(x_add))
self.assertEqual(y.device, '/job:ps/task:1')
self.assertEqual(y_add.device, y.device)
self.assertEqual(y.device, x.device)
z = variable_scope.get_variable(
'z', initializer=10.0,
aggregation=variable_scope.VariableAggregation.SUM)
self.assertEqual(z.device, '/job:ps/task:0')
self.assertNotEqual(z.device, x.device)
with ops.control_dependencies([y_add]):
# We add an identity here to avoid complaints about summing
# non-distributed values.
z_add = z.assign_add(array_ops.identity(y))
with ops.control_dependencies([z_add]):
f = z + c
self.assertEqual(f.device, worker_device + '/' + last_part_device)
# The device scope would merge with the default worker device.
with ops.device('/CPU:1'):
g = e + 1.0
self.assertEqual(g.device, worker_device + '/device:CPU:1')
# This ops.colocate_with will be ignored when defining a variable
# but not for a normal tensor.
with ops.colocate_with(x):
u = variable_scope.get_variable('u', initializer=30.0)
v = variable_scope.get_variable('v', initializer=30.0)
h = f + 1.0
self.assertIn('/job:ps/', u.device)
self.assertIn('/job:ps/', v.device)
# u and v are on different parameter servers.
self.assertTrue(u.device != x.device or v.device != x.device)
self.assertTrue(u.device == x.device or v.device == x.device)
# Here h is not on one worker. Note h.device is canonical while x.device
# is not but.
self.assertIn('/job:ps/', h.device)
return y_add, z_add, f
y, z, f = d.extended.call_for_each_replica(model_fn)
self.assertNotEqual(y, None)
self.assertNotEqual(z, None)
self.assertNotEqual(f, None)
if context.num_gpus() >= 1 and num_gpus <= 1:
self.evaluate(variables.global_variables_initializer())
y_val, z_val, f_val = sess.run([y, z, f])
self.assertEqual(y_val, 33.0)
self.assertEqual(z_val, 43.0)
self.assertEqual(f_val, 46.0)
def _test_device_assignment_distributed_enable_partitioner(
self, task_type, task_id, num_gpus):
d, _, sess_config = self._get_test_objects(task_type, task_id, num_gpus)
num_shards = len(d.extended.parameter_devices)
partitioner = partitioned_variables.fixed_size_partitioner(num_shards)
with ops.Graph().as_default(), \
self.cached_session(target=self._default_target,
config=sess_config) as sess, \
d.scope():
n = variable_scope.get_variable(
'n',
initializer=constant_op.constant([10.0, 20.0]),
aggregation=variable_scope.VariableAggregation.SUM,
partitioner=partitioner)
for part_id, var in enumerate(n):
self.assertEqual(var.device, '/job:ps/task:%d' % part_id)
def model_fn():
a = constant_op.constant([3.0, 5.0])
# The device scope is ignored for variables but not for normal ops.
with ops.device('/job:worker/task:0'):
x = variable_scope.get_variable(
'x',
initializer=constant_op.constant([10.0, 20.0]),
aggregation=variable_scope.VariableAggregation.SUM,
partitioner=partitioner)
x_add = x.assign_add(a, name='x_add')
# The variable x is on the task 1 since the device_function has been
# called once before the model_fn.
for part_id, var in enumerate(x):
self.assertEqual(var.device, '/job:ps/task:%d' % part_id)
self.assertEqual(var.device, x_add[part_id].device)
return x_add
x = d.extended.call_for_each_replica(model_fn)
if context.num_gpus() >= 1:
self.evaluate(variables.global_variables_initializer())
x_val = sess.run(x)
if num_gpus < 1:
self.assertEqual(x_val, [13.0, 25.0])
else:
x_expect = [10.0 + 3 * num_gpus, 20.0 + 5 * num_gpus]
self.assertEqual(x_val, x_expect)
def _test_device_assignment_local(self,
d,
compute_device='CPU',
variable_device='CPU',
num_gpus=0):
with ops.Graph().as_default(), \
self.cached_session(target=self._default_target,
config=self._sess_config) as sess, \
d.scope():
def model_fn():
if 'CPU' in compute_device:
replica_compute_device = '/device:CPU:0'
else:
replica_id = _get_replica_id_integer()
replica_compute_device = ('/device:GPU:%d' % replica_id)
replica_compute_device = device_util.canonicalize(
replica_compute_device)
if 'CPU' in variable_device:
replica_variable_device = '/device:CPU:0'
else:
replica_id = _get_replica_id_integer()
replica_variable_device = ('/device:GPU:%d' % replica_id)
replica_variable_device = device_util.canonicalize(
replica_variable_device)
a = constant_op.constant(1.0)
b = constant_op.constant(2.0)
c = a + b
self.assertEqual(a.device, replica_compute_device)
self.assertEqual(b.device, replica_compute_device)
self.assertEqual(c.device, replica_compute_device)
# The device scope is ignored for variables but not for normal ops.
with ops.device('/device:GPU:2'):
x = variable_scope.get_variable(
'x', initializer=10.0,
aggregation=variable_scope.VariableAggregation.SUM)
x_add = x.assign_add(c)
e = a + c
self.assertEqual(
device_util.canonicalize(x.device), replica_variable_device)
self.assertEqual(x_add.device, x.device)
self.assertEqual(e.device, device_util.canonicalize('/device:GPU:2'))
# The colocate_vars_with can override the distribution's device.
with d.extended.colocate_vars_with(x):
y = variable_scope.get_variable(
'y', initializer=20.0,
aggregation=variable_scope.VariableAggregation.SUM)
# We add an identity here to avoid complaints about summing
# non-distributed values.
y_add = y.assign_add(array_ops.identity(x_add))
self.assertEqual(
device_util.canonicalize(y.device), replica_variable_device)
self.assertEqual(y_add.device, y.device)
self.assertEqual(y.device, x.device)
z = variable_scope.get_variable(
'z', initializer=10.0,
aggregation=variable_scope.VariableAggregation.SUM)
self.assertEqual(
device_util.canonicalize(z.device), replica_variable_device)
with ops.control_dependencies([y_add]):
# We add an identity here to avoid complaints about summing
# non-distributed values.
z_add = z.assign_add(array_ops.identity(y))
with ops.control_dependencies([z_add]):
f = z + c
self.assertEqual(f.device, replica_compute_device)
# The device scope would merge with the default worker device.
with ops.device('/CPU:1'):
g = e + 1.0
self.assertEqual(g.device, device_util.canonicalize('/device:CPU:1'))
# This ops.colocate_with will be ignored when defining a variable
# but not for a normal tensor.
with ops.colocate_with(x):
u = variable_scope.get_variable('u', initializer=30.0)
h = f + 1.0
self.assertEqual(
device_util.canonicalize(u.device), replica_variable_device)
self.assertEqual(
device_util.canonicalize(x.device),
device_util.canonicalize(h.device))
return y_add, z_add, f
y, z, f = d.extended.call_for_each_replica(model_fn)
self.assertNotEqual(y, None)
self.assertNotEqual(z, None)
self.assertNotEqual(f, None)
if context.num_gpus() >= 1 and num_gpus <= 1:
self.evaluate(variables.global_variables_initializer())
y_val, z_val, f_val = sess.run([y, z, f])
self.assertEqual(y_val, 33.0)
self.assertEqual(z_val, 43.0)
self.assertEqual(f_val, 46.0)
def _test_simple_increment(self, task_type, task_id, num_gpus):
d, master_target, sess_config = self._get_test_objects(
task_type, task_id, num_gpus)
if d.extended._cluster_spec:
num_workers = len(d.extended._cluster_spec.as_dict().get(WORKER))
if 'chief' in d.extended._cluster_spec.as_dict():
num_workers += 1
else:
num_workers = 1
with ops.Graph().as_default(), \
self.cached_session(target=master_target,
config=sess_config) as sess, \
d.scope():
def model_fn():
x = variable_scope.get_variable(
'x', initializer=10.0,
aggregation=variable_scope.VariableAggregation.SUM)
y = variable_scope.get_variable(
'y', initializer=20.0,
aggregation=variable_scope.VariableAggregation.SUM)
z = variable_scope.get_variable(
'z', initializer=30.0,
aggregation=variable_scope.VariableAggregation.ONLY_FIRST_REPLICA)
# We explicitly make a constant tensor here to avoid complaints about
# summing non-distributed values.
one = constant_op.constant(1.0)
x_add = x.assign_add(one, use_locking=True)
y_add = y.assign_add(one, use_locking=True)
z_add = z.assign_add(one, use_locking=True)
train_op = control_flow_ops.group(x_add, y_add, z_add)
return x, y, z, train_op
x, y, z, train_op = d.extended.call_for_each_replica(model_fn)
train_op = d.group(train_op)
if task_id == 0:
self.evaluate(variables.global_variables_initializer())
# Workers waiting for chief worker's initializing variables.
self._init_condition.acquire()
self._init_reached += 1
while self._init_reached != num_workers:
self._init_condition.wait()
self._init_condition.notify_all()
self._init_condition.release()
sess.run(train_op)
# Wait for other workers to finish training.
self._finish_condition.acquire()
self._finish_reached += 1
while self._finish_reached != num_workers:
self._finish_condition.wait()
self._finish_condition.notify_all()
self._finish_condition.release()
x_val, y_val, z_val = sess.run([x, y, z])
self.assertEqual(x_val, 10.0 + 1.0 * num_workers * d.num_replicas_in_sync)
self.assertEqual(y_val, 20.0 + 1.0 * num_workers * d.num_replicas_in_sync)
self.assertEqual(z_val, 30.0 + 1.0 * num_workers)
def _test_minimize_loss_graph(self, task_type, task_id, num_gpus):
d, master_target, sess_config = self._get_test_objects(
task_type, task_id, num_gpus)
if task_type:
# Multi-worker
assert hasattr(d.extended, '_cluster_spec') and d.extended._cluster_spec
num_workers = len(d.extended._cluster_spec.as_dict().get(WORKER))
if CHIEF in d.extended._cluster_spec.as_dict():
num_workers += 1
else:
# local
num_workers = 1
with ops.Graph().as_default(), \
self.cached_session(target=master_target,
config=sess_config) as sess, \
d.scope():
kernel = strategy_test_lib.create_variable_like_keras_layer(
'kernel', (1, 1), dtypes.float32,)
def loss_fn(x):
y = array_ops.reshape(
math_ops.matmul(x, kernel), []) - constant_op.constant(1.)
return y * y
# TODO(yuefengz, apassos): eager.backprop.implicit_grad is not safe for
# multiple graphs (b/111216820).
def grad_fn(x):
loss = loss_fn(x)
var_list = (
variables.trainable_variables() + ops.get_collection(
ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES))
grads = gradients.gradients(loss, var_list)
ret = list(zip(grads, var_list))
return ret
def update(v, g):
return v.assign_sub(0.05 * g, use_locking=True)
one = constant_op.constant([[1.]])
def step():
"""Perform one optimization step."""
# Run forward & backward to get gradients, variables list.
g_v = d.extended.call_for_each_replica(grad_fn, args=(one,))
# Update the variables using the gradients and the update() function.
before_list = []
after_list = []
for g, v in g_v:
fetched = d.extended.read_var(v)
before_list.append(fetched)
with ops.control_dependencies([fetched]):
# TODO(yuefengz): support non-Mirrored variable as destinations.
g = d.extended.reduce_to(
reduce_util.ReduceOp.SUM, g, destinations=v)
with ops.control_dependencies(
d.extended.update(v, update, args=(g,), group=False)):
after_list.append(d.extended.read_var(v))
return before_list, after_list
before_out, after_out = step()
if (not task_type or
multi_worker_util.is_chief(
d.extended._cluster_spec, task_type, task_id)):
self.evaluate(variables.global_variables_initializer())
# Workers waiting for chief worker's initializing variables.
self._init_condition.acquire()
self._init_reached += 1
while self._init_reached != num_workers:
self._init_condition.wait()
self._init_condition.notify_all()
self._init_condition.release()
for i in range(10):
b, a = sess.run((before_out, after_out))
if i == 0:
before, = b
after, = a
error_before = abs(before - 1)
error_after = abs(after - 1)
# Error should go down
self.assertLess(error_after, error_before)
def _test_input_fn_iterator(self,
task_type,
task_id,
num_gpus,
input_fn,
expected_values,
test_reinitialize=True,
ignore_order=False):
distribution, master_target, config = self._get_test_objects(
task_type, task_id, num_gpus)
devices = distribution.extended.worker_devices
with ops.Graph().as_default(), \
self.cached_session(config=config,
target=master_target) as sess:
iterator = distribution.make_input_fn_iterator(input_fn)
sess.run(iterator.initializer)
for expected_value in expected_values:
next_element = iterator.get_next()
computed_value = sess.run([distribute_utils.select_replica(
r, next_element) for r in range(len(devices))])
if ignore_order:
self.assertCountEqual(expected_value, computed_value)
else:
self.assertEqual(expected_value, computed_value)
with self.assertRaises(errors.OutOfRangeError):
next_element = iterator.get_next()
sess.run([distribute_utils.select_replica(r, next_element)
for r in range(len(devices))])
# After re-initializing the iterator, should be able to iterate again.
if test_reinitialize:
sess.run(iterator.initializer)
for expected_value in expected_values:
next_element = iterator.get_next()
computed_value = sess.run([distribute_utils.select_replica(
r, next_element) for r in range(len(devices))])
if ignore_order:
self.assertCountEqual(expected_value, computed_value)
else:
self.assertEqual(expected_value, computed_value)
class ParameterServerStrategyTest(
ParameterServerStrategyTestBase,
strategy_test_lib.DistributionTestBase,
strategy_test_lib.TwoDeviceDistributionTestBase,
parameterized.TestCase):
@classmethod
def setUpClass(cls):
cls._cluster_spec = multi_worker_test_base.create_in_process_cluster(
num_workers=3, num_ps=2)
cls._default_target = 'grpc://' + cls._cluster_spec[WORKER][0]
@combinations.generate(combinations.combine(mode=['graph']))
def test_num_replicas_in_sync(self):
strategy, _, _ = create_test_objects(num_gpus=2)
# All the devices on a given worker are in sync which in this case is the
# number of gpus on each worker.
self.assertEqual(2, strategy.num_replicas_in_sync)
@combinations.generate(combinations.combine(mode=['graph']))
def testDeviceAssignmentLocalCPU(self):
strategy, _, _ = create_test_objects(num_gpus=0)
self._test_device_assignment_local(
strategy, compute_device='CPU', variable_device='CPU', num_gpus=0)
@combinations.generate(combinations.combine(mode=['graph']))
def testDeviceAssignmentLocalOneGPU(self):
strategy, _, _ = create_test_objects(num_gpus=1)
self._test_device_assignment_local(
strategy, compute_device='GPU', variable_device='GPU', num_gpus=1)
@combinations.generate(combinations.combine(mode=['graph']))
def testDeviceAssignmentLocalTwoGPUs(self):
strategy, _, _ = create_test_objects(num_gpus=2)
self._test_device_assignment_local(
strategy, compute_device='GPU', variable_device='CPU', num_gpus=2)
@combinations.generate(
combinations.combine(mode=['graph'], num_gpus=[0, 1, 2]))
def testDeviceAssignmentDistributed(self, num_gpus):
self._test_device_assignment_distributed('worker', 1, num_gpus)
@combinations.generate(
combinations.combine(mode=['graph'], num_gpus=[0, 1, 2]))
def testDeviceAssignmentDistributedEnablePartitioner(self, num_gpus):
self._test_device_assignment_distributed_enable_partitioner(
'worker', 1, num_gpus)
@combinations.generate(combinations.combine(mode=['graph']))
def testSimpleBetweenGraph(self):
self._run_between_graph_clients(self._test_simple_increment,
self._cluster_spec, context.num_gpus())
@combinations.generate(
combinations.combine(mode=['graph'], required_gpus=[0, 1, 2]))
def testLocalSimpleIncrement(self, required_gpus):
self._test_simple_increment(None, 0, required_gpus)
@combinations.generate(
combinations.combine(mode=['graph'], required_gpus=[0, 1, 2]))
def testMinimizeLossGraphDistributed(self, required_gpus):
self._run_between_graph_clients(self._test_minimize_loss_graph,
self._cluster_spec, required_gpus)
@combinations.generate(
combinations.combine(mode=['graph'], required_gpus=[0, 1, 2]))
def testMinimizeLossGraphLocal(self, required_gpus):
self._test_minimize_loss_graph(None, None, required_gpus)
# TODO(priyag): Refactor this and other multi worker tests.
@combinations.generate(
combinations.combine(
mode=['graph'], required_gpus=[1, 2], use_dataset=[True, False]))
def testMakeInputFnIteratorDistributed(self, required_gpus, use_dataset):
if use_dataset:
fn = lambda: dataset_ops.Dataset.range(100)
else:
def fn():
dataset = dataset_ops.Dataset.range(100)
it = dataset_ops.make_one_shot_iterator(dataset)
return it.get_next
expected_values = [[i + j
for j in range(required_gpus)]
for i in range(0, 100, required_gpus)]
input_fn = self._input_fn_to_test_input_context(
fn,
expected_num_replicas_in_sync=required_gpus,
expected_num_input_pipelines=3,
expected_input_pipeline_id=1) # because task_id = 1
self._test_input_fn_iterator(
'worker',
1,
required_gpus,
input_fn,
expected_values,
test_reinitialize=use_dataset,
ignore_order=not use_dataset)
@combinations.generate(
combinations.combine(
mode=['graph'], required_gpus=[1, 2], use_dataset=[True, False]))
def testMakeInputFnIteratorLocal(self, required_gpus, use_dataset):
if use_dataset:
fn = lambda: dataset_ops.Dataset.range(100)
else:
def fn():
dataset = dataset_ops.Dataset.range(100)
it = dataset_ops.make_one_shot_iterator(dataset)
return it.get_next
expected_values = [[i + j
for j in range(required_gpus)]
for i in range(0, 100, required_gpus)]
input_fn = self._input_fn_to_test_input_context(
fn,
expected_num_replicas_in_sync=required_gpus,
expected_num_input_pipelines=1,
expected_input_pipeline_id=0) # only one worker and pipeline for local.
self._test_input_fn_iterator(
None,
None,
required_gpus,
input_fn,
expected_values,
test_reinitialize=use_dataset,
ignore_order=not use_dataset)
@combinations.generate(combinations.combine(mode=['graph']))
def testGlobalStepUpdate(self):
strategy, _, _ = create_test_objects()
self._test_global_step_update(strategy)
@combinations.generate(combinations.combine(mode=['graph']))
def testUpdateConfigProtoMultiWorker(self):
strategy, _, _ = create_test_objects(
cluster_spec=self._cluster_spec,
task_type='worker',
task_id=1,
num_gpus=2)
config_proto = config_pb2.ConfigProto(device_filters=['to_be_overridden'])
new_config = strategy.update_config_proto(config_proto)
# Verify device filters.
self.assertEqual(['/job:worker/task:1', '/job:ps'],
new_config.device_filters)
# Verify isolate_session_state
self.assertFalse(new_config.isolate_session_state)
@combinations.generate(combinations.combine(mode=['graph']))
def testUpdateConfigProtoLocal(self):
strategy, _, _ = create_test_objects(num_gpus=2)
config_proto = config_pb2.ConfigProto()
new_config = strategy.update_config_proto(config_proto)
# Verify isolate_session_state
self.assertTrue(new_config.isolate_session_state)
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
def testInMultiWorkerMode(self):
strategy, _, _ = create_test_objects(
cluster_spec=self._cluster_spec,
task_type='worker',
task_id=1,
num_gpus=0)
self.assertTrue(strategy.extended._in_multi_worker_mode())
@combinations.generate(combinations.combine(mode=['eager']))
def testEagerCustomTrainingUnimplementedError(self):
cluster_spec = multi_worker_test_base.create_in_process_cluster(
num_workers=3, num_ps=2)
cluster_resolver = cluster_resolver_lib.SimpleClusterResolver(
cluster_spec=multi_worker_util.normalize_cluster_spec(cluster_spec),
task_type='worker',
task_id=1,
num_accelerators={'GPU': 0})
strategy = parameter_server_strategy.ParameterServerStrategyV1(
cluster_resolver)
dataset = dataset_ops.DatasetV2.from_tensor_slices([5., 6., 7., 8.])
def train_step(data):
return math_ops.square(data)
self.assertRaisesRegex(NotImplementedError, 'ParameterServerStrategy*',
strategy.experimental_distribute_dataset,
dataset.batch(2))
self.assertRaisesRegex(NotImplementedError, 'ParameterServerStrategy*',
strategy.distribute_datasets_from_function,
lambda _: dataset)
self.assertRaisesRegex(NotImplementedError, 'ParameterServerStrategy*',
strategy.scope)
self.assertRaisesRegex(NotImplementedError, 'ParameterServerStrategy*',
strategy.run, train_step)
@combinations.generate(combinations.combine(
mode=['graph'],
prefetch_to_device=[None, True]))
def test_prefetch_to_device_dataset(self, prefetch_to_device):
distribution, _, _ = create_test_objects(
cluster_spec=self._cluster_spec,
task_type='worker',
task_id=0,
num_gpus=2)
if prefetch_to_device is None:
input_options = None
else:
input_options = distribute_lib.InputOptions(
experimental_fetch_to_device=prefetch_to_device)
dataset = dataset_ops.Dataset.range(100)
dataset = dataset.batch(distribution.num_replicas_in_sync)
dataset = distribution.experimental_distribute_dataset( # pylint: disable=assignment-from-no-return
dataset,
options=input_options)
if isinstance(dataset, input_lib_v1.DistributedDatasetV1):
item = dataset.make_initializable_iterator().get_next()
else:
self.skipTest('unsupported test combination')
device_types = {
tf_device.DeviceSpec.from_string(tensor.device).device_type for
tensor in item.values}
self.assertAllEqual(list(device_types), ['GPU'])
@combinations.generate(combinations.combine(mode=['graph']))
def test_prefetch_to_host_dataset(self):
distribution, _, _ = create_test_objects(
cluster_spec=self._cluster_spec,
task_type='worker',
task_id=0,
num_gpus=2)
input_options = distribute_lib.InputOptions(
experimental_fetch_to_device=False)
dataset = dataset_ops.Dataset.range(100)
dataset = dataset.batch(distribution.num_replicas_in_sync)
dataset = distribution.experimental_distribute_dataset( # pylint: disable=assignment-from-no-return
dataset,
options=input_options)
if isinstance(dataset, input_lib_v1.DistributedDatasetV1):
item = dataset.make_initializable_iterator().get_next()
else:
self.skipTest('unsupported test combination')
device_types = {
tf_device.DeviceSpec.from_string(tensor.device).device_type for
tensor in item.values}
self.assertAllEqual(list(device_types), ['CPU'])
class ParameterServerStrategyWithChiefTest(ParameterServerStrategyTestBase,
parameterized.TestCase):
@classmethod
def setUpClass(cls):
cls._cluster_spec = multi_worker_test_base.create_in_process_cluster(
num_workers=3, num_ps=2, has_chief=True)
cls._default_target = 'grpc://' + cls._cluster_spec[CHIEF][0]
@combinations.generate(
combinations.combine(mode=['graph'], required_gpus=[0, 1, 2]))
def testSimpleBetweenGraph(self, required_gpus):
self._run_between_graph_clients(self._test_simple_increment,
self._cluster_spec, required_gpus)
@combinations.generate(
combinations.combine(mode=['graph'], num_gpus=[0, 1, 2]))
def testMinimizeLossGraph(self, num_gpus):
self._run_between_graph_clients(self._test_minimize_loss_graph,
self._cluster_spec, num_gpus)
@combinations.generate(combinations.combine(mode=['graph']))
def testGlobalStepIsWrappedOnTwoGPUs(self):
strategy, _, _ = create_test_objects(num_gpus=2)
with ops.Graph().as_default(), strategy.scope():
created_step = training_util.create_global_step()
get_step = training_util.get_global_step()
self.assertEqual(created_step, get_step,
msg=('created_step %s type %s vs. get_step %s type %s' %
(id(created_step), created_step.__class__.__name__,
id(get_step), get_step.__class__.__name__)))
self.assertIs(ps_values.AggregatingVariable, type(created_step))
self.assertIs(ps_values.AggregatingVariable, type(get_step))
self.assertIs(strategy, created_step.distribute_strategy)
@combinations.generate(combinations.combine(mode=['graph']))
def testGlobalStepIsNotWrappedOnOneGPU(self):
strategy, _, _ = create_test_objects(num_gpus=1)
with ops.Graph().as_default(), strategy.scope():
created_step = training_util.create_global_step()
get_step = training_util.get_global_step()
self.assertEqual(created_step, get_step,
msg=('created_step %s type %s vs. get_step %s type %s' %
(id(created_step), created_step.__class__.__name__,
id(get_step), get_step.__class__.__name__)))
self.assertIs(resource_variable_ops.ResourceVariable, type(created_step))
self.assertIs(resource_variable_ops.ResourceVariable, type(get_step))
# All variables have an _distribute_strategy parameter. Only variable
# subclasses in distribution strategy expose it publicly.
self.assertFalse(hasattr(strategy, 'distribute_strategy'))
self.assertIs(strategy, created_step._distribute_strategy)
@combinations.generate(combinations.combine(mode=['graph'], required_gpus=2))
def testValueContainer(self):
strategy, _, _ = create_test_objects(num_gpus=2)
with ops.Graph().as_default(), strategy.scope():
def f():
with backprop.GradientTape() as tape:
v = variable_scope.get_variable('v', initializer=10.0)
_ = v * v
v, = tape.watched_variables()
w = strategy.extended.value_container(v)
self.assertIs(ps_values.AggregatingVariable, type(w))
strategy.extended.call_for_each_replica(f)
class CentralStorageStrategyTest(strategy_test_lib.DistributionTestBase,
parameterized.TestCase):
@combinations.generate(combinations.combine(mode=['graph', 'eager'],
required_gpus=2))
def testNumpyDataset(self):
strategy, _, _ = create_test_objects(num_gpus=2)
self._test_numpy_dataset(strategy)
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
def testInMultiWorkerMode(self):
strategy, _, _ = create_test_objects(num_gpus=0)
self.assertFalse(strategy.extended._in_multi_worker_mode())
if __name__ == '__main__':
test.main()
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@python@distribute@parameter_server_strategy_test.py@.PATH_END.py
|
{
"filename": "SVFields.ipynb",
"repo_name": "desihub/desiutil",
"repo_path": "desiutil_extracted/desiutil-main/doc/nb/SVFields.ipynb",
"type": "Jupyter Notebook"
}
|
# SV Fields
This example is based on a plot initially developed by Anand Raichoor.
```python
%matplotlib inline
import os
from pkg_resources import resource_filename
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import astropy.units as u
from astropy.coordinates import SkyCoord, HeliocentricTrueEcliptic, ICRS, SkyOffsetFrame, Longitude
from astropy.io import fits
import healpy as hp
from desiutil.plots import init_sky
```
```python
def get_footprint(survey):
"""Define sky areas for various surveys.
Parameters
----------
survey : str
The name of the survey.
Returns
-------
dict
A dictionary containing various sub-regions of `survey`.
"""
mydict = {}
if (survey=='hscdr1'):
mydict['xmm'] = '29,40,-7,-3'
mydict['gama09h'] = '130,138,-1,3'
mydict['cosmos'] = '148,152,0,4'
mydict['wide12h'] = '177,183,-2,2'
mydict['gama15h'] = '213,221,-2,2'
mydict['vvds'] = '331,342,-1,3'
mydict['deep2-3'] = '350,354,-2,2'
mydict['hectomap']= '242,249,42,45'
mydict['aegis'] = '213,217,51,54'
mydict['elais-n1']= '240,246,53,57'
elif (survey=='hscdr2'):
mydict['w05_1'] = '330,4,-2,3'
mydict['w05_2'] = '330,345,3,7'
mydict['w01'] = '15,23,-2,3'
mydict['w02'] = '28,40,-8,0'
mydict['w03'] = '126,162,-3,6'
mydict['w04'] = '165,228,-3,6'
mydict['w06'] = '198,252,41.5,45'
mydict['w07'] = '212,216,51,54'
elif (survey=='hscdr2_dud'):
mydict['xmm'] = '33,38,-7,-3'
mydict['cosmos'] = '148,152,0,4'
mydict['elais'] = '239,246,53,57'
mydict['deep2_3'] = '350,354,-2,2'
elif (survey=='deep2'):
mydict['deep2_1'] = '213,217,51,54'
mydict['deep2_2'] = '251,254,34,36'
mydict['deep2_3'] = '351,354,-1,1'
mydict['deep2_4'] = '36,39,0,1'
elif (survey=='kids'):
mydict['s1'] = '0,53.5,-35.6,-25.7'
mydict['s2'] = '329.5,360,-35.6,-25.7'
mydict['n1'] = '156,225,-5,4'
mydict['n2'] = '225,238,-3,4'
elif (survey=='ebosselg'):
mydict['eboss21'] = '315,360,-2,2'
mydict['eboss22'] = '0,45,-5,5'
mydict['eboss23a']= '126,143,16,29'
mydict['eboss23b']= '136,157,13,27'
mydict['eboss25a']= '131,166,29,32.5'
mydict['eboss25b']= '142.5,166,27,29'
mydict['eboss25c']= '157,166,23,27'
elif (survey=='dr8b'):
mydict['s82'] = '0,45,-1.25,1.25'
mydict['hsc_sgc'] = '30,40,-6.5,-1.25'
mydict['hsc_ngc'] = '177.5,182.5,-1,1'
mydict['edr'] = '240,245,5,12'
mydict['hsc_north']='240,250,42,45'
mydict['egs'] = '213,216.5,52,54'
elif (survey=='cosmos'):
mydict['cosmos'] = '149,151.5,1,3.5'
elif (survey=='vvdswide'):
mydict['10'] = '150.4,152.0,1.0,2.8'
mydict['14'] = '208.9,211.0,4.1,5.6'
mydict['22'] = '333.4,335.1,-0.6,1.3'
elif (survey=='vvdsdeep'):
mydict['02'] = '36.2,37.1,-4.9,-4.0'
mydict['03'] = '52.9,53.3,-28.0,-27.6'
elif (survey=='cfhtls'):
mydict['w1'] = '30,39,-11.5,-3.5'
mydict['w2'] = '131,137,-7.0,-0.5'
mydict['w3'] = '208,221,51,58'
mydict['w4'] = '329.5,336,-1.5,5'
elif (survey=='vipers'):
mydict['w1'] = '30.1,38.8,-6.0,-4.15'
mydict['w4'] = '330,335.4,0.85,2.4'
return mydict
def get_poly_mw(string, ax):
"""Create polygon-compatible arrays from `string`.
"""
ramin,ramax,decmin,decmax = [float(x) for x in string.split(',')]
raminmw,decminmw = ax.projection_ra(np.array([ramin])), ax.projection_dec(np.array([decmin]))
raminmw,decminmw = raminmw[0],decminmw[0]
ramaxmw,decmaxmw = ax.projection_ra(np.array([ramax])), ax.projection_dec(np.array([decmax]))
ramaxmw,decmaxmw = ramaxmw[0],decmaxmw[0]
tmpx = [raminmw, ramaxmw, ramaxmw, raminmw, raminmw]
tmpy = [decminmw,decminmw,decmaxmw,decmaxmw,decminmw]
tmppoly = np.zeros((len(tmpx), 2))
tmppoly[:,0] = tmpx
tmppoly[:,1] = tmpy
return tmppoly
```
```python
#
# General survey areas.
#
surveys = ('hscdr2', 'kids', 'cfhtls', 'cosmos', 'ebosselg', 'vipers', 'vvdswide', 'vvdsdeep', 'deep2')
#
# SV fields
# https://desi.lbl.gov/trac/wiki/TargetSelectionWG/SVFields_for_DR9
#
svdict = {'01_s82': '30,40,-7,2',
'02_egs': '210,220,50,55',
'03_gama09': '129,141,-2,3',
'04_gama12': '174,186,-3,2',
'05_gama15': '212,224,-2,3',
'06_overlap': '135,160,30,35',
'07_refnorth': '215,230,41,46',
'08_ages': '215,220,30,40',
'09_sagittarius': '200,210,5,10',
'10_highebv_n': '140,150,65,70',
'11_highebv_s': '240,245,20,25',
'12_highstardens_n': '273,283,40,45',
'13_highstardens_s': '260,270,15,20',
'14_hscw05': '330,340,-2,3'}
```
```python
#
# DES
#
DESfile = resource_filename('desiutil', 'data/DES_footprint.txt')
desra, desdec = np.loadtxt(DESfile, unpack=True)
```
```python
#
# DECaLS dr8 grz + dec>-30
#
hpdict = {}
dr8fn = os.path.join(os.environ['DESI_ROOT'], 'target', 'catalogs',
'dr8', '0.31.1', 'pixweight', 'pixweight-dr8-0.31.1.fits')
with fits.open(dr8fn) as hdulist:
nside, nest = hdulist[1].header['HPXNSIDE'], hdulist[1].header['HPXNEST']
npix = hp.nside2npix(nside)
theta, phi = hp.pix2ang(nside, np.arange(npix, dtype=int), nest=nest)
hpdict['ra'], hpdict['dec'] = 180./np.pi*phi, 90.-180./np.pi*theta
for key in ('fracarea', 'stardens', 'ebv', 'psfsize_g', 'psfsize_r', 'psfsize_z',
'galdepth_g', 'galdepth_r', 'galdepth_z', 'psfdepth_w1', 'psfdepth_w2'):
if key == 'stardens':
hpdict[key] = np.log10(hdulist[1].data[key])
hpdict[key+'lab'] = 'log10(stardens)'
elif key.startswith('galdepth') or key.startswith('psfdepth'):
hpdict[key] = 22.5 - 2.5*np.log10(5./np.sqrt(hdulist[1].data[key]))
hpdict[key+'lab'] = r'5$\sigma$ '+key
else:
hpdict[key] = hdulist[1].data[key]
hpdict[key+'lab'] = key
if key.startswith('psfsize'):
hpdict[key][hpdict[key]==0] = np.nan
#
# north/south
#
c = SkyCoord(hpdict['ra']*u.degree, hpdict['dec']*u.degree, frame='icrs')
hpdict['north'] = (hpdict['fracarea']>0) & (hpdict['dec']>32.375) & (c.galactic.b.value>0)
hpdict['south'] = ((hpdict['fracarea']>0) & (hpdict['dec']>-30) &
((c.galactic.b.value<0) | ((c.galactic.b.value>0) & (hpdict['dec']<32.375))))
print('North: {0:.0f} deg2'.format(hpdict['north'].sum() * hp.nside2pixarea(nside, degrees=True)))
print('South: {0:.0f} deg2'.format(hpdict['south'].sum() * hp.nside2pixarea(nside, degrees=True)))
#
# SV
#
hpdict['sv'] = np.zeros(npix, dtype=bool)
tmparea = 0
for key in sorted(svdict.keys()):
ramin, ramax, decmin, decmax = [float(x) for x in svdict[key].split(',')]
tmp = (hpdict['ra']>ramin) & (hpdict['ra']<ramax) & (hpdict['dec']>decmin) & (hpdict['dec']<decmax)
hpdict['sv'][tmp] = True
tmparea += tmp.sum() * hp.nside2pixarea(nside, degrees=True)
print('{0}: {1:.0f} deg2'.format(key, tmp.sum() * hp.nside2pixarea(nside, degrees=True)))
print('SV: {0:.0f} deg2'.format(tmparea))
```
```python
ax = init_sky()
#
# dr8
#
ramw, decmw = ax.projection_ra(hpdict['ra']), ax.projection_dec(hpdict['dec'])
for s, a, l in zip([hpdict['north'], hpdict['south']],
[0.05, 0.6],
['ls/dr8_north', 'ls/dr8_south (dec>-30)']):
print('{0}: {1:.0f} deg2'.format(l, s.sum() * hp.nside2pixarea(nside, degrees=True)))
foo = ax.scatter(ramw[s], decmw[s], s=1, c='y', zorder=0, alpha=a, rasterized=True, label=l)
#
# DES
#
foo = ax.plot(ax.projection_ra(desra), ax.projection_dec(desdec), c='k', lw=0.5, label='des')
#
# Other surveys
#
for survey in surveys:
mydict = get_footprint(survey)
count = 0
if survey == 'deep2':
c, lw = 'k', 2
else:
c, lw = ax._get_lines.get_next_color(), 1
for key in mydict:
tmppoly = get_poly_mw(mydict[key], ax)
if count == 0:
label = survey
else:
label = None
foo = ax.plot(tmppoly[:, 0], tmppoly[:, 1], c=c, lw=lw, label=label)
count +=1
#
# SV fields
#
for key in svdict:
tmppoly = get_poly_mw(svdict[key], ax)
foo = ax.add_patch(Polygon(tmppoly, closed=True, fill=False, hatch='xxx', lw=2, color='k', zorder=10))
foo = ax.legend(ncol=2, loc=1)
foo.set_zorder(20)
# plt.savefig(outroot+'.skymap.png', bbox_inches='tight')
```
```python
```
|
desihubREPO_NAMEdesiutilPATH_START.@desiutil_extracted@desiutil-main@doc@nb@SVFields.ipynb@.PATH_END.py
|
{
"filename": "intg.py",
"repo_name": "ja-vazquez/SimpleMC",
"repo_path": "SimpleMC_extracted/SimpleMC-master/simplemc/tools/intg.py",
"type": "Python"
}
|
#!/usr/bin/env python
from RunBase import *
C=LCDMCosmology()
Df=C.Da_z(1090)
lims=[0.25,0.50, 0.95, 0.99, 0.995]
l=lims.pop(0)
z=0.5;
while True:
f=C.Da_z(z)/Df
if (f>l):
print z,f, "XXX"
if len(lims)>0:
l=lims.pop(0)
else:
break
z*=1.03
|
ja-vazquezREPO_NAMESimpleMCPATH_START.@SimpleMC_extracted@SimpleMC-master@simplemc@tools@intg.py@.PATH_END.py
|
{
"filename": "plot_cf_colour.py",
"repo_name": "igmhub/LyaCoLoRe",
"repo_path": "LyaCoLoRe_extracted/LyaCoLoRe-master/picca_analysis/plot_cf_colour.py",
"type": "Python"
}
|
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import sys
from lyacolore import plot_functions
default_location = ['/global/homes/j/jfarr/Programs/picca/picca_analysis_000/picca_00000/']
if len(sys.argv) > 1:
locations = sys.argv[1:]
else:
locations = default_location
#Plotting decisions
plot_system = 'plot_per_file' #per bin or per file
model = 'Slosar11'
fit_type = 'manual'
fit_data = {'b1': 1., 'b2': 1., 'beta1': 0., 'beta2': 0.}
vmax = 10**-4
show_plots = True
save_plots = True
filename = 'berkeley_cross_0.2_DLA.pdf'
figsize=(12,8)
subplots = {}
subplots[(0,0)] = {'location': '/global/homes/j/jfarr/Programs/picca/picca_analysis_045/picca_00240/',
'filename': 'xcf_exp_0.2_randoms.fits.gz',
#'mu_bins': [(0.0,0.5),(0.5,0.8),(0.8,0.95),(0.95,1.0)],
'mu_bins': [(0.0,0.5),(0.5,0.8),(0.8,0.95),(0.95,1.0)],
'mu_bin_colours': ['C0','C1','C2','C3'],
'plot_data': {'r_power': 2, 'nr': 40, 'rmax_plot': 200.0},
'plot_picca_fit': False,
'picca_fit_data': {'rmin': 40., 'rmax': 160., 'afix': 'free'},
'plot_manual_fit': True,
'manual_fit_data': {'b1': -0.1049, 'b2': 2.0, 'beta1': 1.53, 'beta2': 0.48},
'format': {'legend': False, 'xlabel': True, 'ylabel': True},
}
#Get the correlation objects
corr_object = plot_functions.get_correlation_object(subplots[(0,0)])
#Make plots of the objects
plot_functions.make_colour_plots(corr_object,vmax=vmax,save_plots=True,show_plots=True,suffix=suffix)
|
igmhubREPO_NAMELyaCoLoRePATH_START.@LyaCoLoRe_extracted@LyaCoLoRe-master@picca_analysis@plot_cf_colour.py@.PATH_END.py
|
{
"filename": "plotting.py",
"repo_name": "aabdurrouf/piXedfit",
"repo_path": "piXedfit_extracted/piXedfit-main/piXedfit/piXedfit_analysis/plotting.py",
"type": "Python"
}
|
import numpy as np
from math import pow
import sys, os
from operator import itemgetter
import matplotlib
matplotlib.use('agg')
from astropy.io import fits
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
from scipy.interpolate import interp1d
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from astropy.cosmology import *
from ..piXedfit_model.model_utils import construct_SFH, convert_unit_spec_from_ergscm2A, list_default_params_fit, default_params_val, get_no_nebem_wave_fit
from ..piXedfit_model.gen_models import generate_modelSED_spec_decompose
from ..utils.filtering import cwave_filters, filtering
from ..utils.posteriors import plot_triangle_posteriors
__all__ = ["plot_SED", "plot_corner", "plot_sfh_mcmc"]
def plot_SED_rdsps_photo(filters=None,obs_photo=None,bfit_photo=None,bfit_mod_spec=None,minchi2_params=None,header_samplers=None,
logscale_x=True,logscale_y=True,xrange=None,yrange=None,wunit='micron',funit='erg/s/cm2/A',decompose=0,xticks=None,
photo_color='red',residual_range=[-1.0,1.0],fontsize_tick=18,fontsize_label=25,show_legend=True,loc_legend=4,
fontsize_legend=18,markersize=100,lw=2.0,name_plot=None):
from matplotlib.gridspec import GridSpec
# observed SEDs
nbands = len(filters)
obs_fluxes = obs_photo['flux']
obs_flux_err = obs_photo['flux_err']
# central wavelength of all filters
photo_cwave = cwave_filters(filters)
# convert flux unit of observed SED
obs_fluxes = convert_unit_spec_from_ergscm2A(photo_cwave,obs_fluxes,funit=funit)
obs_flux_err = convert_unit_spec_from_ergscm2A(photo_cwave,obs_flux_err,funit=funit)
# plotting
fig1 = plt.figure(figsize=(14,7))
gs = GridSpec(nrows=2, ncols=1, height_ratios=[3, 1], left=0.1, right=0.98, top=0.98, bottom=0.13, hspace=0.001)
f1 = fig1.add_subplot(gs[0])
plt.setp(f1.get_xticklabels(), visible=False)
if logscale_y == True:
f1.set_yscale('log')
if logscale_x == True:
f1.set_xscale('log')
plt.setp(f1.get_yticklabels(), fontsize=int(fontsize_tick))
if wunit==0 or wunit=='angstrom':
plt.xlabel(r'Wavelength $[\AA]$', fontsize=int(fontsize_label))
elif wunit==1 or wunit=='micron':
plt.xlabel(r'Wavelength [$\mu$m]', fontsize=int(fontsize_label))
if funit=='erg/s/cm2/A' or funit==0:
plt.ylabel(r'$F_{\lambda}$ [erg $s^{-1}cm^{-2}\AA^{-1}$]', fontsize=int(fontsize_label))
elif funit=='erg/s/cm2' or funit==1:
plt.ylabel(r'$\lambda F_{\lambda}$ [erg $s^{-1}cm^{-2}$]', fontsize=int(fontsize_label))
elif funit=='Jy' or funit==2:
plt.ylabel(r'$F_{\nu}$ [Jy]', fontsize=int(fontsize_label))
else:
print ("The input funit is not recognized!")
sys.exit()
#if xticks is not None:
# plt.xticks(xticks)
#for axis in [f1.xaxis]:
# axis.set_major_formatter(ScalarFormatter())
if xrange is None:
xmin, xmax = min(photo_cwave)*0.7, max(photo_cwave)*1.3
elif xrange is not None:
xmin, xmax = xrange[0], xrange[1]
if wunit==0 or wunit=='angstrom':
plt.xlim(xmin,xmax)
elif wunit==1 or wunit=='micron':
xmin, xmax = xmin/1e+4, xmax/1e+4
plt.xlim(xmin,xmax)
if yrange is None:
plt.ylim(min(obs_fluxes)*0.5,max(obs_fluxes)*1.8)
elif yrange is not None:
plt.ylim(yrange[0],yrange[1])
if decompose==1 or decompose==True:
def_params = list_default_params_fit()
def_params_val = default_params_val()
# modeling configuration
imf = header_samplers['imf']
sfh_form = header_samplers['sfh_form']
dust_law = header_samplers['dust_law']
duste_switch = header_samplers['duste_stat']
add_neb_emission = header_samplers['add_neb_emission']
add_agn = header_samplers['add_agn']
add_igm_absorption = header_samplers['add_igm_absorption']
if add_igm_absorption == 1:
igm_type = header_samplers['igm_type']
elif add_igm_absorption == 0:
igm_type = 0
smooth_velocity = header_samplers['smooth_velocity']
sigma_smooth = header_samplers['sigma_smooth']
smooth_lsf = header_samplers['smooth_lsf']
if smooth_lsf == 1 or smooth_lsf == True:
name_file_lsf = header_samplers['name_file_lsf']
data = np.loadtxt(temp_dir+name_file_lsf)
lsf_wave, lsf_sigma = data[:,0], data[:,1]
elif smooth_lsf == 0:
lsf_wave, lsf_sigma = None, None
if header_samplers['free_z'] == 0:
def_params_val['z'] = float(header_samplers['gal_z'])
# cosmology parameter
cosmo = header_samplers['cosmo']
H0 = float(header_samplers['H0'])
Om0 = float(header_samplers['Om0'])
params_val = def_params_val
for pp in range(0,int(header_samplers['nparams'])):
str_temp = 'param%d' % pp
if header_samplers[str_temp] in def_params:
params_val[header_samplers[str_temp]] = minchi2_params[header_samplers[str_temp]][0]
spec_SED = generate_modelSED_spec_decompose(params_val=params_val,imf=imf,duste_switch=duste_switch,
add_neb_emission=add_neb_emission,dust_law=dust_law,add_agn=add_agn,
add_igm_absorption=add_igm_absorption,igm_type=igm_type,cosmo=cosmo,
H0=H0,Om0=Om0,sfh_form=sfh_form,funit=funit,smooth_velocity=smooth_velocity,
sigma_smooth=sigma_smooth,smooth_lsf=smooth_lsf,lsf_wave=lsf_wave,lsf_sigma=lsf_sigma)
bfit_photo_fluxes = filtering(spec_SED['wave'],spec_SED['flux_total'],filters)
bfit_spec_wave = spec_SED['wave']
bfit_spec_flux_tot = spec_SED['flux_total']
if wunit==0 or wunit=='angstrom':
plt.plot(spec_SED['wave'],spec_SED['flux_stellar'],lw=lw,color='darkorange',label='stellar emission', zorder=1)
if add_neb_emission == 1:
plt.plot(spec_SED['wave'],spec_SED['flux_nebe'],lw=lw,color='darkcyan',label='nebular emission', zorder=2)
if duste_switch==1:
plt.plot(spec_SED['wave'],spec_SED['flux_duste'],lw=lw,color='darkred',label='dust emission', zorder=3)
if add_agn == 1:
plt.plot(spec_SED['wave'],spec_SED['flux_agn'],lw=lw,color='darkgreen',label='AGN torus emission', zorder=4)
elif wunit==1 or wunit=='micron':
plt.plot(spec_SED['wave']/1.0e+4,spec_SED['flux_stellar'],lw=lw,color='darkorange',label='stellar emission', zorder=1)
if add_neb_emission == 1:
plt.plot(spec_SED['wave']/1.0e+4,spec_SED['flux_nebe'],lw=lw,color='darkcyan',label='nebular emission', zorder=2)
if duste_switch==1:
plt.plot(spec_SED['wave']/1.0e+4,spec_SED['flux_duste'],lw=lw,color='darkred',label='dust emission', zorder=3)
if add_agn == 1:
plt.plot(spec_SED['wave']/1.0e+4,spec_SED['flux_agn'],lw=lw,color='darkgreen',label='AGN torus emission', zorder=4)
elif decompose==0 or decompose==False:
bfit_photo_fluxes = convert_unit_spec_from_ergscm2A(photo_cwave,bfit_photo['flux'],funit=funit)
bfit_spec_wave = bfit_mod_spec['wave']
bfit_spec_flux_tot = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['flux'],funit=funit)
if wunit==0 or wunit=='angstrom':
plt.plot(bfit_spec_wave,bfit_spec_flux_tot,lw=lw,color='black',label='total', zorder=5)
plt.scatter(photo_cwave,bfit_photo_fluxes, s=markersize, marker='s', lw=2, edgecolor='gray', color='none', zorder=6)
plt.errorbar(photo_cwave,obs_fluxes,yerr=obs_flux_err,color=photo_color,markersize=1,fmt='o', zorder=7)
plt.scatter(photo_cwave,obs_fluxes,s=markersize, marker='s', lw=2, edgecolor=photo_color, color='none', zorder=8)
elif wunit==1 or wunit=='micron':
plt.plot(bfit_spec_wave/1.0e+4,bfit_spec_flux_tot,lw=lw,color='black',label='total', zorder=5)
plt.scatter(photo_cwave/1.0e+4,bfit_photo_fluxes, s=markersize, marker='s', lw=2, edgecolor='gray', color='none', zorder=6)
plt.errorbar(photo_cwave/1.0e+4,obs_fluxes,yerr=obs_flux_err,color=photo_color,markersize=1,fmt='o', zorder=7)
plt.scatter(photo_cwave/1.0e+4,obs_fluxes,s=markersize, marker='s', lw=2, edgecolor=photo_color, color='none', zorder=8)
f1.text(0.25, 0.9, "reduced $\chi^2 = %.3f$" % header_samplers['redcd_chi2'], verticalalignment='bottom',
horizontalalignment='right', transform=f1.transAxes, color='black', fontsize=20)
if decompose==1 or decompose==True:
if show_legend == True:
plt.legend(fontsize=int(fontsize_legend), ncol=2, loc=loc_legend)
# plot residual
f1 = fig1.add_subplot(gs[1])
plt.setp(f1.get_yticklabels(), fontsize=int(fontsize_tick))
plt.setp(f1.get_xticklabels(), fontsize=int(fontsize_tick))
if logscale_x == True:
f1.set_xscale('log')
plt.ylabel(r'residual', fontsize=25)
plt.ylim(residual_range[0],residual_range[1])
if wunit==0 or wunit=='angstrom':
plt.xlabel(r'Wavelength $[\AA]$', fontsize=int(fontsize_label))
elif wunit==1 or wunit=='micron':
plt.xlabel(r'Wavelength [$\mu$m]', fontsize=int(fontsize_label))
if xticks is not None:
plt.xticks(xticks)
for axis in [f1.xaxis]:
axis.set_major_formatter(ScalarFormatter())
#if xrange is None:
# if wunit==0 or wunit=='angstrom':
# xmin = min(photo_cwave)*0.7
# xmax = max(photo_cwave)*1.3
# plt.xlim(min(photo_cwave)*0.7,max(photo_cwave)*1.3)
# elif wunit==1 or wunit=='micron':
# plt.xlim(min(photo_cwave)*0.7/1e+4,max(photo_cwave)*1.3/1e+4)
# xmin = min(photo_cwave)*0.7/1e+4
# xmax = max(photo_cwave)*1.3/1e+4
#elif xrange is not None:
# plt.xlim(xrange[0],xrange[1])
# xmin = xrange[0]
# xmax = xrange[1]
plt.xlim(xmin,xmax)
# get residual:
residuals = (obs_fluxes - bfit_photo_fluxes)/obs_fluxes
if wunit==0 or wunit=='angstrom':
plt.scatter(photo_cwave,residuals, s=80, marker='s', lw=3.0, color='gray')
elif wunit==1 or wunit=='micron':
plt.scatter(photo_cwave/1.0e+4,residuals, s=80, marker='s', lw=3.0, color='gray')
x = np.linspace(xmin,xmax,100)
y = x-x
plt.plot(x,y,lw=2,color='black',linestyle='--')
plt.savefig(name_plot, bbox_inches='tight')
return name_plot
def plot_SED_mcmc_photo(filters=None,obs_photo=None,bfit_photo=None,bfit_mod_spec=None,header_samplers=None,
logscale_x=True,logscale_y=True,xrange=None,yrange=None,wunit='micron',funit='erg/s/cm2/A',decompose=1,
xticks=None,photo_color='blue',residual_range=[-1.0,1.0],fontsize_tick=18,fontsize_label=28,
show_legend=True,loc_legend=2,fontsize_legend=20,markersize=100,lw=1.0,name_plot=None):
from matplotlib.gridspec import GridSpec
# observed SEDs
nbands = len(filters)
obs_fluxes = obs_photo['flux']
obs_flux_err = obs_photo['flux_err']
# central wavelength of all filters
photo_cwave = cwave_filters(filters)
# convert flux unit of observed SED
obs_fluxes = convert_unit_spec_from_ergscm2A(photo_cwave,obs_fluxes,funit=funit)
obs_flux_err = convert_unit_spec_from_ergscm2A(photo_cwave,obs_flux_err,funit=funit)
fig1 = plt.figure(figsize=(14,9))
gs = GridSpec(nrows=2, ncols=1, height_ratios=[3, 1], left=0.1, right=0.96, top=0.98, bottom=0.13, hspace=0.001)
f1 = fig1.add_subplot(gs[0])
plt.setp(f1.get_xticklabels(), visible=False)
if logscale_y == True:
f1.set_yscale('log')
if logscale_x == True:
f1.set_xscale('log')
plt.setp(f1.get_yticklabels(), fontsize=int(fontsize_tick))
plt.setp(f1.get_xticklabels(), fontsize=int(fontsize_tick))
if funit=='erg/s/cm2/A' or funit==0:
plt.ylabel(r'$F_{\lambda}$ [erg $s^{-1}cm^{-2}\AA^{-1}$]', fontsize=int(fontsize_label))
elif funit=='erg/s/cm2' or funit==1:
plt.ylabel(r'$\lambda F_{\lambda}$ [erg $s^{-1}cm^{-2}$]', fontsize=int(fontsize_label))
elif funit=='Jy' or funit==2:
plt.ylabel(r'$F_{\nu}$ [Jy]', fontsize=int(fontsize_label))
else:
print ("The input funit is not recognized!")
sys.exit()
if yrange is None:
plt.ylim(min(obs_fluxes)*0.5,max(obs_fluxes)*1.8)
if yrange is not None:
plt.ylim(yrange[0],yrange[1])
#if xrange is None:
# if wunit==0 or wunit=='angstrom':
# plt.xlim(min(photo_cwave)*0.7,max(photo_cwave)*1.3)
# xmin = min(photo_cwave)*0.7
# xmax = max(photo_cwave)*1.3
# elif wunit==1 or wunit=='micron':
# plt.xlim(min(photo_cwave)*0.7/1e+4,max(photo_cwave)*1.3/1e+4)
# xmin = min(photo_cwave)*0.7/1e+4
# xmax = max(photo_cwave)*1.3/1e+4
#elif xrange is not None:
# plt.xlim(xrange[0],xrange[1])
# xmin = xrange[0]
# xmax = xrange[1]
if xrange is None:
xmin, xmax = min(photo_cwave)*0.7, max(photo_cwave)*1.3
elif xrange is not None:
xmin, xmax = xrange[0], xrange[1]
if wunit==0 or wunit=='angstrom':
plt.xlim(xmin,xmax)
elif wunit==1 or wunit=='micron':
xmin, xmax = xmin/1e+4, xmax/1e+4
plt.xlim(xmin,xmax)
#==> best-fit model spectrum
if wunit==0 or wunit=='angstrom':
spec_wave = bfit_mod_spec['wave']
elif wunit==1 or wunit=='micron':
spec_wave = bfit_mod_spec['wave']/1e+4
p16_spec_tot = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['tot_p16'],funit=funit)
p50_spec_tot = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['tot_p50'],funit=funit)
p84_spec_tot = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['tot_p84'],funit=funit)
f1.fill_between(spec_wave,p16_spec_tot,p84_spec_tot,facecolor='gray',alpha=0.5,zorder=9)
if decompose==1 or decompose==True:
plt.plot(spec_wave,p50_spec_tot,lw=lw,color='black',zorder=9,label='total')
elif decompose==0 or decompose==False:
plt.plot(spec_wave,p50_spec_tot,lw=lw,color='black',zorder=9)
if decompose==1 or decompose==True:
# stellar emission
p16_spec_stellar = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['stellar_p16'],funit=funit)
p50_spec_stellar = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['stellar_p50'],funit=funit)
p84_spec_stellar = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['stellar_p84'],funit=funit)
f1.fill_between(spec_wave,p16_spec_stellar,p84_spec_stellar,facecolor='orange',alpha=0.25,zorder=8)
plt.plot(spec_wave,p50_spec_stellar,lw=lw,color='darkorange',zorder=8,label='stellar emission')
# nebular emission
if header_samplers['add_neb_emission'] == 1:
p16_spec_nebe = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['nebe_p16'],funit=funit)
p50_spec_nebe = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['nebe_p50'],funit=funit)
p84_spec_nebe = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['nebe_p84'],funit=funit)
f1.fill_between(spec_wave,p16_spec_nebe,p84_spec_nebe,facecolor='cyan',alpha=0.25,zorder=8)
plt.plot(spec_wave,p50_spec_nebe,lw=lw,color='darkcyan',zorder=8,label='nebular emission')
# dust emission
if header_samplers['duste_stat']==1:
p16_spec_duste = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['duste_p16'],funit=funit)
p50_spec_duste = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['duste_p50'],funit=funit)
p84_spec_duste = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['duste_p84'],funit=funit)
f1.fill_between(spec_wave,p16_spec_duste,p84_spec_duste,facecolor='red',alpha=0.25,zorder=8)
plt.plot(spec_wave,p50_spec_duste,lw=lw,color='darkred',zorder=8,label='dust emission')
# AGN dusty torus emission
if header_samplers['add_agn'] == 1:
p16_spec_agn = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['agn_p16'],funit=funit)
p50_spec_agn = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['agn_p50'],funit=funit)
p84_spec_agn = convert_unit_spec_from_ergscm2A(bfit_mod_spec['wave'],bfit_mod_spec['agn_p84'],funit=funit)
f1.fill_between(spec_wave,p16_spec_agn,p84_spec_agn,facecolor='green',alpha=0.25,zorder=8)
plt.plot(spec_wave,p50_spec_agn,lw=lw,color='darkgreen',zorder=8,label='AGN torus emission')
if show_legend == True:
plt.legend(fontsize=int(fontsize_legend), loc=loc_legend, ncol=2)
#==> plot best-fit photometric SED
p50_photo_flux = convert_unit_spec_from_ergscm2A(photo_cwave,bfit_photo['p50'],funit=funit)
if wunit==0 or wunit=='angstrom':
plt.scatter(photo_cwave,p50_photo_flux, s=markersize, marker='s', lw=2, edgecolor='gray', color='none', zorder=9,alpha=0.5)
elif wunit==1 or wunit=='micron':
plt.scatter(photo_cwave/1.0e+4,p50_photo_flux, s=markersize, marker='s', lw=2, edgecolor='gray', color='none', zorder=9,alpha=0.5)
#==> plot observed SED:
if wunit==0 or wunit=='angstrom':
plt.errorbar(photo_cwave,obs_fluxes,yerr=obs_flux_err,color=photo_color,markersize=1,fmt='o',zorder=10)
plt.scatter(photo_cwave,obs_fluxes, s=markersize, marker='s', lw=2, edgecolor=photo_color, color='none', zorder=11)
elif wunit==1 or wunit=='micron':
plt.errorbar(photo_cwave/1.0e+4,obs_fluxes,yerr=obs_flux_err,color=photo_color,markersize=1,fmt='o',zorder=10)
plt.scatter(photo_cwave/1.0e+4,obs_fluxes, s=markersize, marker='s', lw=2, edgecolor=photo_color, color='none', zorder=11)
## plot residual
f1 = fig1.add_subplot(gs[1])
plt.setp(f1.get_yticklabels(), fontsize=int(fontsize_tick))
plt.setp(f1.get_xticklabels(), fontsize=int(fontsize_tick))
if logscale_x == True:
f1.set_xscale('log')
plt.ylabel(r'residual', fontsize=25)
plt.ylim(residual_range[0],residual_range[1])
if wunit==0 or wunit=='angstrom':
plt.xlabel(r'Wavelength $[\AA]$', fontsize=int(fontsize_label))
elif wunit==1 or wunit=='micron':
plt.xlabel(r'Wavelength [$\mu$m]', fontsize=int(fontsize_label))
if xticks is not None:
plt.xticks(xticks)
for axis in [f1.xaxis]:
axis.set_major_formatter(ScalarFormatter())
#if xrange is None:
# if wunit==0 or wunit=='angstrom':
# plt.xlim(min(photo_cwave)*0.7,max(photo_cwave)*1.3)
# xmin = min(photo_cwave)*0.7
# xmax = max(photo_cwave)*1.3
# elif wunit==1 or wunit=='micron':
# plt.xlim(min(photo_cwave)*0.7/1e+4,max(photo_cwave)*1.3/1e+4)
# xmin = min(photo_cwave)*0.7/1e+4
# xmax = max(photo_cwave)*1.3/1e+4
#elif xrange is not None:
# plt.xlim(xrange[0],xrange[1])
# xmin = xrange[0]
# xmax = xrange[1]
plt.xlim(xmin,xmax)
# get residual:
residuals = (obs_fluxes - p50_photo_flux)/obs_fluxes
if wunit==0 or wunit=='angstrom':
plt.scatter(photo_cwave,residuals, s=80, marker='s', lw=3.5, color='gray', zorder=9, alpha=1.0)
elif wunit==1 or wunit=='micron':
plt.scatter(photo_cwave/1.0e+4,residuals, s=80, marker='s', lw=3.5, color='gray', zorder=9, alpha=1.0)
x = np.linspace(xmin,xmax,100)
y = x-x
plt.plot(x,y,lw=2,color='black',linestyle='--')
plt.subplots_adjust(left=0.25, right=0.98, bottom=0.25, top=0.98)
plt.savefig(name_plot, bbox_inches='tight')
return name_plot
def plot_SED_specphoto_old(filters=None,obs_photo=None,obs_spec=None,bfit_photo=None,bfit_spec=None,bfit_mod_spec=None,minchi2_params=None,
header_samplers=None,logscale_x=True, logscale_y=True, xrange=None, yrange=None, wunit='micron',funit='erg/s/cm2/A',
decompose=1,xticks=None,photo_color='red',residual_range=[-1.0,1.0], fontsize_tick=18,fontsize_label=25,show_legend=True,
loc_legend=4, fontsize_legend=18, markersize=100, lw=2.0, name_plot=None):
# plot 1: photometry
name_plot1 = 'ph_%s' % name_plot
if header_samplers['fitmethod'] == 'mcmc':
plot_SED_mcmc_photo(filters=filters,obs_photo=obs_photo,bfit_photo=bfit_photo,bfit_mod_spec=bfit_mod_spec,header_samplers=header_samplers,
logscale_x=logscale_x,logscale_y=logscale_y,xrange=xrange,yrange=yrange,wunit=wunit,funit=funit,decompose=decompose,
xticks=xticks,photo_color=photo_color,residual_range=residual_range,fontsize_tick=fontsize_tick,fontsize_label=fontsize_label,
show_legend=show_legend,loc_legend=loc_legend,fontsize_legend=fontsize_legend,markersize=markersize,lw=lw,name_plot=name_plot1)
elif header_samplers['fitmethod'] == 'rdsps':
plot_SED_rdsps_photo(filters=filters,obs_photo=obs_photo,bfit_photo=bfit_photo,bfit_mod_spec=bfit_mod_spec,minchi2_params=minchi2_params,
header_samplers=header_samplers,logscale_x=logscale_x,logscale_y=logscale_y,xrange=xrange,yrange=yrange,wunit=wunit,funit=funit,
decompose=decompose,xticks=xticks,photo_color=photo_color,residual_range=residual_range,fontsize_tick=fontsize_tick,
fontsize_label=fontsize_label,show_legend=show_legend,loc_legend=loc_legend,fontsize_legend=fontsize_legend,
markersize=markersize,lw=lw,name_plot=name_plot1)
# plot 2: spectroscopy
fig1 = plt.figure(figsize=(18,6))
f1 = plt.subplot()
plt.setp(f1.get_yticklabels(), fontsize=int(fontsize_tick))
plt.setp(f1.get_xticklabels(), fontsize=int(fontsize_tick))
plt.ylim(min(obs_spec['flux'])*0.4,max(obs_spec['flux'])*1.8)
xmin, xmax = min(obs_spec['wave'])-200, max(obs_spec['wave'])+200
plt.xlim(xmin,xmax)
plt.ylabel(r'$F_{\lambda}$ [erg $s^{-1}cm^{-2}\AA^{-1}$]', fontsize=int(fontsize_label))
plt.xlabel(r'Wavelength $[\AA]$', fontsize=int(fontsize_label))
if logscale_y == True:
f1.set_yscale('log')
if logscale_x == True:
f1.set_xscale('log')
for axis in [f1.xaxis]:
axis.set_major_formatter(ScalarFormatter())
f1.fill_between(obs_spec['wave'],obs_spec['flux']-obs_spec['flux_err'],obs_spec['flux']+obs_spec['flux_err'],
color='gray', alpha=0.2, edgecolor='none', zorder=3)
plt.plot(obs_spec['wave'], obs_spec['flux'], lw=lw, color='black', label='Observed spectrum', zorder=4)
if header_samplers['fitmethod'] == 'mcmc':
f1.fill_between(bfit_spec['wave'], bfit_spec['p16'], bfit_spec['p84'], color='pink', alpha=0.2, edgecolor='none', zorder=5)
plt.plot(bfit_spec['wave'], bfit_spec['p50'], lw=1, color='red', zorder=6)
elif header_samplers['fitmethod'] == 'rdsps':
plt.plot(bfit_spec['wave'], bfit_spec['flux'], lw=1, color='red', zorder=5)
plt.subplots_adjust(bottom=0.2)
name_plot2 = 'sp_%s' % name_plot
plt.savefig(name_plot2, bbox_inches='tight')
# plot 3: photometry + spectroscopy
# observed SEDs
nbands = len(filters)
photo_cwave = cwave_filters(filters)
obs_fluxes = convert_unit_spec_from_ergscm2A(photo_cwave,obs_photo['flux'],funit=funit)
obs_flux_err = convert_unit_spec_from_ergscm2A(photo_cwave,obs_photo['flux_err'],funit=funit)
obs_spec_flux = convert_unit_spec_from_ergscm2A(obs_spec['wave'],obs_spec['flux'],funit=funit)
fig1 = plt.figure(figsize=(18,6))
f1 = plt.subplot()
plt.setp(f1.get_yticklabels(), fontsize=int(fontsize_tick))
plt.setp(f1.get_xticklabels(), fontsize=int(fontsize_tick))
if logscale_y == True:
f1.set_yscale('log')
if logscale_x == True:
f1.set_xscale('log')
if funit=='erg/s/cm2/A' or funit==0:
plt.ylabel(r'$F_{\lambda}$ [erg $s^{-1}cm^{-2}\AA^{-1}$]', fontsize=int(fontsize_label))
elif funit=='erg/s/cm2' or funit==1:
plt.ylabel(r'$\lambda F_{\lambda}$ [erg $s^{-1}cm^{-2}$]', fontsize=int(fontsize_label))
elif funit=='Jy' or funit==2:
plt.ylabel(r'$F_{\nu}$ [Jy]', fontsize=int(fontsize_label))
else:
print ("The input funit is not recognized!")
sys.exit()
if yrange is None:
plt.ylim(min(obs_fluxes)*0.5,max(obs_fluxes)*1.8)
if yrange is not None:
plt.ylim(yrange[0],yrange[1])
if xrange is None:
if wunit==0 or wunit=='angstrom':
plt.xlim(min(photo_cwave)*0.7,max(photo_cwave)*1.3)
xmin = min(photo_cwave)*0.7
xmax = max(photo_cwave)*1.3
elif wunit==1 or wunit=='micron':
plt.xlim(min(photo_cwave)*0.7/1e+4,max(photo_cwave)*1.3/1e+4)
xmin = min(photo_cwave)*0.7/1e+4
xmax = max(photo_cwave)*1.3/1e+4
elif xrange is not None:
plt.xlim(xrange[0],xrange[1])
xmin = xrange[0]
xmax = xrange[1]
if wunit==0 or wunit=='angstrom':
plt.xlabel(r'Wavelength $[\AA]$', fontsize=int(fontsize_label))
elif wunit==1 or wunit=='micron':
plt.xlabel(r'Wavelength [$\mu$m]', fontsize=int(fontsize_label))
if xticks is not None:
plt.xticks(xticks)
for axis in [f1.xaxis]:
axis.set_major_formatter(ScalarFormatter())
#==> best-fit model spectrum
if header_samplers['fitmethod'] == 'mcmc':
bfit_spec_flux = convert_unit_spec_from_ergscm2A(bfit_spec['wave'],bfit_spec['p50'],funit=funit)
bfit_photo_flux = convert_unit_spec_from_ergscm2A(photo_cwave,bfit_photo['p50'],funit=funit)
elif header_samplers['fitmethod'] == 'rdsps':
bfit_spec_flux = convert_unit_spec_from_ergscm2A(bfit_spec['wave'],bfit_spec['flux'],funit=funit)
bfit_photo_flux = convert_unit_spec_from_ergscm2A(photo_cwave,bfit_photo['flux'],funit=funit)
if wunit==0 or wunit=='angstrom':
plt.plot(obs_spec['wave'], obs_spec_flux, lw=lw, color='black', zorder=4)
plt.plot(bfit_spec['wave'], bfit_spec_flux, lw=1, color='red', zorder=5)
plt.scatter(photo_cwave, obs_fluxes, marker='o', s=markersize, color='blue', zorder=6)
plt.scatter(photo_cwave, bfit_photo_flux, marker='o', s=0.7*markersize, color='gray', zorder=7)
elif wunit==1 or wunit=='micron':
plt.plot(obs_spec['wave']/1.0e+4, obs_spec_flux, lw=lw, color='black', zorder=4)
plt.plot(bfit_spec['wave']/1.0e+4, bfit_spec_flux, lw=1, color='red', zorder=5)
plt.scatter(photo_cwave/1.0e+4, obs_fluxes, marker='o', s=markersize, color='blue', zorder=6)
plt.scatter(photo_cwave/1.0e+4, bfit_photo_flux, marker='o', s=0.7*markersize, color='gray', zorder=7)
plt.subplots_adjust(bottom=0.2)
name_plot3 = 'sph_%s' % name_plot
plt.savefig(name_plot3, bbox_inches='tight')
def plot_SED_specphoto(filters=None,obs_photo=None,obs_spec=None,bfit_photo=None,bfit_spec=None,bfit_mod_spec=None,
corr_factor=None,minchi2_params=None,header_samplers=None,logscale_x=True, logscale_y=True, xrange=None, yrange=None,
wunit='micron',funit='erg/s/cm2/A', xticks=None, photo_color='red',residual_range=[-1.0,1.0], show_original_spec=False,
fontsize_tick=18, fontsize_label=25, show_legend=True, loc_legend=4, fontsize_legend=18, markersize=100, lw=2.0, name_plot=None):
from matplotlib.gridspec import GridSpec
from matplotlib.ticker import AutoMinorLocator
gal_z = header_samplers['gal_z']
del_wave_nebem = header_samplers['del_wave_nebem']
photo_flux = obs_photo['flux']
photo_flux_err = obs_photo['flux_err']
photo_wave = bfit_photo['wave']
bfit_photo_flux = bfit_photo['p50']
obs_spec_wave = obs_spec['wave']
obs_spec_flux = obs_spec['flux']
obs_spec_flux_err = obs_spec['flux_err']
bfit_spec_wave = bfit_spec['wave']
bfit_spec_flux = bfit_spec['tot_p50']
bfit_spec_flux_nebe = bfit_spec['nebe_p50']
bfit_spec_flux_nebe_cont = bfit_spec['nebe_cont_p50']
bfit_spec_flux_nebe_lines = bfit_spec_flux_nebe - bfit_spec_flux_nebe_cont
bfit_mod_spec_wave = bfit_mod_spec['wave']
bfit_mod_spec_flux = bfit_mod_spec['tot_p50']
bfit_mod_spec_flux_nebe = bfit_mod_spec['nebe_p50']
bfit_mod_spec_flux_nebe_cont = bfit_mod_spec['nebe_cont_p50']
bfit_mod_spec_flux_nebe_lines = bfit_mod_spec_flux_nebe - bfit_mod_spec_flux_nebe_cont
idx_sel = np.where((bfit_mod_spec_wave>=min(obs_spec_wave)) & (bfit_mod_spec_wave<=max(obs_spec_wave)))
bfit_mod_spec_wave_cut = bfit_mod_spec_wave[idx_sel[0]]
bfit_mod_spec_flux_cut = bfit_mod_spec_flux[idx_sel[0]]
bfit_mod_spec_flux_nebe_cut = bfit_mod_spec_flux_nebe[idx_sel[0]]
bfit_mod_spec_flux_nebe_cont_cut = bfit_mod_spec_flux_nebe_cont[idx_sel[0]]
bfit_mod_spec_flux_nebe_lines_cut = bfit_mod_spec_flux_nebe_lines[idx_sel[0]]
corr_factor_wave = corr_factor['wave']
corr_factor_values = corr_factor['p50']
func = interp1d(corr_factor_wave, corr_factor_values, fill_value='extrapolate')
interp_corr_factor_values = func(bfit_mod_spec_wave_cut)
rescaled_bfit_mod_spec_wave_cut = bfit_mod_spec_wave_cut
rescaled_bfit_mod_spec_flux_cut = bfit_mod_spec_flux_cut*interp_corr_factor_values
rescaled_bfit_mod_spec_flux_nebe_cut = bfit_mod_spec_flux_nebe_cut*interp_corr_factor_values
rescaled_bfit_mod_spec_flux_nebe_cont_cut = bfit_mod_spec_flux_nebe_cont_cut*interp_corr_factor_values
rescaled_bfit_mod_spec_flux_nebe_lines_cut = bfit_mod_spec_flux_nebe_lines_cut*interp_corr_factor_values
rescaled_bfit_mod_spec_flux_no_lines_cut = rescaled_bfit_mod_spec_flux_cut-rescaled_bfit_mod_spec_flux_nebe_lines_cut
# Calculate residuals
obs_spec_wave_clean, waveid_excld = get_no_nebem_wave_fit(gal_z, obs_spec_wave, del_wave_nebem)
waveid_incld = np.delete(np.arange(0,len(obs_spec_wave)), waveid_excld)
photo_residuals = (photo_flux - bfit_photo_flux)/photo_flux
spec_residuals = (obs_spec_flux[waveid_incld] - bfit_spec_flux[waveid_incld])/obs_spec_flux[waveid_incld]
# flux unit:
if funit != 'erg/s/cm2/A':
if show_original_spec == True:
bfit_mod_spec_flux1 = convert_unit_spec_from_ergscm2A(bfit_mod_spec_wave,bfit_mod_spec_flux,funit=funit)
obs_spec_flux1 = convert_unit_spec_from_ergscm2A(obs_spec_wave,obs_spec_flux,funit=funit)
rescaled_bfit_mod_spec_flux_no_lines_cut1 = convert_unit_spec_from_ergscm2A(rescaled_bfit_mod_spec_wave_cut,rescaled_bfit_mod_spec_flux_no_lines_cut,funit=funit)
bfit_photo_flux1 = convert_unit_spec_from_ergscm2A(photo_wave,bfit_photo_flux,funit=funit)
photo_flux1 = convert_unit_spec_from_ergscm2A(photo_wave,photo_flux,funit=funit)
photo_flux_err1 = convert_unit_spec_from_ergscm2A(photo_wave,photo_flux_err,funit=funit)
else:
if show_original_spec == True:
bfit_mod_spec_flux1 = bfit_mod_spec_flux
obs_spec_flux1 = obs_spec_flux
rescaled_bfit_mod_spec_flux_no_lines_cut1 = rescaled_bfit_mod_spec_flux_no_lines_cut
bfit_photo_flux1 = bfit_photo_flux
photo_flux1 = photo_flux
photo_flux_err1 = photo_flux_err
# wavelength units:
if wunit==1 or wunit=='micron':
bfit_mod_spec_wave1 = bfit_mod_spec_wave/1e+4
obs_spec_wave1 = obs_spec_wave/1e+4
rescaled_bfit_mod_spec_wave_cut1 = rescaled_bfit_mod_spec_wave_cut/1e+4
photo_wave1 = photo_wave/1e+4
else:
bfit_mod_spec_wave1 = bfit_mod_spec_wave
obs_spec_wave1 = obs_spec_wave
rescaled_bfit_mod_spec_wave_cut1 = rescaled_bfit_mod_spec_wave_cut
photo_wave1 = photo_wave
###==> plotting
fig1 = plt.figure(figsize=(15,9))
gs = GridSpec(nrows=2, ncols=1, height_ratios=[3,1], left=0.1, right=0.96, top=0.92, bottom=0.13, hspace=0.001)
f1 = fig1.add_subplot(gs[0])
plt.setp(f1.get_xticklabels(), visible=False)
plt.setp(f1.get_yticklabels(), fontsize=15)
if xrange is None:
bulk_waves = photo_wave1.tolist() + obs_spec_wave1.tolist()
xmin, xmax = 0.7*min(bulk_waves), 1.1*max(bulk_waves)
else:
xmin, xmax = xrange[0], xrange[1]
if yrange is None:
bulk_fluxes = photo_flux1.tolist() + bfit_photo_flux1.tolist() + obs_spec_flux1.tolist() + rescaled_bfit_mod_spec_flux_no_lines_cut1.tolist()
ymin, ymax = 0.7*min(bulk_fluxes), 1.2*max(bulk_fluxes)
else:
ymin, ymax = yrange[0], yrange[1]
plt.ylim(ymin,ymax)
if logscale_y == True:
f1.set_yscale('log')
if logscale_x == True:
f1.set_xscale('log')
#if xticks is not None:
# plt.xticks(xticks)
#for axis in [f1.xaxis]:
# axis.set_major_formatter(ScalarFormatter())
plt.xlim(xmin, xmax)
if funit=='erg/s/cm2/A' or funit==0:
plt.ylabel(r'$F_{\lambda}$ [erg $\rm{s}^{-1}\rm{cm}^{-2}\AA^{-1}$]', fontsize=int(fontsize_label))
elif funit=='erg/s/cm2' or funit==1:
plt.ylabel(r'$\lambda F_{\lambda}$ [erg $\rm{s}^{-1}\rm{cm}^{-2}$]', fontsize=int(fontsize_label))
elif funit=='Jy' or funit==2:
plt.ylabel(r'$F_{\nu}$ [Jy]', fontsize=int(fontsize_label))
else:
print ("The input funit is not recognized!")
sys.exit()
if show_original_spec == True:
plt.plot(bfit_mod_spec_wave1, bfit_mod_spec_flux1, lw=0.5, color='lightblue', zorder=0)
plt.plot(obs_spec_wave1, obs_spec_flux1, lw=1, color='black', zorder=1, label='Observed spectrum')
plt.plot(rescaled_bfit_mod_spec_wave_cut1, rescaled_bfit_mod_spec_flux_no_lines_cut1, lw=1, color='red', zorder=2, label='Model continuum')
plt.scatter(photo_wave1, bfit_photo_flux1, s=100, marker='s', edgecolor='blue', color='none', lw=3, zorder=3, label='Model photometry')
plt.errorbar(photo_wave1, photo_flux1, yerr=photo_flux_err1, fmt='o', color='green', markersize=10, lw=2, zorder=4, label='Observed photometry')
plt.legend(fontsize=17)
f2 = f1.twiny()
if wunit==1 or wunit=='micron':
f2.set_xlabel(r'Rest-frame wavelength [$\mu$m]', fontsize=18)
else:
f2.set_xlabel(r'Rest-frame wavelength [$\AA$]', fontsize=18)
plt.setp(f2.get_xticklabels(), fontsize=14)
plt.xlim(xmin/(1.0+gal_z), xmax/(1.0+gal_z))
if logscale_x == True:
f2.set_xscale('log')
f2.xaxis.set_minor_locator(AutoMinorLocator())
### plot residual
f1 = fig1.add_subplot(gs[1])
plt.setp(f1.get_yticklabels(), fontsize=15)
plt.setp(f1.get_xticklabels(), fontsize=15)
plt.ylabel(r'residual', fontsize=22)
if wunit==1 or wunit=='micron':
f1.set_xlabel(r'Observed wavelength [$\mu$m]', fontsize=22)
else:
f1.set_xlabel(r'Observed wavelength [$\AA$]', fontsize=22)
plt.ylim(residual_range[0],residual_range[1])
if logscale_x == True:
f1.set_xscale('log')
if xticks is not None:
plt.xticks(xticks)
for axis in [f1.xaxis]:
axis.set_major_formatter(ScalarFormatter())
plt.xlim(xmin,xmax)
f1.xaxis.set_minor_locator(AutoMinorLocator())
plt.plot(obs_spec_wave1[waveid_incld], spec_residuals, lw=1, color='gray', zorder=0)
plt.scatter(photo_wave1, photo_residuals, s=100, marker='s', edgecolor='black', color='none', lw=4, zorder=1)
x = np.linspace(xmin,xmax,200)
plt.plot(x,x-x,lw=1,color='black',linestyle='--')
plt.savefig(name_plot, bbox_inches='tight')
return name_plot
def plot_SED(name_sampler_fits,logscale_x=False,logscale_y=True,xrange=None,yrange=None,wunit='micron',funit='erg/s/cm2/A',
decompose=0,xticks=None,photo_color='red',residual_range=[-1.0,1.0],show_original_spec=False,fontsize_tick=18,fontsize_label=25,
show_legend=True, loc_legend=4, fontsize_legend=18, markersize=100, lw=2.0, name_plot=None):
"""Function for plotting best-fit (i.e., median posterior) model SED from a result of SED fitting.
:param name_sampler_fits:
Name of input FITS file containing result of an SED fitting.
:param logscale_x:
Choice for plotting x-axis in logarithmic (True) or linear scale (False).
:param logscale_y:
Choice for plotting y-axis in logarithmic (True) or linear scale (False).
:param xrange:
Range in x-axis. The accepted format is: [xmin,xmax]. If xrange=None, the range will be defined based on
the wavelength range of the observed SED.
:param yrange:
Range in y-axis. The accepted format is: [ymin,ymax]. If yrange=None, the range will be defined based on
the fluxes range of the observed SED.
:param wunit:
Wavelength unit. Options are: 0 or 'angstrom' for Angstrom unit and 1 or 'micron' for micron unit.
:param funit:
Flux unit. Options are: 0 or 'erg/s/cm2/A', 1 or 'erg/s/cm2', and 2 or 'Jy'.
:param decompose:
Choice for showing best-fit (i.e., median posterior) model SED broken down into its components (1 or True) or just its total (0 or False).
:param xticks:
xticks in list format.
:param photo_color:
Color of photometric fluxes data points. The accepted colors are those available in the matplotlib.
:param residual_range:
Residuals between observed SED and the median posterior model SED.
The residual is defined as (D - M)/D, where D represents observed SED, while M is model SED.
:param show_original_spec: (default=False)
Show original best-fit model spectrum before rescaling with polnomial correction. This is only relevant if the data is spectrophotometric.
:param fontsize_tick:
Fontsize for the ticks.
:param fontsize_label:
Fontsize for the labels in the x and y axes.
:param show_legend:
Option for showing a legend.
:param loc_legend:
Location of the legend.
:param fontsize_legend:
Fontsize of the legend.
:param markersize:
Size for the markers of the observed and model SEDs.
:param lw: (optional, default: 1)
Line width for the best-fit (i.e., median posterior) model SED.
:param name_plot:
Desired name for the output plot. This is optional. If None, a default name will be used.
"""
hdu = fits.open(name_sampler_fits)
header_samplers = hdu[0].header
obs_photo = hdu['obs_photo'].data
bfit_photo = hdu['bfit_photo'].data
if header_samplers['fitmethod'] == 'rdsps':
minchi2_params = hdu['minchi2_params'].data
if header_samplers['specphot'] == 0:
bfit_mod_spec = hdu['bfit_mod_spec'].data
if header_samplers['specphot'] == 1:
obs_spec = hdu['obs_spec'].data
bfit_spec = hdu['bfit_spec'].data
bfit_mod_spec = hdu['bfit_mod'].data
corr_factor = hdu['corr_factor'].data
hdu.close()
# filters
nbands = int(header_samplers['nfilters'])
filters = []
for bb in range(0,nbands):
filters.append(header_samplers['fil%d' % bb])
if name_plot==None:
name_sampler_fits1 = name_sampler_fits.replace('.fits','')
name_plot = "sed_%s.png" % (name_sampler_fits1)
if header_samplers['specphot'] == 1:
if header_samplers['fitmethod'] == 'mcmc':
minchi2_params = None
plot_SED_specphoto(filters=filters,obs_photo=obs_photo,obs_spec=obs_spec,bfit_photo=bfit_photo,bfit_spec=bfit_spec,
bfit_mod_spec=bfit_mod_spec,corr_factor=corr_factor,minchi2_params=minchi2_params,header_samplers=header_samplers,
logscale_x=logscale_x,logscale_y=logscale_y,xrange=xrange,yrange=yrange,wunit=wunit,funit=funit,xticks=xticks,
photo_color=photo_color,residual_range=residual_range,show_original_spec=show_original_spec,fontsize_tick=fontsize_tick,
fontsize_label=fontsize_label,show_legend=show_legend,loc_legend=loc_legend,fontsize_legend=fontsize_legend,
markersize=markersize,lw=lw,name_plot=name_plot)
elif header_samplers['specphot'] == 0:
if header_samplers['fitmethod'] == 'mcmc':
plot_SED_mcmc_photo(filters=filters,obs_photo=obs_photo,bfit_photo=bfit_photo,bfit_mod_spec=bfit_mod_spec,
header_samplers=header_samplers,logscale_x=logscale_x,logscale_y=logscale_y,xrange=xrange,yrange=yrange,
wunit=wunit,funit=funit,decompose=decompose,xticks=xticks,photo_color=photo_color,residual_range=residual_range,
fontsize_tick=fontsize_tick,fontsize_label=fontsize_label,show_legend=show_legend,loc_legend=loc_legend,
fontsize_legend=fontsize_legend,markersize=markersize,lw=lw,name_plot=name_plot)
elif header_samplers['fitmethod'] == 'rdsps':
plot_SED_rdsps_photo(filters=filters,obs_photo=obs_photo,bfit_photo=bfit_photo,bfit_mod_spec=bfit_mod_spec,minchi2_params=minchi2_params,
header_samplers=header_samplers,logscale_x=logscale_x,logscale_y=logscale_y,xrange=xrange,yrange=yrange,wunit=wunit,
funit=funit,decompose=decompose,xticks=xticks,photo_color=photo_color,residual_range=residual_range,fontsize_tick=fontsize_tick,
fontsize_label=fontsize_label,show_legend=show_legend,loc_legend=loc_legend,fontsize_legend=fontsize_legend,markersize=markersize,
lw=lw,name_plot=name_plot)
def plot_corner(name_sampler_fits, params=['log_sfr','log_mass','log_dustmass','log_fagn','log_fagn_bol','log_tauagn',
'log_qpah','log_umin','log_gamma','dust1','dust2','dust_index','log_mw_age','log_age','log_t0','log_alpha','log_beta',
'log_tau','logzsol','z','gas_logu','gas_logz'], label_params={'log_sfr':'log(SFR)','log_mass':'log($M_{*}$)','log_dustmass':'log($M_{dust}$)',
'log_fagn':'log($f_{AGN,*}$)','log_fagn_bol':'log($f_{AGN,bol}$)','log_tauagn':'log($\\tau_{AGN}$)','log_qpah':'log($Q_{PAH}$)',
'log_umin':'log($U_{min}$)','log_gamma':'log($\gamma_{e}$)','dust1':'$\hat \\tau_{1}$','dust2':'$\hat \\tau_{2}$', 'dust_index':'$n$',
'log_mw_age':'log($\mathrm{age}_{\mathrm{M}}$)','log_age':'log($\mathrm{age}_{\mathrm{sys}}$)','log_t0':'log($t_{0}$)',
'log_alpha':'log($\\alpha$)', 'log_beta':'log($\\beta$)','log_tau':'log($\\tau$)','logzsol':'log($Z/Z_{\odot}$)','z':'z',
'gas_logu':'log($U$)', 'gas_logz':'log($Z_{gas}/Z_{\odot}$)'},
params_ranges = {'log_sfr':[-99.0,-99.0],'log_mass':[-99.0,-99.0],'log_dustmass':[-99.0,-99.0],'log_fagn':[-5.0,0.48],
'log_fagn_bol':[-99.0,-99.0],'log_tauagn':[0.70,2.18],'log_qpah':[-1.0, 0.845],'log_umin':[-1.0, 1.176],'log_gamma':[-3.0,-0.824],
'dust1':[0.0,4.0],'dust2':[0.0,4.0], 'dust_index':[-2.2,0.4],'log_mw_age':[-99.0,-99.0],'log_age': [-3.0, 1.14],
'log_t0': [-2.0, 1.14],'log_alpha':[-2.5,2.5],'log_beta':[-2.5,2.5],'log_tau': [-2.5, 1.5], 'logzsol': [-2.0, 0.5],
'z': [-99.0, -99.0],'gas_logu':[-4.0,-1.0],'gas_logz':[-2.0,0.2]}, factor=1.0, nbins=12, fontsize_label=20, fontsize_tick=14, name_plot=None):
"""Function for producing corner plot that shows 1D and joint 2D posterior probability distributions from a fitting result with the MCMC method.
:param name_sampler_fits:
Name of input FITS file containing result of an SED fitting.
:param params:
List of parameters to be shown in the corner plot. This is optional.
If default input is used, all the parameters incolved in the SED fitting will be shown in the corner plot.
:param label_params:
Labels for the parameters. The accepted format is a python dictionary.
:param params_ranges:
Desired ranges for the parameters.
:param factor:
Multiplication factor to be applied to stellar mass, SFR, and dust mass.
:param nbins:
Number of binning in the parameter space when calculating the joint posteriors.
:param fontsize_label:
Fontsize of labels in the x and y axes.
:param fontsize_tick:
Fontsize for the ticks.
:param name_plot: (optional, default: None)
Desired name for the output plot.
:returns name_plot:
Desired name for the output plot. This is optional. If None, a default name will be used.
"""
def_params0 = list_default_params_fit()
def_params = def_params0 + ['log_sfr', 'log_mass', 'log_mw_age', 'log_dustmass']
# open the input FITS file
hdu = fits.open(name_sampler_fits)
header_samplers = hdu[1].header
data_samplers = hdu[1].data
if hdu[0].header['storesamp'] == 0:
print ("The input FITS file does not contain sampler chains!")
sys.exit()
hdu.close()
# number of parameters
nparams = len(params)
if nparams == len(def_params): # if default set is used
nparams_fit = int(header_samplers['TFIELDS']) - 1
params_new = []
label_params_new = {}
params_ranges_new = {}
for ii in range(0,nparams):
for jj in range(0,nparams_fit):
str_temp = 'TTYPE%d' % (jj+2)
if params[ii] == header_samplers['TTYPE%d' % (jj+2)]:
params_new.append(params[ii])
label_params_new[params[ii]] = label_params[params[ii]]
params_ranges_new[params[ii]] = params_ranges[params[ii]]
else:
params_new = params
label_params_new = label_params
params_ranges_new = params_ranges
# apply multiplication factor
data_samplers['log_mass'] = data_samplers['log_mass'] + np.log10(factor)
data_samplers['log_sfr'] = data_samplers['log_sfr'] + np.log10(factor)
if 'log_dustmass' in params_new:
data_samplers['log_dustmass'] = data_samplers['log_dustmass'] + np.log10(factor)
## exclude saturated samplers: log(SFR)~-29.99..
idx_sel = np.where(data_samplers['log_sfr']>-29.0)
nparams_new = len(params_new)
nchains = len(idx_sel[0])
param_samplers = np.zeros((nparams_new,nchains))
for ii in range(0,nparams_new):
param_samplers[ii] = [data_samplers[params_new[ii]][j] for j in idx_sel[0]]
if name_plot==None:
name_sampler_fits1 = name_sampler_fits.replace('.fits','')
name_plot = "corner_%s.png" % (name_sampler_fits1)
# change the format of label_params, true_params, and postmean_flag:
label_params1 = []
params_ranges1 = np.zeros((nparams_new,2))
for ii in range(0,nparams_new):
label_params1.append(label_params_new[params_new[ii]])
params_ranges1[ii][0] = params_ranges_new[params_new[ii]][0]
params_ranges1[ii][1] = params_ranges_new[params_new[ii]][1]
plot_triangle_posteriors(param_samplers=param_samplers,label_params=label_params1,params_ranges=params_ranges1,
nbins=nbins,fontsize_label=fontsize_label,fontsize_tick=fontsize_tick,output_name=name_plot)
return name_plot
def plot_sfh_mcmc(name_sampler_fits, nchains=200, del_t=0.05, lbacktime_max=None, yrange=None, factor=1.0, loc_legend=2, fontsize_tick=18,
fontsize_label=25, fontsize_legend=26, logscale_x=False, logscale_y=False, name_plot=None):
"""Function for producing SFH plot from a fitting result with the MCMC method. This is only applicable for fitting result
that stores the full sampler chains, which is when we set store_full_samplers=1 in the SED fitting functions.
:param name_sampler_fits:
Name of input FITS file containing result of an SED fitting.
:param nchains:
Number of randomly chosen sampler chains (from the full samplers stored in the FITS file) to be used for calculating the inferred SFH.
:param del_t:
Width of the look back time binning in unit of Gyr for sampling the star formation history (SFH).
:param lbacktime_max:
Maximum look-back time in the SFH plot. If None, the maximum look-back time is defined from the age of universe at the redshift of the galaxy.
:param yrange:
Range in the y-axis.
:param factor:
Multiplication factor to be applied to the SFH.
:param loc_legend:
Where to locate the legend. This is the same as in the `matplotlib`.
:param fontsize_tick:
Fontsize for the ticks.
:param fontsize_label:
Fontsize of the labels in the x and y axes.
:param fontsize_legend:
Fontsize of the legend.
:param logscale_x:
Choice for plotting x-axis in logarithmic (True) or linear scale (False).
:param logscale_y:
Choice for plotting y-axis in logarithmic (True) or linear scale (False).
:returns name_plot:
Desired name for the output plot. This is optional. If None, a default name will be used.
:returns grid_lbt:
Look-back times.
:return grid_sfr_p16:
16th percentile of the SFR(t).
:return grid_sfr_p50:
50th percentile of the SFR(t).
:return grid_sfr_p84:
84th percentile of the SFR(t).
"""
hdu = fits.open(name_sampler_fits)
header_samplers = hdu[0].header
data_samplers = hdu[1].data
hdu.close()
sfh_form = header_samplers['sfh_form']
nsamplers = len(data_samplers['log_age'])
free_z = int(header_samplers['free_z'])
# cosmology parameter
cosmo = header_samplers['cosmo']
H0 = float(header_samplers['H0'])
Om0 = float(header_samplers['Om0'])
if free_z == 0:
gal_z = float(header_samplers['gal_z'])
if cosmo == 'flat_LCDM':
cosmo1 = FlatLambdaCDM(H0=H0, Om0=Om0)
max_lbt = cosmo1.age(gal_z).value
elif cosmo == 'WMAP5':
max_lbt = WMAP5.age(gal_z).value
elif cosmo == 'WMAP7':
max_lbt = WMAP7.age(gal_z).value
elif cosmo == 'WMAP9':
max_lbt = WMAP9.age(gal_z).value
elif cosmo == 'Planck13':
max_lbt = Planck13.age(gal_z).value
elif cosmo == 'Planck15':
max_lbt = Planck15.age(gal_z).value
#elif cosmo == 'Planck18':
# max_lbt = Planck18.age(gal_z).value
elif free_z == 1:
max_z = max(data_samplers['z'])
if cosmo == 'flat_LCDM':
cosmo1 = FlatLambdaCDM(H0=H0, Om0=Om0)
max_lbt = cosmo1.age(max_z).value
elif cosmo == 'WMAP5':
max_lbt = WMAP5.age(max_z).value
elif cosmo == 'WMAP7':
max_lbt = WMAP7.age(max_z).value
elif cosmo == 'WMAP9':
max_lbt = WMAP9.age(max_z).value
elif cosmo == 'Planck13':
max_lbt = Planck13.age(max_z).value
elif cosmo == 'Planck15':
max_lbt = Planck15.age(max_z).value
#elif cosmo == 'Planck18':
# max_lbt = Planck18.age(gal_z).value
nt = int(max_lbt/del_t)
grid_lbt = np.linspace(0.0,max_lbt,nt)
array_sfr_at_lbt = np.zeros((nchains,nt))
## exclude saturated samplers: log(SFR)~-29.99..
idx_sel = np.where(data_samplers['log_sfr']>-29.0)
rand_idx = np.random.uniform(0,len(idx_sel[0]),nchains)
for ii in range(0,nchains):
idx = idx_sel[0][int(rand_idx[ii])]
if free_z == 1:
gal_z = data_samplers['z'][idx]
if cosmo == 'flat_LCDM':
cosmo1 = FlatLambdaCDM(H0=H0, Om0=Om0)
max_lbt1 = cosmo1.age(gal_z).value
elif cosmo == 'WMAP5':
max_lbt1 = WMAP5.age(gal_z).value
elif cosmo == 'WMAP7':
max_lbt1 = WMAP7.age(gal_z).value
elif cosmo == 'WMAP9':
max_lbt1 = WMAP9.age(gal_z).value
elif cosmo == 'Planck13':
max_lbt1 = Planck13.age(gal_z).value
elif cosmo == 'Planck15':
max_lbt1 = Planck15.age(gal_z).value
elif free_z == 0:
max_lbt1 = max_lbt
age = pow(10.0,data_samplers['log_age'][idx])
tau = pow(10.0,data_samplers['log_tau'][idx])
t0 = 0.0
alpha = 0.0
beta = 0.0
if sfh_form==2 or sfh_form==3:
t0 = pow(10.0,data_samplers['log_t0'][idx])
if sfh_form==4:
alpha = pow(10.0,data_samplers['log_alpha'][idx])
beta = pow(10.0,data_samplers['log_beta'][idx])
formed_mass = pow(10.0,data_samplers['log_mass'][idx])
t,SFR_t = construct_SFH(sfh_form=sfh_form,t0=t0,tau=tau,alpha=alpha,beta=beta,age=age,formed_mass=formed_mass)
t_back = np.abs(t - age)
array_sfr_at_lbt[ii] = np.interp(grid_lbt,t_back[::-1],SFR_t[::-1],left=0,right=0)*factor
array_sfr_at_lbt_trans = np.transpose(array_sfr_at_lbt, axes=(1,0))
grid_sfr_p16 = np.percentile(array_sfr_at_lbt_trans, 16, axis=1)
grid_sfr_p50 = np.percentile(array_sfr_at_lbt_trans, 50, axis=1)
grid_sfr_p84 = np.percentile(array_sfr_at_lbt_trans, 84, axis=1)
# plotting
fig = plt.figure(figsize=(8,5))
f1 = plt.subplot()
if logscale_y == True:
f1.set_yscale('log')
if logscale_x == True:
f1.set_xscale('log')
plt.setp(f1.get_xticklabels(), fontsize=int(fontsize_tick))
plt.setp(f1.get_yticklabels(), fontsize=int(fontsize_tick))
plt.tick_params(axis='y', which='both', right='on')
plt.tick_params(axis='x', which='both', top='on')
plt.xlabel('Look back time [Gyr]', fontsize=int(fontsize_label))
plt.ylabel(r'SFR[$M_{\odot}yr^{-1}$]', fontsize=int(fontsize_label))
f1.fill_between(grid_lbt, grid_sfr_p16, grid_sfr_p84, color='gray', alpha=0.5)
plt.plot(grid_lbt,grid_sfr_p50,lw=4,color='black')
# xrange:
if lbacktime_max == None:
xmax = max_lbt
plt.xlim(xmax,0)
elif lbacktime_max != None:
xmax = lbacktime_max
plt.xlim(xmax,0)
# yrange:
if yrange == None:
maxSFR = max(grid_sfr_p84)
plt.ylim(0,maxSFR*1.2)
if yrange != None:
plt.ylim(yrange[0],yrange[1])
plt.subplots_adjust(left=0.15, right=0.95, bottom=0.15, top=0.95)
if name_plot==None:
name_sampler_fits1 = name_sampler_fits.replace('.fits','')
name_plot = "sfh_%s.png" % (name_sampler_fits1)
plt.savefig(name_plot, bbox_inches='tight')
return name_plot, grid_lbt, grid_sfr_p16, grid_sfr_p50, grid_sfr_p84
|
aabdurroufREPO_NAMEpiXedfitPATH_START.@piXedfit_extracted@piXedfit-main@piXedfit@piXedfit_analysis@plotting.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "dmlc/xgboost",
"repo_path": "xgboost_extracted/xgboost-master/R-package/README.md",
"type": "Markdown"
}
|
XGBoost R Package for Scalable GBM
==================================
[](https://cran.r-project.org/web/packages/xgboost)
[](https://cran.rstudio.com/web/packages/xgboost/index.html)
[](http://xgboost.readthedocs.org/en/latest/R-package/index.html)
Resources
---------
* [XGBoost R Package Online Documentation](http://xgboost.readthedocs.org/en/latest/R-package/index.html)
- Check this out for detailed documents, examples and tutorials.
Installation
------------
We are [on CRAN](https://cran.r-project.org/web/packages/xgboost/index.html) now. For stable/pre-compiled(for Windows and OS X) version, please install from CRAN:
```r
install.packages('xgboost')
```
For more detailed installation instructions, please see [here](http://xgboost.readthedocs.org/en/latest/build.html#r-package-installation).
Examples
--------
* Please visit [walk through example](demo).
* See also the [example scripts](../demo/kaggle-higgs) for Kaggle Higgs Challenge, including [speedtest script](../demo/kaggle-higgs/speedtest.R) on this dataset and the one related to [Otto challenge](../demo/kaggle-otto), including a [RMarkdown documentation](../demo/kaggle-otto/understandingXGBoostModel.Rmd).
Development
-----------
* See the [R Package section](https://xgboost.readthedocs.io/en/latest/contrib/coding_guide.html#r-coding-guideline) of the contributors guide.
|
dmlcREPO_NAMExgboostPATH_START.@xgboost_extracted@xgboost-master@R-package@README.md@.PATH_END.py
|
{
"filename": "test_YSEDR1_AD.ipynb",
"repo_name": "patrickaleo/LAISS-local",
"repo_path": "LAISS-local_extracted/LAISS-local-main/notebooks/test_YSEDR1_AD.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import time
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
import seaborn as sns
import os
import sys
import annoy
from annoy import AnnoyIndex
import random
import glob
from IPython.display import display_markdown
from collections import Counter
from sklearn import preprocessing
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.metrics import classification_report
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from scipy.spatial import cKDTree
from sklearn.decomposition import PCA
from sklearn.decomposition import SparsePCA
from sklearn.ensemble import RandomForestClassifier
import antares_client
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
# set random seed for consistent results
import random
random.seed(0)
plt.style.use('fig_publication.mplstyle')
%config InlineBackend.figure_format = 'retina' #for MacOS, make plots crisp
```
```python
# From 106 available features from Kostya's lc_feature_extractor, use the 82 from SNAD Miner paper
# R and g bands
feature_names_r_g = ['feature_amplitude_magn_r',
'feature_anderson_darling_normal_magn_r',
'feature_beyond_1_std_magn_r',
'feature_beyond_2_std_magn_r',
'feature_cusum_magn_r',
#'feature_eta_e_magn_r',
'feature_inter_percentile_range_2_magn_r',
'feature_inter_percentile_range_10_magn_r',
'feature_inter_percentile_range_25_magn_r',
'feature_kurtosis_magn_r',
'feature_linear_fit_slope_magn_r',
'feature_linear_fit_slope_sigma_magn_r',
#'feature_linear_fit_reduced_chi2_magn_r',
#'feature_linear_trend_magn_r', # cadence removal
#'feature_linear_trend_sigma_magn_r', # cadence removal
'feature_magnitude_percentage_ratio_40_5_magn_r',
'feature_magnitude_percentage_ratio_20_5_magn_r',
#'feature_maximum_slope_magn_r',
'feature_mean_magn_r',
'feature_median_absolute_deviation_magn_r',
'feature_percent_amplitude_magn_r',
'feature_median_buffer_range_percentage_10_magn_r',
'feature_median_buffer_range_percentage_20_magn_r',
'feature_percent_difference_magnitude_percentile_5_magn_r',
'feature_percent_difference_magnitude_percentile_10_magn_r',
#'feature_period_0_magn_r', # should be negated
#'feature_period_s_to_n_0_magn_r', # cadence removal
#'feature_period_1_magn_r',
#'feature_period_s_to_n_1_magn_r', # cadence removal
#'feature_period_2_magn_r',
#'feature_period_s_to_n_2_magn_r', # cadence removal
#'feature_period_3_magn_r',
#'feature_period_s_to_n_3_magn_r', # cadence removal
#'feature_period_4_magn_r',
#'feature_period_s_to_n_4_magn_r', # cadence removal
#'feature_periodogram_amplitude_magn_r',
#'feature_periodogram_beyond_2_std_magn_r', # cadence removal
#'feature_periodogram_beyond_3_std_magn_r', # cadence removal
#'feature_periodogram_standard_deviation_magn_r', # cadence removal
#'feature_chi2_magn_r',
'feature_skew_magn_r',
'feature_standard_deviation_magn_r',
'feature_stetson_k_magn_r',
'feature_weighted_mean_magn_r',
'feature_anderson_darling_normal_flux_r',
'feature_cusum_flux_r',
#'feature_eta_e_flux_r',
'feature_excess_variance_flux_r',
'feature_kurtosis_flux_r',
'feature_mean_variance_flux_r',
#'feature_chi2_flux_r',
'feature_skew_flux_r',
'feature_stetson_k_flux_r',
'feature_amplitude_magn_g',
'feature_anderson_darling_normal_magn_g',
'feature_beyond_1_std_magn_g',
'feature_beyond_2_std_magn_g',
'feature_cusum_magn_g',
#'feature_eta_e_magn_g',
'feature_inter_percentile_range_2_magn_g',
'feature_inter_percentile_range_10_magn_g',
'feature_inter_percentile_range_25_magn_g',
'feature_kurtosis_magn_g',
'feature_linear_fit_slope_magn_g',
'feature_linear_fit_slope_sigma_magn_g',
#'feature_linear_fit_reduced_chi2_magn_g',
#'feature_linear_trend_magn_g', # cadence removal
#'feature_linear_trend_sigma_magn_g', # cadence removal
'feature_magnitude_percentage_ratio_40_5_magn_g',
'feature_magnitude_percentage_ratio_20_5_magn_g',
#'feature_maximum_slope_magn_g',
'feature_mean_magn_g',
'feature_median_absolute_deviation_magn_g',
'feature_median_buffer_range_percentage_10_magn_g',
'feature_median_buffer_range_percentage_20_magn_g',
'feature_percent_amplitude_magn_g',
'feature_percent_difference_magnitude_percentile_5_magn_g',
'feature_percent_difference_magnitude_percentile_10_magn_g',
#'feature_period_0_magn_g', # should be negated
#'feature_period_s_to_n_0_magn_g', # cadence removal
#'feature_period_1_magn_g',
#'feature_period_s_to_n_1_magn_g', # cadence removal
#'feature_period_2_magn_g',
#'feature_period_s_to_n_2_magn_g', # cadence removal
#'feature_period_3_magn_g',
#'feature_period_s_to_n_3_magn_g', # cadence removal
#'feature_period_4_magn_g',
#'feature_period_s_to_n_4_magn_g', # cadence removal
#'feature_periodogram_amplitude_magn_g',
#'feature_periodogram_beyond_2_std_magn_g', # cadence removal
#'feature_periodogram_beyond_3_std_magn_g', # cadence removal
#'feature_periodogram_standard_deviation_magn_g', # cadence removal
#'feature_chi2_magn_g',
'feature_skew_magn_g',
'feature_standard_deviation_magn_g',
'feature_stetson_k_magn_g',
'feature_weighted_mean_magn_g',
'feature_anderson_darling_normal_flux_g',
'feature_cusum_flux_g',
#'feature_eta_e_flux_g',
'feature_excess_variance_flux_g',
'feature_kurtosis_flux_g',
'feature_mean_variance_flux_g',
#'feature_chi2_flux_g',
'feature_skew_flux_g',
'feature_stetson_k_flux_g']
ztf_id_and_features_r_g = ['Unnamed: 0'] + ['locus_id', 'ra', 'dec',\
'tags', 'catalogs', 'ztf_object_id', 'ztf_ssnamenr', 'num_alerts',\
'num_mag_values', 'oldest_alert_id', 'oldest_alert_magnitude',\
'oldest_alert_observation_time', 'newest_alert_id',\
'newest_alert_magnitude', 'newest_alert_observation_time',\
'brightest_alert_id', 'brightest_alert_magnitude', \
'brightest_alert_observation_time'] + feature_names_r_g +\
['horizons_targetname', 'anomaly', 'anom_score', 'anomaly_score',\
'anomaly_mask', 'anomaly_type', 'is_corrected', 'vpdf_extreme_version',\
'vpdf_extreme_faint', 'vpdf_extreme_bright', 'locus_gal_l', 'locus_gal_b']
feature_names_hostgal = [
# 'Unnamed: 0',
# 'level_0',
# 'index',
# 'objName',
# 'objAltName1',
# 'objAltName2',
# 'objAltName3',
# 'objID',
# 'uniquePspsOBid',
# 'ippObjID',
# 'surveyID',
# 'htmID',
# 'zoneID',
# 'tessID',
# 'projectionID',
# 'skyCellID',
# 'randomID',
# 'batchID',
# 'dvoRegionID',
# 'processingVersion',
# 'objInfoFlag',
# 'qualityFlag',
# 'raStack',
# 'decStack',
# 'raStackErr',
# 'decStackErr',
# 'raMean',
# 'decMean',
# 'raMeanErr',
# 'decMeanErr',
# 'epochMean',
# 'posMeanChisq',
# 'cx',
# 'cy',
# 'cz',
# 'lambda',
# 'beta',
# 'l',
# 'b',
# 'nStackObjectRows',
# 'nStackDetections',
# 'nDetections',
# 'ng',
# 'nr',
# 'ni',
# 'nz',
# 'ny',
# 'uniquePspsSTid',
# 'primaryDetection',
# 'bestDetection',
# 'gippDetectID',
# 'gstackDetectID',
# 'gstackImageID',
# 'gra',
# 'gdec',
# 'graErr',
# 'gdecErr',
# 'gEpoch',
# 'gPSFMag',
# 'gPSFMagErr',
# 'gApMag',
# 'gApMagErr',
# 'gKronMag',
# 'gKronMagErr',
# 'ginfoFlag',
# 'ginfoFlag2',
# 'ginfoFlag3',
# 'gnFrames',
# 'gxPos',
# 'gyPos',
# 'gxPosErr',
# 'gyPosErr',
# 'gpsfMajorFWHM',
# 'gpsfMinorFWHM',
# 'gpsfTheta',
# 'gpsfCore',
# 'gpsfLikelihood',
# 'gpsfQf',
# 'gpsfQfPerfect',
# 'gpsfChiSq',
'gmomentXX',
'gmomentXY',
'gmomentYY',
'gmomentR1',
'gmomentRH',
'gPSFFlux',
# 'gPSFFluxErr',
'gApFlux',
# 'gApFluxErr',
# 'gApFillFac',
# 'gApRadius',
'gKronFlux',
# 'gKronFluxErr',
'gKronRad',
# 'gexpTime',
'gExtNSigma',
# 'gsky',
# 'gskyErr',
# 'gzp',
# 'gPlateScale',
# 'rippDetectID',
# 'rstackDetectID',
# 'rstackImageID',
# 'rra',
# 'rdec',
# 'rraErr',
# 'rdecErr',
# 'rEpoch',
# 'rPSFMag',
# 'rPSFMagErr',
# 'rApMag',
# 'rApMagErr',
# 'rKronMag',
# 'rKronMagErr',
# 'rinfoFlag',
# 'rinfoFlag2',
# 'rinfoFlag3',
# 'rnFrames',
# 'rxPos',
# 'ryPos',
# 'rxPosErr',
# 'ryPosErr',
# 'rpsfMajorFWHM',
# 'rpsfMinorFWHM',
# 'rpsfTheta',
# 'rpsfCore',
# 'rpsfLikelihood',
# 'rpsfQf',
# 'rpsfQfPerfect',
# 'rpsfChiSq',
'rmomentXX',
'rmomentXY',
'rmomentYY',
'rmomentR1',
'rmomentRH',
'rPSFFlux',
# 'rPSFFluxErr',
'rApFlux',
# 'rApFluxErr',
# 'rApFillFac',
# 'rApRadius',
'rKronFlux',
# 'rKronFluxErr',
'rKronRad',
# 'rexpTime',
'rExtNSigma',
# 'rsky',
# 'rskyErr',
# 'rzp',
# 'rPlateScale',
# 'iippDetectID',
# 'istackDetectID',
# 'istackImageID',
# 'ira',
# 'idec',
# 'iraErr',
# 'idecErr',
# 'iEpoch',
# 'iPSFMag',
# 'iPSFMagErr',
# 'iApMag',
# 'iApMagErr',
# 'iKronMag',
# 'iKronMagErr',
# 'iinfoFlag',
# 'iinfoFlag2',
# 'iinfoFlag3',
# 'inFrames',
# 'ixPos',
# 'iyPos',
# 'ixPosErr',
# 'iyPosErr',
# 'ipsfMajorFWHM',
# 'ipsfMinorFWHM',
# 'ipsfTheta',
# 'ipsfCore',
# 'ipsfLikelihood',
# 'ipsfQf',
# 'ipsfQfPerfect',
# 'ipsfChiSq',
'imomentXX',
'imomentXY',
'imomentYY',
'imomentR1',
'imomentRH',
'iPSFFlux',
# 'iPSFFluxErr',
'iApFlux',
# 'iApFluxErr',
# 'iApFillFac',
# 'iApRadius',
'iKronFlux',
# 'iKronFluxErr',
'iKronRad',
# 'iexpTime',
'iExtNSigma',
# 'isky',
# 'iskyErr',
# 'izp',
# 'iPlateScale',
# 'zippDetectID',
# 'zstackDetectID',
# 'zstackImageID',
# 'zra',
# 'zdec',
# 'zraErr',
# 'zdecErr',
# 'zEpoch',
# 'zPSFMag',
# 'zPSFMagErr',
# 'zApMag',
# 'zApMagErr',
# 'zKronMag',
# 'zKronMagErr',
# 'zinfoFlag',
# 'zinfoFlag2',
# 'zinfoFlag3',
# 'znFrames',
# 'zxPos',
# 'zyPos',
# 'zxPosErr',
# 'zyPosErr',
# 'zpsfMajorFWHM',
# 'zpsfMinorFWHM',
# 'zpsfTheta',
# 'zpsfCore',
# 'zpsfLikelihood',
# 'zpsfQf',
# 'zpsfQfPerfect',
# 'zpsfChiSq',
'zmomentXX',
'zmomentXY',
'zmomentYY',
'zmomentR1',
'zmomentRH',
'zPSFFlux',
# # 'zPSFFluxErr',
'zApFlux',
# # 'zApFluxErr',
# # 'zApFillFac',
# # 'zApRadius',
'zKronFlux',
# # 'zKronFluxErr',
'zKronRad',
# # 'zexpTime',
'zExtNSigma',
# 'zsky',
# 'zskyErr',
# 'zzp',
# 'zPlateScale',
# 'yippDetectID',
# 'ystackDetectID',
# 'ystackImageID',
# 'yra',
# 'ydec',
# 'yraErr',
# 'ydecErr',
# 'yEpoch',
# 'yPSFMag',
# 'yPSFMagErr',
# 'yApMag',
# 'yApMagErr',
# 'yKronMag',
# 'yKronMagErr',
# 'yinfoFlag',
# 'yinfoFlag2',
# 'yinfoFlag3',
# 'ynFrames',
# 'yxPos',
# 'yyPos',
# 'yxPosErr',
# 'yyPosErr',
# 'ypsfMajorFWHM',
# 'ypsfMinorFWHM',
# 'ypsfTheta',
# 'ypsfCore',
# 'ypsfLikelihood',
# 'ypsfQf',
# 'ypsfQfPerfect',
# 'ypsfChiSq',
'ymomentXX',
'ymomentXY',
'ymomentYY',
'ymomentR1',
'ymomentRH',
'yPSFFlux',
# # 'yPSFFluxErr',
'yApFlux',
# # 'yApFluxErr',
# # 'yApFillFac',
# # 'yApRadius',
'yKronFlux',
# # 'yKronFluxErr',
'yKronRad',
# # 'yexpTime',
'yExtNSigma',
# 'ysky',
# 'yskyErr',
# 'yzp',
# 'yPlateScale',
# 'distance',
# 'SkyMapper_StarClass',
# 'gelong',
# 'g_a',
# 'g_b',
# 'g_pa',
# 'relong',
# 'r_a',
# 'r_b',
# 'r_pa',
# 'ielong',
# 'i_a',
# 'i_b',
# 'i_pa',
# 'zelong',
# 'z_a',
# 'z_b',
# 'z_pa',
'i-z', # try throwing in
# 'g-r',
# 'r-i',
# 'g-i',
# 'z-y',
# 'g-rErr',
# 'r-iErr',
# 'i-zErr',
# 'z-yErr',
'gApMag_gKronMag',
'rApMag_rKronMag',
'iApMag_iKronMag',
'zApMag_zKronMag',
'yApMag_yKronMag',
'7DCD',
# 'NED_name',
# 'NED_type',
# 'NED_vel',
# 'NED_redshift',
# 'NED_mag',
# 'class',
'dist/DLR',
# 'dist',
# 'TransientClass',
# 'TransientRA',
# 'TransientDEC'
]
feature_names_tns = ['has_tns', 'tns_cls', 'spec_z', 'report_group']
lc_and_host_features = feature_names_r_g+feature_names_hostgal
lc_and_host_and_tns_features = lc_and_host_features + feature_names_tns
```
```python
def plot_conf_matrix(y_test, y_pred, labels, title, kind, figsize=(6, 4)):
if kind == 'completeness':
counts = confusion_matrix(y_test, y_pred, labels=labels, normalize=None)
recall = confusion_matrix(y_test, y_pred, labels=labels, normalize='true')
annotations = np.vectorize(lambda c, r: f'{r:.2g}\n({c})')(counts, recall)
heatmap = pd.DataFrame(recall, index=labels, columns=labels)
if kind == 'purity':
counts = confusion_matrix(y_test, y_pred, labels=labels, normalize=None)
recall = confusion_matrix(y_test, y_pred, labels=labels, normalize='pred')
annotations = np.vectorize(lambda c, r: f'{r:.2g}\n({c})')(counts, recall)
heatmap = pd.DataFrame(recall, index=labels, columns=labels)
plt.figure(figsize=figsize)
sns.heatmap(heatmap, annot=annotations, fmt='s', cmap='Blues', vmin=0, vmax=1, annot_kws={"fontsize":18})
plt.title(title, fontsize=24)
plt.ylabel('True class', fontsize=24)
plt.xlabel('Predicted class', fontsize=24)
#plt.show()
```
```python
def plot_RFC_prob_vs_lc_ztfid(anom_ztfid, anom_thresh, lc, meta, df_path):
df_path = df_path
anom_thresh = anom_thresh
try:
lc_and_hosts_df = pd.read_csv(f'{df_path}/{anom_ztfid}_timeseries.csv')
except:
print(f"couldn't feature space as func of time for {anom_ztfid}. pass.")
return
lc_and_hosts_df = lc_and_hosts_df.dropna()
try:
anom_obj_df = lc_and_hosts_df[lc_and_host_features]
except:
print(f"{anom_ztfid} has some NaN LC features. Skip!")
return
try:
pred_prob_anom = 100 * clf.predict_proba(anom_obj_df)
pred_prob_anom[:, 0] = [round(a, 1) for a in pred_prob_anom[:, 0]]
pred_prob_anom[:, 1] = [round(b, 1) for b in pred_prob_anom[:, 1]]
num_anom_epochs = len(np.where(pred_prob_anom[:, 1]>=anom_thresh)[0])
except:
print(f"{anom_ztfid} has some NaN host galaxy values from PS1 catalog. Skip!")
return
try:
#anom_idx = np.where(pred_prob_anom[:, 1]>=50)[0][0]
orig = pd.read_csv(f'{df_path}/{anom_ztfid}_timeseries.csv')
#diff = len(orig)-len(pred_prob_anom)
#anom_idx = lc_and_hosts_df.loc[np.where(pred_prob_anom[:, 1]>=anom_thresh)[0][0]]
anom_idx = lc_and_hosts_df.iloc[np.where(pred_prob_anom[:, 1]>=anom_thresh)[0][0]] #+ diff
anom_idx_is = True
print("Anomalous during timeseries!")
except:
print(f"Prediction doesn't exceed anom_threshold of {anom_thresh}% for {anom_ztfid}.")
anom_idx_is = False
max_anom_score = max(pred_prob_anom[:, 1])
print("max_anom_score", round(max_anom_score, 1))
print("num_anom_epochs", num_anom_epochs)
ztf_id_ref = anom_ztfid
df_ref_g = lc[(lc.PASSBAND == 'g')]
df_ref_r = lc[(lc.PASSBAND == 'R')]
mjd_idx_at_min_mag_r_ref = df_ref_r[['MAG']].reset_index().idxmin().MAG
mjd_idx_at_min_mag_g_ref = df_ref_g[['MAG']].reset_index().idxmin().MAG
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(7,10))
ax1.invert_yaxis()
ax1.errorbar(x=df_ref_r.MJD, y=df_ref_r.MAG, yerr=df_ref_r.MAGERR, fmt='o', c='r',
label=f'REF: {ztf_id_ref}')
ax1.errorbar(x=df_ref_g.MJD, y=df_ref_g.MAG, yerr=df_ref_g.MAGERR, fmt='o', c='g')
if anom_idx_is == True:
print("MJD Cutoff anom_idx", anom_idx.mjd_cutoff)
#print(lc_and_hosts_df[lc_and_hosts_df.obs_num == anom_idx])
ax1.axvline(x=lc_and_hosts_df[lc_and_hosts_df.obs_num == anom_idx.obs_num].mjd_cutoff.values[0])
#ax1.set_xlim(min(df_ref_r.MJD), max(df_ref_r.MJD))
print(f'https://ziggy.ucolick.org/yse/transient_detail/{anom_ztfid}/')
ax2.plot(lc_and_hosts_df['mjd_cutoff'], pred_prob_anom[:, 0], label='p(Normal)')
ax2.plot(lc_and_hosts_df['mjd_cutoff'], pred_prob_anom[:, 1], label='p(Other)')
#ax2.axhline(y=50)
#ax2.set_xlim(min(df_ref_r.ant_mjd), max(df_ref_r.ant_mjd))
plt.xlabel('mjd_cutoff')
plt.ylabel('Probability (%)')
plt.legend()
plt.show()
def save_RFC_prob_vs_lc_ztfid(anom_ztfid, anom_thresh, meta, df_path, clf, full_timeseries=True):
df_path = df_path
anom_thresh = anom_thresh
try:
lc_and_hosts_df = pd.read_csv(f'{df_path}/{anom_ztfid}_timeseries.csv')
except:
print(f"couldn't feature space as func of time for {anom_ztfid}. pass.")
return
lc_and_hosts_df = lc_and_hosts_df.dropna()
try:
anom_obj_df = lc_and_hosts_df[lc_and_host_features]
except:
print(f"{anom_ztfid} has some NaN LC features. Skip!")
return
if full_timeseries: #default, full timeseries
try:
pred_prob_anom = 100 * clf.predict_proba(anom_obj_df)
pred_prob_anom[:, 0] = [round(a, 1) for a in pred_prob_anom[:, 0]]
pred_prob_anom[:, 1] = [round(b, 1) for b in pred_prob_anom[:, 1]]
num_anom_epochs = len(np.where(pred_prob_anom[:, 1]>=anom_thresh)[0])
except:
print(f"{anom_ztfid} has some NaN host galaxy values from PS1 catalog. Skip!")
return
try:
#anom_idx = np.where(pred_prob_anom[:, 1]>=50)[0][0]
orig = pd.read_csv(f'{df_path}/{anom_ztfid}_timeseries.csv')
#diff = len(orig)-len(pred_prob_anom)
#anom_idx = lc_and_hosts_df.loc[np.where(pred_prob_anom[:, 1]>=anom_thresh)[0][0]]
anom_idx = lc_and_hosts_df.iloc[np.where(pred_prob_anom[:, 1]>=anom_thresh)[0][0]] #+ diff
anom_idx_is = True
print("Anomalous during timeseries!")
except:
print(f"Prediction doesn't exceed anom_threshold of {anom_thresh}% for {anom_ztfid}.")
anom_idx_is = False
max_anom_score = max(pred_prob_anom[:, 1])
if not full_timeseries: # full LC ONLY
anom_obj_df = pd.DataFrame(anom_obj_df.iloc[-1]).T # last row of df to test "full LC only"
try:
pred_prob_anom = 100 * clf.predict_proba(anom_obj_df)
pred_prob_anom[:, 0] = [round(a, 1) for a in pred_prob_anom[:, 0]]
pred_prob_anom[:, 1] = [round(b, 1) for b in pred_prob_anom[:, 1]]
num_anom_epochs = len(np.where(pred_prob_anom[:, 1]>=anom_thresh)[0])
max_anom_score = max(pred_prob_anom[:, 1])
except:
print(f"{anom_ztfid} has some NaN host galaxy values from PS1 catalog. Skip!")
return
if num_anom_epochs == 1:
anom_idx_is = True
print("Anomalous at end of full LC!")
else:
print(f"Prediction doesn't exceed anom_threshold of {anom_thresh}% for {anom_ztfid}.")
anom_idx_is = False
if anom_idx_is == True:
anom_idx_is = "Other"
elif anom_idx_is == False:
anom_idx_is = "Normal"
else:
anom_idx_is = "???"
spec_class = meta["transient_spec_class"]
best_z = meta["redshift"]
z_type = meta["redshift_type"]
return anom_ztfid, anom_idx_is, max_anom_score, num_anom_epochs, spec_class, best_z, z_type
```
# Load YSE DR1
```python
# Import custom file for parsing data into:
# list of SN names, list of dicts (SN metadata), and list of dataframes (SN photometry)
from read_yse_ztf_snana_dir import read_YSE_ZTF_snana_dir
# Read in YSE DR1 data
full_snid_list, full_meta_list, full_df_list = read_YSE_ZTF_snana_dir(dir_name='./YSEDR1/yse_dr1_zenodo_snr_geq_4')
# Other useful imports
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sys
import random
from math import nan, isnan
import itertools
from collections import Counter
import matplotlib as mpl
mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command
# Make plots nice on Mac
%config InlineBackend.figure_format = 'retina'
```
<ipython-input-5-439435d26f7d>:21: MatplotlibDeprecationWarning: Support for setting the 'text.latex.preamble' or 'pgf.preamble' rcParam to a list of strings is deprecated since 3.3 and will be removed two minor releases later; set it to a single string instead.
mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command
```python
# Other useful functions
def get_param(meta_list, param):
param_list = []
for sn in meta_list:
if param == 'peak_abs_mag':
print(sn['object_id'], sn['transient_spec_class'])
try: param_list.append(float(sn[param]))
except: continue
else:
try:
param_list.append(sn[param])
except: print(f"WARNING: {param} not in parameter list. Check!")
return param_list
def get_SNclass_param(meta_list, param, ifstr, ifprint=False):
all_list, snII_list, snIa_list, snIbc_list, other_list = [], [], [], [], []
for spec_sn in meta_list:
if ifstr==True:
try: all_list.append(str(spec_sn[param]))
except: continue
if spec_sn[param] == 'SNII':
snII_list.append(str(spec_sn[param]))
elif spec_sn[param] == 'SNIa':
snIa_list.append(str(spec_sn[param]))
elif spec_sn[param] == 'SNIbc':
snIbc_list.append(str(spec_sn[param]))
else:
if ifprint==True: print(spec_sn[param])
other_list.append(str(spec_sn[param]))
else:
try: all_list.append(float(spec_sn[param]))
except: continue
if spec_sn['spectype_3class'] == 'SNII':
snII_list.append(float(spec_sn[param]))
elif spec_sn['spectype_3class'] == 'SNIa':
snIa_list.append(float(spec_sn[param]))
elif spec_sn['spectype_3class'] == 'SNIbc':
snIbc_list.append(float(spec_sn[param]))
else:
if ifprint==True: print(spec_sn['spectype_3class'])
other_list.append(float(spec_sn[param]))
return all_list, snII_list, snIa_list, snIbc_list, other_list
```
```python
spectype_3class_l = get_param(meta_list=full_meta_list, param='spectype_3class')
# Separate for spec and phot data to make some analyis easier
# Get indices of Spec, Phot Sne
spec_idx_l, phot_idx_l = [],[]
for i, cls in enumerate(spectype_3class_l):
if cls != 'NA':
spec_idx_l.append(i)
elif cls == 'NA':
phot_idx_l.append(i)
else: print("AHH!")
# Spectroscopic SNe only
spec_snid_list = [full_snid_list[i] for i in spec_idx_l]
spec_meta_list = [full_meta_list[i] for i in spec_idx_l]
spec_df_list = [full_df_list[i] for i in spec_idx_l]
# Photometric SNe only
phot_snid_list = [full_snid_list[i] for i in phot_idx_l]
phot_meta_list = [full_meta_list[i] for i in phot_idx_l]
phot_df_list = [full_df_list[i] for i in phot_idx_l]
```
```python
# list of SN names
full_snid_list[0:3]
```
['2019lbi', '2019pmd', '2019ppi']
```python
full_meta_list[0:3]
```
[{'object_id': '2019lbi',
'original_object_id': '2019lbi',
'ra': 190.088004,
'dec': 1.273998,
'mwebv': 0.015,
'redshift': 0.013,
'redshift_err': 0.005,
'redshift_type': 'SPEC-Z',
'redshift_frame': 'HELIO',
'photo_z': 0.038,
'photoz_err': 0.012,
'sn_offset': 14.831,
'host_gal_name': 'WISEA J124020.15+011624.2',
'host_gal_name_source': '(NED)',
'host_gal_z': 0.0158,
'host_gal_z_err': 0.005,
'host_gal_z_type': 'HOST-Z',
'host_gal_z_frame': 'HELIO',
'peakmjd': 58676.172,
'host_logmass': -99.0,
'peak_abs_mag': -16.708,
'transient_spec_class': 'SNII',
'spectype_3class': 'SNII',
'parsnip_pred_class': 'SNIa',
'parsnip_pred_conf': '49.7',
'parsnip_s1': -0.063,
'parsnip_s1_err': 0.127,
'parsnip_s2': 0.758,
'parsnip_s2_err': 0.273,
'parsnip_s3': -0.112,
'parsnip_s3_err': 0.124,
'superraenn_pred_class': 'SNII',
'superraenn_pred_conf': '91.7',
'ztf_zeropoint': 27.5,
'peakSNR': 61.164,
'max_mjd_gap': 23.018,
'nobs_before_peak': 0,
'nobs_to_peak': 1,
'nobs_after_peak': 92,
'peakmag': 17.042,
'peakflt': 'X',
'peakmag_rY': 17.116,
'peakflt_rY': 'Y',
'passbands': 'zXiYrg',
'num_points': 93},
{'object_id': '2019pmd',
'original_object_id': '2019pmd',
'ra': 49.599161,
'dec': -1.930453,
'mwebv': 0.073,
'redshift': 0.022,
'redshift_err': 0.005,
'redshift_type': 'SPEC-Z',
'redshift_frame': 'HELIO',
'photo_z': 0.026,
'photoz_err': 0.008,
'sn_offset': 4.617,
'host_gal_name': 'KUG 0315-021B',
'host_gal_name_source': '(NED)',
'host_gal_z': 0.022,
'host_gal_z_err': 0.005,
'host_gal_z_type': 'HOST-Z',
'host_gal_z_frame': 'HELIO',
'peakmjd': 58741.441,
'host_logmass': -99.0,
'peak_abs_mag': -18.397,
'transient_spec_class': 'SNIa-norm',
'spectype_3class': 'SNIa',
'parsnip_pred_class': 'SNIa',
'parsnip_pred_conf': '97.2',
'parsnip_s1': -0.13,
'parsnip_s1_err': 0.02,
'parsnip_s2': 0.016,
'parsnip_s2_err': 0.044,
'parsnip_s3': 1.408,
'parsnip_s3_err': 0.034,
'superraenn_pred_class': 'SNIa',
'superraenn_pred_conf': '83.4',
'ztf_zeropoint': 27.5,
'peakSNR': 122.151,
'max_mjd_gap': 16.879,
'nobs_before_peak': 9,
'nobs_to_peak': 10,
'nobs_after_peak': 54,
'peakmag': 16.512,
'peakflt': 'Y',
'peakmag_rY': 16.512,
'peakflt_rY': 'Y',
'passbands': 'zXiYrg',
'num_points': 64},
{'object_id': '2019ppi',
'original_object_id': '2019ppi',
'ra': 133.897475,
'dec': 49.160259,
'mwebv': 0.021,
'redshift': 0.052,
'redshift_err': 0.005,
'redshift_type': 'SPEC-Z',
'redshift_frame': 'HELIO',
'photo_z': 0.135,
'photoz_err': 0.056,
'sn_offset': 5.068,
'host_gal_name': 'SDSS J085534.94+490936.9',
'host_gal_name_source': '(NED)',
'host_gal_z': -99.0,
'host_gal_z_err': 0.005,
'host_gal_z_type': 'HOST-Z',
'host_gal_z_frame': 'HELIO',
'peakmjd': 58759.509,
'host_logmass': -99.0,
'peak_abs_mag': -18.111,
'transient_spec_class': 'SNII',
'spectype_3class': 'SNII',
'parsnip_pred_class': 'SNII',
'parsnip_pred_conf': '49.5',
'parsnip_s1': -1.503,
'parsnip_s1_err': 0.041,
'parsnip_s2': 0.674,
'parsnip_s2_err': 0.078,
'parsnip_s3': -0.758,
'parsnip_s3_err': 0.041,
'superraenn_pred_class': 'SNIbc',
'superraenn_pred_conf': '77.1',
'ztf_zeropoint': 27.5,
'peakSNR': 31.968,
'max_mjd_gap': 11.965,
'nobs_before_peak': 15,
'nobs_to_peak': 16,
'nobs_after_peak': 80,
'peakmag': 18.712,
'peakflt': 'Y',
'peakmag_rY': 18.712,
'peakflt_rY': 'Y',
'passbands': 'zXiYrg',
'num_points': 96}]
```python
# list of dataframes (SN photometry)
full_df_list[0:3]
```
[ MJD PASSBAND FLUX FLUXERR MAG MAGERR PHOTFLAG
0 58676.172 X 15240.973 258.852 17.042 0.018 0x00
1 58676.192 Y 14246.757 232.926 17.116 0.018 0x00
2 58679.187 X 14460.171 311.803 17.100 0.023 0x00
3 58679.191 Y 13726.576 295.383 17.156 0.023 0x00
4 58797.545 Y 6190.193 240.414 18.021 0.042 0x00
.. ... ... ... ... ... ... ...
88 59032.280 z 618.992 98.058 20.521 0.172 0x00
89 59039.300 r 553.884 83.581 20.641 0.164 0x00
90 59041.280 r 583.879 81.826 20.584 0.152 0x00
91 59041.280 z 519.282 103.647 20.711 0.217 0x00
92 59048.280 r 497.846 59.789 20.757 0.130 0x00
[93 rows x 7 columns],
MJD PASSBAND FLUX FLUXERR MAG MAGERR PHOTFLAG
0 58725.462 X 1094.346 78.982 19.902 0.078 0x00
1 58730.443 X 5213.041 120.359 18.207 0.025 0x00
2 58730.496 Y 8545.246 133.232 17.671 0.017 0x00
3 58732.438 X 8121.221 124.098 17.726 0.017 0x00
4 58732.481 Y 12968.024 141.770 17.218 0.012 0x00
.. ... ... ... ... ... ... ...
59 58886.162 X 1253.915 307.993 19.754 0.267 0x00
60 58891.157 X 616.980 119.216 20.577 0.227 0x00
61 58893.148 X 514.027 113.123 20.723 0.239 0x00
62 58894.149 X 486.825 110.338 20.782 0.246 0x00
63 58899.151 X 493.387 114.321 20.767 0.252 0x00
[64 rows x 7 columns],
MJD PASSBAND FLUX FLUXERR MAG MAGERR PHOTFLAG
0 58733.493 X 1994.766 174.776 19.250 0.095 0x00
1 58733.511 Y 2543.512 150.846 18.986 0.064 0x00
2 58736.487 X 2230.263 117.440 19.129 0.057 0x00
3 58736.509 Y 2846.913 153.158 18.864 0.058 0x00
4 58737.506 X 2154.592 103.708 19.167 0.052 0x00
.. ... ... ... ... ... ... ...
91 58893.277 Y 330.078 75.871 21.209 0.252 0x00
92 58895.262 Y 341.423 74.927 21.167 0.238 0x00
93 58896.275 Y 316.788 77.259 21.256 0.271 0x00
94 58898.305 Y 572.566 136.950 20.605 0.260 0x00
95 58904.205 Y 313.690 78.300 21.262 0.272 0x00
[96 rows x 7 columns]]
```python
# TODO: get ZTFID...
```
```python
```
## Load pickle
```python
n_estimators=100 #3500
max_depth=35 #35
random_state=11
max_features=35 # {“sqrt”, “log2”, None}, int or float, default=”sqrt” - sqrt(120) ~ 10
#class_weight={"None": 1, "SLSN-II": 1, "SN II": 1, 'SN IIP': 1, 'SN IIb': 1, 'SN IIn': 1, 'SN Ia': 1,
# 'SN Ia-91T-like': 1, 'SN Ib': 1, 'SN Ic': 1, 'SN Ic-BL': 1, 'TDE': 1} #"balanced"
class_weight={"Normal": 1, "Other": 1} #"balanced"
figure_path = f"/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/RFC/SMOTE_train_test_70-30_min14_kneighbors8/cls=binary_n_estimators={n_estimators}_max_depth={max_depth}_rs={random_state}_max_feats={max_features}_cw=balanced/figures"
model_path = f"/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/RFC/SMOTE_train_test_70-30_min14_kneighbors8/cls=binary_n_estimators={n_estimators}_max_depth={max_depth}_rs={random_state}_max_feats={max_features}_cw=balanced/model"
if not os.path.exists(figure_path):
os.makedirs(figure_path)
if not os.path.exists(model_path):
os.makedirs(model_path)
cm_path = f"{figure_path}/confusion_matrix/"
if not os.path.exists(cm_path):
os.makedirs(cm_path)
```
```python
with open(f'{model_path}/cls=binary_n_estimators={n_estimators}_max_depth={max_depth}_rs={random_state}_max_feats={max_features}_cw=balanced.pkl', 'rb') as f:
clf = pickle.load(f)
```
```python
```
# make timeseries files of YSE DR1
```python
import requests
from requests.auth import HTTPBasicAuth
import astropy.table as at
import matplotlib
from matplotlib.transforms import Bbox
from matplotlib.backends.backend_pdf import PdfPages
from astropy.io import fits
from astropy.wcs import WCS
from astropy.coordinates import SkyCoord
from astropy.coordinates import Angle
import astropy.units as u
from astropy.visualization import PercentileInterval, AsinhStretch
from astroquery.mast import Catalogs
from astroquery.sdss import SDSS
from astroquery.simbad import Simbad
import light_curve as lc
from itertools import chain
import light_curve as lc
from astropy.table import MaskedColumn
from PIL import Image
import os
import sys
import shutil
import glob
import json
import math
import warnings
warnings.filterwarnings("ignore")
import time
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
import os
import sys
import annoy
from annoy import AnnoyIndex
import random
from IPython.display import display_markdown
from collections import Counter
from sklearn import preprocessing
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.metrics import classification_report
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from scipy.spatial import cKDTree
from sklearn.decomposition import PCA
from sklearn.decomposition import SparsePCA
from alerce.core import Alerce
alerce = Alerce()
import antares_client
df_path = "/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries"
plt.style.use('fig_publication.mplstyle')
%config InlineBackend.figure_format = 'retina' #for MacOS, make plots crisp
```
```python
def replace_magn_with_flux(s):
if 'magnitude' in s:
return s.replace('magnitudes', 'fluxes').replace('magnitude', 'flux')
return f'{s} for flux light curve'
def create_base_features_class(
magn_extractor,
flux_extractor,
bands=('R', 'g',),
):
feature_names = ([f'{name}_magn' for name in magn_extractor.names]
+ [f'{name}_flux' for name in flux_extractor.names])
property_names = {band: [f'feature_{name}_{band}'.lower()
for name in feature_names]
for band in bands}
features_count = len(feature_names)
return feature_names, property_names, features_count
MAGN_EXTRACTOR = lc.Extractor(
lc.Amplitude(),
lc.AndersonDarlingNormal(),
lc.BeyondNStd(1.0),
lc.BeyondNStd(2.0),
lc.Cusum(),
lc.EtaE(),
lc.InterPercentileRange(0.02),
lc.InterPercentileRange(0.1),
lc.InterPercentileRange(0.25),
lc.Kurtosis(),
lc.LinearFit(),
lc.LinearTrend(),
lc.MagnitudePercentageRatio(0.4, 0.05),
lc.MagnitudePercentageRatio(0.2, 0.05),
lc.MaximumSlope(),
lc.Mean(),
lc.MedianAbsoluteDeviation(),
lc.PercentAmplitude(),
lc.PercentDifferenceMagnitudePercentile(0.05),
lc.PercentDifferenceMagnitudePercentile(0.1),
lc.MedianBufferRangePercentage(0.1),
lc.MedianBufferRangePercentage(0.2),
lc.Periodogram(
peaks=5,
resolution=10.0,
max_freq_factor=2.0,
nyquist='average',
fast=True,
features=(
lc.Amplitude(),
lc.BeyondNStd(2.0),
lc.BeyondNStd(3.0),
lc.StandardDeviation(),
),
),
lc.ReducedChi2(),
lc.Skew(),
lc.StandardDeviation(),
lc.StetsonK(),
lc.WeightedMean(),
)
FLUX_EXTRACTOR = lc.Extractor(
lc.AndersonDarlingNormal(),
lc.Cusum(),
lc.EtaE(),
lc.ExcessVariance(),
lc.Kurtosis(),
lc.MeanVariance(),
lc.ReducedChi2(),
lc.Skew(),
lc.StetsonK(),
)
def remove_simultaneous_alerts(table):
"""Remove alert duplicates"""
dt = np.diff(table['ant_mjd'], append=np.inf)
return table[dt != 0]
def get_detections(photometry, band):
"""Extract clean light curve in given band from locus photometry"""
band_lc = photometry[(photometry['ant_passband'] == band) & (~photometry['ant_mag'].isna())]
idx = ~MaskedColumn(band_lc['ant_mag']).mask
detections = remove_simultaneous_alerts(band_lc[idx])
return detections
```
```python
import astro_ghost
# from astro_ghost.PS1QueryFunctions import getAllPostageStamps
# from astro_ghost.TNSQueryFunctions import getTNSSpectra
# from astro_ghost.NEDQueryFunctions import getNEDSpectra
from astro_ghost.ghostHelperFunctions import getTransientHosts, getGHOST
from astropy.coordinates import SkyCoord
from astropy import units as u
from datetime import datetime
import tempfile
# Throw RA/DEC into ghost with just DLR method, gentle starcut
# Sets environ var to find ghost.csv
os.environ['GHOST_PATH'] = './host_info'
# Then don't use getGHOST(real=True, verbose=verbose)
getGHOST(real=True,verbose=False)
```
GHOST database already exists in the install path!
```python
astro_ghost.__version__
```
'2.0.16'
```python
def extract_lc_and_host_features_YSEDR1(IAU_name, ysedr1_lightcurve, spec_class, ra, dec, show_lc=False, show_host=False):
IAU_name = IAU_name
spec_class = spec_class
df_path = "/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries"
min_obs_count=4
lightcurve = ysedr1_lightcurve
feature_names, property_names, features_count = create_base_features_class(MAGN_EXTRACTOR, FLUX_EXTRACTOR)
g_obs = list(ysedr1_lightcurve[ysedr1_lightcurve.PASSBAND == "g"].MJD)
r_obs = list(ysedr1_lightcurve[ysedr1_lightcurve.PASSBAND == "R"].MJD)
mjd_l = sorted(g_obs+r_obs)
lc_properties_d_l = []
len_det_counter_r,len_det_counter_g = 0,0
all_detections = ysedr1_lightcurve
for ob, mjd in enumerate(mjd_l): # requires 4 obs
# do time evolution of detections - in chunks
detections_pb = all_detections[all_detections["MJD"].values <= mjd]
#print(detections)
lc_properties_d={}
for band, names in property_names.items():
detections = detections_pb[detections_pb["PASSBAND"] == band]
# Ensure locus has >3 obs for calculation
if (len(detections) < min_obs_count):
continue
#print(detections)
t = detections['MJD'].values
m = detections['MAG'].values
merr = detections['MAGERR'].values
flux = detections['FLUX'].values
fluxerr = detections['FLUXERR'].values
try:
magn_features = MAGN_EXTRACTOR(
t,
m,
merr,
fill_value=None,
)
except:
print(f"{IAU_name} is maybe not sorted?")
return
flux_features = FLUX_EXTRACTOR(
t,
flux,
fluxerr,
fill_value=None,
)
# After successfully calculating features, set locus properties and tag
lc_properties_d["obs_num"] = int(ob)
lc_properties_d["mjd_cutoff"] = mjd
lc_properties_d["ztf_object_id"] = IAU_name
#print(band, m)
for name, value in zip(names, chain(magn_features, flux_features)):
lc_properties_d[name] = value
#if name == "feature_amplitude_magn_g": print(m, value, band)
#print("%%%%%%%%")
lc_properties_d_l.append(lc_properties_d)
lc_properties_d_l = [d for d in lc_properties_d_l if d]
lc_properties_df = pd.DataFrame(lc_properties_d_l)
if len(lc_properties_df) == 0:
print(f"Not enough obs for {IAU_name}. pass!\n")
return
print(f"Extracted LC features for {IAU_name}!")
# Get GHOST features
ra,dec=float(ra),float(dec)
snName=[IAU_name, IAU_name]
snCoord = [SkyCoord(ra*u.deg, dec*u.deg, frame='icrs'), SkyCoord(ra*u.deg, dec*u.deg, frame='icrs')]
with tempfile.TemporaryDirectory() as tmp:
try:
hosts = getTransientHosts(transientName=snName, snCoord=snCoord, GLADE=True, verbose=0,
starcut='gentle', ascentMatch=False, savepath=tmp, redo_search=False)
except:
print(f"GHOST error for {IAU_name}. Retry without GLADE. \n")
hosts = getTransientHosts(transientName=snName, snCoord=snCoord, GLADE=False, verbose=0,
starcut='gentle', ascentMatch=False, savepath=tmp, redo_search=False)
if len(hosts) > 1:
hosts = pd.DataFrame(hosts.loc[0]).T
hosts_df = hosts[feature_names_hostgal]
hosts_df = hosts_df[~hosts_df.isnull().any(axis=1)]
if len(hosts_df) < 1:
# if any features are nan, we can't use as input
print(f"Some features are NaN for {IAU_name}. Skip!\n")
return
if show_host:
print(f'http://ps1images.stsci.edu/cgi-bin/ps1cutouts?pos={hosts.raMean.values[0]}+{hosts.decMean.values[0]}&filter=color')
# Define the label array
label_arr = np.array(['ILRT', 'SLSN-I', 'SLSN-II', 'SN', 'SNII', 'SNII-pec', 'SNIIP',
'SNIIb', 'SNIIn', 'SNIa-norm', 'SN Ia-91T-like', 'SNIa-91bg-like',
'SNIa-CSM', 'SNIa-Ca-rich', 'SNIa-pec', 'SN Iax[02cx-like]',
'SNIb', 'SNIb-Ca-rich', 'SNIb/c', 'SNIbc', 'SNIbn', 'SNIc', 'SNIc-BL',
'SNIcn', 'TDE', "NA", "PHOT"])
# Define the spectroscopic class
spec_class = spec_class
# Find the index of the spectroscopic class in the label array
class_index = np.where(label_arr == spec_class)[0]
# Create a one-hot encoded array with all zeros
onehot_array = np.zeros(len(label_arr), dtype=float)
# Set the corresponding index to 1
onehot_array[class_index] = 1
# Reshape the array to have a shape of (1, num_classes)
onehot_array = onehot_array.reshape(1, -1)
onehot_df = pd.DataFrame(np.array(onehot_array, dtype=float), columns=label_arr)
onehot_df = pd.concat([onehot_df] * len(lc_properties_df), ignore_index=True)
hosts_df = hosts[feature_names_hostgal]
hosts_df = pd.concat([hosts_df] * len(lc_properties_df), ignore_index=True)
lc_and_hosts_df = pd.concat([lc_properties_df, hosts_df, onehot_df], axis=1)
lc_and_hosts_df = lc_and_hosts_df.set_index('ztf_object_id')
lc_and_hosts_df.to_csv(f'{df_path}/{lc_and_hosts_df.index[0]}_timeseries.csv')
print(f"Saved results for {IAU_name}!\n")
```
%%time
for i, (name, lc, meta) in enumerate(zip(full_snid_list[1805:], full_df_list[1805:], full_meta_list[1805:])):
lc_grXY = lc[(lc.PASSBAND != 'i') & (lc.PASSBAND != 'z')].reset_index(drop=True)
# (g, r, i or z for PS1-griz, respectively; X for ZTF-g; Y for ZTF-r)
lc_grXY["PASSBAND"] = lc_grXY["PASSBAND"].map({"X": "g", "g": "g", "Y": "R", "r": "R"})
if i % 50 == 0: print(i)
# extract_lc_and_host_features_YSEDR1(IAU_name=name,
# ysedr1_lightcurve=lc_grXY,
# spec_class=meta["transient_spec_class"],
# ra=meta["ra"],
# dec=meta["dec"],
# show_lc=False,
# show_host=False)
if os.path.exists(f"/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries/{name}_timeseries.csv"):
print(f'{name} is already made. Continue!\n')
continue
else:
# make timeseries files
extract_lc_and_host_features_YSEDR1(IAU_name=name,
ysedr1_lightcurve=lc_grXY,
spec_class=meta["transient_spec_class"],
ra=meta["ra"],
dec=meta["dec"],
show_lc=False,
show_host=False)
# Run AD model for YSE DR1
```python
%%time
fps = glob.glob("/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries/*")
ztfid_l, anom_idx_is_l, max_anom_score_l, num_anom_epochs_l, spec_class_l, best_z_l, z_type_l = [], [], [], [], [], [], []
for fp in fps:
ztfid = fp.split('/')[-1].split('_')[0]
for meta in full_meta_list:
if ztfid == meta["object_id"]:
try:
anom_ztfid, anom_idx_is, max_anom_score, num_anom_epochs, spec_class, best_z, z_type = save_RFC_prob_vs_lc_ztfid(anom_ztfid=ztfid,
anom_thresh=50,
meta=meta,
df_path = "/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries",
clf=clf,
full_timeseries=False
)
except: continue
ztfid_l.append(anom_ztfid), anom_idx_is_l.append(anom_idx_is), max_anom_score_l.append(max_anom_score),
num_anom_epochs_l.append(num_anom_epochs), spec_class_l.append(spec_class), best_z_l.append(best_z), z_type_l.append(z_type)
```
Prediction doesn't exceed anom_threshold of 50% for 2021iyu.
Prediction doesn't exceed anom_threshold of 50% for 2020van.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020mvu.
Prediction doesn't exceed anom_threshold of 50% for 2020olk.
Prediction doesn't exceed anom_threshold of 50% for 2021bbp.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020vuc.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021lzg.
Prediction doesn't exceed anom_threshold of 50% for 2021kbn.
Prediction doesn't exceed anom_threshold of 50% for 2020mks.
Prediction doesn't exceed anom_threshold of 50% for 2020qda.
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qqi.
Anomalous at end of full LC!
2021rge has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020jli.
Prediction doesn't exceed anom_threshold of 50% for 2020zqn.
Prediction doesn't exceed anom_threshold of 50% for 2021vhk.
2021oqc has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020pix.
Prediction doesn't exceed anom_threshold of 50% for 2021abj.
Prediction doesn't exceed anom_threshold of 50% for 2021rbq.
Prediction doesn't exceed anom_threshold of 50% for 2020two.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021sil.
2020aeyo has some NaN LC features. Skip!
2021qsr has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020nki.
Prediction doesn't exceed anom_threshold of 50% for 2019zag.
2020lez has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021gdv.
2021abgy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020uwl.
Prediction doesn't exceed anom_threshold of 50% for 2020dxa.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020wxz.
Prediction doesn't exceed anom_threshold of 50% for 2020aov.
Prediction doesn't exceed anom_threshold of 50% for 2020rom.
2020zrv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021iww.
Prediction doesn't exceed anom_threshold of 50% for 2020sxs.
Prediction doesn't exceed anom_threshold of 50% for 2020tkd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020vzd.
Prediction doesn't exceed anom_threshold of 50% for 2021qgw.
Prediction doesn't exceed anom_threshold of 50% for 2021xgi.
Prediction doesn't exceed anom_threshold of 50% for 2020jts.
Prediction doesn't exceed anom_threshold of 50% for 2020kax.
Prediction doesn't exceed anom_threshold of 50% for 2021kdb.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021acfw.
2020pip has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020amc.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021lhf.
Prediction doesn't exceed anom_threshold of 50% for 2021kpo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020lly.
Prediction doesn't exceed anom_threshold of 50% for 2021bpq.
Prediction doesn't exceed anom_threshold of 50% for 2021nmk.
Prediction doesn't exceed anom_threshold of 50% for 2020rcr.
Prediction doesn't exceed anom_threshold of 50% for 2021mqr.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020rii.
Prediction doesn't exceed anom_threshold of 50% for 2021amm.
Prediction doesn't exceed anom_threshold of 50% for 2020tle.
Prediction doesn't exceed anom_threshold of 50% for 2021zzv.
Prediction doesn't exceed anom_threshold of 50% for 2020ebm.
Prediction doesn't exceed anom_threshold of 50% for 2020iul.
Prediction doesn't exceed anom_threshold of 50% for 2021sne.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
2021lra has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020abrb.
Prediction doesn't exceed anom_threshold of 50% for 2020ikz.
Prediction doesn't exceed anom_threshold of 50% for 2021qas.
Prediction doesn't exceed anom_threshold of 50% for 2020advk.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021doz.
2021aamp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020iio.
Prediction doesn't exceed anom_threshold of 50% for 2021abwm.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021kcc.
Prediction doesn't exceed anom_threshold of 50% for 2021xic.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ltk.
2020aaca has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021yuc.
Prediction doesn't exceed anom_threshold of 50% for 2021obg.
Prediction doesn't exceed anom_threshold of 50% for 2021wlq.
Prediction doesn't exceed anom_threshold of 50% for 2021aox.
Prediction doesn't exceed anom_threshold of 50% for 2020aewm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021isf.
2020bso has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021log.
Prediction doesn't exceed anom_threshold of 50% for 2020lac.
Prediction doesn't exceed anom_threshold of 50% for 2021in.
Prediction doesn't exceed anom_threshold of 50% for 2021buj.
Prediction doesn't exceed anom_threshold of 50% for 2021gik.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021oax.
2021kiw has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kbi.
Prediction doesn't exceed anom_threshold of 50% for 2020abpx.
Prediction doesn't exceed anom_threshold of 50% for 2021hpz.
Prediction doesn't exceed anom_threshold of 50% for 2021apd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020jpm.
Prediction doesn't exceed anom_threshold of 50% for 2021mge.
Prediction doesn't exceed anom_threshold of 50% for 2020acyp.
Prediction doesn't exceed anom_threshold of 50% for 2020wwr.
2021aow has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kwa.
Prediction doesn't exceed anom_threshold of 50% for 2021iyr.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aevg.
Prediction doesn't exceed anom_threshold of 50% for 2020ovk.
Prediction doesn't exceed anom_threshold of 50% for 2020acwz.
Prediction doesn't exceed anom_threshold of 50% for 2021ort.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020dyc.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020wyx.
2020irb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021iwx.
Prediction doesn't exceed anom_threshold of 50% for 2019lbi.
Prediction doesn't exceed anom_threshold of 50% for 2020abul.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ihk.
2020oix has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020achi.
Prediction doesn't exceed anom_threshold of 50% for 2021aceh.
Prediction doesn't exceed anom_threshold of 50% for 2021abpc.
Prediction doesn't exceed anom_threshold of 50% for 2021txi.
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
2020hra has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021upd.
Prediction doesn't exceed anom_threshold of 50% for 2020aapb.
Prediction doesn't exceed anom_threshold of 50% for 2020iqr.
Prediction doesn't exceed anom_threshold of 50% for 2020bta.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021lhi.
Prediction doesn't exceed anom_threshold of 50% for 2021vcz.
Prediction doesn't exceed anom_threshold of 50% for 2020nis.
Prediction doesn't exceed anom_threshold of 50% for 2020gcv.
Prediction doesn't exceed anom_threshold of 50% for 2021tfw.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021uqi.
2019wcb has some NaN LC features. Skip!
2021afeb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021acgu.
Prediction doesn't exceed anom_threshold of 50% for 2021adug.
Prediction doesn't exceed anom_threshold of 50% for 2020woh.
Prediction doesn't exceed anom_threshold of 50% for 2020oa.
2020lyv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021ofq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021ypp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020juq.
Prediction doesn't exceed anom_threshold of 50% for 2021shf.
Prediction doesn't exceed anom_threshold of 50% for 2020kiq.
Prediction doesn't exceed anom_threshold of 50% for 2021gpt.
Prediction doesn't exceed anom_threshold of 50% for 2020bct.
2020vzc has some NaN LC features. Skip!
2020kvb has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tkc.
Prediction doesn't exceed anom_threshold of 50% for 2021bsf.
Prediction doesn't exceed anom_threshold of 50% for 2020qwb.
Prediction doesn't exceed anom_threshold of 50% for 2020iyt.
Prediction doesn't exceed anom_threshold of 50% for 2020jjb.
Prediction doesn't exceed anom_threshold of 50% for 2020roj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021jox.
Prediction doesn't exceed anom_threshold of 50% for 2021tjh.
Prediction doesn't exceed anom_threshold of 50% for 2020qxm.
Prediction doesn't exceed anom_threshold of 50% for 2020ndi.
2020qrv has some NaN LC features. Skip!
2021ctd has some NaN LC features. Skip!
2021wlv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021wms.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021abwj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021lzh.
2020aehy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019yjl.
Prediction doesn't exceed anom_threshold of 50% for 2021jvo.
Prediction doesn't exceed anom_threshold of 50% for 2020advl.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020oot.
Prediction doesn't exceed anom_threshold of 50% for 2021ojn.
Prediction doesn't exceed anom_threshold of 50% for 2021sdy.
Prediction doesn't exceed anom_threshold of 50% for 2020kpf.
Prediction doesn't exceed anom_threshold of 50% for 2020tfy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021mzk.
Prediction doesn't exceed anom_threshold of 50% for 2020hik.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020rok.
Prediction doesn't exceed anom_threshold of 50% for 2021joy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020kvc.
Prediction doesn't exceed anom_threshold of 50% for 2021abgw.
2021van has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020iyu.
Prediction doesn't exceed anom_threshold of 50% for 2021vku.
Prediction doesn't exceed anom_threshold of 50% for 2021zvo.
Prediction doesn't exceed anom_threshold of 50% for 2020qhp.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020fhj.
Prediction doesn't exceed anom_threshold of 50% for 2021ypq.
Prediction doesn't exceed anom_threshold of 50% for 2021qed.
Prediction doesn't exceed anom_threshold of 50% for 2020nva.
Prediction doesn't exceed anom_threshold of 50% for 2020twa.
Prediction doesn't exceed anom_threshold of 50% for 2021sjr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021ygl.
Prediction doesn't exceed anom_threshold of 50% for 2021hid.
Prediction doesn't exceed anom_threshold of 50% for 2019wcc.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ztz.
Prediction doesn't exceed anom_threshold of 50% for 2021bzl.
Prediction doesn't exceed anom_threshold of 50% for 2021xqw.
Prediction doesn't exceed anom_threshold of 50% for 2020pfy.
Prediction doesn't exceed anom_threshold of 50% for 2021hlp.
2020qqg has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ito.
2020tfx has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020kqb.
Prediction doesn't exceed anom_threshold of 50% for 2020zkg.
Prediction doesn't exceed anom_threshold of 50% for 2020nrp.
2020abrd has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021rrc.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021wog.
Prediction doesn't exceed anom_threshold of 50% for 2020oou.
Prediction doesn't exceed anom_threshold of 50% for 2021oaq.
2020oen has some NaN LC features. Skip!
2021abwk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kld.
Prediction doesn't exceed anom_threshold of 50% for 2021lqw.
Prediction doesn't exceed anom_threshold of 50% for 2020qfz.
Prediction doesn't exceed anom_threshold of 50% for 2021ils.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021hzh.
Prediction doesn't exceed anom_threshold of 50% for 2021wmr.
Prediction doesn't exceed anom_threshold of 50% for 2021cte.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021acjn has some NaN LC features. Skip!
2021mgl has some NaN LC features. Skip!
2021jw has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021tji.
2021iys has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020adkc.
Prediction doesn't exceed anom_threshold of 50% for 2021ovd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021owa.
Prediction doesn't exceed anom_threshold of 50% for 2020acyq.
Prediction doesn't exceed anom_threshold of 50% for 2020jnz.
Prediction doesn't exceed anom_threshold of 50% for 2020rkr.
Prediction doesn't exceed anom_threshold of 50% for 2021aov.
Prediction doesn't exceed anom_threshold of 50% for 2021mxw.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jed.
Prediction doesn't exceed anom_threshold of 50% for 2021mgd.
Prediction doesn't exceed anom_threshold of 50% for 2020aahq.
2021sfe has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kgr.
Prediction doesn't exceed anom_threshold of 50% for 2020tzs.
2021vqz has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021accm.
Prediction doesn't exceed anom_threshold of 50% for 2021iok.
Prediction doesn't exceed anom_threshold of 50% for 2021seu.
Prediction doesn't exceed anom_threshold of 50% for 2020ebc.
Prediction doesn't exceed anom_threshold of 50% for 2021btn.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021abje.
Prediction doesn't exceed anom_threshold of 50% for 2021rgc.
Prediction doesn't exceed anom_threshold of 50% for 2020abhk.
Prediction doesn't exceed anom_threshold of 50% for 2020aapc.
Prediction doesn't exceed anom_threshold of 50% for 2020agv.
Prediction doesn't exceed anom_threshold of 50% for 2021aci.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020acun.
2020hfm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021fom.
Prediction doesn't exceed anom_threshold of 50% for 2020abwx.
Prediction doesn't exceed anom_threshold of 50% for 2021gyv.
Prediction doesn't exceed anom_threshold of 50% for 2021kor.
2021sjz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tvl.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020hpu.
Prediction doesn't exceed anom_threshold of 50% for 2020oiy.
Prediction doesn't exceed anom_threshold of 50% for 2020aqn.
Prediction doesn't exceed anom_threshold of 50% for 2020azp.
Prediction doesn't exceed anom_threshold of 50% for 2021scq.
Prediction doesn't exceed anom_threshold of 50% for 2021kfy.
2020imp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021iwy.
Prediction doesn't exceed anom_threshold of 50% for 2019uev.
Prediction doesn't exceed anom_threshold of 50% for 2020tip.
Prediction doesn't exceed anom_threshold of 50% for 2021bzk.
Prediction doesn't exceed anom_threshold of 50% for 2021pgu.
2021ygk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020amb.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020rmy.
Prediction doesn't exceed anom_threshold of 50% for 2020hro.
Prediction doesn't exceed anom_threshold of 50% for 2021dkj.
Prediction doesn't exceed anom_threshold of 50% for 2020ino.
2020vyu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019yfu.
Prediction doesn't exceed anom_threshold of 50% for 2020jtr.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021pzs.
Prediction doesn't exceed anom_threshold of 50% for 2020oiv.
Prediction doesn't exceed anom_threshold of 50% for 2021cqv.
Anomalous at end of full LC!
2020bii has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kcl.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020sga.
Prediction doesn't exceed anom_threshold of 50% for 2020abjq.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020vds.
Prediction doesn't exceed anom_threshold of 50% for 2020kvd.
Prediction doesn't exceed anom_threshold of 50% for 2020lnm.
Prediction doesn't exceed anom_threshold of 50% for 2020sxr.
Prediction doesn't exceed anom_threshold of 50% for 2020rol.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020her.
Prediction doesn't exceed anom_threshold of 50% for 2021pde.
Prediction doesn't exceed anom_threshold of 50% for 2021qvo.
Prediction doesn't exceed anom_threshold of 50% for 2020noq.
Prediction doesn't exceed anom_threshold of 50% for 2019tvv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020lky.
2021aoy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020dwg.
Prediction doesn't exceed anom_threshold of 50% for 2020jzx.
Anomalous at end of full LC!
2021wmu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021dgu.
Prediction doesn't exceed anom_threshold of 50% for 2020ltj.
Prediction doesn't exceed anom_threshold of 50% for 2021kcb.
Prediction doesn't exceed anom_threshold of 50% for 2021abwl.
2021sgo has some NaN LC features. Skip!
2021oav has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020advj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020psv.
Prediction doesn't exceed anom_threshold of 50% for 2021ryz.
Prediction doesn't exceed anom_threshold of 50% for 2020knv.
Prediction doesn't exceed anom_threshold of 50% for 2020nxl.
Prediction doesn't exceed anom_threshold of 50% for 2021ina.
2020abrc has some NaN LC features. Skip!
2021dph has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020opa.
Prediction doesn't exceed anom_threshold of 50% for 2020rcs.
Prediction doesn't exceed anom_threshold of 50% for 2021acwo.
Prediction doesn't exceed anom_threshold of 50% for 2020aczf.
Prediction doesn't exceed anom_threshold of 50% for 2020jmm.
2020tmi has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tfw.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021gcv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020skv.
Prediction doesn't exceed anom_threshold of 50% for 2020aejb.
Prediction doesn't exceed anom_threshold of 50% for 2020shf.
Prediction doesn't exceed anom_threshold of 50% for 2021kbo.
2020jzp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021mgc.
Prediction doesn't exceed anom_threshold of 50% for 2021actw.
2020jdf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021jkg.
Prediction doesn't exceed anom_threshold of 50% for 2021hdq.
Prediction doesn't exceed anom_threshold of 50% for 2020acyv.
Prediction doesn't exceed anom_threshold of 50% for 2020hju.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous at end of full LC!
2021dsp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021iyt.
Prediction doesn't exceed anom_threshold of 50% for 2021bwy.
Prediction doesn't exceed anom_threshold of 50% for 2020aewd.
2020ejj has some NaN LC features. Skip!
2020abjy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021qxm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020tvk.
Prediction doesn't exceed anom_threshold of 50% for 2020sdy.
Prediction doesn't exceed anom_threshold of 50% for 2021gyq.
Prediction doesn't exceed anom_threshold of 50% for 2020zoy.
Prediction doesn't exceed anom_threshold of 50% for 2020pvj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021pyk.
Prediction doesn't exceed anom_threshold of 50% for 2021aahm.
Prediction doesn't exceed anom_threshold of 50% for 2020rxy.
Prediction doesn't exceed anom_threshold of 50% for 2020szo.
Prediction doesn't exceed anom_threshold of 50% for 2021sun.
Prediction doesn't exceed anom_threshold of 50% for 2020btg.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020kzy.
Prediction doesn't exceed anom_threshold of 50% for 2020tlf.
Prediction doesn't exceed anom_threshold of 50% for 2021agu.
2020rij has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021dpj.
Prediction doesn't exceed anom_threshold of 50% for 2021dqo.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020awg.
2021doy has some NaN LC features. Skip!
2020odn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021aalv.
Prediction doesn't exceed anom_threshold of 50% for 2020abra.
Prediction doesn't exceed anom_threshold of 50% for 2020iky.
Prediction doesn't exceed anom_threshold of 50% for 2021inc.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abxz.
Prediction doesn't exceed anom_threshold of 50% for 2020txk.
Prediction doesn't exceed anom_threshold of 50% for 2021wlr.
Prediction doesn't exceed anom_threshold of 50% for 2020yza.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020abpt.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020neh.
Prediction doesn't exceed anom_threshold of 50% for 2021naw.
Prediction doesn't exceed anom_threshold of 50% for 2020sxp.
Prediction doesn't exceed anom_threshold of 50% for 2020kvf.
2021ndc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021qyb.
2020irn has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020hep.
Prediction doesn't exceed anom_threshold of 50% for 2020ovg.
Prediction doesn't exceed anom_threshold of 50% for 2020yac.
Prediction doesn't exceed anom_threshold of 50% for 2020ron.
Prediction doesn't exceed anom_threshold of 50% for 2020wfg.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020apf.
Prediction doesn't exceed anom_threshold of 50% for 2020juu.
Prediction doesn't exceed anom_threshold of 50% for 2021bmt.
Prediction doesn't exceed anom_threshold of 50% for 2020vzg.
Prediction doesn't exceed anom_threshold of 50% for 2020aym.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aeeg.
Prediction doesn't exceed anom_threshold of 50% for 2020nvd.
Anomalous at end of full LC!
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021itd.
Prediction doesn't exceed anom_threshold of 50% for 2021gmv.
2020kuv has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020llz.
Prediction doesn't exceed anom_threshold of 50% for 2020ftl.
Prediction doesn't exceed anom_threshold of 50% for 2020rfe.
Prediction doesn't exceed anom_threshold of 50% for 2020mza.
Prediction doesn't exceed anom_threshold of 50% for 2021ygi.
2019wcf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021wue.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020wzd.
2021yfd has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021yga.
Prediction doesn't exceed anom_threshold of 50% for 2020actn.
Prediction doesn't exceed anom_threshold of 50% for 2020zqm.
Prediction doesn't exceed anom_threshold of 50% for 2020bte.
2020lyz has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021unv.
Prediction doesn't exceed anom_threshold of 50% for 2021rh.
Prediction doesn't exceed anom_threshold of 50% for 2021oov.
Prediction doesn't exceed anom_threshold of 50% for 2020tuy.
Prediction doesn't exceed anom_threshold of 50% for 2021gzc.
Prediction doesn't exceed anom_threshold of 50% for 2020zmn.
Prediction doesn't exceed anom_threshold of 50% for 2021zcj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020ohy.
2020pux has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020acih.
Prediction doesn't exceed anom_threshold of 50% for 2021ufx.
Prediction doesn't exceed anom_threshold of 50% for 2020adc.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021uxn.
Prediction doesn't exceed anom_threshold of 50% for 2020rof.
2021abgz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021gnn.
Prediction doesn't exceed anom_threshold of 50% for 2020adkf.
2020acyt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020lks.
2020tee has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ivw.
Prediction doesn't exceed anom_threshold of 50% for 2021qve.
Prediction doesn't exceed anom_threshold of 50% for 2021iyv.
Prediction doesn't exceed anom_threshold of 50% for 2020nde.
2021kbm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020sia.
2020qfw has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cth.
Prediction doesn't exceed anom_threshold of 50% for 2021wlz.
Prediction doesn't exceed anom_threshold of 50% for 2021mga.
Prediction doesn't exceed anom_threshold of 50% for 2020ikq.
Prediction doesn't exceed anom_threshold of 50% for 2020zjo.
Prediction doesn't exceed anom_threshold of 50% for 2020mju.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021aru.
Prediction doesn't exceed anom_threshold of 50% for 2020rvq.
Prediction doesn't exceed anom_threshold of 50% for 2020oox.
Prediction doesn't exceed anom_threshold of 50% for 2021aafe.
Prediction doesn't exceed anom_threshold of 50% for 2020jlj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aaur.
Prediction doesn't exceed anom_threshold of 50% for 2021adew.
Prediction doesn't exceed anom_threshold of 50% for 2020rhg.
Prediction doesn't exceed anom_threshold of 50% for 2021pcn.
Prediction doesn't exceed anom_threshold of 50% for 2021efy.
2020ngu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020nlk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020urt.
Prediction doesn't exceed anom_threshold of 50% for 2020icp.
Prediction doesn't exceed anom_threshold of 50% for 2021tux.
Prediction doesn't exceed anom_threshold of 50% for 2021aaez.
Prediction doesn't exceed anom_threshold of 50% for 2021ctg.
2021wmp has some NaN LC features. Skip!
2021wlu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021aaoa.
Prediction doesn't exceed anom_threshold of 50% for 2020kns.
Prediction doesn't exceed anom_threshold of 50% for 2020zke.
Prediction doesn't exceed anom_threshold of 50% for 2021oas.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021pca.
Prediction doesn't exceed anom_threshold of 50% for 2021acvo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ghe.
Prediction doesn't exceed anom_threshold of 50% for 2020qqe.
Prediction doesn't exceed anom_threshold of 50% for 2021iqw.
Prediction doesn't exceed anom_threshold of 50% for 2021aaws.
2021hbx has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019wca.
Prediction doesn't exceed anom_threshold of 50% for 2020zj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019vuz.
Prediction doesn't exceed anom_threshold of 50% for 2020vfc.
Prediction doesn't exceed anom_threshold of 50% for 2020aeqm.
Prediction doesn't exceed anom_threshold of 50% for 2021nlj.
Prediction doesn't exceed anom_threshold of 50% for 2020eru.
2021aaie has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020iga.
Prediction doesn't exceed anom_threshold of 50% for 2020kbl.
Prediction doesn't exceed anom_threshold of 50% for 2020kir.
2021she has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021lta.
Prediction doesn't exceed anom_threshold of 50% for 2020jur.
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020iri.
Prediction doesn't exceed anom_threshold of 50% for 2020lom.
Prediction doesn't exceed anom_threshold of 50% for 2020kva.
Prediction doesn't exceed anom_threshold of 50% for 2020lev.
Prediction doesn't exceed anom_threshold of 50% for 2020bwr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aesp.
Prediction doesn't exceed anom_threshold of 50% for 2021qxh.
Prediction doesn't exceed anom_threshold of 50% for 2020add.
Prediction doesn't exceed anom_threshold of 50% for 2020mms.
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021scs.
Prediction doesn't exceed anom_threshold of 50% for 2020hrb.
Prediction doesn't exceed anom_threshold of 50% for 2020dec.
2021sjx has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kzx.
Prediction doesn't exceed anom_threshold of 50% for 2020thx.
Prediction doesn't exceed anom_threshold of 50% for 2020kuy.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019xbj.
Prediction doesn't exceed anom_threshold of 50% for 2020ite.
Prediction doesn't exceed anom_threshold of 50% for 2021dpe.
Prediction doesn't exceed anom_threshold of 50% for 2021im.
Prediction doesn't exceed anom_threshold of 50% for 2021rga.
Prediction doesn't exceed anom_threshold of 50% for 2021rfd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020acdu.
Prediction doesn't exceed anom_threshold of 50% for 2021dov.
Prediction doesn't exceed anom_threshold of 50% for 2020aajf.
Prediction doesn't exceed anom_threshold of 50% for 2021inl.
Prediction doesn't exceed anom_threshold of 50% for 2021uip.
2021wmx has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021qht.
Prediction doesn't exceed anom_threshold of 50% for 2020icx.
Prediction doesn't exceed anom_threshold of 50% for 2021zfy.
Prediction doesn't exceed anom_threshold of 50% for 2021tup.
Prediction doesn't exceed anom_threshold of 50% for 2020shc.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021kbj.
Prediction doesn't exceed anom_threshold of 50% for 2020teb.
2021abbi has some NaN LC features. Skip!
2021aot has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020jnx.
Prediction doesn't exceed anom_threshold of 50% for 2020acys.
2020kux has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020thy.
Prediction doesn't exceed anom_threshold of 50% for 2021abor.
Prediction doesn't exceed anom_threshold of 50% for 2020abim.
Prediction doesn't exceed anom_threshold of 50% for 2020iqp.
Prediction doesn't exceed anom_threshold of 50% for 2020sex.
Prediction doesn't exceed anom_threshold of 50% for 2020xku.
Prediction doesn't exceed anom_threshold of 50% for 2021aabr.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020uaq.
Prediction doesn't exceed anom_threshold of 50% for 2020epi.
Prediction doesn't exceed anom_threshold of 50% for 2020aefy.
Prediction doesn't exceed anom_threshold of 50% for 2020igh.
Prediction doesn't exceed anom_threshold of 50% for 2021scr.
Prediction doesn't exceed anom_threshold of 50% for 2021mim.
2021rui has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020azs.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020achk.
Prediction doesn't exceed anom_threshold of 50% for 2020wyz.
Prediction doesn't exceed anom_threshold of 50% for 2019wkf.
Prediction doesn't exceed anom_threshold of 50% for 2020acwx.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021qxi.
Prediction doesn't exceed anom_threshold of 50% for 2020jny.
Prediction doesn't exceed anom_threshold of 50% for 2020aavd.
Prediction doesn't exceed anom_threshold of 50% for 2020fsc.
Prediction doesn't exceed anom_threshold of 50% for 2020rkq.
2020acyr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ivq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020qsy.
2021iyp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kry.
Prediction doesn't exceed anom_threshold of 50% for 2021sff.
Prediction doesn't exceed anom_threshold of 50% for 2021xin.
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jpo.
Prediction doesn't exceed anom_threshold of 50% for 2021uo.
Prediction doesn't exceed anom_threshold of 50% for 2021uiq.
Prediction doesn't exceed anom_threshold of 50% for 2020rtb.
Prediction doesn't exceed anom_threshold of 50% for 2021snh.
Prediction doesn't exceed anom_threshold of 50% for 2020skr.
Prediction doesn't exceed anom_threshold of 50% for 2021sev.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020zji.
2021dow has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020cyz.
Prediction doesn't exceed anom_threshold of 50% for 2021hxv.
2020jxa has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021fui.
Prediction doesn't exceed anom_threshold of 50% for 2020tgv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021tci.
Prediction doesn't exceed anom_threshold of 50% for 2021abax.
Prediction doesn't exceed anom_threshold of 50% for 2020qql.
Prediction doesn't exceed anom_threshold of 50% for 2021ghd.
Prediction doesn't exceed anom_threshold of 50% for 2020vbs.
Prediction doesn't exceed anom_threshold of 50% for 2021wqr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020duv.
Prediction doesn't exceed anom_threshold of 50% for 2021rgh.
Prediction doesn't exceed anom_threshold of 50% for 2021acvn.
2020oov has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021oar.
2021jvm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021wr.
Prediction doesn't exceed anom_threshold of 50% for 2020aaaq.
2021qjh has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021wlt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021wmq.
Prediction doesn't exceed anom_threshold of 50% for 2021ctf.
Prediction doesn't exceed anom_threshold of 50% for 2021sgk.
Prediction doesn't exceed anom_threshold of 50% for 2021smp.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020aevm has some NaN LC features. Skip!
2021xvu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ixs.
Prediction doesn't exceed anom_threshold of 50% for 2021vjs.
Prediction doesn't exceed anom_threshold of 50% for 2020irh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020fwz.
Prediction doesn't exceed anom_threshold of 50% for 2019yyb.
Prediction doesn't exceed anom_threshold of 50% for 2021joz.
2021ypr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021shd.
Prediction doesn't exceed anom_threshold of 50% for 2021udc.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020rrn.
Prediction doesn't exceed anom_threshold of 50% for 2020sdu.
Prediction doesn't exceed anom_threshold of 50% for 2020twb.
Prediction doesn't exceed anom_threshold of 50% for 2021sjq.
Prediction doesn't exceed anom_threshold of 50% for 2020nvb.
Prediction doesn't exceed anom_threshold of 50% for 2021xpq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qtp.
Prediction doesn't exceed anom_threshold of 50% for 2020aeql.
Prediction doesn't exceed anom_threshold of 50% for 2019zha.
Prediction doesn't exceed anom_threshold of 50% for 2020nbo.
Prediction doesn't exceed anom_threshold of 50% for 2021wuc.
Prediction doesn't exceed anom_threshold of 50% for 2021hby.
Prediction doesn't exceed anom_threshold of 50% for 2020jip.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020yhn.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020lex.
Prediction doesn't exceed anom_threshold of 50% for 2021kxk.
Prediction doesn't exceed anom_threshold of 50% for 2020irg.
2020nap has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021qxn.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020azt.
Prediction doesn't exceed anom_threshold of 50% for 2021abym.
2020pwl has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020hrd.
2021vwz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021iae.
Prediction doesn't exceed anom_threshold of 50% for 2021nma.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020zpi.
2020zql has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021lii.
Prediction doesn't exceed anom_threshold of 50% for 2020jba.
Prediction doesn't exceed anom_threshold of 50% for 2021nip.
Prediction doesn't exceed anom_threshold of 50% for 2020itc.
Prediction doesn't exceed anom_threshold of 50% for 2021zqb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020kpk.
Prediction doesn't exceed anom_threshold of 50% for 2020tlo.
Prediction doesn't exceed anom_threshold of 50% for 2021lgc.
Prediction doesn't exceed anom_threshold of 50% for 2020jlk.
Prediction doesn't exceed anom_threshold of 50% for 2021rgg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ycq.
Prediction doesn't exceed anom_threshold of 50% for 2020hif.
Prediction doesn't exceed anom_threshold of 50% for 2021dpc.
2021achw has some NaN LC features. Skip!
2020acds has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020pyf.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Anomalous at end of full LC!
2020xoc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020dbd.
Prediction doesn't exceed anom_threshold of 50% for 2019wqf.
Prediction doesn't exceed anom_threshold of 50% for 2020jzs.
Prediction doesn't exceed anom_threshold of 50% for 2020kmm.
2021kbl has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qfv.
Prediction doesn't exceed anom_threshold of 50% for 2020ted.
Prediction doesn't exceed anom_threshold of 50% for 2021iyw.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021hol.
Prediction doesn't exceed anom_threshold of 50% for 2020hjv.
Prediction doesn't exceed anom_threshold of 50% for 2021jkd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020fsd.
Prediction doesn't exceed anom_threshold of 50% for 2020nor.
Prediction doesn't exceed anom_threshold of 50% for 2021bbz.
Prediction doesn't exceed anom_threshold of 50% for 2020ppe.
Prediction doesn't exceed anom_threshold of 50% for 2021cta.
Prediction doesn't exceed anom_threshold of 50% for 2021wmv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021wls.
Prediction doesn't exceed anom_threshold of 50% for 2020acgk.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020ikx.
Prediction doesn't exceed anom_threshold of 50% for 2020kop.
Prediction doesn't exceed anom_threshold of 50% for 2021tvn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021dox.
2021agt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020jfx.
Prediction doesn't exceed anom_threshold of 50% for 2020wto.
Prediction doesn't exceed anom_threshold of 50% for 2021phy.
2021afkm has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020nlb.
Prediction doesn't exceed anom_threshold of 50% for 2021tfr.
Prediction doesn't exceed anom_threshold of 50% for 2020kuw.
2021zkm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021vvw.
Prediction doesn't exceed anom_threshold of 50% for 2021sjv.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020fo.
Prediction doesn't exceed anom_threshold of 50% for 2020kit.
2021shc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021zcc.
Prediction doesn't exceed anom_threshold of 50% for 2020igg.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qht.
Prediction doesn't exceed anom_threshold of 50% for 2021bmu.
Prediction doesn't exceed anom_threshold of 50% for 2021ogq.
Prediction doesn't exceed anom_threshold of 50% for 2021ypu.
2020jtq has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021coc.
Prediction doesn't exceed anom_threshold of 50% for 2020roo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021fsb.
2019uez has some NaN LC features. Skip!
2020aot has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021vjt.
Prediction doesn't exceed anom_threshold of 50% for 2020von.
Prediction doesn't exceed anom_threshold of 50% for 2020zyj.
2021acey has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020pth.
Prediction doesn't exceed anom_threshold of 50% for 2021pzl.
2020anm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020adob.
Prediction doesn't exceed anom_threshold of 50% for 2021abmt.
Prediction doesn't exceed anom_threshold of 50% for 2020hmc.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020ciu.
Prediction doesn't exceed anom_threshold of 50% for 2020utj.
Prediction doesn't exceed anom_threshold of 50% for 2020alx.
Prediction doesn't exceed anom_threshold of 50% for 2020rfx.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020ghq.
Prediction doesn't exceed anom_threshold of 50% for 2021kpq.
Prediction doesn't exceed anom_threshold of 50% for 2020lgy.
Prediction doesn't exceed anom_threshold of 50% for 2020iqc.
Prediction doesn't exceed anom_threshold of 50% for 2021key.
Prediction doesn't exceed anom_threshold of 50% for 2020xkf.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021hwj.
Prediction doesn't exceed anom_threshold of 50% for 2020woq.
Prediction doesn't exceed anom_threshold of 50% for 2021acfi.
Prediction doesn't exceed anom_threshold of 50% for 2020rsp.
2020nrh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aekp.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abyb.
Prediction doesn't exceed anom_threshold of 50% for 2021cbe.
Prediction doesn't exceed anom_threshold of 50% for 2021dpw.
Prediction doesn't exceed anom_threshold of 50% for 2019ytc.
2021rgs has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021utd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021gca.
2020kqz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021zqv.
2021fwo has some NaN LC features. Skip!
2020usk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020xst.
Prediction doesn't exceed anom_threshold of 50% for 2021nug.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021kbx.
Prediction doesn't exceed anom_threshold of 50% for 2020kfg.
Prediction doesn't exceed anom_threshold of 50% for 2021mfy.
Prediction doesn't exceed anom_threshold of 50% for 2020ppq.
Prediction doesn't exceed anom_threshold of 50% for 2020qgo.
2020aecv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020qfj.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020xft has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ivj.
Prediction doesn't exceed anom_threshold of 50% for 2021isp.
Prediction doesn't exceed anom_threshold of 50% for 2020svo.
2021aon has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020yeg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021afe.
Prediction doesn't exceed anom_threshold of 50% for 2021tvz.
Prediction doesn't exceed anom_threshold of 50% for 2020ski.
Prediction doesn't exceed anom_threshold of 50% for 2021aahr.
Prediction doesn't exceed anom_threshold of 50% for 2021ueu.
Prediction doesn't exceed anom_threshold of 50% for 2020tvt.
2021nsk has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021suq.
Prediction doesn't exceed anom_threshold of 50% for 2020iqk.
Prediction doesn't exceed anom_threshold of 50% for 2020nib.
Prediction doesn't exceed anom_threshold of 50% for 2020aepz.
Prediction doesn't exceed anom_threshold of 50% for 2020wzq.
Prediction doesn't exceed anom_threshold of 50% for 2020yix.
2021qxr has some NaN LC features. Skip!
2019unp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021rkd.
Prediction doesn't exceed anom_threshold of 50% for 2020uwr.
2020addt has some NaN LC features. Skip!
2020azh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020achp.
2020imh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021foz.
Prediction doesn't exceed anom_threshold of 50% for 2020rrr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020rsw.
Prediction doesn't exceed anom_threshold of 50% for 2020inw.
Prediction doesn't exceed anom_threshold of 50% for 2021nra.
2020aenc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020eyv.
Prediction doesn't exceed anom_threshold of 50% for 2019zix.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020nim.
Prediction doesn't exceed anom_threshold of 50% for 2020aeqp.
2020utm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020yiw.
Prediction doesn't exceed anom_threshold of 50% for 2021pgm.
Prediction doesn't exceed anom_threshold of 50% for 2020admp.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021xyf.
Prediction doesn't exceed anom_threshold of 50% for 2020abji.
Prediction doesn't exceed anom_threshold of 50% for 2020acvi.
Prediction doesn't exceed anom_threshold of 50% for 2021cox.
Prediction doesn't exceed anom_threshold of 50% for 2021jof.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ykb.
Prediction doesn't exceed anom_threshold of 50% for 2021ofo.
Prediction doesn't exceed anom_threshold of 50% for 2020aciz.
Prediction doesn't exceed anom_threshold of 50% for 2020jtj.
Prediction doesn't exceed anom_threshold of 50% for 2021sbc.
Prediction doesn't exceed anom_threshold of 50% for 2020sgy.
Prediction doesn't exceed anom_threshold of 50% for 2020ilb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020mbi.
2020ltr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tqz.
Prediction doesn't exceed anom_threshold of 50% for 2021xbd.
Prediction doesn't exceed anom_threshold of 50% for 2021zmr.
2020iiv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020zhh.
Prediction doesn't exceed anom_threshold of 50% for 2020kxv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021tks.
2020vkd has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020usl.
Prediction doesn't exceed anom_threshold of 50% for 2020aawu.
Prediction doesn't exceed anom_threshold of 50% for 2020iuu.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021afj.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021bf.
Prediction doesn't exceed anom_threshold of 50% for 2020opy.
2021chy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020jyp.
Prediction doesn't exceed anom_threshold of 50% for 2021arg.
Prediction doesn't exceed anom_threshold of 50% for 2020odt.
2020xek has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020knn.
2020trj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020evq.
Prediction doesn't exceed anom_threshold of 50% for 2020nrg.
2020yyc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021achl.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020awu.
Prediction doesn't exceed anom_threshold of 50% for 2021aagz.
Prediction doesn't exceed anom_threshold of 50% for 2020hwk.
Prediction doesn't exceed anom_threshold of 50% for 2021ipg.
2020qpu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020itx.
Prediction doesn't exceed anom_threshold of 50% for 2020able.
Prediction doesn't exceed anom_threshold of 50% for 2020kpp.
Prediction doesn't exceed anom_threshold of 50% for 2020lhy.
Prediction doesn't exceed anom_threshold of 50% for 2021thk.
Prediction doesn't exceed anom_threshold of 50% for 2020has.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cka.
2021aoi has some NaN LC features. Skip!
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021zri.
Prediction doesn't exceed anom_threshold of 50% for 2021khl.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020kfh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021nuh.
2021hqa has some NaN LC features. Skip!
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021pti.
2021rpa has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021us.
Prediction doesn't exceed anom_threshold of 50% for 2020mcd.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
2020lzp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021aeja.
Prediction doesn't exceed anom_threshold of 50% for 2021vtq.
Prediction doesn't exceed anom_threshold of 50% for 2020vpn.
Prediction doesn't exceed anom_threshold of 50% for 2021jzf.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cdn.
Prediction doesn't exceed anom_threshold of 50% for 2020aasl.
Prediction doesn't exceed anom_threshold of 50% for 2021rkc.
Prediction doesn't exceed anom_threshold of 50% for 2021uyq.
Prediction doesn't exceed anom_threshold of 50% for 2021ljb.
Prediction doesn't exceed anom_threshold of 50% for 2021krk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021zvx.
Prediction doesn't exceed anom_threshold of 50% for 2021qxu.
Prediction doesn't exceed anom_threshold of 50% for 2020pcz.
Prediction doesn't exceed anom_threshold of 50% for 2020hgw.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aeqx.
Prediction doesn't exceed anom_threshold of 50% for 2020nie.
Prediction doesn't exceed anom_threshold of 50% for 2021abeu.
Prediction doesn't exceed anom_threshold of 50% for 2021nsl.
Prediction doesn't exceed anom_threshold of 50% for 2020ioz.
2021qes has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020lxe.
Prediction doesn't exceed anom_threshold of 50% for 2020jwr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021dkz.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021uil.
Prediction doesn't exceed anom_threshold of 50% for 2020kfi.
2020aehn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021low.
Prediction doesn't exceed anom_threshold of 50% for 2020iwi.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020ore.
Prediction doesn't exceed anom_threshold of 50% for 2020acyo.
Prediction doesn't exceed anom_threshold of 50% for 2021hem.
Prediction doesn't exceed anom_threshold of 50% for 2020use.
Prediction doesn't exceed anom_threshold of 50% for 2021reh.
Prediction doesn't exceed anom_threshold of 50% for 2020lhx.
Prediction doesn't exceed anom_threshold of 50% for 2021thj.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020mjn.
Prediction doesn't exceed anom_threshold of 50% for 2021achm.
Prediction doesn't exceed anom_threshold of 50% for 2020absw.
2021vrt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020evp.
Prediction doesn't exceed anom_threshold of 50% for 2020kob.
Prediction doesn't exceed anom_threshold of 50% for 2020wnz.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020era.
Prediction doesn't exceed anom_threshold of 50% for 2021nsm.
Prediction doesn't exceed anom_threshold of 50% for 2020lxd.
Prediction doesn't exceed anom_threshold of 50% for 2021kni.
Prediction doesn't exceed anom_threshold of 50% for 2020lsz.
2020qte has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021ehh.
2020abhu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abip.
Prediction doesn't exceed anom_threshold of 50% for 2021kzd.
Prediction doesn't exceed anom_threshold of 50% for 2020mzr.
Prediction doesn't exceed anom_threshold of 50% for 2021vjf.
Prediction doesn't exceed anom_threshold of 50% for 2021dk.
Prediction doesn't exceed anom_threshold of 50% for 2021aczd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019pmd.
Prediction doesn't exceed anom_threshold of 50% for 2020apu.
Prediction doesn't exceed anom_threshold of 50% for 2021atj.
Prediction doesn't exceed anom_threshold of 50% for 2021lux.
Prediction doesn't exceed anom_threshold of 50% for 2020qhn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ibg.
Prediction doesn't exceed anom_threshold of 50% for 2020qbu.
Prediction doesn't exceed anom_threshold of 50% for 2021dhc.
2020wmb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021pn.
Prediction doesn't exceed anom_threshold of 50% for 2020jtk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ulp.
Prediction doesn't exceed anom_threshold of 50% for 2020aafm.
Prediction doesn't exceed anom_threshold of 50% for 2021hjj.
Prediction doesn't exceed anom_threshold of 50% for 2021yny.
Prediction doesn't exceed anom_threshold of 50% for 2021akq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021tex.
Prediction doesn't exceed anom_threshold of 50% for 2020utl.
Prediction doesn't exceed anom_threshold of 50% for 2021hca.
Prediction doesn't exceed anom_threshold of 50% for 2020abix.
2020rsv has some NaN LC features. Skip!
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020acjk.
Prediction doesn't exceed anom_threshold of 50% for 2020trk.
2020odu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021hyf.
Prediction doesn't exceed anom_threshold of 50% for 2021bg.
Prediction doesn't exceed anom_threshold of 50% for 2020aaua.
Prediction doesn't exceed anom_threshold of 50% for 2020acqi.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020itq.
Prediction doesn't exceed anom_threshold of 50% for 2019zfv.
Prediction doesn't exceed anom_threshold of 50% for 2021lgq.
Prediction doesn't exceed anom_threshold of 50% for 2021abam.
Prediction doesn't exceed anom_threshold of 50% for 2020pni.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020usm.
2020tom has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020zbr.
Prediction doesn't exceed anom_threshold of 50% for 2020whv.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021wli.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ufe.
Prediction doesn't exceed anom_threshold of 50% for 2020fle.
Prediction doesn't exceed anom_threshold of 50% for 2020skh.
Prediction doesn't exceed anom_threshold of 50% for 2020war.
Prediction doesn't exceed anom_threshold of 50% for 2020clh.
Prediction doesn't exceed anom_threshold of 50% for 2021iv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021bh.
2021afd has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021gis.
2020kpv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021aapa.
Prediction doesn't exceed anom_threshold of 50% for 2020dwq.
Prediction doesn't exceed anom_threshold of 50% for 2020qyx.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021kwy.
Prediction doesn't exceed anom_threshold of 50% for 2020svn.
Anomalous at end of full LC!
2020ypn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020mbg.
2021puj has some NaN LC features. Skip!
2020adti has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021ibh.
Prediction doesn't exceed anom_threshold of 50% for 2020tue.
Prediction doesn't exceed anom_threshold of 50% for 2021ofa.
2021dl has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020mye.
2020abkb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021bsv.
Prediction doesn't exceed anom_threshold of 50% for 2020rw.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020wzp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021aave.
Prediction doesn't exceed anom_threshold of 50% for 2019wbw.
Prediction doesn't exceed anom_threshold of 50% for 2020jcy.
Prediction doesn't exceed anom_threshold of 50% for 2020rmo.
Prediction doesn't exceed anom_threshold of 50% for 2020yiy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ylc.
Prediction doesn't exceed anom_threshold of 50% for 2021lit.
Prediction doesn't exceed anom_threshold of 50% for 2020ej.
Prediction doesn't exceed anom_threshold of 50% for 2020lxc.
Prediction doesn't exceed anom_threshold of 50% for 2021aabh.
Prediction doesn't exceed anom_threshold of 50% for 2021aacm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020oku.
2020mnx has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aaer.
2020oaf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021ikk.
2021kpp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aly.
2020utk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021vaw.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020ees.
Prediction doesn't exceed anom_threshold of 50% for 2021gda.
Prediction doesn't exceed anom_threshold of 50% for 2020anl.
Prediction doesn't exceed anom_threshold of 50% for 2021mba.
2021jzh has some NaN LC features. Skip!
2021aakk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020acbb.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020acmm.
Prediction doesn't exceed anom_threshold of 50% for 2020aeid.
Prediction doesn't exceed anom_threshold of 50% for 2020vtq.
Prediction doesn't exceed anom_threshold of 50% for 2021qhg.
Prediction doesn't exceed anom_threshold of 50% for 2020zcp.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021xhy.
Prediction doesn't exceed anom_threshold of 50% for 2021smj.
2021sft has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020qrn.
2021abha has some NaN LC features. Skip!
2020kxp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020toj.
Prediction doesn't exceed anom_threshold of 50% for 2021msx.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020usj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aaws.
Prediction doesn't exceed anom_threshold of 50% for 2020akx.
Prediction doesn't exceed anom_threshold of 50% for 2021cjj.
Prediction doesn't exceed anom_threshold of 50% for 2021qua.
Prediction doesn't exceed anom_threshold of 50% for 2021cca.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2019ucc.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021aamo.
Prediction doesn't exceed anom_threshold of 50% for 2021rxa.
Prediction doesn't exceed anom_threshold of 50% for 2019zsy.
Prediction doesn't exceed anom_threshold of 50% for 2021nsh.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021xei.
Prediction doesn't exceed anom_threshold of 50% for 2020far.
Prediction doesn't exceed anom_threshold of 50% for 2021psl.
Prediction doesn't exceed anom_threshold of 50% for 2020adly.
2021wtv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020abcn.
Prediction doesn't exceed anom_threshold of 50% for 2019zit.
2021liv has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020lgr.
Prediction doesn't exceed anom_threshold of 50% for 2020fwj.
Prediction doesn't exceed anom_threshold of 50% for 2020jjp.
Prediction doesn't exceed anom_threshold of 50% for 2021acza.
Prediction doesn't exceed anom_threshold of 50% for 2020irx.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tug.
Prediction doesn't exceed anom_threshold of 50% for 2021luu.
Prediction doesn't exceed anom_threshold of 50% for 2021gqc.
Prediction doesn't exceed anom_threshold of 50% for 2020imk.
Prediction doesn't exceed anom_threshold of 50% for 2020zfn.
Prediction doesn't exceed anom_threshold of 50% for 2021pc.
2020azk has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020hr.
Prediction doesn't exceed anom_threshold of 50% for 2021qbv.
Prediction doesn't exceed anom_threshold of 50% for 2020qgl.
Prediction doesn't exceed anom_threshold of 50% for 2020pqw.
Prediction doesn't exceed anom_threshold of 50% for 2020jpw.
2021rpe has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021uhl.
Prediction doesn't exceed anom_threshold of 50% for 2020acsq.
Prediction doesn't exceed anom_threshold of 50% for 2020rki.
Prediction doesn't exceed anom_threshold of 50% for 2020jdz.
Prediction doesn't exceed anom_threshold of 50% for 2021rem.
Prediction doesn't exceed anom_threshold of 50% for 2020fxe.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020yda.
Prediction doesn't exceed anom_threshold of 50% for 2021srs.
2020abnt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020iwl.
Prediction doesn't exceed anom_threshold of 50% for 2021adnw.
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020uzk.
Prediction doesn't exceed anom_threshold of 50% for 2021ipc.
Prediction doesn't exceed anom_threshold of 50% for 2020evu.
Prediction doesn't exceed anom_threshold of 50% for 2021zep.
Prediction doesn't exceed anom_threshold of 50% for 2021sou.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020sjo.
Prediction doesn't exceed anom_threshold of 50% for 2020prg.
Prediction doesn't exceed anom_threshold of 50% for 2020pyy.
Prediction doesn't exceed anom_threshold of 50% for 2021jvu.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020unn.
Prediction doesn't exceed anom_threshold of 50% for 2020hwg.
Prediction doesn't exceed anom_threshold of 50% for 2020ewx.
2021znf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tfc.
Prediction doesn't exceed anom_threshold of 50% for 2019wmr.
Prediction doesn't exceed anom_threshold of 50% for 2020dtk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021aaqn.
Prediction doesn't exceed anom_threshold of 50% for 2020acxg.
Prediction doesn't exceed anom_threshold of 50% for 2021uww.
Prediction doesn't exceed anom_threshold of 50% for 2020uys.
Prediction doesn't exceed anom_threshold of 50% for 2021hzs.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020wbh.
Prediction doesn't exceed anom_threshold of 50% for 2020aahb.
Prediction doesn't exceed anom_threshold of 50% for 2020xgz.
Prediction doesn't exceed anom_threshold of 50% for 2021atg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020juk.
Prediction doesn't exceed anom_threshold of 50% for 2020zgc.
2021vjk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020yjc.
Prediction doesn't exceed anom_threshold of 50% for 2020ann.
2020nhl has some NaN LC features. Skip!
2020nbw has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021kpr.
Prediction doesn't exceed anom_threshold of 50% for 2021gfv.
Prediction doesn't exceed anom_threshold of 50% for 2021lce.
Prediction doesn't exceed anom_threshold of 50% for 2021abng.
Prediction doesn't exceed anom_threshold of 50% for 2020aazk.
Prediction doesn't exceed anom_threshold of 50% for 2021acgo.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020rss.
Prediction doesn't exceed anom_threshold of 50% for 2020acjn.
2020znh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021xdd.
Anomalous at end of full LC!
2021ath has some NaN LC features. Skip!
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021pd.
Prediction doesn't exceed anom_threshold of 50% for 2021ype.
Prediction doesn't exceed anom_threshold of 50% for 2020apw.
2020nue has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020smi.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abjb.
Prediction doesn't exceed anom_threshold of 50% for 2021kyv.
Prediction doesn't exceed anom_threshold of 50% for 2020ovv.
Prediction doesn't exceed anom_threshold of 50% for 2020abci.
Prediction doesn't exceed anom_threshold of 50% for 2020nbx.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021oet.
Prediction doesn't exceed anom_threshold of 50% for 2021fnt.
Prediction doesn't exceed anom_threshold of 50% for 2020ujp.
Prediction doesn't exceed anom_threshold of 50% for 2020rxb.
Prediction doesn't exceed anom_threshold of 50% for 2021tsj.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020xaq.
Prediction doesn't exceed anom_threshold of 50% for 2020ioy.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021nyt.
Prediction doesn't exceed anom_threshold of 50% for 2021dnm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019wyp.
Prediction doesn't exceed anom_threshold of 50% for 2021arl.
Prediction doesn't exceed anom_threshold of 50% for 2020awv.
2020tra has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020evr.
Anomalous at end of full LC!
2020lhz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020gll.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021ccl.
Prediction doesn't exceed anom_threshold of 50% for 2019yuj.
2021rfz has some NaN LC features. Skip!
2020svk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ydf.
Prediction doesn't exceed anom_threshold of 50% for 2021hdj.
2021dsk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019zqa.
2021bbb has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020sck.
Prediction doesn't exceed anom_threshold of 50% for 2021sgt.
Prediction doesn't exceed anom_threshold of 50% for 2021xbg.
2021rqo has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021dmu.
2020oxt has some NaN LC features. Skip!
2021aaqi has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ivf.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021mpm.
Prediction doesn't exceed anom_threshold of 50% for 2021afi.
Prediction doesn't exceed anom_threshold of 50% for 2021be.
Prediction doesn't exceed anom_threshold of 50% for 2021lfv.
Prediction doesn't exceed anom_threshold of 50% for 2020tga.
2021vmm has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020iuv.
2020qze has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tyw.
2020koh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tri.
Prediction doesn't exceed anom_threshold of 50% for 2021ied.
Prediction doesn't exceed anom_threshold of 50% for 2020qnh.
2020ije has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021qkr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020odw.
Prediction doesn't exceed anom_threshold of 50% for 2020advq.
Prediction doesn't exceed anom_threshold of 50% for 2020mka.
Prediction doesn't exceed anom_threshold of 50% for 2021sjn.
Prediction doesn't exceed anom_threshold of 50% for 2021nsg.
2020pvy has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020cwg.
Prediction doesn't exceed anom_threshold of 50% for 2021yrx.
Prediction doesn't exceed anom_threshold of 50% for 2020rst.
Prediction doesn't exceed anom_threshold of 50% for 2021rbc.
Prediction doesn't exceed anom_threshold of 50% for 2020mqf.
2020tbu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kqp.
2021vcg has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021ehb.
Prediction doesn't exceed anom_threshold of 50% for 2020mrv.
Prediction doesn't exceed anom_threshold of 50% for 2020row.
Prediction doesn't exceed anom_threshold of 50% for 2021bxe.
Prediction doesn't exceed anom_threshold of 50% for 2020ixl.
Prediction doesn't exceed anom_threshold of 50% for 2021gdd.
2021qls has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020xbi.
2021kms has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020xhr.
Prediction doesn't exceed anom_threshold of 50% for 2020aqz.
Prediction doesn't exceed anom_threshold of 50% for 2021pl.
Prediction doesn't exceed anom_threshold of 50% for 2020uhm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021pzh.
Prediction doesn't exceed anom_threshold of 50% for 2021dha.
Prediction doesn't exceed anom_threshold of 50% for 2021hye.
Prediction doesn't exceed anom_threshold of 50% for 2021oal.
Prediction doesn't exceed anom_threshold of 50% for 2021achf.
2021kkv has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020skd.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021xkm.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020nrm.
Prediction doesn't exceed anom_threshold of 50% for 2020lbh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021gba.
Prediction doesn't exceed anom_threshold of 50% for 2020kpz.
Prediction doesn't exceed anom_threshold of 50% for 2019zgp.
Prediction doesn't exceed anom_threshold of 50% for 2021vml.
Prediction doesn't exceed anom_threshold of 50% for 2020zti.
Prediction doesn't exceed anom_threshold of 50% for 2021afh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ivg.
Prediction doesn't exceed anom_threshold of 50% for 2021eng.
Prediction doesn't exceed anom_threshold of 50% for 2020eaf.
Prediction doesn't exceed anom_threshold of 50% for 2021tjt.
Prediction doesn't exceed anom_threshold of 50% for 2021abhe.
2020mwc has some NaN LC features. Skip!
2020usn has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021at.
Prediction doesn't exceed anom_threshold of 50% for 2021rdf.
2020adjs has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021cjn.
2021aaqh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020acxa.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020acmi.
Prediction doesn't exceed anom_threshold of 50% for 2021qbx.
Prediction doesn't exceed anom_threshold of 50% for 2020qmy.
Prediction doesn't exceed anom_threshold of 50% for 2020scj.
2021xix has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019szh.
2020aalu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021huz.
Prediction doesn't exceed anom_threshold of 50% for 2020ptm.
Prediction doesn't exceed anom_threshold of 50% for 2021pqw.
Prediction doesn't exceed anom_threshold of 50% for 2021qgl.
Prediction doesn't exceed anom_threshold of 50% for 2020aegj.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ixm.
Prediction doesn't exceed anom_threshold of 50% for 2020sym.
Prediction doesn't exceed anom_threshold of 50% for 2021jna.
Prediction doesn't exceed anom_threshold of 50% for 2020mrw.
Prediction doesn't exceed anom_threshold of 50% for 2021lix.
2020nhj has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021bzq.
Prediction doesn't exceed anom_threshold of 50% for 2021cmo.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020yiu.
Prediction doesn't exceed anom_threshold of 50% for 2020rmc.
Prediction doesn't exceed anom_threshold of 50% for 2020fuq.
2021aaiz has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021beb.
Prediction doesn't exceed anom_threshold of 50% for 2020aena.
Prediction doesn't exceed anom_threshold of 50% for 2021nsf.
Prediction doesn't exceed anom_threshold of 50% for 2020kak.
2020lyb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021psj.
2020ybc has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021ygy.
Prediction doesn't exceed anom_threshold of 50% for 2020pf.
2020elt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021yow.
2021jni has some NaN LC features. Skip!
2021qsi has some NaN LC features. Skip!
2020abkf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021bsr.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020ldd.
Prediction doesn't exceed anom_threshold of 50% for 2020tkw.
2020imm has some NaN LC features. Skip!
2020nud has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020zmv.
2021lus has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021scl.
Prediction doesn't exceed anom_threshold of 50% for 2021yqa.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ypd.
Prediction doesn't exceed anom_threshold of 50% for 2021ati.
Prediction doesn't exceed anom_threshold of 50% for 2020nqu.
Prediction doesn't exceed anom_threshold of 50% for 2020kfj.
Prediction doesn't exceed anom_threshold of 50% for 2021xbn.
Prediction doesn't exceed anom_threshold of 50% for 2020jpq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020wbf.
Prediction doesn't exceed anom_threshold of 50% for 2021ptk.
2021fvg has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021dsj.
Prediction doesn't exceed anom_threshold of 50% for 2021wrt.
Prediction doesn't exceed anom_threshold of 50% for 2020pog.
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jgl.
Anomalous at end of full LC!
2019ytn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ahd.
2020nfh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020itz.
Prediction doesn't exceed anom_threshold of 50% for 2021aegy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020koa has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021idh.
Prediction doesn't exceed anom_threshold of 50% for 2021wd.
Prediction doesn't exceed anom_threshold of 50% for 2020aww.
Anomalous at end of full LC!
Anomalous at end of full LC!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021wlm.
Prediction doesn't exceed anom_threshold of 50% for 2020fla.
Anomalous at end of full LC!
2020acxf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021cji.
Prediction doesn't exceed anom_threshold of 50% for 2020poh.
Prediction doesn't exceed anom_threshold of 50% for 2020uxw.
Prediction doesn't exceed anom_threshold of 50% for 2021fwm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021amq.
Prediction doesn't exceed anom_threshold of 50% for 2020riu.
Prediction doesn't exceed anom_threshold of 50% for 2021zqt.
Prediction doesn't exceed anom_threshold of 50% for 2020vbj.
Prediction doesn't exceed anom_threshold of 50% for 2020lht.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020tly.
Prediction doesn't exceed anom_threshold of 50% for 2020tfb.
Prediction doesn't exceed anom_threshold of 50% for 2020ijc.
Prediction doesn't exceed anom_threshold of 50% for 2020kon.
Prediction doesn't exceed anom_threshold of 50% for 2020odq.
Prediction doesn't exceed anom_threshold of 50% for 2020iow.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021skm.
Prediction doesn't exceed anom_threshold of 50% for 2020rsr.
Prediction doesn't exceed anom_threshold of 50% for 2021oez.
Prediction doesn't exceed anom_threshold of 50% for 2020rla.
Prediction doesn't exceed anom_threshold of 50% for 2020alz.
Prediction doesn't exceed anom_threshold of 50% for 2020acty.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021kps.
Prediction doesn't exceed anom_threshold of 50% for 2021urb.
2020zrj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020slb.
2021tqq has some NaN LC features. Skip!
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021dib.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021lut.
Prediction doesn't exceed anom_threshold of 50% for 2020nuc.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021kyp.
2021aeuw has some NaN LC features. Skip!
2020abka has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aesh.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2020heg.
Prediction doesn't exceed anom_threshold of 50% for 2021mwb.
Prediction doesn't exceed anom_threshold of 50% for 2021ynu.
Anomalous at end of full LC!
Prediction doesn't exceed anom_threshold of 50% for 2021acfc.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020jww.
Prediction doesn't exceed anom_threshold of 50% for 2020pwr.
Prediction doesn't exceed anom_threshold of 50% for 2021vva.
Prediction doesn't exceed anom_threshold of 50% for 2021nsi.
Prediction doesn't exceed anom_threshold of 50% for 2020vxe.
Prediction doesn't exceed anom_threshold of 50% for 2020zjp.
2020xef has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021lru.
Prediction doesn't exceed anom_threshold of 50% for 2020aeti.
Prediction doesn't exceed anom_threshold of 50% for 2020glj.
Prediction doesn't exceed anom_threshold of 50% for 2021afg.
Prediction doesn't exceed anom_threshold of 50% for 2021adnv.
Prediction doesn't exceed anom_threshold of 50% for 2020xye.
Prediction doesn't exceed anom_threshold of 50% for 2021aaej.
2021ptl has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021sld.
2020qmv has some NaN LC features. Skip!
2020qgm has some NaN LC features. Skip!
CPU times: user 37.3 s, sys: 6.31 s, total: 43.6 s
Wall time: 47.5 s
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
```python
ysedr1_df = pd.DataFrame(zip(ztfid_l, anom_idx_is_l, max_anom_score_l, num_anom_epochs_l, spec_class_l, best_z_l, z_type_l),
columns=["Disc. Internal Name", "RFC_best_cls", "max_anom_score", "num_anom_epochs", "spec_class", "best_z", "z_type"])
ysedr1_df = ysedr1_df.set_index("Disc. Internal Name")
ysedr1_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>RFC_best_cls</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
</tr>
<tr>
<th>Disc. Internal Name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2021iyu</th>
<td>Normal</td>
<td>20.0</td>
<td>0</td>
<td>NA</td>
<td>0.1991</td>
<td>HOST-Z</td>
</tr>
<tr>
<th>2020van</th>
<td>Normal</td>
<td>49.0</td>
<td>0</td>
<td>NA</td>
<td>0.2070</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2020mvu</th>
<td>Normal</td>
<td>33.0</td>
<td>0</td>
<td>NA</td>
<td>0.1530</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2020olk</th>
<td>Normal</td>
<td>30.0</td>
<td>0</td>
<td>NA</td>
<td>0.1140</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2021bbp</th>
<td>Normal</td>
<td>47.0</td>
<td>0</td>
<td>NA</td>
<td>0.1710</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>2021afg</th>
<td>Normal</td>
<td>15.0</td>
<td>0</td>
<td>NA</td>
<td>0.1209</td>
<td>HOST-Z</td>
</tr>
<tr>
<th>2021adnv</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNIa-norm</td>
<td>0.1900</td>
<td>SPEC-Z</td>
</tr>
<tr>
<th>2020xye</th>
<td>Normal</td>
<td>20.0</td>
<td>0</td>
<td>NA</td>
<td>0.1970</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2021aaej</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNII</td>
<td>0.0900</td>
<td>SPEC-Z</td>
</tr>
<tr>
<th>2021sld</th>
<td>Normal</td>
<td>49.0</td>
<td>0</td>
<td>SNIa-norm</td>
<td>0.1040</td>
<td>SPEC-Z</td>
</tr>
</tbody>
</table>
<p>1153 rows × 6 columns</p>
</div>
```python
di = {'CV': 'Other',
'SLSN-I': 'Other',
'SLSN-II': 'Other',
'SNII': 'Normal',
'SNIIP': 'Normal',
'SNIIb': 'Other',
'SNIIn': 'Other',
'SNII-pec': 'Other',
'SNIa-norm': 'Normal',
'SNIa-91T-like': 'Normal',
'SNIa-CSM': 'Other',
'SNIa-SC': 'Other',
'SNIa-91bg-like': 'Normal',
'SNIax[02cx-like]': 'Other',
'SNIax': 'Other',
'SNIa-pec': 'Other',
'SNIb': 'Other',
'SNIb-pec': 'Other',
'SNIb/c': 'Other',
'SNIbn': 'Other',
'SNIc': 'Other',
'SNIc-BL': 'Other',
'SNIcn': 'Other',
'TDE': 'Other',
'None': 'Other',
'SN': 'Other',
'SNI': 'Other',
'Other': 'Other',
'LBV': 'Other',
'LRN': 'Other',
'NA': 'NA'}
ysedr1_df["spec_class_norm_anom"] = ysedr1_df["spec_class"].copy()
ysedr1_df = ysedr1_df.replace({"spec_class_norm_anom": di})
ysedr1_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>RFC_best_cls</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
<th>spec_class_norm_anom</th>
</tr>
<tr>
<th>Disc. Internal Name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2021iyu</th>
<td>Normal</td>
<td>20.0</td>
<td>0</td>
<td>NA</td>
<td>0.1991</td>
<td>HOST-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2020van</th>
<td>Normal</td>
<td>49.0</td>
<td>0</td>
<td>NA</td>
<td>0.2070</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2020mvu</th>
<td>Normal</td>
<td>33.0</td>
<td>0</td>
<td>NA</td>
<td>0.1530</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2020olk</th>
<td>Normal</td>
<td>30.0</td>
<td>0</td>
<td>NA</td>
<td>0.1140</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2021bbp</th>
<td>Normal</td>
<td>47.0</td>
<td>0</td>
<td>NA</td>
<td>0.1710</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>2021afg</th>
<td>Normal</td>
<td>15.0</td>
<td>0</td>
<td>NA</td>
<td>0.1209</td>
<td>HOST-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2021adnv</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNIa-norm</td>
<td>0.1900</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020xye</th>
<td>Normal</td>
<td>20.0</td>
<td>0</td>
<td>NA</td>
<td>0.1970</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2021aaej</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNII</td>
<td>0.0900</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021sld</th>
<td>Normal</td>
<td>49.0</td>
<td>0</td>
<td>SNIa-norm</td>
<td>0.1040</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
</tbody>
</table>
<p>1153 rows × 7 columns</p>
</div>
```python
# full LC only (39 spec+phot w/ "other"/anomaly classification)
higest_anom_score_df = ysedr1_df.nlargest(51, 'max_anom_score')
for obj, score, num_anom_epochs, spec, z, z_type, spec_norm_anom, rfc_cls in zip(higest_anom_score_df.index,
higest_anom_score_df.max_anom_score,
higest_anom_score_df.num_anom_epochs,
higest_anom_score_df["spec_class"],
higest_anom_score_df["best_z"],
higest_anom_score_df["z_type"],
higest_anom_score_df["spec_class_norm_anom"],
higest_anom_score_df.RFC_best_cls):
print(f"https://ziggy.ucolick.org/yse/transient_detail/{obj}/", round(score, 1), num_anom_epochs, spec, z, z_type, spec_norm_anom, rfc_cls)
```
https://ziggy.ucolick.org/yse/transient_detail/2020xsy/ 91.0 1 SLSN-II 0.27 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020acyu/ 89.0 1 NA 0.254 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bmv/ 81.0 1 SNIIn 0.09 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021nxq/ 80.0 1 SLSN-I 0.15 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020qmj/ 79.0 1 SNIIn 0.022 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021hrj/ 78.0 1 SNIb 0.022 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021too/ 77.9 1 SNIc-BL 0.07 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmq/ 73.0 1 NA 0.131 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jvi/ 72.0 1 NA 0.192 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmj/ 71.0 1 NA 0.0799 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021dpa/ 70.0 1 NA 0.152 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 70.0 1 SNIa-norm 0.12 SPEC-Z Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021aadc/ 67.0 1 SLSN-II 0.1953 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 66.0 1 SNIa-norm 0.06 SPEC-Z Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kre/ 66.0 1 SNIa-CSM 0.136 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020wwt/ 65.0 1 NA 0.157 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kps/ 64.0 1 NA 0.134 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofr/ 63.0 1 NA 0.0848 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 61.0 1 SNIa-norm 0.127 SPEC-Z Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mms/ 60.0 1 NA 0.239 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021gno/ 59.0 1 SNIb-pec 0.0062 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020icw/ 58.0 1 NA 0.0803 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/ 57.0 1 NA 0.167 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019uit/ 57.0 1 SNIIn 0.071 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020imb/ 57.0 1 NA 0.0507 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 1 SNII 0.0275 SPEC-Z Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020rdu/ 55.0 1 SNIIn 0.18 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/ 55.0 1 NA 0.197 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pss/ 55.0 1 NA 0.213 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tjd/ 55.0 1 SNIIb 0.01 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020itp/ 55.0 1 NA 0.16 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/ 55.0 1 NA 0.1352 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 55.0 1 SNIa-91T-like 0.209 SPEC-Z Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 1 SNII 0.036 SPEC-Z Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxp/ 55.0 1 NA 0.095 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021pnp/ 54.9 1 SNIIb 0.03 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/ 54.9 1 NA 0.12 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/ 53.0 1 NA 0.131 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/ 52.9 1 NA 0.1121 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 52.0 1 SNII 0.051 SPEC-Z Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ika/ 52.0 1 NA 0.207 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/ 52.0 1 NA 0.099 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/ 51.2 1 NA 0.096 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abgb/ 51.0 1 NA 0.133 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/ 51.0 1 NA 0.262 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/ 51.0 1 NA 0.1753 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020uba/ 51.0 1 NA 0.0832 HOST-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020mor/ 51.0 1 NA 0.152 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/ 51.0 1 NA 0.191 PHOTO-Z NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acct/ 50.0 1 SNIc 0.035 SPEC-Z Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020tan/ 50.0 1 SNIIn 0.079 SPEC-Z Other Other
## vetting
https://ziggy.ucolick.org/yse/transient_detail/2020xsy/ 91.0 SLSN-II Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020acyu/ 89.0 NA NA Other -- IIn if photoz is right (photoz = 0.254, abs mag ~ -20.50)
https://ziggy.ucolick.org/yse/transient_detail/2021bmv/ 81.0 SNIIn Other Other --
https://ziggy.ucolick.org/yse/transient_detail/2021nxq/ 80.0 SLSN-I Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020qmj/ 79.0 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021hrj/ 78.0 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021too/ 77.9 SNIc-BL Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmq/ 73.0 NA NA Other -- II/IIn
https://ziggy.ucolick.org/yse/transient_detail/2020jvi/ 72.0 NA NA Other --IIn if photoz is right (photoz = 0.192, abs mag ~ -20.2)
https://ziggy.ucolick.org/yse/transient_detail/2020kmj/ 71.0 NA NA Other -- normal II if photoz is right (photoz = 0.0799, abs mag ~ -18.25)
https://ziggy.ucolick.org/yse/transient_detail/2021dpa/ 70.0 NA NA Other -- normal II/ IIn w/ very weird slow 120d RISE?
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 70.0 SNIa-norm Normal Other -- Ia-norm spec. But that z=0.12 puts abs mag ~ -20.6 mag. Ia-SC????
https://ziggy.ucolick.org/yse/transient_detail/2021aadc/ 67.0 SLSN-II Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kre/ 66.0 SNIa-CSM Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 66.0 SNIa-norm Normal Other -- no 2nd bump, no rise. ParSNIP/SR also thought Ibc. SALT fit is even bad. Spec is def Ia norm though.
https://ziggy.ucolick.org/yse/transient_detail/2020wwt/ 65.0 NA NA Other -- looks normal Ia
https://ziggy.ucolick.org/yse/transient_detail/2020kps/ 64.0 NA NA Other - blue, nuclear, likely TDE
https://ziggy.ucolick.org/yse/transient_detail/2021ofr/ 63.0 NA NA Other -- likely Ia. One bad epoch. early time excess?
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 61.0 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.127. Faint host.
https://ziggy.ucolick.org/yse/transient_detail/2021mms/ 60.0 NA NA Other -- IIn if photoz is right (z=0.239), abs mag ~ -20.2 mag. Looks IIn evolution
https://ziggy.ucolick.org/yse/transient_detail/2021gno/ 59.0 SNIb-pec Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020icw/ 58.0 NA NA Other -- likely Ia normal, no second max
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/ 57.0 NA NA Other -- def fall of IIn or SLSN (prob IIn). 100d, abs mag ~ -19.6 mag.
https://ziggy.ucolick.org/yse/transient_detail/2020imb/ 57.0 NA NA Other -- likely II, but lasted 500d
https://ziggy.ucolick.org/yse/transient_detail/2019uit/ 57.0 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 SNII Normal Other -- looks normal SN II, between IIL/IIP, kinda weird LC
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/ 55.0 NA NA Other -- not sure.
https://ziggy.ucolick.org/yse/transient_detail/2020tjd/ 55.0 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020pss/ 55.0 NA NA Other -- IIn/SLSN if photo-z is right (z=0.213), then abs mag ~-20.5.
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 55.0 SNIa-91T-like Normal Other -- Ia-91T
https://ziggy.ucolick.org/yse/transient_detail/2020itp/ 55.0 NA NA Other -- not sure. Ia/Ibc ? Bright like Ia if photoz is right, but LC evolution looks more Ibc. elliptical host so Ia?
https://ziggy.ucolick.org/yse/transient_detail/2021qxp/ 55.0 NA NA Other -- II normal?
https://ziggy.ucolick.org/yse/transient_detail/2020rdu/ 55.0 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/ 55.0 NA NA Other -- Ia normal?
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 SNII Normal Other -- II normal
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/ 54.9 NA NA Other -- Ia normal? nuclear
https://ziggy.ucolick.org/yse/transient_detail/2021pnp/ 54.9 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/ 53.0 NA NA Other -- subluminous Ia?
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/ 52.9 NA NA Other -- Ia normal? nuclear
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 52.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ika/ 52.0 NA NA Other -- Ia-SC/91T if photoz is right (photoz = 0.207, abs mag ~ -21)
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/ 52.0 NA NA Other -- nice II normal
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/ 51.2 NA NA Other -- Ia normal? But bad SALT fit.
https://ziggy.ucolick.org/yse/transient_detail/2020abgb/ 51.0 NA NA Other -- likely IIn if photoz is right (z~0.133), abs mag ~ -19.4 mag). evolution is IIn-esque
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/ 51.0 NA NA Other -- II-normal?
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/ 51.0 NA NA Other -- Ia normal?
https://ziggy.ucolick.org/yse/transient_detail/2020uba/ 51.0 NA NA Other -- Ibc? reddened Ia? (but low extinction)
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/ 51.0 NA NA Other -- very high z Ia?/ overluminous Ia? z~0.262 if photoz is right, abs mag ~-20.2 mag
https://ziggy.ucolick.org/yse/transient_detail/2020mor/ 51.0 NA NA Other -- Ia normal? Maybe overluminous Ia if photoz is right.
https://ziggy.ucolick.org/yse/transient_detail/2020qlp/ 50.0 NA NA Other -- high z / overluminous Ia?
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 50.0 SNII Normal Other
```python
plt.hist(ysedr1_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True)
plt.hist(higest_anom_score_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True)
```
(array([1.96078431, 5.88235294, 1.96078431, 4.90196078, 6.8627451 ,
0.98039216, 8.82352941, 3.92156863, 2.94117647, 4.90196078,
2.94117647, 0.98039216, 0.98039216, 1.96078431, 0. ]),
array([0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ,
0.22, 0.24, 0.26, 0.28, 0.3 ]),
<BarContainer object of 15 artists>)
```python
len(ysedr1_df)
```
1153
```python
from scipy import stats
# Assuming ysedr1_df.best_z and higest_anom_score_df.best_z are your data arrays
# Plot histograms
plt.hist(ysedr1_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True, label='ysedr1_df')
plt.hist(higest_anom_score_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True, label='higest_anom_score_df')
plt.legend()
# Perform K-S test
import random
# Assuming ysedr1_df.best_z has 1153 objects and higest_anom_score_df.best_z has 51 objects
# Downsample the larger sample to match the size of the smaller sample
if len(ysedr1_df.best_z) > len(higest_anom_score_df.best_z):
ysedr1_df_best_z_downsample_l = random.sample(list(ysedr1_df.best_z), len(higest_anom_score_df.best_z))
# Now both samples have the same size
# You can proceed with your K-S test or other analyses
ks_statistic, p_value = stats.ks_2samp(ysedr1_df_best_z_downsample_l, higest_anom_score_df.best_z)
# Output the test result
if p_value < 0.05:
print(f"p_value: {p_value}")
print("The distributions are different (reject the null hypothesis)")
else:
print(f"p_value: {p_value}")
print("The distributions are the same (fail to reject the null hypothesis)")
plt.show()
```
p_value: 0.7280106739553731
The distributions are the same (fail to reject the null hypothesis)
Good both samples are same, so anom detection is tagging objects at all z similar to full z distr, so it's not just picking up brightest or farthest objects
```python
np.linspace(0, 0.3, 16)
```
array([0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ,
0.22, 0.24, 0.26, 0.28, 0.3 ])
```python
plt.hist(higest_anom_score_df[higest_anom_score_df.z_type == "SPEC-Z"].best_z, bins=np.linspace(0, 0.3, 16),
alpha=0.5, density=False,
label=f"SPEC-Z ({len(higest_anom_score_df[higest_anom_score_df.z_type == 'SPEC-Z'])} SNe)")
plt.hist(higest_anom_score_df[higest_anom_score_df.z_type == "HOST-Z"].best_z, bins=np.linspace(0, 0.3, 16),
alpha=0.5, density=False,
label=f"HOST-Z ({len(higest_anom_score_df[higest_anom_score_df.z_type == 'HOST-Z'])} SNe)")
plt.hist(higest_anom_score_df[higest_anom_score_df.z_type == "PHOTO-Z"].best_z, bins=np.linspace(0, 0.3, 16),
alpha=0.5, density=False,
label=f"PHOTO-Z ({len(higest_anom_score_df[higest_anom_score_df.z_type == 'PHOTO-Z'])} SNe)")
plt.legend()
```
<matplotlib.legend.Legend at 0x7fe857c84610>
```python
#plot
for obj, score, num_anom_epochs, cls, cls2, rfc_cls in zip(higest_anom_score_df.index,
higest_anom_score_df.max_anom_score,
higest_anom_score_df.num_anom_epochs,
higest_anom_score_df["spec_class"],
higest_anom_score_df["spec_class_norm_anom"],
higest_anom_score_df.RFC_best_cls):
print(obj, score, num_anom_epochs, cls, cls2, rfc_cls)
#if obj != '2019vuz': continue
sn_idx = full_snid_list.index(obj)
meta = full_meta_list[sn_idx]
lc = full_df_list[sn_idx]
lc_grXY = lc[(lc.PASSBAND != 'i') & (lc.PASSBAND != 'z')].reset_index(drop=True)
# (g, r, i or z for PS1-griz, respectively; X for ZTF-g; Y for ZTF-r)
lc_grXY["PASSBAND"] = lc_grXY["PASSBAND"].map({"X": "g", "g": "g", "Y": "R", "r": "R"})
lc = lc_grXY
plot_RFC_prob_vs_lc_ztfid(anom_ztfid=obj,
anom_thresh=50,
lc=lc,
meta=meta,
df_path = "/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries",
)
print("%%%%%%%%%%%%")
```
2020xsy 91.0 1 SLSN-II Other Other
Anomalous during timeseries!
max_anom_score 98.0
num_anom_epochs 93
MJD Cutoff anom_idx 59232.484
https://ziggy.ucolick.org/yse/transient_detail/2020xsy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acyu 89.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 90.0
num_anom_epochs 33
MJD Cutoff anom_idx 59220.301
https://ziggy.ucolick.org/yse/transient_detail/2020acyu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021bmv 81.0 1 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 85.0
num_anom_epochs 11
MJD Cutoff anom_idx 59265.133
https://ziggy.ucolick.org/yse/transient_detail/2021bmv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
%%%%%%%%%%%%
2021nxq 80.0 1 SLSN-I Other Other
Anomalous during timeseries!
max_anom_score 89.0
num_anom_epochs 27
MJD Cutoff anom_idx 59410.198
https://ziggy.ucolick.org/yse/transient_detail/2021nxq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020qmj 79.0 1 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 82.0
num_anom_epochs 51
MJD Cutoff anom_idx 59128.257
https://ziggy.ucolick.org/yse/transient_detail/2020qmj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021hrj 78.0 1 SNIb Other Other
Anomalous during timeseries!
max_anom_score 88.0
num_anom_epochs 79
MJD Cutoff anom_idx 59328.186
https://ziggy.ucolick.org/yse/transient_detail/2021hrj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021too 77.9 1 SNIc-BL Other Other
Anomalous during timeseries!
max_anom_score 81.9
num_anom_epochs 36
MJD Cutoff anom_idx 59440.37
https://ziggy.ucolick.org/yse/transient_detail/2021too/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021rmq 73.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 10
MJD Cutoff anom_idx 59379.224
https://ziggy.ucolick.org/yse/transient_detail/2021rmq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020jvi 72.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 84.0
num_anom_epochs 9
MJD Cutoff anom_idx 59042.36
https://ziggy.ucolick.org/yse/transient_detail/2020jvi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kmj 71.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 76.0
num_anom_epochs 49
MJD Cutoff anom_idx 58992.234
https://ziggy.ucolick.org/yse/transient_detail/2020kmj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021dpa 70.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 20
MJD Cutoff anom_idx 59323.18
https://ziggy.ucolick.org/yse/transient_detail/2021dpa/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kvl 70.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 8
MJD Cutoff anom_idx 59033.172
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021aadc 67.0 1 SLSN-II Other Other
Anomalous during timeseries!
max_anom_score 85.0
num_anom_epochs 15
MJD Cutoff anom_idx 59523.143
https://ziggy.ucolick.org/yse/transient_detail/2021aadc/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021vwx 66.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 69.8
num_anom_epochs 9
MJD Cutoff anom_idx 59525.462
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
%%%%%%%%%%%%
2020kre 66.0 1 SNIa-CSM Other Other
Anomalous during timeseries!
max_anom_score 82.0
num_anom_epochs 70
MJD Cutoff anom_idx 59017.216
https://ziggy.ucolick.org/yse/transient_detail/2020kre/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020wwt 65.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 14
MJD Cutoff anom_idx 59134.298
https://ziggy.ucolick.org/yse/transient_detail/2020wwt/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kps 64.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 10
MJD Cutoff anom_idx 58975.32
https://ziggy.ucolick.org/yse/transient_detail/2020kps/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ofr 63.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 76.0
num_anom_epochs 8
MJD Cutoff anom_idx 59387.194
https://ziggy.ucolick.org/yse/transient_detail/2021ofr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020qkx 61.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 89.0
num_anom_epochs 51
MJD Cutoff anom_idx 59071.386
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021mms 60.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 65.0
num_anom_epochs 9
MJD Cutoff anom_idx 59377.211
https://ziggy.ucolick.org/yse/transient_detail/2021mms/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021gno 59.0 1 SNIb-pec Other Other
Anomalous during timeseries!
max_anom_score 78.0
num_anom_epochs 29
MJD Cutoff anom_idx 59323.317
https://ziggy.ucolick.org/yse/transient_detail/2021gno/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020icw 58.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 3
MJD Cutoff anom_idx 59008.27
https://ziggy.ucolick.org/yse/transient_detail/2020icw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ygj 57.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 6
MJD Cutoff anom_idx 59523.337
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019uit 57.0 1 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 60.0
num_anom_epochs 10
MJD Cutoff anom_idx 59022.27
https://ziggy.ucolick.org/yse/transient_detail/2019uit/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020imb 57.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 31
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 58997.232
https://ziggy.ucolick.org/yse/transient_detail/2020imb/
%%%%%%%%%%%%
2020lrr 56.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 4
MJD Cutoff anom_idx 59082.187
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020rdu 55.0 1 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 21
MJD Cutoff anom_idx 59083.26
https://ziggy.ucolick.org/yse/transient_detail/2020rdu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021sjw 55.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 3
MJD Cutoff anom_idx 59360.39
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020pss 55.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 2
MJD Cutoff anom_idx 59080.26
https://ziggy.ucolick.org/yse/transient_detail/2020pss/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tjd 55.0 1 SNIIb Other Other
Anomalous during timeseries!
max_anom_score 61.0
num_anom_epochs 16
MJD Cutoff anom_idx 59219.315
https://ziggy.ucolick.org/yse/transient_detail/2020tjd/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020itp 55.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 44
MJD Cutoff anom_idx 58987.33
https://ziggy.ucolick.org/yse/transient_detail/2020itp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021rmi 55.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 7
MJD Cutoff anom_idx 59404.221
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021xjh 55.0 1 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 4
MJD Cutoff anom_idx 59484.24
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021nue 55.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 1
MJD Cutoff anom_idx 59509.33
https://ziggy.ucolick.org/yse/transient_detail/2021nue/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021qxp 55.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 66.0
num_anom_epochs 9
MJD Cutoff anom_idx 59409.437
https://ziggy.ucolick.org/yse/transient_detail/2021qxp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021pnp 54.9 1 SNIIb Other Other
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
Anomalous during timeseries!
max_anom_score 79.0
num_anom_epochs 46
MJD Cutoff anom_idx 59408.235
https://ziggy.ucolick.org/yse/transient_detail/2021pnp/
%%%%%%%%%%%%
2020kmv 54.9 1 NA NA Other
Anomalous during timeseries!
max_anom_score 54.9
num_anom_epochs 3
MJD Cutoff anom_idx 59010.29
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021cuw 53.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 59280.271
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kfy 52.9 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.9
num_anom_epochs 1
MJD Cutoff anom_idx 59024.181
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021aff 52.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 16
MJD Cutoff anom_idx 59232.4
https://ziggy.ucolick.org/yse/transient_detail/2021aff/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ika 52.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 2
MJD Cutoff anom_idx 59014.221
https://ziggy.ucolick.org/yse/transient_detail/2020ika/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021bbd 52.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 2
MJD Cutoff anom_idx 59392.194
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020abbj 51.2 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.2
num_anom_epochs 2
MJD Cutoff anom_idx 59223.482
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020abgb 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 51
MJD Cutoff anom_idx 59198.444
https://ziggy.ucolick.org/yse/transient_detail/2020abgb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021wlp 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 9
MJD Cutoff anom_idx 59463.329
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020nwa 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 2
MJD Cutoff anom_idx 59055.37
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020uba 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 3
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59138.296
https://ziggy.ucolick.org/yse/transient_detail/2020uba/
%%%%%%%%%%%%
2020mor 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
MJD Cutoff anom_idx 59062.27
https://ziggy.ucolick.org/yse/transient_detail/2020mor/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020rpl 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
MJD Cutoff anom_idx 59079.379
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acct 50.0 1 SNIc Other Other
Anomalous during timeseries!
max_anom_score 70.0
num_anom_epochs 32
MJD Cutoff anom_idx 59200.508
https://ziggy.ucolick.org/yse/transient_detail/2020acct/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tan 50.0 1 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 60
MJD Cutoff anom_idx 59119.379
https://ziggy.ucolick.org/yse/transient_detail/2020tan/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
```python
Counter(ysedr1_df.num_anom_epochs)
```
Counter({0: 1098, 1: 55})
```python
# Confusion matrices
#spec_ysedr1_df = ysedr1_df[ysedr1_df.spec_class != "NA"]
spec_ysedr1_df = ysedr1_df[(ysedr1_df.spec_class != "NA")]
title='RFC'
true_labels = np.array(spec_ysedr1_df['spec_class_norm_anom'])
predicted_labels = np.array(spec_ysedr1_df['RFC_best_cls'])
# define the class labels
class_names = np.unique(true_labels)
nclasses = len(class_names)
KINDS = ['completeness', 'purity']
for KIND in KINDS:
# Sims test set
plot_conf_matrix(true_labels, predicted_labels, labels=class_names,
title=f'{title} ({KIND})', kind=KIND)
#plt.savefig(f'{cm_path}/confmatrix_YSEDR1_timeseries_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
plt.savefig(f'{cm_path}/confmatrix_YSEDR1_fullLConly_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
plt.show()
plt.close()
```
```python
# full LC only (14 spec w/ "other"/anomaly classification)
```
```python
missclassified_df = spec_ysedr1_df[(spec_ysedr1_df["RFC_best_cls"] == "Other") & (spec_ysedr1_df["spec_class_norm_anom"] == "Normal")]
missclassified_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>RFC_best_cls</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
<th>spec_class_norm_anom</th>
</tr>
<tr>
<th>Disc. Internal Name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2021vwx</th>
<td>Other</td>
<td>66.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.0600</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020kvl</th>
<td>Other</td>
<td>70.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.1200</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020qkx</th>
<td>Other</td>
<td>61.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.1270</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021aff</th>
<td>Other</td>
<td>52.0</td>
<td>1</td>
<td>SNII</td>
<td>0.0510</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020lrr</th>
<td>Other</td>
<td>56.0</td>
<td>1</td>
<td>SNII</td>
<td>0.0275</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021xjh</th>
<td>Other</td>
<td>55.0</td>
<td>1</td>
<td>SNIa-91T-like</td>
<td>0.2090</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2019ppi</th>
<td>Other</td>
<td>50.0</td>
<td>1</td>
<td>SNII</td>
<td>0.0520</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021nue</th>
<td>Other</td>
<td>55.0</td>
<td>1</td>
<td>SNII</td>
<td>0.0360</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020ulz</th>
<td>Other</td>
<td>50.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.1030</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
</tbody>
</table>
</div>
```python
for obj, score, spec, spec_norm_anom, rfc_cls in zip(missclassified_df.index,
missclassified_df.max_anom_score,
missclassified_df["spec_class"],
missclassified_df["spec_class_norm_anom"],
missclassified_df.RFC_best_cls):
print(f"https://ziggy.ucolick.org/yse/transient_detail/{obj}/", round(score, 1), spec, spec_norm_anom, rfc_cls)
```
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 66.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 70.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 61.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 52.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 55.0 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 50.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/ 50.0 SNIa-norm Normal Other
## TODO: check spectra
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 66.0 SNIa-norm Normal Other -- no 2nd bump, no rise. ParSNIP/SR also thought Ibc. SALT fit is even bad. Spec is def Ia norm though.
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 70.0 SNIa-norm Normal Other --Ia-norm spec. But that z=0.12 puts abs mag ~ -20.6 mag. Ia-SC????
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 61.0 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.127. Faint host.
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 52.0 SNII Normal Other -- looks normal SN II, but lasted 300d
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 SNII Normal Other -- looks normal SN II, between IIL/IIP, kinda weird LC
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 55.0 SNIa-91T-like Normal Other -- Ia-91T
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 50.0 SNII Normal Other -- looks normal SN II
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 SNII Normal Other -- II normal
```python
```
```python
fig, ax = plt.subplots(figsize=(10,6))
anom_ysedr1_df = ysedr1_df[ysedr1_df.max_anom_score >= 50]
ax.scatter(anom_ysedr1_df.num_anom_epochs, anom_ysedr1_df.max_anom_score)
for i, txt in enumerate(anom_ysedr1_df.index):
#if ysedr1_df.max_anom_score[i] >= 50:
ax.annotate(txt, (anom_ysedr1_df.num_anom_epochs[i]+0.55, anom_ysedr1_df.max_anom_score[i]+0.55))
plt.title("Full Timeseries Light Curve")
plt.xlabel("Number of Anomalous (>=50% score) Epochs")
plt.ylabel("Max Anomaly Score")
plt.savefig("./YSEDR1/figures/ysedr1_fullLConly_anomaly_scatterplot.pdf", bbox_inches='tight', dpi=300)
plt.show()
```
```python
```
# Full timeseries
```python
%%time
fps = glob.glob("/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries/*")
ztfid_l, anom_idx_is_l, max_anom_score_l, num_anom_epochs_l, spec_class_l, best_z_l, z_type_l = [], [], [], [], [], [], []
for fp in fps:
ztfid = fp.split('/')[-1].split('_')[0]
for meta in full_meta_list:
if ztfid == meta["object_id"]:
try:
anom_ztfid, anom_idx_is, max_anom_score, num_anom_epochs, spec_class, best_z, z_type = save_RFC_prob_vs_lc_ztfid(anom_ztfid=ztfid,
anom_thresh=50,
meta=meta,
df_path = "/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries",
clf=clf,
full_timeseries=True
)
except: continue
ztfid_l.append(anom_ztfid), anom_idx_is_l.append(anom_idx_is), max_anom_score_l.append(max_anom_score),
num_anom_epochs_l.append(num_anom_epochs), spec_class_l.append(spec_class), best_z_l.append(best_z), z_type_l.append(z_type)
```
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021iyu.
Prediction doesn't exceed anom_threshold of 50% for 2020van.
Prediction doesn't exceed anom_threshold of 50% for 2020mvu.
Prediction doesn't exceed anom_threshold of 50% for 2020olk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021bbp.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020vuc.
Prediction doesn't exceed anom_threshold of 50% for 2021lzg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021kbn.
Prediction doesn't exceed anom_threshold of 50% for 2020mks.
Prediction doesn't exceed anom_threshold of 50% for 2020qda.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020qqi.
Anomalous during timeseries!
2021rge has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020jli.
Prediction doesn't exceed anom_threshold of 50% for 2020zqn.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021vhk.
2021oqc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020pix.
Prediction doesn't exceed anom_threshold of 50% for 2021abj.
Prediction doesn't exceed anom_threshold of 50% for 2021rbq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020two.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021sil.
2020aeyo has some NaN LC features. Skip!
2021qsr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020nki.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019zag.
2020lez has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021gdv.
2021abgy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020uwl.
Prediction doesn't exceed anom_threshold of 50% for 2020dxa.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020wxz.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020rom.
2020zrv has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021iww.
Prediction doesn't exceed anom_threshold of 50% for 2020sxs.
Prediction doesn't exceed anom_threshold of 50% for 2020tkd.
Prediction doesn't exceed anom_threshold of 50% for 2020vzd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021qgw.
Prediction doesn't exceed anom_threshold of 50% for 2021xgi.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jts.
Prediction doesn't exceed anom_threshold of 50% for 2020kax.
Prediction doesn't exceed anom_threshold of 50% for 2021kdb.
Prediction doesn't exceed anom_threshold of 50% for 2021acfw.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020pip has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020amc.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021lhf.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021kpo.
Prediction doesn't exceed anom_threshold of 50% for 2020lly.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021nmk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021mqr.
Prediction doesn't exceed anom_threshold of 50% for 2020rii.
Prediction doesn't exceed anom_threshold of 50% for 2021amm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tle.
Prediction doesn't exceed anom_threshold of 50% for 2021zzv.
Prediction doesn't exceed anom_threshold of 50% for 2020ebm.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021sne.
2021lra has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020abrb.
Prediction doesn't exceed anom_threshold of 50% for 2020ikz.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021qas.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021doz.
2021aamp has some NaN LC features. Skip!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021abwm.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021kcc.
Prediction doesn't exceed anom_threshold of 50% for 2021xic.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ltk.
2020aaca has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021yuc.
Prediction doesn't exceed anom_threshold of 50% for 2021obg.
Prediction doesn't exceed anom_threshold of 50% for 2021wlq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021aox.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021isf.
2020bso has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021log.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020lac.
Prediction doesn't exceed anom_threshold of 50% for 2021in.
Prediction doesn't exceed anom_threshold of 50% for 2021buj.
Prediction doesn't exceed anom_threshold of 50% for 2021gik.
Prediction doesn't exceed anom_threshold of 50% for 2021oax.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021kiw has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kbi.
Prediction doesn't exceed anom_threshold of 50% for 2020abpx.
Prediction doesn't exceed anom_threshold of 50% for 2021hpz.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021apd.
Prediction doesn't exceed anom_threshold of 50% for 2020jpm.
Prediction doesn't exceed anom_threshold of 50% for 2021mge.
Prediction doesn't exceed anom_threshold of 50% for 2020acyp.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020wwr.
2021aow has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kwa.
Prediction doesn't exceed anom_threshold of 50% for 2021iyr.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020ovk.
Prediction doesn't exceed anom_threshold of 50% for 2020acwz.
Prediction doesn't exceed anom_threshold of 50% for 2021ort.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020dyc.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020wyx.
2020irb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021iwx.
Prediction doesn't exceed anom_threshold of 50% for 2019lbi.
Prediction doesn't exceed anom_threshold of 50% for 2020abul.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
2020oix has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020achi.
Prediction doesn't exceed anom_threshold of 50% for 2021aceh.
Prediction doesn't exceed anom_threshold of 50% for 2021abpc.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021txi.
Anomalous during timeseries!
2020hra has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021upd.
Prediction doesn't exceed anom_threshold of 50% for 2020aapb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020iqr.
Prediction doesn't exceed anom_threshold of 50% for 2020bta.
Anomalous during timeseries!
Anomalous during timeseries!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020gcv.
Prediction doesn't exceed anom_threshold of 50% for 2021tfw.
Prediction doesn't exceed anom_threshold of 50% for 2021uqi.
2019wcb has some NaN LC features. Skip!
2021afeb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021acgu.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021adug.
Prediction doesn't exceed anom_threshold of 50% for 2020woh.
Prediction doesn't exceed anom_threshold of 50% for 2020oa.
2020lyv has some NaN LC features. Skip!
Anomalous during timeseries!
2021ypp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021shf.
Anomalous during timeseries!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020bct.
2020vzc has some NaN LC features. Skip!
2020kvb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tkc.
Prediction doesn't exceed anom_threshold of 50% for 2021bsf.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qwb.
Prediction doesn't exceed anom_threshold of 50% for 2020iyt.
Prediction doesn't exceed anom_threshold of 50% for 2020jjb.
Prediction doesn't exceed anom_threshold of 50% for 2020roj.
Prediction doesn't exceed anom_threshold of 50% for 2021jox.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021tjh.
Prediction doesn't exceed anom_threshold of 50% for 2020qxm.
Anomalous during timeseries!
2020qrv has some NaN LC features. Skip!
2021ctd has some NaN LC features. Skip!
2021wlv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021wms.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021abwj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021lzh.
2020aehy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019yjl.
Prediction doesn't exceed anom_threshold of 50% for 2021jvo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020advl.
Prediction doesn't exceed anom_threshold of 50% for 2020oot.
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020kpf.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tfy.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021mzk.
Prediction doesn't exceed anom_threshold of 50% for 2020hik.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020rok.
Prediction doesn't exceed anom_threshold of 50% for 2021joy.
Prediction doesn't exceed anom_threshold of 50% for 2020kvc.
Prediction doesn't exceed anom_threshold of 50% for 2021abgw.
2021van has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020iyu.
Prediction doesn't exceed anom_threshold of 50% for 2021vku.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020qhp.
Prediction doesn't exceed anom_threshold of 50% for 2020fhj.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ypq.
Prediction doesn't exceed anom_threshold of 50% for 2021qed.
Prediction doesn't exceed anom_threshold of 50% for 2020nva.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021sjr.
Prediction doesn't exceed anom_threshold of 50% for 2021ygl.
Prediction doesn't exceed anom_threshold of 50% for 2021hid.
Prediction doesn't exceed anom_threshold of 50% for 2019wcc.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ztz.
Prediction doesn't exceed anom_threshold of 50% for 2021bzl.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020pfy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021hlp.
2020qqg has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ito.
2020tfx has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kqb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020nrp.
2020abrd has some NaN LC features. Skip!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021wog.
Prediction doesn't exceed anom_threshold of 50% for 2020oou.
Prediction doesn't exceed anom_threshold of 50% for 2021oaq.
2020oen has some NaN LC features. Skip!
2021abwk has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020kld.
Prediction doesn't exceed anom_threshold of 50% for 2021lqw.
Prediction doesn't exceed anom_threshold of 50% for 2020qfz.
Prediction doesn't exceed anom_threshold of 50% for 2021ils.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021hzh.
Prediction doesn't exceed anom_threshold of 50% for 2021wmr.
Prediction doesn't exceed anom_threshold of 50% for 2021cte.
2021acjn has some NaN LC features. Skip!
2021mgl has some NaN LC features. Skip!
2021jw has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021tji.
2021iys has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020adkc.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021owa.
Prediction doesn't exceed anom_threshold of 50% for 2020acyq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jnz.
Prediction doesn't exceed anom_threshold of 50% for 2020rkr.
Prediction doesn't exceed anom_threshold of 50% for 2021aov.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021mgd.
Anomalous during timeseries!
2021sfe has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kgr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020tzs.
2021vqz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021accm.
Prediction doesn't exceed anom_threshold of 50% for 2021iok.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ebc.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021abje.
Prediction doesn't exceed anom_threshold of 50% for 2021rgc.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abhk.
Prediction doesn't exceed anom_threshold of 50% for 2020aapc.
Prediction doesn't exceed anom_threshold of 50% for 2020agv.
Prediction doesn't exceed anom_threshold of 50% for 2021aci.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
2020hfm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021fom.
Prediction doesn't exceed anom_threshold of 50% for 2020abwx.
Prediction doesn't exceed anom_threshold of 50% for 2021gyv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021kor.
2021sjz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tvl.
Prediction doesn't exceed anom_threshold of 50% for 2020hpu.
Prediction doesn't exceed anom_threshold of 50% for 2020oiy.
Prediction doesn't exceed anom_threshold of 50% for 2020aqn.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020azp.
Prediction doesn't exceed anom_threshold of 50% for 2021scq.
Prediction doesn't exceed anom_threshold of 50% for 2021kfy.
2020imp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021iwy.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021bzk.
Prediction doesn't exceed anom_threshold of 50% for 2021pgu.
2021ygk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020amb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020rmy.
Prediction doesn't exceed anom_threshold of 50% for 2020hro.
Prediction doesn't exceed anom_threshold of 50% for 2021dkj.
Prediction doesn't exceed anom_threshold of 50% for 2020ino.
2020vyu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019yfu.
Prediction doesn't exceed anom_threshold of 50% for 2020jtr.
Prediction doesn't exceed anom_threshold of 50% for 2021pzs.
Prediction doesn't exceed anom_threshold of 50% for 2020oiv.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
2020bii has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kcl.
Prediction doesn't exceed anom_threshold of 50% for 2020sga.
Prediction doesn't exceed anom_threshold of 50% for 2020abjq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020vds.
Prediction doesn't exceed anom_threshold of 50% for 2020kvd.
Prediction doesn't exceed anom_threshold of 50% for 2020lnm.
Prediction doesn't exceed anom_threshold of 50% for 2020sxr.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020rol.
Prediction doesn't exceed anom_threshold of 50% for 2020her.
Prediction doesn't exceed anom_threshold of 50% for 2021pde.
Prediction doesn't exceed anom_threshold of 50% for 2021qvo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020noq.
Prediction doesn't exceed anom_threshold of 50% for 2019tvv.
Prediction doesn't exceed anom_threshold of 50% for 2020lky.
2021aoy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020dwg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jzx.
Anomalous during timeseries!
2021wmu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021dgu.
Prediction doesn't exceed anom_threshold of 50% for 2020ltj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021abwl.
2021sgo has some NaN LC features. Skip!
2021oav has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020advj.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ryz.
Prediction doesn't exceed anom_threshold of 50% for 2020knv.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021ina.
2020abrc has some NaN LC features. Skip!
2021dph has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020opa.
Prediction doesn't exceed anom_threshold of 50% for 2020rcs.
Prediction doesn't exceed anom_threshold of 50% for 2021acwo.
Prediction doesn't exceed anom_threshold of 50% for 2020aczf.
Prediction doesn't exceed anom_threshold of 50% for 2020jmm.
2020tmi has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tfw.
2021gcv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020skv.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020shf.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021kbo.
2020jzp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021mgc.
Prediction doesn't exceed anom_threshold of 50% for 2021actw.
2020jdf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021jkg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021hdq.
Prediction doesn't exceed anom_threshold of 50% for 2020acyv.
Prediction doesn't exceed anom_threshold of 50% for 2020hju.
Anomalous during timeseries!
2021dsp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021iyt.
Prediction doesn't exceed anom_threshold of 50% for 2021bwy.
Prediction doesn't exceed anom_threshold of 50% for 2020aewd.
2020ejj has some NaN LC features. Skip!
2020abjy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021qxm.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tvk.
Prediction doesn't exceed anom_threshold of 50% for 2020sdy.
Prediction doesn't exceed anom_threshold of 50% for 2021gyq.
Prediction doesn't exceed anom_threshold of 50% for 2020zoy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021pyk.
Prediction doesn't exceed anom_threshold of 50% for 2021aahm.
Prediction doesn't exceed anom_threshold of 50% for 2020rxy.
Prediction doesn't exceed anom_threshold of 50% for 2020szo.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021sun.
Prediction doesn't exceed anom_threshold of 50% for 2020btg.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020kzy.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021agu.
2020rij has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021dpj.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021dqo.
Prediction doesn't exceed anom_threshold of 50% for 2020awg.
2021doy has some NaN LC features. Skip!
2020odn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021aalv.
Prediction doesn't exceed anom_threshold of 50% for 2020abra.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020iky.
Prediction doesn't exceed anom_threshold of 50% for 2021inc.
Prediction doesn't exceed anom_threshold of 50% for 2020abxz.
Prediction doesn't exceed anom_threshold of 50% for 2020txk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021wlr.
Prediction doesn't exceed anom_threshold of 50% for 2020yza.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020abpt.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020neh.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020sxp.
Prediction doesn't exceed anom_threshold of 50% for 2020kvf.
2021ndc has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021qyb.
2020irn has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020ovg.
Prediction doesn't exceed anom_threshold of 50% for 2020yac.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020ron.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020apf.
Prediction doesn't exceed anom_threshold of 50% for 2020juu.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021bmt.
Prediction doesn't exceed anom_threshold of 50% for 2020vzg.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020aeeg.
Prediction doesn't exceed anom_threshold of 50% for 2020nvd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021itd.
Prediction doesn't exceed anom_threshold of 50% for 2021gmv.
2020kuv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020llz.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ftl.
Prediction doesn't exceed anom_threshold of 50% for 2020rfe.
Prediction doesn't exceed anom_threshold of 50% for 2020mza.
Prediction doesn't exceed anom_threshold of 50% for 2021ygi.
2019wcf has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Anomalous during timeseries!
2021yfd has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021yga.
Prediction doesn't exceed anom_threshold of 50% for 2020actn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020zqm.
Prediction doesn't exceed anom_threshold of 50% for 2020bte.
2020lyz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021unv.
Prediction doesn't exceed anom_threshold of 50% for 2021rh.
Prediction doesn't exceed anom_threshold of 50% for 2021oov.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tuy.
Prediction doesn't exceed anom_threshold of 50% for 2021gzc.
Prediction doesn't exceed anom_threshold of 50% for 2020zmn.
Prediction doesn't exceed anom_threshold of 50% for 2021zcj.
Prediction doesn't exceed anom_threshold of 50% for 2020ohy.
2020pux has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020acih.
Prediction doesn't exceed anom_threshold of 50% for 2021ufx.
Prediction doesn't exceed anom_threshold of 50% for 2020adc.
Prediction doesn't exceed anom_threshold of 50% for 2021uxn.
Prediction doesn't exceed anom_threshold of 50% for 2020rof.
2021abgz has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021gnn.
Prediction doesn't exceed anom_threshold of 50% for 2020adkf.
2020acyt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020lks.
2020tee has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ivw.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021qve.
Prediction doesn't exceed anom_threshold of 50% for 2021iyv.
Prediction doesn't exceed anom_threshold of 50% for 2020nde.
2021kbm has some NaN LC features. Skip!
Anomalous during timeseries!
2020qfw has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cth.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021mga.
Prediction doesn't exceed anom_threshold of 50% for 2020ikq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020zjo.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021aru.
Prediction doesn't exceed anom_threshold of 50% for 2020rvq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021aafe.
Prediction doesn't exceed anom_threshold of 50% for 2020jlj.
Prediction doesn't exceed anom_threshold of 50% for 2020aaur.
Prediction doesn't exceed anom_threshold of 50% for 2021adew.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020rhg.
Prediction doesn't exceed anom_threshold of 50% for 2021pcn.
Prediction doesn't exceed anom_threshold of 50% for 2021efy.
2020ngu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020nlk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020urt.
Prediction doesn't exceed anom_threshold of 50% for 2020icp.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021aaez.
Prediction doesn't exceed anom_threshold of 50% for 2021ctg.
2021wmp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021wlu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021aaoa.
Prediction doesn't exceed anom_threshold of 50% for 2020kns.
Prediction doesn't exceed anom_threshold of 50% for 2020zke.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021oas.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021pca.
Prediction doesn't exceed anom_threshold of 50% for 2021acvo.
Prediction doesn't exceed anom_threshold of 50% for 2021ghe.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qqe.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021aaws.
2021hbx has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019wca.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020zj.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020vfc.
Prediction doesn't exceed anom_threshold of 50% for 2020aeqm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021nlj.
Prediction doesn't exceed anom_threshold of 50% for 2020eru.
2021aaie has some NaN LC features. Skip!
Anomalous during timeseries!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020kir.
2021she has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021lta.
Prediction doesn't exceed anom_threshold of 50% for 2020jur.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020lom.
Prediction doesn't exceed anom_threshold of 50% for 2020kva.
Prediction doesn't exceed anom_threshold of 50% for 2020lev.
Prediction doesn't exceed anom_threshold of 50% for 2020bwr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021qxh.
Prediction doesn't exceed anom_threshold of 50% for 2020add.
Prediction doesn't exceed anom_threshold of 50% for 2020mms.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021scs.
Prediction doesn't exceed anom_threshold of 50% for 2020hrb.
Prediction doesn't exceed anom_threshold of 50% for 2020dec.
2021sjx has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kzx.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020thx.
Prediction doesn't exceed anom_threshold of 50% for 2020kuy.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020ite.
Prediction doesn't exceed anom_threshold of 50% for 2021dpe.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021im.
Prediction doesn't exceed anom_threshold of 50% for 2021rga.
Prediction doesn't exceed anom_threshold of 50% for 2021rfd.
Prediction doesn't exceed anom_threshold of 50% for 2020acdu.
Prediction doesn't exceed anom_threshold of 50% for 2021dov.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aajf.
Prediction doesn't exceed anom_threshold of 50% for 2021inl.
Prediction doesn't exceed anom_threshold of 50% for 2021uip.
2021wmx has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020icx.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021zfy.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020shc.
Prediction doesn't exceed anom_threshold of 50% for 2021kbj.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020teb.
2021abbi has some NaN LC features. Skip!
2021aot has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020jnx.
Prediction doesn't exceed anom_threshold of 50% for 2020acys.
2020kux has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020thy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021abor.
Prediction doesn't exceed anom_threshold of 50% for 2020abim.
Prediction doesn't exceed anom_threshold of 50% for 2020iqp.
Prediction doesn't exceed anom_threshold of 50% for 2020sex.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021aabr.
Prediction doesn't exceed anom_threshold of 50% for 2020uaq.
Prediction doesn't exceed anom_threshold of 50% for 2020epi.
Prediction doesn't exceed anom_threshold of 50% for 2020aefy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020igh.
Prediction doesn't exceed anom_threshold of 50% for 2021scr.
Prediction doesn't exceed anom_threshold of 50% for 2021mim.
2021rui has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020azs.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020achk.
Prediction doesn't exceed anom_threshold of 50% for 2020wyz.
Prediction doesn't exceed anom_threshold of 50% for 2019wkf.
Prediction doesn't exceed anom_threshold of 50% for 2020acwx.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021qxi.
Prediction doesn't exceed anom_threshold of 50% for 2020jny.
Prediction doesn't exceed anom_threshold of 50% for 2020aavd.
Prediction doesn't exceed anom_threshold of 50% for 2020fsc.
Prediction doesn't exceed anom_threshold of 50% for 2020rkq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020acyr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ivq.
Prediction doesn't exceed anom_threshold of 50% for 2020qsy.
2021iyp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020kry.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021sff.
Prediction doesn't exceed anom_threshold of 50% for 2021xin.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020jpo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021uo.
Prediction doesn't exceed anom_threshold of 50% for 2021uiq.
Anomalous during timeseries!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020skr.
Prediction doesn't exceed anom_threshold of 50% for 2021sev.
Prediction doesn't exceed anom_threshold of 50% for 2020zji.
2021dow has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020cyz.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021hxv.
2020jxa has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021fui.
Prediction doesn't exceed anom_threshold of 50% for 2020tgv.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021tci.
Prediction doesn't exceed anom_threshold of 50% for 2021abax.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021ghd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020vbs.
Prediction doesn't exceed anom_threshold of 50% for 2021wqr.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021rgh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021acvn.
2020oov has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021oar.
2021jvm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021wr.
Prediction doesn't exceed anom_threshold of 50% for 2020aaaq.
2021qjh has some NaN LC features. Skip!
2021wlt has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021wmq.
Prediction doesn't exceed anom_threshold of 50% for 2021ctf.
Prediction doesn't exceed anom_threshold of 50% for 2021sgk.
Prediction doesn't exceed anom_threshold of 50% for 2021smp.
2020aevm has some NaN LC features. Skip!
2021xvu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021vjs.
Prediction doesn't exceed anom_threshold of 50% for 2020irh.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020fwz.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019yyb.
Anomalous during timeseries!
2021ypr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021shd.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020rrn.
Prediction doesn't exceed anom_threshold of 50% for 2020sdu.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021sjq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020nvb.
Prediction doesn't exceed anom_threshold of 50% for 2021xpq.
Prediction doesn't exceed anom_threshold of 50% for 2020qtp.
Prediction doesn't exceed anom_threshold of 50% for 2020aeql.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019zha.
Prediction doesn't exceed anom_threshold of 50% for 2020nbo.
Prediction doesn't exceed anom_threshold of 50% for 2021wuc.
Prediction doesn't exceed anom_threshold of 50% for 2021hby.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jip.
Prediction doesn't exceed anom_threshold of 50% for 2020yhn.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020lex.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021kxk.
Prediction doesn't exceed anom_threshold of 50% for 2020irg.
2020nap has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020azt.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021abym.
2020pwl has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020hrd.
2021vwz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021iae.
Prediction doesn't exceed anom_threshold of 50% for 2021nma.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020zpi.
2020zql has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021lii.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021nip.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020itc.
Prediction doesn't exceed anom_threshold of 50% for 2021zqb.
Prediction doesn't exceed anom_threshold of 50% for 2020kpk.
Prediction doesn't exceed anom_threshold of 50% for 2020tlo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021lgc.
Prediction doesn't exceed anom_threshold of 50% for 2020jlk.
Prediction doesn't exceed anom_threshold of 50% for 2021rgg.
Prediction doesn't exceed anom_threshold of 50% for 2021ycq.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020hif.
Prediction doesn't exceed anom_threshold of 50% for 2021dpc.
2021achw has some NaN LC features. Skip!
2020acds has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020pyf.
Anomalous during timeseries!
2020xoc has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020dbd.
Prediction doesn't exceed anom_threshold of 50% for 2019wqf.
Prediction doesn't exceed anom_threshold of 50% for 2020jzs.
Prediction doesn't exceed anom_threshold of 50% for 2020kmm.
2021kbl has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qfv.
Prediction doesn't exceed anom_threshold of 50% for 2020ted.
Prediction doesn't exceed anom_threshold of 50% for 2021iyw.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021jkd.
Prediction doesn't exceed anom_threshold of 50% for 2020fsd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021bbz.
Prediction doesn't exceed anom_threshold of 50% for 2020ppe.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021wmv.
Prediction doesn't exceed anom_threshold of 50% for 2021wls.
Prediction doesn't exceed anom_threshold of 50% for 2020acgk.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ikx.
Prediction doesn't exceed anom_threshold of 50% for 2020kop.
Prediction doesn't exceed anom_threshold of 50% for 2021tvn.
Prediction doesn't exceed anom_threshold of 50% for 2021dox.
2021agt has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020wto.
Prediction doesn't exceed anom_threshold of 50% for 2021phy.
2021afkm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020nlb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021tfr.
Prediction doesn't exceed anom_threshold of 50% for 2020kuw.
2021zkm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021vvw.
Prediction doesn't exceed anom_threshold of 50% for 2021sjv.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020fo.
Prediction doesn't exceed anom_threshold of 50% for 2020kit.
2021shc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021zcc.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qht.
Prediction doesn't exceed anom_threshold of 50% for 2021bmu.
Prediction doesn't exceed anom_threshold of 50% for 2021ogq.
Prediction doesn't exceed anom_threshold of 50% for 2021ypu.
2020jtq has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021coc.
Prediction doesn't exceed anom_threshold of 50% for 2020roo.
Prediction doesn't exceed anom_threshold of 50% for 2021fsb.
2019uez has some NaN LC features. Skip!
2020aot has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021vjt.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020von.
Prediction doesn't exceed anom_threshold of 50% for 2020zyj.
2021acey has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020pth.
Prediction doesn't exceed anom_threshold of 50% for 2021pzl.
2020anm has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021abmt.
Prediction doesn't exceed anom_threshold of 50% for 2020hmc.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020utj.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020rfx.
Prediction doesn't exceed anom_threshold of 50% for 2020ghq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021kpq.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020iqc.
Prediction doesn't exceed anom_threshold of 50% for 2021key.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020xkf.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021hwj.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021acfi.
Anomalous during timeseries!
2020nrh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aekp.
Prediction doesn't exceed anom_threshold of 50% for 2020abyb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cbe.
Prediction doesn't exceed anom_threshold of 50% for 2021dpw.
Prediction doesn't exceed anom_threshold of 50% for 2019ytc.
2021rgs has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021utd.
Prediction doesn't exceed anom_threshold of 50% for 2021gca.
2020kqz has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021zqv.
2021fwo has some NaN LC features. Skip!
2020usk has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020xst.
Prediction doesn't exceed anom_threshold of 50% for 2021nug.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020kfg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020ppq.
Prediction doesn't exceed anom_threshold of 50% for 2020qgo.
2020aecv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020qfj.
2020xft has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ivj.
Prediction doesn't exceed anom_threshold of 50% for 2021isp.
Prediction doesn't exceed anom_threshold of 50% for 2020svo.
2021aon has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020yeg.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021afe.
Prediction doesn't exceed anom_threshold of 50% for 2021tvz.
Prediction doesn't exceed anom_threshold of 50% for 2020ski.
Prediction doesn't exceed anom_threshold of 50% for 2021aahr.
Prediction doesn't exceed anom_threshold of 50% for 2021ueu.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tvt.
2021nsk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021suq.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020nib.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aepz.
Prediction doesn't exceed anom_threshold of 50% for 2020wzq.
Anomalous during timeseries!
2021qxr has some NaN LC features. Skip!
2019unp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021rkd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020uwr.
2020addt has some NaN LC features. Skip!
2020azh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020achp.
2020imh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021foz.
Prediction doesn't exceed anom_threshold of 50% for 2020rrr.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020rsw.
Prediction doesn't exceed anom_threshold of 50% for 2020inw.
Prediction doesn't exceed anom_threshold of 50% for 2021nra.
2020aenc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020eyv.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2019zix.
Prediction doesn't exceed anom_threshold of 50% for 2020nim.
Prediction doesn't exceed anom_threshold of 50% for 2020aeqp.
2020utm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020yiw.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021pgm.
Prediction doesn't exceed anom_threshold of 50% for 2020admp.
Prediction doesn't exceed anom_threshold of 50% for 2021xyf.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020acvi.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cox.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020ykb.
Prediction doesn't exceed anom_threshold of 50% for 2021ofo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aciz.
Prediction doesn't exceed anom_threshold of 50% for 2020jtj.
Anomalous during timeseries!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ilb.
Prediction doesn't exceed anom_threshold of 50% for 2020mbi.
2020ltr has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020tqz.
Prediction doesn't exceed anom_threshold of 50% for 2021xbd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021zmr.
2020iiv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020zhh.
Prediction doesn't exceed anom_threshold of 50% for 2020kxv.
Prediction doesn't exceed anom_threshold of 50% for 2021tks.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2020vkd has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020usl.
Prediction doesn't exceed anom_threshold of 50% for 2020aawu.
Prediction doesn't exceed anom_threshold of 50% for 2020iuu.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021afj.
Prediction doesn't exceed anom_threshold of 50% for 2021bf.
Anomalous during timeseries!
2021chy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020jyp.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021arg.
Prediction doesn't exceed anom_threshold of 50% for 2020odt.
2020xek has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020knn.
2020trj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020evq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020nrg.
2020yyc has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021achl.
Anomalous during timeseries!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020hwk.
Prediction doesn't exceed anom_threshold of 50% for 2021ipg.
2020qpu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020itx.
Prediction doesn't exceed anom_threshold of 50% for 2020able.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020lhy.
Prediction doesn't exceed anom_threshold of 50% for 2021thk.
Prediction doesn't exceed anom_threshold of 50% for 2020has.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cka.
2021aoi has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021zri.
Prediction doesn't exceed anom_threshold of 50% for 2021khl.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020kfh.
Prediction doesn't exceed anom_threshold of 50% for 2021nuh.
2021hqa has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021pti.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
2021rpa has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021us.
Prediction doesn't exceed anom_threshold of 50% for 2020mcd.
2020lzp has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021aeja.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021vtq.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021jzf.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021rkc.
Prediction doesn't exceed anom_threshold of 50% for 2021uyq.
Prediction doesn't exceed anom_threshold of 50% for 2021ljb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021krk.
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021qxu.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020aeqx.
Prediction doesn't exceed anom_threshold of 50% for 2020nie.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021nsl.
Prediction doesn't exceed anom_threshold of 50% for 2020ioz.
2021qes has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020lxe.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jwr.
Prediction doesn't exceed anom_threshold of 50% for 2021dkz.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021uil.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020kfi.
2020aehn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021low.
Prediction doesn't exceed anom_threshold of 50% for 2020iwi.
Prediction doesn't exceed anom_threshold of 50% for 2020ore.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020acyo.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020use.
Prediction doesn't exceed anom_threshold of 50% for 2021reh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020lhx.
Prediction doesn't exceed anom_threshold of 50% for 2021thj.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021achm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020absw.
2021vrt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020evp.
Prediction doesn't exceed anom_threshold of 50% for 2020kob.
Prediction doesn't exceed anom_threshold of 50% for 2020wnz.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020era.
Prediction doesn't exceed anom_threshold of 50% for 2021nsm.
Prediction doesn't exceed anom_threshold of 50% for 2020lxd.
Prediction doesn't exceed anom_threshold of 50% for 2021kni.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020lsz.
2020qte has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021ehh.
2020abhu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020abip.
Prediction doesn't exceed anom_threshold of 50% for 2021kzd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020mzr.
Prediction doesn't exceed anom_threshold of 50% for 2021vjf.
Prediction doesn't exceed anom_threshold of 50% for 2021dk.
Prediction doesn't exceed anom_threshold of 50% for 2021aczd.
Prediction doesn't exceed anom_threshold of 50% for 2019pmd.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020apu.
Prediction doesn't exceed anom_threshold of 50% for 2021atj.
Prediction doesn't exceed anom_threshold of 50% for 2021lux.
Prediction doesn't exceed anom_threshold of 50% for 2020qhn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ibg.
Prediction doesn't exceed anom_threshold of 50% for 2020qbu.
Prediction doesn't exceed anom_threshold of 50% for 2021dhc.
2020wmb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021pn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jtk.
Prediction doesn't exceed anom_threshold of 50% for 2021ulp.
Prediction doesn't exceed anom_threshold of 50% for 2020aafm.
Prediction doesn't exceed anom_threshold of 50% for 2021hjj.
Prediction doesn't exceed anom_threshold of 50% for 2021yny.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021akq.
Prediction doesn't exceed anom_threshold of 50% for 2021tex.
Prediction doesn't exceed anom_threshold of 50% for 2020utl.
Prediction doesn't exceed anom_threshold of 50% for 2021hca.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abix.
2020rsv has some NaN LC features. Skip!
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020trk.
2020odu has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021hyf.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020aaua.
Prediction doesn't exceed anom_threshold of 50% for 2020acqi.
Prediction doesn't exceed anom_threshold of 50% for 2020itq.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2019zfv.
Prediction doesn't exceed anom_threshold of 50% for 2021lgq.
Prediction doesn't exceed anom_threshold of 50% for 2021abam.
Prediction doesn't exceed anom_threshold of 50% for 2020pni.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020usm.
2020tom has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020zbr.
Prediction doesn't exceed anom_threshold of 50% for 2020whv.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021wli.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ufe.
Prediction doesn't exceed anom_threshold of 50% for 2020fle.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020war.
Prediction doesn't exceed anom_threshold of 50% for 2020clh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021iv.
Prediction doesn't exceed anom_threshold of 50% for 2021bh.
2021afd has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021gis.
2020kpv has some NaN LC features. Skip!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020dwq.
Prediction doesn't exceed anom_threshold of 50% for 2020qyx.
Prediction doesn't exceed anom_threshold of 50% for 2021kwy.
Prediction doesn't exceed anom_threshold of 50% for 2020svn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
2020ypn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020mbg.
2021puj has some NaN LC features. Skip!
2020adti has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020tue.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ofa.
2021dl has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020mye.
2020abkb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021bsv.
Prediction doesn't exceed anom_threshold of 50% for 2020rw.
2020wzp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021aave.
Prediction doesn't exceed anom_threshold of 50% for 2019wbw.
Prediction doesn't exceed anom_threshold of 50% for 2020jcy.
Prediction doesn't exceed anom_threshold of 50% for 2020rmo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020yiy.
Prediction doesn't exceed anom_threshold of 50% for 2021ylc.
Prediction doesn't exceed anom_threshold of 50% for 2021lit.
Prediction doesn't exceed anom_threshold of 50% for 2020ej.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020lxc.
Prediction doesn't exceed anom_threshold of 50% for 2021aabh.
Prediction doesn't exceed anom_threshold of 50% for 2021aacm.
Prediction doesn't exceed anom_threshold of 50% for 2020oku.
2020mnx has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aaer.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
2020oaf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021ikk.
2021kpp has some NaN LC features. Skip!
Anomalous during timeseries!
2020utk has some NaN LC features. Skip!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ees.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020anl.
Prediction doesn't exceed anom_threshold of 50% for 2021mba.
2021jzh has some NaN LC features. Skip!
2021aakk has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020acmm.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020vtq.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021qhg.
Prediction doesn't exceed anom_threshold of 50% for 2020zcp.
Prediction doesn't exceed anom_threshold of 50% for 2021xhy.
Prediction doesn't exceed anom_threshold of 50% for 2021smj.
2021sft has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020qrn.
2021abha has some NaN LC features. Skip!
2020kxp has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020toj.
Prediction doesn't exceed anom_threshold of 50% for 2021msx.
2020usj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aaws.
Prediction doesn't exceed anom_threshold of 50% for 2020akx.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021qua.
Prediction doesn't exceed anom_threshold of 50% for 2021cca.
Prediction doesn't exceed anom_threshold of 50% for 2019ucc.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021aamo.
Prediction doesn't exceed anom_threshold of 50% for 2021rxa.
Prediction doesn't exceed anom_threshold of 50% for 2019zsy.
Prediction doesn't exceed anom_threshold of 50% for 2021nsh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021xei.
Prediction doesn't exceed anom_threshold of 50% for 2020far.
Prediction doesn't exceed anom_threshold of 50% for 2021psl.
Anomalous during timeseries!
2021wtv has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abcn.
Prediction doesn't exceed anom_threshold of 50% for 2019zit.
2021liv has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020fwj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jjp.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020irx.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021luu.
Prediction doesn't exceed anom_threshold of 50% for 2021gqc.
Prediction doesn't exceed anom_threshold of 50% for 2020imk.
Prediction doesn't exceed anom_threshold of 50% for 2020zfn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021pc.
2020azk has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021qbv.
Prediction doesn't exceed anom_threshold of 50% for 2020qgl.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020pqw.
Prediction doesn't exceed anom_threshold of 50% for 2020jpw.
2021rpe has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021uhl.
Prediction doesn't exceed anom_threshold of 50% for 2020acsq.
Prediction doesn't exceed anom_threshold of 50% for 2020rki.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020jdz.
Prediction doesn't exceed anom_threshold of 50% for 2021rem.
Prediction doesn't exceed anom_threshold of 50% for 2020fxe.
Prediction doesn't exceed anom_threshold of 50% for 2020yda.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021srs.
2020abnt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020iwl.
Prediction doesn't exceed anom_threshold of 50% for 2021adnw.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020uzk.
Prediction doesn't exceed anom_threshold of 50% for 2021ipc.
Prediction doesn't exceed anom_threshold of 50% for 2020evu.
Prediction doesn't exceed anom_threshold of 50% for 2021zep.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021sou.
Prediction doesn't exceed anom_threshold of 50% for 2020sjo.
Prediction doesn't exceed anom_threshold of 50% for 2020prg.
Prediction doesn't exceed anom_threshold of 50% for 2020pyy.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021jvu.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020hwg.
Prediction doesn't exceed anom_threshold of 50% for 2020ewx.
2021znf has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tfc.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020dtk.
Prediction doesn't exceed anom_threshold of 50% for 2021aaqn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020acxg.
Prediction doesn't exceed anom_threshold of 50% for 2021uww.
Prediction doesn't exceed anom_threshold of 50% for 2020uys.
Prediction doesn't exceed anom_threshold of 50% for 2021hzs.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020wbh.
Prediction doesn't exceed anom_threshold of 50% for 2020aahb.
Prediction doesn't exceed anom_threshold of 50% for 2020xgz.
Prediction doesn't exceed anom_threshold of 50% for 2021atg.
Prediction doesn't exceed anom_threshold of 50% for 2020juk.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020zgc.
2021vjk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020yjc.
Prediction doesn't exceed anom_threshold of 50% for 2020ann.
2020nhl has some NaN LC features. Skip!
2020nbw has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021kpr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021gfv.
Prediction doesn't exceed anom_threshold of 50% for 2021lce.
Prediction doesn't exceed anom_threshold of 50% for 2021abng.
Prediction doesn't exceed anom_threshold of 50% for 2020aazk.
Prediction doesn't exceed anom_threshold of 50% for 2021acgo.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020acjn.
2020znh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021xdd.
Anomalous during timeseries!
2021ath has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021pd.
Prediction doesn't exceed anom_threshold of 50% for 2021ype.
Prediction doesn't exceed anom_threshold of 50% for 2020apw.
2020nue has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020smi.
Prediction doesn't exceed anom_threshold of 50% for 2020abjb.
Prediction doesn't exceed anom_threshold of 50% for 2021kyv.
Prediction doesn't exceed anom_threshold of 50% for 2020ovv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020abci.
Prediction doesn't exceed anom_threshold of 50% for 2020nbx.
Prediction doesn't exceed anom_threshold of 50% for 2021oet.
Prediction doesn't exceed anom_threshold of 50% for 2021fnt.
Prediction doesn't exceed anom_threshold of 50% for 2020ujp.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020rxb.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020xaq.
Prediction doesn't exceed anom_threshold of 50% for 2020ioy.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021nyt.
Prediction doesn't exceed anom_threshold of 50% for 2021dnm.
Prediction doesn't exceed anom_threshold of 50% for 2019wyp.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020awv.
2020tra has some NaN LC features. Skip!
Anomalous during timeseries!
Anomalous during timeseries!
2020lhz has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021ccl.
Prediction doesn't exceed anom_threshold of 50% for 2019yuj.
2021rfz has some NaN LC features. Skip!
2020svk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ydf.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021hdj.
2021dsk has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019zqa.
2021bbb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020sck.
Prediction doesn't exceed anom_threshold of 50% for 2021sgt.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021xbg.
2021rqo has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021dmu.
2020oxt has some NaN LC features. Skip!
2021aaqi has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ivf.
Prediction doesn't exceed anom_threshold of 50% for 2021mpm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2021afi.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021lfv.
Prediction doesn't exceed anom_threshold of 50% for 2020tga.
2021vmm has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020iuv.
2020qze has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020tyw.
2020koh has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021ied.
Prediction doesn't exceed anom_threshold of 50% for 2020qnh.
2020ije has some NaN LC features. Skip!
2021qkr has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020odw.
Prediction doesn't exceed anom_threshold of 50% for 2020advq.
Prediction doesn't exceed anom_threshold of 50% for 2020mka.
Prediction doesn't exceed anom_threshold of 50% for 2021sjn.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021nsg.
2020pvy has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020cwg.
Prediction doesn't exceed anom_threshold of 50% for 2021yrx.
Prediction doesn't exceed anom_threshold of 50% for 2020rst.
Prediction doesn't exceed anom_threshold of 50% for 2021rbc.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020mqf.
2020tbu has some NaN LC features. Skip!
Anomalous during timeseries!
2021vcg has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021ehb.
Prediction doesn't exceed anom_threshold of 50% for 2020mrv.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021bxe.
Prediction doesn't exceed anom_threshold of 50% for 2020ixl.
Prediction doesn't exceed anom_threshold of 50% for 2021gdd.
2021qls has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020xbi.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
2021kms has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020xhr.
Prediction doesn't exceed anom_threshold of 50% for 2020aqz.
Prediction doesn't exceed anom_threshold of 50% for 2021pl.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020uhm.
Prediction doesn't exceed anom_threshold of 50% for 2021pzh.
Prediction doesn't exceed anom_threshold of 50% for 2021dha.
Prediction doesn't exceed anom_threshold of 50% for 2021hye.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021oal.
Prediction doesn't exceed anom_threshold of 50% for 2021achf.
2021kkv has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020skd.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021xkm.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020nrm.
Prediction doesn't exceed anom_threshold of 50% for 2020lbh.
Prediction doesn't exceed anom_threshold of 50% for 2021gba.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2019zgp.
Prediction doesn't exceed anom_threshold of 50% for 2021vml.
Prediction doesn't exceed anom_threshold of 50% for 2020zti.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021eng.
Prediction doesn't exceed anom_threshold of 50% for 2020eaf.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021tjt.
Prediction doesn't exceed anom_threshold of 50% for 2021abhe.
2020mwc has some NaN LC features. Skip!
2020usn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021at.
Prediction doesn't exceed anom_threshold of 50% for 2021rdf.
2020adjs has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021cjn.
2021aaqh has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020acxa.
Prediction doesn't exceed anom_threshold of 50% for 2020acmi.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020qmy.
Prediction doesn't exceed anom_threshold of 50% for 2020scj.
2021xix has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2019szh.
2020aalu has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021huz.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ptm.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021qgl.
Prediction doesn't exceed anom_threshold of 50% for 2020aegj.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020ixm.
Prediction doesn't exceed anom_threshold of 50% for 2020sym.
Prediction doesn't exceed anom_threshold of 50% for 2021jna.
Prediction doesn't exceed anom_threshold of 50% for 2020mrw.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021lix.
2020nhj has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021bzq.
Prediction doesn't exceed anom_threshold of 50% for 2021cmo.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020yiu.
Prediction doesn't exceed anom_threshold of 50% for 2020rmc.
Prediction doesn't exceed anom_threshold of 50% for 2020fuq.
2021aaiz has some NaN LC features. Skip!
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021nsf.
Prediction doesn't exceed anom_threshold of 50% for 2020kak.
2020lyb has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021psj.
2020ybc has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ygy.
Prediction doesn't exceed anom_threshold of 50% for 2020pf.
2020elt has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021yow.
2021jni has some NaN LC features. Skip!
2021qsi has some NaN LC features. Skip!
2020abkf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021bsr.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ldd.
Prediction doesn't exceed anom_threshold of 50% for 2020tkw.
2020imm has some NaN LC features. Skip!
2020nud has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020zmv.
2021lus has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021scl.
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021ati.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2020nqu.
Prediction doesn't exceed anom_threshold of 50% for 2020kfj.
Prediction doesn't exceed anom_threshold of 50% for 2021xbn.
Prediction doesn't exceed anom_threshold of 50% for 2020jpq.
Prediction doesn't exceed anom_threshold of 50% for 2020wbf.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021ptk.
2021fvg has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021dsj.
Prediction doesn't exceed anom_threshold of 50% for 2021wrt.
Prediction doesn't exceed anom_threshold of 50% for 2020pog.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020jgl.
Anomalous during timeseries!
2019ytn has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020ahd.
2020nfh has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020itz.
Prediction doesn't exceed anom_threshold of 50% for 2021aegy.
2020koa has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021wd.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020aww.
Anomalous during timeseries!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021wlm.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
Prediction doesn't exceed anom_threshold of 50% for 2020fla.
Anomalous during timeseries!
2020acxf has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2021cji.
Prediction doesn't exceed anom_threshold of 50% for 2020poh.
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020uxw.
Prediction doesn't exceed anom_threshold of 50% for 2021fwm.
Prediction doesn't exceed anom_threshold of 50% for 2021amq.
Prediction doesn't exceed anom_threshold of 50% for 2020riu.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021zqt.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020lht.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020tfb.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020ijc.
Prediction doesn't exceed anom_threshold of 50% for 2020kon.
Prediction doesn't exceed anom_threshold of 50% for 2020odq.
Prediction doesn't exceed anom_threshold of 50% for 2020iow.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021skm.
Prediction doesn't exceed anom_threshold of 50% for 2020rsr.
Prediction doesn't exceed anom_threshold of 50% for 2021oez.
Prediction doesn't exceed anom_threshold of 50% for 2020rla.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020alz.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021kps.
Prediction doesn't exceed anom_threshold of 50% for 2021urb.
2020zrj has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
2021tqq has some NaN LC features. Skip!
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021dib.
Prediction doesn't exceed anom_threshold of 50% for 2021lut.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020nuc.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021kyp.
2021aeuw has some NaN LC features. Skip!
2020abka has some NaN LC features. Skip!
Prediction doesn't exceed anom_threshold of 50% for 2020aesh.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020heg.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021ynu.
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2021acfc.
Prediction doesn't exceed anom_threshold of 50% for 2020jww.
Anomalous during timeseries!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2021vva.
Prediction doesn't exceed anom_threshold of 50% for 2021nsi.
Prediction doesn't exceed anom_threshold of 50% for 2020vxe.
Prediction doesn't exceed anom_threshold of 50% for 2020zjp.
2020xef has some NaN LC features. Skip!
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
Prediction doesn't exceed anom_threshold of 50% for 2021lru.
Anomalous during timeseries!
Prediction doesn't exceed anom_threshold of 50% for 2020glj.
Prediction doesn't exceed anom_threshold of 50% for 2021afg.
Prediction doesn't exceed anom_threshold of 50% for 2021adnv.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
Prediction doesn't exceed anom_threshold of 50% for 2020xye.
Prediction doesn't exceed anom_threshold of 50% for 2021aaej.
2021ptl has some NaN LC features. Skip!
Anomalous during timeseries!
2020qmv has some NaN LC features. Skip!
2020qgm has some NaN LC features. Skip!
CPU times: user 51.6 s, sys: 7.21 s, total: 58.8 s
Wall time: 1min 4s
```python
ysedr1_df = pd.DataFrame(zip(ztfid_l, anom_idx_is_l, max_anom_score_l, num_anom_epochs_l, spec_class_l, best_z_l, z_type_l),
columns=["Disc. Internal Name", "RFC_best_cls", "max_anom_score", "num_anom_epochs", "spec_class", "best_z", "z_type"])
ysedr1_df = ysedr1_df.set_index("Disc. Internal Name")
ysedr1_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>RFC_best_cls</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
</tr>
<tr>
<th>Disc. Internal Name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2021iyu</th>
<td>Normal</td>
<td>24.0</td>
<td>0</td>
<td>NA</td>
<td>0.1991</td>
<td>HOST-Z</td>
</tr>
<tr>
<th>2020van</th>
<td>Normal</td>
<td>49.0</td>
<td>0</td>
<td>NA</td>
<td>0.2070</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2020mvu</th>
<td>Normal</td>
<td>33.0</td>
<td>0</td>
<td>NA</td>
<td>0.1530</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2020olk</th>
<td>Normal</td>
<td>30.0</td>
<td>0</td>
<td>NA</td>
<td>0.1140</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2021bbp</th>
<td>Normal</td>
<td>47.0</td>
<td>0</td>
<td>NA</td>
<td>0.1710</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>2021afg</th>
<td>Normal</td>
<td>40.0</td>
<td>0</td>
<td>NA</td>
<td>0.1209</td>
<td>HOST-Z</td>
</tr>
<tr>
<th>2021adnv</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNIa-norm</td>
<td>0.1900</td>
<td>SPEC-Z</td>
</tr>
<tr>
<th>2020xye</th>
<td>Normal</td>
<td>37.0</td>
<td>0</td>
<td>NA</td>
<td>0.1970</td>
<td>PHOTO-Z</td>
</tr>
<tr>
<th>2021aaej</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNII</td>
<td>0.0900</td>
<td>SPEC-Z</td>
</tr>
<tr>
<th>2021sld</th>
<td>Other</td>
<td>52.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.1040</td>
<td>SPEC-Z</td>
</tr>
</tbody>
</table>
<p>1153 rows × 6 columns</p>
</div>
```python
Counter(ysedr1_df.spec_class)
```
Counter({'NA': 843,
'SNIIn': 8,
'SNIa-91T-like': 11,
'SNII': 52,
'SNIa-norm': 195,
'SNIIb': 7,
'SNIc-BL': 3,
'SNIb': 5,
'Other': 1,
'SNIbn': 2,
'TDE': 3,
'LBV': 1,
'SNIc': 7,
'SNIb-pec': 2,
'SNIa-CSM': 3,
'SNIa-91bg-like': 1,
'SN': 4,
'SNIa-SC': 1,
'SLSN-II': 2,
'SLSN-I': 1,
'SNIax': 1})
```python
di = {'CV': 'Other',
'SLSN-I': 'Other',
'SLSN-II': 'Other',
'SNII': 'Normal',
'SNIIP': 'Normal',
'SNIIb': 'Other',
'SNIIn': 'Other',
'SNII-pec': 'Other',
'SNIa-norm': 'Normal',
'SNIa-91T-like': 'Normal',
'SNIa-CSM': 'Other',
'SNIa-SC': 'Other',
'SNIa-91bg-like': 'Normal',
'SNIax[02cx-like]': 'Other',
'SNIax': 'Other',
'SNIa-pec': 'Other',
'SNIb': 'Other',
'SNIb-pec': 'Other',
'SNIb/c': 'Other',
'SNIbn': 'Other',
'SNIc': 'Other',
'SNIc-BL': 'Other',
'SNIcn': 'Other',
'TDE': 'Other',
'None': 'Other',
'SN': 'Other',
'SNI': 'Other',
'Other': 'Other',
'LBV': 'Other',
'LRN': 'Other',
'NA': 'NA'}
ysedr1_df["spec_class_norm_anom"] = ysedr1_df["spec_class"].copy()
ysedr1_df = ysedr1_df.replace({"spec_class_norm_anom": di})
ysedr1_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>RFC_best_cls</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
<th>spec_class_norm_anom</th>
</tr>
<tr>
<th>Disc. Internal Name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2021iyu</th>
<td>Normal</td>
<td>24.0</td>
<td>0</td>
<td>NA</td>
<td>0.1991</td>
<td>HOST-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2020van</th>
<td>Normal</td>
<td>49.0</td>
<td>0</td>
<td>NA</td>
<td>0.2070</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2020mvu</th>
<td>Normal</td>
<td>33.0</td>
<td>0</td>
<td>NA</td>
<td>0.1530</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2020olk</th>
<td>Normal</td>
<td>30.0</td>
<td>0</td>
<td>NA</td>
<td>0.1140</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2021bbp</th>
<td>Normal</td>
<td>47.0</td>
<td>0</td>
<td>NA</td>
<td>0.1710</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>2021afg</th>
<td>Normal</td>
<td>40.0</td>
<td>0</td>
<td>NA</td>
<td>0.1209</td>
<td>HOST-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2021adnv</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNIa-norm</td>
<td>0.1900</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020xye</th>
<td>Normal</td>
<td>37.0</td>
<td>0</td>
<td>NA</td>
<td>0.1970</td>
<td>PHOTO-Z</td>
<td>NA</td>
</tr>
<tr>
<th>2021aaej</th>
<td>Normal</td>
<td>11.0</td>
<td>0</td>
<td>SNII</td>
<td>0.0900</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021sld</th>
<td>Other</td>
<td>52.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.1040</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
</tbody>
</table>
<p>1153 rows × 7 columns</p>
</div>
```python
# full timeseries only (XX spec+phot w/ "other"/anomaly classification)
higest_anom_score_df = ysedr1_df.nlargest(198, 'max_anom_score')
for obj, score, num_anom_epochs, spec, spec_norm_anom, rfc_cls in zip(higest_anom_score_df.index,
higest_anom_score_df.max_anom_score,
higest_anom_score_df.num_anom_epochs,
higest_anom_score_df["spec_class"],
higest_anom_score_df["spec_class_norm_anom"],
higest_anom_score_df.RFC_best_cls):
print(f"https://ziggy.ucolick.org/yse/transient_detail/{obj}/", round(score, 1), num_anom_epochs, spec, spec_norm_anom, rfc_cls)
```
https://ziggy.ucolick.org/yse/transient_detail/2020xsy/ 98.0 93 SLSN-II Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020hjv/ 91.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tip/ 90.0 14 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acyu/ 90.0 33 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 89.0 51 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020opy/ 89.0 83 TDE Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021nxq/ 89.0 27 SLSN-I Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020qql/ 88.0 35 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021hrj/ 88.0 79 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021bmv/ 85.0 11 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021aadc/ 85.0 15 SLSN-II Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020jvi/ 84.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qmj/ 82.0 51 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kre/ 82.0 70 SNIa-CSM Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021too/ 81.9 36 SNIc-BL Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021pnp/ 79.0 46 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2019vuz/ 79.0 17 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021gno/ 78.0 29 SNIb-pec Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kbl/ 77.0 22 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020rss/ 77.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofr/ 76.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmj/ 76.0 49 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bpq/ 75.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021btn/ 75.0 43 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021udc/ 75.0 8 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021ojn/ 74.0 18 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021dpa/ 73.0 20 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmq/ 73.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020itp/ 73.0 44 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tan/ 73.0 60 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020acun/ 72.9 25 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020wwt/ 72.0 14 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 72.0 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020wfg/ 72.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ivg/ 72.0 40 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020iga/ 71.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020sgy/ 71.0 18 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kpz/ 71.0 65 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acct/ 70.0 32 SNIc Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020vpn/ 70.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019wmr/ 70.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 69.8 9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofq/ 68.0 14 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021gpt/ 68.0 85 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wue/ 68.0 7 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tux/ 68.0 7 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020jba/ 68.0 16 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020imb/ 68.0 31 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mxw/ 67.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nxl/ 67.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021afh/ 67.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aym/ 66.0 13 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020skh/ 66.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021vaw/ 66.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxp/ 66.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021vcz/ 65.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020hep/ 65.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mms/ 65.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pvj/ 64.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tlf/ 64.0 63 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021joz/ 64.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020adly/ 64.0 26 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kps/ 64.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 64.0 4 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ihk/ 63.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ndi/ 63.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cqv/ 63.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cta/ 63.0 11 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 63.0 16 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020iul/ 62.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020oox/ 61.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iio/ 61.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tjd/ 61.0 16 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021aapa/ 61.0 9 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021ypd/ 61.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeti/ 61.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/ 60.9 17 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kiq/ 60.0 16 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021snh/ 60.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abji/ 60.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 60.0 34 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019uit/ 60.0 10 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020wzd/ 59.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aesp/ 59.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020twb/ 59.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iqk/ 59.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bg/ 59.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeid/ 59.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021arl/ 59.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021pqw/ 59.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aejb/ 58.9 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/ 58.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020icw/ 58.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019xbj/ 58.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rsp/ 58.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/ 58.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020evr/ 58.0 12 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/ 57.7 9 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvo/ 57.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jed/ 57.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/ 57.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020sia/ 57.0 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlz/ 57.0 21 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020mju/ 57.0 12 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020awu/ 57.0 6 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021be/ 57.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021kqp/ 57.0 1 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020rdu/ 56.0 21 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021ovd/ 56.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019uev/ 56.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020psv/ 56.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021iqw/ 56.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021tup/ 56.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020adob/ 56.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021jof/ 56.0 3 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/ 56.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acjk/ 56.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cjj/ 56.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lgr/ 56.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 4 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/ 56.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020nis/ 55.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020mjn/ 55.9 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abgb/ 55.0 51 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021seu/ 55.0 3 Other Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pss/ 55.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iri/ 55.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nor/ 55.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kpp/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ibh/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020row/ 55.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acty/ 55.0 10 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/ 54.9 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021idh/ 54.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020igg/ 54.6 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aevg/ 54.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020twa/ 54.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021xqw/ 54.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020xku/ 54.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rtb/ 54.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tug/ 54.0 6 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aahq/ 53.9 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qbx/ 53.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020juq/ 53.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020zkg/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rrc/ 53.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qht/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jfx/ 53.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ciu/ 53.0 15 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lgy/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aagz/ 53.0 2 SNIa-SC Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aasl/ 53.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvx/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021abeu/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tri/ 53.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ika/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tly/ 53.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/ 52.9 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021beb/ 52.9 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020unn/ 52.2 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020uba/ 52.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ixs/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hol/ 52.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020yix/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020pcz/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pzl/ 52.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qlp/ 52.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aly/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021acza/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021yqa/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020slb/ 52.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pwr/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sld/ 52.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/ 51.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/ 51.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021gda/ 51.5 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/ 51.2 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aov/ 51.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/ 51.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020duv/ 51.0 2 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxn/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hem/ 51.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020mor/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acbb/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020gll/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rcr/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020advk/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aewm/ 50.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021lhi/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sdy/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021kcb/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021naw/ 50.0 1 NA NA Other
! https://ziggy.ucolick.org/yse/transient_detail/2020xsy/ 98.0 93 SLSN-II Other Other
? https://ziggy.ucolick.org/yse/transient_detail/2020hjv/ 91.0 7 NA NA Other ~ -20 abs mag, if photo-z is right? Ia-SC?
! https://ziggy.ucolick.org/yse/transient_detail/2020tip/ 90.0 13 SNIa-norm Normal Other -- 91T / 99aa like? weird b/c litte/no Si in spectrum...
! https://ziggy.ucolick.org/yse/transient_detail/2020acyu/ 90.0 32 NA NA Other -- IIn if photoz is right (-20.25 abs mag)
! https://ziggy.ucolick.org/yse/transient_detail/2021nxq/ 89.0 27 SLSN-I Other Other
? https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 89.0 51 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.127. Faint host.
! https://ziggy.ucolick.org/yse/transient_detail/2020opy/ 89.0 82 TDE Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2021hrj/ 88.0 79 SNIb Other Other
? https://ziggy.ucolick.org/yse/transient_detail/2020qql/ 88.0 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.076
! https://ziggy.ucolick.org/yse/transient_detail/2021bmv/ 85.0 SNIIn Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2021aadc/ 85.0 SLSN-II Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020jvi/ 84.0 NA NA Other -- IIn if photoz is right (-20 abs mag)
! https://ziggy.ucolick.org/yse/transient_detail/2020qmj/ 82.0 SNIIn Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020kre/ 82.0 SNIa-CSM Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2021too/ 81.9 SNIc-BL Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2019vuz/ 79.0 NA NA Normal -- Blue. nuclear. bright. likely TDE.
! https://ziggy.ucolick.org/yse/transient_detail/2021pnp/ 79.0 SNIIb Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2021gno/ 78.0 SNIb-pec Other Other
X https://ziggy.ucolick.org/yse/transient_detail/2020rss/ 77.0 NA NA Other -- Ia normal?
? https://ziggy.ucolick.org/yse/transient_detail/2020kbl/ 77.0 SNIa-norm Normal Other -- Ia-norm spec. But maxes out color on SALT fit c=0.3. In a merger/spiral gal? Not obviously elliptical.
? https://ziggy.ucolick.org/yse/transient_detail/2020kmj/ 76.0 NA NA Other -- normal II if photoz is right (photoz = 0.0799, abs mag ~ -18.25). But in elliptical galaxy (maybe? hard to tell!)
X https://ziggy.ucolick.org/yse/transient_detail/2021ofr/ 76.0 NA NA Other -- likely Ia. One bad epoch. early time excess?
! https://ziggy.ucolick.org/yse/transient_detail/2021udc/ 75.0 SNIIb Other Other
X https://ziggy.ucolick.org/yse/transient_detail/2021bpq/ 75.0 SNIa-norm Normal Other -- Ia-norm spec. IDK why, need to see timeseries evolution.
! https://ziggy.ucolick.org/yse/transient_detail/2021btn/ 75.0 SNII Normal Normal -- AWFUL spec but RECLASSIFY as Ibc!
? https://ziggy.ucolick.org/yse/transient_detail/2021ojn/ 74.0 SNIa-norm Normal Other -- Ia-norm spec. IDK why, need to see timeseries evolution. BUT it does have 4 spectra so someone thought it was interesting...
! https://ziggy.ucolick.org/yse/transient_detail/2021dpa/ 73.0 NA NA Other -- normal II/ IIn w/ very weird slow 120d RISE?
! https://ziggy.ucolick.org/yse/transient_detail/2020tan/ 73.0 SNIIn Other Other
? https://ziggy.ucolick.org/yse/transient_detail/2021rmq/ 73.0 NA NA Other -- II/IIn?
? https://ziggy.ucolick.org/yse/transient_detail/2020itp/ 73.0 NA NA Other -- not sure. Ia/Ibc ? Bright like Ia if photoz is right, but LC evolution looks more Ibc. elliptical host so Ia?
! https://ziggy.ucolick.org/yse/transient_detail/2020acun/ 72.9 SNII Normal Normal -- RECLASSIFY AS Ic/Ic-BL?! Bad spec though
! https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 72.0 SNIa-norm Normal Other -- Ia-norm spec. But that z=0.12 puts abs mag ~ -20.6 mag. Ia-SC????
? https://ziggy.ucolick.org/yse/transient_detail/2020wwt/ 72.0 NA NA Other -- Not sure. Could be Ia, but in spiral. Also PS1-r image is messed up...
! https://ziggy.ucolick.org/yse/transient_detail/2020ivg/ 72.0 SNIIb Other Other
X https://ziggy.ucolick.org/yse/transient_detail/2020wfg/ 72.0 SNIa-norm Normal Other -- spec Ia normal to me.
! https://ziggy.ucolick.org/yse/transient_detail/2020kpz/ 71.0 SNII Normal Other -- lasted 400d w/ pre-explosion data??
X https://ziggy.ucolick.org/yse/transient_detail/2020sgy/ 71.0 NA NA Other -- prob Ia normal? Faint host. If photoz is right, overluminous Ia. but SALT z estimate is more reasonable.
X https://ziggy.ucolick.org/yse/transient_detail/2020iga/ 71.0 NA NA Other -- prob Ia normal? nuclear.
? https://ziggy.ucolick.org/yse/transient_detail/2020vpn/ 70.0 NA NA Other -- ? Weird LC in elliptical. Ia/II...?
! https://ziggy.ucolick.org/yse/transient_detail/2020acct/ 70.0 SNIc Other Normal
! https://ziggy.ucolick.org/yse/transient_detail/2019wmr/ 70.0 SNII Normal Normal -- spec is normal II (i guess, lots of host contamination) but LC is weird for II
? https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 69.8 SNIa-norm Normal Other -- Ryan thinks it could be interesting
= ! (25/42 = 60%) --> !/? (36/42 = 86%)
= ? (11/42 = 26%) --^
= X (6/42 = 14%)
# What is z range of training set??
# notes: seems to misstag normal Ia w/: little/no 2nd bump, are faint (app mag Ia), those which have lingering r-band obs/plateau at end but g is not observed (too faint), big gaps in one pb (~10d) during big color change
______
X https://ziggy.ucolick.org/yse/transient_detail/2020jba/ 68.0 NA NA Other -- looks Ia normal to me
! https://ziggy.ucolick.org/yse/transient_detail/2021tux/ 68.0 SNIb Other Normal
! https://ziggy.ucolick.org/yse/transient_detail/2020imb/ 68.0 NA NA Other -- likely SN II that lasts 400-500d
? https://ziggy.ucolick.org/yse/transient_detail/2021wue/ 68.0 SNIa-norm Normal Other -- Ia, no strong 2nd bump, maybe 91T like (first classification was this) (photoz = 0.108)
X https://ziggy.ucolick.org/yse/transient_detail/2021ofq/ 68.0 SNIa-norm Normal Other -- Ia, no strong 2nd bump,(photoz = 0.19)
! https://ziggy.ucolick.org/yse/transient_detail/2021gpt/ 68.0 NA NA Other -- lasted for 250d in ysedr1, lasted in total for 800-900d
X https://ziggy.ucolick.org/yse/transient_detail/2020nxl/ 67.0 NA NA Other -- likely Ia normal (photoz = 0.112)
? https://ziggy.ucolick.org/yse/transient_detail/2021mxw/ 67.0 NA NA Normal -- likely faint (app mag) Ia normal, but weird color switch for one epoch on decline (photoz = 0.202)
X https://ziggy.ucolick.org/yse/transient_detail/2021afh/ 67.0 NA NA Other -- likely faint (app mag) Ia normal (photoz = 0.197)
X https://ziggy.ucolick.org/yse/transient_detail/2020aym/ 66.0 NA NA Other -- likely faint (app mag) Ia normal (photoz = 0.19)
? https://ziggy.ucolick.org/yse/transient_detail/2021qxp/ 66.0 NA NA Normal -- likely faint (app mag) II normal? Or Ia in spiral gal? Hard to know what this is...
? https://ziggy.ucolick.org/yse/transient_detail/2020skh/ 66.0 NA NA Other -- likely Ia normal, high color offset (r brighter ~1 mag) on decline for a few epochs. SALT c=0.3
X https://ziggy.ucolick.org/yse/transient_detail/2021vaw/ 66.0 SNIa-norm Normal Other -- likely Ia normal
! https://ziggy.ucolick.org/yse/transient_detail/2021mms/ 65.0 NA NA Other -- likely IIn (z=0.239)
X https://ziggy.ucolick.org/yse/transient_detail/2021vcz/ 65.0 SNIa-norm Normal Other -- big gap in g band but not r band
? https://ziggy.ucolick.org/yse/transient_detail/2020hep/ 65.0 NA NA Other -- Ia, but 2nd bump isn't until about 40d after peak. Stretch SALT x1=3.00.
! https://ziggy.ucolick.org/yse/transient_detail/2020tlf/ 64.0 SNII Normal Other -- we know this is interesting
! https://ziggy.ucolick.org/yse/transient_detail/2020adly/ 64.0 NA NA Other -- IIn/ maybe SLSN
! https://ziggy.ucolick.org/yse/transient_detail/2020kps/ 64.0 NA NA Normal -- likely TDE
! https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 64.0 SNIa-91T-like Normal Other -- faint (app mag) Ia-91T-like (z=0.209)
X https://ziggy.ucolick.org/yse/transient_detail/2020pvj/ 64.0 NA NA Other -- like Ia normal
X https://ziggy.ucolick.org/yse/transient_detail/2021joz/ 64.0 SNII Normal Other -- SN II normal, think a bad epoch messed it up
! https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 63.0 SNII Normal Other -- 300d SN II(P), big color offset
X https://ziggy.ucolick.org/yse/transient_detail/2021ihk/ 63.0 NA NA Other -- faint (app mag) Ia
X https://ziggy.ucolick.org/yse/transient_detail/2021cqv/ 63.0 NA NA Other -- prob Ia
? https://ziggy.ucolick.org/yse/transient_detail/2020ndi/ 63.0 NA NA Other -- prob Ia. Stretch SALT x1=3.00.
https://ziggy.ucolick.org/yse/transient_detail/2021cta/ 63.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020iul/ 62.0 NA NA Other -- gaps
https://ziggy.ucolick.org/yse/transient_detail/2020oox/ 61.9 NA NA Other -- gaps
https://ziggy.ucolick.org/yse/transient_detail/2021ypd/ 61.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeti/ 61.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aapa/ 61.0 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020tjd/ 61.0 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020iio/ 61.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/ 60.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kiq/ 60.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021snh/ 60.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abji/ 60.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 60.0 SNII Normal Other -- looks normal SN II
https://ziggy.ucolick.org/yse/transient_detail/2019uit/ 60.0 SNIIn Other Other
________
https://ziggy.ucolick.org/yse/transient_detail/2021bg/ 59.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2021pqw/ 59.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeid/ 59.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021arl/ 59.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020iqk/ 59.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aesp/ 59.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020twb/ 59.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020wzd/ 59.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aejb/ 58.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019xbj/ 58.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020evr/ 58.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/ 58.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rsp/ 58.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/ 58.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020icw/ 58.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/ 57.7 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020mju/ 57.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021be/ 57.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvo/ 57.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlz/ 57.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/ 57.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020awu/ 57.0 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021kqp/ 57.0 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020jed/ 57.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020sia/ 57.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020adob/ 56.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2021jof/ 56.0 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acjk/ 56.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020lgr/ 56.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021tup/ 56.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ovd/ 56.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/ 56.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019uev/ 56.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020rdu/ 56.0 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021iqw/ 56.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/ 56.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021cjj/ 56.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020psv/ 56.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020nis/ 55.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020mjn/ 55.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abgb/ 55.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/ 55.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kpp/ 55.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pss/ 55.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020iri/ 55.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nor/ 55.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ibh/ 55.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020row/ 55.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021seu/ 55.0 Other Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acty/ 55.0 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/ 54.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021idh/ 54.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020igg/ 54.6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020xku/ 54.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020rtb/ 54.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aevg/ 54.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tug/ 54.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020twa/ 54.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021xqw/ 54.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qbx/ 53.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aahq/ 53.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rrc/ 53.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2021qht/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aasl/ 53.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020ciu/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020juq/ 53.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tri/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvx/ 53.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020zkg/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021abeu/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tly/ 53.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020lgy/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jfx/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ika/ 53.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aagz/ 53.0 SNIa-SC Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/ 52.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021beb/ 52.9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020unn/ 52.2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020yix/ 52.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020pcz/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qlp/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pzl/ 52.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020aly/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021acza/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020slb/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pwr/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sld/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ixs/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021yqa/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020uba/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/ 52.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hol/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/ 51.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/ 51.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021gda/ 51.5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/ 51.2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aov/ 51.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/ 51.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/ 51.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acbb/ 51.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020gll/ 51.0 NA NA Normal
https://ziggy.ucolick.org/yse/transient_detail/2020duv/ 51.0 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxn/ 51.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hem/ 51.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020mor/ 51.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rcr/ 50.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aewm/ 50.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021lhi/ 50.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021naw/ 50.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020alx/ 50.0 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hr/ 50.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aena/ 50.0 NA NA Other
```python
cols_order = ['spec_class', 'best_z', 'z_type', 'max_anom_score', 'num_anom_epochs', 'RFC_best_cls', 'spec_class_norm_anom']
highest_anom_score_df = higest_anom_score_df[cols_order]
highest_anom_score_df["max_anom_score"] = highest_anom_score_df["max_anom_score"].map(round)
highest_anom_score_df = highest_anom_score_df.drop(["RFC_best_cls", "spec_class_norm_anom"], axis=1)
highest_anom_score_df["Notes"] = "abc!"
highest_anom_score_df.index = highest_anom_score_df.index.rename('IAU_name')
highest_anom_score_df.to_csv("./YSEDR1/tables/ysedr1_highest_anom_score_df_198objs.csv")
highest_anom_score_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>Notes</th>
</tr>
<tr>
<th>IAU_name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020xsy</th>
<td>SLSN-II</td>
<td>0.2700</td>
<td>SPEC-Z</td>
<td>98</td>
<td>93</td>
<td>abc!</td>
</tr>
<tr>
<th>2020hjv</th>
<td>NA</td>
<td>0.1410</td>
<td>PHOTO-Z</td>
<td>91</td>
<td>7</td>
<td>abc!</td>
</tr>
<tr>
<th>2020tip</th>
<td>SNIa-norm</td>
<td>0.0950</td>
<td>SPEC-Z</td>
<td>90</td>
<td>14</td>
<td>abc!</td>
</tr>
<tr>
<th>2020acyu</th>
<td>NA</td>
<td>0.2540</td>
<td>PHOTO-Z</td>
<td>90</td>
<td>33</td>
<td>abc!</td>
</tr>
<tr>
<th>2020qkx</th>
<td>SNIa-norm</td>
<td>0.1270</td>
<td>SPEC-Z</td>
<td>89</td>
<td>51</td>
<td>abc!</td>
</tr>
<tr>
<th>...</th>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr>
<th>2020aewm</th>
<td>NA</td>
<td>0.1604</td>
<td>HOST-Z</td>
<td>50</td>
<td>3</td>
<td>abc!</td>
</tr>
<tr>
<th>2021lhi</th>
<td>NA</td>
<td>0.2520</td>
<td>PHOTO-Z</td>
<td>50</td>
<td>1</td>
<td>abc!</td>
</tr>
<tr>
<th>2021sdy</th>
<td>NA</td>
<td>0.1144</td>
<td>HOST-Z</td>
<td>50</td>
<td>1</td>
<td>abc!</td>
</tr>
<tr>
<th>2021kcb</th>
<td>NA</td>
<td>0.1486</td>
<td>HOST-Z</td>
<td>50</td>
<td>1</td>
<td>abc!</td>
</tr>
<tr>
<th>2021naw</th>
<td>NA</td>
<td>0.1990</td>
<td>PHOTO-Z</td>
<td>50</td>
<td>1</td>
<td>abc!</td>
</tr>
</tbody>
</table>
<p>198 rows × 6 columns</p>
</div>
```python
len(highest_anom_score_df[highest_anom_score_df.max_anom_score >= 70])
```
42
```python
plt.hist(ysedr1_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True)
plt.hist(higest_anom_score_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True)
```
(array([0.76142132, 3.55329949, 3.55329949, 4.56852792, 6.09137056,
4.82233503, 6.09137056, 5.58375635, 2.53807107, 4.31472081,
3.29949239, 3.29949239, 1.01522843, 0.50761421, 0. ]),
array([0. , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 ,
0.22, 0.24, 0.26, 0.28, 0.3 ]),
<BarContainer object of 15 artists>)
```python
from scipy import stats
# Assuming ysedr1_df.best_z and higest_anom_score_df.best_z are your data arrays
# Plot histograms
plt.hist(ysedr1_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True, label='ysedr1_df')
plt.hist(higest_anom_score_df.best_z, bins=np.linspace(0, 0.3, 16), alpha=0.5, density=True, label='higest_anom_score_df')
plt.legend()
# Perform K-S test
import random
# Assuming ysedr1_df.best_z has 1153 objects and higest_anom_score_df.best_z has 51 objects
# Downsample the larger sample to match the size of the smaller sample
if len(ysedr1_df.best_z) > len(higest_anom_score_df.best_z):
ysedr1_df_best_z_downsample_l = random.sample(list(ysedr1_df.best_z), len(higest_anom_score_df.best_z))
# Now both samples have the same size
# You can proceed with your K-S test or other analyses
ks_statistic, p_value = stats.ks_2samp(ysedr1_df_best_z_downsample_l, higest_anom_score_df.best_z)
# Output the test result
if p_value < 0.05:
print(f"p_value: {p_value}")
print("The distributions are different (reject the null hypothesis)")
else:
print(f"p_value: {p_value}")
print("The distributions are the same (fail to reject the null hypothesis)")
plt.show()
```
p_value: 0.13832660629112375
The distributions are the same (fail to reject the null hypothesis)
```python
plt.hist(higest_anom_score_df[higest_anom_score_df.z_type == "SPEC-Z"].best_z, bins=np.linspace(0, 0.3, 16),
alpha=0.5, density=False,
label=f"SPEC-Z ({len(higest_anom_score_df[higest_anom_score_df.z_type == 'SPEC-Z'])} SNe)")
plt.hist(higest_anom_score_df[higest_anom_score_df.z_type == "HOST-Z"].best_z, bins=np.linspace(0, 0.3, 16),
alpha=0.5, density=False,
label=f"HOST-Z ({len(higest_anom_score_df[higest_anom_score_df.z_type == 'HOST-Z'])} SNe)")
plt.hist(higest_anom_score_df[higest_anom_score_df.z_type == "PHOTO-Z"].best_z, bins=np.linspace(0, 0.3, 16),
alpha=0.5, density=False,
label=f"PHOTO-Z ({len(higest_anom_score_df[higest_anom_score_df.z_type == 'PHOTO-Z'])} SNe)")
plt.legend()
```
<matplotlib.legend.Legend at 0x7fe8579debb0>
```python
#plot
for obj, score, num_anom_epochs, cls, cls2, rfc_cls in zip(higest_anom_score_df.index,
higest_anom_score_df.max_anom_score,
higest_anom_score_df.num_anom_epochs,
higest_anom_score_df["spec_class"],
higest_anom_score_df["spec_class_norm_anom"],
higest_anom_score_df.RFC_best_cls):
print(obj, score, num_anom_epochs, cls, cls2, rfc_cls)
sn_idx = full_snid_list.index(obj)
meta = full_meta_list[sn_idx]
lc = full_df_list[sn_idx]
lc_grXY = lc[(lc.PASSBAND != 'i') & (lc.PASSBAND != 'z')].reset_index(drop=True)
# (g, r, i or z for PS1-griz, respectively; X for ZTF-g; Y for ZTF-r)
lc_grXY["PASSBAND"] = lc_grXY["PASSBAND"].map({"X": "g", "g": "g", "Y": "R", "r": "R"})
lc = lc_grXY
plot_RFC_prob_vs_lc_ztfid(anom_ztfid=obj,
anom_thresh=50,
lc=lc,
meta=meta,
df_path = "/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries",
)
print("%%%%%%%%%%%%")
```
2020xsy 98.0 93 SLSN-II Other Other
Anomalous during timeseries!
max_anom_score 98.0
num_anom_epochs 93
MJD Cutoff anom_idx 59232.484
https://ziggy.ucolick.org/yse/transient_detail/2020xsy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020hjv 91.0 7 NA NA Other
Anomalous during timeseries!
max_anom_score 91.0
num_anom_epochs 7
MJD Cutoff anom_idx 58978.185
https://ziggy.ucolick.org/yse/transient_detail/2020hjv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tip 90.0 14 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 90.0
num_anom_epochs 14
MJD Cutoff anom_idx 59125.273
https://ziggy.ucolick.org/yse/transient_detail/2020tip/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acyu 90.0 33 NA NA Other
Anomalous during timeseries!
max_anom_score 90.0
num_anom_epochs 33
MJD Cutoff anom_idx 59220.301
https://ziggy.ucolick.org/yse/transient_detail/2020acyu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020qkx 89.0 51 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 89.0
num_anom_epochs 51
MJD Cutoff anom_idx 59071.386
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020opy 89.0 83 TDE Other Other
Anomalous during timeseries!
max_anom_score 89.0
num_anom_epochs 83
MJD Cutoff anom_idx 59089.245
https://ziggy.ucolick.org/yse/transient_detail/2020opy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021nxq 89.0 27 SLSN-I Other Other
Anomalous during timeseries!
max_anom_score 89.0
num_anom_epochs 27
MJD Cutoff anom_idx 59410.198
https://ziggy.ucolick.org/yse/transient_detail/2021nxq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020qql 88.0 35 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 88.0
num_anom_epochs 35
MJD Cutoff anom_idx 59086.175
https://ziggy.ucolick.org/yse/transient_detail/2020qql/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021hrj 88.0 79 SNIb Other Other
Anomalous during timeseries!
max_anom_score 88.0
num_anom_epochs 79
MJD Cutoff anom_idx 59328.186
https://ziggy.ucolick.org/yse/transient_detail/2021hrj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021bmv 85.0 11 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 85.0
num_anom_epochs 11
MJD Cutoff anom_idx 59265.133
https://ziggy.ucolick.org/yse/transient_detail/2021bmv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021aadc 85.0 15 SLSN-II Other Other
Anomalous during timeseries!
max_anom_score 85.0
num_anom_epochs 15
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59523.143
https://ziggy.ucolick.org/yse/transient_detail/2021aadc/
%%%%%%%%%%%%
2020jvi 84.0 9 NA NA Other
Anomalous during timeseries!
max_anom_score 84.0
num_anom_epochs 9
MJD Cutoff anom_idx 59042.36
https://ziggy.ucolick.org/yse/transient_detail/2020jvi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020qmj 82.0 51 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 82.0
num_anom_epochs 51
MJD Cutoff anom_idx 59128.257
https://ziggy.ucolick.org/yse/transient_detail/2020qmj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kre 82.0 70 SNIa-CSM Other Other
Anomalous during timeseries!
max_anom_score 82.0
num_anom_epochs 70
MJD Cutoff anom_idx 59017.216
https://ziggy.ucolick.org/yse/transient_detail/2020kre/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021too 81.9 36 SNIc-BL Other Other
Anomalous during timeseries!
max_anom_score 81.9
num_anom_epochs 36
MJD Cutoff anom_idx 59440.37
https://ziggy.ucolick.org/yse/transient_detail/2021too/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021pnp 79.0 46 SNIIb Other Other
Anomalous during timeseries!
max_anom_score 79.0
num_anom_epochs 46
MJD Cutoff anom_idx 59408.235
https://ziggy.ucolick.org/yse/transient_detail/2021pnp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019vuz 79.0 17 NA NA Other
Anomalous during timeseries!
max_anom_score 79.0
num_anom_epochs 17
MJD Cutoff anom_idx 58833.357
https://ziggy.ucolick.org/yse/transient_detail/2019vuz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021gno 78.0 29 SNIb-pec Other Other
Anomalous during timeseries!
max_anom_score 78.0
num_anom_epochs 29
MJD Cutoff anom_idx 59323.317
https://ziggy.ucolick.org/yse/transient_detail/2021gno/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kbl 77.0 22 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 77.0
num_anom_epochs 22
MJD Cutoff anom_idx 58996.273
https://ziggy.ucolick.org/yse/transient_detail/2020kbl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020rss 77.0 11 NA NA Other
Anomalous during timeseries!
max_anom_score 77.0
num_anom_epochs 11
MJD Cutoff anom_idx 59092.408
https://ziggy.ucolick.org/yse/transient_detail/2020rss/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ofr 76.0 8 NA NA Other
Anomalous during timeseries!
max_anom_score 76.0
num_anom_epochs 8
MJD Cutoff anom_idx 59387.194
https://ziggy.ucolick.org/yse/transient_detail/2021ofr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kmj 76.0 49 NA NA Other
Anomalous during timeseries!
max_anom_score 76.0
num_anom_epochs 49
MJD Cutoff anom_idx 58992.234
https://ziggy.ucolick.org/yse/transient_detail/2020kmj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021bpq 75.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 75.0
num_anom_epochs 5
MJD Cutoff anom_idx 59275.309
https://ziggy.ucolick.org/yse/transient_detail/2021bpq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021btn 75.0 43 SNII Normal Other
Anomalous during timeseries!
max_anom_score 75.0
num_anom_epochs 43
MJD Cutoff anom_idx 59342.56
https://ziggy.ucolick.org/yse/transient_detail/2021btn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021udc 75.0 8 SNIIb Other Other
Anomalous during timeseries!
max_anom_score 75.0
num_anom_epochs 8
MJD Cutoff anom_idx 59460.62
https://ziggy.ucolick.org/yse/transient_detail/2021udc/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ojn 74.0 18 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 74.0
num_anom_epochs 18
MJD Cutoff anom_idx 59391.3
https://ziggy.ucolick.org/yse/transient_detail/2021ojn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021dpa 73.0 20 NA NA Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 20
MJD Cutoff anom_idx 59323.18
https://ziggy.ucolick.org/yse/transient_detail/2021dpa/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021rmq 73.0 10 NA NA Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 10
MJD Cutoff anom_idx 59379.224
https://ziggy.ucolick.org/yse/transient_detail/2021rmq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020itp 73.0 44 NA NA Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 44
MJD Cutoff anom_idx 58987.33
https://ziggy.ucolick.org/yse/transient_detail/2020itp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tan 73.0 60 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 73.0
num_anom_epochs 60
MJD Cutoff anom_idx 59119.379
https://ziggy.ucolick.org/yse/transient_detail/2020tan/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acun 72.9 25 SNII Normal Other
Anomalous during timeseries!
max_anom_score 72.9
num_anom_epochs 25
MJD Cutoff anom_idx 59253.503
https://ziggy.ucolick.org/yse/transient_detail/2020acun/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020wwt 72.0 14 NA NA Other
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 14
MJD Cutoff anom_idx 59134.298
https://ziggy.ucolick.org/yse/transient_detail/2020wwt/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kvl 72.0 8 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 8
MJD Cutoff anom_idx 59033.172
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020wfg 72.0 3 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 3
MJD Cutoff anom_idx 59157.517
https://ziggy.ucolick.org/yse/transient_detail/2020wfg/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ivg 72.0 40 SNIIb Other Other
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 40
MJD Cutoff anom_idx 58971.378
https://ziggy.ucolick.org/yse/transient_detail/2020ivg/
%%%%%%%%%%%%
2020iga 71.0 7 NA NA Other
Anomalous during timeseries!
max_anom_score 71.0
num_anom_epochs 7
MJD Cutoff anom_idx 58973.295
https://ziggy.ucolick.org/yse/transient_detail/2020iga/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020sgy 71.0 18 NA NA Other
Anomalous during timeseries!
max_anom_score 71.0
num_anom_epochs 18
MJD Cutoff anom_idx 59108.48
https://ziggy.ucolick.org/yse/transient_detail/2020sgy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kpz 71.0 65 SNII Normal Other
Anomalous during timeseries!
max_anom_score 71.0
num_anom_epochs 65
MJD Cutoff anom_idx 59042.27
https://ziggy.ucolick.org/yse/transient_detail/2020kpz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acct 70.0 32 SNIc Other Other
Anomalous during timeseries!
max_anom_score 70.0
num_anom_epochs 32
MJD Cutoff anom_idx 59200.508
https://ziggy.ucolick.org/yse/transient_detail/2020acct/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020vpn 70.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 70.0
num_anom_epochs 5
MJD Cutoff anom_idx 59145.18
https://ziggy.ucolick.org/yse/transient_detail/2020vpn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019wmr 70.0 2 SNII Normal Other
Anomalous during timeseries!
max_anom_score 70.0
num_anom_epochs 2
MJD Cutoff anom_idx 58846.491
https://ziggy.ucolick.org/yse/transient_detail/2019wmr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021vwx 69.8 9 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 69.8
num_anom_epochs 9
MJD Cutoff anom_idx 59525.462
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ofq 68.0 14 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 14
MJD Cutoff anom_idx 59391.259
https://ziggy.ucolick.org/yse/transient_detail/2021ofq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021gpt 68.0 85 NA NA Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 85
MJD Cutoff anom_idx 59297.352
https://ziggy.ucolick.org/yse/transient_detail/2021gpt/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021wue 68.0 7 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 7
MJD Cutoff anom_idx 59468.45
https://ziggy.ucolick.org/yse/transient_detail/2021wue/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021tux 68.0 7 SNIb Other Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 7
MJD Cutoff anom_idx 59445.56
https://ziggy.ucolick.org/yse/transient_detail/2021tux/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020jba 68.0 16 NA NA Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 16
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 58992.292
https://ziggy.ucolick.org/yse/transient_detail/2020jba/
%%%%%%%%%%%%
2020imb 68.0 31 NA NA Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 31
MJD Cutoff anom_idx 58997.232
https://ziggy.ucolick.org/yse/transient_detail/2020imb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021mxw 67.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 67.0
num_anom_epochs 4
MJD Cutoff anom_idx 59374.45
https://ziggy.ucolick.org/yse/transient_detail/2021mxw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020nxl 67.0 10 NA NA Other
Anomalous during timeseries!
max_anom_score 67.0
num_anom_epochs 10
MJD Cutoff anom_idx 59049.421
https://ziggy.ucolick.org/yse/transient_detail/2020nxl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021afh 67.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 67.0
num_anom_epochs 5
MJD Cutoff anom_idx 59238.57
https://ziggy.ucolick.org/yse/transient_detail/2021afh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aym 66.0 13 NA NA Other
Anomalous during timeseries!
max_anom_score 66.0
num_anom_epochs 13
MJD Cutoff anom_idx 58867.384
https://ziggy.ucolick.org/yse/transient_detail/2020aym/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020skh 66.0 7 NA NA Other
Anomalous during timeseries!
max_anom_score 66.0
num_anom_epochs 7
MJD Cutoff anom_idx 59112.374
https://ziggy.ucolick.org/yse/transient_detail/2020skh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021vaw 66.0 4 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 66.0
num_anom_epochs 4
MJD Cutoff anom_idx 59459.341
https://ziggy.ucolick.org/yse/transient_detail/2021vaw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021qxp 66.0 9 NA NA Other
Anomalous during timeseries!
max_anom_score 66.0
num_anom_epochs 9
MJD Cutoff anom_idx 59409.437
https://ziggy.ucolick.org/yse/transient_detail/2021qxp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021vcz 65.0 3 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 65.0
num_anom_epochs 3
MJD Cutoff anom_idx 59453.394
https://ziggy.ucolick.org/yse/transient_detail/2021vcz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020hep 65.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 65.0
num_anom_epochs 2
MJD Cutoff anom_idx 58982.52
https://ziggy.ucolick.org/yse/transient_detail/2020hep/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021mms 65.0 9 NA NA Other
Anomalous during timeseries!
max_anom_score 65.0
num_anom_epochs 9
MJD Cutoff anom_idx 59377.211
https://ziggy.ucolick.org/yse/transient_detail/2021mms/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020pvj 64.0 6 NA NA Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 6
MJD Cutoff anom_idx 59061.352
https://ziggy.ucolick.org/yse/transient_detail/2020pvj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tlf 64.0 63 SNII Normal Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 63
MJD Cutoff anom_idx 59253.533
https://ziggy.ucolick.org/yse/transient_detail/2020tlf/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021joz 64.0 2 SNII Normal Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 2
MJD Cutoff anom_idx 59363.32
https://ziggy.ucolick.org/yse/transient_detail/2021joz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020adly 64.0 26 NA NA Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 26
MJD Cutoff anom_idx 59224.61
https://ziggy.ucolick.org/yse/transient_detail/2020adly/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kps 64.0 10 NA NA Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 10
MJD Cutoff anom_idx 58975.32
https://ziggy.ucolick.org/yse/transient_detail/2020kps/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021xjh 64.0 4 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 4
MJD Cutoff anom_idx 59484.24
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ihk 63.0 10 NA NA Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 10
MJD Cutoff anom_idx 59313.353
https://ziggy.ucolick.org/yse/transient_detail/2021ihk/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ndi 63.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 5
MJD Cutoff anom_idx 59037.37
https://ziggy.ucolick.org/yse/transient_detail/2020ndi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021cqv 63.0 6 NA NA Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 6
MJD Cutoff anom_idx 59262.367
https://ziggy.ucolick.org/yse/transient_detail/2021cqv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021cta 63.0 11 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 11
MJD Cutoff anom_idx 59294.236
https://ziggy.ucolick.org/yse/transient_detail/2021cta/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021aff 63.0 16 SNII Normal Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 16
MJD Cutoff anom_idx 59232.4
https://ziggy.ucolick.org/yse/transient_detail/2021aff/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020iul 62.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 62.0
num_anom_epochs 2
MJD Cutoff anom_idx 58985.37
https://ziggy.ucolick.org/yse/transient_detail/2020iul/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020oox 61.9 2 NA NA Other
Anomalous during timeseries!
max_anom_score 61.9
num_anom_epochs 2
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59074.28
https://ziggy.ucolick.org/yse/transient_detail/2020oox/
%%%%%%%%%%%%
2020iio 61.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 61.0
num_anom_epochs 4
MJD Cutoff anom_idx 58983.52
https://ziggy.ucolick.org/yse/transient_detail/2020iio/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tjd 61.0 16 SNIIb Other Other
Anomalous during timeseries!
max_anom_score 61.0
num_anom_epochs 16
MJD Cutoff anom_idx 59219.315
https://ziggy.ucolick.org/yse/transient_detail/2020tjd/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021aapa 61.0 9 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 61.0
num_anom_epochs 9
MJD Cutoff anom_idx 59521.361
https://ziggy.ucolick.org/yse/transient_detail/2021aapa/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ypd 61.0 8 NA NA Other
Anomalous during timeseries!
max_anom_score 61.0
num_anom_epochs 8
MJD Cutoff anom_idx 59474.28
https://ziggy.ucolick.org/yse/transient_detail/2021ypd/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aeti 61.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 61.0
num_anom_epochs 5
MJD Cutoff anom_idx 59228.62
https://ziggy.ucolick.org/yse/transient_detail/2020aeti/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ulz 60.9 17 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 60.9
num_anom_epochs 17
MJD Cutoff anom_idx 59149.38
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kiq 60.0 16 NA NA Other
Anomalous during timeseries!
max_anom_score 60.0
num_anom_epochs 16
MJD Cutoff anom_idx 58960.29
https://ziggy.ucolick.org/yse/transient_detail/2020kiq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021snh 60.0 11 NA NA Other
Anomalous during timeseries!
max_anom_score 60.0
num_anom_epochs 11
MJD Cutoff anom_idx 59403.227
https://ziggy.ucolick.org/yse/transient_detail/2021snh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020abji 60.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 60.0
num_anom_epochs 4
MJD Cutoff anom_idx 59202.13
https://ziggy.ucolick.org/yse/transient_detail/2020abji/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019ppi 60.0 34 SNII Normal Other
Anomalous during timeseries!
max_anom_score 60.0
num_anom_epochs 34
MJD Cutoff anom_idx 58769.473
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019uit 60.0 10 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 60.0
num_anom_epochs 10
MJD Cutoff anom_idx 59022.27
https://ziggy.ucolick.org/yse/transient_detail/2019uit/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020wzd 59.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 4
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59164.284
https://ziggy.ucolick.org/yse/transient_detail/2020wzd/
%%%%%%%%%%%%
2020aesp 59.0 10 NA NA Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 10
MJD Cutoff anom_idx 59255.42
https://ziggy.ucolick.org/yse/transient_detail/2020aesp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020twb 59.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 5
MJD Cutoff anom_idx 59134.187
https://ziggy.ucolick.org/yse/transient_detail/2020twb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020iqk 59.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 5
MJD Cutoff anom_idx 58970.37
https://ziggy.ucolick.org/yse/transient_detail/2020iqk/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021bg 59.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 2
MJD Cutoff anom_idx 59230.22
https://ziggy.ucolick.org/yse/transient_detail/2021bg/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aeid 59.0 11 NA NA Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 11
MJD Cutoff anom_idx 59221.48
https://ziggy.ucolick.org/yse/transient_detail/2020aeid/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021arl 59.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 5
MJD Cutoff anom_idx 59292.39
https://ziggy.ucolick.org/yse/transient_detail/2021arl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021pqw 59.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 3
MJD Cutoff anom_idx 59377.28
https://ziggy.ucolick.org/yse/transient_detail/2021pqw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aejb 58.9 6 NA NA Other
Anomalous during timeseries!
max_anom_score 58.9
num_anom_epochs 6
MJD Cutoff anom_idx 59226.45
https://ziggy.ucolick.org/yse/transient_detail/2020aejb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ygj 58.0 6 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 6
MJD Cutoff anom_idx 59523.337
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020icw 58.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 3
MJD Cutoff anom_idx 59008.27
https://ziggy.ucolick.org/yse/transient_detail/2020icw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019xbj 58.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 1
MJD Cutoff anom_idx 58852.482
https://ziggy.ucolick.org/yse/transient_detail/2019xbj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020rsp 58.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 5
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
MJD Cutoff anom_idx 59070.223
https://ziggy.ucolick.org/yse/transient_detail/2020rsp/
%%%%%%%%%%%%
2021rmi 58.0 7 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 7
MJD Cutoff anom_idx 59404.221
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020evr 58.0 12 NA NA Other
Anomalous during timeseries!
max_anom_score 58.0
num_anom_epochs 12
MJD Cutoff anom_idx 58914.405
https://ziggy.ucolick.org/yse/transient_detail/2020evr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020vbj 57.7 9 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 57.7
num_anom_epochs 9
MJD Cutoff anom_idx 59144.225
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021zvo 57.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 2
MJD Cutoff anom_idx 59472.416
https://ziggy.ucolick.org/yse/transient_detail/2021zvo/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020jed 57.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 4
MJD Cutoff anom_idx 59005.185
https://ziggy.ucolick.org/yse/transient_detail/2020jed/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021wlp 57.0 9 NA NA Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 9
MJD Cutoff anom_idx 59463.329
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020sia 57.0 8 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 8
MJD Cutoff anom_idx 59113.28
https://ziggy.ucolick.org/yse/transient_detail/2020sia/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021wlz 57.0 21 NA NA Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 21
MJD Cutoff anom_idx 59463.32
https://ziggy.ucolick.org/yse/transient_detail/2021wlz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020mju 57.0 12 NA NA Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 12
MJD Cutoff anom_idx 59028.352
https://ziggy.ucolick.org/yse/transient_detail/2020mju/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020awu 57.0 6 SN Other Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 6
MJD Cutoff anom_idx 58895.399
https://ziggy.ucolick.org/yse/transient_detail/2020awu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021be 57.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 3
MJD Cutoff anom_idx 59227.251
https://ziggy.ucolick.org/yse/transient_detail/2021be/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021kqp 57.0 1 SN Other Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 1
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
MJD Cutoff anom_idx 59366.273
https://ziggy.ucolick.org/yse/transient_detail/2021kqp/
%%%%%%%%%%%%
2020rdu 56.0 21 SNIIn Other Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 21
MJD Cutoff anom_idx 59083.26
https://ziggy.ucolick.org/yse/transient_detail/2020rdu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ovd 56.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 3
MJD Cutoff anom_idx 59396.27
https://ziggy.ucolick.org/yse/transient_detail/2021ovd/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019uev 56.0 8 NA NA Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 8
MJD Cutoff anom_idx 58827.6
https://ziggy.ucolick.org/yse/transient_detail/2019uev/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020psv 56.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 5
MJD Cutoff anom_idx 59074.387
https://ziggy.ucolick.org/yse/transient_detail/2020psv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021iqw 56.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 2
MJD Cutoff anom_idx 59334.286
https://ziggy.ucolick.org/yse/transient_detail/2021iqw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021tup 56.0 2 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 2
MJD Cutoff anom_idx 59433.38
https://ziggy.ucolick.org/yse/transient_detail/2021tup/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020adob 56.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 2
MJD Cutoff anom_idx 59168.486
https://ziggy.ucolick.org/yse/transient_detail/2020adob/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021jof 56.0 3 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 3
MJD Cutoff anom_idx 59341.403
https://ziggy.ucolick.org/yse/transient_detail/2021jof/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020hgw 56.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 1
MJD Cutoff anom_idx 58976.298
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acjk 56.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 3
MJD Cutoff anom_idx 59196.49
https://ziggy.ucolick.org/yse/transient_detail/2020acjk/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021cjj 56.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 4
MJD Cutoff anom_idx 59257.52
https://ziggy.ucolick.org/yse/transient_detail/2021cjj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020lgr 56.0 8 NA NA Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 8
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 58998.297
https://ziggy.ucolick.org/yse/transient_detail/2020lgr/
%%%%%%%%%%%%
2020lrr 56.0 4 SNII Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 4
MJD Cutoff anom_idx 59082.187
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021mwb 56.0 4 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 4
MJD Cutoff anom_idx 59386.297
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020nis 55.9 2 NA NA Other
Anomalous during timeseries!
max_anom_score 55.9
num_anom_epochs 2
MJD Cutoff anom_idx 59054.27
https://ziggy.ucolick.org/yse/transient_detail/2020nis/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020mjn 55.9 3 NA NA Other
Anomalous during timeseries!
max_anom_score 55.9
num_anom_epochs 3
MJD Cutoff anom_idx 59037.189
https://ziggy.ucolick.org/yse/transient_detail/2020mjn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020abgb 55.0 51 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 51
MJD Cutoff anom_idx 59198.444
https://ziggy.ucolick.org/yse/transient_detail/2020abgb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021seu 55.0 3 Other Other Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 3
MJD Cutoff anom_idx 59454.174
https://ziggy.ucolick.org/yse/transient_detail/2021seu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021sjw 55.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 3
MJD Cutoff anom_idx 59360.39
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020pss 55.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 2
MJD Cutoff anom_idx 59080.26
https://ziggy.ucolick.org/yse/transient_detail/2020pss/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020iri 55.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 1
MJD Cutoff anom_idx 58981.41
https://ziggy.ucolick.org/yse/transient_detail/2020iri/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020nor 55.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 5
MJD Cutoff anom_idx 59037.37
https://ziggy.ucolick.org/yse/transient_detail/2020nor/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kpp 55.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 3
MJD Cutoff anom_idx 58964.411
https://ziggy.ucolick.org/yse/transient_detail/2020kpp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ibh 55.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 3
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
MJD Cutoff anom_idx 59323.342
https://ziggy.ucolick.org/yse/transient_detail/2021ibh/
%%%%%%%%%%%%
2020row 55.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 5
MJD Cutoff anom_idx 59107.322
https://ziggy.ucolick.org/yse/transient_detail/2020row/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021nue 55.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 1
MJD Cutoff anom_idx 59509.33
https://ziggy.ucolick.org/yse/transient_detail/2021nue/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acty 55.0 10 SNIb Other Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 10
MJD Cutoff anom_idx 59221.372
https://ziggy.ucolick.org/yse/transient_detail/2020acty/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kmv 54.9 3 NA NA Other
Anomalous during timeseries!
max_anom_score 54.9
num_anom_epochs 3
MJD Cutoff anom_idx 59010.29
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021idh 54.9 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 54.9
num_anom_epochs 1
MJD Cutoff anom_idx 59335.17
https://ziggy.ucolick.org/yse/transient_detail/2021idh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020igg 54.6 2 NA NA Other
Anomalous during timeseries!
max_anom_score 54.6
num_anom_epochs 2
MJD Cutoff anom_idx 58985.289
https://ziggy.ucolick.org/yse/transient_detail/2020igg/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aevg 54.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 54.0
num_anom_epochs 4
MJD Cutoff anom_idx 59306.46
https://ziggy.ucolick.org/yse/transient_detail/2020aevg/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020twa 54.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 54.0
num_anom_epochs 5
MJD Cutoff anom_idx 59128.47
https://ziggy.ucolick.org/yse/transient_detail/2020twa/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021xqw 54.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 54.0
num_anom_epochs 1
MJD Cutoff anom_idx 59473.252
https://ziggy.ucolick.org/yse/transient_detail/2021xqw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020xku 54.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 54.0
num_anom_epochs 2
MJD Cutoff anom_idx 59152.213
https://ziggy.ucolick.org/yse/transient_detail/2020xku/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020rtb 54.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 54.0
num_anom_epochs 2
MJD Cutoff anom_idx 59087.454
https://ziggy.ucolick.org/yse/transient_detail/2020rtb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tug 54.0 6 SNIa-norm Normal Other
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 33 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 85 out of 100 | elapsed: 0.2s remaining: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
Anomalous during timeseries!
max_anom_score 54.0
num_anom_epochs 6
MJD Cutoff anom_idx 59163.35
https://ziggy.ucolick.org/yse/transient_detail/2020tug/
%%%%%%%%%%%%
2020aahq 53.9 7 NA NA Other
Anomalous during timeseries!
max_anom_score 53.9
num_anom_epochs 7
MJD Cutoff anom_idx 59198.213
https://ziggy.ucolick.org/yse/transient_detail/2020aahq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021qbx 53.9 2 NA NA Other
Anomalous during timeseries!
max_anom_score 53.9
num_anom_epochs 2
MJD Cutoff anom_idx 59425.18
https://ziggy.ucolick.org/yse/transient_detail/2021qbx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020juq 53.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 58991.29
https://ziggy.ucolick.org/yse/transient_detail/2020juq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020zkg 53.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 59182.491
https://ziggy.ucolick.org/yse/transient_detail/2020zkg/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021rrc 53.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 4
MJD Cutoff anom_idx 59374.405
https://ziggy.ucolick.org/yse/transient_detail/2021rrc/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021qht 53.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 59482.48
https://ziggy.ucolick.org/yse/transient_detail/2021qht/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020jfx 53.0 9 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 9
MJD Cutoff anom_idx 58987.309
https://ziggy.ucolick.org/yse/transient_detail/2020jfx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ciu 53.0 15 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 15
MJD Cutoff anom_idx 59110.358
https://ziggy.ucolick.org/yse/transient_detail/2020ciu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020lgy 53.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 2
MJD Cutoff anom_idx 59031.3
https://ziggy.ucolick.org/yse/transient_detail/2020lgy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021aagz 53.0 2 SNIa-SC Other Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 2
MJD Cutoff anom_idx 59550.457
https://ziggy.ucolick.org/yse/transient_detail/2021aagz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021cuw 53.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 59280.271
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aasl 53.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 3
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59170.545
https://ziggy.ucolick.org/yse/transient_detail/2020aasl/
%%%%%%%%%%%%
2021zvx 53.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 2
MJD Cutoff anom_idx 59514.41
https://ziggy.ucolick.org/yse/transient_detail/2021zvx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021abeu 53.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 59517.13
https://ziggy.ucolick.org/yse/transient_detail/2021abeu/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tri 53.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 3
MJD Cutoff anom_idx 59094.154
https://ziggy.ucolick.org/yse/transient_detail/2020tri/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ika 53.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 2
MJD Cutoff anom_idx 59014.221
https://ziggy.ucolick.org/yse/transient_detail/2020ika/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tly 53.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 59131.234
https://ziggy.ucolick.org/yse/transient_detail/2020tly/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kfy 52.9 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.9
num_anom_epochs 1
MJD Cutoff anom_idx 59024.181
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021beb 52.9 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.9
num_anom_epochs 1
MJD Cutoff anom_idx 59246.35
https://ziggy.ucolick.org/yse/transient_detail/2021beb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020unn 52.2 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.2
num_anom_epochs 1
MJD Cutoff anom_idx 59314.227
https://ziggy.ucolick.org/yse/transient_detail/2020unn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020uba 52.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 3
MJD Cutoff anom_idx 59138.296
https://ziggy.ucolick.org/yse/transient_detail/2020uba/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ixs 52.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 59082.31
https://ziggy.ucolick.org/yse/transient_detail/2020ixs/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021hol 52.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 59317.406
https://ziggy.ucolick.org/yse/transient_detail/2021hol/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020yix 52.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59140.174
https://ziggy.ucolick.org/yse/transient_detail/2020yix/
%%%%%%%%%%%%
2021sbc 52.0 2 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 2
MJD Cutoff anom_idx 59424.331
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020pcz 52.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 58992.333
https://ziggy.ucolick.org/yse/transient_detail/2020pcz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020pzl 52.0 9 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 9
MJD Cutoff anom_idx 59049.422
https://ziggy.ucolick.org/yse/transient_detail/2020pzl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020qlp 52.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 3
MJD Cutoff anom_idx 59081.348
https://ziggy.ucolick.org/yse/transient_detail/2020qlp/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aly 52.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 2
MJD Cutoff anom_idx 58895.2
https://ziggy.ucolick.org/yse/transient_detail/2020aly/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021acza 52.0 2 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 2
MJD Cutoff anom_idx 59530.235
https://ziggy.ucolick.org/yse/transient_detail/2021acza/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021yqa 52.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 59488.286
https://ziggy.ucolick.org/yse/transient_detail/2021yqa/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021bbd 52.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 2
MJD Cutoff anom_idx 59392.194
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020slb 52.0 4 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 4
MJD Cutoff anom_idx 59104.401
https://ziggy.ucolick.org/yse/transient_detail/2020slb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020pwr 52.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 59077.26
https://ziggy.ucolick.org/yse/transient_detail/2020pwr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021sld 52.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 59423.232
https://ziggy.ucolick.org/yse/transient_detail/2021sld/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021mfy 51.9 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 51.9
num_anom_epochs 1
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59379.215
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/
%%%%%%%%%%%%
2021tsj 51.9 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 51.9
num_anom_epochs 1
MJD Cutoff anom_idx 59436.249
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021gda 51.5 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.5
num_anom_epochs 1
MJD Cutoff anom_idx 59297.5
https://ziggy.ucolick.org/yse/transient_detail/2021gda/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020abbj 51.2 2 NA NA Other
Anomalous during timeseries!
max_anom_score 51.2
num_anom_epochs 2
MJD Cutoff anom_idx 59223.482
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aov 51.0 5 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 5
MJD Cutoff anom_idx 58903.429
https://ziggy.ucolick.org/yse/transient_detail/2020aov/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020nwa 51.0 2 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 2
MJD Cutoff anom_idx 59055.37
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020duv 51.0 2 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 2
MJD Cutoff anom_idx 58973.443
https://ziggy.ucolick.org/yse/transient_detail/2020duv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021qxn 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
MJD Cutoff anom_idx 59403.288
https://ziggy.ucolick.org/yse/transient_detail/2021qxn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021hem 51.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
MJD Cutoff anom_idx 59326.46
https://ziggy.ucolick.org/yse/transient_detail/2021hem/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020mor 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
MJD Cutoff anom_idx 59062.27
https://ziggy.ucolick.org/yse/transient_detail/2020mor/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acbb 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
MJD Cutoff anom_idx 59218.42
https://ziggy.ucolick.org/yse/transient_detail/2020acbb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020rpl 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.1s finished
MJD Cutoff anom_idx 59079.379
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/
%%%%%%%%%%%%
2020gll 51.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 51.0
num_anom_epochs 1
MJD Cutoff anom_idx 58962.374
https://ziggy.ucolick.org/yse/transient_detail/2020gll/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020rcr 50.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 50.0
num_anom_epochs 1
MJD Cutoff anom_idx 59074.186
https://ziggy.ucolick.org/yse/transient_detail/2020rcr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020advk 50.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 50.0
num_anom_epochs 1
MJD Cutoff anom_idx 59338.29
https://ziggy.ucolick.org/yse/transient_detail/2020advk/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020aewm 50.0 3 NA NA Other
Anomalous during timeseries!
max_anom_score 50.0
num_anom_epochs 3
MJD Cutoff anom_idx 59353.36
https://ziggy.ucolick.org/yse/transient_detail/2020aewm/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021lhi 50.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 50.0
num_anom_epochs 1
MJD Cutoff anom_idx 59406.234
https://ziggy.ucolick.org/yse/transient_detail/2021lhi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021sdy 50.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 50.0
num_anom_epochs 1
MJD Cutoff anom_idx 59424.366
https://ziggy.ucolick.org/yse/transient_detail/2021sdy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021kcb 50.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 50.0
num_anom_epochs 1
MJD Cutoff anom_idx 59336.43
https://ziggy.ucolick.org/yse/transient_detail/2021kcb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021naw 50.0 1 NA NA Other
Anomalous during timeseries!
max_anom_score 50.0
num_anom_epochs 1
MJD Cutoff anom_idx 59368.219
https://ziggy.ucolick.org/yse/transient_detail/2021naw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
```python
```
```python
# Confusion matrices
#max_anom_score_thresh = 50
#spec_ysedr1_df = ysedr1_df[(ysedr1_df.spec_class != "NA") & (ysedr1_df.max_anom_score >= max_anom_score_thresh)]
#spec_ysedr1_df = ysedr1_df[(ysedr1_df.spec_class != "NA")]
spec_ysedr1_df = ysedr1_df[(ysedr1_df.spec_class != "NA")]
title='RFC'
true_labels = np.array(spec_ysedr1_df['spec_class_norm_anom'])
predicted_labels = np.array(spec_ysedr1_df['RFC_best_cls'])
# define the class labels
class_names = np.unique(true_labels)
nclasses = len(class_names)
KINDS = ['completeness', 'purity']
for KIND in KINDS:
# Sims test set
plot_conf_matrix(true_labels, predicted_labels, labels=class_names,
title=f'{title} ({KIND})', kind=KIND)
plt.savefig(f'{cm_path}/confmatrix_YSEDR1_timeseries_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
#plt.savefig(f'{cm_path}/confmatrix_YSEDR1_timeseries_max_anom_score_geq_{max_anom_score_thresh}_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
#plt.savefig(f'{cm_path}/confmatrix_YSEDR1_fullLConly_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
plt.show()
plt.close()
```
```python
missclassified_df = spec_ysedr1_df[(spec_ysedr1_df["RFC_best_cls"] == "Other") & (spec_ysedr1_df["spec_class_norm_anom"] == "Normal")]
missclassified_df = missclassified_df.nlargest(45, 'max_anom_score')
missclassified_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>RFC_best_cls</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
<th>spec_class_norm_anom</th>
</tr>
<tr>
<th>Disc. Internal Name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020tip</th>
<td>Other</td>
<td>90.0</td>
<td>14</td>
<td>SNIa-norm</td>
<td>0.0950</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020qkx</th>
<td>Other</td>
<td>89.0</td>
<td>51</td>
<td>SNIa-norm</td>
<td>0.1270</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020qql</th>
<td>Other</td>
<td>88.0</td>
<td>35</td>
<td>SNIa-norm</td>
<td>0.0760</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020kbl</th>
<td>Other</td>
<td>77.0</td>
<td>22</td>
<td>SNIa-norm</td>
<td>0.0790</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021bpq</th>
<td>Other</td>
<td>75.0</td>
<td>5</td>
<td>SNIa-norm</td>
<td>0.1000</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021btn</th>
<td>Other</td>
<td>75.0</td>
<td>43</td>
<td>SNII</td>
<td>0.0830</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021ojn</th>
<td>Other</td>
<td>74.0</td>
<td>18</td>
<td>SNIa-norm</td>
<td>0.0800</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020acun</th>
<td>Other</td>
<td>72.9</td>
<td>25</td>
<td>SNII</td>
<td>0.0216</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020kvl</th>
<td>Other</td>
<td>72.0</td>
<td>8</td>
<td>SNIa-norm</td>
<td>0.1200</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020wfg</th>
<td>Other</td>
<td>72.0</td>
<td>3</td>
<td>SNIa-norm</td>
<td>0.1080</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020kpz</th>
<td>Other</td>
<td>71.0</td>
<td>65</td>
<td>SNII</td>
<td>0.0390</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2019wmr</th>
<td>Other</td>
<td>70.0</td>
<td>2</td>
<td>SNII</td>
<td>0.0380</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021vwx</th>
<td>Other</td>
<td>69.8</td>
<td>9</td>
<td>SNIa-norm</td>
<td>0.0600</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021ofq</th>
<td>Other</td>
<td>68.0</td>
<td>14</td>
<td>SNIa-norm</td>
<td>0.1200</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021wue</th>
<td>Other</td>
<td>68.0</td>
<td>7</td>
<td>SNIa-norm</td>
<td>0.0800</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021vaw</th>
<td>Other</td>
<td>66.0</td>
<td>4</td>
<td>SNIa-norm</td>
<td>0.1100</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021vcz</th>
<td>Other</td>
<td>65.0</td>
<td>3</td>
<td>SNIa-norm</td>
<td>0.1080</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020tlf</th>
<td>Other</td>
<td>64.0</td>
<td>63</td>
<td>SNII</td>
<td>0.0085</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021joz</th>
<td>Other</td>
<td>64.0</td>
<td>2</td>
<td>SNII</td>
<td>0.0600</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021xjh</th>
<td>Other</td>
<td>64.0</td>
<td>4</td>
<td>SNIa-91T-like</td>
<td>0.2090</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021cta</th>
<td>Other</td>
<td>63.0</td>
<td>11</td>
<td>SNIa-norm</td>
<td>0.0810</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021aff</th>
<td>Other</td>
<td>63.0</td>
<td>16</td>
<td>SNII</td>
<td>0.0510</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020ulz</th>
<td>Other</td>
<td>60.9</td>
<td>17</td>
<td>SNIa-norm</td>
<td>0.1030</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2019ppi</th>
<td>Other</td>
<td>60.0</td>
<td>34</td>
<td>SNII</td>
<td>0.0520</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021arl</th>
<td>Other</td>
<td>59.0</td>
<td>5</td>
<td>SNIa-norm</td>
<td>0.0690</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020vbj</th>
<td>Other</td>
<td>57.7</td>
<td>9</td>
<td>SNIa-91T-like</td>
<td>0.1160</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020sia</th>
<td>Other</td>
<td>57.0</td>
<td>8</td>
<td>SNIa-norm</td>
<td>0.0990</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020psv</th>
<td>Other</td>
<td>56.0</td>
<td>5</td>
<td>SNIa-norm</td>
<td>0.0934</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021tup</th>
<td>Other</td>
<td>56.0</td>
<td>2</td>
<td>SNIa-norm</td>
<td>0.1200</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021jof</th>
<td>Other</td>
<td>56.0</td>
<td>3</td>
<td>SNIa-91T-like</td>
<td>0.1100</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020hgw</th>
<td>Other</td>
<td>56.0</td>
<td>1</td>
<td>SNII</td>
<td>0.0430</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020lrr</th>
<td>Other</td>
<td>56.0</td>
<td>4</td>
<td>SNII</td>
<td>0.0275</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021mwb</th>
<td>Other</td>
<td>56.0</td>
<td>4</td>
<td>SNIa-norm</td>
<td>0.0430</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020row</th>
<td>Other</td>
<td>55.0</td>
<td>5</td>
<td>SNIa-norm</td>
<td>0.0900</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021nue</th>
<td>Other</td>
<td>55.0</td>
<td>1</td>
<td>SNII</td>
<td>0.0360</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021idh</th>
<td>Other</td>
<td>54.9</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.0950</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020tug</th>
<td>Other</td>
<td>54.0</td>
<td>6</td>
<td>SNIa-norm</td>
<td>0.0500</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020juq</th>
<td>Other</td>
<td>53.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.1200</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2020tly</th>
<td>Other</td>
<td>53.0</td>
<td>1</td>
<td>SNII</td>
<td>0.0580</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021hol</th>
<td>Other</td>
<td>52.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.0840</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021sbc</th>
<td>Other</td>
<td>52.0</td>
<td>2</td>
<td>SNIa-norm</td>
<td>0.1000</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021acza</th>
<td>Other</td>
<td>52.0</td>
<td>2</td>
<td>SNIa-norm</td>
<td>0.1900</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021sld</th>
<td>Other</td>
<td>52.0</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.1040</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021mfy</th>
<td>Other</td>
<td>51.9</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.0870</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
<tr>
<th>2021tsj</th>
<td>Other</td>
<td>51.9</td>
<td>1</td>
<td>SNIa-norm</td>
<td>0.0770</td>
<td>SPEC-Z</td>
<td>Normal</td>
</tr>
</tbody>
</table>
</div>
```python
Counter(missclassified_df.spec_class)
```
Counter({'SNIa-norm': 30, 'SNII': 12, 'SNIa-91T-like': 3})
```python
for obj, score, num_anom_epochs, spec, spec_norm_anom, rfc_cls in zip(missclassified_df.index,
missclassified_df.max_anom_score,
missclassified_df.num_anom_epochs,
missclassified_df["spec_class"],
missclassified_df["spec_class_norm_anom"],
missclassified_df.RFC_best_cls):
print(f"https://ziggy.ucolick.org/yse/transient_detail/{obj}/", round(score, 1), num_anom_epochs, spec, spec_norm_anom, rfc_cls)
```
https://ziggy.ucolick.org/yse/transient_detail/2020tip/ 90.0 14 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 89.0 51 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020qql/ 88.0 35 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kbl/ 77.0 22 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021bpq/ 75.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021btn/ 75.0 43 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ojn/ 74.0 18 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acun/ 72.9 25 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 72.0 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020wfg/ 72.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kpz/ 71.0 65 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019wmr/ 70.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 69.8 9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofq/ 68.0 14 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021wue/ 68.0 7 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021vaw/ 66.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021vcz/ 65.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tlf/ 64.0 63 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021joz/ 64.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 64.0 4 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021cta/ 63.0 11 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 63.0 16 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/ 60.9 17 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 60.0 34 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021arl/ 59.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/ 57.7 9 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020sia/ 57.0 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020psv/ 56.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tup/ 56.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021jof/ 56.0 3 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/ 56.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 4 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/ 56.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020row/ 55.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021idh/ 54.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tug/ 54.0 6 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020juq/ 53.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tly/ 53.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021hol/ 52.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021acza/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021sld/ 52.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/ 51.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/ 51.9 1 SNIa-norm Normal Other
## Vetting
https://ziggy.ucolick.org/yse/transient_detail/2020tip/ 90.0 SNIa-norm Normal Other -- 91T / 99aa like? weird b/c litte/no Si in spectrum...
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 89.0 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.127. Faint host.
https://ziggy.ucolick.org/yse/transient_detail/2020qql/ 88.0 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.076
https://ziggy.ucolick.org/yse/transient_detail/2020kbl/ 77.0 SNIa-norm Normal Other -- Ia-norm spec. But maxes out color on SALT fit c=0.3. In a merger/spiral gal? Not obviously elliptical.
https://ziggy.ucolick.org/yse/transient_detail/2021bpq/ 75.0 SNIa-norm Normal Other -- Ia-norm spec. IDK why, need to see timeseries evolution.
https://ziggy.ucolick.org/yse/transient_detail/2021ojn/ 74.0 SNIa-norm Normal Other -- Ia-norm spec. IDK why, need to see timeseries evolution. BUT it does have 4 spectra so someone thought it was interesting...
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 72.0 SNIa-norm Normal Other -- Ia-norm spec. But that z=0.12 puts abs mag ~ -20.6 mag. Ia-SC????
https://ziggy.ucolick.org/yse/transient_detail/2020wfg/ 72.0 SNIa-norm Normal Other -- Ia-norm spec to me...
https://ziggy.ucolick.org/yse/transient_detail/2020kpz/ 71.0 SNII Normal Other -- lasted 400d w/ pre-explosion data??
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 69.8 SNIa-norm Normal Other -- no 2nd bump, no rise. ParSNIP/SR also thought Ibc. SALT fit is even bad. Spec is def Ia norm though.
https://ziggy.ucolick.org/yse/transient_detail/2021wue/ 68.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofq/ 68.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021vaw/ 66.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021vcz/ 65.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tlf/ 64.0 SNII Normal Other -- we know this one is interesting! ;)
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 64.0 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021joz/ 64.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 63.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021cta/ 63.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/ 60.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 60.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021arl/ 59.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/ 57.7 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020sia/ 57.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021jof/ 56.0 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tup/ 56.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/ 56.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/ 56.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020psv/ 56.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020row/ 55.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021idh/ 54.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tug/ 54.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020juq/ 53.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tly/ 53.0 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021acza/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021sld/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021hol/ 52.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/ 51.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/ 51.9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020duv/ 51.0 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021hem/ 51.0 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020hr/ 50.0 SNIa-norm Normal Other
```python
#plot
for obj, score, num_anom_epochs, cls, cls2, rfc_cls in zip(missclassified_df.index,
missclassified_df.max_anom_score,
missclassified_df.num_anom_epochs,
missclassified_df["spec_class"],
missclassified_df["spec_class_norm_anom"],
missclassified_df.RFC_best_cls):
print(obj, score, num_anom_epochs, cls, cls2, rfc_cls)
sn_idx = full_snid_list.index(obj)
meta = full_meta_list[sn_idx]
lc = full_df_list[sn_idx]
lc_grXY = lc[(lc.PASSBAND != 'i') & (lc.PASSBAND != 'z')].reset_index(drop=True)
# (g, r, i or z for PS1-griz, respectively; X for ZTF-g; Y for ZTF-r)
lc_grXY["PASSBAND"] = lc_grXY["PASSBAND"].map({"X": "g", "g": "g", "Y": "R", "r": "R"})
lc = lc_grXY
plot_RFC_prob_vs_lc_ztfid(anom_ztfid=obj,
anom_thresh=50,
lc=lc,
meta=meta,
df_path = "/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries",
)
print("%%%%%%%%%%%%")
```
2020tip 90.0 14 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 90.0
num_anom_epochs 14
MJD Cutoff anom_idx 59125.273
https://ziggy.ucolick.org/yse/transient_detail/2020tip/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020qkx 89.0 51 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 89.0
num_anom_epochs 51
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
MJD Cutoff anom_idx 59071.386
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/
%%%%%%%%%%%%
2020qql 88.0 35 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 88.0
num_anom_epochs 35
MJD Cutoff anom_idx 59086.175
https://ziggy.ucolick.org/yse/transient_detail/2020qql/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kbl 77.0 22 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 77.0
num_anom_epochs 22
MJD Cutoff anom_idx 58996.273
https://ziggy.ucolick.org/yse/transient_detail/2020kbl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021bpq 75.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 75.0
num_anom_epochs 5
MJD Cutoff anom_idx 59275.309
https://ziggy.ucolick.org/yse/transient_detail/2021bpq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021btn 75.0 43 SNII Normal Other
Anomalous during timeseries!
max_anom_score 75.0
num_anom_epochs 43
MJD Cutoff anom_idx 59342.56
https://ziggy.ucolick.org/yse/transient_detail/2021btn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ojn 74.0 18 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 74.0
num_anom_epochs 18
MJD Cutoff anom_idx 59391.3
https://ziggy.ucolick.org/yse/transient_detail/2021ojn/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020acun 72.9 25 SNII Normal Other
Anomalous during timeseries!
max_anom_score 72.9
num_anom_epochs 25
MJD Cutoff anom_idx 59253.503
https://ziggy.ucolick.org/yse/transient_detail/2020acun/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kvl 72.0 8 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 8
MJD Cutoff anom_idx 59033.172
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020wfg 72.0 3 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 72.0
num_anom_epochs 3
MJD Cutoff anom_idx 59157.517
https://ziggy.ucolick.org/yse/transient_detail/2020wfg/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020kpz 71.0 65 SNII Normal Other
Anomalous during timeseries!
max_anom_score 71.0
num_anom_epochs 65
MJD Cutoff anom_idx 59042.27
https://ziggy.ucolick.org/yse/transient_detail/2020kpz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019wmr 70.0 2 SNII Normal Other
Anomalous during timeseries!
max_anom_score 70.0
num_anom_epochs 2
MJD Cutoff anom_idx 58846.491
https://ziggy.ucolick.org/yse/transient_detail/2019wmr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021vwx 69.8 9 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 69.8
num_anom_epochs 9
MJD Cutoff anom_idx 59525.462
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021ofq 68.0 14 SNIa-norm Normal Other
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.2s finished
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 14
MJD Cutoff anom_idx 59391.259
https://ziggy.ucolick.org/yse/transient_detail/2021ofq/
%%%%%%%%%%%%
2021wue 68.0 7 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 68.0
num_anom_epochs 7
MJD Cutoff anom_idx 59468.45
https://ziggy.ucolick.org/yse/transient_detail/2021wue/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021vaw 66.0 4 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 66.0
num_anom_epochs 4
MJD Cutoff anom_idx 59459.341
https://ziggy.ucolick.org/yse/transient_detail/2021vaw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021vcz 65.0 3 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 65.0
num_anom_epochs 3
MJD Cutoff anom_idx 59453.394
https://ziggy.ucolick.org/yse/transient_detail/2021vcz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tlf 64.0 63 SNII Normal Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 63
MJD Cutoff anom_idx 59253.533
https://ziggy.ucolick.org/yse/transient_detail/2020tlf/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021joz 64.0 2 SNII Normal Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 2
MJD Cutoff anom_idx 59363.32
https://ziggy.ucolick.org/yse/transient_detail/2021joz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021xjh 64.0 4 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 64.0
num_anom_epochs 4
MJD Cutoff anom_idx 59484.24
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021cta 63.0 11 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 11
MJD Cutoff anom_idx 59294.236
https://ziggy.ucolick.org/yse/transient_detail/2021cta/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021aff 63.0 16 SNII Normal Other
Anomalous during timeseries!
max_anom_score 63.0
num_anom_epochs 16
MJD Cutoff anom_idx 59232.4
https://ziggy.ucolick.org/yse/transient_detail/2021aff/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020ulz 60.9 17 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 60.9
num_anom_epochs 17
MJD Cutoff anom_idx 59149.38
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2019ppi 60.0 34 SNII Normal Other
Anomalous during timeseries!
max_anom_score 60.0
num_anom_epochs 34
MJD Cutoff anom_idx 58769.473
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021arl 59.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 59.0
num_anom_epochs 5
MJD Cutoff anom_idx 59292.39
https://ziggy.ucolick.org/yse/transient_detail/2021arl/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020vbj 57.7 9 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 57.7
num_anom_epochs 9
MJD Cutoff anom_idx 59144.225
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020sia 57.0 8 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 57.0
num_anom_epochs 8
MJD Cutoff anom_idx 59113.28
https://ziggy.ucolick.org/yse/transient_detail/2020sia/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020psv 56.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 5
MJD Cutoff anom_idx 59074.387
https://ziggy.ucolick.org/yse/transient_detail/2020psv/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021tup 56.0 2 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 2
MJD Cutoff anom_idx 59433.38
https://ziggy.ucolick.org/yse/transient_detail/2021tup/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021jof 56.0 3 SNIa-91T-like Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 3
MJD Cutoff anom_idx 59341.403
https://ziggy.ucolick.org/yse/transient_detail/2021jof/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020hgw 56.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 1
MJD Cutoff anom_idx 58976.298
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020lrr 56.0 4 SNII Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 4
MJD Cutoff anom_idx 59082.187
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021mwb 56.0 4 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 56.0
num_anom_epochs 4
MJD Cutoff anom_idx 59386.297
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020row 55.0 5 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 5
MJD Cutoff anom_idx 59107.322
https://ziggy.ucolick.org/yse/transient_detail/2020row/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021nue 55.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 55.0
num_anom_epochs 1
MJD Cutoff anom_idx 59509.33
https://ziggy.ucolick.org/yse/transient_detail/2021nue/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021idh 54.9 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 54.9
num_anom_epochs 1
MJD Cutoff anom_idx 59335.17
https://ziggy.ucolick.org/yse/transient_detail/2021idh/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tug 54.0 6 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 54.0
num_anom_epochs 6
MJD Cutoff anom_idx 59163.35
https://ziggy.ucolick.org/yse/transient_detail/2020tug/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020juq 53.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 58991.29
https://ziggy.ucolick.org/yse/transient_detail/2020juq/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2020tly 53.0 1 SNII Normal Other
Anomalous during timeseries!
max_anom_score 53.0
num_anom_epochs 1
MJD Cutoff anom_idx 59131.234
https://ziggy.ucolick.org/yse/transient_detail/2020tly/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021hol 52.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 59317.406
https://ziggy.ucolick.org/yse/transient_detail/2021hol/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021sbc 52.0 2 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 2
MJD Cutoff anom_idx 59424.331
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021acza 52.0 2 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 2
MJD Cutoff anom_idx 59530.235
https://ziggy.ucolick.org/yse/transient_detail/2021acza/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021sld 52.0 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 52.0
num_anom_epochs 1
MJD Cutoff anom_idx 59423.232
https://ziggy.ucolick.org/yse/transient_detail/2021sld/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021mfy 51.9 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 51.9
num_anom_epochs 1
MJD Cutoff anom_idx 59379.215
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
2021tsj 51.9 1 SNIa-norm Normal Other
Anomalous during timeseries!
max_anom_score 51.9
num_anom_epochs 1
MJD Cutoff anom_idx 59436.249
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed: 0.0s finished
%%%%%%%%%%%%
```python
correct_classified_df = spec_ysedr1_df[(spec_ysedr1_df["RFC_best_cls"] == "Other") & (spec_ysedr1_df["spec_class_norm_anom"] == "Other")]
correct_classified_df = correct_classified_df.nlargest(45, 'max_anom_score')
correct_classified_df
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>RFC_best_cls</th>
<th>max_anom_score</th>
<th>num_anom_epochs</th>
<th>spec_class</th>
<th>best_z</th>
<th>z_type</th>
<th>spec_class_norm_anom</th>
</tr>
<tr>
<th>Disc. Internal Name</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020xsy</th>
<td>Other</td>
<td>98.0</td>
<td>93</td>
<td>SLSN-II</td>
<td>0.2700</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021nxq</th>
<td>Other</td>
<td>89.0</td>
<td>27</td>
<td>SLSN-I</td>
<td>0.1500</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020opy</th>
<td>Other</td>
<td>89.0</td>
<td>83</td>
<td>TDE</td>
<td>0.1590</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021hrj</th>
<td>Other</td>
<td>88.0</td>
<td>79</td>
<td>SNIb</td>
<td>0.0220</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021bmv</th>
<td>Other</td>
<td>85.0</td>
<td>11</td>
<td>SNIIn</td>
<td>0.0900</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021aadc</th>
<td>Other</td>
<td>85.0</td>
<td>15</td>
<td>SLSN-II</td>
<td>0.1953</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020qmj</th>
<td>Other</td>
<td>82.0</td>
<td>51</td>
<td>SNIIn</td>
<td>0.0220</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020kre</th>
<td>Other</td>
<td>82.0</td>
<td>70</td>
<td>SNIa-CSM</td>
<td>0.1360</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021too</th>
<td>Other</td>
<td>81.9</td>
<td>36</td>
<td>SNIc-BL</td>
<td>0.0700</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021pnp</th>
<td>Other</td>
<td>79.0</td>
<td>46</td>
<td>SNIIb</td>
<td>0.0300</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021gno</th>
<td>Other</td>
<td>78.0</td>
<td>29</td>
<td>SNIb-pec</td>
<td>0.0062</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021udc</th>
<td>Other</td>
<td>75.0</td>
<td>8</td>
<td>SNIIb</td>
<td>0.0350</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020tan</th>
<td>Other</td>
<td>73.0</td>
<td>60</td>
<td>SNIIn</td>
<td>0.0790</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020ivg</th>
<td>Other</td>
<td>72.0</td>
<td>40</td>
<td>SNIIb</td>
<td>0.0530</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020acct</th>
<td>Other</td>
<td>70.0</td>
<td>32</td>
<td>SNIc</td>
<td>0.0350</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021tux</th>
<td>Other</td>
<td>68.0</td>
<td>7</td>
<td>SNIb</td>
<td>0.0300</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020tjd</th>
<td>Other</td>
<td>61.0</td>
<td>16</td>
<td>SNIIb</td>
<td>0.0100</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021aapa</th>
<td>Other</td>
<td>61.0</td>
<td>9</td>
<td>SNIIn</td>
<td>0.0320</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2019uit</th>
<td>Other</td>
<td>60.0</td>
<td>10</td>
<td>SNIIn</td>
<td>0.0710</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021kqp</th>
<td>Other</td>
<td>57.0</td>
<td>1</td>
<td>SN</td>
<td>0.1000</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020awu</th>
<td>Other</td>
<td>57.0</td>
<td>6</td>
<td>SN</td>
<td>0.0650</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020rdu</th>
<td>Other</td>
<td>56.0</td>
<td>21</td>
<td>SNIIn</td>
<td>0.1800</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021seu</th>
<td>Other</td>
<td>55.0</td>
<td>3</td>
<td>Other</td>
<td>0.0597</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2020acty</th>
<td>Other</td>
<td>55.0</td>
<td>10</td>
<td>SNIb</td>
<td>0.0470</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
<tr>
<th>2021aagz</th>
<td>Other</td>
<td>53.0</td>
<td>2</td>
<td>SNIa-SC</td>
<td>0.0617</td>
<td>SPEC-Z</td>
<td>Other</td>
</tr>
</tbody>
</table>
</div>
```python
plt.hist(correct_classified_df.num_anom_epochs, alpha=0.5, bins=np.linspace(0, 100, 51))
plt.hist(missclassified_df.num_anom_epochs, alpha=0.5, bins=np.linspace(0, 100, 51))
```
(array([9., 8., 8., 2., 4., 1., 0., 2., 2., 1., 0., 1., 1., 0., 0., 0., 0.,
2., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),
array([ 0., 2., 4., 6., 8., 10., 12., 14., 16., 18., 20.,
22., 24., 26., 28., 30., 32., 34., 36., 38., 40., 42.,
44., 46., 48., 50., 52., 54., 56., 58., 60., 62., 64.,
66., 68., 70., 72., 74., 76., 78., 80., 82., 84., 86.,
88., 90., 92., 94., 96., 98., 100.]),
<BarContainer object of 50 artists>)
```python
Counter(ysedr1_df.spec_class)
```
Counter({'NA': 843,
'SNIIn': 8,
'SNIa-91T-like': 11,
'SNII': 52,
'SNIa-norm': 195,
'SNIIb': 7,
'SNIc-BL': 3,
'SNIb': 5,
'Other': 1,
'SNIbn': 2,
'TDE': 3,
'LBV': 1,
'SNIc': 7,
'SNIb-pec': 2,
'SNIa-CSM': 3,
'SNIa-91bg-like': 1,
'SN': 4,
'SNIa-SC': 1,
'SLSN-II': 2,
'SLSN-I': 1,
'SNIax': 1})
```python
Counter(correct_classified_df.spec_class)
```
Counter({'SLSN-II': 2,
'SLSN-I': 1,
'TDE': 1,
'SNIb': 3,
'SNIIn': 6,
'SNIa-CSM': 1,
'SNIc-BL': 1,
'SNIIb': 4,
'SNIb-pec': 1,
'SNIc': 1,
'SN': 2,
'Other': 1,
'SNIa-SC': 1})
### correctly classified by class
'SLSN-II': 2/2,
'SLSN-I': 1/1,
'TDE': 1/3,
'SNIb': 2/5,
'SNIIn': 6/8,
'SNIa-CSM': 1/3,
'SNIc-BL': 1/3,
'SNIIb': 4/7,
'SNIb-pec': 1/2,
'SN': 2/4,
'Other': 1/1,
'SNIa-SC': 1/1}
```python
```
# most num anomalous epochs
```python
# full timeseries only (XX spec+phot w/ "other"/anomaly classification)
highest_num_anom_epochs_df = ysedr1_df.nlargest(198, 'num_anom_epochs')
for obj, score, num_anom_epochs, spec, spec_norm_anom, rfc_cls in zip(highest_num_anom_epochs_df.index,
highest_num_anom_epochs_df.max_anom_score,
highest_num_anom_epochs_df.num_anom_epochs,
highest_num_anom_epochs_df["spec_class"],
highest_num_anom_epochs_df["spec_class_norm_anom"],
highest_num_anom_epochs_df.RFC_best_cls):
print(f"https://ziggy.ucolick.org/yse/transient_detail/{obj}/", round(score, 1), num_anom_epochs, spec, spec_norm_anom, rfc_cls)
```
https://ziggy.ucolick.org/yse/transient_detail/2020xsy/ 98.0 93 SLSN-II Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021gpt/ 68.0 85 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020opy/ 89.0 83 TDE Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021hrj/ 88.0 79 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kre/ 82.0 70 SNIa-CSM Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020kpz/ 71.0 65 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tlf/ 64.0 63 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tan/ 73.0 60 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020qmj/ 82.0 51 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020abgb/ 55.0 51 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 89.0 51 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmj/ 76.0 49 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021pnp/ 79.0 46 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020itp/ 73.0 44 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021btn/ 75.0 43 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ivg/ 72.0 40 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021too/ 81.9 36 SNIc-BL Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020qql/ 88.0 35 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 60.0 34 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020acyu/ 90.0 33 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acct/ 70.0 32 SNIc Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020imb/ 68.0 31 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021gno/ 78.0 29 SNIb-pec Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021nxq/ 89.0 27 SLSN-I Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020adly/ 64.0 26 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acun/ 72.9 25 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kbl/ 77.0 22 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020rdu/ 56.0 21 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlz/ 57.0 21 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021dpa/ 73.0 20 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ojn/ 74.0 18 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020sgy/ 71.0 18 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019vuz/ 79.0 17 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ulz/ 60.9 17 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kiq/ 60.0 16 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tjd/ 61.0 16 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020jba/ 68.0 16 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 63.0 16 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ciu/ 53.0 15 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aadc/ 85.0 15 SLSN-II Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofq/ 68.0 14 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tip/ 90.0 14 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020wwt/ 72.0 14 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aym/ 66.0 13 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020mju/ 57.0 12 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020evr/ 58.0 12 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bmv/ 85.0 11 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021snh/ 60.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cta/ 63.0 11 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeid/ 59.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rss/ 77.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ihk/ 63.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmq/ 73.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nxl/ 67.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aesp/ 59.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kps/ 64.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019uit/ 60.0 10 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020acty/ 55.0 10 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 69.8 9 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020jvi/ 84.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/ 57.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mms/ 65.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jfx/ 53.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pzl/ 52.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aapa/ 61.0 9 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/ 57.7 9 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxp/ 66.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019uev/ 56.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 72.0 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020sia/ 57.0 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofr/ 76.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021udc/ 75.0 8 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020lgr/ 56.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ypd/ 61.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aahq/ 53.9 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wue/ 68.0 7 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tux/ 68.0 7 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020iga/ 71.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hjv/ 91.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020skh/ 66.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/ 58.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/ 58.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cqv/ 63.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aejb/ 58.9 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pvj/ 64.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020awu/ 57.0 6 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020tug/ 54.0 6 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aov/ 51.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bpq/ 75.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ndi/ 63.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020twa/ 54.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020psv/ 56.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020twb/ 59.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nor/ 55.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rsp/ 58.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iqk/ 59.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020vpn/ 70.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021arl/ 59.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020row/ 55.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021afh/ 67.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeti/ 61.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iio/ 61.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aevg/ 54.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rrc/ 53.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mxw/ 67.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jed/ 57.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020wzd/ 59.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abji/ 60.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021vaw/ 66.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021cjj/ 56.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 4 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 64.0 4 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020slb/ 52.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/ 56.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aewm/ 50.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021vcz/ 65.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ovd/ 56.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021seu/ 55.0 3 Other Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020icw/ 58.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020wfg/ 72.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020uba/ 52.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021jof/ 56.0 3 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kpp/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/ 54.9 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aasl/ 53.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020mjn/ 55.9 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acjk/ 56.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qlp/ 52.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ibh/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021be/ 57.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tri/ 53.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021pqw/ 59.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iul/ 62.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nis/ 55.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvo/ 57.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hep/ 65.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/ 51.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020oox/ 61.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pss/ 55.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021iqw/ 56.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021tup/ 56.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020xku/ 54.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rtb/ 54.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020duv/ 51.0 2 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021joz/ 64.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020igg/ 54.6 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020adob/ 56.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lgy/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021aagz/ 53.0 2 SNIa-SC Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvx/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bg/ 59.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aly/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021acza/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019wmr/ 70.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ika/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qbx/ 53.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/ 51.2 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rcr/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020advk/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/ 52.9 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021lhi/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020juq/ 53.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021sdy/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021xqw/ 54.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020zkg/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021kcb/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021naw/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iri/ 55.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019xbj/ 58.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qht/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ixs/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxn/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hol/ 52.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020alx/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020woq/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021kbx/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/ 51.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020yix/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cdn/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pcz/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/ 56.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021abeu/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hem/ 51.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020mor/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021gda/ 51.5 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acbb/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hr/ 50.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020unn/ 52.2 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/ 51.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020gll/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021kqp/ 57.0 1 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021beb/ 52.9 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aena/ 50.0 1 NA NA Other
! https://ziggy.ucolick.org/yse/transient_detail/2020xsy/ 98.0 93 SLSN-II Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2021gpt/ 68.0 82 NA NA Other -- lasted for 250d in ysedr1, lasted in total for 800-900d
! https://ziggy.ucolick.org/yse/transient_detail/2020opy/ 89.0 82 TDE Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2021hrj/ 88.0 79 SNIb Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020kre/ 82.0 69 SNIa-CSM Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020tlf/ 64.0 63 SNII Normal Other -- we know this is interesting
! https://ziggy.ucolick.org/yse/transient_detail/2020tan/ 73.0 59 SNIIn Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020kpz/ 71.0 57 SNII Normal Other -- lasted 400d w/ pre-explosion data??
! https://ziggy.ucolick.org/yse/transient_detail/2020qmj/ 82.0 51 SNIIn Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020abgb/ 55.0 51 NA NA Other -- likely IIn if photoz is right (z~0.133), abs mag ~ -19.4 mag). evolution is IIn-esque. lasted 200d.
? https://ziggy.ucolick.org/yse/transient_detail/2020qkx/ 89.0 51 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.127. Faint host.
X https://ziggy.ucolick.org/yse/transient_detail/2020kmj/ 76.0 49 NA NA Other -- normal II if photoz is right (photoz = 0.0799, abs mag ~ -18.25)
! https://ziggy.ucolick.org/yse/transient_detail/2021pnp/ 79.0 45 SNIIb Other Other
? https://ziggy.ucolick.org/yse/transient_detail/2020itp/ 73.0 44 NA NA Other -- not sure. Ia/Ibc ? Bright like Ia if photoz is right, but LC evolution looks more Ibc. elliptical host so Ia?
! https://ziggy.ucolick.org/yse/transient_detail/2021btn/ 75.0 42 SNII Normal Other -- AWFUL spec but RECLASSIFY as Ibc?
! https://ziggy.ucolick.org/yse/transient_detail/2020ivg/ 72.0 39 SNIIb Other Other
X https://ziggy.ucolick.org/yse/transient_detail/2019ppi/ 60.0 34 SNII Normal Other -- looks normal SN II
! https://ziggy.ucolick.org/yse/transient_detail/2021too/ 81.9 34 SNIc-BL Other Other
? https://ziggy.ucolick.org/yse/transient_detail/2020qql/ 88.0 33 SNIa-norm Normal Other -- Ia-norm spec. But slightly overluminous for Ia, -19.6 abs mag, z=0.076
! https://ziggy.ucolick.org/yse/transient_detail/2020acyu/ 90.0 32 NA NA Other -- IIn if photoz is right (photoz = 0.254, abs mag ~ -20.50)
! https://ziggy.ucolick.org/yse/transient_detail/2020acct/ 70.0 31 SNIc Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020imb/ 68.0 31 NA NA Other -- likely II, but lasted 500d
! https://ziggy.ucolick.org/yse/transient_detail/2021gno/ 78.0 28 SNIb-pec Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2021nxq/ 89.0 27 SLSN-I Other Other
! https://ziggy.ucolick.org/yse/transient_detail/2020adly/ 64.0 26 NA NA Other -- IIn/ maybe SLSN
! https://ziggy.ucolick.org/yse/transient_detail/2020acun/ 72.9 25 SNII Normal Other -- RECLASSIFY AS Ic/Ic-BL?! Bad spec though
! https://ziggy.ucolick.org/yse/transient_detail/2020rdu/ 56.0 21 SNIIn Other Other
? https://ziggy.ucolick.org/yse/transient_detail/2020kbl/ 77.0 21 SNIa-norm Normal Other -- Ia-norm spec. But maxes out color on SALT fit c=0.3. In a merger/spiral gal? Not obviously elliptical.
! https://ziggy.ucolick.org/yse/transient_detail/2021dpa/ 73.0 20 NA NA Other -- normal II/ IIn w/ very weird slow 120d RISE?
_______
? https://ziggy.ucolick.org/yse/transient_detail/2021wlz/ 57.0 19 NA NA Other -- SN Ia/Ibc. SALT x1=3.00.
? https://ziggy.ucolick.org/yse/transient_detail/2021ojn/ 74.0 18 SNIa-norm Normal Other -- Ia norm. But 4 spec so someone thought it was interesting.
! https://ziggy.ucolick.org/yse/transient_detail/2019vuz/ 79.0 17 NA NA Other -- Blue. nuclear. bright. likely TDE.
? https://ziggy.ucolick.org/yse/transient_detail/2020sgy/ 71.0 17 NA NA Other -- prob Ia normal? Faint host. If photoz is right, overluminous Ia. but SALT z estimate is more reasonable.
? https://ziggy.ucolick.org/yse/transient_detail/2020kiq/ 60.0 16 NA NA Other -- faint (app mag), but photo-z=0.19, probably intrinsically bright. Unsure type
X https://ziggy.ucolick.org/yse/transient_detail/2020jba/ 68.0 16 NA NA Other -- Ia normal?
? https://ziggy.ucolick.org/yse/transient_detail/2021aff/ 63.0 16 SNII Normal Other -- 300d SN II(P), big color offset
? https://ziggy.ucolick.org/yse/transient_detail/2020ulz/ 60.9 16 SNIa-norm Normal Other -- Ia normal, but gal looks ring-like/ spiral?
! https://ziggy.ucolick.org/yse/transient_detail/2020tjd/ 61.0 15 SNIIb Other Other
_______
https://ziggy.ucolick.org/yse/transient_detail/2021ofq/ 68.0 14 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020wwt/ 72.0 14 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aadc/ 85.0 14 SLSN-II Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020tip/ 90.0 13 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aym/ 66.0 13 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bmv/ 85.0 11 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020mju/ 57.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021snh/ 60.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cta/ 63.0 11 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeid/ 59.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rss/ 77.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020evr/ 58.0 11 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nxl/ 67.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kps/ 64.0 10 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019uit/ 60.0 10 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021ihk/ 63.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jvi/ 84.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mms/ 65.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020vbj/ 57.7 9 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxp/ 66.0 9 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021vwx/ 69.8 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmq/ 73.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wlp/ 57.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kvl/ 72.0 8 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021ofr/ 76.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020ciu/ 53.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lgr/ 56.0 8 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acty/ 55.0 8 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020aahq/ 53.9 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019uev/ 56.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020sia/ 57.0 7 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021tux/ 68.0 7 SNIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020iga/ 71.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aesp/ 59.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021udc/ 75.0 7 SNIIb Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020hjv/ 91.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jfx/ 53.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020skh/ 66.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aapa/ 61.0 7 SNIIn Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021ypd/ 61.0 7 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021cqv/ 63.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pvj/ 64.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021wue/ 68.0 6 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020awu/ 57.0 6 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021rmi/ 58.0 6 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tug/ 54.0 6 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aov/ 51.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ygj/ 58.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bpq/ 75.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ndi/ 63.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020psv/ 56.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aejb/ 58.9 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020twb/ 59.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rsp/ 58.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iqk/ 59.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020vpn/ 70.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021arl/ 59.0 5 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021afh/ 67.0 5 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aeti/ 61.0 5 NA NA Other
----
https://ziggy.ucolick.org/yse/transient_detail/2020iio/ 61.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021rrc/ 53.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mxw/ 67.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020jed/ 57.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020wzd/ 59.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020abji/ 60.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pzl/ 52.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021vaw/ 66.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021cjj/ 56.0 4 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lrr/ 56.0 4 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020row/ 55.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021xjh/ 64.0 4 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021mwb/ 56.0 4 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021vcz/ 65.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020twa/ 54.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ovd/ 56.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021seu/ 55.0 3 Other Other Other
https://ziggy.ucolick.org/yse/transient_detail/2020wfg/ 72.0 3 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021sjw/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021jof/ 56.0 3 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020kpp/ 55.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kmv/ 54.9 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acjk/ 56.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020qlp/ 52.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021be/ 57.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020tri/ 53.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021pqw/ 59.0 3 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iul/ 62.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nis/ 55.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvo/ 57.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020icw/ 58.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hep/ 65.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020nwa/ 51.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020oox/ 61.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pss/ 55.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021tup/ 56.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020xku/ 54.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020uba/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rtb/ 54.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021joz/ 64.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020igg/ 54.6 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020adob/ 56.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020lgy/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sbc/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020aasl/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021zvx/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020mjn/ 55.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bg/ 59.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021ibh/ 55.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aly/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021acza/ 52.0 2 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2019wmr/ 70.0 2 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ika/ 53.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qbx/ 53.9 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021bbd/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020slb/ 52.0 2 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rcr/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020kfy/ 52.9 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aewm/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aevg/ 54.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021lhi/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020juq/ 53.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021xqw/ 54.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020zkg/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021naw/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021iqw/ 56.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020iri/ 55.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2019xbj/ 58.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qht/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020duv/ 51.0 1 SNIa-91T-like Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020ixs/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021qxn/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hol/ 52.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020nor/ 55.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020alx/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021mfy/ 51.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020yix/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021aagz/ 53.0 1 SNIa-SC Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021cuw/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pcz/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hgw/ 56.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021abeu/ 53.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021hem/ 51.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020mor/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021gda/ 51.5 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020acbb/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020hr/ 50.0 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020unn/ 52.2 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020rpl/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021tsj/ 51.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020gll/ 51.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021kqp/ 57.0 1 SN Other Other
https://ziggy.ucolick.org/yse/transient_detail/2021beb/ 52.9 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020aena/ 50.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021yqa/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021idh/ 54.9 1 SNIa-norm Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2021nue/ 55.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020tly/ 53.0 1 SNII Normal Other
https://ziggy.ucolick.org/yse/transient_detail/2020abbj/ 51.2 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2020pwr/ 52.0 1 NA NA Other
https://ziggy.ucolick.org/yse/transient_detail/2021sld/ 52.0 1 SNIa-norm Normal Other
```python
# Confusion matrices
num_anom_epochs_thresh = 5
#spec_ysedr1_df = ysedr1_df[(ysedr1_df.spec_class != "NA") & (ysedr1_df.max_anom_score >= max_anom_score_thresh)]
#spec_ysedr1_df = ysedr1_df[(ysedr1_df.spec_class != "NA")]
highest_num_anom_epochs_thresh_df = highest_num_anom_epochs_df[(highest_num_anom_epochs_df.spec_class != "NA") & (highest_num_anom_epochs_df.num_anom_epochs >= num_anom_epochs_thresh)]
title='RFC'
true_labels = np.array(highest_num_anom_epochs_thresh_df['spec_class_norm_anom'])
predicted_labels = np.array(highest_num_anom_epochs_thresh_df['RFC_best_cls'])
# define the class labels
class_names = np.unique(true_labels)
nclasses = len(class_names)
KINDS = ['completeness', 'purity']
for KIND in KINDS:
# Sims test set
plot_conf_matrix(true_labels, predicted_labels, labels=class_names,
title=f'{title} ({KIND})', kind=KIND)
plt.savefig(f'{cm_path}/confmatrix_YSEDR1_timeseries_highest_num_anom_epochs_geq_{num_anom_epochs_thresh}_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
#plt.savefig(f'{cm_path}/confmatrix_YSEDR1_timeseries_max_anom_score_geq_{max_anom_score_thresh}_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
#plt.savefig(f'{cm_path}/confmatrix_YSEDR1_fullLConly_{title}_{KIND}.jpg', dpi=300, bbox_inches='tight')
plt.show()
plt.close()
```
```python
```
```python
%%time
for i, (name, lc, meta) in enumerate(zip(full_snid_list, full_df_list, full_meta_list)):
if name != '2021hpr': continue
sn_idx = full_snid_list.index(name)
meta = full_meta_list[sn_idx]
lc = full_df_list[sn_idx]
lc_grXY = lc[(lc.PASSBAND != 'i') & (lc.PASSBAND != 'z')].reset_index(drop=True)
# (g, r, i or z for PS1-griz, respectively; X for ZTF-g; Y for ZTF-r)
lc_grXY["PASSBAND"] = lc_grXY["PASSBAND"].map({"X": "g", "g": "g", "Y": "R", "r": "R"})
if i % 50 == 0: print(i)
# extract_lc_and_host_features_YSEDR1(IAU_name=name,
# ysedr1_lightcurve=lc_grXY,
# spec_class=meta["transient_spec_class"],
# ra=meta["ra"],
# dec=meta["dec"],
# show_lc=False,
# show_host=False)
if os.path.exists(f"/Users/patrickaleo/Desktop/Illinois/LAISS-antares/repo/notebooks/YSEDR1/timeseries/{name}_timeseries.csv"):
print(f'{name} is already made. Continue!\n')
continue
else:
# make timeseries files
extract_lc_and_host_features_YSEDR1(IAU_name=name,
ysedr1_lightcurve=lc_grXY,
spec_class=meta["transient_spec_class"],
ra=meta["ra"],
dec=meta["dec"],
show_lc=False,
show_host=False)
```
Extracted LC features for 2021hpr!
Found 2 hosts in GLADE! See gladeDLR.txt for details.
Found matches for 50.0% of events.
Some features are NaN for 2021hpr. Skip!
CPU times: user 3.98 s, sys: 303 ms, total: 4.28 s
Wall time: 11.5 s
```python
fig, ax = plt.subplots(figsize=(12,14))
class_to_marker = {
'NA': 'o', # Circle marker
'Other': 's', # Square marker
'SLSN-I': '^', # Triangle marker
'SLSN-II': 'v', # Inverted triangle marker
'SN': 'd', # Diamond marker
'SNII': 'p', # Pentagon marker
'SNIIb': '*', # Star marker
'SNIIn': '+', # Plus marker
'SNIa-91T-like': 'x', # X marker
'SNIa-CSM': 'H', # Hexagon1 marker
'SNIa-SC': 'h', # Hexagon2 marker
'SNIa-norm': '8', # Octagon marker
'SNIb': '>', # Right triangle marker
'SNIb-pec': '<', # Left triangle marker
'SNIc': '|', # Vertical bar marker
'SNIc-BL': '_', # Horizontal bar marker
'TDE': '1' # Tri down marker
}
anom_ysedr1_df = ysedr1_df[ysedr1_df.max_anom_score >= 50]
# Create a dictionary to map classes to unique shapes
classes = list(np.unique(anom_ysedr1_df.spec_class))
class_to_shape = {}
unique_shapes = ['o', 's', '^', 'v', 'd', 'p', '*', '+', 'x', 'H', 'h', '8', '>', '<', '|', '_', '1']
for i, class_label in enumerate(classes):
class_to_shape[class_label] = unique_shapes[i % len(unique_shapes)]
# for class_label, x, y in zip(anom_ysedr1_df.spec_class, anom_ysedr1_df.num_anom_epochs, anom_ysedr1_df.max_anom_score):
# marker = class_to_marker.get(class_label, 'o') # Use 'o' (circle) as default marker
# ax.scatter(x, y, label=class_label, marker=marker)
for class_label, x, y in zip(anom_ysedr1_df.spec_class, anom_ysedr1_df.num_anom_epochs, anom_ysedr1_df.max_anom_score):
marker = class_to_shape.get(class_label, 'o') # Use 'o' (circle) as default marker
ax.scatter(x, y, marker=marker, color='c')
for i, txt in enumerate(anom_ysedr1_df.index):
#if ysedr1_df.max_anom_score[i] >= 50:
ax.annotate(txt, (anom_ysedr1_df.num_anom_epochs[i]-2.55, anom_ysedr1_df.max_anom_score[i]+0.55))
plt.title("Full Timeseries Light Curve")
plt.xlabel(r"Number of Anomalous Epochs ($\geq$50% score)")
plt.ylabel("Max Anomaly Score")
# # Create a custom legend with unique shapes
legend_elements = [plt.Line2D([0], [0], marker=shape, color='k', linestyle="None", label=class_label, markersize=10)
for class_label, shape in class_to_shape.items()]
# Add the legend to the plot
plt.legend(handles=legend_elements,
fontsize=12,
frameon=False,
bbox_to_anchor=(1.01, 1)
)
plt.savefig("./YSEDR1/figures/ysedr1_timeseries_anomaly_scatterplot.pdf", bbox_inches='tight', dpi=300)
plt.show()
```
```python
fig, ax = plt.subplots(figsize=(8,10))
class_to_marker = {
'NA': 'o', # Circle marker
'Other': 's', # Square marker
'SLSN-I': '^', # Triangle marker
'SLSN-II': 'v', # Inverted triangle marker
'SN': 'd', # Diamond marker
'SNII': 'p', # Pentagon marker
'SNIIb': '*', # Star marker
'SNIIn': '+', # Plus marker
'SNIa-91T-like': 'x', # X marker
'SNIa-CSM': 'H', # Hexagon1 marker
'SNIa-SC': 'h', # Hexagon2 marker
'SNIa-norm': '8', # Octagon marker
'SNIb': '>', # Right triangle marker
'SNIb-pec': '<', # Left triangle marker
'SNIc': '|', # Vertical bar marker
'SNIc-BL': '_', # Horizontal bar marker
'TDE': '1' # Tri down marker
}
anom_ysedr1_df = ysedr1_df[(ysedr1_df.max_anom_score >= 70)]
# Create a dictionary to map classes to unique shapes
classes = list(np.unique(anom_ysedr1_df.spec_class))
class_to_shape = {}
unique_shapes = ['o', 's', '^', 'v', 'd', 'p', '*', '+', 'x', 'H', 'h', '8', '>', '<', '|', '_', '1']
for i, class_label in enumerate(classes):
class_to_shape[class_label] = unique_shapes[i % len(unique_shapes)]
# for class_label, x, y in zip(anom_ysedr1_df.spec_class, anom_ysedr1_df.num_anom_epochs, anom_ysedr1_df.max_anom_score):
# marker = class_to_marker.get(class_label, 'o') # Use 'o' (circle) as default marker
# ax.scatter(x, y, label=class_label, marker=marker)
for class_label, x, y in zip(anom_ysedr1_df.spec_class, anom_ysedr1_df.num_anom_epochs, anom_ysedr1_df.max_anom_score):
marker = class_to_shape.get(class_label, 'o') # Use 'o' (circle) as default marker
ax.scatter(x, y, marker=marker, color='c')
for i, txt in enumerate(anom_ysedr1_df.index):
#if ysedr1_df.max_anom_score[i] >= 50:
ax.annotate(txt, (anom_ysedr1_df.num_anom_epochs[i]-2.55, anom_ysedr1_df.max_anom_score[i]+0.55))
plt.title("Full Timeseries Light Curve")
plt.xlabel(r"Number of Anomalous Epochs ($\geq$50% score)")
plt.ylabel("Max Anomaly Score")
# # Create a custom legend with unique shapes
legend_elements = [plt.Line2D([0], [0], marker=shape, color='k', linestyle="None", label=class_label, markersize=10)
for class_label, shape in class_to_shape.items()]
# Add the legend to the plot
plt.legend(handles=legend_elements,
fontsize=12,
frameon=False,
bbox_to_anchor=(1.01, 1)
)
plt.savefig("./YSEDR1/figures/ysedr1_timeseries_anomaly_scatterplot_most_anom.pdf", bbox_inches='tight', dpi=300)
plt.show()
```
```python
```
|
patrickaleoREPO_NAMELAISS-localPATH_START.@LAISS-local_extracted@LAISS-local-main@notebooks@test_YSEDR1_AD.ipynb@.PATH_END.py
|
{
"filename": "VariationalEquations.ipynb",
"repo_name": "tigerchenlu98/rebound",
"repo_path": "rebound_extracted/rebound-main/ipython_examples/VariationalEquations.ipynb",
"type": "Jupyter Notebook"
}
|
# Variational Equations
For a complete introduction to variational equations, please read the paper by Rein and Tamayo (2016).
For this tutorial, we work with a two planet system. We vary the initial semi-major axis $a$ of the outer planet. Because the planets interact with each other, the final $x$-position of the inner planet at the end of the simulation will depend on the initial semi-major axis of the outer planet. We run the simulation once for a fixed $a_0$ and then use first and second order variational equations to predict the final position of the outer planet for different $a$s in a neighbourhood of $a_0$.
To do that, let us first import REBOUND, numpy and matplotlib.
```python
import rebound
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
```
Before using variational equations, let us define a function that calculates the final position of the inner planet as a function of $a$ in the brute-force way:
```python
def run_sim(a):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(primary=sim.particles[0],m=1e-3, a=1)
sim.add(primary=sim.particles[0],m=1e-3, a=a)
sim.integrate(2.*np.pi*10.)
return sim.particles[1].x
```
We'll use this function to create a list of *true* final positions to which we later compare our results.
```python
N=400
x_exact = np.zeros((N))
a_grid = np.linspace(1.4,1.7,N)
for i,a in enumerate(a_grid):
x_exact[i] = run_sim(a)
```
Running a simulation with variational equations is very easy. We start by creating a simulation and add the three particles (the star and two planets) just as before. Note that the `vary` convenience function we use below only accepts heliocentric coordinates, so we explicitly tell REBOUND that the star is the primary when adding particles to the simulation.
We then add variational particles to the simulation. We vary one parameter ($a$) and thus need only one set of first order variational equations. The second order variational equations depend on the first order ones. Thus, when initializing them, one has to pass the set of first order variational equations using the 'first_order' parameter.
After adding a variation, one must always initialize it. We do this below with REBOUND's `vary()` convenience function, which makes varying orbital parameters particularly easy. Alternatively, one can also initialize the variational particles directly, e.g. using `var_da.particles[1].x = 1`. Note that variations are implemented as particles, but you they really represent derivatives of a particle's coordinates with respect to some initial parameter. For more details, see Rein and Tamayo (2016).
The function below does all that and returns the final position of the inner planet, as well as the first and second derivatives of the position with respect to $a$.
```python
def run_sim_var(a):
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(primary=sim.particles[0],m=1e-3, a=1)
sim.add(primary=sim.particles[0],m=1e-3, a=a)
var_da = sim.add_variation()
var_dda = sim.add_variation(order=2, first_order=var_da)
var_da.vary(2, "a")
var_dda.vary(2, "a")
sim.integrate(2.*np.pi*10.)
return sim.particles[1].x, var_da.particles[1].x, var_dda.particles[1].x
```
We can now use the variational equations to predict the final position of the inner particle. Note that we only run one simulation, at $a_0=1.56$.
```python
a_0 = 1.56
x, dxda, ddxdda = run_sim_var(a_0)
x_1st_order = np.zeros(N)
x_2nd_order = np.zeros(N)
for i,a in enumerate(a_grid):
x_1st_order[i] = x + (a-a_0)*dxda
x_2nd_order[i] = x + (a-a_0)*dxda + 0.5*(a-a_0)*(a-a_0)*ddxdda
```
In the figure below, we plot the final position as a function of the initial semi-major axis. The black line corresponds to the true final position as calculated by the brute-force approach. The dashed and dotted lines correspond to the approximations using first and second order variational equations. As one can see, the second order approximation is very accurate within a neighbourhood of $a_0$.
```python
fig = plt.figure(figsize=(6,4))
ax = plt.subplot(111)
ax.set_xlim(a_grid[0],a_grid[-1])
ax.set_ylim(np.min(x_exact),np.max(x_exact)*1.01)
ax.set_xlabel("initial semi-major axis of the outer planet")
ax.set_ylabel("$x$ position of inner planet after 10 orbits")
ax.plot(a_grid, x_exact, "-", color="black", lw=2)
ax.plot(a_grid, x_1st_order, "--", color="green")
ax.plot(a_grid, x_2nd_order, ":", color="blue")
ax.plot(a_0, x, "ro",ms=10);
```
For chaotic systems, the coordinates of variational particles grow exponentially. Very quickly, one might run into numerical issues because the finite range of floating point numbers prevents us from working with number larger than $\approx10^{308}$. REBOUND (as of version 3.21) automatically rescales first order variational variables when coordinates become larger than $10^{100}$. This is possible because first order variational equations (in contrast to second order ones) are linear.
Consider the following chaotic planetary system:
```python
sim = rebound.Simulation()
sim.add(m=1.) # Star
sim.add(m=0.000954, a=5.204, M=0.600, omega=0.257, e=0.048) # planet 1
sim.add(m=0.000285, a=7.2, M=0.871, omega=1.616, e=0.12) # planet 2
sim.move_to_com()
```
Let us add a first order set of variational equations:
```python
v = sim.add_variation()
v.particles[1].x = 1
```
Next, we integrate this forward in time, keeping track of the x coordinate of the variational particle as well as the `lrescale` parameter in the `reb_variational_configuration` struct `v`.
```python
sim.integrator = "whfast"
sim.dt = 5.0
times = np.linspace(0,1e6,1000)
xs = np.zeros(len(times))
lrescale = np.zeros(len(times))
for i in range(len(times)):
sim.integrate(times[i], exact_finish_time=0)
xs[i] = v.particles[1].x
lrescale[i] = v.lrescale
```
We can then use `lrescale` to extend our integration beyond what would normally be possible using standard floating point numbers. The `lrescale` parameter is the logarithm of all rescalings that have been applied to the variational particles. Note that we need to do all the calculations in log space because the values are too big for floating point numbers.
```python
log_xs = np.log(np.abs(xs)) + lrescale
log10_xs = log_xs/np.log(10)
```
```python
fig, ax = plt.subplots()
ax.set_ylabel("$|x|$")
ax.set_xlabel("time [orbits]")
ax.plot(times/sim.particles[1].P, np.log10(np.abs(xs)), label= "actual x coordinate of variational particle \n (not taking rescaling into account)")
ax.plot(times/sim.particles[1].P, log10_xs, label= "x coordinate of variational particle \n (rescaling taken into account)")
plt.axhline(y=308, color='k', linestyle='--', label = "maximum range of floating point numbers")
ax.yaxis.set_major_formatter(FormatStrFormatter('$10^{%.f}$'))
ax.legend(loc="upper left");
```
```python
```
|
tigerchenlu98REPO_NAMEreboundPATH_START.@rebound_extracted@rebound-main@ipython_examples@VariationalEquations.ipynb@.PATH_END.py
|
{
"filename": "twoMass.py",
"repo_name": "ProfessorBrunner/rc3-pipeline",
"repo_path": "rc3-pipeline_extracted/rc3-pipeline-master/pipeline/twoMass.py",
"type": "Python"
}
|
# Note: 2MASS Class can not be named starting with number
from survey import Survey
class TwoMass(Survey):
def __init__(self):
self.name = '2MASS'
self.bands=['j','h','k']
self.color_bands=['j','h','k']
self.best_band ='k' #see Fig 2 (Skrutskie et al. 2006)
self.pixel_scale = 2.0
self.data_server = Survey._initServer(self)
# Mosaic Program Settings
self.sextractor_params= " -PIXEL_SCALE {}".format(self.pixel_scale)
self.stiff_param_low = " -MAX_TYPE QUANTILE -MAX_TYPE QUANTILE -MAX_LEVEL 0.997 -COLOUR_SAT 7 -MIN_TYPE QUANTILE -MIN_LEVEL 1 -GAMMA_FAC 0.7 "
self.stiff_param_best = " -MAX_TYPE QUANTILE -MAX_LEVEL 0.99 -COLOUR_SAT 5 -MIN_TYPE QUANTILE -MIN_LEVEL 1 -GAMMA_FAC 0.8"
|
ProfessorBrunnerREPO_NAMErc3-pipelinePATH_START.@rc3-pipeline_extracted@rc3-pipeline-master@pipeline@twoMass.py@.PATH_END.py
|
{
"filename": "spiefigures.ipynb",
"repo_name": "Jashcraf/poke",
"repo_path": "poke_extracted/poke-main/docs/_build/html/notebooks/spiefigures.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import numpy as np
import matplotlib.pyplot as plt
from poke.writing import read_serial_to_rayfront
from poke.poke_core import Rayfront
import poke.plotting as plot
```
```python
rf = read_serial_to_rayfront('sample_rayfront.msgpack')
```
```python
rayset = 0
surf = -1
size = 1e-10
r = rf.xData[rayset,0]**2 + rf.yData[rayset,0]**2
mask = r < np.max(rf.xData[rayset,0])
fig = plt.figure(figsize=[13,5])
plt.title('Fooprint Diagram')
plt.subplot(121)
plt.title('Wavefront OPD')
plt.scatter(rf.xData[rayset,0][mask],rf.yData[rayset,0][mask],c=1e6*(rf.opd[rayset,surf]-np.mean(rf.opd[rayset,surf]))[mask],cmap='coolwarm')
plt.colorbar(label='OPD nm')
plt.ylabel('Entrance Pupil Y [m]',fontsize=14)
plt.xlabel('Entrance Pupil X [m] \n (a)',fontsize=14)
ax = plt.subplot(122)
plt.title('Spot Diagram at Image')
offset = -0.4061995389968682
plt.scatter((rf.xData[rayset,surf][mask])/size,(rf.yData[rayset,surf][mask]-offset)/size)
plt.xlabel('Image X [nm] \n (b)',fontsize=14)
circ = plt.Circle((0, 0), 144,fill=0,edgecolor='black')
ax.add_patch(circ)
ax.set_aspect('equal')
plt.xlim([-500,500])
plt.ylim([-500,500])
plt.show()
```
<ipython-input-7-b1ca02b7f1bf>:8: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.
plt.subplot(121)
```python
surfaces = rf.surfaces
print(surfaces)
rf.surfaces = surfaces[:-1]
rf.compute_jones_pupil(aloc=np.array([0.,1.,0.]))
```
[{'surf': 2, 'coating': (1.0194+6.6388j), 'mode': 'reflect'}, {'surf': 4, 'coating': (1.0194+6.6388j), 'mode': 'reflect'}, {'surf': 8, 'coating': (1.0194+6.6388j), 'mode': 'reflect'}, {'surf': 11, 'coating': 1, 'mode': 'reflect'}]
```python
import poke.plotting as plot
plot.jones_pupil(rf)
```
```python
from hcipy import *
from poke.interfaces import rayfront_to_hcipy_wavefront
# define HCIPy parameters
npix = 256
pupil_grid = make_pupil_grid(npix)
focal_grid = make_focal_grid(8,12)
prop = FraunhoferPropagator(pupil_grid,focal_grid)
telescope_aperture = make_magellan_aperture(True)(pupil_grid)
# convert the Rayfront's jones pupil to an HCIPy wavefront
wavefront = rayfront_to_hcipy_wavefront(rf,npix,pupil_grid)
# apply aperture and propagate
wavefront.electric_field *= telescope_aperture
focused_wavefront = prop(wavefront)
```
```python
fig,ax = plt.subplots(ncols=2,nrows=2,figsize=[7,7])
title = ['Axx','Axy','Ayx','Ayy']
k = 0
for i in range(2):
for j in range(2):
toplot = np.abs(arm[i,j])**2
im = ax[i,j].imshow(np.log10(toplot),cmap='inferno',vmin=-10,vmax=1)
ax[i,j].set_xticks([0])
ax[i,j].set_xticklabels([''])
ax[i,j].set_yticks([0])
ax[i,j].set_yticklabels([''])
ax[i,j].set_title(title[k])
k += 1
fig.colorbar(im,ax=ax,fraction=0.05,label='Log Irradiance')
plt.show()
```
```python
arm.electric_field.shaped.shape
```
(2, 2, 192, 192)
|
JashcrafREPO_NAMEpokePATH_START.@poke_extracted@poke-main@docs@_build@html@notebooks@spiefigures.ipynb@.PATH_END.py
|
{
"filename": "DopplerImagingGallery_Variable.ipynb",
"repo_name": "rodluger/starry",
"repo_path": "starry_extracted/starry-master/notebooks/DopplerImagingGallery_Variable.ipynb",
"type": "Jupyter Notebook"
}
|
# Spectrally variable `SPOT`
<script>
// Force "pop out" links to open in the browser (rather than download)
document.addEventListener("DOMContentLoaded", function(event) {
var links = document.getElementsByClassName("reference download internal")
for (var i = 0; i < links.length; i++) {
links[i].outerHTML = links[i].outerHTML.replace("download=\"\"", "");
}
});
</script>
```python
%matplotlib inline
```
```python
%run notebook_setup.py
```
```python
import starry
from pathlib import Path
starry_path = Path(starry.__file__).parents[0]
starry.config.lazy = False
starry.config.quiet = True
```
In this notebook we'll instantiate a Doppler map with spectrally variable features and visualize it with the interactive ``show()`` method. The plots below are fully interactive: move the mouse and scroll over the maps to control the spectra that are displayed below them.
```python
import starry
import numpy as np
```
```python
# Instantiate
map = starry.DopplerMap(15, nt=20, nc=4, inc=60, veq=30000)
# Four images containing each of the letters in "SPOT"
# We'll flip them so that they are bright
image = np.flipud(plt.imread(starry_path / "img" / "spot.png"))
image = np.mean(image[:, :, :3], axis=2)
nlat, nlon = image.shape
images = np.zeros((4, nlat, nlon))
for n in range(4):
images[n] = np.zeros_like(image)
idx = slice(n * nlon // 4, (n + 1) * nlon // 4)
images[n][:, idx] = 1 - image[:, idx]
images += 0.1
# Four corresponding absorption lines
mu = np.array([642.925, 642.975, 643.025, 643.075])
sig = 0.0085
dw = map.wav0.reshape(1, -1) - mu.reshape(-1, 1)
spectra = 1.0 - np.exp(-0.5 * dw ** 2 / sig ** 2)
# Load it all into the map
map.load(maps=images, spectra=spectra, smoothing=0.075)
# Visualize
map.visualize()
```
```python
map.visualize(file="doppler_variable.html")
```
|
rodlugerREPO_NAMEstarryPATH_START.@starry_extracted@starry-master@notebooks@DopplerImagingGallery_Variable.ipynb@.PATH_END.py
|
{
"filename": "nnu-H0-sigma8.py",
"repo_name": "cmbant/CosmoMC",
"repo_path": "CosmoMC_extracted/CosmoMC-master/batch3/outputs/nnu-H0-sigma8.py",
"type": "Python"
}
|
import planckStyle as s
from pylab import *
g = s.getSinglePlotter()
g.settings.param_names_for_labels = 'clik_Hunits.paramnames'
base = 'base_nnu_'
roots = [base + s.defdata_all]
neff = [1, 5]
updated = True
if updated:
g.add_y_bands(74.03, 1.42)
g.add_text_left('Riess et al. (2019)', 0.03, 0.8, color='k', fontsize=7)
else:
g.add_y_bands(73.45, 1.66)
g.add_text_left('Riess et al. (2018)', 0.03, 0.76, color='k', fontsize=7)
g.plot_3d(roots, ['nnu', 'H0', 'sigma8'])
norm = 3.046
g.add_x_marker(norm, ls='-')
# g.add_x_marker(norm + 0.39)
# g.add_x_marker(norm + 0.57)
g # .add_x_marker(norm + 1)
g.add_2d_contours(g.getRoot('nnu', s.defdata_all_lensing + '_BAO'), 'nnu', 'H0', color='black')
# g.add_2d_contours(g.getRoot('nnu', s.defdata_all + '_abundances'), 'nnu', 'H0', color='red', ls='--')
# g.add_2d_contours(g.getRoot('nnu', s.defdata + '_H073p9'), 'nnu', 'H0', color='magenta', ls='-')
g.add_2d_contours(g.getRoot('nnu', s.defdata_all + '_Riess18_post_BAO_lensing'), 'nnu', 'H0', color='darkblue', ls='--',
alpha=0.7)
# g.add_2d_contours(g.getRoot('nnu', s.defdata_all + '_BAO_Cooke17_Aver15'), 'nnu', 'H0', color='green', filled=False,
# alpha=1)
# g.add_legend([s.planckall + '+BAO'], legend_loc='upper left', colored_text=True)
gca().set_xticks([2, 2.5, 3.0, 3.5, 4])
ylim([57, 78])
xlim([2, 4.2])
g.export()
|
cmbantREPO_NAMECosmoMCPATH_START.@CosmoMC_extracted@CosmoMC-master@batch3@outputs@nnu-H0-sigma8.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "amerand/CANDID",
"repo_path": "CANDID_extracted/CANDID-master/README.md",
"type": "Markdown"
}
|
# [C]ompanion [A]nalysis and [N]on-[D]etection in [I]nterferometric [D]ata
This is a suite of Python 2/3 tools to find faint companion around star in interferometric data in the [OIFITS format](http://www.mrao.cam.ac.uk/research/optical-interferometry/oifits/). This tool allows to systematically search for faint companions in OIFITS data, and if not found, estimates the detection limit. This code is presented in the article [Gallenne et al. 2015](http://adsabs.harvard.edu/abs/2015A%26A...579A..68G), although it has evolved slightly since the paper was published.
WARNING: before version 1.0.5 (April 2021), there is a bug in the computation of the statistical significance: the computation are correct below ~4 sigmas, but get progressively overestimated, to double by 14 sigma. In practice, this does not change results, as the significance is only important around or below 4 sigmas. Also, note that the number of sigma cannot go above ~8 with the new (and correct) formula.
To install, run in the top directory (containing [pyproject.toml](pyproject.toml)):
```
python3 -m pip install .
```
To remove CANDID:
```
python3 -m pip uninstall candid
```
## What does it do for you?
### Companion Search
The tool is based on model fitting and Chi2 minimization ([scipy.optimize.leastsq](http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.optimize.leastsq.html)), with a grid for the starting points of the companion position. It ensures that all positions are explored by estimating a-posteriori if the grid was dense enough, and provide an estimate of the optimum grid density (see example).
Note that if you use [LITpro](http://www.jmmc.fr/litpro_page.htm), you might find that the results given by CANDID are different:
* In general, position of the found companion is the same for LITpro and CANDID
* LITpro tends to overestimate the flux ratio, because it does not take into account bandwidth smearing, whereas CANDID does. For AX Cir, the flux ratio found bt CANDID is 1%, whereas it is 0.8% for LITpro (or CANDID when bandwidth smearing is disabled). Note that the latest versions of CANDID compute numerically the bandwidth smearing to make sure we never run into restrictions of the analytical approximation of Lachaume & Berger (2013). the variable `CONFIG['Nsmear']` is automatically adjusted, so do not try to adjust it yourself.
### Detection limit
It uses Chi2 statistics to estimate the level of detection in "number of sigmas".
### Non-Detection Limit
There are two approaches implemented: [Absil et al. 2011](http://adsabs.harvard.edu/abs/2011A%26A...535A..68A) and CANDID's Analytical Companion Injection [Gallenne et al. 2015](http://adsabs.harvard.edu/abs/2015A%26A...579A..68G).
## Known limitations
The code has *not* been deeply error proofed. If you encounter problems or bugs, do not hesitate to contact the developers.
* **The (non-)detection levels are given assuming the errors bars in the data are uncorrelated**. This is of course not the case for real data, in particular when a lot of spectral channels are present.
* The works only with simple OIFITS files: all observations should be with the same instrument (same OI_WAVELENGTH) and all data will be taken, assuming a single target is present. Several OIFITS files can be loaded at once (giving a list of files) but it assumes that all files contain only one target.
* The UD visibility is computed using a polynomial approximation, so only first and second lobe visibilities for the primary can be handled. That should be enough but might lead to some (unknown yet) side effects.
* The code has been tested of OIFITS files form CHARA/MIRC and VLTI/PIONIER. If you use other instruments and encounter problems, please contact the developers!
* The code can take lots of memory because it stores lots of intermediate results, so using a 64bit python is advisable.
* The code is not particularly fast, but uses [multiprocessing](https://docs.python.org/2/library/multiprocessing.html): our experience on Macs is that it leads to several problems:
* It does not work properly with IPython Notebooks. [It seems to be known](https://github.com/ipython/ipython/issues/6109).
* It works better with the IPython console, though it sometimes seems unresponsive (it makes sometimes the estimation of running time unreliable). Moreover, ctrl+C sometimes does not stop the code.
* The smoothest behavior is obtained by running in a python shell.
## Examples:
The following example can be found in [axcir.py](candid/demo/axcir.py).
* **chi2 Maps**. These are useful because fast, but dangerous because it is easy to miss a companion just using those. On FIG1 and FIG2, we show to runs for different diameters and flux ratio: either the diameter is fitted to the V2 data ([FIG1](candid/doc/figure_1.png)). The chi2 map shows a minimum only if the grid if fine enough (the structure in the map should be clear, not pixelated) but also if the parameters (inc. the flux ratio) are very close to the actual ones.
* **fit Maps**. These are **MUCH** better, but slower than chi2 maps. If V2 are present, the diameter will be fitted ([FIG2](candid/doc/figure_2.png)). Note that once a companion is found, it can be removed analytically from the data and the fit map ran again ([FIG4](candid/doc/figure_4.png)): this demonstrates that, in the case of our example, the secondary "detections" are only artifact from the main companion.
* **detection limits**. We implemented 2 methods; Absil's and our companion injection ([Gallenne et al. 2015](http://arxiv.org/abs/1505.02715)). Note that they give slightly different results: we argue that our method is more robust to correlated noise (read our paper!). When you have detected a companion and wish to estimate the detection limit, it is important to first remove the companion ([FIG5](candid/doc/figure_5.png)).
To run the demo, you can do:
```
>>> import candid.demo.axcir
>>> candid.demo.axcir.runAll()
```
### Open OIFITS file with CANDID:
```
>>> import candid
>>> o = candid.Open('AXCir.oifits')
> loading file AXCir.oifits
| WARNING: no valid T3AMP values in this HDU
| WARNING: no valid T3AMP values in this HDU
| Smallest spatial scale: 2.68 mas
| Diffraction Field of view: 221.88 mas
| WL Smearing Field of view: 55.24 mas
| observables available: [ 'v2', 'cp']
| instruments: [ 'VLTI-PIONIER_Pnat(1.6135391/1.7698610)']
| rmin= not given, set to smallest spatial scale: rmin= 2.68 mas
| rmax= not given, set to Field of View: rmax=55.24 mas
```
One might want to limit the observables and/or instrumental setups fitted. VLTI/GRAVITY data, for example, provide no meaningful closure amplitude. If our "o" variable was from a GRAVITY dataset, one should add `o.observables=['cp','v2']; o.instruments = ['SPECTRO_FT']` after opening the file in order to limit the following fits to phase closure and V2, as well as instrument to the fringe tracker (or `o.instruments = ['SPECTRO_SC']` for the science spectrograph, but very slow...).
### CHI2MAP: fitted diameter and fixed flux ratio=1%:
The easiest thing to try is a chi2 map, assuming a certain flux ratio for the companion. This is quite inefficient but CANDID allows to do it. If no parametrization is given (step size 'step=', maximum radius for search 'rmax'), CANDID will guess some values based on the angular resolution and the wavelength smearing field of view. The flux ratio is given in percent.
```
>>> o.chi2Map(fig=1, fratio=1.0)
> step= not given, using 1/4 X smallest spatial scale = 0.67 mas
> observables: ['v2', 'cp']
> UD diam Fit
| best fit diameter: 0.932 +- 0.006 mas
| chi2 = 0.975
|================================================== | 99% 0 s remaining
| chi2 Min: 0.959
| at X,Y : 6.38, -28.55 mas
| NDOF=1499 > n sigma detection: 0.99 (fully uncorrelated errors)
```
### FITMAP:
Doing a grid of fit is much more efficient than doing a simple Chi2 Map ([FIG1](candid/doc/figure_1.png)). In a FITMAP, a set of binary fits are performed starting from a 2D grid of companion position. The plot displays the interpolated map of the chi2 minima (left), with the path of the fit, from start to finish (yellow lines). FITMAP will compute, a posteriori, what was the correct step size `step=`. In our example below, we let CANDID chose the step size, based on the angular resolution of the data (1.2 wavelength/baseline). The companion is detected at the same position as for the previous example, with a much better dynamic range.
```
>>> o.fitMap(fig=2)
> step= not given, using sqrt(2) x smallest spatial scale = 3.78 mas
> observables: ['v2', 'cp']
> Preliminary analysis
> UD diam Fit
| best fit diameter: 0.932 +- 0.006 mas
| chi2 = 0.975
|================================================== | 99% 0 s remaining
| Computing map of interpolated Chi2 minima
| 195 individual minima for 643 fits
| 10, 50, 90 percentiles for fit displacement: 1.2, 3.1, 6.1 mas
| Grid has the correct steps of 3.78mas, optimimum step size found to be 4.26mas
| Rbf interpolating: 195 points -> 3721 pixels map
> BEST FIT 0: chi2= 0.73
| x= 6.2468 +- 0.0580 mas
| y= -28.5056 +- 0.1017 mas
| f= 1.0411 +- 0.0472 %
| diam*= 0.8007 +- 0.0088 mas
| chi2r_UD=0.98, chi2r_BIN=0.73, NDOF=1499 -> n sigma: 14.37 (assumes uncorr data)
```
FITMAP allows to provide additional parameters, as well as fixing parameters in the fit. Additional parameters are passed using `addParam={'diamc':0.5, 'fres':0.1}` for a resolved flux of 10% (of the centra star's flux) and a companion angular diameter of 0.5mas. If ones want to prevent fitting parameters, `doNotFit=['fres', 'diamc']`.
### ERROR BARS by bootstrapping:
In order to better estimate the uncertainties on the companion we found, we can use bootstrapping to estimate the uncertainties around the best fit parameters. The default starting is the best fitted position: since we made a fit with an analytical removal of the companion, the currently stored companion is not the one found on Fig2; thankfully we stored it in the variable 'p'.
On the correlations plots, the red dot with error bars is the fitted position; the blue ellipses are derived from the bootstrap (using a principal component analysis); the values given for each parameters are the median; the uncertainties are the 16% and 84% percentile (one sigma).
```
>>> o.fitBoot(fig=3)
> 'N=' not given, setting it to Ndata/2
|================================================== | 99% 0 s remaining
> sigma clipping in position and flux ratio for nSigmaClip= 3.5
| 0 fits ignored
| diam* = 0.8002 + 0.0144 - 0.0153 mas
| f = 1.0483 + 0.0891 - 0.0895 %
| x = 6.2435 + 0.1029 - 0.1183 mas
| y = -28.5068 + 0.1419 - 0.1686 mas
```
FITBOOT, similar to FITMAP, accepts the `doNotFit=` input as a list of non-fitted parameters.
### FITMAP, after analytically removing companion
CANDID offers the possibility, once a companion has been detected, to remove it analytically from the data and rerun a FITMAP. This allows to estimate the dynamic range of the data set, but also to detect fainter tertiary components. fitMap stores the best fit in the dictionary bestFit, which key best contains the dictionary containing the parameters. Note that `o.bestFit['uncer']` contains the corresponding error bars.
```
>>> p = o.bestFit['best']
>>> o.fitMap(fig=4, removeCompanion=p)
> step= not given, using sqrt(2) x smallest spatial scale = 3.78 mas
> observables: ['v2', 'cp']
> Preliminary analysis
> UD diam Fit
| best fit diameter: 0.800 +- 0.006 mas
| chi2 = 0.739
|================================================== | 99% 0 s remaining
| Computing map of interpolated Chi2 minima
| 189 individual minima for 643 fits
| 10, 50, 90 percentiles for fit displacement: 1.2, 3.3, 7.5 mas
| current grid step (3.69mas) is too fine!!! --> 4.62mas should be enough
| Rbf interpolating: 189 points -> 3721 pixels map
> BEST FIT 0: chi2= 0.69
| x= 6.6809 +- 0.1332 mas
| y= 2.4402 +- 0.1770 mas
| f= 0.3640 +- 0.0381 %
| diam*= 0.7332 +- 0.0097 mas
| chi2r_UD=0.74, chi2r_BIN=0.69, NDOF=1499 -> n sigma: 2.02 (assumes uncorr data)
```
### DETECTION LIMIT, after analytically removing companion:
We here remove the companion analytically from the V2 and CP data. This is mandatory in order to estimate the detection limit: the statistical hypothesis of the test is that the data are best described by a uniform disk.
```
>>> o.detectionLimit(fig=5, step=1.5, removeCompanion=p)
> step= not given, using 1/2 X smallest spatial scale = 1.34 mas
> observables: ['v2', 'cp']
> UD diam Fit
| best fit diameter: 0.800 +- 0.006 mas
| chi2 = 0.739
> Detection Limit Map 83x83
> Method: Absil
|================================================== | 99% 0 s remaining
> Method: injection
|================================================== | 99% 0 s remaining
```
### CONFIG parameters
A global dictionary CONFIG to set parameters. The list of default parameters is shown each time the library is loaded. To modify the parameters, you should do it as such:
```
>>> candid.CONFIG['longExecWarning'] = 300
```
This will, for example, set the maximum computing time to 300s (instead of the default 180s). Note that this will have to be done every time you import or reload the library.
## Performances
Note that with the release of SciPy 1.9, `scipy.weave` has been phased out, hence CANDID has taken a hit in terms of performances by reversing to Numpy. Starting in version 0.3 of CANDID (early 2018), Cython is used to accelerate by a factor 2 over Numpy. It is not as fast as `scipy.weave` but still twice as fast as Numpy.
### Multiprocessing
CANDID is parallelized and the number of core used can be set. By default, it will use all the cores: `candid.CONFIG['Ncores'] = None`. One can set manually how many cores to be used:
```
>>> candid.CONFIG['Ncores'] = 8
```
will use 8 processes, or the actual number of cores present on your machine if you have less than 8 cores. To know how many cores you have, run:
```
>>> import multiprocessing
>>> multiprocessing.cpu_counts()
```
Because not all computation are parallelized and there are some overheads due to the 'multiprocessing' library, the gain is not linear with the number of cores used. The measured speedups (on a 96 cores machine) for `fitMap` and `detectionLimit` are:
|N cores: | 1 | 4 | 8 | 16 | 32 | 64 |
|-----------------|---|----|---|----|----|----|
|`fitMap` | 1 | 3.5| 7 | 11 | 15 | 16 |
|`detectionLimit` | 1 | 3.5| 6 | 8 | 9 | 10 |
8 cores is hence a good compromise. The gains match [Amdhal's law](https://en.wikipedia.org/wiki/Amdahl%27s_law) for 96% of `fitMap` being parallelized. The fraction for `detectionLimit` is roughly 92%.
## Informations
### Link
https://github.com/amerand/CANDID
### Developpers
[Antoine Mérand](mailto:amerand-at-eso.org) and [Alexandre Gallenne](mailto:agallenn-at-eso.org)
### Python dependencies
Python >=3.7, Numpy, Scipy, Matplotlib and astropy (for reading FITS files).
### LICENCE (BSD)
Copyright (c) 2015-2018, Antoine Mérand
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
amerandREPO_NAMECANDIDPATH_START.@CANDID_extracted@CANDID-master@README.md@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/graph_objs/pointcloud/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._hoverlabel import Hoverlabel
from ._marker import Marker
from ._stream import Stream
from . import hoverlabel
from . import marker
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[".hoverlabel", ".marker"],
["._hoverlabel.Hoverlabel", "._marker.Marker", "._stream.Stream"],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@graph_objs@pointcloud@__init__.py@.PATH_END.py
|
{
"filename": "opfilt_kk.py",
"repo_name": "carronj/plancklens",
"repo_path": "plancklens_extracted/plancklens-master/plancklens/qcinv/opfilt_kk.py",
"type": "Python"
}
|
"""lending map Wiener and inverse variance filtering module.
This is literally the very same spin-0 inverse variance filtering codes than for temperatures,
with indices 'tt' replaced with 'pp' and potential to k remapping
"""
from __future__ import absolute_import
from __future__ import print_function
import hashlib
import numpy as np
import healpy as hp
from healpy import alm2map, map2alm
#: Exporting these two methods so that they can be easily customized / optimized.
from plancklens.utils import clhash, enumerate_progress
from . import util
from . import template_removal
from . import dense
def _cli(cl):
ret = np.zeros_like(cl)
ret[np.where(cl != 0.)] = 1. / cl[np.where(cl != 0.)]
return ret
def p2k(lmax):
return 0.5 * np.arange(lmax + 1) * np.arange(1, lmax + 2, dtype=float)
def pp2kk(lmax):
return p2k(lmax) ** 2
def calc_prep(m, s_cls, n_inv_filt):
"""Missing doc."""
kmap = np.copy(m)
n_inv_filt.apply_map(kmap)
alm = map2alm(kmap, lmax=len(n_inv_filt.b_transf) - 1, iter=0)
hp.almxfl(alm, n_inv_filt.b_transf * (len(m) / (4. * np.pi)), inplace=True)
return alm
def apply_fini(alm, s_cls, n_inv_filt):
""" This final operation turns the Wiener-filtered klm cg-solution to the inverse-variance filtered klm. """
hp.almxfl(alm, _cli(s_cls['pp'] * pp2kk(len(s_cls['pp']) - 1)), inplace=True)
class dot_op:
"""Scalar product definition for kk cg-inversion
"""
def __init__(self):
pass
def __call__(self, alm1, alm2):
lmax1 = hp.Alm.getlmax(alm1.size)
assert lmax1 == hp.Alm.getlmax(alm2.size)
return np.sum(hp.alm2cl(alm1, alms2=alm2) * (2. * np.arange(0, lmax1 + 1) + 1))
class fwd_op:
"""Conjugate-gradient inversion forward operation definition. """
def __init__(self, s_cls, n_inv_filt):
self.clkk_inv = _cli(s_cls['pp'] * pp2kk(len(s_cls['pp']) - 1))
self.n_inv_filt = n_inv_filt
def hashdict(self):
return {'clkk_inv': clhash(self.clkk_inv),
'n_inv_filt': self.n_inv_filt.hashdict()}
def __call__(self, klm):
return self.calc(klm)
def calc(self, klm):
if np.all(klm == 0): # do nothing if zero
return klm
alm = np.copy(klm)
self.n_inv_filt.apply_alm(alm)
alm += hp.almxfl(klm, self.clkk_inv)
return alm
class pre_op_diag:
def __init__(self, s_cls, n_inv_filt):
"""Harmonic space diagonal pre-conditioner operation. """
clkk = pp2kk(len(s_cls['pp']) - 1) * s_cls['pp']
assert len(clkk) >= len(n_inv_filt.b_transf)
n_inv_cl = np.sum(n_inv_filt.n_inv) / (4.0 * np.pi)
lmax = len(n_inv_filt.b_transf) - 1
assert lmax <= (len(clkk) - 1)
filt = _cli(clkk[:lmax + 1])
filt += n_inv_cl * n_inv_filt.b_transf[:lmax + 1] ** 2
self.filt = _cli(filt)
def __call__(self, klm):
return self.calc(klm)
def calc(self, klm):
return hp.almxfl(klm, self.filt)
def pre_op_dense(lmax, fwd_op, cache_fname=None):
"""Missing doc. """
return dense.pre_op_dense_kk(lmax, fwd_op, cache_fname=cache_fname)
class alm_filter_ninv(object):
"""Missing doc. """
def __init__(self, n_inv, b_transf,
marge_monopole=False, marge_dipole=False, marge_uptolmin=-1, marge_maps=(), nlev_fkl=None):
if isinstance(n_inv, list):
n_inv_prod = util.load_map(n_inv[0])
if len(n_inv) > 1:
for n in n_inv[1:]:
n_inv_prod = n_inv_prod * util.load_map(n)
n_inv = n_inv_prod
else:
n_inv = util.load_map(n_inv)
print("opfilt_kk: inverse noise map std dev / av = %.3e" % (
np.std(n_inv[np.where(n_inv != 0.0)]) / np.average(n_inv[np.where(n_inv != 0.0)])))
templates = []
templates_hash = []
for kmap in [util.load_map(m) for m in marge_maps]:
assert (len(n_inv) == len(kmap))
templates.append(template_removal.template_map(kmap))
templates_hash.append(hashlib.sha1(kmap.view(np.uint8)).hexdigest())
if marge_uptolmin >= 0:
templates.append(template_removal.template_uptolmin(marge_uptolmin))
else:
if marge_monopole: templates.append(template_removal.template_monopole())
if marge_dipole: templates.append(template_removal.template_dipole())
if len(templates) != 0:
nmodes = int(np.sum([t.nmodes for t in templates]))
modes_idx_t = np.concatenate(([t.nmodes * [int(im)] for im, t in enumerate(templates)]))
modes_idx_i = np.concatenate(([range(0, t.nmodes) for t in templates]))
Pt_Nn1_P = np.zeros((nmodes, nmodes))
for i, ir in enumerate_progress(range(nmodes), label='filling template (%s) projection matrix'%nmodes):
kmap = np.copy(n_inv)
templates[modes_idx_t[ir]].apply_mode(kmap, int(modes_idx_i[ir]))
ic = 0
for tc in templates[0:modes_idx_t[ir] + 1]:
Pt_Nn1_P[ir, ic:(ic + tc.nmodes)] = tc.dot(kmap)
Pt_Nn1_P[ic:(ic + tc.nmodes), ir] = Pt_Nn1_P[ir, ic:(ic + tc.nmodes)]
ic += tc.nmodes
eigv, eigw = np.linalg.eigh(Pt_Nn1_P)
eigv_inv = 1.0 / eigv
self.Pt_Nn1_P_inv = np.dot(np.dot(eigw, np.diag(eigv_inv)), np.transpose(eigw))
self.n_inv = n_inv
self.b_transf = b_transf
self.npix = len(self.n_inv)
self.nside = hp.npix2nside(self.npix)
self.marge_monopole = marge_monopole
self.marge_dipole = marge_dipole
self.marge_uptolmin = marge_uptolmin
self.templates = templates
self.templates_hash = templates_hash
if nlev_fkl is None:
nlev_fkl = 10800. / np.sqrt(np.sum(self.n_inv) / (4.0 * np.pi)) / np.pi
self.nlev_fkl = nlev_fkl
print("ninv_fkl: using %.2e uK-amin noise Cl"%self.nlev_fkl)
def hashdict(self):
return {'n_inv': clhash(self.n_inv),
'b_transf': clhash(self.b_transf),
'marge_monopole': self.marge_monopole,
'marge_dipole': self.marge_dipole,
'templates_hash': self.templates_hash,
'marge_uptolmin': self.marge_uptolmin}
def get_fkl(self):
return self.b_transf ** 2 / (self.nlev_fkl / 60. /180. * np.pi) ** 2
def degrade(self, nside):
"""Missing doc. """
if nside == hp.npix2nside(len(self.n_inv)):
return self
else:
print("DEGRADING WITH NO MARGE MAPS")
marge_maps = []
return alm_filter_ninv(hp.ud_grade(self.n_inv, nside, power=-2), self.b_transf,
marge_monopole=self.marge_monopole, marge_dipole= self.marge_dipole,
marge_uptolmin=self.marge_uptolmin, marge_maps=marge_maps)
def apply_alm(self, alm):
"""Missing doc. """
npix = len(self.n_inv)
hp.almxfl(alm, self.b_transf, inplace=True)
kmap = alm2map(alm, hp.npix2nside(npix), verbose=False)
self.apply_map(kmap)
alm[:] = map2alm(kmap, lmax=hp.Alm.getlmax(alm.size), iter=0)
hp.almxfl(alm, self.b_transf * (npix / (4. * np.pi)), inplace=True)
def apply_map(self, kmap):
"""Missing doc. """
kmap *= self.n_inv
if len(self.templates) != 0:
coeffs = np.concatenate(([t.dot(kmap) for t in self.templates]))
coeffs = np.dot(self.Pt_Nn1_P_inv, coeffs)
pmodes = np.zeros(len(self.n_inv))
im = 0
for t in self.templates:
t.accum(pmodes, coeffs[im:(im + t.nmodes)])
im += t.nmodes
pmodes *= self.n_inv
kmap -= pmodes
|
carronjREPO_NAMEplancklensPATH_START.@plancklens_extracted@plancklens-master@plancklens@qcinv@opfilt_kk.py@.PATH_END.py
|
{
"filename": "_variantsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattergeo/textfont/_variantsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class VariantsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self, plotly_name="variantsrc", parent_name="scattergeo.textfont", **kwargs
):
super(VariantsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattergeo@textfont@_variantsrc.py@.PATH_END.py
|
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