From 74162cfb743256c8cb95360d8c20a4cb9074bfca Mon Sep 17 00:00:00 2001
From: michael <m.langguth@fz-juelich.de>
Date: Tue, 26 May 2020 13:00:07 +0200
Subject: [PATCH] Some rename for Python's naming convention.
---
video_prediction/datasets/era5_dataset_v2.py | 15 ++++++++-------
1 file changed, 8 insertions(+), 7 deletions(-)
diff --git a/video_prediction/datasets/era5_dataset_v2.py b/video_prediction/datasets/era5_dataset_v2.py
index f25fbc13..00111565 100644
--- a/video_prediction/datasets/era5_dataset_v2.py
+++ b/video_prediction/datasets/era5_dataset_v2.py
@@ -14,8 +14,9 @@ from video_prediction.datasets.base_dataset import VarLenFeatureVideoDataset
from os import path
import sys
sys.path.append(path.abspath('../../workflow_parallel_frame_prediction/'))
+import DataPreprocess.process_netCDF_v2
from DataPreprocess.process_netCDF_v2 import get_unique_vars
-from DataPreprocess.process_netCDF_v2 import calc_data_stat.get_stat_vars
+from DataPreprocess.process_netCDF_v2 import Calc_data_stat
#from base_dataset import VarLenFeatureVideoDataset
from collections import OrderedDict
from tensorflow.contrib.training import HParams
@@ -161,7 +162,7 @@ def save_tf_record(output_fname, sequences):
example = tf.train.Example(features=features)
writer.write(example.SerializeToString())
-class norm_data:
+class Norm_data:
"""
Class for normalizing data. The statistical data for normalization (minimum, maximum, average, standard deviation etc.) is expected to be available from a statistics-dictionary
created with the calc_data_stat-class (see 'process_netCDF_v2.py'.
@@ -171,7 +172,7 @@ class norm_data:
known_norms = {}
known_norms["minmax"] = ["min","max"]
known_norms["znorm"] = ["avg","sigma"]
-
+
def __init__(self,varnames):
"""Initialize the instance by setting the variable names to be handled and the status (for sanity checks only) as attributes."""
varnames_uni, _, nvars = get_unique_vars(varnames)
@@ -205,7 +206,7 @@ class norm_data:
for varname in self.varnames:
for stat_name in self.known_norms[norm]:
#setattr(self,varname+stat_name,stat_dict[varname][0][stat_name])
- setattr(self,varname+stat_name,get_stat_vars(stat_dict,stat_name,varname))
+ setattr(self,varname+stat_name,Calc_data_stat.get_stat_vars(stat_dict,stat_name,varname))
self.status_ok = True # set status for normalization -> ready
@@ -215,7 +216,7 @@ class norm_data:
"""
# some sanity checks
- if not self.status_ok: raise ValueError("norm_data-object needs to be initialized and checked first.") # status ready?
+ if not self.status_ok: raise ValueError("Norm_data-instance needs to be initialized and checked first.") # status ready?
if not norm in self.known_norms.keys(): # valid normalization requested?
print("Please select one of the following known normalizations: ")
@@ -235,7 +236,7 @@ class norm_data:
"""
# some sanity checks
- if not self.status_ok: raise ValueError("norm_data-object needs to be initialized and checked first.") # status ready?
+ if not self.status_ok: raise ValueError("Norm_data-instance needs to be initialized and checked first.") # status ready?
if not norm in self.known_norms.keys(): # valid normalization requested?
print("Please select one of the following known normalizations: ")
@@ -264,7 +265,7 @@ def read_frames_and_save_tf_records(output_dir,input_dir,partition_name,vars_in,
output_dir = os.path.join(output_dir,partition_name)
os.makedirs(output_dir,exist_ok=True)
- norm_cls = norm_data(vars_in) # init normalization-instance
+ norm_cls = Norm_data(vars_in) # init normalization-instance
nvars = len(vars_in)
# open statistics file and feed it to norm-instance
--
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