Calculate standard error of mean if not in HDF5 file

plot_results.py

@@ -31,12 +31,12 @@ file_names = []
 # file_names.append("jackknife_U4_b2_w16_n1e4_n4iwf20_2019-07-05_12-58-01.hdf5")
 # file_names.append("jackknife_U4_b2_w16_n1e4_n4iwf20_2019-07-05_14-20-41.hdf5")
 
-# file_names.append("U4_b2_w4_n64e3_n4iwf20_2019-07-11_14-49-57.hdf5")
-# file_names.append("U4_b2_w8_n32e3_n4iwf20_2019-07-11_14-50-20.hdf5")
-# file_names.append("U4_b2_w16_n16e3_n4iwf20_2019-07-11_10-50-56.hdf5")
-# file_names.append("U4_b2_w32_n8e3_n4iwf20_2019-07-11_10-51-25.hdf5")
+file_names.append("U4_b2_w4_n64e3_n4iwf20_2019-07-11_14-49-57.hdf5")
+file_names.append("U4_b2_w8_n32e3_n4iwf20_2019-07-11_14-50-20.hdf5")
+file_names.append("U4_b2_w16_n16e3_n4iwf20_2019-07-11_10-50-56.hdf5")
+file_names.append("U4_b2_w32_n8e3_n4iwf20_2019-07-11_10-51-25.hdf5")
 file_names.append("U4_b2_w50_n5120_n4iwf20_2019-07-18_17-13-41.hdf5")
-# file_names.append("U4_b2_w64_n4e3_n4iwf20_2019-07-11_10-51-47.hdf5")
+file_names.append("U4_b2_w64_n4e3_n4iwf20_2019-07-11_10-51-47.hdf5")
 file_names.append("U4_b2_w80_n3200_n4iwf20_2019-07-18_17-13-12.hdf5")
 
 
@@ -53,11 +53,9 @@ file_names.append("U4_b2_w80_n3200_n4iwf20_2019-07-18_17-13-12.hdf5")
 file_paths = [os.path.join(directory, name) for name in file_names]
 files = [h5py.File(path, "r") for path in file_paths]
 means = np.array([np.array(f["mean"][...]) for f in files])
-errors = np.array([np.array(f["standard_error_of_mean"][...]) for f in files])
 transformed_input_means = np.array([np.array(f["transformed_input_mean"][...])
                                     for f in files])
 try:
-    # TODO: Check why this doesn't work and fix it
     n_meas = np.array([f[".config"].attrs["qmc.nmeas"] for f in files])
     print(n_meas)
     n_worm_samples = np.array([f[".config"].attrs["n_samples"] for f in files])
@@ -65,6 +63,18 @@ try:
 except Exception as e:
     print("Could not read file attributes")
     print(e)
+try:
+    errors = np.array([np.array(f["standard_error_of_mean"][...])
+                       for f in files])
+except:
+    try:
+        errors = np.array([np.array(f["standard_deviation"][...])
+                        for f in files])
+        dimensions = np.ones(errors.ndim, int)
+        dimensions[0] = errors.shape[0]
+        errors /= np.sqrt(np.reshape(n_worm_samples, dimensions))
+    except Exception as e:
+        print(e)
 try:
     samples = np.array([np.array([np.array(f["output_samples"][str(i)][...])
                                   for i in range(n - 1)])