A project for Ar-Ar geochronology

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Project links

Homepage

Project description

ArArPy

ArArPy is a module for the reduction of ⁴⁰Ar/³⁹Ar geochronologic data.

It packages the whole processing steps, including reading data from local files, blank correction, decay correction, interference reactions correction, age calculation, isochron regression, etc.

The current version supports exported files in Thermo Scientific Qtegra (ISDS) platform software.

ArArPy is written in Python language combined with some open source packages, such as numpy, pandas, os, scipy, pickle, xlrd, xlsxwriter, and json.

Installing from PyPI

ArArPy can be installed via pip from PyPI.

pip install ararpy

API

Class: Sample

new Sample(**kwargs)

__init__(
    Doi = "",
    RawData = RawData(),
    Info = Info(),
    SequenceName = [],
    SequenceValue = [],
    SequenceUnit = [],
    NewIntercept = [],
    NewBlank = [],
    NewParam = [],
    SampleIntercept = [],
    BlankIntercept = [],
    AnalysisDateTime = [],
    BlankCorrected = [],
    MassDiscrCorrected = [],
    DecayCorrected = [],
    InterferenceCorrected = [],
    CorrectedValues = [],
    DegasValues = [],
    ApparentAgeValues = [],
    IsochronValues = [],
    TotalParam = [],
    PublishValues = [],
    SelectedSequence1 = [],
    SelectedSequence2 = [],
    UnselectedSequence = [],
    IsochronMark = [],
    UnknownTable = Table(),
    BlankTable = Table(),
    CorrectedTable = Table(),
    DegasPatternTable = Table(),
    PublishTable = Table(),
    AgeSpectraTable = Table(),
    IsochronsTable = Table(),
    TotalParamsTable = Table(),
    AgeSpectraPlot = Plot(),
    NorIsochronPlot = Plot(),
    InvIsochronPlot = Plot(),
    KClAr1IsochronPlot = Plot(),
    KClAr2IsochronPlot = Plot(),
    KClAr3IsochronPlot = Plot(),
    ThreeDIsochronPlot = Plot(),
    CorrelationPlot = Plot(),
    DegasPatternPlot = Plot(),
    AgeDistributionPlot = Plot(),
)

Doi type: str "" default: ""

Instance id, created by uuid.uuid4().hex.
RawData type: RawData()

RawData instance, contains information and data of the imported raw files.
Info type: Info()

Info instance. it may contain:
- attr_name type: str Info
- id type: str 0
- name type: str info
- type type: str Info
- sample Info instance.
  - name type: str Sample name.
  - material type: str Sample material.
  - location type: str Sample location.
- researcher Info instance
  - name type: str Researcher name.
  - email type: str Researcher email.
- laboratory Info instance
  - name type: str Laboratory name.
  - email type: str Laboratory email.
  - addr type: str Laboratory address.
  - analyst type: str Laboratory analyst.
  - info type: str Laboratory info.
- results Info instance
  - name type: str RESULTS
  - age_plateau type: List[float] Age plateau.
  - age_spectra type: List[float] Age spectra.
  - isochron type: List[float] Isochron.
  - isochron_F type: List[float] Isochron F.
  - isochron_age type: List[float] Isochron age.
  - J type: List[float] J value, a list of value and error.
  - plateau_F type: List[float] Plateau F.
  - plateau_age type: List[float] Plateau age.
  - total_F type: List[float] total F.
  - total_age type: List[float] total age.
- reference Info instance
  - name type: str REFERENCE.
  - doi type: str Paper doi.
  - journal type: str Journal name.
SequenceName = [] type: List[str]

Sequence name list.
SequenceValue = [] type: List[str]

Sequence label list.
SequenceUnit = [] type: List[str]

Sequence unit list.
NewIntercept = [] type: List[str]

New intercept list, 2d list, shape = (10, n), n is the number of sample sequences.
NewBlank = [] type: List[str]

New Blank list, 2d list, shape = (10, n), n is the number of sample sequences.
NewParam = [] type: List[str]

New Param list, 2d list, shape = (123, n), n is the number of sample sequences.
SampleIntercept = [] type: List[str]

Unknown intercept list, 2d list, shape = (10, n), n is the number of sample sequences.
BlankIntercept = [] type: List[str]

Blank intercept list, 2d list, shape = (10, n), n is the number of sample sequences.
AnalysisDateTime = [] type: List[str]

Analysis DateTime list, 1d list, length equals the number of sample sequences.
BlankCorrected = [] type: List[str]

Blank-corrected list, 2d list, shape = (10, n), n is the number of sample sequences.
MassDiscrCorrected = [] type: List[str]

Mass discrimination corrected list, 2d list, shape = (10, n), n is the number of sample sequences.
DecayCorrected = [] type: List[str]

Decay corrected list, 2d list, shape = (10, n), n is the number of sample sequences.
InterferenceCorrected = [] type: List[str]

Interference corrected values, 2d list, shape = (10, n), n is the number of sample sequences.
CorrectedValues = [] type: List[str]

Corrected values, 2d list, shape = (10, n), n is the number of sample sequences.
DegasValues = [] type: List[str]

Degas values, 2d list, shape = (10, n), n is the number of sample sequences.
ApparentAgeValues = [] type: List[str]

Degas values, 2d list, shape = (10, n), n is the number of sample sequences.
IsochronValues = [] type: List[str]

Isochron ratio values, 2d list, shape = (39, 0)
TotalParam = [] type: List[str]

Parameters values, 2d list, shape = (123, 0)
PublishValues = [] type: List[str]

Publish values, 2d list, shape = (11, 0)
SelectedSequence1 = [] type: List[str]

Selected sequence values of set 1, 1d list, shape = (n, ), n is the number of set 1 selected sequences
SelectedSequence2 = [] type: List[str]

Selected sequence values of set 2, 1d list, shape = (n, ), n is the number of set 2 selected sequences
UnselectedSequence = [] type: List[str]

Unselected sequence values, 1d list, shape = (n, ), n is the number of unselected sequences
IsochronMark = [] type: List[str]

Isochron mark values, 1d list, shape = (n, ), n is the number of whole sequences
UnknownTable = Table() type: Table

Unknown intercept Table.
BlankTable = Table() type: Table

Blank intercept Table.
CorrectedTable = Table() type: Table

Corrected values Table.
DegasPatternTable = Table() type: Table

Degas values Table.
PublishTable = Table() type: Table

Publish values Table.
AgeSpectraTable = Table() type: Table

Age spectra values Table.
IsochronsTable = Table() type: Table

Isochron values Table.
TotalParamsTable = Table() type: Table

Total parameters Table.
AgeSpectraPlot = Plot() type: Plot

Age spectra Plot.
NorIsochronPlot = Plot() type: Plot

Normal Isochron Plot.
InvIsochronPlot = Plot() type: Plot

Inverse Isochron Plot.
KClAr1IsochronPlot = Plot() type: Plot

K-Cl-Ar 1 Isochron Plot.
KClAr2IsochronPlot = Plot() type: Plot

K-Cl-Ar 2 Isochron Plot.
KClAr3IsochronPlot = Plot() type: Plot

K-Cl-Ar 3 Isochron Plot.
ThreeDIsochronPlot = Plot() type: Plot

Three dimensional isochron Plot.
CorrelationPlot = Plot() type: Plot

Correlation Plot.
DegasPatternPlot = Plot() type: Plot

Degas pattern Plot.
AgeDistributionPlot = Plot() type: Plot

Age distribution Plot.

name()

Get sample name.

doi()

Get sample doi.

sample()

Get sample info.

researcher()

Get researcher info.

laboratory()

Get laboratory info.

results()

Get results, a ArArBasic class.

For example:

{
    'isochron': {
        'normal': {
            'set1': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, 
                    'abs_conv': nan, 'iter': nan, 'mag': nan, 'R2': nan, 
                    'Chisq': nan, 'Pvalue': nan, 'rs': nan, 'age': nan, 
                    's1': nan, 's2': nan, 's3': nan, 'conv': nan, 'initial': nan, 
                    'sinitial': nan, 'F': nan, 'sF': nan}, 
            'set2': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'unselected': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}
        }, 
        'inverse': {
            'set1': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'set2': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'unselected': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}
        }, 
        'cl_1': {
            'set1': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'set2': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'unselected': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}
        }, 
        'cl_2': {
            'set1': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'set2': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'unselected': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}
        }, 
        'cl_3': {
            'set1': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'set2': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'unselected': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}
        }, 
        'three_d': {'set1': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'set2': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}, 
            'unselected': {'k': nan, 'sk': nan, 'm1': nan, 'sm1': nan, 'MSWD': nan, ...}}
    }, 
    'age_plateau': {
        'set1': {'F': nan, 'sF': nan, 'Num': nan, 'MSWD': nan, 'Chisq': nan, 'Pvalue': nan, 
                'age': nan, 's1': nan, 's2': nan, 's3': nan, 'Ar39': nan, 'rs': nan}, 
        'set2': {'F': nan, 'sF': nan, 'Num': nan, 'MSWD': nan, 'Chisq': nan, 'Pvalue': nan, ...}, 
        'unselected': {'F': nan, 'sF': nan, 'Num': nan, 'MSWD': nan, 'Chisq': nan, 'Pvalue': nan, ...}
    }
}

sequence()

Get sequence, a ArArBasic class.

sample.sequence() = ArArBasic(
    size=len(_smp.SequenceName), name=_smp.SequenceName,
    value=_smp.SequenceValue, unit=_smp.SequenceUnit,
    mark=ArArBasic(
        size=len(_smp.IsochronMark),
        set1=ArArBasic(
            size=sum([1 if i == 1 else 0 for i in _smp.IsochronMark]),
            index=[index for index, _ in enumerate(_smp.IsochronMark) if _ == 1],
        ),
        set2=ArArBasic(
            size=sum([1 if i == 2 else 0 for i in _smp.IsochronMark]),
            index=[index for index, _ in enumerate(_smp.IsochronMark) if _ == 2],
        ),
        unselected=ArArBasic(
            size=sum([0 if i == 2 or i == 1 else 1 for i in _smp.IsochronMark]),
            index=[index for index, _ in enumerate(_smp.IsochronMark) if _ != 1 and _ != 2],
        ),
        value=_smp.IsochronMark,
    )
)

initial()

Initialize sample instance.

set_selection(index, mark)

args: index, mark

index: int, index of the selected data point

mark: 1 or 2 for set 1 or set 2

update_table(data, table_id)

Update table data.

unknown()

Get sample intercept data.

blank()

Get blank intercept data.

parameters()

Get parameters data.

corrected()

Get corrected data.

degas()

Get degas data.

isochron()

Get isochron data.

apparent_ages()

Get apparent ages data.

publish()

Get publish data.

corr_blank()

Do correction for blank.

corr_massdiscr()

Do correction for mass discrimination.

corr_decay()

Do correction for decay.

corr_ca()

Do correction for ca.

corr_k()

Do correction for k.

corr_cl()

Do correction for cl.

corr_atm()

Do correction for atm.

corr_r()

Do calculation of radiogenic 40Ar.

corr_ratio()

Do calculation of ratios.

set_params()

Set parameters

set_info()

Set sample info

recalculate()

Re-calculate

plot_init()

Re-calculate initialize

plot_isochron()

Re-calculate plot isochron

plot_age_plateau()

Re-calculate plot age plateau

plot_normal()

Re-calculate plot normal isochron

plot_inverse()

Re-calculate plot inverse isochron

plot_cl_1()

Re-calculate plot K-Cl-Ar correlation 1

plot_cl_2()

Re-calculate plot K-Cl-Ar correlation 2

plot_cl_3()

Re-calculate plot K-Cl-Ar correlation 3

plot_3D()

Re-calculate plot 3D diagram

show_data()

Show all data

Testing

1. Running the test function from a Python terminal

>>> import ararpy as ap
>>> ap.test()
Running: ararpy.test()
============= Open an example .arr file =============
file_path = 'your_dir\\examples\\22WHA0433.arr'
sample = from_arr(file_path=file_path)
sample.name() = '22WHA0433 -PFI'
sample.help = 'builtin methods:\n __class__\t__delattr__\t__dir__\t__eq__\t__format__\t__ge__\t__getattribute__\t__gt__\t__hash__\t__init__\t__init_subclass__\t__le__\t__lt__\t__ne__\t__new__\t__reduce__\t__reduce_ex__\t__repr__\t__setattr__\t__sizeof__\t__str__\t__subclasshook__\ndunder-excluded methods:\n apparent_ages\tblank\tcalc_ratio\tcorr_atm\tcorr_blank\tcorr_ca\tcorr_cl\tcorr_decay\tcorr_k\tcorr_massdiscr\tcorr_r\tdoi\tinitial\tisochron\tlaboratory\tname\tparameters\tpublish\trecalculation\tresearcher\tresults\tsample\tsequence\tset_selection\tunknown\tupdate_table\n'
sample.parameters() = <ararpy.ArArData object at 0x0000027F7FBEC9D0>
sample.parameters().to_df() = 
         0    1      2       3       4    5  ...   117     118   119 120 121 122
0   298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
1   298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
2   298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
3   298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
4   298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
... ...     ...  ...    ...     ...     ...  ...  ...   ...     ...    ... ... ...
22  298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
23  298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
24  298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
25  298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1
26  298.56  0.0  0.018  0.0063  0.1885  0.0  ...  0.31  298.56  0.31   1   1   1

2. Example 1： create an empty sample

>>> import ararpy as ap    
>>> sample = ap.from_empty()  # create new sample instance
>>> print(sample.show_data())
# Sample Name:
#
# Doi:
#    9a43b5c1a99747ee8608676ac31814da  # uuid
# Corrected Values:
#     Empty DataFrame
# Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# Index: []
# Parameters:
#     Empty DataFrame
# Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
#           30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
#           57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
#           84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]
# Index: []
#
# [0 rows x 123 columns]
# Isochron Values:
#     Empty DataFrame
# Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
#           30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46]
# Index: []
# Apparent Ages:
#     Empty DataFrame
# Columns: [0, 1, 2, 3, 4, 5, 6, 7]
# Index: []
# Publish Table:
#     Empty DataFrame
# Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# Index: []

3. Example 2： change data point selection and recalculate

>>> import ararpy as ap 
>>> import os
>>> example_dir = os.path.join(os.path.dirname(os.path.abspath(ap.__file__)), r'examples')
>>> file_path = os.path.join(example_dir, r'22WHA0433.arr')
>>> sample = ap.from_arr(file_path)
# normal isochron age
>>> print(f"{sample.results().isochron.inverse.set1.age = }")
# sample.results().isochron.inverse.set1.age = 163.10336210925516
# check current data point selection
>>> print(f"{sample.sequence().mark.value}")
# [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
>>> print(f"{sample.sequence().mark.set1.index}")
# [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]

# change data point selection
>>> sample.set_selection(10, 1)
# check new data point selection
>>> print(f"{sample.sequence().mark.set1.index}")
# [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]

# recalculate
>>> sample.recalculate(re_plot=True)
# check new results
>>> print(f"{sample.results().isochron.inverse.set1.age = }")
# sample.results().isochron.inverse.set1.age = 164.57644271385772

Classes

Info
Plot
Sample
Table

class Info(builtins.object)
 |  Info(id='', name='', type='Info', **kwargs)
 |  
 |  Methods defined here:
 |  
 |  __init__(self, id='', name='', type='Info', **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)

class Plot(builtins.object)
 |  Plot(id='', type='', name='', data=None, info=None, **kwargs)
 |  
 |  Methods defined here:
 |  
 |  __init__(self, id='', type='', name='', data=None, info=None, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  Axis = <class 'sample.Plot.Axis'>
 |  
 |  BasicAttr = <class 'sample.Plot.BasicAttr'>
 |  
 |  Label = <class 'sample.Plot.Label'>
 |  
 |  Set = <class 'sample.Plot.Set'>
 |  
 |  Text = <class 'sample.Plot.Text'>

class Sample(builtins.object)
 |  Sample(**kwargs)
 |  
 |  Methods defined here:
 |  
 |  __init__(self, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  apparent_ages(self)
 |  
 |  blank(self)
 |  
 |  calc_ratio(self)
 |  
 |  corr_atm(self)
 |  
 |  corr_blank(self)
 |  
 |  corr_ca(self)
 |  
 |  corr_cl(self)
 |  
 |  corr_decay(self)
 |  
 |  corr_k(self)
 |  
 |  corr_massdiscr(self)
 |  
 |  corr_r(self)
 |  
 |  corrected(self)
 |  
 |  doi(self)
 |
 |  degas(self)
 |  
 |  initial(self)
 |  
 |  isochron(self)
 |  
 |  laboratory(self)
 |  
 |  name(self)
 |  
 |  parameters(self)
 |  
 |  publish(self)
 |  
 |  recalculation(self)
 |  
 |  researcher(self)
 |  
 |  results(self)
 |  
 |  sample(self)
 |  
 |  sequence(self)
 |  
 |  set_selection(self)
 |  
 |  show_data(self)
 |  
 |  unknown(self)
 |  
 |  update_table(self)
 |  
 |  ----------------------------------------------------------------------
 |  Readonly properties defined here:
 |  
 |  version
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)

class Table(builtins.object)
 |  Table(id='', name='Table', colcount=None, rowcount=None, header=None, data=None, coltypes=None, textindexs=None, numericindexs=None, **kwargs)
 |  
 |  Methods defined here:
 |  
 |  __init__(self, id='', name='Table', colcount=None, rowcount=None, header=None, data=None, coltypes=None, textindexs=None, numericindexs=None, **kwargs)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)

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0.1.13

Oct 29, 2024

This version

0.1.12

Oct 8, 2024

0.1.11

Sep 26, 2024

0.1.1

Sep 26, 2024

0.0.24

Feb 12, 2024

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ararpy 0.1.12

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ArArPy

Installing from PyPI

API

Class: Sample

new Sample(**kwargs)

name()

doi()

sample()

researcher()

laboratory()

results()

sequence()

initial()

set_selection(index, mark)

update_table(data, table_id)

unknown()

blank()

parameters()

corrected()

degas()

isochron()

apparent_ages()

publish()

corr_blank()

corr_massdiscr()

corr_decay()

corr_ca()

corr_k()

corr_cl()

corr_atm()

corr_r()

corr_ratio()

set_params()

set_info()

recalculate()

plot_init()

plot_isochron()

plot_age_plateau()

plot_normal()

plot_inverse()

plot_cl_1()

plot_cl_2()

plot_cl_3()

plot_3D()

show_data()

Testing

1. Running the test function from a Python terminal

2. Example 1： create an empty sample

3. Example 2： change data point selection and recalculate

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