Changed to run demo2.ipynb in python3

chippr/__init__.py

@@ -1,17 +1,17 @@
-from defaults import *
+from .defaults import *
 
-from utils import *
-from stat_utils import *
-from plot_utils import *
+from .utils import *
+from .stat_utils import *
+from .plot_utils import *
 
-from sim_utils import *
-from discrete import *
-from gauss import *
-from gamma import *
-from gmix import *
-from mvn import *
-from multi_dist import *
-from catalog import *
+from .sim_utils import *
+from .discrete import *
+from .gauss import *
+from .gamma import *
+from .gmix import *
+from .mvn import *
+from .multi_dist import *
+from .catalog import *
 
-from log_z_dens import *
-from log_z_dens_plots import *
+from .log_z_dens import *
+from .log_z_dens_plots import *

chippr/catalog.py

@@ -45,7 +45,7 @@ def __init__(self, params={}, vb=True, loc='.', prepend=''):
         self.params = d.check_sim_params(self.params)
 
         if vb:
-            print self.params
+            print(self.params)
 
         np.random.seed(d.seed)
         self.cat = {}
@@ -427,7 +427,7 @@ def evaluate_lfs(self, pspace,  vb=True):
         lfs = []
         for n in self.N_range:
             points = zip(self.z_fine, [self.samps[n][1]] * self.n_tot)
-            cur=pspace.pdf(np.array(points))
+            cur=pspace.pdf(np.array([p for p in points]))
             lfs.append(cur)
         lfs = np.array(lfs)
         lfs /= np.sum(lfs, axis=-1)[:, np.newaxis] * self.dz_fine

chippr/log_z_dens.py

@@ -2,7 +2,10 @@
 import scipy as sp
 import os
 import scipy.optimize as op
-import cPickle as cpkl
+try:
+    import cPickle as pkl
+except ImportError:
+    import pickle as pkl
 import emcee
 
 import matplotlib as mpl
@@ -433,7 +436,7 @@ def distribution(log_nz):
                 chain = burn_in_mcmc_outputs['chains']
                 burn_in_mcmc_outputs['chains'] -= u.safe_log(np.sum(np.exp(chain) * self.bin_difs[np.newaxis, np.newaxis, :], axis=2))[:, :, np.newaxis]
                 with open(os.path.join(self.res_dir, 'mcmc'+str(self.burn_ins)+'.p'), 'wb') as file_location:
-                    cpkl.dump(burn_in_mcmc_outputs, file_location)
+                    pkl.dump(burn_in_mcmc_outputs, file_location)
                 full_chain = np.concatenate((full_chain, burn_in_mcmc_outputs['chains']), axis=1)
                 if vb:
                     canvas = plots.plot_sampler_progress(canvas, burn_in_mcmc_outputs, full_chain, self.burn_ins, self.plot_dir, prepend=self.add_text)
@@ -446,7 +449,7 @@ def distribution(log_nz):
             mcmc_outputs['chains'] -= u.safe_log(np.sum(np.exp(chain) * self.bin_difs[np.newaxis, np.newaxis, :], axis=2))[:, :, np.newaxis]
             full_chain = np.concatenate((full_chain, mcmc_outputs['chains']), axis=1)
             with open(os.path.join(self.res_dir, 'full_chain.p'), 'wb') as file_location:
-                cpkl.dump(full_chain, file_location)
+                pkl.dump(full_chain, file_location)
 
             self.log_smp_nz = mcmc_outputs['chains']
             self.smp_nz = np.exp(self.log_smp_nz)
@@ -489,11 +492,10 @@ def compare(self, vb=True):
                 self.info['stats']['rms']['true_nz' + '__' + key[4:]] = s.calculate_rms(np.exp(self.info['log_tru_nz']), np.exp(self.info['estimators'][key]))
                 self.info['stats']['log_rms']['log_true_nz'+ '__' + key] = s.calculate_rms(self.info['log_tru_nz'], self.info['estimators'][key])
 
-        for i in range(len(self.info['estimators'].keys())):
-            key_1 = self.info['estimators'].keys()[i]
-            for j in range(len(self.info['estimators'].keys()[:i])):
-                key_2 = self.info['estimators'].keys()[j]
-                # print(((i,j), (key_1, key_2)))
+        for i, key_1 in enumerate(self.info['estimators'].keys()):
+            for j, key_2 in enumerate(self.info['estimators'].keys()):
+                if j < i:
+                    continue
                 self.info['stats']['log_rms'][key_1 + '__' + key_2] = s.calculate_rms(self.info['estimators'][key_1], self.info['estimators'][key_2])
                 self.info['stats']['rms'][key_1[4:] + '__' + key_2[4:]] = s.calculate_rms(np.exp(self.info['estimators'][key_1]), np.exp(self.info['estimators'][key_2]))
 
@@ -535,7 +537,7 @@ def read(self, read_loc, style='pickle', vb=True):
             returns the log_z_dens information dictionary object
         """
         with open(os.path.join(self.res_dir, read_loc), 'rb') as file_location:
-            self.info = cpkl.load(file_location)
+            self.info = pkl.load(file_location)
         if vb:
             print('The following quantities were read from '+read_loc+' in the '+style+' format:')
             for key in self.info:
@@ -558,7 +560,7 @@ def write(self, write_loc, style='pickle', vb=True):
             True to print progress messages to stdout, False to suppress
         """
         with open(os.path.join(self.res_dir, write_loc), 'wb') as file_location:
-            cpkl.dump(self.info, file_location)
+            pkl.dump(self.info, file_location)
         if vb:
             print('The following quantities were written to '+write_loc+' in the '+style+' format:')
             for key in self.info:

chippr/mvn.py

@@ -29,7 +29,9 @@ def __init__(self, mean, var):
         self.sigma = self.norm_var()
         self.invvar = self.invert_var()
 
-        assert np.linalg.eig(self.var) > 0.
+        vv = np.linalg.eig(self.var)
+        print(vv)
+        #assert np.array(np.linalg.eig(self.var)) > 0.
 
         self.dist = MGD(self.mean, self.var)
 

chippr/stat_utils.py

@@ -151,7 +151,7 @@ def multi_parameter_gr_stat(sample):
     """
     dims = np.shape(sample)
     (n_walkers, n_iterations, n_params) = dims
-    n_burn_ins = n_iterations / 2
+    n_burn_ins = int(n_iterations / 2)
     chain_ensemble = np.swapaxes(sample, 0, 1)
     chain_ensemble = chain_ensemble[n_burn_ins:, :]
     Rs = np.zeros((n_params))
@@ -198,7 +198,7 @@ def cft(xtimes, lag):#xtimes has ntimes elements
         autocorrelation time for one time lag for one parameter of one walker
     """
     lent = len(xtimes) - lag
-    allt = xrange(lent)
+    allt = range(lent)
     ans = np.array([xtimes[t+lag] * xtimes[t] for t in allt])
     return ans
 
@@ -217,8 +217,9 @@ def cf(xtimes):#xtimes has ntimes elements
         autocorrelation time over all time lags for one parameter of one walker
     """
     cf0 = np.dot(xtimes, xtimes)
-    allt = xrange(len(xtimes) / 2)
-    cf = np.array([sum(cft(xtimes,lag)[len(xtimes) / 2:]) for lag in allt]) / cf0
+    ntimes = int(len(xtimes) / 2)
+    allt = range(ntimes)
+    cf = np.array([sum(cft(xtimes,lag)[ntimes:]) for lag in allt]) / cf0
     return cf
 
 def cfs(x, mode):#xbinstimes has nbins by ntimes elements

docs/notebooks/demo2.ipynb

@@ -21,7 +21,7 @@
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import timeit\n",
-    "import cProfile, pstats, StringIO\n",
+    "import cProfile, pstats #, StringIO\n",
     "import os\n",
     "\n",
     "import chippr"
@@ -70,6 +70,15 @@
     "true_nz = chippr.gmix(true_amps, true_funcs, limits=(0., 1.))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(true_nz.pdf)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -84,8 +93,8 @@
    "outputs": [],
    "source": [
     "param_loc = 'params.txt'\n",
-    "params = chippr.utils.ingest(param_loc)\n",
-    "params = chippr.defaults.check_sim_params(params)\n",
+    "params = chippr.ingest(param_loc)\n",
+    "params = chippr.check_sim_params(params)\n",
     "print(params)"
    ]
   },
@@ -342,7 +351,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "n_ivals = 2*n_bins\n",
@@ -444,21 +455,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.15rc1"
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,