Plotting Interface

BRAINS provides a Python plotting interface (named bbackend) to visualize the results. The source code is placed in the subdirectory analysis/. The interface is independent from the BRAINS and can be installed separately.

Change the path into analysis and install the plotting interface as follows.

python setup.py install --user

Then open up a Python environment and import the interface as

from bbackend import bplotlib

Load the parameter file (e.g., ../param/param) as

pb = bplotlib("../param/param")

Then plot the results as follows. An examplary script example.py is also provided to guide the useage.

flagdim = int(pb.param["flagdim"])

#===============================================
# continuum results, flagdim == 0
if flagdim == 0:
# plot continuum DRW parameters
pb.plot_drw_parameters(doshow=False)

# plot CDNest diagnoistics
temperature = 1
pb.postprocess(temperature, doshow=False)

pb.plot_results_con()

#===============================================
# RM 1D results, flagdim == 1
if flagdim == 1:
# plot continuum DRW parameters
pb.plot_drw_parameters(doshow=False)

# plot CDNest diagnoistics
temperature = 1
pb.postprocess(temperature, doshow=False)

pb.plot_results_1d(doshow=False)

# plot 1d transfer function
# pb.plot_tran1d(tau_range=[a, b])
pb.plot_tran1d(doshow=False)

# plot histograms of BLR model parameters
pb.plot_blrmodel_para_hist(doshow=False)

# plot clouds' distribution
pb.plot_clouds("../data/clouds.txt", doshow=False)
#pb.plot_clouds("../data/clouds.txt", range=[-10, 10], objname="target", format="jpg", velocity=False, doshow=False)

# plot clouds' distribution viewd from line of sight
pb.plot_clouds_los("../data/clouds.txt", doshow=False)
#pb.plot_clouds_los("../data/clouds.txt", range=[-10, 10], objname="target", format="jpg", velocity=False, doshow=False)


#===============================================
# RM 2D results, flagdim == 2
#
if flagdim == 2:
# print parameter names
pb.print_blrmodel_para_names()

# plot continuum DRW parameters
pb.plot_drw_parameters(doshow=False)

# plot CDNest diagnoistics
temperature = 1
pb.postprocess(temperature, doshow=False)

# get continuum data
con_data = pb.get_con_data()

# get line 2D data
line2d_data = pb.get_line2d_data()

# pb.plot_results_2d_style2018(doshow=False)
pb.plot_results_2d_style2022(doshow=False)

# plot 2d tranfer function with the maximum prob
# pb.plot_tran2d(tau_range=[a, b])
# pb.plot_tran2d(vel_range=[a, b])
# pb.plot_tran2d(tau_range=[a, b], vel_range=[c, d], doshow=False)
pb.plot_tran2d(doshow=False)

# plot 1d transfer function
# pb.plot_tran1d(tau_range=[a, b], doshow=False)
pb.plot_tran1d(doshow=False)

# plot histograms of BLR model parameters
pb.plot_blrmodel_para_hist(doshow=False)

# plot clouds' distribution
pb.plot_clouds("../data/clouds.txt", doshow=False)
#pb.plot_clouds("../data/clouds.txt", range=[-10, 10], objname="target", format="jpg", velocity=True, doshow=False)


# plot clouds' distribution viewd from line of sight
pb.plot_clouds_los("../data/clouds.txt", doshow=False)
#pb.plot_clouds_los("../data/clouds.txt", range=[-10, 10], objname="target", format="jpg", velocity=True, doshow=False)