added folder archiv

archiv/test_data.json

+{
+    "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
+        }
+    ]
+}
\ No newline at end of file

archiv/trial_json.py

+# -*- 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.")