Final Pull Request

.travis.yml

@@ -0,0 +1,8 @@
+language: python
+
+python:
+ - "2.6.0"
+ - "2.7.0"
+ - "3.6.0"
+
+script: pytest

exoplanet_mcmc/.cache/v/cache/lastfailed

@@ -0,0 +1,3 @@
+{
+  "plot_test.py": true
+}

readme.md

@@ -0,0 +1,114 @@
+# Exoplanet_MCMC Demonstration
+
+The goal of this notebook is to demonstrate the effect of different properties of astrometric data on the posterior distributions resulting from the MCMC package I have written. I will show the effect of having more or less densely sampled data, greater or fewer points included, and smaller or larger uncertainties on the observations.
+
+## Using the module exoplanet_mcmc
+
+To get to a corner plot of the orbital parameters of a system, only four functions must be used:
+
+### Create an instance of System
+
+```python
+print(exmc.System.__init__.__doc__)
+# Instantiates a System
+#            Arguments [all without units attached]:
+#                mstar - stellar mass in units of Msun
+#                mplanet - planetary mass in units of Mjup
+#                distance - distance to system in parsecs
+#                semimajor_axis - in au
+#                eccentricity
+#                time_of_periastron [between -1 and 1, units of period]
+#                argument_of_periastron [-2pi to 2pi]
+#                timesteps - number of timesteps in an orbit
+
+sys=exmc.System(mstar=2., mplanet=2., semimajor_axis=2., eccentricity=.2, distance=5., argument_of_periastron=np.pi/4., time_periastron=0, timesteps=40)
+
+sys.plot_orbit()
+```
+
+<img src="https://raw.githubusercontent.com/jacobhhamer/Exoplanet_MCMC/master/orbit_plot.png" width="500">
+
+### Pick a sample of the orbit to add noise to and use as observations for the MCMC
+
+```python
+print(exmc.System.generate_mcmc_sample.__doc__)
+# For an instance of system, generates a sample to run MCMC on.
+#            Arguments:
+#                mas_unc - uncertainty in observations in mas, no units attached
+#                sigma_true_anomaly - uncertainty in observations of position angle in degrees
+#                sigma_mass - uncertainty in the stellar mass as a fraction of true stellar mass
+#                indices - slice of the time, true_anomaly, and radius arrays to use
+
+sys.generate_mcmc_sample(indices=[10,20], mas_unc=5., sigma_true_anomaly=3., sigma_mass=.03)
+
+sys.plot_mcmc_sample()
+```
+
+<img src="https://raw.githubusercontent.com/jacobhhamer/Exoplanet_MCMC/master/mcmc_sample_plot.png" width="500">
+
+### Generate the first guess of the parameters for the sample observations
+
+```python
+print(exmc.System.generate_first_guess.__doc__)
+# Method to generate the first guess [initial position in parameter space] for the MCMC, by minimizing the
+#        likelihood function.
+
+sys.generate_first_guess()
+```
+
+### Run the MCMC on the sample of the data
+
+```python
+print(exmc.System.runmcmc.__doc__)
+# Method to run emcee on an instance of System for which generate_sample_data has been run.
+#            Arguments:
+#                p0spread - fraction of range of parameter space which is used as variance of normal distribution to sample from
+#                nwalker - number of MCMC walkers
+#                nburn - number of burn-in steps
+#                nsteps - number of MCMC steps
+#            Note: For the EnsembleSampler default affine invariant algorithm, it is good to use many walkers and fewer steps.
+
+sys.runmcmc(p0spread=.01, nwalker=500, nburn=200, nsteps=400)
+
+#3.8032890891e+30 4.17956644614e+30
+#0.172149097695 0.230489784341
+#287480747958.0 313270933742.0
+#-0.0728754135833 0.0707064628404
+#-0.344134986222 0.42200270733
+#Burn in progress:  9.5%
+#Burn in progress: 19.5%
+#Burn in progress: 29.5%
+#Burn in progress: 39.5%
+#Burn in progress: 49.5%
+#Burn in progress: 59.5%
+#Burn in progress: 69.5%
+#Burn in progress: 79.5%
+#Burn in progress: 89.5%
+#Burn in progress: 99.5%
+#MCMC progress:  9.8%
+#MCMC progress: 19.8%
+#MCMC progress: 29.8%
+#MCMC progress: 39.8%
+#MCMC progress: 49.8%
+#MCMC progress: 59.8%
+#MCMC progress: 69.8%
+#MCMC progress: 79.8%
+#MCMC progress: 89.8%
+#MCMC progress: 99.8%
+```
+
+## Plot the results
+
+```sys.walker_plot()```
+
+<img src="https://raw.githubusercontent.com/jacobhhamer/Exoplanet_MCMC/master/walker_plot.png" height="1000">
+
+If the MCMC was run with enough steps, the plot of the walkers in each parameter space should look like "white noise", as above.
+
+```sys.corner_plot()```
+
+<img src="https://raw.githubusercontent.com/jacobhhamer/Exoplanet_MCMC/master/corner_plot.png" width="750">
+
+The true values of the parameters are overplotted in blue. We can see that they fall near the regions of maximum probability.
+
+

requirements.txt

@@ -0,0 +1,6 @@
+matplotlib
+astropy
+scipy
+emcee
+corner
+numpy

tests/test_exoplanet_mcmc.py

@@ -0,0 +1,26 @@
+from exoplanet_mcmc.exoplanet_mcmc import *
+import numpy as np
+
+def test_system1():
+	sys=random_orbit()
+	assert sys.mstar.value>0
+
+def test_system2():
+	sys=random_orbit()
+	assert sys.mplanet.value>0
+
+def test_system3():
+	sys=random_orbit()
+	assert (sys.e>0)&(sys.e<1)
+
+def test_system4():
+	sys=random_orbit()
+	assert sys.a.value>0
+
+def test_system5():
+	sys=random_orbit()
+	assert (sys.tau>-1)&(sys.tau<1)
+
+def test_system6():
+	sys=random_orbit()
+	assert (sys.arg.value>-2*np.pi)&(sys.arg.value<2*np.pi)