updated documentation

README.rst

@@ -1,7 +1,5 @@
 Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation
 -----------------------------------------------------------------------------
 
-Bagpipes is a state of the art code for generating realistic model galaxy spectra and fitting these to spectroscopic and photometric observations.
-
-For further information please see the Bagpipes documentation at `bagpipes.readthedocs.io <http://bagpipes.readthedocs.io>`_.
+Bagpipes is a state of the art code for generating realistic model galaxy spectra and fitting these to spectroscopic and photometric observations. For further information please see the Bagpipes documentation at `bagpipes.readthedocs.io <http://bagpipes.readthedocs.io>`_.
 

bagpipes/model_galaxy.py

@@ -14,13 +14,6 @@
 import star_formation_history
 import model_manager as models
 
-if not os.path.exists(models.install_dir + "/tables/IGM/D_IGM_grid_Inoue14.fits"):
-	import IGM_Inoue2014 as igm 
-	igm.make_table()
-
-D_IGM_grid_global = fits.open(models.install_dir + "/tables/IGM/D_IGM_grid_Inoue14.fits")[1].data
-IGM_redshifts = np.arange(0.0, 10.01, 0.01)
-IGM_wavs_global = np.arange(1.0, 1225.01, 1.0)
 
 class Model_Galaxy:
 	""" Build a model galaxy spectrum.
@@ -52,6 +45,7 @@ def __init__(self, model_components, filtlist=None, output_specwavs=None, out_un
 
 		models.set_cosmology()
 		models.set_model_type(models.model_type)
+		models.get_igm_grid()
 
 		self.model_comp = model_components
 
@@ -196,10 +190,10 @@ def __init__(self, model_components, filtlist=None, output_specwavs=None, out_un
 			self.keep_ionizing_continuum = False
 
 		# D_IGM_grid: a grid of correction factors for IGM attenuation as a function of wavelength and redshift taken from Inoue et al. (2014)
-		self.D_IGM_grid = np.zeros((IGM_redshifts.shape[0], self.chosen_modelgrid_wavs[(self.chosen_modelgrid_wavs > 911.8) & (self.chosen_modelgrid_wavs < 1220.)].shape[0]))
+		self.D_IGM_grid = np.zeros((models.IGM_redshifts.shape[0], self.chosen_modelgrid_wavs[(self.chosen_modelgrid_wavs > 911.8) & (self.chosen_modelgrid_wavs < 1220.)].shape[0]))
 
-		for i in range(IGM_redshifts.shape[0]):
-			self.D_IGM_grid[i,:] = interp(self.chosen_modelgrid_wavs[(self.chosen_modelgrid_wavs > 911.8) & (self.chosen_modelgrid_wavs < 1220.)], IGM_wavs_global, D_IGM_grid_global[i,:])
+		for i in range(models.IGM_redshifts.shape[0]):
+			self.D_IGM_grid[i,:] = interp(self.chosen_modelgrid_wavs[(self.chosen_modelgrid_wavs > 911.8) & (self.chosen_modelgrid_wavs < 1220.)], models.IGM_wavs, models.D_IGM_grid[i,:])
 
 		# If filtlist is not None, finish off necessary calculations for photometry calculation
 		if self.filtlist is not None:
@@ -236,10 +230,10 @@ def __init__(self, model_components, filtlist=None, output_specwavs=None, out_un
 			if "dust" in list(self.model_comp):
 				self.k_lambda_lines[self.model_comp["dust"]["type"]] = self.get_dust_model(self.model_comp["dust"]["type"], wavs=self.cloudylinewavs)
 
-			self.D_IGM_grid_lines = np.zeros((IGM_redshifts.shape[0], self.cloudylinewavs.shape[0]))
+			self.D_IGM_grid_lines = np.zeros((models.IGM_redshifts.shape[0], self.cloudylinewavs.shape[0]))
 
-			for i in range(IGM_redshifts.shape[0]):
-				self.D_IGM_grid_lines[i,:] = interp(self.cloudylinewavs, IGM_wavs_global, D_IGM_grid_global[i,:], left=0., right=1.)
+			for i in range(models.IGM_redshifts.shape[0]):
+				self.D_IGM_grid_lines[i,:] = interp(self.cloudylinewavs, models.IGM_wavs, models.D_IGM_grid[i,:], left=0., right=1.)
 
 		self.update(self.model_comp)
 
@@ -484,9 +478,9 @@ def update(self, model_components):
 
 		# Apply intergalactic medium absorption to the model spectrum
 		if self.model_comp["redshift"] > 0.:
-			zred_ind = IGM_redshifts[IGM_redshifts < self.model_comp["redshift"]].shape[0]
+			zred_ind = models.IGM_redshifts[models.IGM_redshifts < self.model_comp["redshift"]].shape[0]
 
-			high_zred_factor = (IGM_redshifts[zred_ind] - self.model_comp["redshift"])/(IGM_redshifts[zred_ind] - IGM_redshifts[zred_ind-1])
+			high_zred_factor = (models.IGM_redshifts[zred_ind] - self.model_comp["redshift"])/(models.IGM_redshifts[zred_ind] - models.IGM_redshifts[zred_ind-1])
 			low_zred_factor = 1. - high_zred_factor
 
 			# Interpolate IGM transmission from pre-loaded grid

bagpipes/model_manager.py

@@ -64,6 +64,22 @@ def set_cosmology(H0=70., Om0=0.3):
 
 
 
+def get_igm_grid():
+    """ Loads the grid of IGM transmission factors. """
+    global D_IGM_grid
+    global IGM_redshifts
+    global IGM_wavs
+
+    if not os.path.exists(install_dir + "/tables/IGM/D_IGM_grid_Inoue14.fits"):
+        import IGM_Inoue2014 as igm 
+        igm.make_table()
+
+    D_IGM_grid = fits.open(install_dir + "/tables/IGM/D_IGM_grid_Inoue14.fits")[1].data
+    IGM_redshifts = np.arange(0.0, 10.01, 0.01)
+    IGM_wavs = np.arange(1.0, 1225.01, 1.0)
+
+
+
 def make_dirs():
     """ Make local Bagpipes directory structure. """
     if not os.path.exists(working_dir + "/pipes"):

bagpipes/star_formation_history.py

@@ -99,7 +99,7 @@ def burst(self, par_dict):
                 sys.exit("BAGPIPES: The time at which the burst started was less than zero.")
 
             const_par_dict = {}
-            const_par_dict["agemax"] = par_dict["age"]
+            const_par_dict["age"] = par_dict["age"]
             const_par_dict["agemin"] = par_dict["age"] - par_dict["width"]
             const_par_dict["massformed"] = par_dict["massformed"]
 
@@ -198,7 +198,7 @@ def constant(self, par_dict):
         if par_dict["age"] == "hubble time":
             par_dict["age"] = np.interp(self.model_components["redshift"], models.z_array, models.age_at_z)
 
-        if par_dict["agemin"] > par_dict["agemax"]:
+        if par_dict["agemin"] > par_dict["age"]:
             sys.exit("Minimum constant age exceeded maximum.")
 
         age_ind = self.ages[self.ages < par_dict["age"]*10**9].shape[0]
@@ -207,12 +207,12 @@ def constant(self, par_dict):
 
         widths_constant = np.copy(self.age_widths)
 
-        if self.age_lhs[age_ind] - par_dict["agemax"]*10**9 > 0:
+        if self.age_lhs[age_ind] - par_dict["age"]*10**9 > 0:
             widths_constant[age_ind] = 0.
-            widths_constant[age_ind-1] = par_dict["agemax"]*10**9 - self.age_lhs[age_ind-1]
+            widths_constant[age_ind-1] = par_dict["age"]*10**9 - self.age_lhs[age_ind-1]
 
         else:
-            widths_constant[age_ind] = par_dict["agemax"]*10**9 - self.age_lhs[age_ind]
+            widths_constant[age_ind] = par_dict["age"]*10**9 - self.age_lhs[age_ind]
 
         if self.age_lhs[age_min_ind] - par_dict["agemin"]*10**9 < 0:
             widths_constant[age_min_ind-1] = 0.
@@ -226,8 +226,8 @@ def constant(self, par_dict):
 
         widths_constant[age_ind+1:] *= 0.
 
-        if age_ind == age_min_ind and self.age_lhs[age_ind] - par_dict["agemax"]*10**9 < 0 and self.age_lhs[age_min_ind] - par_dict["agemin"]*10**9 < 0:
-            widths_constant[age_ind] = (par_dict["agemax"] - par_dict["agemin"])*10**9
+        if age_ind == age_min_ind and self.age_lhs[age_ind] - par_dict["age"]*10**9 < 0 and self.age_lhs[age_min_ind] - par_dict["agemin"]*10**9 < 0:
+            widths_constant[age_ind] = (par_dict["age"] - par_dict["agemin"])*10**9
 
         weight_widths = widths_constant
         weight_widths /= np.sum(weight_widths)

docs/filtlists.rst

@@ -7,9 +7,9 @@ Bagpipes uses filter lists to load up filter curve files which are in turn used
 
 To define a filter list (referred to as ``filtlist`` within the code) you'll first have to set up some of the :ref:`directory structure <directory-structure>`. Specifically, within your working directory you'll have to make a ``pipes/`` directory, and within ``pipes/`` a ``filters/`` directory.
 
-Now, within the ``pipes/filters/`` directory you should create a file called ``<name of filter list>.filtlist``. For example, if I wanted to set up a PanSTARRS filter list, I could call my file ``PanSTARRS.filtlist``. 
+Now, within the ``pipes/filters/`` directory you should create a file called ``<filter list name>.filtlist``. For example, if I wanted to set up a PanSTARRS filter list, I could call my file ``PanSTARRS.filtlist``. 
 
-In this file, you'll have to add paths from the ``pipes/filters/`` directory to the locations the filter curves you want to include are stored. In order to find the curves you want I recommend the `SVO filter profile service <http://svo2.cab.inta-csic.es/svo/theory/fps>`_.
+In this file, you'll have to add paths from the ``pipes/filters/`` directory to the locations the filter curves you want to include are stored. In order to find the curves you want I recommend the `SVO filter profile service <http://svo2.cab.inta-csic.es/svo/theory/fps>`_. Bagpipes expects filter curve files to contain a column of wavelengths in Angstroms followed by a column of relative transmission values (normalisation not important).
 
 For example, if you downloaded the PS1 grizy filters and put them in a directory called ``PanSTARRS/`` within the ``pipes/filters/`` directory, you'd need the following in ``PanSTARRS.filtlist``:
 

docs/getting_started.rst

@@ -3,13 +3,13 @@
 Getting Started
 ===============
 
-Bagpipes is structured around three core classes. The ``Model_Galaxy`` class, which allows model galaxy spectra, photometry and emission line strengths to be generated, the **Galaxy** glass, which allows the user to input and plot observational data, and the ``Fit`` class, which allows the data within a ``Galaxy`` object to be fitted with Bagpipes models.
+Bagpipes is structured around three core classes. The ``Model_Galaxy`` class, which allows model galaxy spectra, photometry and emission line strengths to be generated, the ``Galaxy`` class, which allows the user to input and plot observational data, and the ``Fit`` class, which allows the data within a ``Galaxy`` object to be fitted with Bagpipes models.
 
 
 Your first model galaxy spectrum
 --------------------------------
 
-Using the ``Model_Galaxy`` class to generate model galaxies is described fully in the :ref:`Making Model Galaxies <making-model-galaxies>` section. However, for those wishing to start quickly, we can generate and plot a simple model galaxy spectrum as follows:
+Using the ``Model_Galaxy`` class to generate model galaxies is described fully in the :ref:`Making model galaxies <making-model-galaxies>` section. However, for those wishing to start quickly, we can generate and plot a simple model galaxy spectrum as follows:
 
 .. code:: python
 

docs/model_galaxies.rst

@@ -1,7 +1,7 @@
 .. _making-model-galaxies:
 
-Making Model Galaxies
-======================
+Making model galaxies
+=====================
 
 Model galaxies in Bagpipes are created using the ``Model_Galaxy`` class. The most important argument passed to ``Model_Galaxy`` is the ``model_components`` dictionary, which contains all the physical parameters of the model.