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author
Katzberg
committed
changed label of Substrate to actual name to avoid confusion
1 parent 5e64bc7 commit cb0514b

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Lines changed: 53 additions & 52 deletions

‎ToDo.md‎

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Auflistung der Beobachtung aus Test und BEschreibung des eigentlich gewünschten Verhaltens in Form einer Ticketliste
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## #1 cS1 und CS2 nicht aussagekräftig
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### Problem
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Die Achsenbezeichung c_S1 und c_S2 passt nicht zur Eingabetabelle und ist nicht selbst erklärend
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### Lösung
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Umbenennen in c_C-Quelle und c_N-Quelle
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## #2 Fenstergrößen änderung führt zu nicht sichtbaren Fenstern
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### Problem
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Durch versehentliches Kleinziehen der Fenster werden diese so klein, dass Sie nicht erkennbar sind
@@ -29,3 +22,11 @@ maximalen Zoom- Out auf Basis der Größe der angezeigten Fenster festlegen
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### Problem
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Da die Position der Inputfelder absolut eingetragen ist, darf so lange kein Resize möglich sein, solange die Inputfeldposition und Größe nicht realtiv zur Fenstergröße festegelegt werden
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# Erledigt
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## #1 cS1 und CS2 nicht aussagekräftig
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### Problem
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Die Achsenbezeichung c_S1 und c_S2 passt nicht zur Eingabetabelle und ist nicht selbst erklärend
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### Lösung
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Umbenennen in c_C-Quelle und c_N-Quelle

‎ausgabe.json‎

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‎src/DataModels/model_db.json‎

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1-
{"0":{"Modell":1,"Beschreibung":"nicht wachstumsassoziierte Ethanolbildung mit hohem Ks --> h\u00f6here Biomassegehalte bei niedrigen Substrat 1 konz.","Mikroorganismus":"S.cerevisiae","Substrat 1":"Glucose","Substrat 2":"(NH4)2SO4","Produkt 1":"Ethanol","mumax":0.467,"KS1":0.1,"KS2":0.01,"KMS1":0.01,"YXS1":0.48,"YXS2":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":0.88,"YPS1":0.46,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.00703,"pH_min":2.5,"pH_opt":5.0,"pH_max":8.2,"T_min":1.45,"T_opt":35.26,"T_max":41.48,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter (Lui et al), (Salam et al., 2024), (Lip et al., 2020); mu_max (Minimalmedium) (Lip et al., 2020);m_s (Vos et al., 2016)"},"1":{"Modell":2,"Beschreibung":"E.coli Biomassenbildung und Acetatbildung","Mikroorganismus":"E.coli","Substrat 1":"Glucose","Substrat 2":"NH4Cl","Produkt 1":"Acetat","mumax":0.7,"KS1":0.1,"KS2":0.01,"KMS1":0.05,"YXS1":0.52,"YXS2":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":0.88,"YPS1":0.48,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.0667,"pH_min":3.95,"pH_opt":6.16,"pH_max":9.71,"T_min":9.23,"T_opt":35.46,"T_max":45.77,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter f\u00fcr E. coli K12 ( Baka et al.);(Minimalmedium) mu_max (Simensen et al., 2022); Y_x\/s1, Y_p\/s1 (Acetat) (Pramanik & Keasling, 1997); m_s (Nanchen et. al, 2006)"},"2":{"Modell":3,"Beschreibung":"E.coli ausschlie\u00dfliche Biomassebildung","Mikroorganismus":"E.coli Biomass","Substrat 1":"Glucose","Substrat 2":"NH4Cl","Produkt 1":null,"mumax":0.7,"KS1":0.1,"KS2":0.01,"KMS1":0.05,"YXS1":0.52,"YXS2":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":0,"alpha":0,"beta":0.0,"YPS1":0.0,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.0667,"pH_min":3.95,"pH_opt":6.16,"pH_max":9.71,"T_min":9.23,"T_opt":35.46,"T_max":45.77,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter f\u00fcr E. coli K12 ( Baka et al.);(Komplexmedium) mu_max ( Baka et al.); m_s (Nanchen et. al, 2006)"},"3":{"Modell":4,"Beschreibung":"V. natriegens Biomassebildung und Acetatbildung","Mikroorganismus":"V. natriegens","Substrat 1":"Glucose","Substrat 2":"N-Quelle","Produkt 1":"Acetat","mumax":1.65,"KS1":0.05,"KS2":0.05,"KMS1":0.01,"YXS1":0.42,"YXS2":8,"YXO2":1.75,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":1.0,"YPS1":0.65,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.06,"pH_min":5.25,"pH_opt":7.75,"pH_max":10.0,"T_min":10.0,"T_opt":37.0,"T_max":44.0,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter (\u00d6rencik et al., 2019); mu_max (Minimalmedium) , YXS1, YXO2,YPS1 (Acetat) (Thoma & Blombach, 2021); m_s (Long et al., 2017)"},"4":{"Modell":5,"Beschreibung":"B. subtilis Biomassebildung","Mikroorganismus":"B. subtilis","Substrat 1":"C-Quelle","Substrat 2":"N-Quelle","Produkt 1":"Produkt","mumax":1.9,"KS1":0.03,"KS2":0.1,"KMS1":0.01,"YXS1":0.48,"YXS2":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":1.0,"YPS1":0.46,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.055,"pH_min":4.58,"pH_opt":6.9,"pH_max":9.1,"T_min":5.5,"T_opt":37.0,"T_max":55.7,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter, mu_max (Hafdane et al., 2025),m_s (T\u00e4nnler et al., 2008)"},"5":{"Modell":6,"Beschreibung":"PHB Produktion","Mikroorganismus":"Cupriavidus necator","Substrat 1":"Fructose","Substrat 2":"NH4-N","Produkt 1":"PHB","mumax":0.2,"KS1":0.01,"KS2":0.02,"KMS1":0.01,"YXS1":0.5,"YXS2":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":0.015,"YPS1":0.3,"RQ_x":1,"Y_CO2_P":1.0234883721,"m_s":0.0,"pH_min":5.0,"pH_opt":7.0,"pH_max":7.5,"T_min":15.0,"T_opt":37.0,"T_max":40.0,"Bemerkung":"Parameter aus Praktikum; Kardinalparameter gesch\u00e4tzt"}}
1+
{"0":{"Modell":1,"Beschreibung":"nicht wachstumsassoziierte Ethanolbildung mit hohem Ks --> h\u00f6here Biomassegehalte bei niedrigen Substrat 1 konz.","Mikroorganismus":"S.cerevisiae","C-Quelle":"Glucose","N-Quelle":"(NH4)2SO4","Produkt":"Ethanol","mumax":0.467,"KS_C":0.1,"KS_N":0.01,"KM_C":0.01,"YXC":0.48,"YXN":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":0.88,"YPC":0.46,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.00703,"pH_min":2.5,"pH_opt":5.0,"pH_max":8.2,"T_min":1.45,"T_opt":35.26,"T_max":41.48,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter (Lui et al), (Salam et al., 2024), (Lip et al., 2020); mu_max (Minimalmedium) (Lip et al., 2020);m_s (Vos et al., 2016)"},"1":{"Modell":2,"Beschreibung":"E.coli Biomassenbildung und Acetatbildung","Mikroorganismus":"E.coli","C-Quelle":"Glucose","N-Quelle":"NH4Cl","Produkt":"Acetat","mumax":0.7,"KS_C":0.1,"KS_N":0.01,"KM_C":0.05,"YXC":0.52,"YXN":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":0.88,"YPC":0.48,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.0667,"pH_min":3.95,"pH_opt":6.16,"pH_max":9.71,"T_min":9.23,"T_opt":35.46,"T_max":45.77,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter f\u00fcr E. coli K12 ( Baka et al.);(Minimalmedium) mu_max (Simensen et al., 2022); Y_x\/s1, Y_p\/s1 (Acetat) (Pramanik & Keasling, 1997); m_s (Nanchen et. al, 2006)"},"2":{"Modell":3,"Beschreibung":"E.coli ausschlie\u00dfliche Biomassebildung","Mikroorganismus":"E.coli Biomass","C-Quelle":"Glucose","N-Quelle":"NH4Cl","Produkt":null,"mumax":0.7,"KS_C":0.1,"KS_N":0.01,"KM_C":0.05,"YXC":0.52,"YXN":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":0,"alpha":0,"beta":0.0,"YPC":0.0,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.0667,"pH_min":3.95,"pH_opt":6.16,"pH_max":9.71,"T_min":9.23,"T_opt":35.46,"T_max":45.77,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter f\u00fcr E. coli K12 ( Baka et al.);(Komplexmedium) mu_max ( Baka et al.); m_s (Nanchen et. al, 2006)"},"3":{"Modell":4,"Beschreibung":"V. natriegens Biomassebildung und Acetatbildung","Mikroorganismus":"V. natriegens","C-Quelle":"Glucose","N-Quelle":"N-Quelle","Produkt":"Acetat","mumax":1.65,"KS_C":0.05,"KS_N":0.05,"KM_C":0.01,"YXC":0.42,"YXN":8,"YXO2":1.75,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":1.0,"YPC":0.65,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.06,"pH_min":5.25,"pH_opt":7.75,"pH_max":10.0,"T_min":10.0,"T_opt":37.0,"T_max":44.0,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter (\u00d6rencik et al., 2019); mu_max (Minimalmedium) , YXS1, YXO2,YPS1 (Acetat) (Thoma & Blombach, 2021); m_s (Long et al., 2017)"},"4":{"Modell":5,"Beschreibung":"B. subtilis Biomassebildung","Mikroorganismus":"B. subtilis","C-Quelle":"C-Quelle","N-Quelle":"N-Quelle","Produkt":"Produkt","mumax":1.9,"KS_C":0.03,"KS_N":0.1,"KM_C":0.01,"YXC":0.48,"YXN":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":1.0,"YPC":0.46,"RQ_x":1,"Y_CO2_P":0.9552854352,"m_s":0.055,"pH_min":4.58,"pH_opt":6.9,"pH_max":9.1,"T_min":5.5,"T_opt":37.0,"T_max":55.7,"Bemerkung":"erdachte Parameter! Nicht getestet; Kardinalparameter, mu_max (Hafdane et al., 2025),m_s (T\u00e4nnler et al., 2008)"},"5":{"Modell":6,"Beschreibung":"PHB Produktion","Mikroorganismus":"Cupriavidus necator","C-Quelle":"Fructose","N-Quelle":"NH4-N","Produkt":"PHB","mumax":0.2,"KS_C":0.01,"KS_N":0.02,"KM_C":0.01,"YXC":0.5,"YXN":8,"YXO2":4.81,"KS_O2":0.000096,"Prod":1,"alpha":0,"beta":0.015,"YPC":0.3,"RQ_x":1,"Y_CO2_P":1.0234883721,"m_s":0.0,"pH_min":5.0,"pH_opt":7.0,"pH_max":7.5,"T_min":15.0,"T_opt":37.0,"T_max":40.0,"Bemerkung":"Parameter aus Praktikum; Kardinalparameter gesch\u00e4tzt"}}

‎src/DataModels/model_db.xlsx‎

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‎src/DataModels/output_definition.py‎

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class OutputKeys(str, Enum):
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time = "t", #elapsed fermentation time in h
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c_x = "c_x", #conc. of biomass (cell dry weight) in g/L
6-
c_S1 = "c_S1", # conc. of Substrate 1 in g/L
7-
c_S2 = "c_S2", # conc. of Substrate 2 in g/L
6+
c_C_Src = "c_C_Quelle", # conc. of Carbon-Source 1 in g/L
7+
c_N_Src = "c_N_Quelle", # conc. of Nitrogen Source 2 in g/L
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#Sum_Feed = "Sum_Feed",
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Druck = "Druck", # pressure in barg
1010
Q_Air = "Begasungsrate", # aeration rate

‎src/DataModels/~$model_db.xlsx‎

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‎src/OutputAdapter/chart_js_adapter.py‎

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@@ -41,24 +41,24 @@ def transform_data(self, df: pd.DataFrame) -> Dict[str, ChartDataset]:
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"labels": df[OutputKeys.time].to_list(),
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"datasets": [
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{
44-
"label": "Substrat 1 (S1)",
44+
"label": "C-Quelle",
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"yAxisID": "yL",
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"fill": False,
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"lineTension": 0.2,
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"pointRadius": 0,
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"backgroundColor": orange,
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"borderColor": orange,
51-
"data": df[OutputKeys.c_S1].to_list(),
51+
"data": df[OutputKeys.c_C_Src].to_list(),
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},
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{
54-
"label": "Substrat 2 (S2)",
54+
"label": "N-Quelle",
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"yAxisID": "yR",
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"fill": False,
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"lineTension": 0.2,
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"pointRadius": 0,
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"backgroundColor": "petrol",
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"borderColor": "petrol",
61-
"data": df[OutputKeys.c_S2].to_list(),
61+
"data": df[OutputKeys.c_N_Src].to_list(),
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},
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],
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},
@@ -82,7 +82,7 @@ def transform_data(self, df: pd.DataFrame) -> Dict[str, ChartDataset]:
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"min": 0,
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"title": {
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"display": True,
85-
"text": "c(S1) [g" + cdot + "L" + hochminus1 + "]",
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"text": "c(C-Quelle) [g" + cdot + "L" + hochminus1 + "]",
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"font": {"size": labelfontsize},
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},
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},
@@ -96,7 +96,7 @@ def transform_data(self, df: pd.DataFrame) -> Dict[str, ChartDataset]:
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"min": 0,
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"title": {
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"display": True,
99-
"text": "c(S2) [g" + cdot + "L" + hochminus1 + "]",
99+
"text": "c(N-Quelle) [g" + cdot + "L" + hochminus1 + "]",
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"font": {"size": labelfontsize},
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"color": petrol,
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},

‎src/Util/multiplot_ferm.py‎

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@@ -18,18 +18,18 @@ def multiplot_ferm(results_df):
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fig,((ax1,ax2),(ax3,ax4))=plt.subplots(2,2)
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2020
# ax1.plot(results_df[OutputKeys.time], results_df["Sum_Feed"], label = "sum_feeding", color=gelb)
21-
ax1.plot(results_df[OutputKeys.time], results_df[OutputKeys.c_S1], label = "$c_{S1}$", color=orange)
21+
ax1.plot(results_df[OutputKeys.time], results_df[OutputKeys.c_C_Src], label = "$c_{C-Quelle}$", color=orange)
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#ax1.set_ylabel("$c_{S1} [g/L], m_{Feed S1} [g]$",fontsize=labelsize)
23-
ax1.set_ylabel("$c_{S1} [g/L]$",fontsize=labelsize)
23+
ax1.set_ylabel("$c_{C_Quelle} [g/L]$",fontsize=labelsize)
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ax1.grid()
2525
ax1.set_ylim(bottom=0)
2626
ax1.set_title("Substrate", fontsize=labelsize+3)
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2828
ax12=ax1.twinx()
29-
ax1.plot(np.nan,color=blau, label="$c_{S2}$") #only to get an entry in ax1 legend
30-
ax12.plot(results_df[OutputKeys.time], results_df[OutputKeys.c_S2], color=blau)
29+
ax1.plot(np.nan,color=blau, label="$c_{N-Quelle}$") #only to get an entry in ax1 legend
30+
ax12.plot(results_df[OutputKeys.time], results_df[OutputKeys.c_N_Src], color=blau)
3131
ax12.set_ylim(bottom=0)
32-
ax12.set_ylabel("$c_{S2} [g/L]$", fontsize=labelsize)
32+
ax12.set_ylabel("$c_{N-Quelle} [g/L]$", fontsize=labelsize)
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ax12.yaxis.label.set_color(blau)
3434
ax12.tick_params(axis="y",labelcolor=blau)
3535
ax12.spines["right"].set_color(blau)

‎src/calc/Fx_ODE_Bioreaktor.py‎

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# Features:
55
# * Drei limitierende Substrate - eines davon Sauerstoff
66
#
7-
# S1 - Kohlenstoffquelle
8-
# S2 - Stickstoff oder Phosphor oder limitierender Nährstoff bei S1 Überschuss
9-
# S3 - Sauerstoff
7+
# C - Kohlenstoffquelle
8+
# N - Stickstoff oder Phosphor oder limitierender Nährstoff bei S1 Überschuss
9+
# O - Sauerstoff
1010
# * Bildung eines Produktes auf Basis des Substrats 1
1111
# * Erhaltungsstoffwechsel - einfach ohne Konsum der Biomasse
1212
# * aerober Prozess
@@ -35,8 +35,8 @@ def Bioreaktor_ODE(x, y, Mpar, Fpar): #Hier werden die ODEs definiert
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3636
#Zuweisungen der Eingangswerte / = Konzentrationen die das Model berechnet
3737
c_x = y[0] #% Konzentration Biotrockenmasse in g/L
38-
c_S1 = y[1] #% Substratkonzentration 1 in g/L
39-
c_S2 = y[2] # % Substratkonzentration 2 in g/L
38+
c_C = y[1] #% Substratkonzentration 1 in g/L
39+
c_N = y[2] # % Substratkonzentration 2 in g/L
4040
c_P = y[3] #% Produktkonzentration in g/L
4141
c_DO = y[4] #% Gelöstsaurestoffkonzentration in g/L
4242
c_O2_Out = y[5] #% Abluftkonzentration O2
@@ -57,7 +57,7 @@ def Bioreaktor_ODE(x, y, Mpar, Fpar): #Hier werden die ODEs definiert
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5858
#spez. Wachstumsrate in Abhängigkeit von 3 limitierenden Substraten
5959
#Monod Kinetik fuer spezifische Wachstumsrate Varinate "Min-Verknüpfung"
60-
mu_Faktor=min([c_S1/(Mpar["KS1"]+c_S1), c_S2/(Mpar["KS2"]+c_S2), c_DO/(Mpar["KS_O2"]+c_DO)])
60+
mu_Faktor=min([c_C/(Mpar["KS_C"]+c_C), c_N/(Mpar["KS_N"]+c_N), c_DO/(Mpar["KS_O2"]+c_DO)])
6161
#mu_Faktor = Mpar["mumax"]*c_S1/(Mpar["KS1"]+c_S1)*c_S2/(Mpar["KS2"]+c_S2)*c_DO/(Mpar["KS_O2"]+c_DO)
6262
mu=Mpar["mumax"]*mu_Faktor #spez. Wachstumsrate in 1/h
6363
#mu_s = mumax*cs1/(KS1+cs1)*cs2/(KS2+cs2)*c_ox/(KS_O2+c_ox); % Monod Kinetik fuer spezifische Wachstumsrate Varinate "Produkt-Verknüpfung"
@@ -68,19 +68,19 @@ def Bioreaktor_ODE(x, y, Mpar, Fpar): #Hier werden die ODEs definiert
6868
if mu > 1*10^(-3): #wenn mu größer ~0 dann keine nicht wachstumsassoziierte Produktbildung; bei numerischer Integration wird mu nie exakt 0
6969
beta=0
7070
else:
71-
beta = Mpar["beta"]*c_S1/(Mpar["KMS1"]+c_S1)
71+
beta = Mpar["beta"]*c_C/(Mpar["KM_C"]+c_C)
7272

7373
if (Fpar["Feed_C"]+Fpar["Feed_N"])>0:
74-
c_S1_Feed=Fpar["c_C_Source"]*Fpar["Feed_C"]/1000*1/Q_In # since 2 substrates are fed we need to calc the c_ substrate in the combined feed
75-
c_S2_Feed=Fpar["c_N_Source"]*Fpar["Feed_N"]/1000*1/Q_In # since 2 substrates are fed we need to calc the c_ substrate in the combined feed
74+
c_C_Feed=Fpar["c_C_Source"]*Fpar["Feed_C"]/1000*1/Q_In # since 2 substrates are fed we need to calc the c_ substrate in the combined feed
75+
c_N_Feed=Fpar["c_N_Source"]*Fpar["Feed_N"]/1000*1/Q_In # since 2 substrates are fed we need to calc the c_ substrate in the combined feed
7676

7777
else:
78-
c_S1_Feed=0
79-
c_S2_Feed=0
78+
c_C_Feed=0
79+
c_N_Feed=0
8080

8181
qp=Mpar["Prod"]*(mu*Mpar["alpha"]+beta) #Luedking Piret product formation
82-
q_S1=-mu/Mpar["YXS1"]-qp/Mpar["YPS1"]
83-
q_S2=-mu/Mpar["YXS2"]
82+
q_C=-mu/Mpar["YXC"]-qp/Mpar["YPC"]
83+
q_N=-mu/Mpar["YXN"]
8484
OUR=c_x*(mu/Mpar["YXO2"]+0.001) #in g/(L*h) die 0.001 ist ein Platzhalter für den Erhaltungsstoffwechsel
8585
OUR=OUR/32 #Umrechung in mol/(L*h)
8686
#Begasung / Sauerstoff
@@ -93,11 +93,11 @@ def Bioreaktor_ODE(x, y, Mpar, Fpar): #Hier werden die ODEs definiert
9393

9494
#Substratverbrauchsgeschwindigkeit Substrat 1 durch Biomassebildung und Produktbildung
9595

96-
c_S1_r=D*(c_S1_Feed-c_S1)+q_S1*c_x
96+
c_C_r=D*(c_C_Feed-c_C)+q_C*c_x
9797

9898
#Substratverbrauchsgeschwindigkeit Substrat 2 durch Biomassebildung NICHT Genutzt zur Produktbildung Produktbildung
9999

100-
c_S2_r=D*(c_S2_Feed-c_S2)+q_S2*c_x
100+
c_N_r=D*(c_N_Feed-c_N)+q_N*c_x
101101

102102
#Produktbildung nach Luedeking Piret;
103103
c_P_r=qp*c_x-D*c_P
@@ -115,4 +115,4 @@ def Bioreaktor_ODE(x, y, Mpar, Fpar): #Hier werden die ODEs definiert
115115

116116
v_L_r=Q_In-Fpar["Q_out"]/1000
117117

118-
return [cx_r, c_S1_r, c_S2_r, c_P_r, c_DO_r, c_O2_Out_r, c_CO2_Out_r, v_L_r]
118+
return [cx_r, c_C_r, c_N_r, c_P_r, c_DO_r, c_O2_Out_r, c_CO2_Out_r, v_L_r]

‎src/calc/Nebenrechnungen.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -19,12 +19,12 @@ def Nebenrechnungen(Mpars_in, df_Fpar_in):
1919
#############################################################
2020
# Sicherstellen das folgende Werte nicht exakt Null sind
2121
#############################################################
22-
# Mpars_out["YPS1"]=Mpars_out["YPS1"].astype(float) #just to make sure it is really float
22+
# Mpars_out["YPC"]=Mpars_out["YPC"].astype(float) #just to make sure it is really float
2323
# Mpars_out["Y_CO2_P"]=Mpars_out["Y_CO2_P"].astype(float)
2424
# produces a warning...and mybe is not needed for a series
2525

26-
if Mpars_in["YPS1"] == 0:
27-
Mpars_out["YPS1"] = 1 * 10 ** (-25) # avoid division by Zero in calc of mu_s by setting Yps to a very small number
26+
if Mpars_in["YPC"] == 0:
27+
Mpars_out["YPC"] = 1 * 10 ** (-25) # avoid division by Zero in calc of mu_s by setting Yps to a very small number
2828

2929
if Mpars_in["Y_CO2_P"] == 0:
3030
Mpars_out["Y_CO2_P"] = 1 * 10 ** (-25)

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