Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
# -*- coding: utf-8 -*-
"""
Created on Thu May 5 17:27:38 2022 .
@author: Richter
testfile for operating with json-files
"""
# %% import moduls
import json
# import numpy as np
import pandas as pd
# import h5py as h5
# %% define functions
def findkeys(node, kv):
"""
https://stackoverflow.com/questions/9807634/find-all-occurrences-of-a-key-in-nested-dictionaries-and-lists.
Parameters
----------
node : TYPE
DESCRIPTION.
kv : TYPE
DESCRIPTION.
Yields
------
TYPE
DESCRIPTION.
"""
if isinstance(node, list):
for i in node:
for x in findkeys(i, kv):
yield x
elif isinstance(node, dict):
if kv in node:
yield node[kv]
for j in node.values():
for x in findkeys(j, kv):
yield x
def find_attribut(data, attribut):
"""
Find attribute of .
----------
data : TYPE
DESCRIPTION.
attribut : TYPE
DESCRIPTION.
Returns
-------
name_pump : TYPE
DESCRIPTION.
efficiency_pump : TYPE
DESCRIPTION.
"""
name_pump = list(findkeys(data, "Name"))
efficiency_pump = list(findkeys(data, attribut))
# print("Die Pumpe", name_pump[0], "besitzt den Wirkungsgrad",
# efficiency_pump[0])
return name_pump, efficiency_pump
def calculate_efficiency(eta_1, eta_2):
"""
Parameters.
----------
eta_1 : TYPE
DESCRIPTION.
eta_2 : TYPE
DESCRIPTION.
Returns
-------
eta : TYPE
DESCRIPTION.
"""
eta = eta_1 * eta_2
return eta
# %% main script
with open("test_data.json", "r+") as file:
data = json.load(file)
a = data.keys()
# print(a)
# read out key - value - pairs
for i in a:
key = data[i]
for j in range(0, len(key)):
# print(j)
dic = key[j]
attrs = list(dic.keys())
for x in attrs:
name = list(findkeys(dic, x))
# print("Maschinenart", i, "Das Attribut",x, "hat den Wert", name)
# find specific data
machine = "Motors"
attribut = "Efficiency"
data_pumps = data["Pumps"]
data_motors = data["Motors"]
dataset = pd.DataFrame()
# pump_1 = data_pumps[0]
# Iteration über Attribute einfügen
for i in range(0, len(data_pumps)):
attr_name = list(findkeys(data_pumps[i], "Name"))
attr_value = list(findkeys(data_pumps[i], attribut))
# attr_name, attr_value = find_attribut(data_pumps[i], attribut)
# efficiency_pump)
print("Die", machine, attr_name[0], "besitzt das Attribut",
attribut, "mit dem Wert", attr_value[0])
# multiplicate the pump efficiency with the motor efficiency
eta_motor_1 = list(findkeys(data["Motors"][0], "Efficiency"))[0]
eta_motor_2 = list(findkeys(data["Motors"][1], "Efficiency"))[0]
eta_pumpe_1 = list(findkeys(data["Pumps"][0], "Efficiency"))[0]
eta_pumpe_2 = list(findkeys(data["Pumps"][1], "Efficiency"))[0]
print(eta_motor_1, eta_motor_2)
count = 0
# iteration over all pumps and motors
for p in range(0, len(data_pumps)):
for m in range(0, len(data_motors)):
eta_pumpe = list(findkeys(data_pumps[p], attribut))[0]/100
eta_motor = list(findkeys(data_motors[m], attribut))[0]/100
eta_ges = calculate_efficiency(eta_pumpe, eta_motor)
scenario = "Szenario_" + str(count)
dataset[scenario] = [eta_pumpe, eta_motor, eta_ges]
dataset.index = ["eta_pumpe", "eta_motor", "eta_ges"]
count += 1
print(p, m, eta_ges)
# %% store dataframe in hdf5-file
filename = "example_kpi.h5"
with pd.HDFStore(filename, "a") as hdf:
try:
dataset.to_hdf(hdf, "Berechnung")
hdf.get_storer("Berechnung").attrs.Link = ("https://git.rwth-aachen.de/"
"fst-tuda/projects/lehre/praktikum_digitalisierung/quality-kpi.git")
except ValueError:
print("Gruppe existiert bereits.")