from IPython.display import display, Markdown as md, HTML as html, FileLink
import math
import time
import json
import zipfile

try:
    import pandas as pd
    import ipywidgets as widgets
    from ipywidgets import interact, interactive, interactive_output
    import numpy as np
    import matplotlib.pylab as plt
    import scipy
    from sklearn.preprocessing import minmax_scale
    import plotly.express as px
    from plotly.subplots import make_subplots
    import plotly.graph_objects as go
    import statsmodels.api as sm
    from matplotlib import pyplot
    from scipy.optimize import curve_fit

except Exception as e:
    !pip install pandas ipywidgets matplotlib plotly pandas-bokeh scipy scikit-learn statsmodels
    import pandas as pd
    import ipywidgets as widgets
    from ipywidgets import interact, interactive
    import numpy as np
    import matplotlib.pylab as plt
    import scipy
    from sklearn.preprocessing import minmax_scale
    import plotly.express as px
    from plotly.subplots import make_subplots
    import plotly.graph_objects as go
    import statsmodels.api as sm
    from matplotlib import pyplot
    from scipy.optimize import curve_fit
    
#pd.options.plotting.backend = "plotly"

display(md("# Grenzsetzungfunktion"))
df = pd.read_csv('gsf2.csv')
df
#df.to_csv('gsf2.csv',encoding='utf-8', index=False)

def gsf1(_K, _V, _F, _pK):
    return _pK * (0.1 * _K +4) / 5.659 * 30/ math.pow(_V, 0.4) * (1 - math.pow( math.e, -3.53 * _F))
    
def pk(_K):
    if _K == 16: return 1.5
    if _K == 32: return 1.25
    if _K == 45: return 1.09
print(math.exp( -3.53 * 10), math.pow( math.e, -3.53 * 10))
    
def gsf2(_row, _F):
    _K = _row["K"]
    _V= _row["V"]
    print(math.exp( -3.53 * _F), math.pow( math.e, -3.53 * _F))
    if math.isnan(_K) or math.isnan(_V): return
        
    return pk(_K) * (0.1 * _K +4) / 5.659 * 30/ math.pow(_V, 0.4) * (1 - math.exp( -3.53 * _F))

    # _K 45 32 16
# _V 80,4 100,2, 59,2 77,9, 38,8, 95,1 109,7 165,4, 103,9
# _F 250 375
# _pK 1.5:16er 1.25:32er 1.09:45er

#print(gsf1(45, 100.2, 250, 1.09))

df["250_r"] = df.apply(gsf2, _F=250, axis=1)
df["375_r"] = df.apply(gsf2, _F=375, axis=1)

df["250_d"] = df["250_r"] - df["250_c"]
df["375_d"] = df["375_r"] - df["375_c"]

df.to_csv('gsf3.csv',encoding='utf-8', index=False)
df

4.670945379442584e-16 4.670945379442593e-16
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0

	Nr.	Material	250_c	375_c	V	K
0	1	KG 2, 0/45 Grauwacke	1.441	2.384	80.4	45.0
1	2	KG 1, 0/45 Granodiorid	5.592	6.181	100.2	45.0
2	3	Schotter, 31,5/63, Diabas	7.866	9.455	NaN	NaN
3	11	KG 2, 0/45, Grauwacke	9.444	11.373	59.2	45.0
4	12	KG 2, 0/32, Grauwacke	5.465	6.707	NaN	32.0
5	13	KG 2, 0/45, Grauwacke	6.063	7.398	77.9	45.0
6	14	KG 1, 0/45, Grauwacke	8.457	10.233	38.8	45.0
7	15	KG 2, 0/32, Grauwacke	4.465	5.896	95.1	32.0
8	16	KG 2, 0/45, Grauwacke	3.353	4.178	109.7	45.0
9	17	KG 2, 0/32, RC-Material	3.525	4.597	165.4	32.0
10	18	KG 2, 0/16, Grauwacke	3.978	5.176	103.9	16.0

	Nr.	Material	250_c	375_c	V	K	250_r	375_r	250_d	375_d
0	1	KG 2, 0/45 Grauwacke	1.441	2.384	80.4	45.0	8.494241	8.494241	7.053241	6.110241
1	2	KG 1, 0/45 Granodiorid	5.592	6.181	100.2	45.0	7.778214	7.778214	2.186214	1.597214
2	3	Schotter, 31,5/63, Diabas	7.866	9.455	NaN	NaN	NaN	NaN	NaN	NaN
3	11	KG 2, 0/45, Grauwacke	9.444	11.373	59.2	45.0	9.600598	9.600598	0.156598	-1.772402
4	12	KG 2, 0/32, Grauwacke	5.465	6.707	NaN	32.0	NaN	NaN	NaN	NaN
5	13	KG 2, 0/45, Grauwacke	6.063	7.398	77.9	45.0	8.602249	8.602249	2.539249	1.204249
6	14	KG 1, 0/45, Grauwacke	8.457	10.233	38.8	45.0	11.368268	11.368268	2.911268	1.135268
7	15	KG 2, 0/32, Grauwacke	4.465	5.896	95.1	32.0	7.715283	7.715283	3.250283	1.819283
8	16	KG 2, 0/45, Grauwacke	3.353	4.178	109.7	45.0	7.501435	7.501435	4.148435	3.323435
9	17	KG 2, 0/32, RC-Material	3.525	4.597	165.4	32.0	6.183151	6.183151	2.658151	1.586151
10	18	KG 2, 0/16, Grauwacke	3.978	5.176	103.9	16.0	6.950477	6.950477	2.972477	1.774477

Grenzsetzungfunktion¶