MICA2

Read the documentation.

If you make use of this code, please cite our paper Li, Y.-R., Wang, J.-M., & Bai, J.-M. 2016, ApJ, 831, 206.

A Quick Tutorial

Types of Transfer Functions

MICA2 supports serveral types of tranfer functions:

• Gaussian
• Tophat
• Gamma distribution (k=2)
• Expenential

Exectuable Binary Version: mica2

Prepare the parameter file (e.g., named param) and input data file (placed in sub-folder data/), and then run mica in a terminal , e.g.,

mpiexec -n 6 ./mica2 param

Here -n 6 means using 6 cores. Change the number according to your need.

Python Module Version: pymica

Edit a Python script named, e.g., example.py, as the following.

from mpi4py import MPI
import numpy as np
import pymica
import matplotlib.pyplot as plt

# initiate MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()

# load data
if rank == 0:
  data = np.loadtxt("./sim_data.txt")
  con = data[:126, :]
  line= data[126:, :]

  # make a data dict
  data_input = {"set1":[con, line]}

  # if multiple datasets, e.g.,
  #data_input = {"set1":[con1, line1], "set2":[con2, line2]}

  # if a dataset has multiple lines, e.g.,
  #data_input = {"set1":[con, line1, line2]}
else:
  data_input = None

data_input = comm.bcast(data_input, root=0)

model = pymica.gmodel()
model.setup(data=data_input, type_tf='gaussian', lag_limit=[0, 100], number_component=[2, 2], max_num_saves=200)
# if using tophats, set type_tf='tophat'

#the full arguments are
#model.setup(data_file=None, data=None,
#            type_tf='gaussian', max_num_saves=2000,
#            flag_uniform_var_params=False, flag_uniform_tranfuns=False,
#            flag_trend=0, flag_lag_posivity=False,
#            lag_limit=[0, 100], number_component=[1, 1],
#            flag_con_sys_err=False, flag_line_sys_err=False,
#            type_lag_prior=0)

#run mica
model.run()

# plot results
if rank == 0:
  # get the full sample
  # sample is a list, each element contains an array of posterior samples
  # sample[0] is for the case of number_component[0]
  # sample[1] is for the case of number_component[1]
  # ...
  sample = model.get_posterior_sample()

  # get the posterior sample of time lags of the "line" in the dataset "set"
  # timelag is a list, each element contains an array of posterior samples
  # timelag[0] is for the case of number_component[0]
  # timelag[1] is for the case of number_component[1]
  # ...
  timelag = model.get_posterior_sample_timelag(set=0, line=0)
  plt.plot(timelag[0][:, 0])
  plt.plot(timelag[0][:, 1])
  plt.show()

  # get the posterior sample of widths of the "line" in the dataset "set"
  # width is a list, each element contains an array of posterior samples
  # width[0] is for the case of number_component[0]
  # width[1] is for the case of number_component[1]
  # ...
  width = model.get_posterior_sample_width(set=0, line=0)
  plt.plot(width[0][:, 0])
  plt.plot(width[0][:, 1])
  plt.show()

  model.plot_results() # plot results
  model.post_process()  # generate plots for the properties of MCMC sampling

Run this script using the terminal command as

mpiexec -n 6 python example.py

If you want to use only one core, just run as

python example.py

Photometric Reverberation Mapping

MICA2 can also do reverberation mapping analysis between two photometric light curves, in which the photometric bands may contain broad-line emissions or other components so that there may exist multiple responses. For simplicity, MICA2 assumes that the driving photometric light curve does not contain those contaminations and purely reflects continuum variations.

from mpi4py import MPI
import numpy as np
import pymica
import matplotlib.pyplot as plt

# initiate MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()

# load data
if rank == 0:
  data = np.loadtxt("./sim_data.txt")
  band1 = data[:126, :]
  band2 = data[126:, :]

  # make a data dict
  data_input = {"set1":[band1, band2]}
else:
  data_input = None

data_input = comm.bcast(data_input, root=0)

model = pymica.pmap()
model.setup(data=data_input, type_tf='gaussian', max_num_saves=2000, lag_prior=[[-5, 5],[0, 50]], ratio_prior=[0.01, 0.5])
# if using tophats, set type_tf='tophat'

#run mica
model.run()

# plot results
if rank == 0:

  model.plot_results() # plot results
  model.post_process()  # generate plots for the properties of MCMC sampling

Virtual Reverberation Mapping

MICA2 also provides a vmap mode to do reverberation mapping analysis with a virtual driving light curve. This mode applies in cases where the dirving light curve cannot be chosen or the driving light curve has a poor qaulity that is not suitable to act as the dirving one.

To this end, MICA2 assumes that the virtual drving light curve follows a DRW process with a variation amplitude (\sigma) of 0.1 and has a time lag of zero with respect to the first light curve of the input data. The remaining analysis is trival and similar to the normal modes.

from mpi4py import MPI
import numpy as np
import pymica
import matplotlib.pyplot as plt

# initiate MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()

# load data
if rank == 0:
  lc0 = np.empty(0)  # virtual light curve, empty
  lc1 = np.loadtxt("g.txt")
  lc2 = np.loadtxt("r.txt")

  # make a data dict
  data_input = {"set1":[lc0, lc1, lc2]}
else:
  data_input = None

data_input = comm.bcast(data_input, root=0)

model = pymica.vmap()
model.setup(data=data_input, type_tf='gaussian', lag_limit=[-2, 5], number_component=[1, 1], max_num_saves=2000)
# if using tophats, set type_tf='tophat'
# see the documentation for the format of vmap data.

#run mica
model.run()

# plot results
if rank == 0:

  model.plot_results() # plot results
  model.post_process()  # generate plots for the properties of MCMC sampling