MiBiPreT example: Contaminant Data Screening with Amersfoort data¶

Diagnostic plots for data analysis on microbial biodegredation at the contaminant Amersfoort site. Author: Alraune Zech

Data based on the PhD thesis of Johan van Leeuwen, 2021 'Biodegredation of mono- and polyaromatic hydrocarbons in a contaminated aquifer originating from a former Pintsch gas factory site' which is equivalent to the manuscript of van Leeuwen et al., 2022 'Anaerobic degradation of benzene and other aromatic hydrocarbons in a tar-derived plume: Nitrate versus iron reducing conditions', J. of Cont. Hydrol. The data was provided by Johan van Leeuwen.

Background: Amersfoort contaminant site¶

Close to the train station in Amersfoort, the Netherlands, the subsurface is contaminated with organic hydrocarbons forming a NAPL. Contamination originates from decades of operating manufactured gas plant, which dumped tar by-products in waste lagoons. The tar is a DNAPL and has spread into the underlying shallow unconfined aquifer. Sample wells were installed to measure various characteristics of the subsurface. The raw data contains measurements on

• environmental conditions, such as pH, redox potential, concentrations of oxygen, nitrate, etc
• contaminant concentration such as BTEX, indene, indane, naphtalene and multiple other (typically cyclic) petroleum hydrocarbons
• metabolite concentration, i.e. byproducts of degredation processes of contaminant
• isotope measurments for specific contaminants and samples
• counts of genes (RNA/DNA) of mibrobiota that is know to perform biodegredation as well as functional enzymes know to be responsible for biodegredation

Required packages

In [1]:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt import numpy as np import pandas as pd

In [2]:

import mibiscreen as mbs

import mibiscreen as mbs

Data loading¶

In [3]:

file_path = './amersfoort.xlsx'

file_path = './amersfoort.xlsx'

Load and standardize data of contaminants:

In [4]:

contaminants_raw,units = mbs.load_excel(file_path,
                                        sheet_name = 'contaminants',
                                        verbose = False)
contaminants,units = mbs.standardize(contaminants_raw,
                                      reduce = False,
                                      verbose = False)

contaminants_raw,units = mbs.load_excel(file_path, sheet_name = 'contaminants', verbose = False) contaminants,units = mbs.standardize(contaminants_raw, reduce = False, verbose = False)

Load and standardize data of metabolites

Basic analysis of contaminant concentrations per sample¶

Concentrations of contaminants subsets¶

Total concentration of all contaminants provided in Excel-sheet/Dataframe:

In [5]:

contaminants_concentration = mbs.total_concentration(contaminants,
                                                     name_list = 'all',
                                                     #include_as = "concentration_contaminants",
                                                     verbose = True)

contaminants_concentration = mbs.total_concentration(contaminants, name_list = 'all', #include_as = "concentration_contaminants", verbose = True)

==============================================================
 Running function 'total_concentration()' on data
==============================================================
Selecting all data columns except for those with settings.
Selected set of quantities: 
---------------------------
isopropylbenzene
indene
2_ethylnaphthalene
pm_xylene
styrene
4_ethyltoluene
naphthalene
2_ethyltoluene
o_xylene
1_methylnaphthalene
123_trimethylbenzene
124_trimethylbenzene
benzene
2_methylnaphthalene
ethylbenzene
1245_tetramethylbenzene
toluene
2_methylindene
16_dimethylnaphthalene
135_trimethylbenzene
n_propylbenzene
3_ethyltoluene
26_dimethylnaphthalene
13_diethylbenzene
4_isopropyltouene
_____________________________________________________________
________________________________________________________________
total concentration in [ug/l] is:
1          9
2      13344
3       4337
4       5781
5      87452
6     150695
7      57598
8      59496
9       3699
10       734
11         9
12        40
13    102811
14     66182
15     45938
16     26463
17      7911
18      9922
19     18826
20     27114
21     25733
22     10167
23      2301
24      1137
25       113
26        83
27        27
28     14985
29     10376
30         9
31         9
32         9
33        13
dtype: int64
--------------------------------------------------

equivalent to:

(now including results as own data column to data frame of contaminants)

In [6]:

mbs.total_contaminant_concentration(contaminants,
                                    include = True,
                                    verbose = True,
                                    )
#display(contaminants)

mbs.total_contaminant_concentration(contaminants, include = True, verbose = True, ) #display(contaminants)

==============================================================
 Running function 'total_contaminant_concentration()' on data
==============================================================
==============================================================
 Running function 'total_concentration()' on data
==============================================================
Selecting specific group of contaminants: all_cont
_____________________________________________________________
Selected set of quantities: 
---------------------------
isopropylbenzene
indene
2_ethylnaphthalene
pm_xylene
styrene
4_ethyltoluene
naphthalene
2_ethyltoluene
o_xylene
1_methylnaphthalene
123_trimethylbenzene
124_trimethylbenzene
benzene
2_methylnaphthalene
ethylbenzene
1245_tetramethylbenzene
toluene
2_methylindene
16_dimethylnaphthalene
135_trimethylbenzene
n_propylbenzene
3_ethyltoluene
26_dimethylnaphthalene
13_diethylbenzene
4_isopropyltouene
_____________________________________________________________
WARNING: There are quantities from name list not in data
p_cresol
benzo[a]pyrene
35_dimethylphenol
18_dimethylnaphthalene
o_cresol
17_dimethylnaphthalene
naphthalene_PAH
phenanthrene
14_diethylbenzene
15_dimethylnaphthalene
23_dimethylnaphthalene
23_dimethylphenol
14_dimethylnaphthalene
benzo[k]fluoranthene
p_[tert]butylphenol
phenol
benzo[b]fluoranthene
cresol
tetramethylbenzene
13_dimethylnaphthalene
3_ethylphenol
indane
benzo[ghi]perylene
26_dimethylphenol
chrysene
methylindene
4_ethylphenol
benzo[a]anthracene
naphthalene_VOC
m_cresol
2_isopropylphenol
12_diethylbenzene
1235_tetramethylbenzene
diethylbenzene
acenaphthylene
1234_tetramethylbenzene
12_dimethylnaphthalene
24_dimethylphenol
235_trimethylphenol
indeno[1,2,3-cd]pyrene
34_dimethylphenol
anthracene
2_ethylphenol
345_trimethylphenol
acenaphthene
1_ethylnaphthalene
25_dimethylphenol
1_methylindene
pyrene
fluoranthene
thymol
dibenz[a,h]anthracene
27_dimethylnaphthalene
ethyltoluene
trimethylbenzene
dimethylnaphthalene
fluorene
ethylnaphthalene
methylnaphthalene
Maybe data not in standardized format. Run 'standardize()' first.
_________________________________________________________________
________________________________________________________________
total concentration in [ug/l] is:
1          9
2      13344
3       4337
4       5781
5      87452
6     150695
7      57598
8      59496
9       3699
10       734
11         9
12        40
13    102811
14     66182
15     45938
16     26463
17      7911
18      9922
19     18826
20     27114
21     25733
22     10167
23      2301
24      1137
25       113
26        83
27        27
28     14985
29     10376
30         9
31         9
32         9
33        13
dtype: int64
--------------------------------------------------
Series saved as column 'concentration_contaminants' within provided DataFrame
---------------------------------------------------------------------------

Out[6]:

1          9
2      13344
3       4337
4       5781
5      87452
6     150695
7      57598
8      59496
9       3699
10       734
11         9
12        40
13    102811
14     66182
15     45938
16     26463
17      7911
18      9922
19     18826
20     27114
21     25733
22     10167
23      2301
24      1137
25       113
26        83
27        27
28     14985
29     10376
30         9
31         9
32         9
33        13
dtype: int64

Total concentration of contaminant subgroup: BTEX (Benzene, Toluene, Ethylbenzene, Xylene)

In [7]:

contaminants_BTEX = mbs.total_concentration(contaminants,
                                            name_list = 'BTEX',
                                            #include_as = "concentration_BTEX",
                                            verbose = True,
                                            )

contaminants_BTEX = mbs.total_concentration(contaminants, name_list = 'BTEX', #include_as = "concentration_BTEX", verbose = True, )

==============================================================
 Running function 'total_concentration()' on data
==============================================================
Selecting specific group of contaminants: BTEX
_____________________________________________________________
Selected set of quantities: 
---------------------------
benzene
ethylbenzene
o_xylene
toluene
pm_xylene
_____________________________________________________________
________________________________________________________________
total concentration in [ug/l] is:
1         9
2      4136
3       620
4       880
5     32200
6     75984
7     40733
8     41599
9       361
10      378
11        9
12       12
13    40505
14    24700
15    26341
16    12531
17     3974
18     6756
19    10338
20    20638
21    18938
22     3771
23     1145
24      612
25       69
26       74
27       11
28    10741
29     7209
30        9
31        9
32        9
33       13
dtype: int64
--------------------------------------------------

equivalent to:(now including results as own data column to data frame of contaminants)

In [8]:

mbs.total_contaminant_concentration(contaminants,
                                    contaminant_group = 'BTEX',
                                    include = True,
                                    verbose = False)

mbs.total_contaminant_concentration(contaminants, contaminant_group = 'BTEX', include = True, verbose = False)

Out[8]:

1         9
2      4136
3       620
4       880
5     32200
6     75984
7     40733
8     41599
9       361
10      378
11        9
12       12
13    40505
14    24700
15    26341
16    12531
17     3974
18     6756
19    10338
20    20638
21    18938
22     3771
23     1145
24      612
25       69
26       74
27       11
28    10741
29     7209
30        9
31        9
32        9
33       13
dtype: int64

Total concentration of contaminant subgroup: BTEX + Indene,Indane and Naphthalene

In [9]:

contaminants_BTEXIIN = mbs.total_concentration(contaminants,
                                               name_list = 'BTEXIIN',
                                               #include_as = "concentration_BTEXIIN",
                                               verbose = True,
                                               )

contaminants_BTEXIIN = mbs.total_concentration(contaminants, name_list = 'BTEXIIN', #include_as = "concentration_BTEXIIN", verbose = True, )

==============================================================
 Running function 'total_concentration()' on data
==============================================================
Selecting specific group of contaminants: BTEXIIN
_____________________________________________________________
Selected set of quantities: 
---------------------------
indene
benzene
ethylbenzene
o_xylene
toluene
pm_xylene
naphthalene
_____________________________________________________________
WARNING: There are quantities from name list not in data
indane
Maybe data not in standardized format. Run 'standardize()' first.
_________________________________________________________________
________________________________________________________________
total concentration in [ug/l] is:
1          9
2      12028
3       3513
4       4002
5      62336
6     114532
7      51158
8      52901
9       2029
10       603
11         9
12        18
13     75039
14     48031
15     37864
16     21281
17      7062
18      9406
19     17118
20     26253
21     25057
22      8855
23      1811
24      1033
25       101
26        82
27        27
28     14099
29      9651
30         9
31         9
32         9
33        13
dtype: int64
--------------------------------------------------

Using wrapper functions for specific type of data, the previous commands are equivalent to:

(now including results as own data column with standard name to data frame of contaminants)

In [10]:

mbs.total_contaminant_concentration(contaminants,
                                    contaminant_group = 'BTEXIIN',
                                    include = True,
                                    verbose = False)

mbs.total_contaminant_concentration(contaminants, contaminant_group = 'BTEXIIN', include = True, verbose = False)

WARNING: There are quantities from name list not in data
Maybe data not in standardized format. Run 'standardize()' first.
_________________________________________________________________

Out[10]:

1          9
2      12028
3       3513
4       4002
5      62336
6     114532
7      51158
8      52901
9       2029
10       603
11         9
12        18
13     75039
14     48031
15     37864
16     21281
17      7062
18      9406
19     17118
20     26253
21     25057
22      8855
23      1811
24      1033
25       101
26        82
27        27
28     14099
29      9651
30         9
31         9
32         9
33        13
dtype: int64

Summed concentration of selected subgroup of contaminants: benzene & toluene

In [11]:

contaminants_BT = mbs.total_concentration(contaminants,
                                          name_list = ['benzene','toluene'],
                                          include_as = "concentration_BT",
                                          verbose = True,
                                          )

contaminants_BT = mbs.total_concentration(contaminants, name_list = ['benzene','toluene'], include_as = "concentration_BT", verbose = True, )

==============================================================
 Running function 'total_concentration()' on data
==============================================================
Selecting all names from provided list.
Selected set of quantities: 
---------------------------
toluene
benzene
_____________________________________________________________
________________________________________________________________
total concentration in [ug/l] is:
1         7
2      2116
3       210
4       248
5     22287
6     62374
7     35246
8     36156
9       237
10      341
11        7
12        9
13    29769
14    17743
15    19817
16     8501
17     2982
18     6268
19     7704
20    15459
21    14925
22     2436
23     1074
24      559
25       55
26       69
27       10
28     7671
29     5895
30        7
31        7
32        7
33       13
dtype: int64
--------------------------------------------------
Series saved as column 'concentration_BT' within provided DataFrame
---------------------------------------------------------------------------

Visualization of contaminant concentrations per sample¶

Using mibiscreen plotting routine for concentration bar plots

In [12]:

list_contaminants = ['concentration_contaminants','concentration_BTEXIIN',
                     'concentration_BTEX','concentration_BT','benzene']

mbs.contaminants_bar(contaminants,
                 list_contaminants,
                 list_labels = ['all','BTEXIIN','BTEX','BT','B'],
                 sort = True,
                 figsize = [5.2,3],
                 textsize = 12,
                 save_fig = 'contaminants_bar.png',
                 yscale = 'log',
                 loc='upper left',
                 title_text = False,
                 )

list_contaminants = ['concentration_contaminants','concentration_BTEXIIN', 'concentration_BTEX','concentration_BT','benzene'] mbs.contaminants_bar(contaminants, list_contaminants, list_labels = ['all','BTEXIIN','BTEX','BT','B'], sort = True, figsize = [5.2,3], textsize = 12, save_fig = 'contaminants_bar.png', yscale = 'log', loc='upper left', title_text = False, )

Save Figure to file:
 contaminants_bar.png

Out[12]:

(<Figure size 520x300 with 1 Axes>,
 <Axes: xlabel='Samples', ylabel='Total concentration [$\\mu$g/l]'>)

Producing the plot individually using matplotlib and numpy for individual adaptions:

In [13]:

sort_args = np.argsort(contaminants_concentration.values)
#sort_args = np.arange(len(contaminants_concentration.values))
plt.bar(np.arange(len(contaminants_concentration.values)),contaminants_concentration.values[sort_args],label='all')
plt.bar(np.arange(len(contaminants_BTEXIIN.values)),contaminants_BTEXIIN.values[sort_args],label='BTEXIIN')
plt.bar(np.arange(len(contaminants_BTEX.values)),contaminants_BTEX.values[sort_args],label='BTEX')
plt.bar(np.arange(len(contaminants_BT.values)),contaminants_BT.values[sort_args],label='BT')
plt.bar(np.arange(len(contaminants['toluene'].values)),contaminants['toluene'].values[sort_args],label='T')
plt.xlabel('Samples')
plt.ylabel(r'Total concentration [$\mu$g/l]')
plt.yscale('log')
plt.legend()
plt.title('Total concentration of contaminants per sample')

sort_args = np.argsort(contaminants_concentration.values) #sort_args = np.arange(len(contaminants_concentration.values)) plt.bar(np.arange(len(contaminants_concentration.values)),contaminants_concentration.values[sort_args],label='all') plt.bar(np.arange(len(contaminants_BTEXIIN.values)),contaminants_BTEXIIN.values[sort_args],label='BTEXIIN') plt.bar(np.arange(len(contaminants_BTEX.values)),contaminants_BTEX.values[sort_args],label='BTEX') plt.bar(np.arange(len(contaminants_BT.values)),contaminants_BT.values[sort_args],label='BT') plt.bar(np.arange(len(contaminants['toluene'].values)),contaminants['toluene'].values[sort_args],label='T') plt.xlabel('Samples') plt.ylabel(r'Total concentration [$\mu$g/l]') plt.yscale('log') plt.legend() plt.title('Total concentration of contaminants per sample')

Out[13]:

Text(0.5, 1.0, 'Total concentration of contaminants per sample')

Analysis of number of contaminants per sample¶

In [14]:

count_contaminants = mbs.total_count(contaminants,
                                     name_list = 'all_cont',
                                     verbose = True)

count_BTEXIIN = mbs.total_count(contaminants,
                                name_list = 'BTEXIIN',
                                verbose = True)

count_BTEX = mbs.total_count(contaminants,
                             name_list = 'BTEX',
                             verbose = True)

mbs.total_count(contaminants,
                name_list = ['benzene'],
                include_as = "count_benzene",
                verbose = True)

count_contaminants = mbs.total_count(contaminants, name_list = 'all_cont', verbose = True) count_BTEXIIN = mbs.total_count(contaminants, name_list = 'BTEXIIN', verbose = True) count_BTEX = mbs.total_count(contaminants, name_list = 'BTEX', verbose = True) mbs.total_count(contaminants, name_list = ['benzene'], include_as = "count_benzene", verbose = True)

==============================================================
 Running function 'total_count()' on data
==============================================================
Selecting specific group of contaminants: all_cont
_____________________________________________________________
Selected set of quantities: 
---------------------------
isopropylbenzene
indene
2_ethylnaphthalene
pm_xylene
styrene
4_ethyltoluene
naphthalene
2_ethyltoluene
o_xylene
1_methylnaphthalene
123_trimethylbenzene
124_trimethylbenzene
benzene
2_methylnaphthalene
ethylbenzene
1245_tetramethylbenzene
toluene
2_methylindene
16_dimethylnaphthalene
135_trimethylbenzene
n_propylbenzene
3_ethyltoluene
26_dimethylnaphthalene
13_diethylbenzene
4_isopropyltouene
_____________________________________________________________
WARNING: There are quantities from name list not in data
p_cresol
benzo[a]pyrene
35_dimethylphenol
18_dimethylnaphthalene
o_cresol
17_dimethylnaphthalene
naphthalene_PAH
phenanthrene
14_diethylbenzene
15_dimethylnaphthalene
23_dimethylnaphthalene
23_dimethylphenol
14_dimethylnaphthalene
benzo[k]fluoranthene
p_[tert]butylphenol
phenol
benzo[b]fluoranthene
cresol
tetramethylbenzene
13_dimethylnaphthalene
3_ethylphenol
indane
benzo[ghi]perylene
26_dimethylphenol
chrysene
methylindene
4_ethylphenol
benzo[a]anthracene
naphthalene_VOC
m_cresol
2_isopropylphenol
12_diethylbenzene
1235_tetramethylbenzene
diethylbenzene
acenaphthylene
1234_tetramethylbenzene
12_dimethylnaphthalene
24_dimethylphenol
235_trimethylphenol
indeno[1,2,3-cd]pyrene
34_dimethylphenol
anthracene
2_ethylphenol
345_trimethylphenol
acenaphthene
1_ethylnaphthalene
25_dimethylphenol
1_methylindene
pyrene
fluoranthene
thymol
dibenz[a,h]anthracene
27_dimethylnaphthalene
ethyltoluene
trimethylbenzene
dimethylnaphthalene
fluorene
ethylnaphthalene
methylnaphthalene
Maybe data not in standardized format. Run 'standardize()' first.
_________________________________________________________________
________________________________________________________________
Number of quantities out of 25 exceeding               concentration of 0.00 ug/l :
1      3
2     18
3     17
4     25
5     25
6     24
7     25
8     25
9     25
10    25
11     3
12    10
13    25
14    25
15    25
16    25
17    21
18    24
19    18
20    15
21    17
22    25
23    20
24    21
25    16
26     7
27     5
28    23
29    25
30     3
31     3
32     3
33     2
dtype: int64
--------------------------------------------------
==============================================================
 Running function 'total_count()' on data
==============================================================
Selecting specific group of contaminants: BTEXIIN
_____________________________________________________________
Selected set of quantities: 
---------------------------
indene
benzene
ethylbenzene
o_xylene
toluene
pm_xylene
naphthalene
_____________________________________________________________
WARNING: There are quantities from name list not in data
indane
Maybe data not in standardized format. Run 'standardize()' first.
_________________________________________________________________
________________________________________________________________
Number of quantities out of 7 exceeding               concentration of 0.00 ug/l :
1     3
2     7
3     7
4     7
5     7
6     7
7     7
8     7
9     7
10    7
11    3
12    6
13    7
14    7
15    7
16    7
17    7
18    7
19    7
20    7
21    7
22    7
23    7
24    7
25    7
26    6
27    5
28    7
29    7
30    3
31    3
32    3
33    2
dtype: int64
--------------------------------------------------
==============================================================
 Running function 'total_count()' on data
==============================================================
Selecting specific group of contaminants: BTEX
_____________________________________________________________
Selected set of quantities: 
---------------------------
benzene
ethylbenzene
o_xylene
toluene
pm_xylene
_____________________________________________________________
________________________________________________________________
Number of quantities out of 5 exceeding               concentration of 0.00 ug/l :
1     3
2     5
3     5
4     5
5     5
6     5
7     5
8     5
9     5
10    5
11    3
12    5
13    5
14    5
15    5
16    5
17    5
18    5
19    5
20    5
21    5
22    5
23    5
24    5
25    5
26    5
27    3
28    5
29    5
30    3
31    3
32    3
33    2
dtype: int64
--------------------------------------------------
==============================================================
 Running function 'total_count()' on data
==============================================================
Selecting all names from provided list.
Selected set of quantities: 
---------------------------
benzene
_____________________________________________________________
________________________________________________________________
Number of quantities out of 1 exceeding               concentration of 0.00 ug/l :
1     0
2     1
3     1
4     1
5     1
6     1
7     1
8     1
9     1
10    1
11    0
12    1
13    1
14    1
15    1
16    1
17    1
18    1
19    1
20    1
21    1
22    1
23    1
24    1
25    1
26    1
27    1
28    1
29    1
30    0
31    0
32    0
33    1
dtype: int64
--------------------------------------------------
Series saved as column 'count_benzene' within provided DataFrame
---------------------------------------------------------------------------

Out[14]:

1     0
2     1
3     1
4     1
5     1
6     1
7     1
8     1
9     1
10    1
11    0
12    1
13    1
14    1
15    1
16    1
17    1
18    1
19    1
20    1
21    1
22    1
23    1
24    1
25    1
26    1
27    1
28    1
29    1
30    0
31    0
32    0
33    1
dtype: int64

Using wrapper functions for specific type of data, the previous commands are equivalent to:

(now including results with standard names as own data column to data frame of contaminants)

In [15]:

mbs.total_contaminant_count(contaminants,
                contaminant_group = 'all',
                include = True)

mbs.total_contaminant_count(contaminants,
                contaminant_group = 'BTEXIIN',
                include = True)

mbs.total_contaminant_count(contaminants,
                contaminant_group = 'BTEX',
                include = True)

mbs.total_contaminant_count(contaminants, contaminant_group = 'all', include = True) mbs.total_contaminant_count(contaminants, contaminant_group = 'BTEXIIN', include = True) mbs.total_contaminant_count(contaminants, contaminant_group = 'BTEX', include = True)

WARNING: There are quantities from name list not in data
Maybe data not in standardized format. Run 'standardize()' first.
_________________________________________________________________

Out[15]:

1     3
2     5
3     5
4     5
5     5
6     5
7     5
8     5
9     5
10    5
11    3
12    5
13    5
14    5
15    5
16    5
17    5
18    5
19    5
20    5
21    5
22    5
23    5
24    5
25    5
26    5
27    3
28    5
29    5
30    3
31    3
32    3
33    2
dtype: int64

Visualization of number of contaminants per sample¶

In [16]:

list_counts = ['count_contaminants','count_BTEXIIN','count_BTEX','count_benzene']

mbs.contaminants_bar(contaminants,
                     list_counts,
                     list_labels = ['all','BTEXIIN','BTEX','B'],
                     sort = True,
                     figsize = [5.2,3],
                     textsize = 12,
                     ylabel = 'Total count',
                     yscale = 'linear',
                     save_fig = 'count_bar.png',
                     loc='upper left',
                     title_text = False,
                     )

list_counts = ['count_contaminants','count_BTEXIIN','count_BTEX','count_benzene'] mbs.contaminants_bar(contaminants, list_counts, list_labels = ['all','BTEXIIN','BTEX','B'], sort = True, figsize = [5.2,3], textsize = 12, ylabel = 'Total count', yscale = 'linear', save_fig = 'count_bar.png', loc='upper left', title_text = False, )

Save Figure to file:
 count_bar.png

Out[16]:

(<Figure size 520x300 with 1 Axes>,
 <Axes: xlabel='Samples', ylabel='Total count'>)

Producing the plot individually using matplotlib and numpy for individual adaptions:

In [17]:

plt.figure(num=2)
plt.bar(np.arange(len(count_contaminants.values)),np.sort(count_contaminants.values),label='all')
plt.bar(np.arange(len(count_BTEXIIN.values)),np.sort(count_BTEXIIN.values),label='BTEXIIN')
plt.bar(np.arange(len(count_BTEX.values)),np.sort(count_BTEX.values),label='BTEX')
plt.xlabel('Samples')
plt.ylabel('Total number')
plt.title('Total number of contaminants per sample')
plt.legend()

plt.figure(num=2) plt.bar(np.arange(len(count_contaminants.values)),np.sort(count_contaminants.values),label='all') plt.bar(np.arange(len(count_BTEXIIN.values)),np.sort(count_BTEXIIN.values),label='BTEXIIN') plt.bar(np.arange(len(count_BTEX.values)),np.sort(count_BTEX.values),label='BTEX') plt.xlabel('Samples') plt.ylabel('Total number') plt.title('Total number of contaminants per sample') plt.legend()

Out[17]:

<matplotlib.legend.Legend at 0x7f115fd0e710>

Evaluation of intervention threshold exceedance¶

In [18]:

data_thresh_ratio = mbs.thresholds_for_intervention_ratio(contaminants)
display(data_thresh_ratio)

data_thresh_ratio = mbs.thresholds_for_intervention_ratio(contaminants) display(data_thresh_ratio)

WARNING: There are quantities from name list not in data
Maybe data not in standardized format. Run 'standardize()' first.
_________________________________________________________________

	indene_thr_ratio	benzene_thr_ratio	ethylbenzene_thr_ratio	o_xylene_thr_ratio	toluene_thr_ratio	pm_xylene_thr_ratio	naphthalene_thr_ratio
1	0.000000	0.000000	0.006667	0.000000	0.007	0.014286	0.000000
2	18.328571	1.166667	1.886667	6.485714	2.081	18.328571	94.414286
3	1.971429	0.200000	0.480000	1.128571	0.204	3.700000	39.357143
4	5.657143	0.400000	0.633333	2.557143	0.236	5.114286	38.942857
5	89.514286	33.566667	9.153333	38.257143	21.280	83.742857	341.000000
6	100.800000	1012.333333	13.953333	52.757143	32.004	111.771429	449.885714
7	53.900000	751.866667	7.153333	22.628571	12.690	40.428571	95.028571
8	53.071429	768.800000	5.793333	22.271429	13.092	43.071429	108.385714
9	2.257143	1.000000	0.093333	0.857143	0.207	0.714286	21.571429
10	0.557143	11.033333	0.113333	0.128571	0.010	0.157143	2.657143
11	0.000000	0.000000	0.006667	0.000000	0.007	0.014286	0.000000
12	0.085714	0.066667	0.006667	0.014286	0.007	0.014286	0.000000
13	97.071429	503.866667	11.120000	42.957143	14.653	86.585714	396.271429
14	62.314286	231.500000	7.180000	28.171429	10.798	55.828571	270.985714
15	51.942857	271.666667	9.526667	25.314286	11.667	47.471429	112.671429
16	42.971429	28.233333	3.933333	17.585714	7.654	31.557143	82.028571
17	18.342857	71.766667	2.906667	4.385714	0.829	3.557143	25.771429
18	19.142857	206.666667	1.913333	1.328571	0.068	1.542857	18.714286
19	24.300000	57.900000	3.080000	10.200000	5.967	20.828571	72.557143
20	19.271429	503.433333	21.426667	11.300000	0.356	16.771429	60.942857
21	21.071429	464.533333	15.046667	10.385714	0.989	14.700000	66.342857
22	16.885714	71.800000	4.586667	4.871429	0.282	4.371429	55.742857
23	4.271429	35.033333	0.340000	0.185714	0.023	0.100000	5.242857
24	4.857143	17.500000	0.033333	0.457143	0.034	0.228571	1.157143
25	0.185714	1.300000	0.040000	0.042857	0.016	0.071429	0.271429
26	0.114286	1.866667	0.013333	0.014286	0.013	0.028571	0.000000
27	0.042857	0.100000	0.000000	0.000000	0.007	0.014286	0.185714
28	13.314286	251.666667	12.640000	6.157143	0.121	10.614286	34.657143
29	14.171429	193.333333	6.866667	1.685714	0.095	2.371429	20.714286
30	0.000000	0.000000	0.006667	0.000000	0.007	0.014286	0.000000
31	0.000000	0.000000	0.006667	0.000000	0.007	0.014286	0.000000
32	0.000000	0.000000	0.006667	0.000000	0.007	0.014286	0.000000
33	0.000000	0.066667	0.000000	0.000000	0.011	0.000000	0.000000

In [19]:

fig,ax = mbs.threshold_ratio_bar(data_thresh_ratio,
                                 list_samples =  [31,9,11],
                                 figsize = [12,3],
                                 nrows=1,ncols=3,
                                 list_colors = ['olive','lightblue','tomato'],
                                 sharey = True,
                                 grid = True,
                                )

fig,ax = mbs.threshold_ratio_bar(data_thresh_ratio, list_samples = [31,9,11], figsize = [12,3], nrows=1,ncols=3, list_colors = ['olive','lightblue','tomato'], sharey = True, grid = True, )

In [20]:

quantities = ['toluene','naphthalene','indene','pm_xylene','ethylbenzene','o_xylene','benzene']
mbs.threshold_ratio_bar(data_thresh_ratio,
                        list_samples = [9],
                        list_labels =  quantities,
                        figsize = [6,3],
                        unity_line = True,
                        title_text= 'Evaluation of threshold exceedance for BTEXIIN',
                        )

quantities = ['toluene','naphthalene','indene','pm_xylene','ethylbenzene','o_xylene','benzene'] mbs.threshold_ratio_bar(data_thresh_ratio, list_samples = [9], list_labels = quantities, figsize = [6,3], unity_line = True, title_text= 'Evaluation of threshold exceedance for BTEXIIN', )

Out[20]:

(<Figure size 600x300 with 1 Axes>,
 <Axes: title={'center': 'Evaluation of threshold exceedance for BTEXIIN'}, xlabel='ratio to threshold concentration $C/C_\\mathrm{threshold}$'>)