params = ch_frb_rfi.transform_parameters(
plot_type='web_viewer',
plot_nypix=1024,
plot_nxpix=256,
plot_downsample_nt=16,
plot_nzoom=4,
max_nt_buffer=4,
make_plots=make_plots,
bonsai_plot_nypix=1024,
bonsai_output_plot_stem='triggers' if make_plots else None,
maskpath='/data/chimefrb/badchannel_mask_2018-11-02.dat',
detrender_niter=2,
clipper_niter=6,
two_pass=False,
rfi_level=2,
aux_clip_first=False,
aux_clip_last=False,
aux_detrend_first=False,
spline=True,
bonsai_use_analytic_normalization=False,
bonsai_hdf5_output_filename=None,
bonsai_nt_per_hdf5_file=None,
bonsai_fill_rfi_mask=True,
var_est=False,
mask_filler=False,
mask_filler_w_cutoff=0.5,
bonsai_plot_threshold1=7,
bonsai_plot_threshold2=10,
bonsai_dynamic_plotter=False,
bonsai_plot_all_trees=make_plots,
detrend_last=False,
mask_counter=True)
import rf_pipelines
s = ch_frb_rfi.acquisitions.incoherent_pathfinder(
search_name='frb_incoherent_2d', sample_index=3)
p = ch_frb_rfi.transform_parameters(
plot_type='web_viewer',
make_plots=True,
bonsai_output_plot_stem='triggers',
maskpath='/data/pathfinder/rfi_masks/rfi_20160705.dat',
detrender_niter=2,
clipper_niter=6,
spline=True,
bonsai_use_analytic_normalization=False,
bonsai_hdf5_output_filename=None,
bonsai_nt_per_hdf5_file=None,
bonsai_fill_rfi_mask=True,
var_est=False,
mask_filler=False,
mask_filler_w_cutoff=0.5,
bonsai_plot_threshold1=7,
bonsai_plot_threshold2=10,
bonsai_dynamic_plotter=False,
bonsai_plot_all_trees=True,
L1Grouper_thr=10,
bonsai_event_outfile='events_s6')
t = ch_frb_rfi.transform_chain(p)
t += [ch_frb_rfi.bonsai.nfreq1K_7tree(p, fpga_counts_per_sample=512, v=3)]
ch_frb_rfi.run_for_web_viewer('s6', s, t)
# If 'clobber' is False, then when a json file is created with rf_pipelines.json_write(filename, j),
# we throw an exception if 'filename' already exists, and its contents differ from 'j'. This is
# to prevent git-managed json files from being modified accidentally.
clobber = False
for make_plots in [False, True]:
params = ch_frb_rfi.transform_parameters(
plot_type='web_viewer',
make_plots=make_plots,
bonsai_output_plot_stem='triggers' if make_plots else None,
maskpath=None,
two_pass=False,
detrender_niter=2,
clipper_niter=6,
spline=True,
bonsai_use_analytic_normalization=False,
bonsai_hdf5_output_filename=None,
bonsai_nt_per_hdf5_file=None,
bonsai_fill_rfi_mask=True,
var_est=False,
mask_filler=False,
mask_filler_w_cutoff=0.5,
bonsai_plot_threshold1=7,
bonsai_plot_threshold2=10,
bonsai_dynamic_plotter=False,
bonsai_plot_all_trees=make_plots)
t1k = ch_frb_rfi.transform_chain(params)
p1k = rf_pipelines.pipeline(t1k)
params.detrend_last = False
params.mask_counter = False
# If 'clobber' is False, then when a json file is created with rf_pipelines.json_write(filename, j),
# we throw an exception if 'filename' already exists, and its contents differ from 'j'. This is
# to prevent git-managed json files from being modified accidentally.
clobber = False
params = ch_frb_rfi.transform_parameters(plot_downsample_nt = 16,
two_pass = False,
plot_nxpix = 256,
plot_nypix = 1024,
plot_nzoom = 4,
make_plots = True,
bonsai_output_plot_stem = 'triggers',
maskpath = '/data/pathfinder/rfi_masks/rfi_20160705.dat',
detrender_niter = 2,
clipper_niter = 6,
spline = True,
bonsai_use_analytic_normalization = False,
bonsai_hdf5_output_filename = None,
bonsai_nt_per_hdf5_file = None,
bonsai_fill_rfi_mask = True,
var_est = False,
mask_filler = False,
mask_filler_w_cutoff = 0.5,
bonsai_plot_threshold1 = 7,
bonsai_plot_threshold2 = 10,
bonsai_dynamic_plotter = False,
bonsai_plot_all_trees = True)
t1k = ch_frb_rfi.transform_chain(params)
p1k = rf_pipelines.pipeline(t1k)
t16k = [ rf_pipelines.wi_sub_pipeline(p1k, nfreq_out=1024, nds_out=1) ]
# This script is intended to run on frb1.physics.mcgill.ca.
import ch_frb_rfi
# First, make the 'transform_parameters' object.
# This is an aggregator for parameters which determine the transform chain.
# All parameters have defaults which can be overridden as constructor arguments.
# Here, we just override a few parameters which are related to plotting (by default,
# no plots will be produced).
#
# See the ch_frb_rfi.transform_parameters docstring for more details, and a list
# of all parameters available.
p = ch_frb_rfi.transform_parameters(
plot_type=
'web_viewer', # write plots with appropriate resolution for web viewer
bonsai_output_plot_stem='triggers'
) # write bonsai plots to filenames beginning with 'triggers_...'
# Second, make a stream (object of type rf_pipelines.wi_stream).
# The submodule ch_frb_rfi.acquisitions defines some "standard" acquisitions on frb1.
# (See 'dir(ch_frb_rfi.acquisitions) for a complete list'.)
s = ch_frb_rfi.acquisitions.small(
) # A small example acq containing a few minutes of data
# Third, make a transform chain (list of rf_pipelines.wi_transform objects)
# There are lots of functions returning transform_chains, which aren't documented systematically
# since our RFI removal code is still under development.
#
# The function ch_frb_io.transform_chain() returns "the" CHIME RFI-removing chain.
detrend_16k = True
params = ch_frb_rfi.transform_parameters(
plot_type='web_viewer',
plot_nypix=1024,
plot_nxpix=256,
plot_downsample_nt=16,
plot_nzoom=4,
make_plots=False,
bonsai_plot_nypix=1024,
maskpath=None,
detrender_niter=3,
clipper_niter=5,
spline=True,
bonsai_use_analytic_normalization=False,
bonsai_hdf5_output_filename=None,
bonsai_nt_per_hdf5_file=None,
bonsai_fill_rfi_mask=False,
var_est=True,
var_filename='./astro-events_var.h5',
variance_estimator_v1_chunk=16,
variance_estimator_v2_chunk=64,
mask_filler=False,
mask_filler_w_cutoff=0.5,
bonsai_plot_threshold1=7,
bonsai_plot_threshold2=10,
bonsai_dynamic_plotter=False,
bonsai_plot_all_trees=False,
detrend_last=not detrend_16k)
t1k = ch_frb_rfi.transform_chain(params)
# This fill-in helps the bonsai dedisperser compute running trigger variance estimates.
# It's an open question whether we'll use it "in production" on the telescope, but our
# working assumption is that we will do so, and will write a fast C++ implementation
# at some point.
p = ch_frb_rfi.transform_parameters(plot_type = 'web_viewer',
make_plots = False,
bonsai_output_plot_stem = 'triggers',
maskpath = '/data/pathfinder/rfi_masks/rfi_20160705.dat',
rfi_level = rfi_level,
bonsai_use_analytic_normalization = False,
bonsai_hdf5_output_filename = None,
bonsai_nt_per_hdf5_file = None,
var_est = True,
mask_filler = False,
var_filename = './var_example2.h5',
variance_estimator_v1_chunk = v1_chunk,
variance_estimator_v2_chunk = v2_chunk,
mask_filler_w_cutoff = 0.5,
bonsai_plot_threshold1 = 7,
bonsai_plot_threshold2 = 10,
bonsai_dynamic_plotter = False,
bonsai_event_outfile = './events_example2.dat',
L1Grouper_thr = 10,
L1Grouper_beam = 0,
L1Grouper_addr = None)
# Using the specified parameters make a chain of transforms for estimating the variance.
t = ch_frb_rfi.transform_chain(p)
# Combine stream and transforms into a pipeline.
# to prevent git-managed json files from being modified accidentally.
clobber = False
for make_plots in [ True ]:
params = ch_frb_rfi.transform_parameters(plot_type = 'web_viewer',
plot_nypix = 1024,
plot_nxpix = 256,
plot_downsample_nt = 16,
plot_nzoom = 4,
make_plots = make_plots,
bonsai_output_plot_stem = 'triggers' if make_plots else None,
maskpath = None,
rfi_level = 1,
spline = True,
bonsai_use_analytic_normalization = False,
bonsai_hdf5_output_filename = None,
bonsai_nt_per_hdf5_file = None,
bonsai_fill_rfi_mask = True,
var_est = False,
mask_filler = False,
mask_filler_w_cutoff = 0.5,
bonsai_plot_threshold1 = 7,
bonsai_plot_threshold2 = 10,
bonsai_dynamic_plotter = False,
bonsai_plot_all_trees = make_plots)
t1k = ch_frb_rfi.transform_chain(params)
p1k = rf_pipelines.pipeline(t1k)
params.detrend_last = False
# This script is intended to run on frb1.physics.mcgill.ca.
import numpy as np
import ch_frb_rfi
import rf_pipelines
s = ch_frb_rfi.acquisitions.crab_16k(n=300)
p = ch_frb_rfi.transform_parameters(
plot_type='web_viewer',
make_plots=False,
bonsai_output_plot_stem='triggers',
rfi_level=1,
bonsai_use_analytic_normalization=False,
var_est=True,
var_filename='./var_example3.h5',
variance_estimator_v1_chunk=64,
variance_estimator_v2_chunk=100,
mask_filler_w_cutoff=0.25,
bonsai_plot_threshold1=7,
bonsai_plot_threshold2=10,
bonsai_dynamic_plotter=False,
bonsai_event_outfile='./events_example3.dat')
t = ch_frb_rfi.transform_chain(p)
# Combine stream and transforms into a pipeline.
pipeline = rf_pipelines.pipeline([s] + t)
ch_frb_rfi.run_in_scratch_dir('example3', pipeline)
