setting up na_screening analysis and linting of data scripts

.gitignore

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 *.py[cod]
 *.egg-info
 *.eggs
+*.ipynb_checkpoint/
 
 dist
 .cache

mibipret/analysis/__init__.py

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+"""mibipret module for data analysis."""
+
+

mibipret/analysis/sample/__init__.py

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+"""mibipret module for data analysis performed on each sample."""
+
+

mibipret/analysis/sample/properties.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Properties for Natural Attenuation Screening.
+
+File containing name specifications of quantities and parameters measured in
+groundwater samples useful for biodegredation and bioremediation analysis
+
+@author: A. Zech
+"""
+from mibipret.data.names import name_benzene
+from mibipret.data.names import name_ethylbenzene
+from mibipret.data.names import name_indane
+from mibipret.data.names import name_indene
+from mibipret.data.names import name_ironII
+from mibipret.data.names import name_naphthalene
+from mibipret.data.names import name_nitrate
+from mibipret.data.names import name_o_xylene
+from mibipret.data.names import name_oxygen
+from mibipret.data.names import name_pm_xylene
+from mibipret.data.names import name_sulfate
+from mibipret.data.names import name_toluene
+from mibipret.data.names import name_xylene
+
+#name_phosphate,name_nitrite
+#name_sulfide,name_ammonium,name_methane,name_manganese,
+
+properties = dict()
+
+properties[name_benzene]=dict(
+    chemical_formula = 'c6h6',
+    molecular_mass = 78.,
+    cs = 6,
+    factor_stoichiometry = 30.,
+    thresholds_for_intervention_NL = 30,
+    other_names = ["Benzene", "benzene", "C6H6", "c6h6", "Benzeen", "benzeen", "Benzol", "benzol"],
+    )
+
+properties[name_toluene]=dict(
+    chemical_formula = 'c6h5ch3',
+    molecular_mass = 106.,
+    cs = 7.,
+    factor_stoichiometry = 36.,
+    thresholds_for_intervention_NL = 1000,
+    other_names = ["toluene", "Toluene","Tolueen", "tolueen", "C7H8", "c7h8","C6H5CH3",'c6h5ch3'],
+    )
+
+properties[name_ethylbenzene]=dict(
+    chemical_formula = 'c6h5ch2ch3',
+    molecular_mass = 106.,
+    cs = 8.,
+    factor_stoichiometry = 42.,
+    thresholds_for_intervention_NL = 150.,
+    other_names = ["C6H5CH2CH3","c6h5ch2ch3","ethylbenzene","Ethylbenzene","ethylbenzeen","Ethylbenzeen"],
+    )
+
+properties[name_pm_xylene]=dict(
+    chemical_formula =  "c6h4ch3ch3",
+    molecular_mass = 106.,
+    cs = 8.,
+    factor_stoichiometry = 42.,
+    thresholds_for_intervention_NL = 70.,
+    other_names = ["pm-xylene","PM-Xylene","pm_xylene","PM_Xylene","p/m xylene","P/M Xylene", "c6h4ch3ch3",\
+                   "C6H4CH3CH3","c6h4c2h6","C6H4C2H6","c8h14","C8H14"],
+    )
+
+properties[name_o_xylene]=dict(
+    chemical_formula = "c6h4ch3ch3" ,
+    molecular_mass = 106.,
+    cs = 8.,
+    factor_stoichiometry = 42.,
+    thresholds_for_intervention_NL = 70.,
+    other_names =  ["o-xylene", "O-Xylene", "O xylene", "O Xylene", "c6h4ch3ch3","C6H4CH3CH3","c6h4c2h6",\
+                    "C6H4C2H6","c8h14","C8H14"],
+    )
+
+properties[name_xylene]=dict(
+    chemical_formula = "c6h4ch3ch3",
+    molecular_mass = 106.,
+    cs = 8.,
+    factor_stoichiometry = 42.,
+    thresholds_for_intervention_NL = 70.,
+    other_names = ["xylene", "Xylene", "c6h4ch3ch3","C6H4CH3CH3","c6h4c2h6","C6H4C2H6","c8h14","C8H14"],
+    )
+
+properties[name_indene]=dict(
+    chemical_formula = "c9h8",
+    molecular_mass = 116.,
+    cs = 9.,
+    factor_stoichiometry = 44.,
+    thresholds_for_intervention_NL = 70.,
+    other_names = ["indene", "Indene", "c9h8", "C9H8"],
+    )
+
+properties[name_indane]=dict(
+    chemical_formula = "c9h10",
+    molecular_mass = 118.,
+    cs = 9.,
+    factor_stoichiometry = 46.,
+    thresholds_for_intervention_NL = 70.,
+    other_names = ["indane", "Indane", "c9h10", "C9H10"],
+    )
+
+properties[name_naphthalene]=dict(
+    chemical_formula = "c10h8",
+    molecular_mass = 128,
+    cs = 10.,
+    factor_stoichiometry = 48.,
+    thresholds_for_intervention_NL = 70.,
+    other_names = ["naphthalene","Naphthalene","c10h8","C10H8"],
+    )
+
+properties[name_oxygen]=dict(
+    chemical_formula = 'o2',
+    molecular_mass = 32.,
+    factor_stoichiometry = 4.,
+    other_names = ["oxygen","Oxygen","o2","O2"] ,
+    )
+
+properties[name_nitrate]=dict(
+    chemical_formula = 'no3',
+    molecular_mass = 62.,
+    factor_stoichiometry = 5.,
+    other_names = ["nitrate","Nitrate","NO3","no3"],
+    )
+properties[name_sulfate]=dict(
+    chemical_formula = "so42-",
+    molecular_mass = 96.1,
+    factor_stoichiometry = 8.,
+    other_names = ["sulfate", "Sulfate","so4", "so42-", "SO4", "SO42-"],
+    )
+properties[name_ironII]=dict(
+    chemical_formula = "fe2+",
+    molecular_mass = 106.,
+    factor_stoichiometry = 1,
+    other_names = ["Iron","iron","Fe","fe","Fe II","fe II","FeII","feII",\
+                   "Fe_II","fe_II","Fe2",'fe2',"Fe 2","fe 2","Fe_2","fe_2",\
+                   "Fe2+",'fe2+',"Fe 2+","fe 2+","Iron2","iron2","Iron 2",\
+                   "iron 2","Iron2+","iron2+","Iron 2+","iron 2+","IronII",\
+                   "ironII","Iron II","iron II"],
+    )
+

mibipret/analysis/sample/screening_NA.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Routines for calculating natural attenuation potential.
+
+@author: alraune
+"""
+
+import pandas as pd
+import mibipret.data.names as ean
+from .properties import properties
+
+
+def reductors(
+    data,
+    verbose = True,
+    **kwargs,
+    ):
+    """Calculate the amount of electron reductors [mmol e-/l].
+
+    making use of imported molecular mass values for quantities in [mg/mmol]
+
+    Input
+    -----
+        electron_acceptors: dict
+            concentration values of electron acceptors in [mg/l]
+
+    Output
+    ------
+        tot_reduct: float
+        Total amount of electrons needed for reduction in [mmol e-/l]
+    """
+    tot_reduct = 0.
+    if isinstance(data, pd.DataFrame):
+        cols = data.columns.to_list()
+    elif isinstance(data, pd.Series):
+        cols = [data.name]
+    else:
+        raise ValueError("Calculation of total amount of electron reductors \
+                          not possible with given data. Data has to be a \
+                          panda-DataFrame or Series but is given as \
+                          type {}".format(type(data)))
+
+    for ea in [ean.name_oxygen,ean.name_nitrate, ean.name_sulfate, ean.name_ironII]:
+
+        if ea in cols:
+            tot_reduct += properties[ea]['factor_stoichiometry']* \
+                pd.to_numeric(data[ea]) / properties[ea]['molecular_mass']
+
+        else:
+            print("WARNING: No data on {} given, zero concentration assumed.".format(ea))
+
+    if isinstance(tot_reduct, float):
+        print("\nWARNING: No data on electron acceptor concentrations given.")
+        return False
+    elif isinstance(tot_reduct, pd.Series):
+        tot_reduct.rename("total_reductors",inplace = True)
+        if verbose:
+            print("Total amount of electron reductors per column in [mmol e-/l] is:\n{}".format(tot_reduct))
+        return tot_reduct