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) print ":::::::::::: s6 done ::::::::::::"
bonsai_config_filename = '/data/bonsai_configs/bonsai_nfreq1024_7tree_f512_v3.hdf5' nt_tot = 8192 * 1024 n_zoom = 8 s = rf_pipelines.gaussian_noise_stream(nfreq=1024, nt_tot=nt_tot, freq_lo_MHz=400.0, freq_hi_MHz=800.0, dt_sample=1.31072e-3) t_masker = rf_pipelines.adversarial_masker() t_plotter = rf_pipelines.plotter_transform(img_prefix='waterfall1', img_nfreq=256, img_nt=256, downsample_nt=16, n_zoom=n_zoom) t_dedisp = rf_pipelines.bonsai_dedisperser( config_filename=bonsai_config_filename, fill_rfi_mask=True, img_prefix='toy_pipeline', img_ndm=256, img_nt=256, downsample_nt=16, n_zoom=n_zoom, plot_all_trees=True) ch_frb_rfi.run_for_web_viewer('adversarial_masker', s, [t_masker, t_plotter, t_dedisp])
'/data/17-10-01-16k-to-1k/17-04-25-utkarsh-26m-part0/*.h5', 0, 1) 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_derippled') t = ch_frb_rfi.transform_chain(p) t += [ch_frb_rfi.bonsai.nfreq1K_7tree(p, fpga_counts_per_sample=384, v=3)] ch_frb_rfi.run_for_web_viewer('derippled', s, t) print ":::::::::::: derippled done ::::::::::::"
bonsai_dynamic_plotter = False, bonsai_plot_all_trees = make_plots, detrend_last = not detrend_16k, mask_counter = 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) ] if detrend_16k: params.detrend_last = True params.mask_counter = False t16k += ch_frb_rfi.chains.detrender_chain(params, ix=1, jx=0) params.append_plotter_transform(t16k, 'dc_out_last') if write_json: assert isinstance(output_path, str) and output_path.endswith('.json') p16k = rf_pipelines.pipeline(t16k) rf_pipelines.utils.json_write(output_path, p16k, clobber=True) #rf_pipelines.utils.json_write('design-rfi-config_acq.json', s, clobber=True) #w = ch_frb_rfi.WriteWeights(nt_chunk=1024*2) #t16k += [ w, ch_frb_rfi.bonsai.nfreq16K_production(params, v=4, beta=2, u=False) ] t16k.append(ch_frb_rfi.bonsai.nfreq16K_production(params, v=4, beta=2, u=False)) p16k = rf_pipelines.pipeline([s]+t16k) ch_frb_rfi.run_for_web_viewer('design-rfi-config', p16k) print 'design-rfi-config done!'
clobber=True) t16k += [ch_frb_rfi.bonsai.nfreq16K_production(params, 2, False)] p16k = rf_pipelines.pipeline([s] + t16k) ch_frb_rfi.run_in_scratch_dir('astro-events', None, p16k) p16k.unbind() params.var_est = False params.mask_filler = True params.make_plots = True params.bonsai_plot_all_trees = True params.bonsai_output_plot_stem = 'triggers' 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)] if detrend_16k: params.detrend_last = True t16k += ch_frb_rfi.chains.detrender_chain(params, ix=1, jx=0) params.append_plotter_transform(t16k, 'dc_out_last') t16k += [ch_frb_rfi.bonsai.nfreq16K_production(params, 2, False)] p16k = rf_pipelines.pipeline([s] + t16k) ch_frb_rfi.run_for_web_viewer('astro-events', p16k) print 'astro-events done!'
# 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. pipeline = rf_pipelines.pipeline([s]+t) # The purpose of the first pipeline run is to create the h5 file containing variance # estimates (p.var_filename = './var_example2.h5'). We do this pipeline run using the # wrapper function run_in_scratch_dir(), which does not index the run with the web viewer. ch_frb_rfi.run_in_scratch_dir('example2', pipeline) # In the v16 API, need to "unbind" the pipeline after running, before its constituent # pipeline_objects can be reused in another pipeline run. pipeline.unbind() # Remove the variance_estimator, append the mask_filler and plotter transforms. p.var_est = False p.mask_filler = True p.make_plots = True t = ch_frb_rfi.transform_chain(p) t += [ ch_frb_rfi.bonsai.nfreq1K_3tree(p, fpga_counts_per_sample=512, v=1) ] pipeline = rf_pipelines.pipeline([s]+t) # Second pipeline run: we use the wrapper function run_for_web_viewer(). # Run the pipeline (again) but now with the mask_filler and bonsai dedisperser. ch_frb_rfi.run_for_web_viewer('example2', pipeline) print "example2.py: pipeline run successful!" print "You can view the result at http://frb1.physics.mcgill.ca:5000/"