For more reliability we now save the chi² by blocks. Otherwise it could lead...

For more reliability we now save the chi² by blocks. Otherwise it could lead to some crashes when there was more than 1 redshift.

pcigale/analysis_modules/pdf_analysis/utils.py

@@ -6,7 +6,6 @@
 # Author: Yannick Roehlly & Médéric Boquien
 
 from functools import lru_cache
-import os
 
 from astropy import log
 from astropy.cosmology import WMAP7 as cosmo
@@ -21,15 +20,12 @@ def save_chi2(obs, variable, models, chi2, values):
     """Save the chi² and the associated physocal properties
 
     """
-    fname = 'out/{}_{}_chi2.npy'.format(obs['id'], variable)
-    if os.path.exists(fname):
-        data = np.memmap(fname, dtype=np.float64, mode='r+',
-                         shape=(2, models.params.size))
-    else:
+    fname = 'out/{}_{}_chi2-block-{}.npy'.format(obs['id'], variable,
+                                                 models.iblock)
     data = np.memmap(fname, dtype=np.float64, mode='w+',
-                         shape=(2, models.params.size))
-    data[0, models.block] = chi2
-    data[1, models.block] = values
+                     shape=(2, chi2.size))
+    data[0, :] = chi2
+    data[1, :] = values
 
 
 @lru_cache(maxsize=None)

pcigale/managers/models.py

@@ -30,6 +30,7 @@ class ModelsManager(object):
         self.conf = conf
         self.obs = obs
         self.params = params
+        self.iblock = iblock
         self.block = params.blocks[iblock]
 
         self.propertiesnames = conf['analysis_params']['variables']

pcigale_plots/__init__.py

@@ -7,6 +7,7 @@
 # Author: Yannick Roehlly, Médéric Boquien & Denis Burgarella
 
 import argparse
+import glob
 from itertools import product, repeat
 from collections import OrderedDict
 import sys
@@ -49,12 +50,13 @@ def _chi2_worker(obj_name, var_name):
         Name of the analysed variable..
 
     """
-    fname = "out/{}_{}_chi2.npy".format(obj_name, var_name)
-    if os.path.isfile(fname):
-        data = np.memmap(fname, dtype=np.float64)
-        data = np.memmap(fname, dtype=np.float64, shape=(2, data.size // 2))
     figure = plt.figure()
     ax = figure.add_subplot(111)
+
+    fnames = glob.glob("out/{}_{}_chi2-block-*.npy".format(obj_name, var_name))
+    for fname in fnames:
+        data = np.memmap(fname, dtype=np.float64)
+        data = np.memmap(fname, dtype=np.float64, shape=(2, data.size // 2))
         ax.scatter(data[1, :], data[0, :], color='k', s=.1)
     ax.set_xlabel(var_name)
     ax.set_ylabel("Reduced $\chi^2$")
@@ -64,8 +66,6 @@ def _chi2_worker(obj_name, var_name):
                     .format(var_name, obj_name))
     figure.savefig("out/{}_{}_chi2.pdf".format(obj_name, var_name))
     plt.close(figure)
-    else:
-        print("No chi² found for {}. No plot created.".format(obj_name))
 
 
 def _pdf_worker(obj_name, var_name):
@@ -79,13 +79,18 @@ def _pdf_worker(obj_name, var_name):
         Name of the analysed variable..
 
     """
-    fname = "out/{}_{}_chi2.npy".format(obj_name, var_name)
-    if os.path.isfile(fname):
+
+    fnames = glob.glob("out/{}_{}_chi2-block-*.npy".format(obj_name, var_name))
+    likelihood = []
+    model_variable = []
+    for fname in fnames:
         data = np.memmap(fname, dtype=np.float64)
         data = np.memmap(fname, dtype=np.float64, shape=(2, data.size // 2))
 
-        likelihood = np.exp(-data[0, :] / 2.)
-        model_variable = data[1, :]
+        likelihood.append(np.exp(-data[0, :] / 2.))
+        model_variable.append(data[1, :])
+    likelihood = np.concatenate(likelihood)
+    model_variable = np.concatenate(model_variable)
 
     Npdf = 100
     min_hist = np.min(model_variable)
@@ -114,8 +119,6 @@ def _pdf_worker(obj_name, var_name):
                     .format(var_name, obj_name))
     figure.savefig("out/{}_{}_pdf.pdf".format(obj_name, var_name))
     plt.close(figure)
-    else:
-        print("No PDF found for {}. No plot created.".format(obj_name))
 
 
 def _sed_worker(obs, mod, filters, sed_type, nologo):