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Commit 48fb3d6f authored by Richter, Manuela's avatar Richter, Manuela
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clean directory for new concept

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archiv/example_kpi.h5 deleted 100644 → 0
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archiv/test_data.json deleted 100644 → 0
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{
"Pumps":[
{"Name": "Pump_1",
"Manufacturer": "Company A",
"Unit": "Percentage",
"Efficiency": 43},
{"Name": "Pump_2",
"Manufacturer": "Company B",
"Unit": "Percentage",
"Efficiency": 56
}
],
"Motors":[
{"Name": "Motor_1",
"Manufacturer": "Company A",
"Unit": "Percentage",
"Efficiency": 90},
{"Name": "Motor_2",
"Manufacturer": "Company B",
"Unit": "Percentage",
"Efficiency": 85
}
]
}
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archiv/trial_json.py deleted 100644 → 0
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# -*- 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.")
example_kpi.h5 deleted 100644 → 0
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test_data.json deleted 100644 → 0
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{
"Pumps":[
{"Name": "Pump_1",
"Manufacturer": "Company A",
"Unit": "Percentage",
"Efficiency": 43},
{"Name": "Pump_2",
"Manufacturer": "Company B",
"Unit": "Percentage",
"Efficiency": 56
}
],
"Motors":[
{"Name": "Motor_1",
"Manufacturer": "Company A",
"Unit": "Percentage",
"Efficiency": 90},
{"Name": "Motor_2",
"Manufacturer": "Company B",
"Unit": "Percentage",
"Efficiency": 85
}
]
}
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trial_json.py deleted 100644 → 0
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# -*- 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.")
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