Merge pull request #46 from MiBiPreT/45-update-data-handling

Restructure and clean up data handling

mibipret/analysis/reduction/ordination.py

@@ -9,7 +9,7 @@
 import pandas as pd
 import skbio.stats.ordination as sciord
 from sklearn import decomposition
-from mibipret.data.names import name_sample
+from mibipret.data.names_data import name_sample
 
 
 def pca(data_frame,
@@ -293,21 +293,23 @@ def constrained_ordination(data_frame,
         (data_independent_variables.shape[0] < data_independent_variables.shape[1]):
         raise ValueError("Ordination method {} not possible with more variables than samples.".format(method))
 
-    try:
-        # Performing constrained ordination using function from scikit-bio.
-        if method == 'cca':
+    # Performing constrained ordination using function from scikit-bio.
+    if method == 'cca':
+        try:
             sci_ordination = sciord.cca(data_dependent_variables, data_independent_variables, scaling = n_comp)
-        elif method == 'rda':
+        except(TypeError,ValueError):
+            raise TypeError("Not all column values are numeric values. Consider standardizing data first.")
+    elif method == 'rda':
+        try:
             sci_ordination = sciord.rda(data_dependent_variables, data_independent_variables, scaling = n_comp)
-        else:
-            raise ValueError("Ordination method {} not a valid option.".format(method))
-
-        loadings_independent = sci_ordination.biplot_scores.to_numpy()[:,0:n_comp]
-        loadings_dependent = sci_ordination.features.to_numpy()[:,0:n_comp]
-        scores = sci_ordination.samples.to_numpy()[:,0:n_comp]
+        except(TypeError,ValueError):
+            raise TypeError("Not all column values are numeric values. Consider standardizing data first.")
+    else:
+        raise ValueError("Ordination method {} not a valid option.".format(method))
 
-    except(TypeError):
-        raise TypeError("Not all column values are numeric values. Consider standardizing data first.")
+    loadings_independent = sci_ordination.biplot_scores.to_numpy()[:,0:n_comp]
+    loadings_dependent = sci_ordination.features.to_numpy()[:,0:n_comp]
+    scores = sci_ordination.samples.to_numpy()[:,0:n_comp]
 
     if loadings_independent.shape[1]<n_comp:
         raise ValueError("Number of dependent variables too small.")

mibipret/analysis/reduction/stable_isotope_regression.py

@@ -5,8 +5,8 @@
 @author: Alraune Zech
 """
 import numpy as np
-from mibipret.data.names import names_contaminants
-from mibipret.data.names import names_isotopes
+from mibipret.data.names_data import names_contaminants
+from mibipret.data.names_data import names_isotopes
 
 
 def Lambda_regression(delta_C,

mibipret/analysis/sample/properties.py

@@ -8,7 +8,7 @@
 @author: A. Zech
 """
 
-import mibipret.data.names as names
+import mibipret.data.names_data as names
 
 #name_phosphate,name_nitrite
 #name_sulfide,name_ammonium,name_methane,name_manganese,

mibipret/analysis/sample/screening_NA.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 import pandas as pd
-import mibipret.data.names as ean
+import mibipret.data.names_data as names
 from .properties import properties
 
 
@@ -48,7 +48,7 @@ def reductors(
     cols= check_data(data)
 
     try:
-        for ea in ean.electron_acceptors[ea_group]:
+        for ea in names.electron_acceptors[ea_group]:
             if ea in cols:
                 tot_reduct += properties[ea]['factor_stoichiometry']* data[ea]/properties[ea]['molecular_mass']
                 #     pd.to_numeric(data[ea]) / properties[ea]['molecular_mass']
@@ -61,7 +61,7 @@ def reductors(
         raise ValueError("Data not in standardized format. Run 'standardize()' first.")
 
     if isinstance(tot_reduct, pd.Series):
-        tot_reduct.rename(ean.name_total_reductors,inplace = True)
+        tot_reduct.rename(names.name_total_reductors,inplace = True)
         if verbose:
             print("Total amount of electron reductors per well in [mmol e-/l] is:\n{}".format(tot_reduct))
             print('----------------------------------------------------------------')
@@ -72,7 +72,7 @@ def reductors(
     #     tot_reduct = False
 
     if inplace:
-        data[ean.name_total_reductors] = tot_reduct
+        data[names.name_total_reductors] = tot_reduct
 
     return tot_reduct
 
@@ -128,9 +128,9 @@ def oxidators(
         NP_avail = available_NP(data)
 
     try:
-        eas = ean.contaminants[contaminant_group].copy()
-        if (ean.name_o_xylene in cols) and (ean.name_pm_xylene in cols): # and (ean.name_xylene in cols):
-            eas.remove(ean.name_xylene)
+        eas = names.contaminants[contaminant_group].copy()
+        if (names.name_o_xylene in cols) and (names.name_pm_xylene in cols): # and (names.name_xylene in cols):
+            eas.remove(names.name_xylene)
 
         for cont in eas:
             if cont in cols:
@@ -158,15 +158,15 @@ def oxidators(
     #     print('________________________________________________________________')
     #     tot_oxi = False
     if isinstance(tot_oxi, pd.Series):
-        tot_oxi.rename(ean.name_total_oxidators,inplace = True)
+        tot_oxi.rename(names.name_total_oxidators,inplace = True)
         if verbose:
             print("Total amount of oxidators per well in [mmol e-/l] is:\n{}".format(tot_oxi))
             print('-----------------------------------------------------')
     else:
         raise ValueError("No data on oxidators or only zero concentrations given.")
 
     if inplace:
-        data[ean.name_total_oxidators] = tot_oxi
+        data[names.name_total_oxidators] = tot_oxi
 
     return tot_oxi
 
@@ -203,7 +203,7 @@ def available_NP(
 
     cols = check_data(data)
 
-    nutrient_list = [ean.name_nitrate, ean.name_nitrite, ean.name_phosphate]
+    nutrient_list = [names.name_nitrate, names.name_nitrite, names.name_phosphate]
     list_nut_miss = []
 
     for nut in nutrient_list:
@@ -212,13 +212,13 @@ def available_NP(
     if len(list_nut_miss)>0:
         raise ValueError("Concentrations of nutrient(s) missing:", list_nut_miss)
 
-    CNs = (data[ean.name_nitrate] + data[ean.name_nitrite]) * (39. / 4.5)
-    CPs = data[ean.name_phosphate] * (39. / 1.)
+    CNs = (data[names.name_nitrate] + data[names.name_nitrite]) * (39. / 4.5)
+    CPs = data[names.name_phosphate] * (39. / 1.)
     NP_avail =CNs.combine(CPs, min, 0)
-    NP_avail.name = ean.name_NP_avail
+    NP_avail.name = names.name_NP_avail
 
     if inplace:
-        data[ean.name_NP_avail] = NP_avail
+        data[names.name_NP_avail] = NP_avail
 
     if verbose:
         print("Total NP available is:\n{}".format(NP_avail))
@@ -262,23 +262,23 @@ def electron_balance(
 
     cols = check_data(data)
 
-    if ean.name_total_reductors in cols:
-        tot_reduct = data[ean.name_total_reductors]
+    if names.name_total_reductors in cols:
+        tot_reduct = data[names.name_total_reductors]
     else:
         tot_reduct = reductors(data,**kwargs)
         # raise ValueError("Total amount of oxidators not given in data.")
 
-    if ean.name_total_oxidators in cols:
-        tot_oxi = data[ean.name_total_oxidators]
+    if names.name_total_oxidators in cols:
+        tot_oxi = data[names.name_total_oxidators]
     else:
         tot_oxi = oxidators(data,**kwargs)
         # raise ValueError("Total amount of reductors not given in data.")
 
     e_bal = tot_reduct.div(tot_oxi, axis=0)
-    e_bal.name = ean.name_e_balance
+    e_bal.name = names.name_e_balance
 
     if inplace:
-        data[ean.name_e_balance] = e_bal
+        data[names.name_e_balance] = e_bal
 
     if verbose:
         print("Electron balance e_red/e_cont is:\n{}".format(e_bal))
@@ -319,8 +319,8 @@ def NA_traffic(
 
     cols = check_data(data)
 
-    if ean.name_e_balance in cols:
-        e_balance = data[ean.name_e_balance]
+    if names.name_e_balance in cols:
+        e_balance = data[names.name_e_balance]
     else:
         e_balance = electron_balance(data,**kwargs)
         # raise ValueError("Electron balance not given in data.")
@@ -329,10 +329,10 @@ def NA_traffic(
     traffic = np.where(e_bal<1,"red","green")
     traffic[np.isnan(e_bal)] = 'y'
 
-    NA_traffic = pd.Series(name =ean.name_na_traffic_light,data = traffic,index = e_balance.index)
+    NA_traffic = pd.Series(name =names.name_na_traffic_light,data = traffic,index = e_balance.index)
 
     if inplace:
-        data[ean.name_na_traffic_light] = NA_traffic
+        data[names.name_na_traffic_light] = NA_traffic
 
     if verbose:
         print("Evaluation if natural attenuation (NA) is ongoing:")#" for {}".format(contaminant_group))
@@ -383,9 +383,9 @@ def total_contaminant_concentration(
     tot_conc = 0.
     cols = check_data(data)
     try:
-        eas = ean.contaminants[contaminant_group].copy()
-        if (ean.name_o_xylene in cols) and (ean.name_pm_xylene in cols): # and (ean.name_xylene in cols):
-            eas.remove(ean.name_xylene)
+        eas = names.contaminants[contaminant_group].copy()
+        if (names.name_o_xylene in cols) and (names.name_pm_xylene in cols): # and (names.name_xylene in cols):
+            eas.remove(names.name_xylene)
         for cont in eas:
             if cont in cols:
                 tot_conc += data[cont] # mass concentration in ug/l
@@ -402,15 +402,15 @@ def total_contaminant_concentration(
     #     print('________________________________________________________________')
     #     tot_conc = False
     if isinstance(tot_conc, pd.Series):
-        tot_conc.rename(ean.name_total_contaminants,inplace = True)
+        tot_conc.rename(names.name_total_contaminants,inplace = True)
         if verbose:
             print("Total concentration of {} in [ug/l] is:\n{}".format(contaminant_group,tot_conc))
             print('--------------------------------------------------')
     else:
         raise ValueError("No data on contaminants or only zero concentrations given.")
 
     if inplace:
-        data[ean.name_total_contaminants] = tot_conc
+        data[names.name_total_contaminants] = tot_conc
 
     return tot_conc
 
@@ -458,14 +458,14 @@ def thresholds_for_intervention(
     if inplace:
         na_intervention = data
     else:
-        na_intervention= pd.DataFrame(data, columns=[ean.name_sample,ean.name_observation_well])
+        na_intervention= pd.DataFrame(data, columns=[names.name_sample,names.name_observation_well])
     traffic = np.zeros(data.shape[0],dtype=int)
     intervention = [[] for i in range(data.shape[0])]
 
     try:
-        eas = ean.contaminants[contaminant_group].copy()
-        if (ean.name_o_xylene in cols) and (ean.name_pm_xylene in cols): # and (ean.name_xylene in cols):
-            eas.remove(ean.name_xylene)
+        eas = names.contaminants[contaminant_group].copy()
+        if (names.name_o_xylene in cols) and (names.name_pm_xylene in cols): # and (names.name_xylene in cols):
+            eas.remove(names.name_xylene)
         for cont in eas:
             if cont in cols:
                 th_value = properties[cont]['thresholds_for_intervention_NL']
@@ -479,9 +479,9 @@ def thresholds_for_intervention(
 
         traffic_light = np.where(traffic>0,"red","green")
         traffic_light[np.isnan(traffic)] = 'y'
-        na_intervention[ean.name_intervention_traffic] = traffic_light
-        na_intervention[ean.name_intervention_number] = traffic
-        na_intervention[ean.name_intervention_contaminants] = intervention
+        na_intervention[names.name_intervention_traffic] = traffic_light
+        na_intervention[names.name_intervention_number] = traffic
+        na_intervention[names.name_intervention_contaminants] = intervention
 
         if verbose:
             print("Evaluation of contaminant concentrations exceeding intervention values for {}:".format(