Merge pull request #67 from hande-qmc/bug_fix/pyhande_pandas_1.0_cherrp

Bug fix/pyhande pandas 1.0 cherrp

tools/pyhande/pyhande/analysis.py

@@ -53,8 +53,8 @@ def projected_energy(reblock_data, covariance, data_length,
 :func:`pyblock.pd_utils.reblock` for producing the input parameters.
 '''
 
-    proje_sum = reblock_data.ix[:, sum_key]
-    ref_pop = reblock_data.ix[:, ref_key]
+    proje_sum = reblock_data[sum_key]
+    ref_pop = reblock_data[ref_key]
     proje_ref_cov = covariance.xs(ref_key, level=1)[sum_key]
     proje = pyblock.error.ratio(proje_sum, ref_pop, proje_ref_cov, data_length)
     proje.columns = pd.MultiIndex.from_tuples(
@@ -90,7 +90,7 @@ def qmc_summary(data, keys=('\sum H_0j N_j', 'N_0', 'Shift', 'Proj. Energy'),
         (opt_data, no_opt) = ([], [])
     for col in keys:
         if col in data:
-            summary = pyblock.pd_utils.reblock_summary(data.ix[:, col])
+            summary = pyblock.pd_utils.reblock_summary(data[col])
             if summary.empty:
                 no_opt.append(col)
             else:

tools/pyhande/pyhande/lazy.py

@@ -68,7 +68,7 @@ def find_starting_iteration_mser_min(data, md, start_max_frac=0.9, n_blocks=100,
     if end is None:
         end = data['iterations'].iloc[-1]
     before_end_indx = data['iterations'] <= end
-    data_before_end = data.ix[before_end_indx]
+    data_before_end = data[before_end_indx]
 
     list = data_before_end['Proj. Energy']
     n_data=len(list)
@@ -128,16 +128,16 @@ def lazy_hybrid(calc, md, start=0, end=None, batch_size=1):
     if end is None:
         end = calc['iterations'].iloc[-1]
     before_end_indx = calc['iterations'] <= end
-    data_before_end = calc.ix[before_end_indx]
+    data_before_end = calc[before_end_indx]
 
     after_start_indx = data_before_end['iterations'] >= start
     calc_tr          = data_before_end[after_start_indx]
 
     #list = calc_tr['Proj. Energy'].as_matrix()
     #n_data = len(list)
 
-    list_org = calc_tr['Proj. Energy'].as_matrix()
-    n_data = len(list_org) / batch_size
+    list_org = calc_tr['Proj. Energy'].values
+    n_data = len(list_org) // batch_size
     list = [0]*n_data
     for i in range(n_data):
         list[i] = numpy.mean(list_org[i*batch_size:(i+1)*batch_size])
@@ -392,7 +392,7 @@ def lazy_block(calc, md, start=0, end=None, select_function=None,
     if extract_rep_loop_time:
         to_block.append('time')
 
-    mc_data = calc.ix[indx, to_block]
+    mc_data = calc.loc[indx, to_block]
     if mc_data['Shift'].iloc[0] == mc_data['Shift'].iloc[1]:
         if calc['Shift'][~indx].iloc[-1] == mc_data['Shift'].iloc[0]:
             warnings.warn('The blocking analysis starts from before the shift '
@@ -417,7 +417,7 @@ def lazy_block(calc, md, start=0, end=None, select_function=None,
     if calc_inefficiency:
         # Calculate quantities needed for the inefficiency.
         dtau = md['qmc']['tau']
-        reblocked_iters = calc.ix[indx, 'iterations']
+        reblocked_iters = calc.loc[indx, 'iterations']
         N = reblocked_iters.iloc[-1] - reblocked_iters.iloc[0]
 
         # This returns a data frame with inefficiency data from the
@@ -636,7 +636,7 @@ def find_starting_iteration(data, md, frac_screen_interval=300,
         # Default end is the last iteration.
         end = data['iterations'].iloc[-1]
     before_end_indx = data['iterations'] <= end
-    data_before_end = data.ix[before_end_indx]
+    data_before_end = data[before_end_indx]
 
     # Find the point the shift began to vary.
     variable_shift = data_before_end['Shift'] != data_before_end['Shift'].iloc[0]

tools/pyhande/pyhande/testcode.py

@@ -46,13 +46,12 @@ def extract_test_data(fname, underscore=True):
                 # Compare all bar the first entry (which contains a legitimate
                 # NaN in the standard error, which is not handled well by
                 # testcode).
-                indxs = range(len(data))[1:]
-                test_data = data.ix[indxs]
+                test_data = data.iloc[1:]
                 output[metadata['calc_type'] + calc_ind] = test_data
             elif len(data) > 10:
                 # Compare every 1/4 of (non-trivial) QMC calculation...
                 indxs = [0] + [int((i*len(data))/4)-1 for i in range(1,5)]
-                test_data = data.ix[indxs]
+                test_data = data.iloc[indxs]
                 output[metadata['calc_type'] + calc_ind] = test_data
             else:
                 output[metadata['calc_type'] + calc_ind] = data