D47crunch
Standardization and analytical error propagation of Δ47 and Δ48 clumped-isotope measurements
Process and standardize carbonate and/or CO2 clumped-isotope analyses, from low-level data out of a dual-inlet mass spectrometer to final, “absolute” Δ47 and Δ48 values with fully propagated analytical error estimates (Daëron, 2021).
The tutorial section takes you through a series of simple steps to import/process data and print out the results. The how-to section provides instructions applicable to various specific tasks.
1. Tutorial
1.1 Installation
The easy option is to use pip; open a shell terminal and simply type:
python -m pip install D47crunch
For those wishing to experiment with the bleeding-edge development version, this can be done through the following steps:
- Download the
devbranch source code here and rename it toD47crunch.py. - Do any of the following:
- copy
D47crunch.pyto somewhere in your Python path - copy
D47crunch.pyto a working directory (import D47crunchwill only work if called within that directory) - copy
D47crunch.pyto any other location (e.g.,/foo/bar) and then use the following code snippet in your own code to importD47crunch:
- copy
import sys
sys.path.append('/foo/bar')
import D47crunch
Documentation for the development version can be downloaded here (save html file and open it locally).
1.2 Usage
Start by creating a file named rawdata.csv with the following contents:
UID, Sample, d45, d46, d47, d48, d49
A01, ETH-1, 5.79502, 11.62767, 16.89351, 24.56708, 0.79486
A02, MYSAMPLE-1, 6.21907, 11.49107, 17.27749, 24.58270, 1.56318
A03, ETH-2, -6.05868, -4.81718, -11.63506, -10.32578, 0.61352
A04, MYSAMPLE-2, -3.86184, 4.94184, 0.60612, 10.52732, 0.57118
A05, ETH-3, 5.54365, 12.05228, 17.40555, 25.96919, 0.74608
A06, ETH-2, -6.06706, -4.87710, -11.69927, -10.64421, 1.61234
A07, ETH-1, 5.78821, 11.55910, 16.80191, 24.56423, 1.47963
A08, MYSAMPLE-2, -3.87692, 4.86889, 0.52185, 10.40390, 1.07032
Then instantiate a D47data object which will store and process this data:
import D47crunch
mydata = D47crunch.D47data()
For now, this object is empty:
>>> print(mydata)
[]
To load the analyses saved in rawdata.csv into our D47data object and process the data:
mydata.read('rawdata.csv')
# compute δ13C, δ18O of working gas:
mydata.wg()
# compute δ13C, δ18O, raw Δ47 values for each analysis:
mydata.crunch()
# compute absolute Δ47 values for each analysis
# as well as average Δ47 values for each sample:
mydata.standardize()
We can now print a summary of the data processing:
>>> mydata.summary(verbose = True, save_to_file = False)
[summary]
––––––––––––––––––––––––––––––– –––––––––
N samples (anchors + unknowns) 5 (3 + 2)
N analyses (anchors + unknowns) 8 (5 + 3)
Repeatability of δ13C_VPDB 4.2 ppm
Repeatability of δ18O_VSMOW 47.5 ppm
Repeatability of Δ47 (anchors) 13.4 ppm
Repeatability of Δ47 (unknowns) 2.5 ppm
Repeatability of Δ47 (all) 9.6 ppm
Model degrees of freedom 3
Student's 95% t-factor 3.18
Standardization method pooled
––––––––––––––––––––––––––––––– –––––––––
This tells us that our data set contains 5 different samples: 3 anchors (ETH-1, ETH-2, ETH-3) and 2 unknowns (MYSAMPLE-1, MYSAMPLE-2). The total number of analyses is 8, with 5 anchor analyses and 3 unknown analyses. We get an estimate of the analytical repeatability (i.e. the overall, pooled standard deviation) for δ13C, δ18O and Δ47, as well as the number of degrees of freedom (here, 3) that these estimated standard deviations are based on, along with the corresponding Student's t-factor (here, 3.18) for 95 % confidence limits. Finally, the summary indicates that we used a “pooled” standardization approach (see [Daëron, 2021]).
To see the actual results:
>>> mydata.table_of_samples(verbose = True, save_to_file = False)
[table_of_samples]
–––––––––– – ––––––––– –––––––––– –––––– –––––– –––––––– –––––– ––––––––
Sample N d13C_VPDB d18O_VSMOW D47 SE 95% CL SD p_Levene
–––––––––– – ––––––––– –––––––––– –––––– –––––– –––––––– –––––– ––––––––
ETH-1 2 2.01 37.01 0.2052 0.0131
ETH-2 2 -10.17 19.88 0.2085 0.0026
ETH-3 1 1.73 37.49 0.6132
MYSAMPLE-1 1 2.48 36.90 0.2996 0.0091 ± 0.0291
MYSAMPLE-2 2 -8.17 30.05 0.6600 0.0115 ± 0.0366 0.0025
–––––––––– – ––––––––– –––––––––– –––––– –––––– –––––––– –––––– ––––––––
This table lists, for each sample, the number of analytical replicates, average δ13C and δ18O values (for the analyte CO2 , not for the carbonate itself), the average Δ47 value and the SD of Δ47 for all replicates of this sample. For unknown samples, the SE and 95 % confidence limits for mean Δ47 are also listed These 95 % CL take into account the number of degrees of freedom of the regression model, so that in large datasets the 95 % CL will tend to 1.96 times the SE, but in this case the applicable t-factor is much larger.
We can also generate a table of all analyses in the data set (again, note that d18O_VSMOW is the composition of the CO2 analyte):
>>> mydata.table_of_analyses(verbose = True, save_to_file = False)
[table_of_analyses]
––– ––––––––– –––––––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––– –––––––––– –––––––––– ––––––––– ––––––––– –––––––––– ––––––––
UID Session Sample d13Cwg_VPDB d18Owg_VSMOW d45 d46 d47 d48 d49 d13C_VPDB d18O_VSMOW D47raw D48raw D49raw D47
––– ––––––––– –––––––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––– –––––––––– –––––––––– ––––––––– ––––––––– –––––––––– ––––––––
A01 mySession ETH-1 -3.807 24.921 5.795020 11.627670 16.893510 24.567080 0.794860 2.014086 37.041843 -0.574686 1.149684 -27.690250 0.214454
A02 mySession MYSAMPLE-1 -3.807 24.921 6.219070 11.491070 17.277490 24.582700 1.563180 2.476827 36.898281 -0.499264 1.435380 -27.122614 0.299589
A03 mySession ETH-2 -3.807 24.921 -6.058680 -4.817180 -11.635060 -10.325780 0.613520 -10.166796 19.907706 -0.685979 -0.721617 16.716901 0.206693
A04 mySession MYSAMPLE-2 -3.807 24.921 -3.861840 4.941840 0.606120 10.527320 0.571180 -8.159927 30.087230 -0.248531 0.613099 -4.979413 0.658270
A05 mySession ETH-3 -3.807 24.921 5.543650 12.052280 17.405550 25.969190 0.746080 1.727029 37.485567 -0.226150 1.678699 -28.280301 0.613200
A06 mySession ETH-2 -3.807 24.921 -6.067060 -4.877100 -11.699270 -10.644210 1.612340 -10.173599 19.845192 -0.683054 -0.922832 17.861363 0.210328
A07 mySession ETH-1 -3.807 24.921 5.788210 11.559100 16.801910 24.564230 1.479630 2.009281 36.970298 -0.591129 1.282632 -26.888335 0.195926
A08 mySession MYSAMPLE-2 -3.807 24.921 -3.876920 4.868890 0.521850 10.403900 1.070320 -8.173486 30.011134 -0.245768 0.636159 -4.324964 0.661803
––– ––––––––– –––––––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––– –––––––––– –––––––––– ––––––––– ––––––––– –––––––––– ––––––––
2. How-to
2.1 Simulate a virtual data set to play with
It is sometimes convenient to quickly build a virtual data set of analyses, for instance to assess the final analytical precision achievable for a given combination of anchor and unknown analyses (see also Fig. 6 of Daëron, 2021).
This can be achieved with virtual_data(). The example below creates a dataset with four sessions, each of which comprises four analyses of anchor ETH-1, five of ETH-2, six of ETH-3, and two analyses of an unknown sample named FOO with an arbitrarily defined isotopic composition. Analytical repeatabilities for Δ47 and Δ48 are also specified arbitrarily. See the virtual_data() documentation for additional configuration parameters.
from D47crunch import *
args = dict(
samples = [
dict(Sample = 'ETH-1', N = 4),
dict(Sample = 'ETH-2', N = 5),
dict(Sample = 'ETH-3', N = 6),
dict(
Sample = 'FOO',
N = 2,
d13C_VPDB = -5.,
d18O_VPDB = -10.,
D47 = 0.3,
D48 = 0.15
),
],
rD47 = 0.010,
rD48 = 0.030,
)
session1 = virtual_data(session = 'Session_01', **args)
session2 = virtual_data(session = 'Session_02', **args)
session3 = virtual_data(session = 'Session_03', **args)
session4 = virtual_data(session = 'Session_04', **args)
D = D47data(session1 + session2 + session3 + session4)
D.crunch()
D.standardize()
D.table_of_sessions(verbose = True, save_to_file = False)
D.table_of_samples(verbose = True, save_to_file = False)
D.table_of_analyses(verbose = True, save_to_file = False)
2.2 Control data quality
D47crunch offers several tools to visualize processed data. The examples below use the same virtual data set, generated with:
from D47crunch import *
from random import shuffle
# generate virtual data:
args = dict(
samples = [
dict(Sample = 'ETH-1', N = 8),
dict(Sample = 'ETH-2', N = 8),
dict(Sample = 'ETH-3', N = 8),
dict(Sample = 'FOO', N = 4,
d13C_VPDB = -5., d18O_VPDB = -10.,
D47 = 0.3, D48 = 0.15),
dict(Sample = 'BAR', N = 4,
d13C_VPDB = -15., d18O_VPDB = -15.,
D47 = 0.5, D48 = 0.2),
])
sessions = [
virtual_data(session = f'Session_{k+1:02.0f}', seed = int('1234567890'[:k+1]), **args)
for k in range(10)]
# shuffle the data:
data = [r for s in sessions for r in s]
shuffle(data)
data = sorted(data, key = lambda r: r['Session'])
# create D47data instance:
data47 = D47data(data)
# process D47data instance:
data47.crunch()
data47.standardize()
2.2.1 Plotting the distribution of analyses through time
data47.plot_distribution_of_analyses(filename = 'time_distribution.pdf')
The plot above shows the succession of analyses as if they were all distributed at regular time intervals. See D4xdata.plot_distribution_of_analyses() for how to plot analyses as a function of “true” time (based on the TimeTag for each analysis).
2.2.2 Generating session plots
data47.plot_sessions()
Below is one of the resulting sessions plots. Each cross marker is an analysis. Anchors are in red and unknowns in blue. Short horizontal lines show the nominal Δ47 value for anchors, in red, or the average Δ47 value for unknowns, in blue (overall average for all sessions). Curved grey contours correspond to Δ47 standardization errors in this session.
2.2.3 Plotting Δ47 or Δ48 residuals
data47.plot_residuals(filename = 'residuals.pdf')
Again, note that this plot only shows the succession of analyses as if they were all distributed at regular time intervals.
2.2.4 Checking δ13C and δ18O dispersion
mydata = D47data(virtual_data(
session = 'mysession',
samples = [
dict(Sample = 'ETH-1', N = 4),
dict(Sample = 'ETH-2', N = 4),
dict(Sample = 'ETH-3', N = 4),
dict(Sample = 'MYSAMPLE', N = 8, D47 = 0.6, D48 = 0.1, d13C_VPDB = -4.0, d18O_VPDB = -12.0),
], seed = 123))
mydata.refresh()
mydata.wg()
mydata.crunch()
mydata.plot_bulk_compositions()
D4xdata.plot_bulk_compositions() produces a series of plots, one for each sample, and an additional plot with all samples together. For example, here is the plot for sample MYSAMPLE:
2.3 Use a different set of anchors, change anchor nominal values, and/or change oxygen-17 correction parameters
Nominal values for various carbonate standards are defined in four places:
D4xdata.Nominal_d13C_VPDBD4xdata.Nominal_d18O_VPDBD47data.Nominal_D4x(also accessible throughD47data.Nominal_D47)D48data.Nominal_D4x(also accessible throughD48data.Nominal_D48)
17O correction parameters are defined by:
D4xdata.R13_VPDBD4xdata.R18_VSMOWD4xdata.R18_VPDBD4xdata.LAMBDA_17D4xdata.R17_VSMOWD4xdata.R17_VPDB
When creating a new instance of D47data or D48data, the current values of these variables are copied as properties of the new object. Applying custom values for, e.g., R17_VSMOW and Nominal_D47 can thus be done in several ways:
Option 1: by redefining D4xdata.R17_VSMOW and D47data.Nominal_D47 _before_ creating a D47data object:
from D47crunch import D4xdata, D47data
# redefine R17_VSMOW:
D4xdata.R17_VSMOW = 0.00037 # new value
# redefine R17_VPDB for consistency:
D4xdata.R17_VPDB = D4xdata.R17_VSMOW * (D4xdata.R18_VPDB/D4xdata.R18_VSMOW) ** D4xdata.LAMBDA_17
# edit Nominal_D47 to only include ETH-1/2/3:
D47data.Nominal_D4x = {
a: D47data.Nominal_D4x[a]
for a in ['ETH-1', 'ETH-2', 'ETH-3']
}
# redefine ETH-3:
D47data.Nominal_D4x['ETH-3'] = 0.600
# only now create D47data object:
mydata = D47data()
# check the results:
print(mydata.R17_VSMOW, mydata.R17_VPDB)
print(mydata.Nominal_D47)
# NB: mydata.Nominal_D47 is just an alias for mydata.Nominal_D4x
# should print out:
# 0.00037 0.00037599710894149464
# {'ETH-1': 0.2052, 'ETH-2': 0.2085, 'ETH-3': 0.6}
Option 2: by redefining R17_VSMOW and Nominal_D47 _after_ creating a D47data object:
from D47crunch import D47data
# first create D47data object:
mydata = D47data()
# redefine R17_VSMOW:
mydata.R17_VSMOW = 0.00037 # new value
# redefine R17_VPDB for consistency:
mydata.R17_VPDB = mydata.R17_VSMOW * (mydata.R18_VPDB/mydata.R18_VSMOW) ** mydata.LAMBDA_17
# edit Nominal_D47 to only include ETH-1/2/3:
mydata.Nominal_D47 = {
a: mydata.Nominal_D47[a]
for a in ['ETH-1', 'ETH-2', 'ETH-3']
}
# redefine ETH-3:
mydata.Nominal_D47['ETH-3'] = 0.600
# check the results:
print(mydata.R17_VSMOW, mydata.R17_VPDB)
print(mydata.Nominal_D47)
# should print out:
# 0.00037 0.00037599710894149464
# {'ETH-1': 0.2052, 'ETH-2': 0.2085, 'ETH-3': 0.6}
The two options above are equivalent, but the latter provides a simple way to compare different data processing choices:
from D47crunch import D47data
# create two D47data objects:
foo = D47data()
bar = D47data()
# modify foo in various ways:
foo.LAMBDA_17 = 0.52
foo.R17_VSMOW = 0.00037 # new value
foo.R17_VPDB = foo.R17_VSMOW * (foo.R18_VPDB/foo.R18_VSMOW) ** foo.LAMBDA_17
foo.Nominal_D47 = {
'ETH-1': foo.Nominal_D47['ETH-1'],
'ETH-2': foo.Nominal_D47['ETH-1'],
'IAEA-C2': foo.Nominal_D47['IAEA-C2'],
'INLAB_REF_MATERIAL': 0.666,
}
# now import the same raw data into foo and bar:
foo.read('rawdata.csv')
foo.wg() # compute δ13C, δ18O of working gas
foo.crunch() # compute all δ13C, δ18O and raw Δ47 values
foo.standardize() # compute absolute Δ47 values
bar.read('rawdata.csv')
bar.wg() # compute δ13C, δ18O of working gas
bar.crunch() # compute all δ13C, δ18O and raw Δ47 values
bar.standardize() # compute absolute Δ47 values
# and compare the final results:
foo.table_of_samples(verbose = True, save_to_file = False)
bar.table_of_samples(verbose = True, save_to_file = False)
2.4 Process paired Δ47 and Δ48 values
Purely in terms of data processing, it is not obvious why Δ47 and Δ48 data should not be handled separately. For now, D47crunch uses two independent classes — D47data and D48data — which crunch numbers and deal with standardization in very similar ways. The following example demonstrates how to print out combined outputs for D47data and D48data.
from D47crunch import *
# generate virtual data:
args = dict(
samples = [
dict(Sample = 'ETH-1', N = 3),
dict(Sample = 'ETH-2', N = 3),
dict(Sample = 'ETH-3', N = 3),
dict(Sample = 'FOO', N = 3,
d13C_VPDB = -5., d18O_VPDB = -10.,
D47 = 0.3, D48 = 0.15),
], rD47 = 0.010, rD48 = 0.030)
session1 = virtual_data(session = 'Session_01', **args)
session2 = virtual_data(session = 'Session_02', **args)
# create D47data instance:
data47 = D47data(session1 + session2)
# process D47data instance:
data47.crunch()
data47.standardize()
# create D48data instance:
data48 = D48data(data47) # alternatively: data48 = D48data(session1 + session2)
# process D48data instance:
data48.crunch()
data48.standardize()
# output combined results:
table_of_sessions(data47, data48)
table_of_samples(data47, data48)
table_of_analyses(data47, data48)
Expected output:
–––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– ––––––––––––––– –––––––––––––– –––––– ––––––––––––– ––––––––––––––– ––––––––––––––
Session Na Nu d13Cwg_VPDB d18Owg_VSMOW r_d13C r_d18O r_D47 a_47 ± SE 1e3 x b_47 ± SE c_47 ± SE r_D48 a_48 ± SE 1e3 x b_48 ± SE c_48 ± SE
–––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– ––––––––––––––– –––––––––––––– –––––– ––––––––––––– ––––––––––––––– ––––––––––––––
Session_01 9 3 -4.000 26.000 0.0000 0.0000 0.0098 1.021 ± 0.019 -0.398 ± 0.260 -0.903 ± 0.006 0.0486 0.540 ± 0.151 1.235 ± 0.607 -0.390 ± 0.025
Session_02 9 3 -4.000 26.000 0.0000 0.0000 0.0090 1.015 ± 0.019 0.376 ± 0.260 -0.905 ± 0.006 0.0186 1.350 ± 0.156 -0.871 ± 0.608 -0.504 ± 0.027
–––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– ––––––––––––––– –––––––––––––– –––––– ––––––––––––– ––––––––––––––– ––––––––––––––
–––––– – ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– –––––– –––––– –––––––– –––––– ––––––––
Sample N d13C_VPDB d18O_VSMOW D47 SE 95% CL SD p_Levene D48 SE 95% CL SD p_Levene
–––––– – ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– –––––– –––––– –––––––– –––––– ––––––––
ETH-1 6 2.02 37.02 0.2052 0.0078 0.1380 0.0223
ETH-2 6 -10.17 19.88 0.2085 0.0036 0.1380 0.0482
ETH-3 6 1.71 37.45 0.6132 0.0080 0.2700 0.0176
FOO 6 -5.00 28.91 0.3026 0.0044 ± 0.0093 0.0121 0.164 0.1397 0.0121 ± 0.0255 0.0267 0.127
–––––– – ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– –––––– –––––– –––––––– –––––– ––––––––
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– ––––––––
UID Session Sample d13Cwg_VPDB d18Owg_VSMOW d45 d46 d47 d48 d49 d13C_VPDB d18O_VSMOW D47raw D48raw D49raw D47 D48
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– ––––––––
1 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.120787 21.286237 27.780042 2.020000 37.024281 -0.708176 -0.316435 -0.000013 0.197297 0.087763
2 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.132240 21.307795 27.780042 2.020000 37.024281 -0.696913 -0.295333 -0.000013 0.208328 0.126791
3 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.132438 21.313884 27.780042 2.020000 37.024281 -0.696718 -0.289374 -0.000013 0.208519 0.137813
4 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.700300 -12.210735 -18.023381 -10.170000 19.875825 -0.683938 -0.297902 -0.000002 0.209785 0.198705
5 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.707421 -12.270781 -18.023381 -10.170000 19.875825 -0.691145 -0.358673 -0.000002 0.202726 0.086308
6 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.700061 -12.278310 -18.023381 -10.170000 19.875825 -0.683696 -0.366292 -0.000002 0.210022 0.072215
7 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.684379 22.225827 28.306614 1.710000 37.450394 -0.273094 -0.216392 -0.000014 0.623472 0.270873
8 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.660163 22.233729 28.306614 1.710000 37.450394 -0.296906 -0.208664 -0.000014 0.600150 0.285167
9 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.675191 22.215632 28.306614 1.710000 37.450394 -0.282128 -0.226363 -0.000014 0.614623 0.252432
10 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.328380 5.374933 4.665655 -5.000000 28.907344 -0.582131 -0.288924 -0.000006 0.314928 0.175105
11 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.302220 5.384454 4.665655 -5.000000 28.907344 -0.608241 -0.279457 -0.000006 0.289356 0.192614
12 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.322530 5.372841 4.665655 -5.000000 28.907344 -0.587970 -0.291004 -0.000006 0.309209 0.171257
13 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.140853 21.267202 27.780042 2.020000 37.024281 -0.688442 -0.335067 -0.000013 0.207730 0.138730
14 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.127087 21.256983 27.780042 2.020000 37.024281 -0.701980 -0.345071 -0.000013 0.194396 0.131311
15 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.148253 21.287779 27.780042 2.020000 37.024281 -0.681165 -0.314926 -0.000013 0.214898 0.153668
16 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.715859 -12.204791 -18.023381 -10.170000 19.875825 -0.699685 -0.291887 -0.000002 0.207349 0.149128
17 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.709763 -12.188685 -18.023381 -10.170000 19.875825 -0.693516 -0.275587 -0.000002 0.213426 0.161217
18 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.715427 -12.253049 -18.023381 -10.170000 19.875825 -0.699249 -0.340727 -0.000002 0.207780 0.112907
19 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.685994 22.249463 28.306614 1.710000 37.450394 -0.271506 -0.193275 -0.000014 0.618328 0.244431
20 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.681351 22.298166 28.306614 1.710000 37.450394 -0.276071 -0.145641 -0.000014 0.613831 0.279758
21 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.676169 22.306848 28.306614 1.710000 37.450394 -0.281167 -0.137150 -0.000014 0.608813 0.286056
22 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.324359 5.339497 4.665655 -5.000000 28.907344 -0.586144 -0.324160 -0.000006 0.314015 0.136535
23 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.297658 5.325854 4.665655 -5.000000 28.907344 -0.612794 -0.337727 -0.000006 0.287767 0.126473
24 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.310185 5.339898 4.665655 -5.000000 28.907344 -0.600291 -0.323761 -0.000006 0.300082 0.136830
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API Documentation
1''' 2Standardization and analytical error propagation of Δ47 and Δ48 clumped-isotope measurements 3 4Process and standardize carbonate and/or CO2 clumped-isotope analyses, 5from low-level data out of a dual-inlet mass spectrometer to final, “absolute” 6Δ47 and Δ48 values with fully propagated analytical error estimates 7([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). 8 9The **tutorial** section takes you through a series of simple steps to import/process data and print out the results. 10The **how-to** section provides instructions applicable to various specific tasks. 11 12.. include:: ../docs/tutorial.md 13.. include:: ../docs/howto.md 14 15## API Documentation 16''' 17 18__docformat__ = "restructuredtext" 19__author__ = 'Mathieu Daëron' 20__contact__ = 'daeron@lsce.ipsl.fr' 21__copyright__ = 'Copyright (c) 2023 Mathieu Daëron' 22__license__ = 'Modified BSD License - https://opensource.org/licenses/BSD-3-Clause' 23__date__ = '2023-05-16' 24__version__ = '2.1.1' 25 26import os 27import numpy as np 28from statistics import stdev 29from scipy.stats import t as tstudent 30from scipy.stats import levene 31from scipy.interpolate import interp1d 32from numpy import linalg 33from lmfit import Minimizer, Parameters, report_fit 34from matplotlib import pyplot as ppl 35from datetime import datetime as dt 36from functools import wraps 37from colorsys import hls_to_rgb 38from matplotlib import rcParams 39 40rcParams['font.family'] = 'sans-serif' 41rcParams['font.sans-serif'] = 'Helvetica' 42rcParams['font.size'] = 10 43rcParams['mathtext.fontset'] = 'custom' 44rcParams['mathtext.rm'] = 'sans' 45rcParams['mathtext.bf'] = 'sans:bold' 46rcParams['mathtext.it'] = 'sans:italic' 47rcParams['mathtext.cal'] = 'sans:italic' 48rcParams['mathtext.default'] = 'rm' 49rcParams['xtick.major.size'] = 4 50rcParams['xtick.major.width'] = 1 51rcParams['ytick.major.size'] = 4 52rcParams['ytick.major.width'] = 1 53rcParams['axes.grid'] = False 54rcParams['axes.linewidth'] = 1 55rcParams['grid.linewidth'] = .75 56rcParams['grid.linestyle'] = '-' 57rcParams['grid.alpha'] = .15 58rcParams['savefig.dpi'] = 150 59 60Petersen_etal_CO2eqD47 = np.array([[-12, 1.147113572], [-11, 1.139961218], [-10, 1.132872856], [-9, 1.125847677], [-8, 1.118884889], [-7, 1.111983708], [-6, 1.105143366], [-5, 1.098363105], [-4, 1.091642182], [-3, 1.084979862], [-2, 1.078375423], [-1, 1.071828156], [0, 1.065337360], [1, 1.058902349], [2, 1.052522443], [3, 1.046196976], [4, 1.039925291], [5, 1.033706741], [6, 1.027540690], [7, 1.021426510], [8, 1.015363585], [9, 1.009351306], [10, 1.003389075], [11, 0.997476303], [12, 0.991612409], [13, 0.985796821], [14, 0.980028975], [15, 0.974308318], [16, 0.968634304], [17, 0.963006392], [18, 0.957424055], [19, 0.951886769], [20, 0.946394020], [21, 0.940945302], [22, 0.935540114], [23, 0.930177964], [24, 0.924858369], [25, 0.919580851], [26, 0.914344938], [27, 0.909150167], [28, 0.903996080], [29, 0.898882228], [30, 0.893808167], [31, 0.888773459], [32, 0.883777672], [33, 0.878820382], [34, 0.873901170], [35, 0.869019623], [36, 0.864175334], [37, 0.859367901], [38, 0.854596929], [39, 0.849862028], [40, 0.845162813], [41, 0.840498905], [42, 0.835869931], [43, 0.831275522], [44, 0.826715314], [45, 0.822188950], [46, 0.817696075], [47, 0.813236341], [48, 0.808809404], [49, 0.804414926], [50, 0.800052572], [51, 0.795722012], [52, 0.791422922], [53, 0.787154979], [54, 0.782917869], [55, 0.778711277], [56, 0.774534898], [57, 0.770388426], [58, 0.766271562], [59, 0.762184010], [60, 0.758125479], [61, 0.754095680], [62, 0.750094329], [63, 0.746121147], [64, 0.742175856], [65, 0.738258184], [66, 0.734367860], [67, 0.730504620], [68, 0.726668201], [69, 0.722858343], [70, 0.719074792], [71, 0.715317295], [72, 0.711585602], [73, 0.707879469], [74, 0.704198652], [75, 0.700542912], [76, 0.696912012], [77, 0.693305719], [78, 0.689723802], [79, 0.686166034], [80, 0.682632189], [81, 0.679122047], [82, 0.675635387], [83, 0.672171994], [84, 0.668731654], [85, 0.665314156], [86, 0.661919291], [87, 0.658546854], [88, 0.655196641], [89, 0.651868451], [90, 0.648562087], [91, 0.645277352], [92, 0.642014054], [93, 0.638771999], [94, 0.635551001], [95, 0.632350872], [96, 0.629171428], [97, 0.626012487], [98, 0.622873870], [99, 0.619755397], [100, 0.616656895], [102, 0.610519107], [104, 0.604459143], [106, 0.598475670], [108, 0.592567388], [110, 0.586733026], [112, 0.580971342], [114, 0.575281125], [116, 0.569661187], [118, 0.564110371], [120, 0.558627545], [122, 0.553211600], [124, 0.547861454], [126, 0.542576048], [128, 0.537354347], [130, 0.532195337], [132, 0.527098028], [134, 0.522061450], [136, 0.517084654], [138, 0.512166711], [140, 0.507306712], [142, 0.502503768], [144, 0.497757006], [146, 0.493065573], [148, 0.488428634], [150, 0.483845370], [152, 0.479314980], [154, 0.474836677], [156, 0.470409692], [158, 0.466033271], [160, 0.461706674], [162, 0.457429176], [164, 0.453200067], [166, 0.449018650], [168, 0.444884242], [170, 0.440796174], [172, 0.436753787], [174, 0.432756438], [176, 0.428803494], [178, 0.424894334], [180, 0.421028350], [182, 0.417204944], [184, 0.413423530], [186, 0.409683531], [188, 0.405984383], [190, 0.402325531], [192, 0.398706429], [194, 0.395126543], [196, 0.391585347], [198, 0.388082324], [200, 0.384616967], [202, 0.381188778], [204, 0.377797268], [206, 0.374441954], [208, 0.371122364], [210, 0.367838033], [212, 0.364588505], [214, 0.361373329], [216, 0.358192065], [218, 0.355044277], [220, 0.351929540], [222, 0.348847432], [224, 0.345797540], [226, 0.342779460], [228, 0.339792789], [230, 0.336837136], [232, 0.333912113], [234, 0.331017339], [236, 0.328152439], [238, 0.325317046], [240, 0.322510795], [242, 0.319733329], [244, 0.316984297], [246, 0.314263352], [248, 0.311570153], [250, 0.308904364], [252, 0.306265654], [254, 0.303653699], [256, 0.301068176], [258, 0.298508771], [260, 0.295975171], [262, 0.293467070], [264, 0.290984167], [266, 0.288526163], [268, 0.286092765], [270, 0.283683684], [272, 0.281298636], [274, 0.278937339], [276, 0.276599517], [278, 0.274284898], [280, 0.271993211], [282, 0.269724193], [284, 0.267477582], [286, 0.265253121], [288, 0.263050554], [290, 0.260869633], [292, 0.258710110], [294, 0.256571741], [296, 0.254454286], [298, 0.252357508], [300, 0.250281174], [302, 0.248225053], [304, 0.246188917], [306, 0.244172542], [308, 0.242175707], [310, 0.240198194], [312, 0.238239786], [314, 0.236300272], [316, 0.234379441], [318, 0.232477087], [320, 0.230593005], [322, 0.228726993], [324, 0.226878853], [326, 0.225048388], [328, 0.223235405], [330, 0.221439711], [332, 0.219661118], [334, 0.217899439], [336, 0.216154491], [338, 0.214426091], [340, 0.212714060], [342, 0.211018220], [344, 0.209338398], [346, 0.207674420], [348, 0.206026115], [350, 0.204393315], [355, 0.200378063], [360, 0.196456139], [365, 0.192625077], [370, 0.188882487], [375, 0.185226048], [380, 0.181653511], [385, 0.178162694], [390, 0.174751478], [395, 0.171417807], [400, 0.168159686], [405, 0.164975177], [410, 0.161862398], [415, 0.158819521], [420, 0.155844772], [425, 0.152936426], [430, 0.150092806], [435, 0.147312286], [440, 0.144593281], [445, 0.141934254], [450, 0.139333710], [455, 0.136790195], [460, 0.134302294], [465, 0.131868634], [470, 0.129487876], [475, 0.127158722], [480, 0.124879906], [485, 0.122650197], [490, 0.120468398], [495, 0.118333345], [500, 0.116243903], [505, 0.114198970], [510, 0.112197471], [515, 0.110238362], [520, 0.108320625], [525, 0.106443271], [530, 0.104605335], [535, 0.102805877], [540, 0.101043985], [545, 0.099318768], [550, 0.097629359], [555, 0.095974915], [560, 0.094354612], [565, 0.092767650], [570, 0.091213248], [575, 0.089690648], [580, 0.088199108], [585, 0.086737906], [590, 0.085306341], [595, 0.083903726], [600, 0.082529395], [605, 0.081182697], [610, 0.079862998], [615, 0.078569680], [620, 0.077302141], [625, 0.076059794], [630, 0.074842066], [635, 0.073648400], [640, 0.072478251], [645, 0.071331090], [650, 0.070206399], [655, 0.069103674], [660, 0.068022424], [665, 0.066962168], [670, 0.065922439], [675, 0.064902780], [680, 0.063902748], [685, 0.062921909], [690, 0.061959837], [695, 0.061016122], [700, 0.060090360], [705, 0.059182157], [710, 0.058291131], [715, 0.057416907], [720, 0.056559120], [725, 0.055717414], [730, 0.054891440], [735, 0.054080860], [740, 0.053285343], [745, 0.052504565], [750, 0.051738210], [755, 0.050985971], [760, 0.050247546], [765, 0.049522643], [770, 0.048810974], [775, 0.048112260], [780, 0.047426227], [785, 0.046752609], [790, 0.046091145], [795, 0.045441581], [800, 0.044803668], [805, 0.044177164], [810, 0.043561831], [815, 0.042957438], [820, 0.042363759], [825, 0.041780573], [830, 0.041207664], [835, 0.040644822], [840, 0.040091839], [845, 0.039548516], [850, 0.039014654], [855, 0.038490063], [860, 0.037974554], [865, 0.037467944], [870, 0.036970054], [875, 0.036480707], [880, 0.035999734], [885, 0.035526965], [890, 0.035062238], [895, 0.034605393], [900, 0.034156272], [905, 0.033714724], [910, 0.033280598], [915, 0.032853749], [920, 0.032434032], [925, 0.032021309], [930, 0.031615443], [935, 0.031216300], [940, 0.030823749], [945, 0.030437663], [950, 0.030057915], [955, 0.029684385], [960, 0.029316951], [965, 0.028955498], [970, 0.028599910], [975, 0.028250075], [980, 0.027905884], [985, 0.027567229], [990, 0.027234006], [995, 0.026906112], [1000, 0.026583445], [1005, 0.026265908], [1010, 0.025953405], [1015, 0.025645841], [1020, 0.025343124], [1025, 0.025045163], [1030, 0.024751871], [1035, 0.024463160], [1040, 0.024178947], [1045, 0.023899147], [1050, 0.023623680], [1055, 0.023352467], [1060, 0.023085429], [1065, 0.022822491], [1070, 0.022563577], [1075, 0.022308615], [1080, 0.022057533], [1085, 0.021810260], [1090, 0.021566729], [1095, 0.021326872], [1100, 0.021090622]]) 61_fCO2eqD47_Petersen = interp1d(Petersen_etal_CO2eqD47[:,0], Petersen_etal_CO2eqD47[:,1]) 62def fCO2eqD47_Petersen(T): 63 ''' 64 CO2 equilibrium Δ47 value as a function of T (in degrees C) 65 according to [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127). 66 67 ''' 68 return float(_fCO2eqD47_Petersen(T)) 69 70 71Wang_etal_CO2eqD47 = np.array([[-83., 1.8954], [-73., 1.7530], [-63., 1.6261], [-53., 1.5126], [-43., 1.4104], [-33., 1.3182], [-23., 1.2345], [-13., 1.1584], [-3., 1.0888], [7., 1.0251], [17., 0.9665], [27., 0.9125], [37., 0.8626], [47., 0.8164], [57., 0.7734], [67., 0.7334], [87., 0.6612], [97., 0.6286], [107., 0.5980], [117., 0.5693], [127., 0.5423], [137., 0.5169], [147., 0.4930], [157., 0.4704], [167., 0.4491], [177., 0.4289], [187., 0.4098], [197., 0.3918], [207., 0.3747], [217., 0.3585], [227., 0.3431], [237., 0.3285], [247., 0.3147], [257., 0.3015], [267., 0.2890], [277., 0.2771], [287., 0.2657], [297., 0.2550], [307., 0.2447], [317., 0.2349], [327., 0.2256], [337., 0.2167], [347., 0.2083], [357., 0.2002], [367., 0.1925], [377., 0.1851], [387., 0.1781], [397., 0.1714], [407., 0.1650], [417., 0.1589], [427., 0.1530], [437., 0.1474], [447., 0.1421], [457., 0.1370], [467., 0.1321], [477., 0.1274], [487., 0.1229], [497., 0.1186], [507., 0.1145], [517., 0.1105], [527., 0.1068], [537., 0.1031], [547., 0.0997], [557., 0.0963], [567., 0.0931], [577., 0.0901], [587., 0.0871], [597., 0.0843], [607., 0.0816], [617., 0.0790], [627., 0.0765], [637., 0.0741], [647., 0.0718], [657., 0.0695], [667., 0.0674], [677., 0.0654], [687., 0.0634], [697., 0.0615], [707., 0.0597], [717., 0.0579], [727., 0.0562], [737., 0.0546], [747., 0.0530], [757., 0.0515], [767., 0.0500], [777., 0.0486], [787., 0.0472], [797., 0.0459], [807., 0.0447], [817., 0.0435], [827., 0.0423], [837., 0.0411], [847., 0.0400], [857., 0.0390], [867., 0.0380], [877., 0.0370], [887., 0.0360], [897., 0.0351], [907., 0.0342], [917., 0.0333], [927., 0.0325], [937., 0.0317], [947., 0.0309], [957., 0.0302], [967., 0.0294], [977., 0.0287], [987., 0.0281], [997., 0.0274], [1007., 0.0268], [1017., 0.0261], [1027., 0.0255], [1037., 0.0249], [1047., 0.0244], [1057., 0.0238], [1067., 0.0233], [1077., 0.0228], [1087., 0.0223], [1097., 0.0218]]) 72_fCO2eqD47_Wang = interp1d(Wang_etal_CO2eqD47[:,0] - 0.15, Wang_etal_CO2eqD47[:,1]) 73def fCO2eqD47_Wang(T): 74 ''' 75 CO2 equilibrium Δ47 value as a function of `T` (in degrees C) 76 according to [Wang et al. (2004)](https://doi.org/10.1016/j.gca.2004.05.039) 77 (supplementary data of [Dennis et al., 2011](https://doi.org/10.1016/j.gca.2011.09.025)). 78 ''' 79 return float(_fCO2eqD47_Wang(T)) 80 81 82def correlated_sum(X, C, w = None): 83 ''' 84 Compute covariance-aware linear combinations 85 86 **Parameters** 87 88 + `X`: list or 1-D array of values to sum 89 + `C`: covariance matrix for the elements of `X` 90 + `w`: list or 1-D array of weights to apply to the elements of `X` 91 (all equal to 1 by default) 92 93 Return the sum (and its SE) of the elements of `X`, with optional weights equal 94 to the elements of `w`, accounting for covariances between the elements of `X`. 95 ''' 96 if w is None: 97 w = [1 for x in X] 98 return np.dot(w,X), (np.dot(w,np.dot(C,w)))**.5 99 100 101def make_csv(x, hsep = ',', vsep = '\n'): 102 ''' 103 Formats a list of lists of strings as a CSV 104 105 **Parameters** 106 107 + `x`: the list of lists of strings to format 108 + `hsep`: the field separator (`,` by default) 109 + `vsep`: the line-ending convention to use (`\\n` by default) 110 111 **Example** 112 113 ```py 114 print(make_csv([['a', 'b', 'c'], ['d', 'e', 'f']])) 115 ``` 116 117 outputs: 118 119 ```py 120 a,b,c 121 d,e,f 122 ``` 123 ''' 124 return vsep.join([hsep.join(l) for l in x]) 125 126 127def pf(txt): 128 ''' 129 Modify string `txt` to follow `lmfit.Parameter()` naming rules. 130 ''' 131 return txt.replace('-','_').replace('.','_').replace(' ','_') 132 133 134def smart_type(x): 135 ''' 136 Tries to convert string `x` to a float if it includes a decimal point, or 137 to an integer if it does not. If both attempts fail, return the original 138 string unchanged. 139 ''' 140 try: 141 y = float(x) 142 except ValueError: 143 return x 144 if '.' not in x: 145 return int(y) 146 return y 147 148 149def pretty_table(x, header = 1, hsep = ' ', vsep = '–', align = '<'): 150 ''' 151 Reads a list of lists of strings and outputs an ascii table 152 153 **Parameters** 154 155 + `x`: a list of lists of strings 156 + `header`: the number of lines to treat as header lines 157 + `hsep`: the horizontal separator between columns 158 + `vsep`: the character to use as vertical separator 159 + `align`: string of left (`<`) or right (`>`) alignment characters. 160 161 **Example** 162 163 ```py 164 x = [['A', 'B', 'C'], ['1', '1.9999', 'foo'], ['10', 'x', 'bar']] 165 print(pretty_table(x)) 166 ``` 167 yields: 168 ``` 169 -- ------ --- 170 A B C 171 -- ------ --- 172 1 1.9999 foo 173 10 x bar 174 -- ------ --- 175 ``` 176 177 ''' 178 txt = [] 179 widths = [np.max([len(e) for e in c]) for c in zip(*x)] 180 181 if len(widths) > len(align): 182 align += '>' * (len(widths)-len(align)) 183 sepline = hsep.join([vsep*w for w in widths]) 184 txt += [sepline] 185 for k,l in enumerate(x): 186 if k and k == header: 187 txt += [sepline] 188 txt += [hsep.join([f'{e:{a}{w}}' for e, w, a in zip(l, widths, align)])] 189 txt += [sepline] 190 txt += [''] 191 return '\n'.join(txt) 192 193 194def transpose_table(x): 195 ''' 196 Transpose a list if lists 197 198 **Parameters** 199 200 + `x`: a list of lists 201 202 **Example** 203 204 ```py 205 x = [[1, 2], [3, 4]] 206 print(transpose_table(x)) # yields: [[1, 3], [2, 4]] 207 ``` 208 ''' 209 return [[e for e in c] for c in zip(*x)] 210 211 212def w_avg(X, sX) : 213 ''' 214 Compute variance-weighted average 215 216 Returns the value and SE of the weighted average of the elements of `X`, 217 with relative weights equal to their inverse variances (`1/sX**2`). 218 219 **Parameters** 220 221 + `X`: array-like of elements to average 222 + `sX`: array-like of the corresponding SE values 223 224 **Tip** 225 226 If `X` and `sX` are initially arranged as a list of `(x, sx)` doublets, 227 they may be rearranged using `zip()`: 228 229 ```python 230 foo = [(0, 1), (1, 0.5), (2, 0.5)] 231 print(w_avg(*zip(*foo))) # yields: (1.3333333333333333, 0.3333333333333333) 232 ``` 233 ''' 234 X = [ x for x in X ] 235 sX = [ sx for sx in sX ] 236 W = [ sx**-2 for sx in sX ] 237 W = [ w/sum(W) for w in W ] 238 Xavg = sum([ w*x for w,x in zip(W,X) ]) 239 sXavg = sum([ w**2*sx**2 for w,sx in zip(W,sX) ])**.5 240 return Xavg, sXavg 241 242 243def read_csv(filename, sep = ''): 244 ''' 245 Read contents of `filename` in csv format and return a list of dictionaries. 246 247 In the csv string, spaces before and after field separators (`','` by default) 248 are optional. 249 250 **Parameters** 251 252 + `filename`: the csv file to read 253 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`, 254 whichever appers most often in the contents of `filename`. 255 ''' 256 with open(filename) as fid: 257 txt = fid.read() 258 259 if sep == '': 260 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0] 261 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()] 262 return [{k: smart_type(v) for k,v in zip(txt[0], l) if v} for l in txt[1:]] 263 264 265def simulate_single_analysis( 266 sample = 'MYSAMPLE', 267 d13Cwg_VPDB = -4., d18Owg_VSMOW = 26., 268 d13C_VPDB = None, d18O_VPDB = None, 269 D47 = None, D48 = None, D49 = 0., D17O = 0., 270 a47 = 1., b47 = 0., c47 = -0.9, 271 a48 = 1., b48 = 0., c48 = -0.45, 272 Nominal_D47 = None, 273 Nominal_D48 = None, 274 Nominal_d13C_VPDB = None, 275 Nominal_d18O_VPDB = None, 276 ALPHA_18O_ACID_REACTION = None, 277 R13_VPDB = None, 278 R17_VSMOW = None, 279 R18_VSMOW = None, 280 LAMBDA_17 = None, 281 R18_VPDB = None, 282 ): 283 ''' 284 Compute working-gas delta values for a single analysis, assuming a stochastic working 285 gas and a “perfect” measurement (i.e. raw Δ values are identical to absolute values). 286 287 **Parameters** 288 289 + `sample`: sample name 290 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas 291 (respectively –4 and +26 ‰ by default) 292 + `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample 293 + `D47`, `D48`, `D49`, `D17O`: clumped-isotope and oxygen-17 anomalies 294 of the carbonate sample 295 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and 296 Δ48 values if `D47` or `D48` are not specified 297 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and 298 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified 299 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor 300 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17 301 correction parameters (by default equal to the `D4xdata` default values) 302 303 Returns a dictionary with fields 304 `['Sample', 'D17O', 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'd45', 'd46', 'd47', 'd48', 'd49']`. 305 ''' 306 307 if Nominal_d13C_VPDB is None: 308 Nominal_d13C_VPDB = D4xdata().Nominal_d13C_VPDB 309 310 if Nominal_d18O_VPDB is None: 311 Nominal_d18O_VPDB = D4xdata().Nominal_d18O_VPDB 312 313 if ALPHA_18O_ACID_REACTION is None: 314 ALPHA_18O_ACID_REACTION = D4xdata().ALPHA_18O_ACID_REACTION 315 316 if R13_VPDB is None: 317 R13_VPDB = D4xdata().R13_VPDB 318 319 if R17_VSMOW is None: 320 R17_VSMOW = D4xdata().R17_VSMOW 321 322 if R18_VSMOW is None: 323 R18_VSMOW = D4xdata().R18_VSMOW 324 325 if LAMBDA_17 is None: 326 LAMBDA_17 = D4xdata().LAMBDA_17 327 328 if R18_VPDB is None: 329 R18_VPDB = D4xdata().R18_VPDB 330 331 R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW) ** LAMBDA_17 332 333 if Nominal_D47 is None: 334 Nominal_D47 = D47data().Nominal_D47 335 336 if Nominal_D48 is None: 337 Nominal_D48 = D48data().Nominal_D48 338 339 if d13C_VPDB is None: 340 if sample in Nominal_d13C_VPDB: 341 d13C_VPDB = Nominal_d13C_VPDB[sample] 342 else: 343 raise KeyError(f"Sample {sample} is missing d13C_VDP value, and it is not defined in Nominal_d13C_VDP.") 344 345 if d18O_VPDB is None: 346 if sample in Nominal_d18O_VPDB: 347 d18O_VPDB = Nominal_d18O_VPDB[sample] 348 else: 349 raise KeyError(f"Sample {sample} is missing d18O_VPDB value, and it is not defined in Nominal_d18O_VPDB.") 350 351 if D47 is None: 352 if sample in Nominal_D47: 353 D47 = Nominal_D47[sample] 354 else: 355 raise KeyError(f"Sample {sample} is missing D47 value, and it is not defined in Nominal_D47.") 356 357 if D48 is None: 358 if sample in Nominal_D48: 359 D48 = Nominal_D48[sample] 360 else: 361 raise KeyError(f"Sample {sample} is missing D48 value, and it is not defined in Nominal_D48.") 362 363 X = D4xdata() 364 X.R13_VPDB = R13_VPDB 365 X.R17_VSMOW = R17_VSMOW 366 X.R18_VSMOW = R18_VSMOW 367 X.LAMBDA_17 = LAMBDA_17 368 X.R18_VPDB = R18_VPDB 369 X.R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW)**LAMBDA_17 370 371 R45wg, R46wg, R47wg, R48wg, R49wg = X.compute_isobar_ratios( 372 R13 = R13_VPDB * (1 + d13Cwg_VPDB/1000), 373 R18 = R18_VSMOW * (1 + d18Owg_VSMOW/1000), 374 ) 375 R45, R46, R47, R48, R49 = X.compute_isobar_ratios( 376 R13 = R13_VPDB * (1 + d13C_VPDB/1000), 377 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION, 378 D17O=D17O, D47=D47, D48=D48, D49=D49, 379 ) 380 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = X.compute_isobar_ratios( 381 R13 = R13_VPDB * (1 + d13C_VPDB/1000), 382 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION, 383 D17O=D17O, 384 ) 385 386 d45 = 1000 * (R45/R45wg - 1) 387 d46 = 1000 * (R46/R46wg - 1) 388 d47 = 1000 * (R47/R47wg - 1) 389 d48 = 1000 * (R48/R48wg - 1) 390 d49 = 1000 * (R49/R49wg - 1) 391 392 for k in range(3): # dumb iteration to adjust for small changes in d47 393 R47raw = (1 + (a47 * D47 + b47 * d47 + c47)/1000) * R47stoch 394 R48raw = (1 + (a48 * D48 + b48 * d48 + c48)/1000) * R48stoch 395 d47 = 1000 * (R47raw/R47wg - 1) 396 d48 = 1000 * (R48raw/R48wg - 1) 397 398 return dict( 399 Sample = sample, 400 D17O = D17O, 401 d13Cwg_VPDB = d13Cwg_VPDB, 402 d18Owg_VSMOW = d18Owg_VSMOW, 403 d45 = d45, 404 d46 = d46, 405 d47 = d47, 406 d48 = d48, 407 d49 = d49, 408 ) 409 410 411def virtual_data( 412 samples = [], 413 a47 = 1., b47 = 0., c47 = -0.9, 414 a48 = 1., b48 = 0., c48 = -0.45, 415 rd45 = 0.020, rd46 = 0.060, 416 rD47 = 0.015, rD48 = 0.045, 417 d13Cwg_VPDB = None, d18Owg_VSMOW = None, 418 session = None, 419 Nominal_D47 = None, Nominal_D48 = None, 420 Nominal_d13C_VPDB = None, Nominal_d18O_VPDB = None, 421 ALPHA_18O_ACID_REACTION = None, 422 R13_VPDB = None, 423 R17_VSMOW = None, 424 R18_VSMOW = None, 425 LAMBDA_17 = None, 426 R18_VPDB = None, 427 seed = 0, 428 ): 429 ''' 430 Return list with simulated analyses from a single session. 431 432 **Parameters** 433 434 + `samples`: a list of entries; each entry is a dictionary with the following fields: 435 * `Sample`: the name of the sample 436 * `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample 437 * `D47`, `D48`, `D49`, `D17O` (all optional): clumped-isotope and oxygen-17 anomalies of the carbonate sample 438 * `N`: how many analyses to generate for this sample 439 + `a47`: scrambling factor for Δ47 440 + `b47`: compositional nonlinearity for Δ47 441 + `c47`: working gas offset for Δ47 442 + `a48`: scrambling factor for Δ48 443 + `b48`: compositional nonlinearity for Δ48 444 + `c48`: working gas offset for Δ48 445 + `rd45`: analytical repeatability of δ45 446 + `rd46`: analytical repeatability of δ46 447 + `rD47`: analytical repeatability of Δ47 448 + `rD48`: analytical repeatability of Δ48 449 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas 450 (by default equal to the `simulate_single_analysis` default values) 451 + `session`: name of the session (no name by default) 452 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and Δ48 values 453 if `D47` or `D48` are not specified (by default equal to the `simulate_single_analysis` defaults) 454 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and 455 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified 456 (by default equal to the `simulate_single_analysis` defaults) 457 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor 458 (by default equal to the `simulate_single_analysis` defaults) 459 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17 460 correction parameters (by default equal to the `simulate_single_analysis` default) 461 + `seed`: explicitly set to a non-zero value to achieve random but repeatable simulations 462 463 464 Here is an example of using this method to generate an arbitrary combination of 465 anchors and unknowns for a bunch of sessions: 466 467 ```py 468 args = dict( 469 samples = [ 470 dict(Sample = 'ETH-1', N = 4), 471 dict(Sample = 'ETH-2', N = 5), 472 dict(Sample = 'ETH-3', N = 6), 473 dict(Sample = 'FOO', N = 2, 474 d13C_VPDB = -5., d18O_VPDB = -10., 475 D47 = 0.3, D48 = 0.15), 476 ], rD47 = 0.010, rD48 = 0.030) 477 478 session1 = virtual_data(session = 'Session_01', **args, seed = 123) 479 session2 = virtual_data(session = 'Session_02', **args, seed = 1234) 480 session3 = virtual_data(session = 'Session_03', **args, seed = 12345) 481 session4 = virtual_data(session = 'Session_04', **args, seed = 123456) 482 483 D = D47data(session1 + session2 + session3 + session4) 484 485 D.crunch() 486 D.standardize() 487 488 D.table_of_sessions(verbose = True, save_to_file = False) 489 D.table_of_samples(verbose = True, save_to_file = False) 490 D.table_of_analyses(verbose = True, save_to_file = False) 491 ``` 492 493 This should output something like: 494 495 ``` 496 [table_of_sessions] 497 –––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– –––––––––––––– 498 Session Na Nu d13Cwg_VPDB d18Owg_VSMOW r_d13C r_d18O r_D47 a ± SE 1e3 x b ± SE c ± SE 499 –––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– –––––––––––––– 500 Session_01 15 2 -4.000 26.000 0.0000 0.0000 0.0110 0.997 ± 0.017 -0.097 ± 0.244 -0.896 ± 0.006 501 Session_02 15 2 -4.000 26.000 0.0000 0.0000 0.0109 1.002 ± 0.017 -0.110 ± 0.244 -0.901 ± 0.006 502 Session_03 15 2 -4.000 26.000 0.0000 0.0000 0.0107 1.010 ± 0.017 -0.037 ± 0.244 -0.904 ± 0.006 503 Session_04 15 2 -4.000 26.000 0.0000 0.0000 0.0106 1.001 ± 0.017 -0.181 ± 0.244 -0.894 ± 0.006 504 –––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– –––––––––––––– 505 506 [table_of_samples] 507 –––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– 508 Sample N d13C_VPDB d18O_VSMOW D47 SE 95% CL SD p_Levene 509 –––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– 510 ETH-1 16 2.02 37.02 0.2052 0.0079 511 ETH-2 20 -10.17 19.88 0.2085 0.0100 512 ETH-3 24 1.71 37.45 0.6132 0.0105 513 FOO 8 -5.00 28.91 0.2989 0.0040 ± 0.0080 0.0101 0.638 514 –––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– 515 516 [table_of_analyses] 517 ––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– 518 UID Session Sample d13Cwg_VPDB d18Owg_VSMOW d45 d46 d47 d48 d49 d13C_VPDB d18O_VSMOW D47raw D48raw D49raw D47 519 ––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– 520 1 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.122986 21.273526 27.780042 2.020000 37.024281 -0.706013 -0.328878 -0.000013 0.192554 521 2 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.130144 21.282615 27.780042 2.020000 37.024281 -0.698974 -0.319981 -0.000013 0.199615 522 3 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.149219 21.299572 27.780042 2.020000 37.024281 -0.680215 -0.303383 -0.000013 0.218429 523 4 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.136616 21.233128 27.780042 2.020000 37.024281 -0.692609 -0.368421 -0.000013 0.205998 524 5 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.697171 -12.203054 -18.023381 -10.170000 19.875825 -0.680771 -0.290128 -0.000002 0.215054 525 6 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701124 -12.184422 -18.023381 -10.170000 19.875825 -0.684772 -0.271272 -0.000002 0.211041 526 7 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.715105 -12.195251 -18.023381 -10.170000 19.875825 -0.698923 -0.282232 -0.000002 0.196848 527 8 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701529 -12.204963 -18.023381 -10.170000 19.875825 -0.685182 -0.292061 -0.000002 0.210630 528 9 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.711420 -12.228478 -18.023381 -10.170000 19.875825 -0.695193 -0.315859 -0.000002 0.200589 529 10 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.666719 22.296486 28.306614 1.710000 37.450394 -0.290459 -0.147284 -0.000014 0.609363 530 11 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.671553 22.291060 28.306614 1.710000 37.450394 -0.285706 -0.152592 -0.000014 0.614130 531 12 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.652854 22.273271 28.306614 1.710000 37.450394 -0.304093 -0.169990 -0.000014 0.595689 532 13 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.684168 22.263156 28.306614 1.710000 37.450394 -0.273302 -0.179883 -0.000014 0.626572 533 14 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.662702 22.253578 28.306614 1.710000 37.450394 -0.294409 -0.189251 -0.000014 0.605401 534 15 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.681957 22.230907 28.306614 1.710000 37.450394 -0.275476 -0.211424 -0.000014 0.624391 535 16 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.312044 5.395798 4.665655 -5.000000 28.907344 -0.598436 -0.268176 -0.000006 0.298996 536 17 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.328123 5.307086 4.665655 -5.000000 28.907344 -0.582387 -0.356389 -0.000006 0.315092 537 18 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.122201 21.340606 27.780042 2.020000 37.024281 -0.706785 -0.263217 -0.000013 0.195135 538 19 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.134868 21.305714 27.780042 2.020000 37.024281 -0.694328 -0.297370 -0.000013 0.207564 539 20 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.140008 21.261931 27.780042 2.020000 37.024281 -0.689273 -0.340227 -0.000013 0.212607 540 21 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.135540 21.298472 27.780042 2.020000 37.024281 -0.693667 -0.304459 -0.000013 0.208224 541 22 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701213 -12.202602 -18.023381 -10.170000 19.875825 -0.684862 -0.289671 -0.000002 0.213842 542 23 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.685649 -12.190405 -18.023381 -10.170000 19.875825 -0.669108 -0.277327 -0.000002 0.229559 543 24 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.719003 -12.257955 -18.023381 -10.170000 19.875825 -0.702869 -0.345692 -0.000002 0.195876 544 25 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.700592 -12.204641 -18.023381 -10.170000 19.875825 -0.684233 -0.291735 -0.000002 0.214469 545 26 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.720426 -12.214561 -18.023381 -10.170000 19.875825 -0.704308 -0.301774 -0.000002 0.194439 546 27 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.673044 22.262090 28.306614 1.710000 37.450394 -0.284240 -0.180926 -0.000014 0.616730 547 28 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.666542 22.263401 28.306614 1.710000 37.450394 -0.290634 -0.179643 -0.000014 0.610350 548 29 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.680487 22.243486 28.306614 1.710000 37.450394 -0.276921 -0.199121 -0.000014 0.624031 549 30 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.663900 22.245175 28.306614 1.710000 37.450394 -0.293231 -0.197469 -0.000014 0.607759 550 31 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.674379 22.301309 28.306614 1.710000 37.450394 -0.282927 -0.142568 -0.000014 0.618039 551 32 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.660825 22.270466 28.306614 1.710000 37.450394 -0.296255 -0.172733 -0.000014 0.604742 552 33 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.294076 5.349940 4.665655 -5.000000 28.907344 -0.616369 -0.313776 -0.000006 0.283707 553 34 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.313775 5.292121 4.665655 -5.000000 28.907344 -0.596708 -0.371269 -0.000006 0.303323 554 35 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.121613 21.259909 27.780042 2.020000 37.024281 -0.707364 -0.342207 -0.000013 0.194934 555 36 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.145714 21.304889 27.780042 2.020000 37.024281 -0.683661 -0.298178 -0.000013 0.218401 556 37 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.126573 21.325093 27.780042 2.020000 37.024281 -0.702485 -0.278401 -0.000013 0.199764 557 38 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.132057 21.323211 27.780042 2.020000 37.024281 -0.697092 -0.280244 -0.000013 0.205104 558 39 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.708448 -12.232023 -18.023381 -10.170000 19.875825 -0.692185 -0.319447 -0.000002 0.208915 559 40 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.714417 -12.202504 -18.023381 -10.170000 19.875825 -0.698226 -0.289572 -0.000002 0.202934 560 41 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.720039 -12.264469 -18.023381 -10.170000 19.875825 -0.703917 -0.352285 -0.000002 0.197300 561 42 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701953 -12.228550 -18.023381 -10.170000 19.875825 -0.685611 -0.315932 -0.000002 0.215423 562 43 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.704535 -12.213634 -18.023381 -10.170000 19.875825 -0.688224 -0.300836 -0.000002 0.212837 563 44 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.652920 22.230043 28.306614 1.710000 37.450394 -0.304028 -0.212269 -0.000014 0.594265 564 45 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.691485 22.261017 28.306614 1.710000 37.450394 -0.266106 -0.181975 -0.000014 0.631810 565 46 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.679119 22.305357 28.306614 1.710000 37.450394 -0.278266 -0.138609 -0.000014 0.619771 566 47 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.663623 22.327286 28.306614 1.710000 37.450394 -0.293503 -0.117161 -0.000014 0.604685 567 48 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.678524 22.282103 28.306614 1.710000 37.450394 -0.278851 -0.161352 -0.000014 0.619192 568 49 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.666246 22.283361 28.306614 1.710000 37.450394 -0.290925 -0.160121 -0.000014 0.607238 569 50 Session_03 FOO -4.000 26.000 -0.840413 2.828738 1.309929 5.340249 4.665655 -5.000000 28.907344 -0.600546 -0.323413 -0.000006 0.300148 570 51 Session_03 FOO -4.000 26.000 -0.840413 2.828738 1.317548 5.334102 4.665655 -5.000000 28.907344 -0.592942 -0.329524 -0.000006 0.307676 571 52 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.136865 21.300298 27.780042 2.020000 37.024281 -0.692364 -0.302672 -0.000013 0.204033 572 53 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.133538 21.291260 27.780042 2.020000 37.024281 -0.695637 -0.311519 -0.000013 0.200762 573 54 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.139991 21.319865 27.780042 2.020000 37.024281 -0.689290 -0.283519 -0.000013 0.207107 574 55 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.145748 21.330075 27.780042 2.020000 37.024281 -0.683629 -0.273524 -0.000013 0.212766 575 56 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.702989 -12.202762 -18.023381 -10.170000 19.875825 -0.686660 -0.289833 -0.000002 0.204507 576 57 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.692830 -12.240287 -18.023381 -10.170000 19.875825 -0.676377 -0.327811 -0.000002 0.214786 577 58 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.702899 -12.180291 -18.023381 -10.170000 19.875825 -0.686568 -0.267091 -0.000002 0.204598 578 59 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.709282 -12.282257 -18.023381 -10.170000 19.875825 -0.693029 -0.370287 -0.000002 0.198140 579 60 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.679330 -12.235994 -18.023381 -10.170000 19.875825 -0.662712 -0.323466 -0.000002 0.228446 580 61 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.695594 22.238663 28.306614 1.710000 37.450394 -0.262066 -0.203838 -0.000014 0.634200 581 62 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.663504 22.286354 28.306614 1.710000 37.450394 -0.293620 -0.157194 -0.000014 0.602656 582 63 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.666457 22.254290 28.306614 1.710000 37.450394 -0.290717 -0.188555 -0.000014 0.605558 583 64 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.666910 22.223232 28.306614 1.710000 37.450394 -0.290271 -0.218930 -0.000014 0.606004 584 65 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.679662 22.257256 28.306614 1.710000 37.450394 -0.277732 -0.185653 -0.000014 0.618539 585 66 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.676768 22.267680 28.306614 1.710000 37.450394 -0.280578 -0.175459 -0.000014 0.615693 586 67 Session_04 FOO -4.000 26.000 -0.840413 2.828738 1.307663 5.317330 4.665655 -5.000000 28.907344 -0.602808 -0.346202 -0.000006 0.290853 587 68 Session_04 FOO -4.000 26.000 -0.840413 2.828738 1.308562 5.331400 4.665655 -5.000000 28.907344 -0.601911 -0.332212 -0.000006 0.291749 588 ––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– 589 ``` 590 ''' 591 592 kwargs = locals().copy() 593 594 from numpy import random as nprandom 595 if seed: 596 rng = nprandom.default_rng(seed) 597 else: 598 rng = nprandom.default_rng() 599 600 N = sum([s['N'] for s in samples]) 601 errors45 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 602 errors45 *= rd45 / stdev(errors45) # scale errors to rd45 603 errors46 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 604 errors46 *= rd46 / stdev(errors46) # scale errors to rd46 605 errors47 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 606 errors47 *= rD47 / stdev(errors47) # scale errors to rD47 607 errors48 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 608 errors48 *= rD48 / stdev(errors48) # scale errors to rD48 609 610 k = 0 611 out = [] 612 for s in samples: 613 kw = {} 614 kw['sample'] = s['Sample'] 615 kw = { 616 **kw, 617 **{var: kwargs[var] 618 for var in [ 619 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'ALPHA_18O_ACID_REACTION', 620 'Nominal_D47', 'Nominal_D48', 'Nominal_d13C_VPDB', 'Nominal_d18O_VPDB', 621 'R13_VPDB', 'R17_VSMOW', 'R18_VSMOW', 'LAMBDA_17', 'R18_VPDB', 622 'a47', 'b47', 'c47', 'a48', 'b48', 'c48', 623 ] 624 if kwargs[var] is not None}, 625 **{var: s[var] 626 for var in ['d13C_VPDB', 'd18O_VPDB', 'D47', 'D48', 'D49', 'D17O'] 627 if var in s}, 628 } 629 630 sN = s['N'] 631 while sN: 632 out.append(simulate_single_analysis(**kw)) 633 out[-1]['d45'] += errors45[k] 634 out[-1]['d46'] += errors46[k] 635 out[-1]['d47'] += (errors45[k] + errors46[k] + errors47[k]) * a47 636 out[-1]['d48'] += (2*errors46[k] + errors48[k]) * a48 637 sN -= 1 638 k += 1 639 640 if session is not None: 641 for r in out: 642 r['Session'] = session 643 return out 644 645def table_of_samples( 646 data47 = None, 647 data48 = None, 648 dir = 'output', 649 filename = None, 650 save_to_file = True, 651 print_out = True, 652 output = None, 653 ): 654 ''' 655 Print out, save to disk and/or return a combined table of samples 656 for a pair of `D47data` and `D48data` objects. 657 658 **Parameters** 659 660 + `data47`: `D47data` instance 661 + `data48`: `D48data` instance 662 + `dir`: the directory in which to save the table 663 + `filename`: the name to the csv file to write to 664 + `save_to_file`: whether to save the table to disk 665 + `print_out`: whether to print out the table 666 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 667 if set to `'raw'`: return a list of list of strings 668 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 669 ''' 670 if data47 is None: 671 if data48 is None: 672 raise TypeError("Arguments must include at least one D47data() or D48data() instance.") 673 else: 674 return data48.table_of_samples( 675 dir = dir, 676 filename = filename, 677 save_to_file = save_to_file, 678 print_out = print_out, 679 output = output 680 ) 681 else: 682 if data48 is None: 683 return data47.table_of_samples( 684 dir = dir, 685 filename = filename, 686 save_to_file = save_to_file, 687 print_out = print_out, 688 output = output 689 ) 690 else: 691 out47 = data47.table_of_samples(save_to_file = False, print_out = False, output = 'raw') 692 out48 = data48.table_of_samples(save_to_file = False, print_out = False, output = 'raw') 693 out = transpose_table(transpose_table(out47) + transpose_table(out48)[4:]) 694 695 if save_to_file: 696 if not os.path.exists(dir): 697 os.makedirs(dir) 698 if filename is None: 699 filename = f'D47D48_samples.csv' 700 with open(f'{dir}/{filename}', 'w') as fid: 701 fid.write(make_csv(out)) 702 if print_out: 703 print('\n'+pretty_table(out)) 704 if output == 'raw': 705 return out 706 elif output == 'pretty': 707 return pretty_table(out) 708 709 710def table_of_sessions( 711 data47 = None, 712 data48 = None, 713 dir = 'output', 714 filename = None, 715 save_to_file = True, 716 print_out = True, 717 output = None, 718 ): 719 ''' 720 Print out, save to disk and/or return a combined table of sessions 721 for a pair of `D47data` and `D48data` objects. 722 ***Only applicable if the sessions in `data47` and those in `data48` 723 consist of the exact same sets of analyses.*** 724 725 **Parameters** 726 727 + `data47`: `D47data` instance 728 + `data48`: `D48data` instance 729 + `dir`: the directory in which to save the table 730 + `filename`: the name to the csv file to write to 731 + `save_to_file`: whether to save the table to disk 732 + `print_out`: whether to print out the table 733 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 734 if set to `'raw'`: return a list of list of strings 735 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 736 ''' 737 if data47 is None: 738 if data48 is None: 739 raise TypeError("Arguments must include at least one D47data() or D48data() instance.") 740 else: 741 return data48.table_of_sessions( 742 dir = dir, 743 filename = filename, 744 save_to_file = save_to_file, 745 print_out = print_out, 746 output = output 747 ) 748 else: 749 if data48 is None: 750 return data47.table_of_sessions( 751 dir = dir, 752 filename = filename, 753 save_to_file = save_to_file, 754 print_out = print_out, 755 output = output 756 ) 757 else: 758 out47 = data47.table_of_sessions(save_to_file = False, print_out = False, output = 'raw') 759 out48 = data48.table_of_sessions(save_to_file = False, print_out = False, output = 'raw') 760 for k,x in enumerate(out47[0]): 761 if k>7: 762 out47[0][k] = out47[0][k].replace('a', 'a_47').replace('b', 'b_47').replace('c', 'c_47') 763 out48[0][k] = out48[0][k].replace('a', 'a_48').replace('b', 'b_48').replace('c', 'c_48') 764 out = transpose_table(transpose_table(out47) + transpose_table(out48)[7:]) 765 766 if save_to_file: 767 if not os.path.exists(dir): 768 os.makedirs(dir) 769 if filename is None: 770 filename = f'D47D48_sessions.csv' 771 with open(f'{dir}/{filename}', 'w') as fid: 772 fid.write(make_csv(out)) 773 if print_out: 774 print('\n'+pretty_table(out)) 775 if output == 'raw': 776 return out 777 elif output == 'pretty': 778 return pretty_table(out) 779 780 781def table_of_analyses( 782 data47 = None, 783 data48 = None, 784 dir = 'output', 785 filename = None, 786 save_to_file = True, 787 print_out = True, 788 output = None, 789 ): 790 ''' 791 Print out, save to disk and/or return a combined table of analyses 792 for a pair of `D47data` and `D48data` objects. 793 794 If the sessions in `data47` and those in `data48` do not consist of 795 the exact same sets of analyses, the table will have two columns 796 `Session_47` and `Session_48` instead of a single `Session` column. 797 798 **Parameters** 799 800 + `data47`: `D47data` instance 801 + `data48`: `D48data` instance 802 + `dir`: the directory in which to save the table 803 + `filename`: the name to the csv file to write to 804 + `save_to_file`: whether to save the table to disk 805 + `print_out`: whether to print out the table 806 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 807 if set to `'raw'`: return a list of list of strings 808 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 809 ''' 810 if data47 is None: 811 if data48 is None: 812 raise TypeError("Arguments must include at least one D47data() or D48data() instance.") 813 else: 814 return data48.table_of_analyses( 815 dir = dir, 816 filename = filename, 817 save_to_file = save_to_file, 818 print_out = print_out, 819 output = output 820 ) 821 else: 822 if data48 is None: 823 return data47.table_of_analyses( 824 dir = dir, 825 filename = filename, 826 save_to_file = save_to_file, 827 print_out = print_out, 828 output = output 829 ) 830 else: 831 out47 = data47.table_of_analyses(save_to_file = False, print_out = False, output = 'raw') 832 out48 = data48.table_of_analyses(save_to_file = False, print_out = False, output = 'raw') 833 834 if [l[1] for l in out47[1:]] == [l[1] for l in out48[1:]]: # if sessions are identical 835 out = transpose_table(transpose_table(out47) + transpose_table(out48)[-1:]) 836 else: 837 out47[0][1] = 'Session_47' 838 out48[0][1] = 'Session_48' 839 out47 = transpose_table(out47) 840 out48 = transpose_table(out48) 841 out = transpose_table(out47[:2] + out48[1:2] + out47[2:] + out48[-1:]) 842 843 if save_to_file: 844 if not os.path.exists(dir): 845 os.makedirs(dir) 846 if filename is None: 847 filename = f'D47D48_sessions.csv' 848 with open(f'{dir}/{filename}', 'w') as fid: 849 fid.write(make_csv(out)) 850 if print_out: 851 print('\n'+pretty_table(out)) 852 if output == 'raw': 853 return out 854 elif output == 'pretty': 855 return pretty_table(out) 856 857 858def _fullcovar(minresult, epsilon = 0.01, named = False): 859 ''' 860 Construct full covariance matrix in the case of constrained parameters 861 ''' 862 863 import asteval 864 865 def f(values): 866 interp = asteval.Interpreter() 867 for n,v in zip(minresult.var_names, values): 868 interp(f'{n} = {v}') 869 for q in minresult.params: 870 if minresult.params[q].expr: 871 interp(f'{q} = {minresult.params[q].expr}') 872 return np.array([interp.symtable[q] for q in minresult.params]) 873 874 # construct Jacobian 875 J = np.zeros((minresult.nvarys, len(minresult.params))) 876 X = np.array([minresult.params[p].value for p in minresult.var_names]) 877 sX = np.array([minresult.params[p].stderr for p in minresult.var_names]) 878 879 for j in range(minresult.nvarys): 880 x1 = [_ for _ in X] 881 x1[j] += epsilon * sX[j] 882 x2 = [_ for _ in X] 883 x2[j] -= epsilon * sX[j] 884 J[j,:] = (f(x1) - f(x2)) / (2 * epsilon * sX[j]) 885 886 _names = [q for q in minresult.params] 887 _covar = J.T @ minresult.covar @ J 888 _se = np.diag(_covar)**.5 889 _correl = _covar.copy() 890 for k,s in enumerate(_se): 891 if s: 892 _correl[k,:] /= s 893 _correl[:,k] /= s 894 895 if named: 896 _covar = {i: {j:_covar[i,j] for j in minresult.params} for i in minresult.params} 897 _se = {i: _se[i] for i in minresult.params} 898 _correl = {i: {j:_correl[i,j] for j in minresult.params} for i in minresult.params} 899 900 return _names, _covar, _se, _correl 901 902 903class D4xdata(list): 904 ''' 905 Store and process data for a large set of Δ47 and/or Δ48 906 analyses, usually comprising more than one analytical session. 907 ''' 908 909 ### 17O CORRECTION PARAMETERS 910 R13_VPDB = 0.01118 # (Chang & Li, 1990) 911 ''' 912 Absolute (13C/12C) ratio of VPDB. 913 By default equal to 0.01118 ([Chang & Li, 1990](http://www.cnki.com.cn/Article/CJFDTotal-JXTW199004006.htm)) 914 ''' 915 916 R18_VSMOW = 0.0020052 # (Baertschi, 1976) 917 ''' 918 Absolute (18O/16C) ratio of VSMOW. 919 By default equal to 0.0020052 ([Baertschi, 1976](https://doi.org/10.1016/0012-821X(76)90115-1)) 920 ''' 921 922 LAMBDA_17 = 0.528 # (Barkan & Luz, 2005) 923 ''' 924 Mass-dependent exponent for triple oxygen isotopes. 925 By default equal to 0.528 ([Barkan & Luz, 2005](https://doi.org/10.1002/rcm.2250)) 926 ''' 927 928 R17_VSMOW = 0.00038475 # (Assonov & Brenninkmeijer, 2003, rescaled to R13_VPDB) 929 ''' 930 Absolute (17O/16C) ratio of VSMOW. 931 By default equal to 0.00038475 932 ([Assonov & Brenninkmeijer, 2003](https://dx.doi.org/10.1002/rcm.1011), 933 rescaled to `R13_VPDB`) 934 ''' 935 936 R18_VPDB = R18_VSMOW * 1.03092 937 ''' 938 Absolute (18O/16C) ratio of VPDB. 939 By definition equal to `R18_VSMOW * 1.03092`. 940 ''' 941 942 R17_VPDB = R17_VSMOW * 1.03092 ** LAMBDA_17 943 ''' 944 Absolute (17O/16C) ratio of VPDB. 945 By definition equal to `R17_VSMOW * 1.03092 ** LAMBDA_17`. 946 ''' 947 948 LEVENE_REF_SAMPLE = 'ETH-3' 949 ''' 950 After the Δ4x standardization step, each sample is tested to 951 assess whether the Δ4x variance within all analyses for that 952 sample differs significantly from that observed for a given reference 953 sample (using [Levene's test](https://en.wikipedia.org/wiki/Levene%27s_test), 954 which yields a p-value corresponding to the null hypothesis that the 955 underlying variances are equal). 956 957 `LEVENE_REF_SAMPLE` (by default equal to `'ETH-3'`) specifies which 958 sample should be used as a reference for this test. 959 ''' 960 961 ALPHA_18O_ACID_REACTION = round(np.exp(3.59 / (90 + 273.15) - 1.79e-3), 6) # (Kim et al., 2007, calcite) 962 ''' 963 Specifies the 18O/16O fractionation factor generally applicable 964 to acid reactions in the dataset. Currently used by `D4xdata.wg()`, 965 `D4xdata.standardize_d13C`, and `D4xdata.standardize_d18O`. 966 967 By default equal to 1.008129 (calcite reacted at 90 °C, 968 [Kim et al., 2007](https://dx.doi.org/10.1016/j.chemgeo.2007.08.005)). 969 ''' 970 971 Nominal_d13C_VPDB = { 972 'ETH-1': 2.02, 973 'ETH-2': -10.17, 974 'ETH-3': 1.71, 975 } # (Bernasconi et al., 2018) 976 ''' 977 Nominal δ13C_VPDB values assigned to carbonate standards, used by 978 `D4xdata.standardize_d13C()`. 979 980 By default equal to `{'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71}` after 981 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385). 982 ''' 983 984 Nominal_d18O_VPDB = { 985 'ETH-1': -2.19, 986 'ETH-2': -18.69, 987 'ETH-3': -1.78, 988 } # (Bernasconi et al., 2018) 989 ''' 990 Nominal δ18O_VPDB values assigned to carbonate standards, used by 991 `D4xdata.standardize_d18O()`. 992 993 By default equal to `{'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78}` after 994 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385). 995 ''' 996 997 d13C_STANDARDIZATION_METHOD = '2pt' 998 ''' 999 Method by which to standardize δ13C values: 1000 1001 + `none`: do not apply any δ13C standardization. 1002 + `'1pt'`: within each session, offset all initial δ13C values so as to 1003 minimize the difference between final δ13C_VPDB values and 1004 `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` is defined). 1005 + `'2pt'`: within each session, apply a affine trasformation to all δ13C 1006 values so as to minimize the difference between final δ13C_VPDB 1007 values and `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` 1008 is defined). 1009 ''' 1010 1011 d18O_STANDARDIZATION_METHOD = '2pt' 1012 ''' 1013 Method by which to standardize δ18O values: 1014 1015 + `none`: do not apply any δ18O standardization. 1016 + `'1pt'`: within each session, offset all initial δ18O values so as to 1017 minimize the difference between final δ18O_VPDB values and 1018 `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` is defined). 1019 + `'2pt'`: within each session, apply a affine trasformation to all δ18O 1020 values so as to minimize the difference between final δ18O_VPDB 1021 values and `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` 1022 is defined). 1023 ''' 1024 1025 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False): 1026 ''' 1027 **Parameters** 1028 1029 + `l`: a list of dictionaries, with each dictionary including at least the keys 1030 `Sample`, `d45`, `d46`, and `d47` or `d48`. 1031 + `mass`: `'47'` or `'48'` 1032 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods. 1033 + `session`: define session name for analyses without a `Session` key 1034 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods. 1035 1036 Returns a `D4xdata` object derived from `list`. 1037 ''' 1038 self._4x = mass 1039 self.verbose = verbose 1040 self.prefix = 'D4xdata' 1041 self.logfile = logfile 1042 list.__init__(self, l) 1043 self.Nf = None 1044 self.repeatability = {} 1045 self.refresh(session = session) 1046 1047 1048 def make_verbal(oldfun): 1049 ''' 1050 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`. 1051 ''' 1052 @wraps(oldfun) 1053 def newfun(*args, verbose = '', **kwargs): 1054 myself = args[0] 1055 oldprefix = myself.prefix 1056 myself.prefix = oldfun.__name__ 1057 if verbose != '': 1058 oldverbose = myself.verbose 1059 myself.verbose = verbose 1060 out = oldfun(*args, **kwargs) 1061 myself.prefix = oldprefix 1062 if verbose != '': 1063 myself.verbose = oldverbose 1064 return out 1065 return newfun 1066 1067 1068 def msg(self, txt): 1069 ''' 1070 Log a message to `self.logfile`, and print it out if `verbose = True` 1071 ''' 1072 self.log(txt) 1073 if self.verbose: 1074 print(f'{f"[{self.prefix}]":<16} {txt}') 1075 1076 1077 def vmsg(self, txt): 1078 ''' 1079 Log a message to `self.logfile` and print it out 1080 ''' 1081 self.log(txt) 1082 print(txt) 1083 1084 1085 def log(self, *txts): 1086 ''' 1087 Log a message to `self.logfile` 1088 ''' 1089 if self.logfile: 1090 with open(self.logfile, 'a') as fid: 1091 for txt in txts: 1092 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}') 1093 1094 1095 def refresh(self, session = 'mySession'): 1096 ''' 1097 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`. 1098 ''' 1099 self.fill_in_missing_info(session = session) 1100 self.refresh_sessions() 1101 self.refresh_samples() 1102 1103 1104 def refresh_sessions(self): 1105 ''' 1106 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift` 1107 to `False` for all sessions. 1108 ''' 1109 self.sessions = { 1110 s: {'data': [r for r in self if r['Session'] == s]} 1111 for s in sorted({r['Session'] for r in self}) 1112 } 1113 for s in self.sessions: 1114 self.sessions[s]['scrambling_drift'] = False 1115 self.sessions[s]['slope_drift'] = False 1116 self.sessions[s]['wg_drift'] = False 1117 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD 1118 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD 1119 1120 1121 def refresh_samples(self): 1122 ''' 1123 Define `self.samples`, `self.anchors`, and `self.unknowns`. 1124 ''' 1125 self.samples = { 1126 s: {'data': [r for r in self if r['Sample'] == s]} 1127 for s in sorted({r['Sample'] for r in self}) 1128 } 1129 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x} 1130 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x} 1131 1132 1133 def read(self, filename, sep = '', session = ''): 1134 ''' 1135 Read file in csv format to load data into a `D47data` object. 1136 1137 In the csv file, spaces before and after field separators (`','` by default) 1138 are optional. Each line corresponds to a single analysis. 1139 1140 The required fields are: 1141 1142 + `UID`: a unique identifier 1143 + `Session`: an identifier for the analytical session 1144 + `Sample`: a sample identifier 1145 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values 1146 1147 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to 1148 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48` 1149 and `d49` are optional, and set to NaN by default. 1150 1151 **Parameters** 1152 1153 + `fileneme`: the path of the file to read 1154 + `sep`: csv separator delimiting the fields 1155 + `session`: set `Session` field to this string for all analyses 1156 ''' 1157 with open(filename) as fid: 1158 self.input(fid.read(), sep = sep, session = session) 1159 1160 1161 def input(self, txt, sep = '', session = ''): 1162 ''' 1163 Read `txt` string in csv format to load analysis data into a `D47data` object. 1164 1165 In the csv string, spaces before and after field separators (`','` by default) 1166 are optional. Each line corresponds to a single analysis. 1167 1168 The required fields are: 1169 1170 + `UID`: a unique identifier 1171 + `Session`: an identifier for the analytical session 1172 + `Sample`: a sample identifier 1173 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values 1174 1175 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to 1176 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48` 1177 and `d49` are optional, and set to NaN by default. 1178 1179 **Parameters** 1180 1181 + `txt`: the csv string to read 1182 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`, 1183 whichever appers most often in `txt`. 1184 + `session`: set `Session` field to this string for all analyses 1185 ''' 1186 if sep == '': 1187 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0] 1188 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()] 1189 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]] 1190 1191 if session != '': 1192 for r in data: 1193 r['Session'] = session 1194 1195 self += data 1196 self.refresh() 1197 1198 1199 @make_verbal 1200 def wg(self, samples = None, a18_acid = None): 1201 ''' 1202 Compute bulk composition of the working gas for each session based on 1203 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and 1204 `self.Nominal_d18O_VPDB`. 1205 ''' 1206 1207 self.msg('Computing WG composition:') 1208 1209 if a18_acid is None: 1210 a18_acid = self.ALPHA_18O_ACID_REACTION 1211 if samples is None: 1212 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB] 1213 1214 assert a18_acid, f'Acid fractionation factor should not be zero.' 1215 1216 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB] 1217 R45R46_standards = {} 1218 for sample in samples: 1219 d13C_vpdb = self.Nominal_d13C_VPDB[sample] 1220 d18O_vpdb = self.Nominal_d18O_VPDB[sample] 1221 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000) 1222 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17 1223 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid 1224 1225 C12_s = 1 / (1 + R13_s) 1226 C13_s = R13_s / (1 + R13_s) 1227 C16_s = 1 / (1 + R17_s + R18_s) 1228 C17_s = R17_s / (1 + R17_s + R18_s) 1229 C18_s = R18_s / (1 + R17_s + R18_s) 1230 1231 C626_s = C12_s * C16_s ** 2 1232 C627_s = 2 * C12_s * C16_s * C17_s 1233 C628_s = 2 * C12_s * C16_s * C18_s 1234 C636_s = C13_s * C16_s ** 2 1235 C637_s = 2 * C13_s * C16_s * C17_s 1236 C727_s = C12_s * C17_s ** 2 1237 1238 R45_s = (C627_s + C636_s) / C626_s 1239 R46_s = (C628_s + C637_s + C727_s) / C626_s 1240 R45R46_standards[sample] = (R45_s, R46_s) 1241 1242 for s in self.sessions: 1243 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples] 1244 assert db, f'No sample from {samples} found in session "{s}".' 1245# dbsamples = sorted({r['Sample'] for r in db}) 1246 1247 X = [r['d45'] for r in db] 1248 Y = [R45R46_standards[r['Sample']][0] for r in db] 1249 x1, x2 = np.min(X), np.max(X) 1250 1251 if x1 < x2: 1252 wgcoord = x1/(x1-x2) 1253 else: 1254 wgcoord = 999 1255 1256 if wgcoord < -.5 or wgcoord > 1.5: 1257 # unreasonable to extrapolate to d45 = 0 1258 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)]) 1259 else : 1260 # d45 = 0 is reasonably well bracketed 1261 R45_wg = np.polyfit(X, Y, 1)[1] 1262 1263 X = [r['d46'] for r in db] 1264 Y = [R45R46_standards[r['Sample']][1] for r in db] 1265 x1, x2 = np.min(X), np.max(X) 1266 1267 if x1 < x2: 1268 wgcoord = x1/(x1-x2) 1269 else: 1270 wgcoord = 999 1271 1272 if wgcoord < -.5 or wgcoord > 1.5: 1273 # unreasonable to extrapolate to d46 = 0 1274 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)]) 1275 else : 1276 # d46 = 0 is reasonably well bracketed 1277 R46_wg = np.polyfit(X, Y, 1)[1] 1278 1279 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg) 1280 1281 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}') 1282 1283 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB 1284 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW 1285 for r in self.sessions[s]['data']: 1286 r['d13Cwg_VPDB'] = d13Cwg_VPDB 1287 r['d18Owg_VSMOW'] = d18Owg_VSMOW 1288 1289 1290 def compute_bulk_delta(self, R45, R46, D17O = 0): 1291 ''' 1292 Compute δ13C_VPDB and δ18O_VSMOW, 1293 by solving the generalized form of equation (17) from 1294 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05), 1295 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and 1296 solving the corresponding second-order Taylor polynomial. 1297 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014)) 1298 ''' 1299 1300 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17 1301 1302 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17) 1303 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17 1304 C = 2 * self.R18_VSMOW 1305 D = -R46 1306 1307 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2 1308 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C 1309 cc = A + B + C + D 1310 1311 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa) 1312 1313 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW 1314 R17 = K * R18 ** self.LAMBDA_17 1315 R13 = R45 - 2 * R17 1316 1317 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1) 1318 1319 return d13C_VPDB, d18O_VSMOW 1320 1321 1322 @make_verbal 1323 def crunch(self, verbose = ''): 1324 ''' 1325 Compute bulk composition and raw clumped isotope anomalies for all analyses. 1326 ''' 1327 for r in self: 1328 self.compute_bulk_and_clumping_deltas(r) 1329 self.standardize_d13C() 1330 self.standardize_d18O() 1331 self.msg(f"Crunched {len(self)} analyses.") 1332 1333 1334 def fill_in_missing_info(self, session = 'mySession'): 1335 ''' 1336 Fill in optional fields with default values 1337 ''' 1338 for i,r in enumerate(self): 1339 if 'D17O' not in r: 1340 r['D17O'] = 0. 1341 if 'UID' not in r: 1342 r['UID'] = f'{i+1}' 1343 if 'Session' not in r: 1344 r['Session'] = session 1345 for k in ['d47', 'd48', 'd49']: 1346 if k not in r: 1347 r[k] = np.nan 1348 1349 1350 def standardize_d13C(self): 1351 ''' 1352 Perform δ13C standadization within each session `s` according to 1353 `self.sessions[s]['d13C_standardization_method']`, which is defined by default 1354 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but 1355 may be redefined abitrarily at a later stage. 1356 ''' 1357 for s in self.sessions: 1358 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']: 1359 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB] 1360 X,Y = zip(*XY) 1361 if self.sessions[s]['d13C_standardization_method'] == '1pt': 1362 offset = np.mean(Y) - np.mean(X) 1363 for r in self.sessions[s]['data']: 1364 r['d13C_VPDB'] += offset 1365 elif self.sessions[s]['d13C_standardization_method'] == '2pt': 1366 a,b = np.polyfit(X,Y,1) 1367 for r in self.sessions[s]['data']: 1368 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b 1369 1370 def standardize_d18O(self): 1371 ''' 1372 Perform δ18O standadization within each session `s` according to 1373 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`, 1374 which is defined by default by `D47data.refresh_sessions()`as equal to 1375 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage. 1376 ''' 1377 for s in self.sessions: 1378 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']: 1379 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB] 1380 X,Y = zip(*XY) 1381 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y] 1382 if self.sessions[s]['d18O_standardization_method'] == '1pt': 1383 offset = np.mean(Y) - np.mean(X) 1384 for r in self.sessions[s]['data']: 1385 r['d18O_VSMOW'] += offset 1386 elif self.sessions[s]['d18O_standardization_method'] == '2pt': 1387 a,b = np.polyfit(X,Y,1) 1388 for r in self.sessions[s]['data']: 1389 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b 1390 1391 1392 def compute_bulk_and_clumping_deltas(self, r): 1393 ''' 1394 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`. 1395 ''' 1396 1397 # Compute working gas R13, R18, and isobar ratios 1398 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000) 1399 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000) 1400 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg) 1401 1402 # Compute analyte isobar ratios 1403 R45 = (1 + r['d45'] / 1000) * R45_wg 1404 R46 = (1 + r['d46'] / 1000) * R46_wg 1405 R47 = (1 + r['d47'] / 1000) * R47_wg 1406 R48 = (1 + r['d48'] / 1000) * R48_wg 1407 R49 = (1 + r['d49'] / 1000) * R49_wg 1408 1409 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O']) 1410 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB 1411 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW 1412 1413 # Compute stochastic isobar ratios of the analyte 1414 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios( 1415 R13, R18, D17O = r['D17O'] 1416 ) 1417 1418 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1, 1419 # and raise a warning if the corresponding anomalies exceed 0.02 ppm. 1420 if (R45 / R45stoch - 1) > 5e-8: 1421 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm') 1422 if (R46 / R46stoch - 1) > 5e-8: 1423 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm') 1424 1425 # Compute raw clumped isotope anomalies 1426 r['D47raw'] = 1000 * (R47 / R47stoch - 1) 1427 r['D48raw'] = 1000 * (R48 / R48stoch - 1) 1428 r['D49raw'] = 1000 * (R49 / R49stoch - 1) 1429 1430 1431 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0): 1432 ''' 1433 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`, 1434 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope 1435 anomalies (`D47`, `D48`, `D49`), all expressed in permil. 1436 ''' 1437 1438 # Compute R17 1439 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17 1440 1441 # Compute isotope concentrations 1442 C12 = (1 + R13) ** -1 1443 C13 = C12 * R13 1444 C16 = (1 + R17 + R18) ** -1 1445 C17 = C16 * R17 1446 C18 = C16 * R18 1447 1448 # Compute stochastic isotopologue concentrations 1449 C626 = C16 * C12 * C16 1450 C627 = C16 * C12 * C17 * 2 1451 C628 = C16 * C12 * C18 * 2 1452 C636 = C16 * C13 * C16 1453 C637 = C16 * C13 * C17 * 2 1454 C638 = C16 * C13 * C18 * 2 1455 C727 = C17 * C12 * C17 1456 C728 = C17 * C12 * C18 * 2 1457 C737 = C17 * C13 * C17 1458 C738 = C17 * C13 * C18 * 2 1459 C828 = C18 * C12 * C18 1460 C838 = C18 * C13 * C18 1461 1462 # Compute stochastic isobar ratios 1463 R45 = (C636 + C627) / C626 1464 R46 = (C628 + C637 + C727) / C626 1465 R47 = (C638 + C728 + C737) / C626 1466 R48 = (C738 + C828) / C626 1467 R49 = C838 / C626 1468 1469 # Account for stochastic anomalies 1470 R47 *= 1 + D47 / 1000 1471 R48 *= 1 + D48 / 1000 1472 R49 *= 1 + D49 / 1000 1473 1474 # Return isobar ratios 1475 return R45, R46, R47, R48, R49 1476 1477 1478 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'): 1479 ''' 1480 Split unknown samples by UID (treat all analyses as different samples) 1481 or by session (treat analyses of a given sample in different sessions as 1482 different samples). 1483 1484 **Parameters** 1485 1486 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']` 1487 + `grouping`: `by_uid` | `by_session` 1488 ''' 1489 if samples_to_split == 'all': 1490 samples_to_split = [s for s in self.unknowns] 1491 gkeys = {'by_uid':'UID', 'by_session':'Session'} 1492 self.grouping = grouping.lower() 1493 if self.grouping in gkeys: 1494 gkey = gkeys[self.grouping] 1495 for r in self: 1496 if r['Sample'] in samples_to_split: 1497 r['Sample_original'] = r['Sample'] 1498 r['Sample'] = f"{r['Sample']}__{r[gkey]}" 1499 elif r['Sample'] in self.unknowns: 1500 r['Sample_original'] = r['Sample'] 1501 self.refresh_samples() 1502 1503 1504 def unsplit_samples(self, tables = False): 1505 ''' 1506 Reverse the effects of `D47data.split_samples()`. 1507 1508 This should only be used after `D4xdata.standardize()` with `method='pooled'`. 1509 1510 After `D4xdata.standardize()` with `method='indep_sessions'`, one should 1511 probably use `D4xdata.combine_samples()` instead to reverse the effects of 1512 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the 1513 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in 1514 that case session-averaged Δ4x values are statistically independent). 1515 ''' 1516 unknowns_old = sorted({s for s in self.unknowns}) 1517 CM_old = self.standardization.covar[:,:] 1518 VD_old = self.standardization.params.valuesdict().copy() 1519 vars_old = self.standardization.var_names 1520 1521 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r}) 1522 1523 Ns = len(vars_old) - len(unknowns_old) 1524 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new] 1525 VD_new = {k: VD_old[k] for k in vars_old[:Ns]} 1526 1527 W = np.zeros((len(vars_new), len(vars_old))) 1528 W[:Ns,:Ns] = np.eye(Ns) 1529 for u in unknowns_new: 1530 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u}) 1531 if self.grouping == 'by_session': 1532 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits] 1533 elif self.grouping == 'by_uid': 1534 weights = [1 for s in splits] 1535 sw = sum(weights) 1536 weights = [w/sw for w in weights] 1537 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:] 1538 1539 CM_new = W @ CM_old @ W.T 1540 V = W @ np.array([[VD_old[k]] for k in vars_old]) 1541 VD_new = {k:v[0] for k,v in zip(vars_new, V)} 1542 1543 self.standardization.covar = CM_new 1544 self.standardization.params.valuesdict = lambda : VD_new 1545 self.standardization.var_names = vars_new 1546 1547 for r in self: 1548 if r['Sample'] in self.unknowns: 1549 r['Sample_split'] = r['Sample'] 1550 r['Sample'] = r['Sample_original'] 1551 1552 self.refresh_samples() 1553 self.consolidate_samples() 1554 self.repeatabilities() 1555 1556 if tables: 1557 self.table_of_analyses() 1558 self.table_of_samples() 1559 1560 def assign_timestamps(self): 1561 ''' 1562 Assign a time field `t` of type `float` to each analysis. 1563 1564 If `TimeTag` is one of the data fields, `t` is equal within a given session 1565 to `TimeTag` minus the mean value of `TimeTag` for that session. 1566 Otherwise, `TimeTag` is by default equal to the index of each analysis 1567 in the dataset and `t` is defined as above. 1568 ''' 1569 for session in self.sessions: 1570 sdata = self.sessions[session]['data'] 1571 try: 1572 t0 = np.mean([r['TimeTag'] for r in sdata]) 1573 for r in sdata: 1574 r['t'] = r['TimeTag'] - t0 1575 except KeyError: 1576 t0 = (len(sdata)-1)/2 1577 for t,r in enumerate(sdata): 1578 r['t'] = t - t0 1579 1580 1581 def report(self): 1582 ''' 1583 Prints a report on the standardization fit. 1584 Only applicable after `D4xdata.standardize(method='pooled')`. 1585 ''' 1586 report_fit(self.standardization) 1587 1588 1589 def combine_samples(self, sample_groups): 1590 ''' 1591 Combine analyses of different samples to compute weighted average Δ4x 1592 and new error (co)variances corresponding to the groups defined by the `sample_groups` 1593 dictionary. 1594 1595 Caution: samples are weighted by number of replicate analyses, which is a 1596 reasonable default behavior but is not always optimal (e.g., in the case of strongly 1597 correlated analytical errors for one or more samples). 1598 1599 Returns a tuplet of: 1600 1601 + the list of group names 1602 + an array of the corresponding Δ4x values 1603 + the corresponding (co)variance matrix 1604 1605 **Parameters** 1606 1607 + `sample_groups`: a dictionary of the form: 1608 ```py 1609 {'group1': ['sample_1', 'sample_2'], 1610 'group2': ['sample_3', 'sample_4', 'sample_5']} 1611 ``` 1612 ''' 1613 1614 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])] 1615 groups = sorted(sample_groups.keys()) 1616 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups} 1617 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples]) 1618 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples]) 1619 W = np.array([ 1620 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples] 1621 for j in groups]) 1622 D4x_new = W @ D4x_old 1623 CM_new = W @ CM_old @ W.T 1624 1625 return groups, D4x_new[:,0], CM_new 1626 1627 1628 @make_verbal 1629 def standardize(self, 1630 method = 'pooled', 1631 weighted_sessions = [], 1632 consolidate = True, 1633 consolidate_tables = False, 1634 consolidate_plots = False, 1635 constraints = {}, 1636 ): 1637 ''' 1638 Compute absolute Δ4x values for all replicate analyses and for sample averages. 1639 If `method` argument is set to `'pooled'`, the standardization processes all sessions 1640 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous, 1641 i.e. that their true Δ4x value does not change between sessions, 1642 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to 1643 `'indep_sessions'`, the standardization processes each session independently, based only 1644 on anchors analyses. 1645 ''' 1646 1647 self.standardization_method = method 1648 self.assign_timestamps() 1649 1650 if method == 'pooled': 1651 if weighted_sessions: 1652 for session_group in weighted_sessions: 1653 if self._4x == '47': 1654 X = D47data([r for r in self if r['Session'] in session_group]) 1655 elif self._4x == '48': 1656 X = D48data([r for r in self if r['Session'] in session_group]) 1657 X.Nominal_D4x = self.Nominal_D4x.copy() 1658 X.refresh() 1659 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False) 1660 w = np.sqrt(result.redchi) 1661 self.msg(f'Session group {session_group} MRSWD = {w:.4f}') 1662 for r in X: 1663 r[f'wD{self._4x}raw'] *= w 1664 else: 1665 self.msg(f'All D{self._4x}raw weights set to 1 ‰') 1666 for r in self: 1667 r[f'wD{self._4x}raw'] = 1. 1668 1669 params = Parameters() 1670 for k,session in enumerate(self.sessions): 1671 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.") 1672 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.") 1673 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.") 1674 s = pf(session) 1675 params.add(f'a_{s}', value = 0.9) 1676 params.add(f'b_{s}', value = 0.) 1677 params.add(f'c_{s}', value = -0.9) 1678 params.add(f'a2_{s}', value = 0., 1679# vary = self.sessions[session]['scrambling_drift'], 1680 ) 1681 params.add(f'b2_{s}', value = 0., 1682# vary = self.sessions[session]['slope_drift'], 1683 ) 1684 params.add(f'c2_{s}', value = 0., 1685# vary = self.sessions[session]['wg_drift'], 1686 ) 1687 if not self.sessions[session]['scrambling_drift']: 1688 params[f'a2_{s}'].expr = '0' 1689 if not self.sessions[session]['slope_drift']: 1690 params[f'b2_{s}'].expr = '0' 1691 if not self.sessions[session]['wg_drift']: 1692 params[f'c2_{s}'].expr = '0' 1693 1694 for sample in self.unknowns: 1695 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5) 1696 1697 for k in constraints: 1698 params[k].expr = constraints[k] 1699 1700 def residuals(p): 1701 R = [] 1702 for r in self: 1703 session = pf(r['Session']) 1704 sample = pf(r['Sample']) 1705 if r['Sample'] in self.Nominal_D4x: 1706 R += [ ( 1707 r[f'D{self._4x}raw'] - ( 1708 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']] 1709 + p[f'b_{session}'] * r[f'd{self._4x}'] 1710 + p[f'c_{session}'] 1711 + r['t'] * ( 1712 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']] 1713 + p[f'b2_{session}'] * r[f'd{self._4x}'] 1714 + p[f'c2_{session}'] 1715 ) 1716 ) 1717 ) / r[f'wD{self._4x}raw'] ] 1718 else: 1719 R += [ ( 1720 r[f'D{self._4x}raw'] - ( 1721 p[f'a_{session}'] * p[f'D{self._4x}_{sample}'] 1722 + p[f'b_{session}'] * r[f'd{self._4x}'] 1723 + p[f'c_{session}'] 1724 + r['t'] * ( 1725 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}'] 1726 + p[f'b2_{session}'] * r[f'd{self._4x}'] 1727 + p[f'c2_{session}'] 1728 ) 1729 ) 1730 ) / r[f'wD{self._4x}raw'] ] 1731 return R 1732 1733 M = Minimizer(residuals, params) 1734 result = M.least_squares() 1735 self.Nf = result.nfree 1736 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf) 1737 new_names, new_covar, new_se = _fullcovar(result)[:3] 1738 result.var_names = new_names 1739 result.covar = new_covar 1740 1741 for r in self: 1742 s = pf(r["Session"]) 1743 a = result.params.valuesdict()[f'a_{s}'] 1744 b = result.params.valuesdict()[f'b_{s}'] 1745 c = result.params.valuesdict()[f'c_{s}'] 1746 a2 = result.params.valuesdict()[f'a2_{s}'] 1747 b2 = result.params.valuesdict()[f'b2_{s}'] 1748 c2 = result.params.valuesdict()[f'c2_{s}'] 1749 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t']) 1750 1751 1752 self.standardization = result 1753 1754 for session in self.sessions: 1755 self.sessions[session]['Np'] = 3 1756 for k in ['scrambling', 'slope', 'wg']: 1757 if self.sessions[session][f'{k}_drift']: 1758 self.sessions[session]['Np'] += 1 1759 1760 if consolidate: 1761 self.consolidate(tables = consolidate_tables, plots = consolidate_plots) 1762 return result 1763 1764 1765 elif method == 'indep_sessions': 1766 1767 if weighted_sessions: 1768 for session_group in weighted_sessions: 1769 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x) 1770 X.Nominal_D4x = self.Nominal_D4x.copy() 1771 X.refresh() 1772 # This is only done to assign r['wD47raw'] for r in X: 1773 X.standardize(method = method, weighted_sessions = [], consolidate = False) 1774 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}') 1775 else: 1776 self.msg('All weights set to 1 ‰') 1777 for r in self: 1778 r[f'wD{self._4x}raw'] = 1 1779 1780 for session in self.sessions: 1781 s = self.sessions[session] 1782 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2'] 1783 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']] 1784 s['Np'] = sum(p_active) 1785 sdata = s['data'] 1786 1787 A = np.array([ 1788 [ 1789 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'], 1790 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'], 1791 1 / r[f'wD{self._4x}raw'], 1792 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'], 1793 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'], 1794 r['t'] / r[f'wD{self._4x}raw'] 1795 ] 1796 for r in sdata if r['Sample'] in self.anchors 1797 ])[:,p_active] # only keep columns for the active parameters 1798 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors]) 1799 s['Na'] = Y.size 1800 CM = linalg.inv(A.T @ A) 1801 bf = (CM @ A.T @ Y).T[0,:] 1802 k = 0 1803 for n,a in zip(p_names, p_active): 1804 if a: 1805 s[n] = bf[k] 1806# self.msg(f'{n} = {bf[k]}') 1807 k += 1 1808 else: 1809 s[n] = 0. 1810# self.msg(f'{n} = 0.0') 1811 1812 for r in sdata : 1813 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2'] 1814 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t']) 1815 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t']) 1816 1817 s['CM'] = np.zeros((6,6)) 1818 i = 0 1819 k_active = [j for j,a in enumerate(p_active) if a] 1820 for j,a in enumerate(p_active): 1821 if a: 1822 s['CM'][j,k_active] = CM[i,:] 1823 i += 1 1824 1825 if not weighted_sessions: 1826 w = self.rmswd()['rmswd'] 1827 for r in self: 1828 r[f'wD{self._4x}'] *= w 1829 r[f'wD{self._4x}raw'] *= w 1830 for session in self.sessions: 1831 self.sessions[session]['CM'] *= w**2 1832 1833 for session in self.sessions: 1834 s = self.sessions[session] 1835 s['SE_a'] = s['CM'][0,0]**.5 1836 s['SE_b'] = s['CM'][1,1]**.5 1837 s['SE_c'] = s['CM'][2,2]**.5 1838 s['SE_a2'] = s['CM'][3,3]**.5 1839 s['SE_b2'] = s['CM'][4,4]**.5 1840 s['SE_c2'] = s['CM'][5,5]**.5 1841 1842 if not weighted_sessions: 1843 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions]) 1844 else: 1845 self.Nf = 0 1846 for sg in weighted_sessions: 1847 self.Nf += self.rmswd(sessions = sg)['Nf'] 1848 1849 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf) 1850 1851 avgD4x = { 1852 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample]) 1853 for sample in self.samples 1854 } 1855 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self]) 1856 rD4x = (chi2/self.Nf)**.5 1857 self.repeatability[f'sigma_{self._4x}'] = rD4x 1858 1859 if consolidate: 1860 self.consolidate(tables = consolidate_tables, plots = consolidate_plots) 1861 1862 1863 def standardization_error(self, session, d4x, D4x, t = 0): 1864 ''' 1865 Compute standardization error for a given session and 1866 (δ47, Δ47) composition. 1867 ''' 1868 a = self.sessions[session]['a'] 1869 b = self.sessions[session]['b'] 1870 c = self.sessions[session]['c'] 1871 a2 = self.sessions[session]['a2'] 1872 b2 = self.sessions[session]['b2'] 1873 c2 = self.sessions[session]['c2'] 1874 CM = self.sessions[session]['CM'] 1875 1876 x, y = D4x, d4x 1877 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t 1878# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t) 1879 dxdy = -(b+b2*t) / (a+a2*t) 1880 dxdz = 1. / (a+a2*t) 1881 dxda = -x / (a+a2*t) 1882 dxdb = -y / (a+a2*t) 1883 dxdc = -1. / (a+a2*t) 1884 dxda2 = -x * a2 / (a+a2*t) 1885 dxdb2 = -y * t / (a+a2*t) 1886 dxdc2 = -t / (a+a2*t) 1887 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2]) 1888 sx = (V @ CM @ V.T) ** .5 1889 return sx 1890 1891 1892 @make_verbal 1893 def summary(self, 1894 dir = 'output', 1895 filename = None, 1896 save_to_file = True, 1897 print_out = True, 1898 ): 1899 ''' 1900 Print out an/or save to disk a summary of the standardization results. 1901 1902 **Parameters** 1903 1904 + `dir`: the directory in which to save the table 1905 + `filename`: the name to the csv file to write to 1906 + `save_to_file`: whether to save the table to disk 1907 + `print_out`: whether to print out the table 1908 ''' 1909 1910 out = [] 1911 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]] 1912 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]] 1913 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]] 1914 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]] 1915 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]] 1916 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]] 1917 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]] 1918 out += [['Model degrees of freedom', f"{self.Nf}"]] 1919 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]] 1920 out += [['Standardization method', self.standardization_method]] 1921 1922 if save_to_file: 1923 if not os.path.exists(dir): 1924 os.makedirs(dir) 1925 if filename is None: 1926 filename = f'D{self._4x}_summary.csv' 1927 with open(f'{dir}/{filename}', 'w') as fid: 1928 fid.write(make_csv(out)) 1929 if print_out: 1930 self.msg('\n' + pretty_table(out, header = 0)) 1931 1932 1933 @make_verbal 1934 def table_of_sessions(self, 1935 dir = 'output', 1936 filename = None, 1937 save_to_file = True, 1938 print_out = True, 1939 output = None, 1940 ): 1941 ''' 1942 Print out an/or save to disk a table of sessions. 1943 1944 **Parameters** 1945 1946 + `dir`: the directory in which to save the table 1947 + `filename`: the name to the csv file to write to 1948 + `save_to_file`: whether to save the table to disk 1949 + `print_out`: whether to print out the table 1950 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 1951 if set to `'raw'`: return a list of list of strings 1952 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 1953 ''' 1954 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions]) 1955 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions]) 1956 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions]) 1957 1958 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']] 1959 if include_a2: 1960 out[-1] += ['a2 ± SE'] 1961 if include_b2: 1962 out[-1] += ['b2 ± SE'] 1963 if include_c2: 1964 out[-1] += ['c2 ± SE'] 1965 for session in self.sessions: 1966 out += [[ 1967 session, 1968 f"{self.sessions[session]['Na']}", 1969 f"{self.sessions[session]['Nu']}", 1970 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}", 1971 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}", 1972 f"{self.sessions[session]['r_d13C_VPDB']:.4f}", 1973 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}", 1974 f"{self.sessions[session][f'r_D{self._4x}']:.4f}", 1975 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}", 1976 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}", 1977 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}", 1978 ]] 1979 if include_a2: 1980 if self.sessions[session]['scrambling_drift']: 1981 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"] 1982 else: 1983 out[-1] += [''] 1984 if include_b2: 1985 if self.sessions[session]['slope_drift']: 1986 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"] 1987 else: 1988 out[-1] += [''] 1989 if include_c2: 1990 if self.sessions[session]['wg_drift']: 1991 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"] 1992 else: 1993 out[-1] += [''] 1994 1995 if save_to_file: 1996 if not os.path.exists(dir): 1997 os.makedirs(dir) 1998 if filename is None: 1999 filename = f'D{self._4x}_sessions.csv' 2000 with open(f'{dir}/{filename}', 'w') as fid: 2001 fid.write(make_csv(out)) 2002 if print_out: 2003 self.msg('\n' + pretty_table(out)) 2004 if output == 'raw': 2005 return out 2006 elif output == 'pretty': 2007 return pretty_table(out) 2008 2009 2010 @make_verbal 2011 def table_of_analyses( 2012 self, 2013 dir = 'output', 2014 filename = None, 2015 save_to_file = True, 2016 print_out = True, 2017 output = None, 2018 ): 2019 ''' 2020 Print out an/or save to disk a table of analyses. 2021 2022 **Parameters** 2023 2024 + `dir`: the directory in which to save the table 2025 + `filename`: the name to the csv file to write to 2026 + `save_to_file`: whether to save the table to disk 2027 + `print_out`: whether to print out the table 2028 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 2029 if set to `'raw'`: return a list of list of strings 2030 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2031 ''' 2032 2033 out = [['UID','Session','Sample']] 2034 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}] 2035 for f in extra_fields: 2036 out[-1] += [f[0]] 2037 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}'] 2038 for r in self: 2039 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]] 2040 for f in extra_fields: 2041 out[-1] += [f"{r[f[0]]:{f[1]}}"] 2042 out[-1] += [ 2043 f"{r['d13Cwg_VPDB']:.3f}", 2044 f"{r['d18Owg_VSMOW']:.3f}", 2045 f"{r['d45']:.6f}", 2046 f"{r['d46']:.6f}", 2047 f"{r['d47']:.6f}", 2048 f"{r['d48']:.6f}", 2049 f"{r['d49']:.6f}", 2050 f"{r['d13C_VPDB']:.6f}", 2051 f"{r['d18O_VSMOW']:.6f}", 2052 f"{r['D47raw']:.6f}", 2053 f"{r['D48raw']:.6f}", 2054 f"{r['D49raw']:.6f}", 2055 f"{r[f'D{self._4x}']:.6f}" 2056 ] 2057 if save_to_file: 2058 if not os.path.exists(dir): 2059 os.makedirs(dir) 2060 if filename is None: 2061 filename = f'D{self._4x}_analyses.csv' 2062 with open(f'{dir}/{filename}', 'w') as fid: 2063 fid.write(make_csv(out)) 2064 if print_out: 2065 self.msg('\n' + pretty_table(out)) 2066 return out 2067 2068 @make_verbal 2069 def covar_table( 2070 self, 2071 correl = False, 2072 dir = 'output', 2073 filename = None, 2074 save_to_file = True, 2075 print_out = True, 2076 output = None, 2077 ): 2078 ''' 2079 Print out, save to disk and/or return the variance-covariance matrix of D4x 2080 for all unknown samples. 2081 2082 **Parameters** 2083 2084 + `dir`: the directory in which to save the csv 2085 + `filename`: the name of the csv file to write to 2086 + `save_to_file`: whether to save the csv 2087 + `print_out`: whether to print out the matrix 2088 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`); 2089 if set to `'raw'`: return a list of list of strings 2090 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2091 ''' 2092 samples = sorted([u for u in self.unknowns]) 2093 out = [[''] + samples] 2094 for s1 in samples: 2095 out.append([s1]) 2096 for s2 in samples: 2097 if correl: 2098 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}') 2099 else: 2100 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}') 2101 2102 if save_to_file: 2103 if not os.path.exists(dir): 2104 os.makedirs(dir) 2105 if filename is None: 2106 if correl: 2107 filename = f'D{self._4x}_correl.csv' 2108 else: 2109 filename = f'D{self._4x}_covar.csv' 2110 with open(f'{dir}/{filename}', 'w') as fid: 2111 fid.write(make_csv(out)) 2112 if print_out: 2113 self.msg('\n'+pretty_table(out)) 2114 if output == 'raw': 2115 return out 2116 elif output == 'pretty': 2117 return pretty_table(out) 2118 2119 @make_verbal 2120 def table_of_samples( 2121 self, 2122 dir = 'output', 2123 filename = None, 2124 save_to_file = True, 2125 print_out = True, 2126 output = None, 2127 ): 2128 ''' 2129 Print out, save to disk and/or return a table of samples. 2130 2131 **Parameters** 2132 2133 + `dir`: the directory in which to save the csv 2134 + `filename`: the name of the csv file to write to 2135 + `save_to_file`: whether to save the csv 2136 + `print_out`: whether to print out the table 2137 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 2138 if set to `'raw'`: return a list of list of strings 2139 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2140 ''' 2141 2142 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']] 2143 for sample in self.anchors: 2144 out += [[ 2145 f"{sample}", 2146 f"{self.samples[sample]['N']}", 2147 f"{self.samples[sample]['d13C_VPDB']:.2f}", 2148 f"{self.samples[sample]['d18O_VSMOW']:.2f}", 2149 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','', 2150 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', '' 2151 ]] 2152 for sample in self.unknowns: 2153 out += [[ 2154 f"{sample}", 2155 f"{self.samples[sample]['N']}", 2156 f"{self.samples[sample]['d13C_VPDB']:.2f}", 2157 f"{self.samples[sample]['d18O_VSMOW']:.2f}", 2158 f"{self.samples[sample][f'D{self._4x}']:.4f}", 2159 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}", 2160 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}", 2161 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', 2162 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else '' 2163 ]] 2164 if save_to_file: 2165 if not os.path.exists(dir): 2166 os.makedirs(dir) 2167 if filename is None: 2168 filename = f'D{self._4x}_samples.csv' 2169 with open(f'{dir}/{filename}', 'w') as fid: 2170 fid.write(make_csv(out)) 2171 if print_out: 2172 self.msg('\n'+pretty_table(out)) 2173 if output == 'raw': 2174 return out 2175 elif output == 'pretty': 2176 return pretty_table(out) 2177 2178 2179 def plot_sessions(self, dir = 'output', figsize = (8,8)): 2180 ''' 2181 Generate session plots and save them to disk. 2182 2183 **Parameters** 2184 2185 + `dir`: the directory in which to save the plots 2186 + `figsize`: the width and height (in inches) of each plot 2187 ''' 2188 if not os.path.exists(dir): 2189 os.makedirs(dir) 2190 2191 for session in self.sessions: 2192 sp = self.plot_single_session(session, xylimits = 'constant') 2193 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.pdf') 2194 ppl.close(sp.fig) 2195 2196 2197 @make_verbal 2198 def consolidate_samples(self): 2199 ''' 2200 Compile various statistics for each sample. 2201 2202 For each anchor sample: 2203 2204 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x` 2205 + `SE_D47` or `SE_D48`: set to zero by definition 2206 2207 For each unknown sample: 2208 2209 + `D47` or `D48`: the standardized Δ4x value for this unknown 2210 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown 2211 2212 For each anchor and unknown: 2213 2214 + `N`: the total number of analyses of this sample 2215 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample 2216 + `d13C_VPDB`: the average δ13C_VPDB value for this sample 2217 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2) 2218 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal 2219 variance, indicating whether the Δ4x repeatability this sample differs significantly from 2220 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`. 2221 ''' 2222 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']] 2223 for sample in self.samples: 2224 self.samples[sample]['N'] = len(self.samples[sample]['data']) 2225 if self.samples[sample]['N'] > 1: 2226 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']]) 2227 2228 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']]) 2229 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']]) 2230 2231 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']] 2232 if len(D4x_pop) > 2: 2233 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1] 2234 2235 if self.standardization_method == 'pooled': 2236 for sample in self.anchors: 2237 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample] 2238 self.samples[sample][f'SE_D{self._4x}'] = 0. 2239 for sample in self.unknowns: 2240 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}'] 2241 try: 2242 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5 2243 except ValueError: 2244 # when `sample` is constrained by self.standardize(constraints = {...}), 2245 # it is no longer listed in self.standardization.var_names. 2246 # Temporary fix: define SE as zero for now 2247 self.samples[sample][f'SE_D4{self._4x}'] = 0. 2248 2249 elif self.standardization_method == 'indep_sessions': 2250 for sample in self.anchors: 2251 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample] 2252 self.samples[sample][f'SE_D{self._4x}'] = 0. 2253 for sample in self.unknowns: 2254 self.msg(f'Consolidating sample {sample}') 2255 self.unknowns[sample][f'session_D{self._4x}'] = {} 2256 session_avg = [] 2257 for session in self.sessions: 2258 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample] 2259 if sdata: 2260 self.msg(f'{sample} found in session {session}') 2261 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata]) 2262 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata]) 2263 # !! TODO: sigma_s below does not account for temporal changes in standardization error 2264 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x) 2265 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5 2266 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5]) 2267 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1] 2268 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg)) 2269 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']} 2270 wsum = sum([weights[s] for s in weights]) 2271 for s in weights: 2272 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum] 2273 2274 for r in self: 2275 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}'] 2276 2277 2278 2279 def consolidate_sessions(self): 2280 ''' 2281 Compute various statistics for each session. 2282 2283 + `Na`: Number of anchor analyses in the session 2284 + `Nu`: Number of unknown analyses in the session 2285 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session 2286 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session 2287 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session 2288 + `a`: scrambling factor 2289 + `b`: compositional slope 2290 + `c`: WG offset 2291 + `SE_a`: Model stadard erorr of `a` 2292 + `SE_b`: Model stadard erorr of `b` 2293 + `SE_c`: Model stadard erorr of `c` 2294 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`) 2295 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`) 2296 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`) 2297 + `a2`: scrambling factor drift 2298 + `b2`: compositional slope drift 2299 + `c2`: WG offset drift 2300 + `Np`: Number of standardization parameters to fit 2301 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`) 2302 + `d13Cwg_VPDB`: δ13C_VPDB of WG 2303 + `d18Owg_VSMOW`: δ18O_VSMOW of WG 2304 ''' 2305 for session in self.sessions: 2306 if 'd13Cwg_VPDB' not in self.sessions[session]: 2307 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB'] 2308 if 'd18Owg_VSMOW' not in self.sessions[session]: 2309 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW'] 2310 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors]) 2311 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns]) 2312 2313 self.msg(f'Computing repeatabilities for session {session}') 2314 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session]) 2315 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session]) 2316 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session]) 2317 2318 if self.standardization_method == 'pooled': 2319 for session in self.sessions: 2320 2321 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}'] 2322 i = self.standardization.var_names.index(f'a_{pf(session)}') 2323 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5 2324 2325 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}'] 2326 i = self.standardization.var_names.index(f'b_{pf(session)}') 2327 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5 2328 2329 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}'] 2330 i = self.standardization.var_names.index(f'c_{pf(session)}') 2331 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5 2332 2333 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}'] 2334 if self.sessions[session]['scrambling_drift']: 2335 i = self.standardization.var_names.index(f'a2_{pf(session)}') 2336 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5 2337 else: 2338 self.sessions[session]['SE_a2'] = 0. 2339 2340 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}'] 2341 if self.sessions[session]['slope_drift']: 2342 i = self.standardization.var_names.index(f'b2_{pf(session)}') 2343 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5 2344 else: 2345 self.sessions[session]['SE_b2'] = 0. 2346 2347 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}'] 2348 if self.sessions[session]['wg_drift']: 2349 i = self.standardization.var_names.index(f'c2_{pf(session)}') 2350 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5 2351 else: 2352 self.sessions[session]['SE_c2'] = 0. 2353 2354 i = self.standardization.var_names.index(f'a_{pf(session)}') 2355 j = self.standardization.var_names.index(f'b_{pf(session)}') 2356 k = self.standardization.var_names.index(f'c_{pf(session)}') 2357 CM = np.zeros((6,6)) 2358 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]] 2359 try: 2360 i2 = self.standardization.var_names.index(f'a2_{pf(session)}') 2361 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]] 2362 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2] 2363 try: 2364 j2 = self.standardization.var_names.index(f'b2_{pf(session)}') 2365 CM[3,4] = self.standardization.covar[i2,j2] 2366 CM[4,3] = self.standardization.covar[j2,i2] 2367 except ValueError: 2368 pass 2369 try: 2370 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2371 CM[3,5] = self.standardization.covar[i2,k2] 2372 CM[5,3] = self.standardization.covar[k2,i2] 2373 except ValueError: 2374 pass 2375 except ValueError: 2376 pass 2377 try: 2378 j2 = self.standardization.var_names.index(f'b2_{pf(session)}') 2379 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]] 2380 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2] 2381 try: 2382 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2383 CM[4,5] = self.standardization.covar[j2,k2] 2384 CM[5,4] = self.standardization.covar[k2,j2] 2385 except ValueError: 2386 pass 2387 except ValueError: 2388 pass 2389 try: 2390 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2391 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]] 2392 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2] 2393 except ValueError: 2394 pass 2395 2396 self.sessions[session]['CM'] = CM 2397 2398 elif self.standardization_method == 'indep_sessions': 2399 pass # Not implemented yet 2400 2401 2402 @make_verbal 2403 def repeatabilities(self): 2404 ''' 2405 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x 2406 (for all samples, for anchors, and for unknowns). 2407 ''' 2408 self.msg('Computing reproducibilities for all sessions') 2409 2410 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors') 2411 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors') 2412 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors') 2413 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns') 2414 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples') 2415 2416 2417 @make_verbal 2418 def consolidate(self, tables = True, plots = True): 2419 ''' 2420 Collect information about samples, sessions and repeatabilities. 2421 ''' 2422 self.consolidate_samples() 2423 self.consolidate_sessions() 2424 self.repeatabilities() 2425 2426 if tables: 2427 self.summary() 2428 self.table_of_sessions() 2429 self.table_of_analyses() 2430 self.table_of_samples() 2431 2432 if plots: 2433 self.plot_sessions() 2434 2435 2436 @make_verbal 2437 def rmswd(self, 2438 samples = 'all samples', 2439 sessions = 'all sessions', 2440 ): 2441 ''' 2442 Compute the χ2, root mean squared weighted deviation 2443 (i.e. reduced χ2), and corresponding degrees of freedom of the 2444 Δ4x values for samples in `samples` and sessions in `sessions`. 2445 2446 Only used in `D4xdata.standardize()` with `method='indep_sessions'`. 2447 ''' 2448 if samples == 'all samples': 2449 mysamples = [k for k in self.samples] 2450 elif samples == 'anchors': 2451 mysamples = [k for k in self.anchors] 2452 elif samples == 'unknowns': 2453 mysamples = [k for k in self.unknowns] 2454 else: 2455 mysamples = samples 2456 2457 if sessions == 'all sessions': 2458 sessions = [k for k in self.sessions] 2459 2460 chisq, Nf = 0, 0 2461 for sample in mysamples : 2462 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2463 if len(G) > 1 : 2464 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G]) 2465 Nf += (len(G) - 1) 2466 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G]) 2467 r = (chisq / Nf)**.5 if Nf > 0 else 0 2468 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.') 2469 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf} 2470 2471 2472 @make_verbal 2473 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'): 2474 ''' 2475 Compute the repeatability of `[r[key] for r in self]` 2476 ''' 2477 2478 if samples == 'all samples': 2479 mysamples = [k for k in self.samples] 2480 elif samples == 'anchors': 2481 mysamples = [k for k in self.anchors] 2482 elif samples == 'unknowns': 2483 mysamples = [k for k in self.unknowns] 2484 else: 2485 mysamples = samples 2486 2487 if sessions == 'all sessions': 2488 sessions = [k for k in self.sessions] 2489 2490 if key in ['D47', 'D48']: 2491 # Full disclosure: the definition of Nf is tricky/debatable 2492 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions] 2493 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum() 2494 Nf = len(G) 2495# print(f'len(G) = {Nf}') 2496 Nf -= len([s for s in mysamples if s in self.unknowns]) 2497# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider') 2498 for session in sessions: 2499 Np = len([ 2500 _ for _ in self.standardization.params 2501 if ( 2502 self.standardization.params[_].expr is not None 2503 and ( 2504 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session)) 2505 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session)) 2506 ) 2507 ) 2508 ]) 2509# print(f'session {session}: {Np} parameters to consider') 2510 Na = len({ 2511 r['Sample'] for r in self.sessions[session]['data'] 2512 if r['Sample'] in self.anchors and r['Sample'] in mysamples 2513 }) 2514# print(f'session {session}: {Na} different anchors in that session') 2515 Nf -= min(Np, Na) 2516# print(f'Nf = {Nf}') 2517 2518# for sample in mysamples : 2519# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2520# if len(X) > 1 : 2521# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ]) 2522# if sample in self.unknowns: 2523# Nf += len(X) - 1 2524# else: 2525# Nf += len(X) 2526# if samples in ['anchors', 'all samples']: 2527# Nf -= sum([self.sessions[s]['Np'] for s in sessions]) 2528 r = (chisq / Nf)**.5 if Nf > 0 else 0 2529 2530 else: # if key not in ['D47', 'D48'] 2531 chisq, Nf = 0, 0 2532 for sample in mysamples : 2533 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2534 if len(X) > 1 : 2535 Nf += len(X) - 1 2536 chisq += np.sum([ (x-np.mean(X))**2 for x in X ]) 2537 r = (chisq / Nf)**.5 if Nf > 0 else 0 2538 2539 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.') 2540 return r 2541 2542 def sample_average(self, samples, weights = 'equal', normalize = True): 2543 ''' 2544 Weighted average Δ4x value of a group of samples, accounting for covariance. 2545 2546 Returns the weighed average Δ4x value and associated SE 2547 of a group of samples. Weights are equal by default. If `normalize` is 2548 true, `weights` will be rescaled so that their sum equals 1. 2549 2550 **Examples** 2551 2552 ```python 2553 self.sample_average(['X','Y'], [1, 2]) 2554 ``` 2555 2556 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3, 2557 where Δ4x(X) and Δ4x(Y) are the average Δ4x 2558 values of samples X and Y, respectively. 2559 2560 ```python 2561 self.sample_average(['X','Y'], [1, -1], normalize = False) 2562 ``` 2563 2564 returns the value and SE of the difference Δ4x(X) - Δ4x(Y). 2565 ''' 2566 if weights == 'equal': 2567 weights = [1/len(samples)] * len(samples) 2568 2569 if normalize: 2570 s = sum(weights) 2571 if s: 2572 weights = [w/s for w in weights] 2573 2574 try: 2575# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples] 2576# C = self.standardization.covar[indices,:][:,indices] 2577 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples]) 2578 X = [self.samples[sample][f'D{self._4x}'] for sample in samples] 2579 return correlated_sum(X, C, weights) 2580 except ValueError: 2581 return (0., 0.) 2582 2583 2584 def sample_D4x_covar(self, sample1, sample2 = None): 2585 ''' 2586 Covariance between Δ4x values of samples 2587 2588 Returns the error covariance between the average Δ4x values of two 2589 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`), 2590 returns the Δ4x variance for that sample. 2591 ''' 2592 if sample2 is None: 2593 sample2 = sample1 2594 if self.standardization_method == 'pooled': 2595 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}') 2596 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}') 2597 return self.standardization.covar[i, j] 2598 elif self.standardization_method == 'indep_sessions': 2599 if sample1 == sample2: 2600 return self.samples[sample1][f'SE_D{self._4x}']**2 2601 else: 2602 c = 0 2603 for session in self.sessions: 2604 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1] 2605 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2] 2606 if sdata1 and sdata2: 2607 a = self.sessions[session]['a'] 2608 # !! TODO: CM below does not account for temporal changes in standardization parameters 2609 CM = self.sessions[session]['CM'][:3,:3] 2610 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1]) 2611 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1]) 2612 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2]) 2613 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2]) 2614 c += ( 2615 self.unknowns[sample1][f'session_D{self._4x}'][session][2] 2616 * self.unknowns[sample2][f'session_D{self._4x}'][session][2] 2617 * np.array([[avg_D4x_1, avg_d4x_1, 1]]) 2618 @ CM 2619 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T 2620 ) / a**2 2621 return float(c) 2622 2623 def sample_D4x_correl(self, sample1, sample2 = None): 2624 ''' 2625 Correlation between Δ4x errors of samples 2626 2627 Returns the error correlation between the average Δ4x values of two samples. 2628 ''' 2629 if sample2 is None or sample2 == sample1: 2630 return 1. 2631 return ( 2632 self.sample_D4x_covar(sample1, sample2) 2633 / self.unknowns[sample1][f'SE_D{self._4x}'] 2634 / self.unknowns[sample2][f'SE_D{self._4x}'] 2635 ) 2636 2637 def plot_single_session(self, 2638 session, 2639 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4), 2640 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4), 2641 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75), 2642 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75), 2643 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75), 2644 xylimits = 'free', # | 'constant' 2645 x_label = None, 2646 y_label = None, 2647 error_contour_interval = 'auto', 2648 fig = 'new', 2649 ): 2650 ''' 2651 Generate plot for a single session 2652 ''' 2653 if x_label is None: 2654 x_label = f'δ$_{{{self._4x}}}$ (‰)' 2655 if y_label is None: 2656 y_label = f'Δ$_{{{self._4x}}}$ (‰)' 2657 2658 out = _SessionPlot() 2659 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]] 2660 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]] 2661 2662 if fig == 'new': 2663 out.fig = ppl.figure(figsize = (6,6)) 2664 ppl.subplots_adjust(.1,.1,.9,.9) 2665 2666 out.anchor_analyses, = ppl.plot( 2667 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors], 2668 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors], 2669 **kw_plot_anchors) 2670 out.unknown_analyses, = ppl.plot( 2671 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns], 2672 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns], 2673 **kw_plot_unknowns) 2674 out.anchor_avg = ppl.plot( 2675 np.array([ np.array([ 2676 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1, 2677 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1 2678 ]) for sample in anchors]).T, 2679 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T, 2680 **kw_plot_anchor_avg) 2681 out.unknown_avg = ppl.plot( 2682 np.array([ np.array([ 2683 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1, 2684 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1 2685 ]) for sample in unknowns]).T, 2686 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T, 2687 **kw_plot_unknown_avg) 2688 if xylimits == 'constant': 2689 x = [r[f'd{self._4x}'] for r in self] 2690 y = [r[f'D{self._4x}'] for r in self] 2691 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y) 2692 w, h = x2-x1, y2-y1 2693 x1 -= w/20 2694 x2 += w/20 2695 y1 -= h/20 2696 y2 += h/20 2697 ppl.axis([x1, x2, y1, y2]) 2698 elif xylimits == 'free': 2699 x1, x2, y1, y2 = ppl.axis() 2700 else: 2701 x1, x2, y1, y2 = ppl.axis(xylimits) 2702 2703 if error_contour_interval != 'none': 2704 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2) 2705 XI,YI = np.meshgrid(xi, yi) 2706 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi]) 2707 if error_contour_interval == 'auto': 2708 rng = np.max(SI) - np.min(SI) 2709 if rng <= 0.01: 2710 cinterval = 0.001 2711 elif rng <= 0.03: 2712 cinterval = 0.004 2713 elif rng <= 0.1: 2714 cinterval = 0.01 2715 elif rng <= 0.3: 2716 cinterval = 0.03 2717 elif rng <= 1.: 2718 cinterval = 0.1 2719 else: 2720 cinterval = 0.5 2721 else: 2722 cinterval = error_contour_interval 2723 2724 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval) 2725 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error) 2726 out.clabel = ppl.clabel(out.contour) 2727 2728 ppl.xlabel(x_label) 2729 ppl.ylabel(y_label) 2730 ppl.title(session, weight = 'bold') 2731 ppl.grid(alpha = .2) 2732 out.ax = ppl.gca() 2733 2734 return out 2735 2736 def plot_residuals( 2737 self, 2738 kde = False, 2739 hist = False, 2740 binwidth = 2/3, 2741 dir = 'output', 2742 filename = None, 2743 highlight = [], 2744 colors = None, 2745 figsize = None, 2746 ): 2747 ''' 2748 Plot residuals of each analysis as a function of time (actually, as a function of 2749 the order of analyses in the `D4xdata` object) 2750 2751 + `kde`: whether to add a kernel density estimate of residuals 2752 + `hist`: whether to add a histogram of residuals (incompatible with `kde`) 2753 + `histbins`: specify bin edges for the histogram 2754 + `dir`: the directory in which to save the plot 2755 + `highlight`: a list of samples to highlight 2756 + `colors`: a dict of `{<sample>: <color>}` for all samples 2757 + `figsize`: (width, height) of figure 2758 ''' 2759 2760 from matplotlib import ticker 2761 2762 # Layout 2763 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize) 2764 if hist or kde: 2765 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72) 2766 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15) 2767 else: 2768 ppl.subplots_adjust(.08,.05,.78,.8) 2769 ax1 = ppl.subplot(111) 2770 2771 # Colors 2772 N = len(self.anchors) 2773 if colors is None: 2774 if len(highlight) > 0: 2775 Nh = len(highlight) 2776 if Nh == 1: 2777 colors = {highlight[0]: (0,0,0)} 2778 elif Nh == 3: 2779 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])} 2780 elif Nh == 4: 2781 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])} 2782 else: 2783 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)} 2784 else: 2785 if N == 3: 2786 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])} 2787 elif N == 4: 2788 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])} 2789 else: 2790 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)} 2791 2792 ppl.sca(ax1) 2793 2794 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75) 2795 2796 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$')) 2797 2798 session = self[0]['Session'] 2799 x1 = 0 2800# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self]) 2801 x_sessions = {} 2802 one_or_more_singlets = False 2803 one_or_more_multiplets = False 2804 multiplets = set() 2805 for k,r in enumerate(self): 2806 if r['Session'] != session: 2807 x2 = k-1 2808 x_sessions[session] = (x1+x2)/2 2809 ppl.axvline(k - 0.5, color = 'k', lw = .5) 2810 session = r['Session'] 2811 x1 = k 2812 singlet = len(self.samples[r['Sample']]['data']) == 1 2813 if not singlet: 2814 multiplets.add(r['Sample']) 2815 if r['Sample'] in self.unknowns: 2816 if singlet: 2817 one_or_more_singlets = True 2818 else: 2819 one_or_more_multiplets = True 2820 kw = dict( 2821 marker = 'x' if singlet else '+', 2822 ms = 4 if singlet else 5, 2823 ls = 'None', 2824 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0), 2825 mew = 1, 2826 alpha = 0.2 if singlet else 1, 2827 ) 2828 if highlight and r['Sample'] not in highlight: 2829 kw['alpha'] = 0.2 2830 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw) 2831 x2 = k 2832 x_sessions[session] = (x1+x2)/2 2833 2834 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1) 2835 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1) 2836 if not (hist or kde): 2837 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability['r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center') 2838 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability['r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center') 2839 2840 xmin, xmax, ymin, ymax = ppl.axis() 2841 for s in x_sessions: 2842 ppl.text( 2843 x_sessions[s], 2844 ymax +1, 2845 s, 2846 va = 'bottom', 2847 **( 2848 dict(ha = 'center') 2849 if len(self.sessions[s]['data']) > (0.15 * len(self)) 2850 else dict(ha = 'left', rotation = 45) 2851 ) 2852 ) 2853 2854 if hist or kde: 2855 ppl.sca(ax2) 2856 2857 for s in colors: 2858 kw['marker'] = '+' 2859 kw['ms'] = 5 2860 kw['mec'] = colors[s] 2861 kw['label'] = s 2862 kw['alpha'] = 1 2863 ppl.plot([], [], **kw) 2864 2865 kw['mec'] = (0,0,0) 2866 2867 if one_or_more_singlets: 2868 kw['marker'] = 'x' 2869 kw['ms'] = 4 2870 kw['alpha'] = .2 2871 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other' 2872 ppl.plot([], [], **kw) 2873 2874 if one_or_more_multiplets: 2875 kw['marker'] = '+' 2876 kw['ms'] = 4 2877 kw['alpha'] = 1 2878 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other' 2879 ppl.plot([], [], **kw) 2880 2881 if hist or kde: 2882 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9) 2883 else: 2884 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5) 2885 leg.set_zorder(-1000) 2886 2887 ppl.sca(ax1) 2888 2889 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)') 2890 ppl.xticks([]) 2891 ppl.axis([-1, len(self), None, None]) 2892 2893 if hist or kde: 2894 ppl.sca(ax2) 2895 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors]) 2896 2897 if kde: 2898 from scipy.stats import gaussian_kde 2899 yi = np.linspace(ymin, ymax, 201) 2900 xi = gaussian_kde(X).evaluate(yi) 2901 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1)) 2902# ppl.plot(xi, yi, 'k-', lw = 1) 2903 ppl.axis([0, None, ymin, ymax]) 2904 elif hist: 2905 ppl.hist( 2906 X, 2907 orientation = 'horizontal', 2908 histtype = 'stepfilled', 2909 ec = [.4]*3, 2910 fc = [.25]*3, 2911 alpha = .25, 2912 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)), 2913 ) 2914 ppl.axis([None, None, ymin, ymax]) 2915 ppl.text(0, 0, 2916 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", 2917 size = 7.5, 2918 alpha = 1, 2919 va = 'center', 2920 ha = 'left', 2921 ) 2922 2923 ppl.xticks([]) 2924 ppl.yticks([]) 2925# ax2.spines['left'].set_visible(False) 2926 ax2.spines['right'].set_visible(False) 2927 ax2.spines['top'].set_visible(False) 2928 ax2.spines['bottom'].set_visible(False) 2929 2930 2931 if not os.path.exists(dir): 2932 os.makedirs(dir) 2933 if filename is None: 2934 return fig 2935 elif filename == '': 2936 filename = f'D{self._4x}_residuals.pdf' 2937 ppl.savefig(f'{dir}/{filename}') 2938 ppl.close(fig) 2939 2940 2941 def simulate(self, *args, **kwargs): 2942 ''' 2943 Legacy function with warning message pointing to `virtual_data()` 2944 ''' 2945 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()') 2946 2947 def plot_distribution_of_analyses( 2948 self, 2949 dir = 'output', 2950 filename = None, 2951 vs_time = False, 2952 figsize = (6,4), 2953 subplots_adjust = (0.02, 0.13, 0.85, 0.8), 2954 output = None, 2955 ): 2956 ''' 2957 Plot temporal distribution of all analyses in the data set. 2958 2959 **Parameters** 2960 2961 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially. 2962 ''' 2963 2964 asamples = [s for s in self.anchors] 2965 usamples = [s for s in self.unknowns] 2966 if output is None or output == 'fig': 2967 fig = ppl.figure(figsize = figsize) 2968 ppl.subplots_adjust(*subplots_adjust) 2969 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)]) 2970 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)]) 2971 Xmax += (Xmax-Xmin)/40 2972 Xmin -= (Xmax-Xmin)/41 2973 for k, s in enumerate(asamples + usamples): 2974 if vs_time: 2975 X = [r['TimeTag'] for r in self if r['Sample'] == s] 2976 else: 2977 X = [x for x,r in enumerate(self) if r['Sample'] == s] 2978 Y = [-k for x in X] 2979 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75) 2980 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25) 2981 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r') 2982 ppl.axis([Xmin, Xmax, -k-1, 1]) 2983 ppl.xlabel('\ntime') 2984 ppl.gca().annotate('', 2985 xy = (0.6, -0.02), 2986 xycoords = 'axes fraction', 2987 xytext = (.4, -0.02), 2988 arrowprops = dict(arrowstyle = "->", color = 'k'), 2989 ) 2990 2991 2992 x2 = -1 2993 for session in self.sessions: 2994 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session]) 2995 if vs_time: 2996 ppl.axvline(x1, color = 'k', lw = .75) 2997 if x2 > -1: 2998 if not vs_time: 2999 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5) 3000 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session]) 3001# from xlrd import xldate_as_datetime 3002# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0)) 3003 if vs_time: 3004 ppl.axvline(x2, color = 'k', lw = .75) 3005 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15) 3006 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8) 3007 3008 ppl.xticks([]) 3009 ppl.yticks([]) 3010 3011 if output is None: 3012 if not os.path.exists(dir): 3013 os.makedirs(dir) 3014 if filename == None: 3015 filename = f'D{self._4x}_distribution_of_analyses.pdf' 3016 ppl.savefig(f'{dir}/{filename}') 3017 ppl.close(fig) 3018 elif output == 'ax': 3019 return ppl.gca() 3020 elif output == 'fig': 3021 return fig 3022 3023 3024 def plot_bulk_compositions( 3025 self, 3026 samples = None, 3027 dir = 'output/bulk_compositions', 3028 figsize = (6,6), 3029 subplots_adjust = (0.15, 0.12, 0.95, 0.92), 3030 show = False, 3031 sample_color = (0,.5,1), 3032 analysis_color = (.7,.7,.7), 3033 labeldist = 0.3, 3034 radius = 0.05, 3035 ): 3036 ''' 3037 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses. 3038 3039 By default, creates a directory `./output/bulk_compositions` where plots for 3040 each sample are saved. Another plot named `__all__.pdf` shows all analyses together. 3041 3042 3043 **Parameters** 3044 3045 + `samples`: Only these samples are processed (by default: all samples). 3046 + `dir`: where to save the plots 3047 + `figsize`: (width, height) of figure 3048 + `subplots_adjust`: passed to `subplots_adjust()` 3049 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples, 3050 allowing for interactive visualization/exploration in (δ13C, δ18O) space. 3051 + `sample_color`: color used for replicate markers/labels 3052 + `analysis_color`: color used for sample markers/labels 3053 + `labeldist`: distance (in inches) from replicate markers to replicate labels 3054 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`. 3055 ''' 3056 3057 from matplotlib.patches import Ellipse 3058 3059 if samples is None: 3060 samples = [_ for _ in self.samples] 3061 3062 saved = {} 3063 3064 for s in samples: 3065 3066 fig = ppl.figure(figsize = figsize) 3067 fig.subplots_adjust(*subplots_adjust) 3068 ax = ppl.subplot(111) 3069 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)') 3070 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)') 3071 ppl.title(s) 3072 3073 3074 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']]) 3075 UID = [_['UID'] for _ in self.samples[s]['data']] 3076 XY0 = XY.mean(0) 3077 3078 for xy in XY: 3079 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color) 3080 3081 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color) 3082 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color) 3083 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold') 3084 saved[s] = [XY, XY0] 3085 3086 x1, x2, y1, y2 = ppl.axis() 3087 x0, dx = (x1+x2)/2, (x2-x1)/2 3088 y0, dy = (y1+y2)/2, (y2-y1)/2 3089 dx, dy = [max(max(dx, dy), radius)]*2 3090 3091 ppl.axis([ 3092 x0 - 1.2*dx, 3093 x0 + 1.2*dx, 3094 y0 - 1.2*dy, 3095 y0 + 1.2*dy, 3096 ]) 3097 3098 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0)) 3099 3100 for xy, uid in zip(XY, UID): 3101 3102 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy)) 3103 vector_in_display_space = xy_in_display_space - XY0_in_display_space 3104 3105 if (vector_in_display_space**2).sum() > 0: 3106 3107 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5 3108 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist 3109 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space 3110 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space)) 3111 3112 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color) 3113 3114 else: 3115 3116 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color) 3117 3118 if radius: 3119 ax.add_artist(Ellipse( 3120 xy = XY0, 3121 width = radius*2, 3122 height = radius*2, 3123 ls = (0, (2,2)), 3124 lw = .7, 3125 ec = analysis_color, 3126 fc = 'None', 3127 )) 3128 ppl.text( 3129 XY0[0], 3130 XY0[1]-radius, 3131 f'\n± {radius*1e3:.0f} ppm', 3132 color = analysis_color, 3133 va = 'top', 3134 ha = 'center', 3135 linespacing = 0.4, 3136 size = 8, 3137 ) 3138 3139 if not os.path.exists(dir): 3140 os.makedirs(dir) 3141 fig.savefig(f'{dir}/{s}.pdf') 3142 ppl.close(fig) 3143 3144 fig = ppl.figure(figsize = figsize) 3145 fig.subplots_adjust(*subplots_adjust) 3146 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)') 3147 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)') 3148 3149 for s in saved: 3150 for xy in saved[s][0]: 3151 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color) 3152 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color) 3153 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color) 3154 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold') 3155 3156 x1, x2, y1, y2 = ppl.axis() 3157 ppl.axis([ 3158 x1 - (x2-x1)/10, 3159 x2 + (x2-x1)/10, 3160 y1 - (y2-y1)/10, 3161 y2 + (y2-y1)/10, 3162 ]) 3163 3164 3165 if not os.path.exists(dir): 3166 os.makedirs(dir) 3167 fig.savefig(f'{dir}/__all__.pdf') 3168 if show: 3169 ppl.show() 3170 ppl.close(fig) 3171 3172 3173 3174class D47data(D4xdata): 3175 ''' 3176 Store and process data for a large set of Δ47 analyses, 3177 usually comprising more than one analytical session. 3178 ''' 3179 3180 Nominal_D4x = { 3181 'ETH-1': 0.2052, 3182 'ETH-2': 0.2085, 3183 'ETH-3': 0.6132, 3184 'ETH-4': 0.4511, 3185 'IAEA-C1': 0.3018, 3186 'IAEA-C2': 0.6409, 3187 'MERCK': 0.5135, 3188 } # I-CDES (Bernasconi et al., 2021) 3189 ''' 3190 Nominal Δ47 values assigned to the Δ47 anchor samples, used by 3191 `D47data.standardize()` to normalize unknown samples to an absolute Δ47 3192 reference frame. 3193 3194 By default equal to (after [Bernasconi et al. (2021)](https://doi.org/10.1029/2020GC009588)): 3195 ```py 3196 { 3197 'ETH-1' : 0.2052, 3198 'ETH-2' : 0.2085, 3199 'ETH-3' : 0.6132, 3200 'ETH-4' : 0.4511, 3201 'IAEA-C1' : 0.3018, 3202 'IAEA-C2' : 0.6409, 3203 'MERCK' : 0.5135, 3204 } 3205 ``` 3206 ''' 3207 3208 3209 @property 3210 def Nominal_D47(self): 3211 return self.Nominal_D4x 3212 3213 3214 @Nominal_D47.setter 3215 def Nominal_D47(self, new): 3216 self.Nominal_D4x = dict(**new) 3217 self.refresh() 3218 3219 3220 def __init__(self, l = [], **kwargs): 3221 ''' 3222 **Parameters:** same as `D4xdata.__init__()` 3223 ''' 3224 D4xdata.__init__(self, l = l, mass = '47', **kwargs) 3225 3226 3227 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'): 3228 ''' 3229 Find all samples for which `Teq` is specified, compute equilibrium Δ47 3230 value for that temperature, and add treat these samples as additional anchors. 3231 3232 **Parameters** 3233 3234 + `fCo2eqD47`: Which CO2 equilibrium law to use 3235 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127); 3236 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)). 3237 + `priority`: if `replace`: forget old anchors and only use the new ones; 3238 if `new`: keep pre-existing anchors but update them in case of conflict 3239 between old and new Δ47 values; 3240 if `old`: keep pre-existing anchors but preserve their original Δ47 3241 values in case of conflict. 3242 ''' 3243 f = { 3244 'petersen': fCO2eqD47_Petersen, 3245 'wang': fCO2eqD47_Wang, 3246 }[fCo2eqD47] 3247 foo = {} 3248 for r in self: 3249 if 'Teq' in r: 3250 if r['Sample'] in foo: 3251 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.' 3252 else: 3253 foo[r['Sample']] = f(r['Teq']) 3254 else: 3255 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.' 3256 3257 if priority == 'replace': 3258 self.Nominal_D47 = {} 3259 for s in foo: 3260 if priority != 'old' or s not in self.Nominal_D47: 3261 self.Nominal_D47[s] = foo[s] 3262 3263 3264 3265 3266class D48data(D4xdata): 3267 ''' 3268 Store and process data for a large set of Δ48 analyses, 3269 usually comprising more than one analytical session. 3270 ''' 3271 3272 Nominal_D4x = { 3273 'ETH-1': 0.138, 3274 'ETH-2': 0.138, 3275 'ETH-3': 0.270, 3276 'ETH-4': 0.223, 3277 'GU-1': -0.419, 3278 } # (Fiebig et al., 2019, 2021) 3279 ''' 3280 Nominal Δ48 values assigned to the Δ48 anchor samples, used by 3281 `D48data.standardize()` to normalize unknown samples to an absolute Δ48 3282 reference frame. 3283 3284 By default equal to (after [Fiebig et al. (2019)](https://doi.org/10.1016/j.chemgeo.2019.05.019), 3285 Fiebig et al. (in press)): 3286 3287 ```py 3288 { 3289 'ETH-1' : 0.138, 3290 'ETH-2' : 0.138, 3291 'ETH-3' : 0.270, 3292 'ETH-4' : 0.223, 3293 'GU-1' : -0.419, 3294 } 3295 ``` 3296 ''' 3297 3298 3299 @property 3300 def Nominal_D48(self): 3301 return self.Nominal_D4x 3302 3303 3304 @Nominal_D48.setter 3305 def Nominal_D48(self, new): 3306 self.Nominal_D4x = dict(**new) 3307 self.refresh() 3308 3309 3310 def __init__(self, l = [], **kwargs): 3311 ''' 3312 **Parameters:** same as `D4xdata.__init__()` 3313 ''' 3314 D4xdata.__init__(self, l = l, mass = '48', **kwargs) 3315 3316 3317class _SessionPlot(): 3318 ''' 3319 Simple placeholder class 3320 ''' 3321 def __init__(self): 3322 pass
def
fCO2eqD47_Petersen(T):
63def fCO2eqD47_Petersen(T): 64 ''' 65 CO2 equilibrium Δ47 value as a function of T (in degrees C) 66 according to [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127). 67 68 ''' 69 return float(_fCO2eqD47_Petersen(T))
CO2 equilibrium Δ47 value as a function of T (in degrees C) according to Petersen et al. (2019).
def
fCO2eqD47_Wang(T):
74def fCO2eqD47_Wang(T): 75 ''' 76 CO2 equilibrium Δ47 value as a function of `T` (in degrees C) 77 according to [Wang et al. (2004)](https://doi.org/10.1016/j.gca.2004.05.039) 78 (supplementary data of [Dennis et al., 2011](https://doi.org/10.1016/j.gca.2011.09.025)). 79 ''' 80 return float(_fCO2eqD47_Wang(T))
CO2 equilibrium Δ47 value as a function of T (in degrees C)
according to Wang et al. (2004)
(supplementary data of Dennis et al., 2011).
def
make_csv(x, hsep=',', vsep='\n'):
102def make_csv(x, hsep = ',', vsep = '\n'): 103 ''' 104 Formats a list of lists of strings as a CSV 105 106 **Parameters** 107 108 + `x`: the list of lists of strings to format 109 + `hsep`: the field separator (`,` by default) 110 + `vsep`: the line-ending convention to use (`\\n` by default) 111 112 **Example** 113 114 ```py 115 print(make_csv([['a', 'b', 'c'], ['d', 'e', 'f']])) 116 ``` 117 118 outputs: 119 120 ```py 121 a,b,c 122 d,e,f 123 ``` 124 ''' 125 return vsep.join([hsep.join(l) for l in x])
Formats a list of lists of strings as a CSV
Parameters
x: the list of lists of strings to formathsep: the field separator (,by default)vsep: the line-ending convention to use (\nby default)
Example
print(make_csv([['a', 'b', 'c'], ['d', 'e', 'f']]))
outputs:
a,b,c
d,e,f
def
pf(txt):
128def pf(txt): 129 ''' 130 Modify string `txt` to follow `lmfit.Parameter()` naming rules. 131 ''' 132 return txt.replace('-','_').replace('.','_').replace(' ','_')
Modify string txt to follow lmfit.Parameter() naming rules.
def
smart_type(x):
135def smart_type(x): 136 ''' 137 Tries to convert string `x` to a float if it includes a decimal point, or 138 to an integer if it does not. If both attempts fail, return the original 139 string unchanged. 140 ''' 141 try: 142 y = float(x) 143 except ValueError: 144 return x 145 if '.' not in x: 146 return int(y) 147 return y
Tries to convert string x to a float if it includes a decimal point, or
to an integer if it does not. If both attempts fail, return the original
string unchanged.
def
pretty_table(x, header=1, hsep=' ', vsep='–', align='<'):
150def pretty_table(x, header = 1, hsep = ' ', vsep = '–', align = '<'): 151 ''' 152 Reads a list of lists of strings and outputs an ascii table 153 154 **Parameters** 155 156 + `x`: a list of lists of strings 157 + `header`: the number of lines to treat as header lines 158 + `hsep`: the horizontal separator between columns 159 + `vsep`: the character to use as vertical separator 160 + `align`: string of left (`<`) or right (`>`) alignment characters. 161 162 **Example** 163 164 ```py 165 x = [['A', 'B', 'C'], ['1', '1.9999', 'foo'], ['10', 'x', 'bar']] 166 print(pretty_table(x)) 167 ``` 168 yields: 169 ``` 170 -- ------ --- 171 A B C 172 -- ------ --- 173 1 1.9999 foo 174 10 x bar 175 -- ------ --- 176 ``` 177 178 ''' 179 txt = [] 180 widths = [np.max([len(e) for e in c]) for c in zip(*x)] 181 182 if len(widths) > len(align): 183 align += '>' * (len(widths)-len(align)) 184 sepline = hsep.join([vsep*w for w in widths]) 185 txt += [sepline] 186 for k,l in enumerate(x): 187 if k and k == header: 188 txt += [sepline] 189 txt += [hsep.join([f'{e:{a}{w}}' for e, w, a in zip(l, widths, align)])] 190 txt += [sepline] 191 txt += [''] 192 return '\n'.join(txt)
Reads a list of lists of strings and outputs an ascii table
Parameters
x: a list of lists of stringsheader: the number of lines to treat as header lineshsep: the horizontal separator between columnsvsep: the character to use as vertical separatoralign: string of left (<) or right (>) alignment characters.
Example
x = [['A', 'B', 'C'], ['1', '1.9999', 'foo'], ['10', 'x', 'bar']]
print(pretty_table(x))
yields:
-- ------ ---
A B C
-- ------ ---
1 1.9999 foo
10 x bar
-- ------ ---
def
transpose_table(x):
195def transpose_table(x): 196 ''' 197 Transpose a list if lists 198 199 **Parameters** 200 201 + `x`: a list of lists 202 203 **Example** 204 205 ```py 206 x = [[1, 2], [3, 4]] 207 print(transpose_table(x)) # yields: [[1, 3], [2, 4]] 208 ``` 209 ''' 210 return [[e for e in c] for c in zip(*x)]
Transpose a list if lists
Parameters
x: a list of lists
Example
x = [[1, 2], [3, 4]]
print(transpose_table(x)) # yields: [[1, 3], [2, 4]]
def
w_avg(X, sX):
213def w_avg(X, sX) : 214 ''' 215 Compute variance-weighted average 216 217 Returns the value and SE of the weighted average of the elements of `X`, 218 with relative weights equal to their inverse variances (`1/sX**2`). 219 220 **Parameters** 221 222 + `X`: array-like of elements to average 223 + `sX`: array-like of the corresponding SE values 224 225 **Tip** 226 227 If `X` and `sX` are initially arranged as a list of `(x, sx)` doublets, 228 they may be rearranged using `zip()`: 229 230 ```python 231 foo = [(0, 1), (1, 0.5), (2, 0.5)] 232 print(w_avg(*zip(*foo))) # yields: (1.3333333333333333, 0.3333333333333333) 233 ``` 234 ''' 235 X = [ x for x in X ] 236 sX = [ sx for sx in sX ] 237 W = [ sx**-2 for sx in sX ] 238 W = [ w/sum(W) for w in W ] 239 Xavg = sum([ w*x for w,x in zip(W,X) ]) 240 sXavg = sum([ w**2*sx**2 for w,sx in zip(W,sX) ])**.5 241 return Xavg, sXavg
Compute variance-weighted average
Returns the value and SE of the weighted average of the elements of X,
with relative weights equal to their inverse variances (1/sX**2).
Parameters
X: array-like of elements to averagesX: array-like of the corresponding SE values
Tip
If X and sX are initially arranged as a list of (x, sx) doublets,
they may be rearranged using zip():
foo = [(0, 1), (1, 0.5), (2, 0.5)]
print(w_avg(*zip(*foo))) # yields: (1.3333333333333333, 0.3333333333333333)
def
read_csv(filename, sep=''):
244def read_csv(filename, sep = ''): 245 ''' 246 Read contents of `filename` in csv format and return a list of dictionaries. 247 248 In the csv string, spaces before and after field separators (`','` by default) 249 are optional. 250 251 **Parameters** 252 253 + `filename`: the csv file to read 254 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`, 255 whichever appers most often in the contents of `filename`. 256 ''' 257 with open(filename) as fid: 258 txt = fid.read() 259 260 if sep == '': 261 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0] 262 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()] 263 return [{k: smart_type(v) for k,v in zip(txt[0], l) if v} for l in txt[1:]]
Read contents of filename in csv format and return a list of dictionaries.
In the csv string, spaces before and after field separators (',' by default)
are optional.
Parameters
filename: the csv file to readsep: csv separator delimiting the fields. By default, use,,;, or, whichever appers most often in the contents offilename.
def
simulate_single_analysis( sample='MYSAMPLE', d13Cwg_VPDB=-4.0, d18Owg_VSMOW=26.0, d13C_VPDB=None, d18O_VPDB=None, D47=None, D48=None, D49=0.0, D17O=0.0, a47=1.0, b47=0.0, c47=-0.9, a48=1.0, b48=0.0, c48=-0.45, Nominal_D47=None, Nominal_D48=None, Nominal_d13C_VPDB=None, Nominal_d18O_VPDB=None, ALPHA_18O_ACID_REACTION=None, R13_VPDB=None, R17_VSMOW=None, R18_VSMOW=None, LAMBDA_17=None, R18_VPDB=None):
266def simulate_single_analysis( 267 sample = 'MYSAMPLE', 268 d13Cwg_VPDB = -4., d18Owg_VSMOW = 26., 269 d13C_VPDB = None, d18O_VPDB = None, 270 D47 = None, D48 = None, D49 = 0., D17O = 0., 271 a47 = 1., b47 = 0., c47 = -0.9, 272 a48 = 1., b48 = 0., c48 = -0.45, 273 Nominal_D47 = None, 274 Nominal_D48 = None, 275 Nominal_d13C_VPDB = None, 276 Nominal_d18O_VPDB = None, 277 ALPHA_18O_ACID_REACTION = None, 278 R13_VPDB = None, 279 R17_VSMOW = None, 280 R18_VSMOW = None, 281 LAMBDA_17 = None, 282 R18_VPDB = None, 283 ): 284 ''' 285 Compute working-gas delta values for a single analysis, assuming a stochastic working 286 gas and a “perfect” measurement (i.e. raw Δ values are identical to absolute values). 287 288 **Parameters** 289 290 + `sample`: sample name 291 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas 292 (respectively –4 and +26 ‰ by default) 293 + `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample 294 + `D47`, `D48`, `D49`, `D17O`: clumped-isotope and oxygen-17 anomalies 295 of the carbonate sample 296 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and 297 Δ48 values if `D47` or `D48` are not specified 298 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and 299 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified 300 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor 301 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17 302 correction parameters (by default equal to the `D4xdata` default values) 303 304 Returns a dictionary with fields 305 `['Sample', 'D17O', 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'd45', 'd46', 'd47', 'd48', 'd49']`. 306 ''' 307 308 if Nominal_d13C_VPDB is None: 309 Nominal_d13C_VPDB = D4xdata().Nominal_d13C_VPDB 310 311 if Nominal_d18O_VPDB is None: 312 Nominal_d18O_VPDB = D4xdata().Nominal_d18O_VPDB 313 314 if ALPHA_18O_ACID_REACTION is None: 315 ALPHA_18O_ACID_REACTION = D4xdata().ALPHA_18O_ACID_REACTION 316 317 if R13_VPDB is None: 318 R13_VPDB = D4xdata().R13_VPDB 319 320 if R17_VSMOW is None: 321 R17_VSMOW = D4xdata().R17_VSMOW 322 323 if R18_VSMOW is None: 324 R18_VSMOW = D4xdata().R18_VSMOW 325 326 if LAMBDA_17 is None: 327 LAMBDA_17 = D4xdata().LAMBDA_17 328 329 if R18_VPDB is None: 330 R18_VPDB = D4xdata().R18_VPDB 331 332 R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW) ** LAMBDA_17 333 334 if Nominal_D47 is None: 335 Nominal_D47 = D47data().Nominal_D47 336 337 if Nominal_D48 is None: 338 Nominal_D48 = D48data().Nominal_D48 339 340 if d13C_VPDB is None: 341 if sample in Nominal_d13C_VPDB: 342 d13C_VPDB = Nominal_d13C_VPDB[sample] 343 else: 344 raise KeyError(f"Sample {sample} is missing d13C_VDP value, and it is not defined in Nominal_d13C_VDP.") 345 346 if d18O_VPDB is None: 347 if sample in Nominal_d18O_VPDB: 348 d18O_VPDB = Nominal_d18O_VPDB[sample] 349 else: 350 raise KeyError(f"Sample {sample} is missing d18O_VPDB value, and it is not defined in Nominal_d18O_VPDB.") 351 352 if D47 is None: 353 if sample in Nominal_D47: 354 D47 = Nominal_D47[sample] 355 else: 356 raise KeyError(f"Sample {sample} is missing D47 value, and it is not defined in Nominal_D47.") 357 358 if D48 is None: 359 if sample in Nominal_D48: 360 D48 = Nominal_D48[sample] 361 else: 362 raise KeyError(f"Sample {sample} is missing D48 value, and it is not defined in Nominal_D48.") 363 364 X = D4xdata() 365 X.R13_VPDB = R13_VPDB 366 X.R17_VSMOW = R17_VSMOW 367 X.R18_VSMOW = R18_VSMOW 368 X.LAMBDA_17 = LAMBDA_17 369 X.R18_VPDB = R18_VPDB 370 X.R17_VPDB = R17_VSMOW * (R18_VPDB / R18_VSMOW)**LAMBDA_17 371 372 R45wg, R46wg, R47wg, R48wg, R49wg = X.compute_isobar_ratios( 373 R13 = R13_VPDB * (1 + d13Cwg_VPDB/1000), 374 R18 = R18_VSMOW * (1 + d18Owg_VSMOW/1000), 375 ) 376 R45, R46, R47, R48, R49 = X.compute_isobar_ratios( 377 R13 = R13_VPDB * (1 + d13C_VPDB/1000), 378 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION, 379 D17O=D17O, D47=D47, D48=D48, D49=D49, 380 ) 381 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = X.compute_isobar_ratios( 382 R13 = R13_VPDB * (1 + d13C_VPDB/1000), 383 R18 = R18_VPDB * (1 + d18O_VPDB/1000) * ALPHA_18O_ACID_REACTION, 384 D17O=D17O, 385 ) 386 387 d45 = 1000 * (R45/R45wg - 1) 388 d46 = 1000 * (R46/R46wg - 1) 389 d47 = 1000 * (R47/R47wg - 1) 390 d48 = 1000 * (R48/R48wg - 1) 391 d49 = 1000 * (R49/R49wg - 1) 392 393 for k in range(3): # dumb iteration to adjust for small changes in d47 394 R47raw = (1 + (a47 * D47 + b47 * d47 + c47)/1000) * R47stoch 395 R48raw = (1 + (a48 * D48 + b48 * d48 + c48)/1000) * R48stoch 396 d47 = 1000 * (R47raw/R47wg - 1) 397 d48 = 1000 * (R48raw/R48wg - 1) 398 399 return dict( 400 Sample = sample, 401 D17O = D17O, 402 d13Cwg_VPDB = d13Cwg_VPDB, 403 d18Owg_VSMOW = d18Owg_VSMOW, 404 d45 = d45, 405 d46 = d46, 406 d47 = d47, 407 d48 = d48, 408 d49 = d49, 409 )
Compute working-gas delta values for a single analysis, assuming a stochastic working gas and a “perfect” measurement (i.e. raw Δ values are identical to absolute values).
Parameters
sample: sample named13Cwg_VPDB,d18Owg_VSMOW: bulk composition of the working gas (respectively –4 and +26 ‰ by default)d13C_VPDB,d18O_VPDB: bulk composition of the carbonate sampleD47,D48,D49,D17O: clumped-isotope and oxygen-17 anomalies of the carbonate sampleNominal_D47,Nominal_D48: where to lookup Δ47 and Δ48 values ifD47orD48are not specifiedNominal_d13C_VPDB,Nominal_d18O_VPDB: where to lookup δ13C and δ18O values ifd13C_VPDBord18O_VPDBare not specifiedALPHA_18O_ACID_REACTION: 18O/16O acid fractionation factorR13_VPDB,R17_VSMOW,R18_VSMOW,LAMBDA_17,R18_VPDB: oxygen-17 correction parameters (by default equal to theD4xdatadefault values)
Returns a dictionary with fields
['Sample', 'D17O', 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'd45', 'd46', 'd47', 'd48', 'd49'].
def
virtual_data( samples=[], a47=1.0, b47=0.0, c47=-0.9, a48=1.0, b48=0.0, c48=-0.45, rd45=0.02, rd46=0.06, rD47=0.015, rD48=0.045, d13Cwg_VPDB=None, d18Owg_VSMOW=None, session=None, Nominal_D47=None, Nominal_D48=None, Nominal_d13C_VPDB=None, Nominal_d18O_VPDB=None, ALPHA_18O_ACID_REACTION=None, R13_VPDB=None, R17_VSMOW=None, R18_VSMOW=None, LAMBDA_17=None, R18_VPDB=None, seed=0):
412def virtual_data( 413 samples = [], 414 a47 = 1., b47 = 0., c47 = -0.9, 415 a48 = 1., b48 = 0., c48 = -0.45, 416 rd45 = 0.020, rd46 = 0.060, 417 rD47 = 0.015, rD48 = 0.045, 418 d13Cwg_VPDB = None, d18Owg_VSMOW = None, 419 session = None, 420 Nominal_D47 = None, Nominal_D48 = None, 421 Nominal_d13C_VPDB = None, Nominal_d18O_VPDB = None, 422 ALPHA_18O_ACID_REACTION = None, 423 R13_VPDB = None, 424 R17_VSMOW = None, 425 R18_VSMOW = None, 426 LAMBDA_17 = None, 427 R18_VPDB = None, 428 seed = 0, 429 ): 430 ''' 431 Return list with simulated analyses from a single session. 432 433 **Parameters** 434 435 + `samples`: a list of entries; each entry is a dictionary with the following fields: 436 * `Sample`: the name of the sample 437 * `d13C_VPDB`, `d18O_VPDB`: bulk composition of the carbonate sample 438 * `D47`, `D48`, `D49`, `D17O` (all optional): clumped-isotope and oxygen-17 anomalies of the carbonate sample 439 * `N`: how many analyses to generate for this sample 440 + `a47`: scrambling factor for Δ47 441 + `b47`: compositional nonlinearity for Δ47 442 + `c47`: working gas offset for Δ47 443 + `a48`: scrambling factor for Δ48 444 + `b48`: compositional nonlinearity for Δ48 445 + `c48`: working gas offset for Δ48 446 + `rd45`: analytical repeatability of δ45 447 + `rd46`: analytical repeatability of δ46 448 + `rD47`: analytical repeatability of Δ47 449 + `rD48`: analytical repeatability of Δ48 450 + `d13Cwg_VPDB`, `d18Owg_VSMOW`: bulk composition of the working gas 451 (by default equal to the `simulate_single_analysis` default values) 452 + `session`: name of the session (no name by default) 453 + `Nominal_D47`, `Nominal_D48`: where to lookup Δ47 and Δ48 values 454 if `D47` or `D48` are not specified (by default equal to the `simulate_single_analysis` defaults) 455 + `Nominal_d13C_VPDB`, `Nominal_d18O_VPDB`: where to lookup δ13C and 456 δ18O values if `d13C_VPDB` or `d18O_VPDB` are not specified 457 (by default equal to the `simulate_single_analysis` defaults) 458 + `ALPHA_18O_ACID_REACTION`: 18O/16O acid fractionation factor 459 (by default equal to the `simulate_single_analysis` defaults) 460 + `R13_VPDB`, `R17_VSMOW`, `R18_VSMOW`, `LAMBDA_17`, `R18_VPDB`: oxygen-17 461 correction parameters (by default equal to the `simulate_single_analysis` default) 462 + `seed`: explicitly set to a non-zero value to achieve random but repeatable simulations 463 464 465 Here is an example of using this method to generate an arbitrary combination of 466 anchors and unknowns for a bunch of sessions: 467 468 ```py 469 args = dict( 470 samples = [ 471 dict(Sample = 'ETH-1', N = 4), 472 dict(Sample = 'ETH-2', N = 5), 473 dict(Sample = 'ETH-3', N = 6), 474 dict(Sample = 'FOO', N = 2, 475 d13C_VPDB = -5., d18O_VPDB = -10., 476 D47 = 0.3, D48 = 0.15), 477 ], rD47 = 0.010, rD48 = 0.030) 478 479 session1 = virtual_data(session = 'Session_01', **args, seed = 123) 480 session2 = virtual_data(session = 'Session_02', **args, seed = 1234) 481 session3 = virtual_data(session = 'Session_03', **args, seed = 12345) 482 session4 = virtual_data(session = 'Session_04', **args, seed = 123456) 483 484 D = D47data(session1 + session2 + session3 + session4) 485 486 D.crunch() 487 D.standardize() 488 489 D.table_of_sessions(verbose = True, save_to_file = False) 490 D.table_of_samples(verbose = True, save_to_file = False) 491 D.table_of_analyses(verbose = True, save_to_file = False) 492 ``` 493 494 This should output something like: 495 496 ``` 497 [table_of_sessions] 498 –––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– –––––––––––––– 499 Session Na Nu d13Cwg_VPDB d18Owg_VSMOW r_d13C r_d18O r_D47 a ± SE 1e3 x b ± SE c ± SE 500 –––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– –––––––––––––– 501 Session_01 15 2 -4.000 26.000 0.0000 0.0000 0.0110 0.997 ± 0.017 -0.097 ± 0.244 -0.896 ± 0.006 502 Session_02 15 2 -4.000 26.000 0.0000 0.0000 0.0109 1.002 ± 0.017 -0.110 ± 0.244 -0.901 ± 0.006 503 Session_03 15 2 -4.000 26.000 0.0000 0.0000 0.0107 1.010 ± 0.017 -0.037 ± 0.244 -0.904 ± 0.006 504 Session_04 15 2 -4.000 26.000 0.0000 0.0000 0.0106 1.001 ± 0.017 -0.181 ± 0.244 -0.894 ± 0.006 505 –––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– –––––––––––––– 506 507 [table_of_samples] 508 –––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– 509 Sample N d13C_VPDB d18O_VSMOW D47 SE 95% CL SD p_Levene 510 –––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– 511 ETH-1 16 2.02 37.02 0.2052 0.0079 512 ETH-2 20 -10.17 19.88 0.2085 0.0100 513 ETH-3 24 1.71 37.45 0.6132 0.0105 514 FOO 8 -5.00 28.91 0.2989 0.0040 ± 0.0080 0.0101 0.638 515 –––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– –––––––– 516 517 [table_of_analyses] 518 ––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– 519 UID Session Sample d13Cwg_VPDB d18Owg_VSMOW d45 d46 d47 d48 d49 d13C_VPDB d18O_VSMOW D47raw D48raw D49raw D47 520 ––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– 521 1 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.122986 21.273526 27.780042 2.020000 37.024281 -0.706013 -0.328878 -0.000013 0.192554 522 2 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.130144 21.282615 27.780042 2.020000 37.024281 -0.698974 -0.319981 -0.000013 0.199615 523 3 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.149219 21.299572 27.780042 2.020000 37.024281 -0.680215 -0.303383 -0.000013 0.218429 524 4 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.136616 21.233128 27.780042 2.020000 37.024281 -0.692609 -0.368421 -0.000013 0.205998 525 5 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.697171 -12.203054 -18.023381 -10.170000 19.875825 -0.680771 -0.290128 -0.000002 0.215054 526 6 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701124 -12.184422 -18.023381 -10.170000 19.875825 -0.684772 -0.271272 -0.000002 0.211041 527 7 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.715105 -12.195251 -18.023381 -10.170000 19.875825 -0.698923 -0.282232 -0.000002 0.196848 528 8 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701529 -12.204963 -18.023381 -10.170000 19.875825 -0.685182 -0.292061 -0.000002 0.210630 529 9 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.711420 -12.228478 -18.023381 -10.170000 19.875825 -0.695193 -0.315859 -0.000002 0.200589 530 10 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.666719 22.296486 28.306614 1.710000 37.450394 -0.290459 -0.147284 -0.000014 0.609363 531 11 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.671553 22.291060 28.306614 1.710000 37.450394 -0.285706 -0.152592 -0.000014 0.614130 532 12 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.652854 22.273271 28.306614 1.710000 37.450394 -0.304093 -0.169990 -0.000014 0.595689 533 13 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.684168 22.263156 28.306614 1.710000 37.450394 -0.273302 -0.179883 -0.000014 0.626572 534 14 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.662702 22.253578 28.306614 1.710000 37.450394 -0.294409 -0.189251 -0.000014 0.605401 535 15 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.681957 22.230907 28.306614 1.710000 37.450394 -0.275476 -0.211424 -0.000014 0.624391 536 16 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.312044 5.395798 4.665655 -5.000000 28.907344 -0.598436 -0.268176 -0.000006 0.298996 537 17 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.328123 5.307086 4.665655 -5.000000 28.907344 -0.582387 -0.356389 -0.000006 0.315092 538 18 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.122201 21.340606 27.780042 2.020000 37.024281 -0.706785 -0.263217 -0.000013 0.195135 539 19 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.134868 21.305714 27.780042 2.020000 37.024281 -0.694328 -0.297370 -0.000013 0.207564 540 20 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.140008 21.261931 27.780042 2.020000 37.024281 -0.689273 -0.340227 -0.000013 0.212607 541 21 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.135540 21.298472 27.780042 2.020000 37.024281 -0.693667 -0.304459 -0.000013 0.208224 542 22 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701213 -12.202602 -18.023381 -10.170000 19.875825 -0.684862 -0.289671 -0.000002 0.213842 543 23 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.685649 -12.190405 -18.023381 -10.170000 19.875825 -0.669108 -0.277327 -0.000002 0.229559 544 24 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.719003 -12.257955 -18.023381 -10.170000 19.875825 -0.702869 -0.345692 -0.000002 0.195876 545 25 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.700592 -12.204641 -18.023381 -10.170000 19.875825 -0.684233 -0.291735 -0.000002 0.214469 546 26 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.720426 -12.214561 -18.023381 -10.170000 19.875825 -0.704308 -0.301774 -0.000002 0.194439 547 27 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.673044 22.262090 28.306614 1.710000 37.450394 -0.284240 -0.180926 -0.000014 0.616730 548 28 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.666542 22.263401 28.306614 1.710000 37.450394 -0.290634 -0.179643 -0.000014 0.610350 549 29 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.680487 22.243486 28.306614 1.710000 37.450394 -0.276921 -0.199121 -0.000014 0.624031 550 30 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.663900 22.245175 28.306614 1.710000 37.450394 -0.293231 -0.197469 -0.000014 0.607759 551 31 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.674379 22.301309 28.306614 1.710000 37.450394 -0.282927 -0.142568 -0.000014 0.618039 552 32 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.660825 22.270466 28.306614 1.710000 37.450394 -0.296255 -0.172733 -0.000014 0.604742 553 33 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.294076 5.349940 4.665655 -5.000000 28.907344 -0.616369 -0.313776 -0.000006 0.283707 554 34 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.313775 5.292121 4.665655 -5.000000 28.907344 -0.596708 -0.371269 -0.000006 0.303323 555 35 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.121613 21.259909 27.780042 2.020000 37.024281 -0.707364 -0.342207 -0.000013 0.194934 556 36 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.145714 21.304889 27.780042 2.020000 37.024281 -0.683661 -0.298178 -0.000013 0.218401 557 37 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.126573 21.325093 27.780042 2.020000 37.024281 -0.702485 -0.278401 -0.000013 0.199764 558 38 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.132057 21.323211 27.780042 2.020000 37.024281 -0.697092 -0.280244 -0.000013 0.205104 559 39 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.708448 -12.232023 -18.023381 -10.170000 19.875825 -0.692185 -0.319447 -0.000002 0.208915 560 40 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.714417 -12.202504 -18.023381 -10.170000 19.875825 -0.698226 -0.289572 -0.000002 0.202934 561 41 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.720039 -12.264469 -18.023381 -10.170000 19.875825 -0.703917 -0.352285 -0.000002 0.197300 562 42 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701953 -12.228550 -18.023381 -10.170000 19.875825 -0.685611 -0.315932 -0.000002 0.215423 563 43 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.704535 -12.213634 -18.023381 -10.170000 19.875825 -0.688224 -0.300836 -0.000002 0.212837 564 44 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.652920 22.230043 28.306614 1.710000 37.450394 -0.304028 -0.212269 -0.000014 0.594265 565 45 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.691485 22.261017 28.306614 1.710000 37.450394 -0.266106 -0.181975 -0.000014 0.631810 566 46 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.679119 22.305357 28.306614 1.710000 37.450394 -0.278266 -0.138609 -0.000014 0.619771 567 47 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.663623 22.327286 28.306614 1.710000 37.450394 -0.293503 -0.117161 -0.000014 0.604685 568 48 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.678524 22.282103 28.306614 1.710000 37.450394 -0.278851 -0.161352 -0.000014 0.619192 569 49 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.666246 22.283361 28.306614 1.710000 37.450394 -0.290925 -0.160121 -0.000014 0.607238 570 50 Session_03 FOO -4.000 26.000 -0.840413 2.828738 1.309929 5.340249 4.665655 -5.000000 28.907344 -0.600546 -0.323413 -0.000006 0.300148 571 51 Session_03 FOO -4.000 26.000 -0.840413 2.828738 1.317548 5.334102 4.665655 -5.000000 28.907344 -0.592942 -0.329524 -0.000006 0.307676 572 52 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.136865 21.300298 27.780042 2.020000 37.024281 -0.692364 -0.302672 -0.000013 0.204033 573 53 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.133538 21.291260 27.780042 2.020000 37.024281 -0.695637 -0.311519 -0.000013 0.200762 574 54 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.139991 21.319865 27.780042 2.020000 37.024281 -0.689290 -0.283519 -0.000013 0.207107 575 55 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.145748 21.330075 27.780042 2.020000 37.024281 -0.683629 -0.273524 -0.000013 0.212766 576 56 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.702989 -12.202762 -18.023381 -10.170000 19.875825 -0.686660 -0.289833 -0.000002 0.204507 577 57 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.692830 -12.240287 -18.023381 -10.170000 19.875825 -0.676377 -0.327811 -0.000002 0.214786 578 58 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.702899 -12.180291 -18.023381 -10.170000 19.875825 -0.686568 -0.267091 -0.000002 0.204598 579 59 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.709282 -12.282257 -18.023381 -10.170000 19.875825 -0.693029 -0.370287 -0.000002 0.198140 580 60 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.679330 -12.235994 -18.023381 -10.170000 19.875825 -0.662712 -0.323466 -0.000002 0.228446 581 61 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.695594 22.238663 28.306614 1.710000 37.450394 -0.262066 -0.203838 -0.000014 0.634200 582 62 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.663504 22.286354 28.306614 1.710000 37.450394 -0.293620 -0.157194 -0.000014 0.602656 583 63 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.666457 22.254290 28.306614 1.710000 37.450394 -0.290717 -0.188555 -0.000014 0.605558 584 64 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.666910 22.223232 28.306614 1.710000 37.450394 -0.290271 -0.218930 -0.000014 0.606004 585 65 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.679662 22.257256 28.306614 1.710000 37.450394 -0.277732 -0.185653 -0.000014 0.618539 586 66 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.676768 22.267680 28.306614 1.710000 37.450394 -0.280578 -0.175459 -0.000014 0.615693 587 67 Session_04 FOO -4.000 26.000 -0.840413 2.828738 1.307663 5.317330 4.665655 -5.000000 28.907344 -0.602808 -0.346202 -0.000006 0.290853 588 68 Session_04 FOO -4.000 26.000 -0.840413 2.828738 1.308562 5.331400 4.665655 -5.000000 28.907344 -0.601911 -0.332212 -0.000006 0.291749 589 ––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– –––––––– 590 ``` 591 ''' 592 593 kwargs = locals().copy() 594 595 from numpy import random as nprandom 596 if seed: 597 rng = nprandom.default_rng(seed) 598 else: 599 rng = nprandom.default_rng() 600 601 N = sum([s['N'] for s in samples]) 602 errors45 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 603 errors45 *= rd45 / stdev(errors45) # scale errors to rd45 604 errors46 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 605 errors46 *= rd46 / stdev(errors46) # scale errors to rd46 606 errors47 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 607 errors47 *= rD47 / stdev(errors47) # scale errors to rD47 608 errors48 = rng.normal(loc = 0, scale = 1, size = N) # generate random measurement errors 609 errors48 *= rD48 / stdev(errors48) # scale errors to rD48 610 611 k = 0 612 out = [] 613 for s in samples: 614 kw = {} 615 kw['sample'] = s['Sample'] 616 kw = { 617 **kw, 618 **{var: kwargs[var] 619 for var in [ 620 'd13Cwg_VPDB', 'd18Owg_VSMOW', 'ALPHA_18O_ACID_REACTION', 621 'Nominal_D47', 'Nominal_D48', 'Nominal_d13C_VPDB', 'Nominal_d18O_VPDB', 622 'R13_VPDB', 'R17_VSMOW', 'R18_VSMOW', 'LAMBDA_17', 'R18_VPDB', 623 'a47', 'b47', 'c47', 'a48', 'b48', 'c48', 624 ] 625 if kwargs[var] is not None}, 626 **{var: s[var] 627 for var in ['d13C_VPDB', 'd18O_VPDB', 'D47', 'D48', 'D49', 'D17O'] 628 if var in s}, 629 } 630 631 sN = s['N'] 632 while sN: 633 out.append(simulate_single_analysis(**kw)) 634 out[-1]['d45'] += errors45[k] 635 out[-1]['d46'] += errors46[k] 636 out[-1]['d47'] += (errors45[k] + errors46[k] + errors47[k]) * a47 637 out[-1]['d48'] += (2*errors46[k] + errors48[k]) * a48 638 sN -= 1 639 k += 1 640 641 if session is not None: 642 for r in out: 643 r['Session'] = session 644 return out
Return list with simulated analyses from a single session.
Parameters
samples: a list of entries; each entry is a dictionary with the following fields:Sample: the name of the sampled13C_VPDB,d18O_VPDB: bulk composition of the carbonate sampleD47,D48,D49,D17O(all optional): clumped-isotope and oxygen-17 anomalies of the carbonate sampleN: how many analyses to generate for this sample
a47: scrambling factor for Δ47b47: compositional nonlinearity for Δ47c47: working gas offset for Δ47a48: scrambling factor for Δ48b48: compositional nonlinearity for Δ48c48: working gas offset for Δ48rd45: analytical repeatability of δ45rd46: analytical repeatability of δ46rD47: analytical repeatability of Δ47rD48: analytical repeatability of Δ48d13Cwg_VPDB,d18Owg_VSMOW: bulk composition of the working gas (by default equal to thesimulate_single_analysisdefault values)session: name of the session (no name by default)Nominal_D47,Nominal_D48: where to lookup Δ47 and Δ48 values ifD47orD48are not specified (by default equal to thesimulate_single_analysisdefaults)Nominal_d13C_VPDB,Nominal_d18O_VPDB: where to lookup δ13C and δ18O values ifd13C_VPDBord18O_VPDBare not specified (by default equal to thesimulate_single_analysisdefaults)ALPHA_18O_ACID_REACTION: 18O/16O acid fractionation factor (by default equal to thesimulate_single_analysisdefaults)R13_VPDB,R17_VSMOW,R18_VSMOW,LAMBDA_17,R18_VPDB: oxygen-17 correction parameters (by default equal to thesimulate_single_analysisdefault)seed: explicitly set to a non-zero value to achieve random but repeatable simulations
Here is an example of using this method to generate an arbitrary combination of anchors and unknowns for a bunch of sessions:
args = dict(
samples = [
dict(Sample = 'ETH-1', N = 4),
dict(Sample = 'ETH-2', N = 5),
dict(Sample = 'ETH-3', N = 6),
dict(Sample = 'FOO', N = 2,
d13C_VPDB = -5., d18O_VPDB = -10.,
D47 = 0.3, D48 = 0.15),
], rD47 = 0.010, rD48 = 0.030)
session1 = virtual_data(session = 'Session_01', **args, seed = 123)
session2 = virtual_data(session = 'Session_02', **args, seed = 1234)
session3 = virtual_data(session = 'Session_03', **args, seed = 12345)
session4 = virtual_data(session = 'Session_04', **args, seed = 123456)
D = D47data(session1 + session2 + session3 + session4)
D.crunch()
D.standardize()
D.table_of_sessions(verbose = True, save_to_file = False)
D.table_of_samples(verbose = True, save_to_file = False)
D.table_of_analyses(verbose = True, save_to_file = False)
This should output something like:
[table_of_sessions]
–––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– ––––––––––––––
Session Na Nu d13Cwg_VPDB d18Owg_VSMOW r_d13C r_d18O r_D47 a ± SE 1e3 x b ± SE c ± SE
–––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– ––––––––––––––
Session_01 15 2 -4.000 26.000 0.0000 0.0000 0.0110 0.997 ± 0.017 -0.097 ± 0.244 -0.896 ± 0.006
Session_02 15 2 -4.000 26.000 0.0000 0.0000 0.0109 1.002 ± 0.017 -0.110 ± 0.244 -0.901 ± 0.006
Session_03 15 2 -4.000 26.000 0.0000 0.0000 0.0107 1.010 ± 0.017 -0.037 ± 0.244 -0.904 ± 0.006
Session_04 15 2 -4.000 26.000 0.0000 0.0000 0.0106 1.001 ± 0.017 -0.181 ± 0.244 -0.894 ± 0.006
–––––––––– –– –– ––––––––––– –––––––––––– –––––– –––––– –––––– ––––––––––––– –––––––––––––– ––––––––––––––
[table_of_samples]
–––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– ––––––––
Sample N d13C_VPDB d18O_VSMOW D47 SE 95% CL SD p_Levene
–––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– ––––––––
ETH-1 16 2.02 37.02 0.2052 0.0079
ETH-2 20 -10.17 19.88 0.2085 0.0100
ETH-3 24 1.71 37.45 0.6132 0.0105
FOO 8 -5.00 28.91 0.2989 0.0040 ± 0.0080 0.0101 0.638
–––––– –– ––––––––– –––––––––– –––––– –––––– –––––––– –––––– ––––––––
[table_of_analyses]
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– ––––––––
UID Session Sample d13Cwg_VPDB d18Owg_VSMOW d45 d46 d47 d48 d49 d13C_VPDB d18O_VSMOW D47raw D48raw D49raw D47
––– –––––––––– –––––– ––––––––––– –––––––––––– ––––––––– ––––––––– –––––––––– –––––––––– –––––––––– –––––––––– –––––––––– ––––––––– ––––––––– ––––––––– ––––––––
1 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.122986 21.273526 27.780042 2.020000 37.024281 -0.706013 -0.328878 -0.000013 0.192554
2 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.130144 21.282615 27.780042 2.020000 37.024281 -0.698974 -0.319981 -0.000013 0.199615
3 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.149219 21.299572 27.780042 2.020000 37.024281 -0.680215 -0.303383 -0.000013 0.218429
4 Session_01 ETH-1 -4.000 26.000 6.018962 10.747026 16.136616 21.233128 27.780042 2.020000 37.024281 -0.692609 -0.368421 -0.000013 0.205998
5 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.697171 -12.203054 -18.023381 -10.170000 19.875825 -0.680771 -0.290128 -0.000002 0.215054
6 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701124 -12.184422 -18.023381 -10.170000 19.875825 -0.684772 -0.271272 -0.000002 0.211041
7 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.715105 -12.195251 -18.023381 -10.170000 19.875825 -0.698923 -0.282232 -0.000002 0.196848
8 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701529 -12.204963 -18.023381 -10.170000 19.875825 -0.685182 -0.292061 -0.000002 0.210630
9 Session_01 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.711420 -12.228478 -18.023381 -10.170000 19.875825 -0.695193 -0.315859 -0.000002 0.200589
10 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.666719 22.296486 28.306614 1.710000 37.450394 -0.290459 -0.147284 -0.000014 0.609363
11 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.671553 22.291060 28.306614 1.710000 37.450394 -0.285706 -0.152592 -0.000014 0.614130
12 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.652854 22.273271 28.306614 1.710000 37.450394 -0.304093 -0.169990 -0.000014 0.595689
13 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.684168 22.263156 28.306614 1.710000 37.450394 -0.273302 -0.179883 -0.000014 0.626572
14 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.662702 22.253578 28.306614 1.710000 37.450394 -0.294409 -0.189251 -0.000014 0.605401
15 Session_01 ETH-3 -4.000 26.000 5.742374 11.161270 16.681957 22.230907 28.306614 1.710000 37.450394 -0.275476 -0.211424 -0.000014 0.624391
16 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.312044 5.395798 4.665655 -5.000000 28.907344 -0.598436 -0.268176 -0.000006 0.298996
17 Session_01 FOO -4.000 26.000 -0.840413 2.828738 1.328123 5.307086 4.665655 -5.000000 28.907344 -0.582387 -0.356389 -0.000006 0.315092
18 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.122201 21.340606 27.780042 2.020000 37.024281 -0.706785 -0.263217 -0.000013 0.195135
19 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.134868 21.305714 27.780042 2.020000 37.024281 -0.694328 -0.297370 -0.000013 0.207564
20 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.140008 21.261931 27.780042 2.020000 37.024281 -0.689273 -0.340227 -0.000013 0.212607
21 Session_02 ETH-1 -4.000 26.000 6.018962 10.747026 16.135540 21.298472 27.780042 2.020000 37.024281 -0.693667 -0.304459 -0.000013 0.208224
22 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701213 -12.202602 -18.023381 -10.170000 19.875825 -0.684862 -0.289671 -0.000002 0.213842
23 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.685649 -12.190405 -18.023381 -10.170000 19.875825 -0.669108 -0.277327 -0.000002 0.229559
24 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.719003 -12.257955 -18.023381 -10.170000 19.875825 -0.702869 -0.345692 -0.000002 0.195876
25 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.700592 -12.204641 -18.023381 -10.170000 19.875825 -0.684233 -0.291735 -0.000002 0.214469
26 Session_02 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.720426 -12.214561 -18.023381 -10.170000 19.875825 -0.704308 -0.301774 -0.000002 0.194439
27 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.673044 22.262090 28.306614 1.710000 37.450394 -0.284240 -0.180926 -0.000014 0.616730
28 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.666542 22.263401 28.306614 1.710000 37.450394 -0.290634 -0.179643 -0.000014 0.610350
29 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.680487 22.243486 28.306614 1.710000 37.450394 -0.276921 -0.199121 -0.000014 0.624031
30 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.663900 22.245175 28.306614 1.710000 37.450394 -0.293231 -0.197469 -0.000014 0.607759
31 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.674379 22.301309 28.306614 1.710000 37.450394 -0.282927 -0.142568 -0.000014 0.618039
32 Session_02 ETH-3 -4.000 26.000 5.742374 11.161270 16.660825 22.270466 28.306614 1.710000 37.450394 -0.296255 -0.172733 -0.000014 0.604742
33 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.294076 5.349940 4.665655 -5.000000 28.907344 -0.616369 -0.313776 -0.000006 0.283707
34 Session_02 FOO -4.000 26.000 -0.840413 2.828738 1.313775 5.292121 4.665655 -5.000000 28.907344 -0.596708 -0.371269 -0.000006 0.303323
35 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.121613 21.259909 27.780042 2.020000 37.024281 -0.707364 -0.342207 -0.000013 0.194934
36 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.145714 21.304889 27.780042 2.020000 37.024281 -0.683661 -0.298178 -0.000013 0.218401
37 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.126573 21.325093 27.780042 2.020000 37.024281 -0.702485 -0.278401 -0.000013 0.199764
38 Session_03 ETH-1 -4.000 26.000 6.018962 10.747026 16.132057 21.323211 27.780042 2.020000 37.024281 -0.697092 -0.280244 -0.000013 0.205104
39 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.708448 -12.232023 -18.023381 -10.170000 19.875825 -0.692185 -0.319447 -0.000002 0.208915
40 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.714417 -12.202504 -18.023381 -10.170000 19.875825 -0.698226 -0.289572 -0.000002 0.202934
41 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.720039 -12.264469 -18.023381 -10.170000 19.875825 -0.703917 -0.352285 -0.000002 0.197300
42 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.701953 -12.228550 -18.023381 -10.170000 19.875825 -0.685611 -0.315932 -0.000002 0.215423
43 Session_03 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.704535 -12.213634 -18.023381 -10.170000 19.875825 -0.688224 -0.300836 -0.000002 0.212837
44 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.652920 22.230043 28.306614 1.710000 37.450394 -0.304028 -0.212269 -0.000014 0.594265
45 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.691485 22.261017 28.306614 1.710000 37.450394 -0.266106 -0.181975 -0.000014 0.631810
46 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.679119 22.305357 28.306614 1.710000 37.450394 -0.278266 -0.138609 -0.000014 0.619771
47 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.663623 22.327286 28.306614 1.710000 37.450394 -0.293503 -0.117161 -0.000014 0.604685
48 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.678524 22.282103 28.306614 1.710000 37.450394 -0.278851 -0.161352 -0.000014 0.619192
49 Session_03 ETH-3 -4.000 26.000 5.742374 11.161270 16.666246 22.283361 28.306614 1.710000 37.450394 -0.290925 -0.160121 -0.000014 0.607238
50 Session_03 FOO -4.000 26.000 -0.840413 2.828738 1.309929 5.340249 4.665655 -5.000000 28.907344 -0.600546 -0.323413 -0.000006 0.300148
51 Session_03 FOO -4.000 26.000 -0.840413 2.828738 1.317548 5.334102 4.665655 -5.000000 28.907344 -0.592942 -0.329524 -0.000006 0.307676
52 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.136865 21.300298 27.780042 2.020000 37.024281 -0.692364 -0.302672 -0.000013 0.204033
53 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.133538 21.291260 27.780042 2.020000 37.024281 -0.695637 -0.311519 -0.000013 0.200762
54 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.139991 21.319865 27.780042 2.020000 37.024281 -0.689290 -0.283519 -0.000013 0.207107
55 Session_04 ETH-1 -4.000 26.000 6.018962 10.747026 16.145748 21.330075 27.780042 2.020000 37.024281 -0.683629 -0.273524 -0.000013 0.212766
56 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.702989 -12.202762 -18.023381 -10.170000 19.875825 -0.686660 -0.289833 -0.000002 0.204507
57 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.692830 -12.240287 -18.023381 -10.170000 19.875825 -0.676377 -0.327811 -0.000002 0.214786
58 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.702899 -12.180291 -18.023381 -10.170000 19.875825 -0.686568 -0.267091 -0.000002 0.204598
59 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.709282 -12.282257 -18.023381 -10.170000 19.875825 -0.693029 -0.370287 -0.000002 0.198140
60 Session_04 ETH-2 -4.000 26.000 -5.995859 -5.976076 -12.679330 -12.235994 -18.023381 -10.170000 19.875825 -0.662712 -0.323466 -0.000002 0.228446
61 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.695594 22.238663 28.306614 1.710000 37.450394 -0.262066 -0.203838 -0.000014 0.634200
62 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.663504 22.286354 28.306614 1.710000 37.450394 -0.293620 -0.157194 -0.000014 0.602656
63 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.666457 22.254290 28.306614 1.710000 37.450394 -0.290717 -0.188555 -0.000014 0.605558
64 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.666910 22.223232 28.306614 1.710000 37.450394 -0.290271 -0.218930 -0.000014 0.606004
65 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.679662 22.257256 28.306614 1.710000 37.450394 -0.277732 -0.185653 -0.000014 0.618539
66 Session_04 ETH-3 -4.000 26.000 5.742374 11.161270 16.676768 22.267680 28.306614 1.710000 37.450394 -0.280578 -0.175459 -0.000014 0.615693
67 Session_04 FOO -4.000 26.000 -0.840413 2.828738 1.307663 5.317330 4.665655 -5.000000 28.907344 -0.602808 -0.346202 -0.000006 0.290853
68 Session_04 FOO -4.000 26.000 -0.840413 2.828738 1.308562 5.331400 4.665655 -5.000000 28.907344 -0.601911 -0.332212 -0.000006 0.291749
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def
table_of_samples( data47=None, data48=None, dir='output', filename=None, save_to_file=True, print_out=True, output=None):
646def table_of_samples( 647 data47 = None, 648 data48 = None, 649 dir = 'output', 650 filename = None, 651 save_to_file = True, 652 print_out = True, 653 output = None, 654 ): 655 ''' 656 Print out, save to disk and/or return a combined table of samples 657 for a pair of `D47data` and `D48data` objects. 658 659 **Parameters** 660 661 + `data47`: `D47data` instance 662 + `data48`: `D48data` instance 663 + `dir`: the directory in which to save the table 664 + `filename`: the name to the csv file to write to 665 + `save_to_file`: whether to save the table to disk 666 + `print_out`: whether to print out the table 667 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 668 if set to `'raw'`: return a list of list of strings 669 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 670 ''' 671 if data47 is None: 672 if data48 is None: 673 raise TypeError("Arguments must include at least one D47data() or D48data() instance.") 674 else: 675 return data48.table_of_samples( 676 dir = dir, 677 filename = filename, 678 save_to_file = save_to_file, 679 print_out = print_out, 680 output = output 681 ) 682 else: 683 if data48 is None: 684 return data47.table_of_samples( 685 dir = dir, 686 filename = filename, 687 save_to_file = save_to_file, 688 print_out = print_out, 689 output = output 690 ) 691 else: 692 out47 = data47.table_of_samples(save_to_file = False, print_out = False, output = 'raw') 693 out48 = data48.table_of_samples(save_to_file = False, print_out = False, output = 'raw') 694 out = transpose_table(transpose_table(out47) + transpose_table(out48)[4:]) 695 696 if save_to_file: 697 if not os.path.exists(dir): 698 os.makedirs(dir) 699 if filename is None: 700 filename = f'D47D48_samples.csv' 701 with open(f'{dir}/{filename}', 'w') as fid: 702 fid.write(make_csv(out)) 703 if print_out: 704 print('\n'+pretty_table(out)) 705 if output == 'raw': 706 return out 707 elif output == 'pretty': 708 return pretty_table(out)
Print out, save to disk and/or return a combined table of samples
for a pair of D47data and D48data objects.
Parameters
data47:D47datainstancedata48:D48datainstancedir: the directory in which to save the tablefilename: the name to the csv file to write tosave_to_file: whether to save the table to diskprint_out: whether to print out the tableoutput: if set to'pretty': return a pretty text table (seepretty_table()); if set to'raw': return a list of list of strings (e.g.,[['header1', 'header2'], ['0.1', '0.2']])
def
table_of_sessions( data47=None, data48=None, dir='output', filename=None, save_to_file=True, print_out=True, output=None):
711def table_of_sessions( 712 data47 = None, 713 data48 = None, 714 dir = 'output', 715 filename = None, 716 save_to_file = True, 717 print_out = True, 718 output = None, 719 ): 720 ''' 721 Print out, save to disk and/or return a combined table of sessions 722 for a pair of `D47data` and `D48data` objects. 723 ***Only applicable if the sessions in `data47` and those in `data48` 724 consist of the exact same sets of analyses.*** 725 726 **Parameters** 727 728 + `data47`: `D47data` instance 729 + `data48`: `D48data` instance 730 + `dir`: the directory in which to save the table 731 + `filename`: the name to the csv file to write to 732 + `save_to_file`: whether to save the table to disk 733 + `print_out`: whether to print out the table 734 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 735 if set to `'raw'`: return a list of list of strings 736 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 737 ''' 738 if data47 is None: 739 if data48 is None: 740 raise TypeError("Arguments must include at least one D47data() or D48data() instance.") 741 else: 742 return data48.table_of_sessions( 743 dir = dir, 744 filename = filename, 745 save_to_file = save_to_file, 746 print_out = print_out, 747 output = output 748 ) 749 else: 750 if data48 is None: 751 return data47.table_of_sessions( 752 dir = dir, 753 filename = filename, 754 save_to_file = save_to_file, 755 print_out = print_out, 756 output = output 757 ) 758 else: 759 out47 = data47.table_of_sessions(save_to_file = False, print_out = False, output = 'raw') 760 out48 = data48.table_of_sessions(save_to_file = False, print_out = False, output = 'raw') 761 for k,x in enumerate(out47[0]): 762 if k>7: 763 out47[0][k] = out47[0][k].replace('a', 'a_47').replace('b', 'b_47').replace('c', 'c_47') 764 out48[0][k] = out48[0][k].replace('a', 'a_48').replace('b', 'b_48').replace('c', 'c_48') 765 out = transpose_table(transpose_table(out47) + transpose_table(out48)[7:]) 766 767 if save_to_file: 768 if not os.path.exists(dir): 769 os.makedirs(dir) 770 if filename is None: 771 filename = f'D47D48_sessions.csv' 772 with open(f'{dir}/{filename}', 'w') as fid: 773 fid.write(make_csv(out)) 774 if print_out: 775 print('\n'+pretty_table(out)) 776 if output == 'raw': 777 return out 778 elif output == 'pretty': 779 return pretty_table(out)
Print out, save to disk and/or return a combined table of sessions
for a pair of D47data and D48data objects.
Only applicable if the sessions in data47 and those in data48
consist of the exact same sets of analyses.
Parameters
data47:D47datainstancedata48:D48datainstancedir: the directory in which to save the tablefilename: the name to the csv file to write tosave_to_file: whether to save the table to diskprint_out: whether to print out the tableoutput: if set to'pretty': return a pretty text table (seepretty_table()); if set to'raw': return a list of list of strings (e.g.,[['header1', 'header2'], ['0.1', '0.2']])
def
table_of_analyses( data47=None, data48=None, dir='output', filename=None, save_to_file=True, print_out=True, output=None):
782def table_of_analyses( 783 data47 = None, 784 data48 = None, 785 dir = 'output', 786 filename = None, 787 save_to_file = True, 788 print_out = True, 789 output = None, 790 ): 791 ''' 792 Print out, save to disk and/or return a combined table of analyses 793 for a pair of `D47data` and `D48data` objects. 794 795 If the sessions in `data47` and those in `data48` do not consist of 796 the exact same sets of analyses, the table will have two columns 797 `Session_47` and `Session_48` instead of a single `Session` column. 798 799 **Parameters** 800 801 + `data47`: `D47data` instance 802 + `data48`: `D48data` instance 803 + `dir`: the directory in which to save the table 804 + `filename`: the name to the csv file to write to 805 + `save_to_file`: whether to save the table to disk 806 + `print_out`: whether to print out the table 807 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 808 if set to `'raw'`: return a list of list of strings 809 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 810 ''' 811 if data47 is None: 812 if data48 is None: 813 raise TypeError("Arguments must include at least one D47data() or D48data() instance.") 814 else: 815 return data48.table_of_analyses( 816 dir = dir, 817 filename = filename, 818 save_to_file = save_to_file, 819 print_out = print_out, 820 output = output 821 ) 822 else: 823 if data48 is None: 824 return data47.table_of_analyses( 825 dir = dir, 826 filename = filename, 827 save_to_file = save_to_file, 828 print_out = print_out, 829 output = output 830 ) 831 else: 832 out47 = data47.table_of_analyses(save_to_file = False, print_out = False, output = 'raw') 833 out48 = data48.table_of_analyses(save_to_file = False, print_out = False, output = 'raw') 834 835 if [l[1] for l in out47[1:]] == [l[1] for l in out48[1:]]: # if sessions are identical 836 out = transpose_table(transpose_table(out47) + transpose_table(out48)[-1:]) 837 else: 838 out47[0][1] = 'Session_47' 839 out48[0][1] = 'Session_48' 840 out47 = transpose_table(out47) 841 out48 = transpose_table(out48) 842 out = transpose_table(out47[:2] + out48[1:2] + out47[2:] + out48[-1:]) 843 844 if save_to_file: 845 if not os.path.exists(dir): 846 os.makedirs(dir) 847 if filename is None: 848 filename = f'D47D48_sessions.csv' 849 with open(f'{dir}/{filename}', 'w') as fid: 850 fid.write(make_csv(out)) 851 if print_out: 852 print('\n'+pretty_table(out)) 853 if output == 'raw': 854 return out 855 elif output == 'pretty': 856 return pretty_table(out)
Print out, save to disk and/or return a combined table of analyses
for a pair of D47data and D48data objects.
If the sessions in data47 and those in data48 do not consist of
the exact same sets of analyses, the table will have two columns
Session_47 and Session_48 instead of a single Session column.
Parameters
data47:D47datainstancedata48:D48datainstancedir: the directory in which to save the tablefilename: the name to the csv file to write tosave_to_file: whether to save the table to diskprint_out: whether to print out the tableoutput: if set to'pretty': return a pretty text table (seepretty_table()); if set to'raw': return a list of list of strings (e.g.,[['header1', 'header2'], ['0.1', '0.2']])
class
D4xdata(builtins.list):
904class D4xdata(list): 905 ''' 906 Store and process data for a large set of Δ47 and/or Δ48 907 analyses, usually comprising more than one analytical session. 908 ''' 909 910 ### 17O CORRECTION PARAMETERS 911 R13_VPDB = 0.01118 # (Chang & Li, 1990) 912 ''' 913 Absolute (13C/12C) ratio of VPDB. 914 By default equal to 0.01118 ([Chang & Li, 1990](http://www.cnki.com.cn/Article/CJFDTotal-JXTW199004006.htm)) 915 ''' 916 917 R18_VSMOW = 0.0020052 # (Baertschi, 1976) 918 ''' 919 Absolute (18O/16C) ratio of VSMOW. 920 By default equal to 0.0020052 ([Baertschi, 1976](https://doi.org/10.1016/0012-821X(76)90115-1)) 921 ''' 922 923 LAMBDA_17 = 0.528 # (Barkan & Luz, 2005) 924 ''' 925 Mass-dependent exponent for triple oxygen isotopes. 926 By default equal to 0.528 ([Barkan & Luz, 2005](https://doi.org/10.1002/rcm.2250)) 927 ''' 928 929 R17_VSMOW = 0.00038475 # (Assonov & Brenninkmeijer, 2003, rescaled to R13_VPDB) 930 ''' 931 Absolute (17O/16C) ratio of VSMOW. 932 By default equal to 0.00038475 933 ([Assonov & Brenninkmeijer, 2003](https://dx.doi.org/10.1002/rcm.1011), 934 rescaled to `R13_VPDB`) 935 ''' 936 937 R18_VPDB = R18_VSMOW * 1.03092 938 ''' 939 Absolute (18O/16C) ratio of VPDB. 940 By definition equal to `R18_VSMOW * 1.03092`. 941 ''' 942 943 R17_VPDB = R17_VSMOW * 1.03092 ** LAMBDA_17 944 ''' 945 Absolute (17O/16C) ratio of VPDB. 946 By definition equal to `R17_VSMOW * 1.03092 ** LAMBDA_17`. 947 ''' 948 949 LEVENE_REF_SAMPLE = 'ETH-3' 950 ''' 951 After the Δ4x standardization step, each sample is tested to 952 assess whether the Δ4x variance within all analyses for that 953 sample differs significantly from that observed for a given reference 954 sample (using [Levene's test](https://en.wikipedia.org/wiki/Levene%27s_test), 955 which yields a p-value corresponding to the null hypothesis that the 956 underlying variances are equal). 957 958 `LEVENE_REF_SAMPLE` (by default equal to `'ETH-3'`) specifies which 959 sample should be used as a reference for this test. 960 ''' 961 962 ALPHA_18O_ACID_REACTION = round(np.exp(3.59 / (90 + 273.15) - 1.79e-3), 6) # (Kim et al., 2007, calcite) 963 ''' 964 Specifies the 18O/16O fractionation factor generally applicable 965 to acid reactions in the dataset. Currently used by `D4xdata.wg()`, 966 `D4xdata.standardize_d13C`, and `D4xdata.standardize_d18O`. 967 968 By default equal to 1.008129 (calcite reacted at 90 °C, 969 [Kim et al., 2007](https://dx.doi.org/10.1016/j.chemgeo.2007.08.005)). 970 ''' 971 972 Nominal_d13C_VPDB = { 973 'ETH-1': 2.02, 974 'ETH-2': -10.17, 975 'ETH-3': 1.71, 976 } # (Bernasconi et al., 2018) 977 ''' 978 Nominal δ13C_VPDB values assigned to carbonate standards, used by 979 `D4xdata.standardize_d13C()`. 980 981 By default equal to `{'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71}` after 982 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385). 983 ''' 984 985 Nominal_d18O_VPDB = { 986 'ETH-1': -2.19, 987 'ETH-2': -18.69, 988 'ETH-3': -1.78, 989 } # (Bernasconi et al., 2018) 990 ''' 991 Nominal δ18O_VPDB values assigned to carbonate standards, used by 992 `D4xdata.standardize_d18O()`. 993 994 By default equal to `{'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78}` after 995 [Bernasconi et al. (2018)](https://doi.org/10.1029/2017GC007385). 996 ''' 997 998 d13C_STANDARDIZATION_METHOD = '2pt' 999 ''' 1000 Method by which to standardize δ13C values: 1001 1002 + `none`: do not apply any δ13C standardization. 1003 + `'1pt'`: within each session, offset all initial δ13C values so as to 1004 minimize the difference between final δ13C_VPDB values and 1005 `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` is defined). 1006 + `'2pt'`: within each session, apply a affine trasformation to all δ13C 1007 values so as to minimize the difference between final δ13C_VPDB 1008 values and `Nominal_d13C_VPDB` (averaged over all analyses for which `Nominal_d13C_VPDB` 1009 is defined). 1010 ''' 1011 1012 d18O_STANDARDIZATION_METHOD = '2pt' 1013 ''' 1014 Method by which to standardize δ18O values: 1015 1016 + `none`: do not apply any δ18O standardization. 1017 + `'1pt'`: within each session, offset all initial δ18O values so as to 1018 minimize the difference between final δ18O_VPDB values and 1019 `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` is defined). 1020 + `'2pt'`: within each session, apply a affine trasformation to all δ18O 1021 values so as to minimize the difference between final δ18O_VPDB 1022 values and `Nominal_d18O_VPDB` (averaged over all analyses for which `Nominal_d18O_VPDB` 1023 is defined). 1024 ''' 1025 1026 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False): 1027 ''' 1028 **Parameters** 1029 1030 + `l`: a list of dictionaries, with each dictionary including at least the keys 1031 `Sample`, `d45`, `d46`, and `d47` or `d48`. 1032 + `mass`: `'47'` or `'48'` 1033 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods. 1034 + `session`: define session name for analyses without a `Session` key 1035 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods. 1036 1037 Returns a `D4xdata` object derived from `list`. 1038 ''' 1039 self._4x = mass 1040 self.verbose = verbose 1041 self.prefix = 'D4xdata' 1042 self.logfile = logfile 1043 list.__init__(self, l) 1044 self.Nf = None 1045 self.repeatability = {} 1046 self.refresh(session = session) 1047 1048 1049 def make_verbal(oldfun): 1050 ''' 1051 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`. 1052 ''' 1053 @wraps(oldfun) 1054 def newfun(*args, verbose = '', **kwargs): 1055 myself = args[0] 1056 oldprefix = myself.prefix 1057 myself.prefix = oldfun.__name__ 1058 if verbose != '': 1059 oldverbose = myself.verbose 1060 myself.verbose = verbose 1061 out = oldfun(*args, **kwargs) 1062 myself.prefix = oldprefix 1063 if verbose != '': 1064 myself.verbose = oldverbose 1065 return out 1066 return newfun 1067 1068 1069 def msg(self, txt): 1070 ''' 1071 Log a message to `self.logfile`, and print it out if `verbose = True` 1072 ''' 1073 self.log(txt) 1074 if self.verbose: 1075 print(f'{f"[{self.prefix}]":<16} {txt}') 1076 1077 1078 def vmsg(self, txt): 1079 ''' 1080 Log a message to `self.logfile` and print it out 1081 ''' 1082 self.log(txt) 1083 print(txt) 1084 1085 1086 def log(self, *txts): 1087 ''' 1088 Log a message to `self.logfile` 1089 ''' 1090 if self.logfile: 1091 with open(self.logfile, 'a') as fid: 1092 for txt in txts: 1093 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}') 1094 1095 1096 def refresh(self, session = 'mySession'): 1097 ''' 1098 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`. 1099 ''' 1100 self.fill_in_missing_info(session = session) 1101 self.refresh_sessions() 1102 self.refresh_samples() 1103 1104 1105 def refresh_sessions(self): 1106 ''' 1107 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift` 1108 to `False` for all sessions. 1109 ''' 1110 self.sessions = { 1111 s: {'data': [r for r in self if r['Session'] == s]} 1112 for s in sorted({r['Session'] for r in self}) 1113 } 1114 for s in self.sessions: 1115 self.sessions[s]['scrambling_drift'] = False 1116 self.sessions[s]['slope_drift'] = False 1117 self.sessions[s]['wg_drift'] = False 1118 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD 1119 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD 1120 1121 1122 def refresh_samples(self): 1123 ''' 1124 Define `self.samples`, `self.anchors`, and `self.unknowns`. 1125 ''' 1126 self.samples = { 1127 s: {'data': [r for r in self if r['Sample'] == s]} 1128 for s in sorted({r['Sample'] for r in self}) 1129 } 1130 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x} 1131 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x} 1132 1133 1134 def read(self, filename, sep = '', session = ''): 1135 ''' 1136 Read file in csv format to load data into a `D47data` object. 1137 1138 In the csv file, spaces before and after field separators (`','` by default) 1139 are optional. Each line corresponds to a single analysis. 1140 1141 The required fields are: 1142 1143 + `UID`: a unique identifier 1144 + `Session`: an identifier for the analytical session 1145 + `Sample`: a sample identifier 1146 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values 1147 1148 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to 1149 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48` 1150 and `d49` are optional, and set to NaN by default. 1151 1152 **Parameters** 1153 1154 + `fileneme`: the path of the file to read 1155 + `sep`: csv separator delimiting the fields 1156 + `session`: set `Session` field to this string for all analyses 1157 ''' 1158 with open(filename) as fid: 1159 self.input(fid.read(), sep = sep, session = session) 1160 1161 1162 def input(self, txt, sep = '', session = ''): 1163 ''' 1164 Read `txt` string in csv format to load analysis data into a `D47data` object. 1165 1166 In the csv string, spaces before and after field separators (`','` by default) 1167 are optional. Each line corresponds to a single analysis. 1168 1169 The required fields are: 1170 1171 + `UID`: a unique identifier 1172 + `Session`: an identifier for the analytical session 1173 + `Sample`: a sample identifier 1174 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values 1175 1176 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to 1177 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48` 1178 and `d49` are optional, and set to NaN by default. 1179 1180 **Parameters** 1181 1182 + `txt`: the csv string to read 1183 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`, 1184 whichever appers most often in `txt`. 1185 + `session`: set `Session` field to this string for all analyses 1186 ''' 1187 if sep == '': 1188 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0] 1189 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()] 1190 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]] 1191 1192 if session != '': 1193 for r in data: 1194 r['Session'] = session 1195 1196 self += data 1197 self.refresh() 1198 1199 1200 @make_verbal 1201 def wg(self, samples = None, a18_acid = None): 1202 ''' 1203 Compute bulk composition of the working gas for each session based on 1204 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and 1205 `self.Nominal_d18O_VPDB`. 1206 ''' 1207 1208 self.msg('Computing WG composition:') 1209 1210 if a18_acid is None: 1211 a18_acid = self.ALPHA_18O_ACID_REACTION 1212 if samples is None: 1213 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB] 1214 1215 assert a18_acid, f'Acid fractionation factor should not be zero.' 1216 1217 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB] 1218 R45R46_standards = {} 1219 for sample in samples: 1220 d13C_vpdb = self.Nominal_d13C_VPDB[sample] 1221 d18O_vpdb = self.Nominal_d18O_VPDB[sample] 1222 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000) 1223 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17 1224 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid 1225 1226 C12_s = 1 / (1 + R13_s) 1227 C13_s = R13_s / (1 + R13_s) 1228 C16_s = 1 / (1 + R17_s + R18_s) 1229 C17_s = R17_s / (1 + R17_s + R18_s) 1230 C18_s = R18_s / (1 + R17_s + R18_s) 1231 1232 C626_s = C12_s * C16_s ** 2 1233 C627_s = 2 * C12_s * C16_s * C17_s 1234 C628_s = 2 * C12_s * C16_s * C18_s 1235 C636_s = C13_s * C16_s ** 2 1236 C637_s = 2 * C13_s * C16_s * C17_s 1237 C727_s = C12_s * C17_s ** 2 1238 1239 R45_s = (C627_s + C636_s) / C626_s 1240 R46_s = (C628_s + C637_s + C727_s) / C626_s 1241 R45R46_standards[sample] = (R45_s, R46_s) 1242 1243 for s in self.sessions: 1244 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples] 1245 assert db, f'No sample from {samples} found in session "{s}".' 1246# dbsamples = sorted({r['Sample'] for r in db}) 1247 1248 X = [r['d45'] for r in db] 1249 Y = [R45R46_standards[r['Sample']][0] for r in db] 1250 x1, x2 = np.min(X), np.max(X) 1251 1252 if x1 < x2: 1253 wgcoord = x1/(x1-x2) 1254 else: 1255 wgcoord = 999 1256 1257 if wgcoord < -.5 or wgcoord > 1.5: 1258 # unreasonable to extrapolate to d45 = 0 1259 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)]) 1260 else : 1261 # d45 = 0 is reasonably well bracketed 1262 R45_wg = np.polyfit(X, Y, 1)[1] 1263 1264 X = [r['d46'] for r in db] 1265 Y = [R45R46_standards[r['Sample']][1] for r in db] 1266 x1, x2 = np.min(X), np.max(X) 1267 1268 if x1 < x2: 1269 wgcoord = x1/(x1-x2) 1270 else: 1271 wgcoord = 999 1272 1273 if wgcoord < -.5 or wgcoord > 1.5: 1274 # unreasonable to extrapolate to d46 = 0 1275 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)]) 1276 else : 1277 # d46 = 0 is reasonably well bracketed 1278 R46_wg = np.polyfit(X, Y, 1)[1] 1279 1280 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg) 1281 1282 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}') 1283 1284 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB 1285 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW 1286 for r in self.sessions[s]['data']: 1287 r['d13Cwg_VPDB'] = d13Cwg_VPDB 1288 r['d18Owg_VSMOW'] = d18Owg_VSMOW 1289 1290 1291 def compute_bulk_delta(self, R45, R46, D17O = 0): 1292 ''' 1293 Compute δ13C_VPDB and δ18O_VSMOW, 1294 by solving the generalized form of equation (17) from 1295 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05), 1296 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and 1297 solving the corresponding second-order Taylor polynomial. 1298 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014)) 1299 ''' 1300 1301 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17 1302 1303 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17) 1304 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17 1305 C = 2 * self.R18_VSMOW 1306 D = -R46 1307 1308 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2 1309 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C 1310 cc = A + B + C + D 1311 1312 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa) 1313 1314 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW 1315 R17 = K * R18 ** self.LAMBDA_17 1316 R13 = R45 - 2 * R17 1317 1318 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1) 1319 1320 return d13C_VPDB, d18O_VSMOW 1321 1322 1323 @make_verbal 1324 def crunch(self, verbose = ''): 1325 ''' 1326 Compute bulk composition and raw clumped isotope anomalies for all analyses. 1327 ''' 1328 for r in self: 1329 self.compute_bulk_and_clumping_deltas(r) 1330 self.standardize_d13C() 1331 self.standardize_d18O() 1332 self.msg(f"Crunched {len(self)} analyses.") 1333 1334 1335 def fill_in_missing_info(self, session = 'mySession'): 1336 ''' 1337 Fill in optional fields with default values 1338 ''' 1339 for i,r in enumerate(self): 1340 if 'D17O' not in r: 1341 r['D17O'] = 0. 1342 if 'UID' not in r: 1343 r['UID'] = f'{i+1}' 1344 if 'Session' not in r: 1345 r['Session'] = session 1346 for k in ['d47', 'd48', 'd49']: 1347 if k not in r: 1348 r[k] = np.nan 1349 1350 1351 def standardize_d13C(self): 1352 ''' 1353 Perform δ13C standadization within each session `s` according to 1354 `self.sessions[s]['d13C_standardization_method']`, which is defined by default 1355 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but 1356 may be redefined abitrarily at a later stage. 1357 ''' 1358 for s in self.sessions: 1359 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']: 1360 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB] 1361 X,Y = zip(*XY) 1362 if self.sessions[s]['d13C_standardization_method'] == '1pt': 1363 offset = np.mean(Y) - np.mean(X) 1364 for r in self.sessions[s]['data']: 1365 r['d13C_VPDB'] += offset 1366 elif self.sessions[s]['d13C_standardization_method'] == '2pt': 1367 a,b = np.polyfit(X,Y,1) 1368 for r in self.sessions[s]['data']: 1369 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b 1370 1371 def standardize_d18O(self): 1372 ''' 1373 Perform δ18O standadization within each session `s` according to 1374 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`, 1375 which is defined by default by `D47data.refresh_sessions()`as equal to 1376 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage. 1377 ''' 1378 for s in self.sessions: 1379 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']: 1380 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB] 1381 X,Y = zip(*XY) 1382 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y] 1383 if self.sessions[s]['d18O_standardization_method'] == '1pt': 1384 offset = np.mean(Y) - np.mean(X) 1385 for r in self.sessions[s]['data']: 1386 r['d18O_VSMOW'] += offset 1387 elif self.sessions[s]['d18O_standardization_method'] == '2pt': 1388 a,b = np.polyfit(X,Y,1) 1389 for r in self.sessions[s]['data']: 1390 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b 1391 1392 1393 def compute_bulk_and_clumping_deltas(self, r): 1394 ''' 1395 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`. 1396 ''' 1397 1398 # Compute working gas R13, R18, and isobar ratios 1399 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000) 1400 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000) 1401 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg) 1402 1403 # Compute analyte isobar ratios 1404 R45 = (1 + r['d45'] / 1000) * R45_wg 1405 R46 = (1 + r['d46'] / 1000) * R46_wg 1406 R47 = (1 + r['d47'] / 1000) * R47_wg 1407 R48 = (1 + r['d48'] / 1000) * R48_wg 1408 R49 = (1 + r['d49'] / 1000) * R49_wg 1409 1410 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O']) 1411 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB 1412 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW 1413 1414 # Compute stochastic isobar ratios of the analyte 1415 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios( 1416 R13, R18, D17O = r['D17O'] 1417 ) 1418 1419 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1, 1420 # and raise a warning if the corresponding anomalies exceed 0.02 ppm. 1421 if (R45 / R45stoch - 1) > 5e-8: 1422 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm') 1423 if (R46 / R46stoch - 1) > 5e-8: 1424 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm') 1425 1426 # Compute raw clumped isotope anomalies 1427 r['D47raw'] = 1000 * (R47 / R47stoch - 1) 1428 r['D48raw'] = 1000 * (R48 / R48stoch - 1) 1429 r['D49raw'] = 1000 * (R49 / R49stoch - 1) 1430 1431 1432 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0): 1433 ''' 1434 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`, 1435 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope 1436 anomalies (`D47`, `D48`, `D49`), all expressed in permil. 1437 ''' 1438 1439 # Compute R17 1440 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17 1441 1442 # Compute isotope concentrations 1443 C12 = (1 + R13) ** -1 1444 C13 = C12 * R13 1445 C16 = (1 + R17 + R18) ** -1 1446 C17 = C16 * R17 1447 C18 = C16 * R18 1448 1449 # Compute stochastic isotopologue concentrations 1450 C626 = C16 * C12 * C16 1451 C627 = C16 * C12 * C17 * 2 1452 C628 = C16 * C12 * C18 * 2 1453 C636 = C16 * C13 * C16 1454 C637 = C16 * C13 * C17 * 2 1455 C638 = C16 * C13 * C18 * 2 1456 C727 = C17 * C12 * C17 1457 C728 = C17 * C12 * C18 * 2 1458 C737 = C17 * C13 * C17 1459 C738 = C17 * C13 * C18 * 2 1460 C828 = C18 * C12 * C18 1461 C838 = C18 * C13 * C18 1462 1463 # Compute stochastic isobar ratios 1464 R45 = (C636 + C627) / C626 1465 R46 = (C628 + C637 + C727) / C626 1466 R47 = (C638 + C728 + C737) / C626 1467 R48 = (C738 + C828) / C626 1468 R49 = C838 / C626 1469 1470 # Account for stochastic anomalies 1471 R47 *= 1 + D47 / 1000 1472 R48 *= 1 + D48 / 1000 1473 R49 *= 1 + D49 / 1000 1474 1475 # Return isobar ratios 1476 return R45, R46, R47, R48, R49 1477 1478 1479 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'): 1480 ''' 1481 Split unknown samples by UID (treat all analyses as different samples) 1482 or by session (treat analyses of a given sample in different sessions as 1483 different samples). 1484 1485 **Parameters** 1486 1487 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']` 1488 + `grouping`: `by_uid` | `by_session` 1489 ''' 1490 if samples_to_split == 'all': 1491 samples_to_split = [s for s in self.unknowns] 1492 gkeys = {'by_uid':'UID', 'by_session':'Session'} 1493 self.grouping = grouping.lower() 1494 if self.grouping in gkeys: 1495 gkey = gkeys[self.grouping] 1496 for r in self: 1497 if r['Sample'] in samples_to_split: 1498 r['Sample_original'] = r['Sample'] 1499 r['Sample'] = f"{r['Sample']}__{r[gkey]}" 1500 elif r['Sample'] in self.unknowns: 1501 r['Sample_original'] = r['Sample'] 1502 self.refresh_samples() 1503 1504 1505 def unsplit_samples(self, tables = False): 1506 ''' 1507 Reverse the effects of `D47data.split_samples()`. 1508 1509 This should only be used after `D4xdata.standardize()` with `method='pooled'`. 1510 1511 After `D4xdata.standardize()` with `method='indep_sessions'`, one should 1512 probably use `D4xdata.combine_samples()` instead to reverse the effects of 1513 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the 1514 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in 1515 that case session-averaged Δ4x values are statistically independent). 1516 ''' 1517 unknowns_old = sorted({s for s in self.unknowns}) 1518 CM_old = self.standardization.covar[:,:] 1519 VD_old = self.standardization.params.valuesdict().copy() 1520 vars_old = self.standardization.var_names 1521 1522 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r}) 1523 1524 Ns = len(vars_old) - len(unknowns_old) 1525 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new] 1526 VD_new = {k: VD_old[k] for k in vars_old[:Ns]} 1527 1528 W = np.zeros((len(vars_new), len(vars_old))) 1529 W[:Ns,:Ns] = np.eye(Ns) 1530 for u in unknowns_new: 1531 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u}) 1532 if self.grouping == 'by_session': 1533 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits] 1534 elif self.grouping == 'by_uid': 1535 weights = [1 for s in splits] 1536 sw = sum(weights) 1537 weights = [w/sw for w in weights] 1538 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:] 1539 1540 CM_new = W @ CM_old @ W.T 1541 V = W @ np.array([[VD_old[k]] for k in vars_old]) 1542 VD_new = {k:v[0] for k,v in zip(vars_new, V)} 1543 1544 self.standardization.covar = CM_new 1545 self.standardization.params.valuesdict = lambda : VD_new 1546 self.standardization.var_names = vars_new 1547 1548 for r in self: 1549 if r['Sample'] in self.unknowns: 1550 r['Sample_split'] = r['Sample'] 1551 r['Sample'] = r['Sample_original'] 1552 1553 self.refresh_samples() 1554 self.consolidate_samples() 1555 self.repeatabilities() 1556 1557 if tables: 1558 self.table_of_analyses() 1559 self.table_of_samples() 1560 1561 def assign_timestamps(self): 1562 ''' 1563 Assign a time field `t` of type `float` to each analysis. 1564 1565 If `TimeTag` is one of the data fields, `t` is equal within a given session 1566 to `TimeTag` minus the mean value of `TimeTag` for that session. 1567 Otherwise, `TimeTag` is by default equal to the index of each analysis 1568 in the dataset and `t` is defined as above. 1569 ''' 1570 for session in self.sessions: 1571 sdata = self.sessions[session]['data'] 1572 try: 1573 t0 = np.mean([r['TimeTag'] for r in sdata]) 1574 for r in sdata: 1575 r['t'] = r['TimeTag'] - t0 1576 except KeyError: 1577 t0 = (len(sdata)-1)/2 1578 for t,r in enumerate(sdata): 1579 r['t'] = t - t0 1580 1581 1582 def report(self): 1583 ''' 1584 Prints a report on the standardization fit. 1585 Only applicable after `D4xdata.standardize(method='pooled')`. 1586 ''' 1587 report_fit(self.standardization) 1588 1589 1590 def combine_samples(self, sample_groups): 1591 ''' 1592 Combine analyses of different samples to compute weighted average Δ4x 1593 and new error (co)variances corresponding to the groups defined by the `sample_groups` 1594 dictionary. 1595 1596 Caution: samples are weighted by number of replicate analyses, which is a 1597 reasonable default behavior but is not always optimal (e.g., in the case of strongly 1598 correlated analytical errors for one or more samples). 1599 1600 Returns a tuplet of: 1601 1602 + the list of group names 1603 + an array of the corresponding Δ4x values 1604 + the corresponding (co)variance matrix 1605 1606 **Parameters** 1607 1608 + `sample_groups`: a dictionary of the form: 1609 ```py 1610 {'group1': ['sample_1', 'sample_2'], 1611 'group2': ['sample_3', 'sample_4', 'sample_5']} 1612 ``` 1613 ''' 1614 1615 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])] 1616 groups = sorted(sample_groups.keys()) 1617 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups} 1618 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples]) 1619 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples]) 1620 W = np.array([ 1621 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples] 1622 for j in groups]) 1623 D4x_new = W @ D4x_old 1624 CM_new = W @ CM_old @ W.T 1625 1626 return groups, D4x_new[:,0], CM_new 1627 1628 1629 @make_verbal 1630 def standardize(self, 1631 method = 'pooled', 1632 weighted_sessions = [], 1633 consolidate = True, 1634 consolidate_tables = False, 1635 consolidate_plots = False, 1636 constraints = {}, 1637 ): 1638 ''' 1639 Compute absolute Δ4x values for all replicate analyses and for sample averages. 1640 If `method` argument is set to `'pooled'`, the standardization processes all sessions 1641 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous, 1642 i.e. that their true Δ4x value does not change between sessions, 1643 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to 1644 `'indep_sessions'`, the standardization processes each session independently, based only 1645 on anchors analyses. 1646 ''' 1647 1648 self.standardization_method = method 1649 self.assign_timestamps() 1650 1651 if method == 'pooled': 1652 if weighted_sessions: 1653 for session_group in weighted_sessions: 1654 if self._4x == '47': 1655 X = D47data([r for r in self if r['Session'] in session_group]) 1656 elif self._4x == '48': 1657 X = D48data([r for r in self if r['Session'] in session_group]) 1658 X.Nominal_D4x = self.Nominal_D4x.copy() 1659 X.refresh() 1660 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False) 1661 w = np.sqrt(result.redchi) 1662 self.msg(f'Session group {session_group} MRSWD = {w:.4f}') 1663 for r in X: 1664 r[f'wD{self._4x}raw'] *= w 1665 else: 1666 self.msg(f'All D{self._4x}raw weights set to 1 ‰') 1667 for r in self: 1668 r[f'wD{self._4x}raw'] = 1. 1669 1670 params = Parameters() 1671 for k,session in enumerate(self.sessions): 1672 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.") 1673 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.") 1674 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.") 1675 s = pf(session) 1676 params.add(f'a_{s}', value = 0.9) 1677 params.add(f'b_{s}', value = 0.) 1678 params.add(f'c_{s}', value = -0.9) 1679 params.add(f'a2_{s}', value = 0., 1680# vary = self.sessions[session]['scrambling_drift'], 1681 ) 1682 params.add(f'b2_{s}', value = 0., 1683# vary = self.sessions[session]['slope_drift'], 1684 ) 1685 params.add(f'c2_{s}', value = 0., 1686# vary = self.sessions[session]['wg_drift'], 1687 ) 1688 if not self.sessions[session]['scrambling_drift']: 1689 params[f'a2_{s}'].expr = '0' 1690 if not self.sessions[session]['slope_drift']: 1691 params[f'b2_{s}'].expr = '0' 1692 if not self.sessions[session]['wg_drift']: 1693 params[f'c2_{s}'].expr = '0' 1694 1695 for sample in self.unknowns: 1696 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5) 1697 1698 for k in constraints: 1699 params[k].expr = constraints[k] 1700 1701 def residuals(p): 1702 R = [] 1703 for r in self: 1704 session = pf(r['Session']) 1705 sample = pf(r['Sample']) 1706 if r['Sample'] in self.Nominal_D4x: 1707 R += [ ( 1708 r[f'D{self._4x}raw'] - ( 1709 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']] 1710 + p[f'b_{session}'] * r[f'd{self._4x}'] 1711 + p[f'c_{session}'] 1712 + r['t'] * ( 1713 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']] 1714 + p[f'b2_{session}'] * r[f'd{self._4x}'] 1715 + p[f'c2_{session}'] 1716 ) 1717 ) 1718 ) / r[f'wD{self._4x}raw'] ] 1719 else: 1720 R += [ ( 1721 r[f'D{self._4x}raw'] - ( 1722 p[f'a_{session}'] * p[f'D{self._4x}_{sample}'] 1723 + p[f'b_{session}'] * r[f'd{self._4x}'] 1724 + p[f'c_{session}'] 1725 + r['t'] * ( 1726 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}'] 1727 + p[f'b2_{session}'] * r[f'd{self._4x}'] 1728 + p[f'c2_{session}'] 1729 ) 1730 ) 1731 ) / r[f'wD{self._4x}raw'] ] 1732 return R 1733 1734 M = Minimizer(residuals, params) 1735 result = M.least_squares() 1736 self.Nf = result.nfree 1737 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf) 1738 new_names, new_covar, new_se = _fullcovar(result)[:3] 1739 result.var_names = new_names 1740 result.covar = new_covar 1741 1742 for r in self: 1743 s = pf(r["Session"]) 1744 a = result.params.valuesdict()[f'a_{s}'] 1745 b = result.params.valuesdict()[f'b_{s}'] 1746 c = result.params.valuesdict()[f'c_{s}'] 1747 a2 = result.params.valuesdict()[f'a2_{s}'] 1748 b2 = result.params.valuesdict()[f'b2_{s}'] 1749 c2 = result.params.valuesdict()[f'c2_{s}'] 1750 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t']) 1751 1752 1753 self.standardization = result 1754 1755 for session in self.sessions: 1756 self.sessions[session]['Np'] = 3 1757 for k in ['scrambling', 'slope', 'wg']: 1758 if self.sessions[session][f'{k}_drift']: 1759 self.sessions[session]['Np'] += 1 1760 1761 if consolidate: 1762 self.consolidate(tables = consolidate_tables, plots = consolidate_plots) 1763 return result 1764 1765 1766 elif method == 'indep_sessions': 1767 1768 if weighted_sessions: 1769 for session_group in weighted_sessions: 1770 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x) 1771 X.Nominal_D4x = self.Nominal_D4x.copy() 1772 X.refresh() 1773 # This is only done to assign r['wD47raw'] for r in X: 1774 X.standardize(method = method, weighted_sessions = [], consolidate = False) 1775 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}') 1776 else: 1777 self.msg('All weights set to 1 ‰') 1778 for r in self: 1779 r[f'wD{self._4x}raw'] = 1 1780 1781 for session in self.sessions: 1782 s = self.sessions[session] 1783 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2'] 1784 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']] 1785 s['Np'] = sum(p_active) 1786 sdata = s['data'] 1787 1788 A = np.array([ 1789 [ 1790 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'], 1791 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'], 1792 1 / r[f'wD{self._4x}raw'], 1793 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'], 1794 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'], 1795 r['t'] / r[f'wD{self._4x}raw'] 1796 ] 1797 for r in sdata if r['Sample'] in self.anchors 1798 ])[:,p_active] # only keep columns for the active parameters 1799 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors]) 1800 s['Na'] = Y.size 1801 CM = linalg.inv(A.T @ A) 1802 bf = (CM @ A.T @ Y).T[0,:] 1803 k = 0 1804 for n,a in zip(p_names, p_active): 1805 if a: 1806 s[n] = bf[k] 1807# self.msg(f'{n} = {bf[k]}') 1808 k += 1 1809 else: 1810 s[n] = 0. 1811# self.msg(f'{n} = 0.0') 1812 1813 for r in sdata : 1814 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2'] 1815 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t']) 1816 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t']) 1817 1818 s['CM'] = np.zeros((6,6)) 1819 i = 0 1820 k_active = [j for j,a in enumerate(p_active) if a] 1821 for j,a in enumerate(p_active): 1822 if a: 1823 s['CM'][j,k_active] = CM[i,:] 1824 i += 1 1825 1826 if not weighted_sessions: 1827 w = self.rmswd()['rmswd'] 1828 for r in self: 1829 r[f'wD{self._4x}'] *= w 1830 r[f'wD{self._4x}raw'] *= w 1831 for session in self.sessions: 1832 self.sessions[session]['CM'] *= w**2 1833 1834 for session in self.sessions: 1835 s = self.sessions[session] 1836 s['SE_a'] = s['CM'][0,0]**.5 1837 s['SE_b'] = s['CM'][1,1]**.5 1838 s['SE_c'] = s['CM'][2,2]**.5 1839 s['SE_a2'] = s['CM'][3,3]**.5 1840 s['SE_b2'] = s['CM'][4,4]**.5 1841 s['SE_c2'] = s['CM'][5,5]**.5 1842 1843 if not weighted_sessions: 1844 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions]) 1845 else: 1846 self.Nf = 0 1847 for sg in weighted_sessions: 1848 self.Nf += self.rmswd(sessions = sg)['Nf'] 1849 1850 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf) 1851 1852 avgD4x = { 1853 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample]) 1854 for sample in self.samples 1855 } 1856 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self]) 1857 rD4x = (chi2/self.Nf)**.5 1858 self.repeatability[f'sigma_{self._4x}'] = rD4x 1859 1860 if consolidate: 1861 self.consolidate(tables = consolidate_tables, plots = consolidate_plots) 1862 1863 1864 def standardization_error(self, session, d4x, D4x, t = 0): 1865 ''' 1866 Compute standardization error for a given session and 1867 (δ47, Δ47) composition. 1868 ''' 1869 a = self.sessions[session]['a'] 1870 b = self.sessions[session]['b'] 1871 c = self.sessions[session]['c'] 1872 a2 = self.sessions[session]['a2'] 1873 b2 = self.sessions[session]['b2'] 1874 c2 = self.sessions[session]['c2'] 1875 CM = self.sessions[session]['CM'] 1876 1877 x, y = D4x, d4x 1878 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t 1879# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t) 1880 dxdy = -(b+b2*t) / (a+a2*t) 1881 dxdz = 1. / (a+a2*t) 1882 dxda = -x / (a+a2*t) 1883 dxdb = -y / (a+a2*t) 1884 dxdc = -1. / (a+a2*t) 1885 dxda2 = -x * a2 / (a+a2*t) 1886 dxdb2 = -y * t / (a+a2*t) 1887 dxdc2 = -t / (a+a2*t) 1888 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2]) 1889 sx = (V @ CM @ V.T) ** .5 1890 return sx 1891 1892 1893 @make_verbal 1894 def summary(self, 1895 dir = 'output', 1896 filename = None, 1897 save_to_file = True, 1898 print_out = True, 1899 ): 1900 ''' 1901 Print out an/or save to disk a summary of the standardization results. 1902 1903 **Parameters** 1904 1905 + `dir`: the directory in which to save the table 1906 + `filename`: the name to the csv file to write to 1907 + `save_to_file`: whether to save the table to disk 1908 + `print_out`: whether to print out the table 1909 ''' 1910 1911 out = [] 1912 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]] 1913 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]] 1914 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]] 1915 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]] 1916 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]] 1917 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]] 1918 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]] 1919 out += [['Model degrees of freedom', f"{self.Nf}"]] 1920 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]] 1921 out += [['Standardization method', self.standardization_method]] 1922 1923 if save_to_file: 1924 if not os.path.exists(dir): 1925 os.makedirs(dir) 1926 if filename is None: 1927 filename = f'D{self._4x}_summary.csv' 1928 with open(f'{dir}/{filename}', 'w') as fid: 1929 fid.write(make_csv(out)) 1930 if print_out: 1931 self.msg('\n' + pretty_table(out, header = 0)) 1932 1933 1934 @make_verbal 1935 def table_of_sessions(self, 1936 dir = 'output', 1937 filename = None, 1938 save_to_file = True, 1939 print_out = True, 1940 output = None, 1941 ): 1942 ''' 1943 Print out an/or save to disk a table of sessions. 1944 1945 **Parameters** 1946 1947 + `dir`: the directory in which to save the table 1948 + `filename`: the name to the csv file to write to 1949 + `save_to_file`: whether to save the table to disk 1950 + `print_out`: whether to print out the table 1951 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 1952 if set to `'raw'`: return a list of list of strings 1953 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 1954 ''' 1955 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions]) 1956 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions]) 1957 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions]) 1958 1959 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']] 1960 if include_a2: 1961 out[-1] += ['a2 ± SE'] 1962 if include_b2: 1963 out[-1] += ['b2 ± SE'] 1964 if include_c2: 1965 out[-1] += ['c2 ± SE'] 1966 for session in self.sessions: 1967 out += [[ 1968 session, 1969 f"{self.sessions[session]['Na']}", 1970 f"{self.sessions[session]['Nu']}", 1971 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}", 1972 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}", 1973 f"{self.sessions[session]['r_d13C_VPDB']:.4f}", 1974 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}", 1975 f"{self.sessions[session][f'r_D{self._4x}']:.4f}", 1976 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}", 1977 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}", 1978 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}", 1979 ]] 1980 if include_a2: 1981 if self.sessions[session]['scrambling_drift']: 1982 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"] 1983 else: 1984 out[-1] += [''] 1985 if include_b2: 1986 if self.sessions[session]['slope_drift']: 1987 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"] 1988 else: 1989 out[-1] += [''] 1990 if include_c2: 1991 if self.sessions[session]['wg_drift']: 1992 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"] 1993 else: 1994 out[-1] += [''] 1995 1996 if save_to_file: 1997 if not os.path.exists(dir): 1998 os.makedirs(dir) 1999 if filename is None: 2000 filename = f'D{self._4x}_sessions.csv' 2001 with open(f'{dir}/{filename}', 'w') as fid: 2002 fid.write(make_csv(out)) 2003 if print_out: 2004 self.msg('\n' + pretty_table(out)) 2005 if output == 'raw': 2006 return out 2007 elif output == 'pretty': 2008 return pretty_table(out) 2009 2010 2011 @make_verbal 2012 def table_of_analyses( 2013 self, 2014 dir = 'output', 2015 filename = None, 2016 save_to_file = True, 2017 print_out = True, 2018 output = None, 2019 ): 2020 ''' 2021 Print out an/or save to disk a table of analyses. 2022 2023 **Parameters** 2024 2025 + `dir`: the directory in which to save the table 2026 + `filename`: the name to the csv file to write to 2027 + `save_to_file`: whether to save the table to disk 2028 + `print_out`: whether to print out the table 2029 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 2030 if set to `'raw'`: return a list of list of strings 2031 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2032 ''' 2033 2034 out = [['UID','Session','Sample']] 2035 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}] 2036 for f in extra_fields: 2037 out[-1] += [f[0]] 2038 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}'] 2039 for r in self: 2040 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]] 2041 for f in extra_fields: 2042 out[-1] += [f"{r[f[0]]:{f[1]}}"] 2043 out[-1] += [ 2044 f"{r['d13Cwg_VPDB']:.3f}", 2045 f"{r['d18Owg_VSMOW']:.3f}", 2046 f"{r['d45']:.6f}", 2047 f"{r['d46']:.6f}", 2048 f"{r['d47']:.6f}", 2049 f"{r['d48']:.6f}", 2050 f"{r['d49']:.6f}", 2051 f"{r['d13C_VPDB']:.6f}", 2052 f"{r['d18O_VSMOW']:.6f}", 2053 f"{r['D47raw']:.6f}", 2054 f"{r['D48raw']:.6f}", 2055 f"{r['D49raw']:.6f}", 2056 f"{r[f'D{self._4x}']:.6f}" 2057 ] 2058 if save_to_file: 2059 if not os.path.exists(dir): 2060 os.makedirs(dir) 2061 if filename is None: 2062 filename = f'D{self._4x}_analyses.csv' 2063 with open(f'{dir}/{filename}', 'w') as fid: 2064 fid.write(make_csv(out)) 2065 if print_out: 2066 self.msg('\n' + pretty_table(out)) 2067 return out 2068 2069 @make_verbal 2070 def covar_table( 2071 self, 2072 correl = False, 2073 dir = 'output', 2074 filename = None, 2075 save_to_file = True, 2076 print_out = True, 2077 output = None, 2078 ): 2079 ''' 2080 Print out, save to disk and/or return the variance-covariance matrix of D4x 2081 for all unknown samples. 2082 2083 **Parameters** 2084 2085 + `dir`: the directory in which to save the csv 2086 + `filename`: the name of the csv file to write to 2087 + `save_to_file`: whether to save the csv 2088 + `print_out`: whether to print out the matrix 2089 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`); 2090 if set to `'raw'`: return a list of list of strings 2091 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2092 ''' 2093 samples = sorted([u for u in self.unknowns]) 2094 out = [[''] + samples] 2095 for s1 in samples: 2096 out.append([s1]) 2097 for s2 in samples: 2098 if correl: 2099 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}') 2100 else: 2101 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}') 2102 2103 if save_to_file: 2104 if not os.path.exists(dir): 2105 os.makedirs(dir) 2106 if filename is None: 2107 if correl: 2108 filename = f'D{self._4x}_correl.csv' 2109 else: 2110 filename = f'D{self._4x}_covar.csv' 2111 with open(f'{dir}/{filename}', 'w') as fid: 2112 fid.write(make_csv(out)) 2113 if print_out: 2114 self.msg('\n'+pretty_table(out)) 2115 if output == 'raw': 2116 return out 2117 elif output == 'pretty': 2118 return pretty_table(out) 2119 2120 @make_verbal 2121 def table_of_samples( 2122 self, 2123 dir = 'output', 2124 filename = None, 2125 save_to_file = True, 2126 print_out = True, 2127 output = None, 2128 ): 2129 ''' 2130 Print out, save to disk and/or return a table of samples. 2131 2132 **Parameters** 2133 2134 + `dir`: the directory in which to save the csv 2135 + `filename`: the name of the csv file to write to 2136 + `save_to_file`: whether to save the csv 2137 + `print_out`: whether to print out the table 2138 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 2139 if set to `'raw'`: return a list of list of strings 2140 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2141 ''' 2142 2143 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']] 2144 for sample in self.anchors: 2145 out += [[ 2146 f"{sample}", 2147 f"{self.samples[sample]['N']}", 2148 f"{self.samples[sample]['d13C_VPDB']:.2f}", 2149 f"{self.samples[sample]['d18O_VSMOW']:.2f}", 2150 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','', 2151 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', '' 2152 ]] 2153 for sample in self.unknowns: 2154 out += [[ 2155 f"{sample}", 2156 f"{self.samples[sample]['N']}", 2157 f"{self.samples[sample]['d13C_VPDB']:.2f}", 2158 f"{self.samples[sample]['d18O_VSMOW']:.2f}", 2159 f"{self.samples[sample][f'D{self._4x}']:.4f}", 2160 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}", 2161 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}", 2162 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', 2163 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else '' 2164 ]] 2165 if save_to_file: 2166 if not os.path.exists(dir): 2167 os.makedirs(dir) 2168 if filename is None: 2169 filename = f'D{self._4x}_samples.csv' 2170 with open(f'{dir}/{filename}', 'w') as fid: 2171 fid.write(make_csv(out)) 2172 if print_out: 2173 self.msg('\n'+pretty_table(out)) 2174 if output == 'raw': 2175 return out 2176 elif output == 'pretty': 2177 return pretty_table(out) 2178 2179 2180 def plot_sessions(self, dir = 'output', figsize = (8,8)): 2181 ''' 2182 Generate session plots and save them to disk. 2183 2184 **Parameters** 2185 2186 + `dir`: the directory in which to save the plots 2187 + `figsize`: the width and height (in inches) of each plot 2188 ''' 2189 if not os.path.exists(dir): 2190 os.makedirs(dir) 2191 2192 for session in self.sessions: 2193 sp = self.plot_single_session(session, xylimits = 'constant') 2194 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.pdf') 2195 ppl.close(sp.fig) 2196 2197 2198 @make_verbal 2199 def consolidate_samples(self): 2200 ''' 2201 Compile various statistics for each sample. 2202 2203 For each anchor sample: 2204 2205 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x` 2206 + `SE_D47` or `SE_D48`: set to zero by definition 2207 2208 For each unknown sample: 2209 2210 + `D47` or `D48`: the standardized Δ4x value for this unknown 2211 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown 2212 2213 For each anchor and unknown: 2214 2215 + `N`: the total number of analyses of this sample 2216 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample 2217 + `d13C_VPDB`: the average δ13C_VPDB value for this sample 2218 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2) 2219 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal 2220 variance, indicating whether the Δ4x repeatability this sample differs significantly from 2221 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`. 2222 ''' 2223 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']] 2224 for sample in self.samples: 2225 self.samples[sample]['N'] = len(self.samples[sample]['data']) 2226 if self.samples[sample]['N'] > 1: 2227 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']]) 2228 2229 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']]) 2230 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']]) 2231 2232 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']] 2233 if len(D4x_pop) > 2: 2234 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1] 2235 2236 if self.standardization_method == 'pooled': 2237 for sample in self.anchors: 2238 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample] 2239 self.samples[sample][f'SE_D{self._4x}'] = 0. 2240 for sample in self.unknowns: 2241 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}'] 2242 try: 2243 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5 2244 except ValueError: 2245 # when `sample` is constrained by self.standardize(constraints = {...}), 2246 # it is no longer listed in self.standardization.var_names. 2247 # Temporary fix: define SE as zero for now 2248 self.samples[sample][f'SE_D4{self._4x}'] = 0. 2249 2250 elif self.standardization_method == 'indep_sessions': 2251 for sample in self.anchors: 2252 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample] 2253 self.samples[sample][f'SE_D{self._4x}'] = 0. 2254 for sample in self.unknowns: 2255 self.msg(f'Consolidating sample {sample}') 2256 self.unknowns[sample][f'session_D{self._4x}'] = {} 2257 session_avg = [] 2258 for session in self.sessions: 2259 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample] 2260 if sdata: 2261 self.msg(f'{sample} found in session {session}') 2262 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata]) 2263 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata]) 2264 # !! TODO: sigma_s below does not account for temporal changes in standardization error 2265 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x) 2266 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5 2267 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5]) 2268 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1] 2269 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg)) 2270 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']} 2271 wsum = sum([weights[s] for s in weights]) 2272 for s in weights: 2273 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum] 2274 2275 for r in self: 2276 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}'] 2277 2278 2279 2280 def consolidate_sessions(self): 2281 ''' 2282 Compute various statistics for each session. 2283 2284 + `Na`: Number of anchor analyses in the session 2285 + `Nu`: Number of unknown analyses in the session 2286 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session 2287 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session 2288 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session 2289 + `a`: scrambling factor 2290 + `b`: compositional slope 2291 + `c`: WG offset 2292 + `SE_a`: Model stadard erorr of `a` 2293 + `SE_b`: Model stadard erorr of `b` 2294 + `SE_c`: Model stadard erorr of `c` 2295 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`) 2296 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`) 2297 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`) 2298 + `a2`: scrambling factor drift 2299 + `b2`: compositional slope drift 2300 + `c2`: WG offset drift 2301 + `Np`: Number of standardization parameters to fit 2302 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`) 2303 + `d13Cwg_VPDB`: δ13C_VPDB of WG 2304 + `d18Owg_VSMOW`: δ18O_VSMOW of WG 2305 ''' 2306 for session in self.sessions: 2307 if 'd13Cwg_VPDB' not in self.sessions[session]: 2308 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB'] 2309 if 'd18Owg_VSMOW' not in self.sessions[session]: 2310 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW'] 2311 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors]) 2312 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns]) 2313 2314 self.msg(f'Computing repeatabilities for session {session}') 2315 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session]) 2316 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session]) 2317 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session]) 2318 2319 if self.standardization_method == 'pooled': 2320 for session in self.sessions: 2321 2322 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}'] 2323 i = self.standardization.var_names.index(f'a_{pf(session)}') 2324 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5 2325 2326 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}'] 2327 i = self.standardization.var_names.index(f'b_{pf(session)}') 2328 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5 2329 2330 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}'] 2331 i = self.standardization.var_names.index(f'c_{pf(session)}') 2332 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5 2333 2334 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}'] 2335 if self.sessions[session]['scrambling_drift']: 2336 i = self.standardization.var_names.index(f'a2_{pf(session)}') 2337 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5 2338 else: 2339 self.sessions[session]['SE_a2'] = 0. 2340 2341 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}'] 2342 if self.sessions[session]['slope_drift']: 2343 i = self.standardization.var_names.index(f'b2_{pf(session)}') 2344 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5 2345 else: 2346 self.sessions[session]['SE_b2'] = 0. 2347 2348 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}'] 2349 if self.sessions[session]['wg_drift']: 2350 i = self.standardization.var_names.index(f'c2_{pf(session)}') 2351 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5 2352 else: 2353 self.sessions[session]['SE_c2'] = 0. 2354 2355 i = self.standardization.var_names.index(f'a_{pf(session)}') 2356 j = self.standardization.var_names.index(f'b_{pf(session)}') 2357 k = self.standardization.var_names.index(f'c_{pf(session)}') 2358 CM = np.zeros((6,6)) 2359 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]] 2360 try: 2361 i2 = self.standardization.var_names.index(f'a2_{pf(session)}') 2362 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]] 2363 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2] 2364 try: 2365 j2 = self.standardization.var_names.index(f'b2_{pf(session)}') 2366 CM[3,4] = self.standardization.covar[i2,j2] 2367 CM[4,3] = self.standardization.covar[j2,i2] 2368 except ValueError: 2369 pass 2370 try: 2371 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2372 CM[3,5] = self.standardization.covar[i2,k2] 2373 CM[5,3] = self.standardization.covar[k2,i2] 2374 except ValueError: 2375 pass 2376 except ValueError: 2377 pass 2378 try: 2379 j2 = self.standardization.var_names.index(f'b2_{pf(session)}') 2380 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]] 2381 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2] 2382 try: 2383 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2384 CM[4,5] = self.standardization.covar[j2,k2] 2385 CM[5,4] = self.standardization.covar[k2,j2] 2386 except ValueError: 2387 pass 2388 except ValueError: 2389 pass 2390 try: 2391 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2392 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]] 2393 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2] 2394 except ValueError: 2395 pass 2396 2397 self.sessions[session]['CM'] = CM 2398 2399 elif self.standardization_method == 'indep_sessions': 2400 pass # Not implemented yet 2401 2402 2403 @make_verbal 2404 def repeatabilities(self): 2405 ''' 2406 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x 2407 (for all samples, for anchors, and for unknowns). 2408 ''' 2409 self.msg('Computing reproducibilities for all sessions') 2410 2411 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors') 2412 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors') 2413 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors') 2414 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns') 2415 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples') 2416 2417 2418 @make_verbal 2419 def consolidate(self, tables = True, plots = True): 2420 ''' 2421 Collect information about samples, sessions and repeatabilities. 2422 ''' 2423 self.consolidate_samples() 2424 self.consolidate_sessions() 2425 self.repeatabilities() 2426 2427 if tables: 2428 self.summary() 2429 self.table_of_sessions() 2430 self.table_of_analyses() 2431 self.table_of_samples() 2432 2433 if plots: 2434 self.plot_sessions() 2435 2436 2437 @make_verbal 2438 def rmswd(self, 2439 samples = 'all samples', 2440 sessions = 'all sessions', 2441 ): 2442 ''' 2443 Compute the χ2, root mean squared weighted deviation 2444 (i.e. reduced χ2), and corresponding degrees of freedom of the 2445 Δ4x values for samples in `samples` and sessions in `sessions`. 2446 2447 Only used in `D4xdata.standardize()` with `method='indep_sessions'`. 2448 ''' 2449 if samples == 'all samples': 2450 mysamples = [k for k in self.samples] 2451 elif samples == 'anchors': 2452 mysamples = [k for k in self.anchors] 2453 elif samples == 'unknowns': 2454 mysamples = [k for k in self.unknowns] 2455 else: 2456 mysamples = samples 2457 2458 if sessions == 'all sessions': 2459 sessions = [k for k in self.sessions] 2460 2461 chisq, Nf = 0, 0 2462 for sample in mysamples : 2463 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2464 if len(G) > 1 : 2465 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G]) 2466 Nf += (len(G) - 1) 2467 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G]) 2468 r = (chisq / Nf)**.5 if Nf > 0 else 0 2469 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.') 2470 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf} 2471 2472 2473 @make_verbal 2474 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'): 2475 ''' 2476 Compute the repeatability of `[r[key] for r in self]` 2477 ''' 2478 2479 if samples == 'all samples': 2480 mysamples = [k for k in self.samples] 2481 elif samples == 'anchors': 2482 mysamples = [k for k in self.anchors] 2483 elif samples == 'unknowns': 2484 mysamples = [k for k in self.unknowns] 2485 else: 2486 mysamples = samples 2487 2488 if sessions == 'all sessions': 2489 sessions = [k for k in self.sessions] 2490 2491 if key in ['D47', 'D48']: 2492 # Full disclosure: the definition of Nf is tricky/debatable 2493 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions] 2494 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum() 2495 Nf = len(G) 2496# print(f'len(G) = {Nf}') 2497 Nf -= len([s for s in mysamples if s in self.unknowns]) 2498# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider') 2499 for session in sessions: 2500 Np = len([ 2501 _ for _ in self.standardization.params 2502 if ( 2503 self.standardization.params[_].expr is not None 2504 and ( 2505 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session)) 2506 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session)) 2507 ) 2508 ) 2509 ]) 2510# print(f'session {session}: {Np} parameters to consider') 2511 Na = len({ 2512 r['Sample'] for r in self.sessions[session]['data'] 2513 if r['Sample'] in self.anchors and r['Sample'] in mysamples 2514 }) 2515# print(f'session {session}: {Na} different anchors in that session') 2516 Nf -= min(Np, Na) 2517# print(f'Nf = {Nf}') 2518 2519# for sample in mysamples : 2520# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2521# if len(X) > 1 : 2522# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ]) 2523# if sample in self.unknowns: 2524# Nf += len(X) - 1 2525# else: 2526# Nf += len(X) 2527# if samples in ['anchors', 'all samples']: 2528# Nf -= sum([self.sessions[s]['Np'] for s in sessions]) 2529 r = (chisq / Nf)**.5 if Nf > 0 else 0 2530 2531 else: # if key not in ['D47', 'D48'] 2532 chisq, Nf = 0, 0 2533 for sample in mysamples : 2534 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2535 if len(X) > 1 : 2536 Nf += len(X) - 1 2537 chisq += np.sum([ (x-np.mean(X))**2 for x in X ]) 2538 r = (chisq / Nf)**.5 if Nf > 0 else 0 2539 2540 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.') 2541 return r 2542 2543 def sample_average(self, samples, weights = 'equal', normalize = True): 2544 ''' 2545 Weighted average Δ4x value of a group of samples, accounting for covariance. 2546 2547 Returns the weighed average Δ4x value and associated SE 2548 of a group of samples. Weights are equal by default. If `normalize` is 2549 true, `weights` will be rescaled so that their sum equals 1. 2550 2551 **Examples** 2552 2553 ```python 2554 self.sample_average(['X','Y'], [1, 2]) 2555 ``` 2556 2557 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3, 2558 where Δ4x(X) and Δ4x(Y) are the average Δ4x 2559 values of samples X and Y, respectively. 2560 2561 ```python 2562 self.sample_average(['X','Y'], [1, -1], normalize = False) 2563 ``` 2564 2565 returns the value and SE of the difference Δ4x(X) - Δ4x(Y). 2566 ''' 2567 if weights == 'equal': 2568 weights = [1/len(samples)] * len(samples) 2569 2570 if normalize: 2571 s = sum(weights) 2572 if s: 2573 weights = [w/s for w in weights] 2574 2575 try: 2576# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples] 2577# C = self.standardization.covar[indices,:][:,indices] 2578 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples]) 2579 X = [self.samples[sample][f'D{self._4x}'] for sample in samples] 2580 return correlated_sum(X, C, weights) 2581 except ValueError: 2582 return (0., 0.) 2583 2584 2585 def sample_D4x_covar(self, sample1, sample2 = None): 2586 ''' 2587 Covariance between Δ4x values of samples 2588 2589 Returns the error covariance between the average Δ4x values of two 2590 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`), 2591 returns the Δ4x variance for that sample. 2592 ''' 2593 if sample2 is None: 2594 sample2 = sample1 2595 if self.standardization_method == 'pooled': 2596 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}') 2597 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}') 2598 return self.standardization.covar[i, j] 2599 elif self.standardization_method == 'indep_sessions': 2600 if sample1 == sample2: 2601 return self.samples[sample1][f'SE_D{self._4x}']**2 2602 else: 2603 c = 0 2604 for session in self.sessions: 2605 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1] 2606 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2] 2607 if sdata1 and sdata2: 2608 a = self.sessions[session]['a'] 2609 # !! TODO: CM below does not account for temporal changes in standardization parameters 2610 CM = self.sessions[session]['CM'][:3,:3] 2611 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1]) 2612 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1]) 2613 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2]) 2614 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2]) 2615 c += ( 2616 self.unknowns[sample1][f'session_D{self._4x}'][session][2] 2617 * self.unknowns[sample2][f'session_D{self._4x}'][session][2] 2618 * np.array([[avg_D4x_1, avg_d4x_1, 1]]) 2619 @ CM 2620 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T 2621 ) / a**2 2622 return float(c) 2623 2624 def sample_D4x_correl(self, sample1, sample2 = None): 2625 ''' 2626 Correlation between Δ4x errors of samples 2627 2628 Returns the error correlation between the average Δ4x values of two samples. 2629 ''' 2630 if sample2 is None or sample2 == sample1: 2631 return 1. 2632 return ( 2633 self.sample_D4x_covar(sample1, sample2) 2634 / self.unknowns[sample1][f'SE_D{self._4x}'] 2635 / self.unknowns[sample2][f'SE_D{self._4x}'] 2636 ) 2637 2638 def plot_single_session(self, 2639 session, 2640 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4), 2641 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4), 2642 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75), 2643 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75), 2644 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75), 2645 xylimits = 'free', # | 'constant' 2646 x_label = None, 2647 y_label = None, 2648 error_contour_interval = 'auto', 2649 fig = 'new', 2650 ): 2651 ''' 2652 Generate plot for a single session 2653 ''' 2654 if x_label is None: 2655 x_label = f'δ$_{{{self._4x}}}$ (‰)' 2656 if y_label is None: 2657 y_label = f'Δ$_{{{self._4x}}}$ (‰)' 2658 2659 out = _SessionPlot() 2660 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]] 2661 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]] 2662 2663 if fig == 'new': 2664 out.fig = ppl.figure(figsize = (6,6)) 2665 ppl.subplots_adjust(.1,.1,.9,.9) 2666 2667 out.anchor_analyses, = ppl.plot( 2668 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors], 2669 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors], 2670 **kw_plot_anchors) 2671 out.unknown_analyses, = ppl.plot( 2672 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns], 2673 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns], 2674 **kw_plot_unknowns) 2675 out.anchor_avg = ppl.plot( 2676 np.array([ np.array([ 2677 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1, 2678 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1 2679 ]) for sample in anchors]).T, 2680 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T, 2681 **kw_plot_anchor_avg) 2682 out.unknown_avg = ppl.plot( 2683 np.array([ np.array([ 2684 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1, 2685 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1 2686 ]) for sample in unknowns]).T, 2687 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T, 2688 **kw_plot_unknown_avg) 2689 if xylimits == 'constant': 2690 x = [r[f'd{self._4x}'] for r in self] 2691 y = [r[f'D{self._4x}'] for r in self] 2692 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y) 2693 w, h = x2-x1, y2-y1 2694 x1 -= w/20 2695 x2 += w/20 2696 y1 -= h/20 2697 y2 += h/20 2698 ppl.axis([x1, x2, y1, y2]) 2699 elif xylimits == 'free': 2700 x1, x2, y1, y2 = ppl.axis() 2701 else: 2702 x1, x2, y1, y2 = ppl.axis(xylimits) 2703 2704 if error_contour_interval != 'none': 2705 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2) 2706 XI,YI = np.meshgrid(xi, yi) 2707 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi]) 2708 if error_contour_interval == 'auto': 2709 rng = np.max(SI) - np.min(SI) 2710 if rng <= 0.01: 2711 cinterval = 0.001 2712 elif rng <= 0.03: 2713 cinterval = 0.004 2714 elif rng <= 0.1: 2715 cinterval = 0.01 2716 elif rng <= 0.3: 2717 cinterval = 0.03 2718 elif rng <= 1.: 2719 cinterval = 0.1 2720 else: 2721 cinterval = 0.5 2722 else: 2723 cinterval = error_contour_interval 2724 2725 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval) 2726 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error) 2727 out.clabel = ppl.clabel(out.contour) 2728 2729 ppl.xlabel(x_label) 2730 ppl.ylabel(y_label) 2731 ppl.title(session, weight = 'bold') 2732 ppl.grid(alpha = .2) 2733 out.ax = ppl.gca() 2734 2735 return out 2736 2737 def plot_residuals( 2738 self, 2739 kde = False, 2740 hist = False, 2741 binwidth = 2/3, 2742 dir = 'output', 2743 filename = None, 2744 highlight = [], 2745 colors = None, 2746 figsize = None, 2747 ): 2748 ''' 2749 Plot residuals of each analysis as a function of time (actually, as a function of 2750 the order of analyses in the `D4xdata` object) 2751 2752 + `kde`: whether to add a kernel density estimate of residuals 2753 + `hist`: whether to add a histogram of residuals (incompatible with `kde`) 2754 + `histbins`: specify bin edges for the histogram 2755 + `dir`: the directory in which to save the plot 2756 + `highlight`: a list of samples to highlight 2757 + `colors`: a dict of `{<sample>: <color>}` for all samples 2758 + `figsize`: (width, height) of figure 2759 ''' 2760 2761 from matplotlib import ticker 2762 2763 # Layout 2764 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize) 2765 if hist or kde: 2766 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72) 2767 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15) 2768 else: 2769 ppl.subplots_adjust(.08,.05,.78,.8) 2770 ax1 = ppl.subplot(111) 2771 2772 # Colors 2773 N = len(self.anchors) 2774 if colors is None: 2775 if len(highlight) > 0: 2776 Nh = len(highlight) 2777 if Nh == 1: 2778 colors = {highlight[0]: (0,0,0)} 2779 elif Nh == 3: 2780 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])} 2781 elif Nh == 4: 2782 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])} 2783 else: 2784 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)} 2785 else: 2786 if N == 3: 2787 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])} 2788 elif N == 4: 2789 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])} 2790 else: 2791 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)} 2792 2793 ppl.sca(ax1) 2794 2795 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75) 2796 2797 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$')) 2798 2799 session = self[0]['Session'] 2800 x1 = 0 2801# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self]) 2802 x_sessions = {} 2803 one_or_more_singlets = False 2804 one_or_more_multiplets = False 2805 multiplets = set() 2806 for k,r in enumerate(self): 2807 if r['Session'] != session: 2808 x2 = k-1 2809 x_sessions[session] = (x1+x2)/2 2810 ppl.axvline(k - 0.5, color = 'k', lw = .5) 2811 session = r['Session'] 2812 x1 = k 2813 singlet = len(self.samples[r['Sample']]['data']) == 1 2814 if not singlet: 2815 multiplets.add(r['Sample']) 2816 if r['Sample'] in self.unknowns: 2817 if singlet: 2818 one_or_more_singlets = True 2819 else: 2820 one_or_more_multiplets = True 2821 kw = dict( 2822 marker = 'x' if singlet else '+', 2823 ms = 4 if singlet else 5, 2824 ls = 'None', 2825 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0), 2826 mew = 1, 2827 alpha = 0.2 if singlet else 1, 2828 ) 2829 if highlight and r['Sample'] not in highlight: 2830 kw['alpha'] = 0.2 2831 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw) 2832 x2 = k 2833 x_sessions[session] = (x1+x2)/2 2834 2835 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1) 2836 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1) 2837 if not (hist or kde): 2838 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability['r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center') 2839 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability['r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center') 2840 2841 xmin, xmax, ymin, ymax = ppl.axis() 2842 for s in x_sessions: 2843 ppl.text( 2844 x_sessions[s], 2845 ymax +1, 2846 s, 2847 va = 'bottom', 2848 **( 2849 dict(ha = 'center') 2850 if len(self.sessions[s]['data']) > (0.15 * len(self)) 2851 else dict(ha = 'left', rotation = 45) 2852 ) 2853 ) 2854 2855 if hist or kde: 2856 ppl.sca(ax2) 2857 2858 for s in colors: 2859 kw['marker'] = '+' 2860 kw['ms'] = 5 2861 kw['mec'] = colors[s] 2862 kw['label'] = s 2863 kw['alpha'] = 1 2864 ppl.plot([], [], **kw) 2865 2866 kw['mec'] = (0,0,0) 2867 2868 if one_or_more_singlets: 2869 kw['marker'] = 'x' 2870 kw['ms'] = 4 2871 kw['alpha'] = .2 2872 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other' 2873 ppl.plot([], [], **kw) 2874 2875 if one_or_more_multiplets: 2876 kw['marker'] = '+' 2877 kw['ms'] = 4 2878 kw['alpha'] = 1 2879 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other' 2880 ppl.plot([], [], **kw) 2881 2882 if hist or kde: 2883 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9) 2884 else: 2885 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5) 2886 leg.set_zorder(-1000) 2887 2888 ppl.sca(ax1) 2889 2890 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)') 2891 ppl.xticks([]) 2892 ppl.axis([-1, len(self), None, None]) 2893 2894 if hist or kde: 2895 ppl.sca(ax2) 2896 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors]) 2897 2898 if kde: 2899 from scipy.stats import gaussian_kde 2900 yi = np.linspace(ymin, ymax, 201) 2901 xi = gaussian_kde(X).evaluate(yi) 2902 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1)) 2903# ppl.plot(xi, yi, 'k-', lw = 1) 2904 ppl.axis([0, None, ymin, ymax]) 2905 elif hist: 2906 ppl.hist( 2907 X, 2908 orientation = 'horizontal', 2909 histtype = 'stepfilled', 2910 ec = [.4]*3, 2911 fc = [.25]*3, 2912 alpha = .25, 2913 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)), 2914 ) 2915 ppl.axis([None, None, ymin, ymax]) 2916 ppl.text(0, 0, 2917 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", 2918 size = 7.5, 2919 alpha = 1, 2920 va = 'center', 2921 ha = 'left', 2922 ) 2923 2924 ppl.xticks([]) 2925 ppl.yticks([]) 2926# ax2.spines['left'].set_visible(False) 2927 ax2.spines['right'].set_visible(False) 2928 ax2.spines['top'].set_visible(False) 2929 ax2.spines['bottom'].set_visible(False) 2930 2931 2932 if not os.path.exists(dir): 2933 os.makedirs(dir) 2934 if filename is None: 2935 return fig 2936 elif filename == '': 2937 filename = f'D{self._4x}_residuals.pdf' 2938 ppl.savefig(f'{dir}/{filename}') 2939 ppl.close(fig) 2940 2941 2942 def simulate(self, *args, **kwargs): 2943 ''' 2944 Legacy function with warning message pointing to `virtual_data()` 2945 ''' 2946 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()') 2947 2948 def plot_distribution_of_analyses( 2949 self, 2950 dir = 'output', 2951 filename = None, 2952 vs_time = False, 2953 figsize = (6,4), 2954 subplots_adjust = (0.02, 0.13, 0.85, 0.8), 2955 output = None, 2956 ): 2957 ''' 2958 Plot temporal distribution of all analyses in the data set. 2959 2960 **Parameters** 2961 2962 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially. 2963 ''' 2964 2965 asamples = [s for s in self.anchors] 2966 usamples = [s for s in self.unknowns] 2967 if output is None or output == 'fig': 2968 fig = ppl.figure(figsize = figsize) 2969 ppl.subplots_adjust(*subplots_adjust) 2970 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)]) 2971 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)]) 2972 Xmax += (Xmax-Xmin)/40 2973 Xmin -= (Xmax-Xmin)/41 2974 for k, s in enumerate(asamples + usamples): 2975 if vs_time: 2976 X = [r['TimeTag'] for r in self if r['Sample'] == s] 2977 else: 2978 X = [x for x,r in enumerate(self) if r['Sample'] == s] 2979 Y = [-k for x in X] 2980 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75) 2981 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25) 2982 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r') 2983 ppl.axis([Xmin, Xmax, -k-1, 1]) 2984 ppl.xlabel('\ntime') 2985 ppl.gca().annotate('', 2986 xy = (0.6, -0.02), 2987 xycoords = 'axes fraction', 2988 xytext = (.4, -0.02), 2989 arrowprops = dict(arrowstyle = "->", color = 'k'), 2990 ) 2991 2992 2993 x2 = -1 2994 for session in self.sessions: 2995 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session]) 2996 if vs_time: 2997 ppl.axvline(x1, color = 'k', lw = .75) 2998 if x2 > -1: 2999 if not vs_time: 3000 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5) 3001 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session]) 3002# from xlrd import xldate_as_datetime 3003# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0)) 3004 if vs_time: 3005 ppl.axvline(x2, color = 'k', lw = .75) 3006 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15) 3007 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8) 3008 3009 ppl.xticks([]) 3010 ppl.yticks([]) 3011 3012 if output is None: 3013 if not os.path.exists(dir): 3014 os.makedirs(dir) 3015 if filename == None: 3016 filename = f'D{self._4x}_distribution_of_analyses.pdf' 3017 ppl.savefig(f'{dir}/{filename}') 3018 ppl.close(fig) 3019 elif output == 'ax': 3020 return ppl.gca() 3021 elif output == 'fig': 3022 return fig 3023 3024 3025 def plot_bulk_compositions( 3026 self, 3027 samples = None, 3028 dir = 'output/bulk_compositions', 3029 figsize = (6,6), 3030 subplots_adjust = (0.15, 0.12, 0.95, 0.92), 3031 show = False, 3032 sample_color = (0,.5,1), 3033 analysis_color = (.7,.7,.7), 3034 labeldist = 0.3, 3035 radius = 0.05, 3036 ): 3037 ''' 3038 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses. 3039 3040 By default, creates a directory `./output/bulk_compositions` where plots for 3041 each sample are saved. Another plot named `__all__.pdf` shows all analyses together. 3042 3043 3044 **Parameters** 3045 3046 + `samples`: Only these samples are processed (by default: all samples). 3047 + `dir`: where to save the plots 3048 + `figsize`: (width, height) of figure 3049 + `subplots_adjust`: passed to `subplots_adjust()` 3050 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples, 3051 allowing for interactive visualization/exploration in (δ13C, δ18O) space. 3052 + `sample_color`: color used for replicate markers/labels 3053 + `analysis_color`: color used for sample markers/labels 3054 + `labeldist`: distance (in inches) from replicate markers to replicate labels 3055 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`. 3056 ''' 3057 3058 from matplotlib.patches import Ellipse 3059 3060 if samples is None: 3061 samples = [_ for _ in self.samples] 3062 3063 saved = {} 3064 3065 for s in samples: 3066 3067 fig = ppl.figure(figsize = figsize) 3068 fig.subplots_adjust(*subplots_adjust) 3069 ax = ppl.subplot(111) 3070 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)') 3071 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)') 3072 ppl.title(s) 3073 3074 3075 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']]) 3076 UID = [_['UID'] for _ in self.samples[s]['data']] 3077 XY0 = XY.mean(0) 3078 3079 for xy in XY: 3080 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color) 3081 3082 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color) 3083 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color) 3084 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold') 3085 saved[s] = [XY, XY0] 3086 3087 x1, x2, y1, y2 = ppl.axis() 3088 x0, dx = (x1+x2)/2, (x2-x1)/2 3089 y0, dy = (y1+y2)/2, (y2-y1)/2 3090 dx, dy = [max(max(dx, dy), radius)]*2 3091 3092 ppl.axis([ 3093 x0 - 1.2*dx, 3094 x0 + 1.2*dx, 3095 y0 - 1.2*dy, 3096 y0 + 1.2*dy, 3097 ]) 3098 3099 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0)) 3100 3101 for xy, uid in zip(XY, UID): 3102 3103 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy)) 3104 vector_in_display_space = xy_in_display_space - XY0_in_display_space 3105 3106 if (vector_in_display_space**2).sum() > 0: 3107 3108 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5 3109 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist 3110 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space 3111 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space)) 3112 3113 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color) 3114 3115 else: 3116 3117 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color) 3118 3119 if radius: 3120 ax.add_artist(Ellipse( 3121 xy = XY0, 3122 width = radius*2, 3123 height = radius*2, 3124 ls = (0, (2,2)), 3125 lw = .7, 3126 ec = analysis_color, 3127 fc = 'None', 3128 )) 3129 ppl.text( 3130 XY0[0], 3131 XY0[1]-radius, 3132 f'\n± {radius*1e3:.0f} ppm', 3133 color = analysis_color, 3134 va = 'top', 3135 ha = 'center', 3136 linespacing = 0.4, 3137 size = 8, 3138 ) 3139 3140 if not os.path.exists(dir): 3141 os.makedirs(dir) 3142 fig.savefig(f'{dir}/{s}.pdf') 3143 ppl.close(fig) 3144 3145 fig = ppl.figure(figsize = figsize) 3146 fig.subplots_adjust(*subplots_adjust) 3147 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)') 3148 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)') 3149 3150 for s in saved: 3151 for xy in saved[s][0]: 3152 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color) 3153 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color) 3154 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color) 3155 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold') 3156 3157 x1, x2, y1, y2 = ppl.axis() 3158 ppl.axis([ 3159 x1 - (x2-x1)/10, 3160 x2 + (x2-x1)/10, 3161 y1 - (y2-y1)/10, 3162 y2 + (y2-y1)/10, 3163 ]) 3164 3165 3166 if not os.path.exists(dir): 3167 os.makedirs(dir) 3168 fig.savefig(f'{dir}/__all__.pdf') 3169 if show: 3170 ppl.show() 3171 ppl.close(fig)
Store and process data for a large set of Δ47 and/or Δ48 analyses, usually comprising more than one analytical session.
D4xdata(l=[], mass='47', logfile='', session='mySession', verbose=False)
1026 def __init__(self, l = [], mass = '47', logfile = '', session = 'mySession', verbose = False): 1027 ''' 1028 **Parameters** 1029 1030 + `l`: a list of dictionaries, with each dictionary including at least the keys 1031 `Sample`, `d45`, `d46`, and `d47` or `d48`. 1032 + `mass`: `'47'` or `'48'` 1033 + `logfile`: if specified, write detailed logs to this file path when calling `D4xdata` methods. 1034 + `session`: define session name for analyses without a `Session` key 1035 + `verbose`: if `True`, print out detailed logs when calling `D4xdata` methods. 1036 1037 Returns a `D4xdata` object derived from `list`. 1038 ''' 1039 self._4x = mass 1040 self.verbose = verbose 1041 self.prefix = 'D4xdata' 1042 self.logfile = logfile 1043 list.__init__(self, l) 1044 self.Nf = None 1045 self.repeatability = {} 1046 self.refresh(session = session)
Parameters
l: a list of dictionaries, with each dictionary including at least the keysSample,d45,d46, andd47ord48.mass:'47'or'48'logfile: if specified, write detailed logs to this file path when callingD4xdatamethods.session: define session name for analyses without aSessionkeyverbose: ifTrue, print out detailed logs when callingD4xdatamethods.
Returns a D4xdata object derived from list.
R18_VSMOW =
0.0020052
Absolute (18O/16C) ratio of VSMOW. By default equal to 0.0020052 (Baertschi, 1976)
LAMBDA_17 =
0.528
Mass-dependent exponent for triple oxygen isotopes. By default equal to 0.528 (Barkan & Luz, 2005)
R17_VSMOW =
0.00038475
Absolute (17O/16C) ratio of VSMOW.
By default equal to 0.00038475
(Assonov & Brenninkmeijer, 2003,
rescaled to R13_VPDB)
R18_VPDB =
0.0020672007840000003
Absolute (18O/16C) ratio of VPDB.
By definition equal to R18_VSMOW * 1.03092.
R17_VPDB =
0.0003909861828790272
Absolute (17O/16C) ratio of VPDB.
By definition equal to R17_VSMOW * 1.03092 ** LAMBDA_17.
LEVENE_REF_SAMPLE =
'ETH-3'
After the Δ4x standardization step, each sample is tested to assess whether the Δ4x variance within all analyses for that sample differs significantly from that observed for a given reference sample (using Levene's test, which yields a p-value corresponding to the null hypothesis that the underlying variances are equal).
LEVENE_REF_SAMPLE (by default equal to 'ETH-3') specifies which
sample should be used as a reference for this test.
ALPHA_18O_ACID_REACTION =
1.008129
Specifies the 18O/16O fractionation factor generally applicable
to acid reactions in the dataset. Currently used by D4xdata.wg(),
D4xdata.standardize_d13C, and D4xdata.standardize_d18O.
By default equal to 1.008129 (calcite reacted at 90 °C, Kim et al., 2007).
Nominal_d13C_VPDB =
{'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71}
Nominal δ13CVPDB values assigned to carbonate standards, used by
D4xdata.standardize_d13C().
By default equal to {'ETH-1': 2.02, 'ETH-2': -10.17, 'ETH-3': 1.71} after
Bernasconi et al. (2018).
Nominal_d18O_VPDB =
{'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78}
Nominal δ18OVPDB values assigned to carbonate standards, used by
D4xdata.standardize_d18O().
By default equal to {'ETH-1': -2.19, 'ETH-2': -18.69, 'ETH-3': -1.78} after
Bernasconi et al. (2018).
d13C_STANDARDIZATION_METHOD =
'2pt'
Method by which to standardize δ13C values:
none: do not apply any δ13C standardization.'1pt': within each session, offset all initial δ13C values so as to minimize the difference between final δ13CVPDB values andNominal_d13C_VPDB(averaged over all analyses for whichNominal_d13C_VPDBis defined).'2pt': within each session, apply a affine trasformation to all δ13C values so as to minimize the difference between final δ13CVPDB values andNominal_d13C_VPDB(averaged over all analyses for whichNominal_d13C_VPDBis defined).
d18O_STANDARDIZATION_METHOD =
'2pt'
Method by which to standardize δ18O values:
none: do not apply any δ18O standardization.'1pt': within each session, offset all initial δ18O values so as to minimize the difference between final δ18OVPDB values andNominal_d18O_VPDB(averaged over all analyses for whichNominal_d18O_VPDBis defined).'2pt': within each session, apply a affine trasformation to all δ18O values so as to minimize the difference between final δ18OVPDB values andNominal_d18O_VPDB(averaged over all analyses for whichNominal_d18O_VPDBis defined).
def
make_verbal(oldfun):
1049 def make_verbal(oldfun): 1050 ''' 1051 Decorator: allow temporarily changing `self.prefix` and overriding `self.verbose`. 1052 ''' 1053 @wraps(oldfun) 1054 def newfun(*args, verbose = '', **kwargs): 1055 myself = args[0] 1056 oldprefix = myself.prefix 1057 myself.prefix = oldfun.__name__ 1058 if verbose != '': 1059 oldverbose = myself.verbose 1060 myself.verbose = verbose 1061 out = oldfun(*args, **kwargs) 1062 myself.prefix = oldprefix 1063 if verbose != '': 1064 myself.verbose = oldverbose 1065 return out 1066 return newfun
Decorator: allow temporarily changing self.prefix and overriding self.verbose.
def
msg(self, txt):
1069 def msg(self, txt): 1070 ''' 1071 Log a message to `self.logfile`, and print it out if `verbose = True` 1072 ''' 1073 self.log(txt) 1074 if self.verbose: 1075 print(f'{f"[{self.prefix}]":<16} {txt}')
Log a message to self.logfile, and print it out if verbose = True
def
vmsg(self, txt):
1078 def vmsg(self, txt): 1079 ''' 1080 Log a message to `self.logfile` and print it out 1081 ''' 1082 self.log(txt) 1083 print(txt)
Log a message to self.logfile and print it out
def
log(self, *txts):
1086 def log(self, *txts): 1087 ''' 1088 Log a message to `self.logfile` 1089 ''' 1090 if self.logfile: 1091 with open(self.logfile, 'a') as fid: 1092 for txt in txts: 1093 fid.write(f'\n{dt.now().strftime("%Y-%m-%d %H:%M:%S")} {f"[{self.prefix}]":<16} {txt}')
Log a message to self.logfile
def
refresh(self, session='mySession'):
1096 def refresh(self, session = 'mySession'): 1097 ''' 1098 Update `self.sessions`, `self.samples`, `self.anchors`, and `self.unknowns`. 1099 ''' 1100 self.fill_in_missing_info(session = session) 1101 self.refresh_sessions() 1102 self.refresh_samples()
Update self.sessions, self.samples, self.anchors, and self.unknowns.
def
refresh_sessions(self):
1105 def refresh_sessions(self): 1106 ''' 1107 Update `self.sessions` and set `scrambling_drift`, `slope_drift`, and `wg_drift` 1108 to `False` for all sessions. 1109 ''' 1110 self.sessions = { 1111 s: {'data': [r for r in self if r['Session'] == s]} 1112 for s in sorted({r['Session'] for r in self}) 1113 } 1114 for s in self.sessions: 1115 self.sessions[s]['scrambling_drift'] = False 1116 self.sessions[s]['slope_drift'] = False 1117 self.sessions[s]['wg_drift'] = False 1118 self.sessions[s]['d13C_standardization_method'] = self.d13C_STANDARDIZATION_METHOD 1119 self.sessions[s]['d18O_standardization_method'] = self.d18O_STANDARDIZATION_METHOD
Update self.sessions and set scrambling_drift, slope_drift, and wg_drift
to False for all sessions.
def
refresh_samples(self):
1122 def refresh_samples(self): 1123 ''' 1124 Define `self.samples`, `self.anchors`, and `self.unknowns`. 1125 ''' 1126 self.samples = { 1127 s: {'data': [r for r in self if r['Sample'] == s]} 1128 for s in sorted({r['Sample'] for r in self}) 1129 } 1130 self.anchors = {s: self.samples[s] for s in self.samples if s in self.Nominal_D4x} 1131 self.unknowns = {s: self.samples[s] for s in self.samples if s not in self.Nominal_D4x}
Define self.samples, self.anchors, and self.unknowns.
def
read(self, filename, sep='', session=''):
1134 def read(self, filename, sep = '', session = ''): 1135 ''' 1136 Read file in csv format to load data into a `D47data` object. 1137 1138 In the csv file, spaces before and after field separators (`','` by default) 1139 are optional. Each line corresponds to a single analysis. 1140 1141 The required fields are: 1142 1143 + `UID`: a unique identifier 1144 + `Session`: an identifier for the analytical session 1145 + `Sample`: a sample identifier 1146 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values 1147 1148 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to 1149 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48` 1150 and `d49` are optional, and set to NaN by default. 1151 1152 **Parameters** 1153 1154 + `fileneme`: the path of the file to read 1155 + `sep`: csv separator delimiting the fields 1156 + `session`: set `Session` field to this string for all analyses 1157 ''' 1158 with open(filename) as fid: 1159 self.input(fid.read(), sep = sep, session = session)
Read file in csv format to load data into a D47data object.
In the csv file, spaces before and after field separators (',' by default)
are optional. Each line corresponds to a single analysis.
The required fields are:
UID: a unique identifierSession: an identifier for the analytical sessionSample: a sample identifierd45,d46, and at least one ofd47ord48: the working-gas delta values
Independently known oxygen-17 anomalies may be provided as D17O (in ‰ relative to
VSMOW, λ = self.LAMBDA_17), and are otherwise assumed to be zero. Working-gas deltas d47, d48
and d49 are optional, and set to NaN by default.
Parameters
fileneme: the path of the file to readsep: csv separator delimiting the fieldssession: setSessionfield to this string for all analyses
def
input(self, txt, sep='', session=''):
1162 def input(self, txt, sep = '', session = ''): 1163 ''' 1164 Read `txt` string in csv format to load analysis data into a `D47data` object. 1165 1166 In the csv string, spaces before and after field separators (`','` by default) 1167 are optional. Each line corresponds to a single analysis. 1168 1169 The required fields are: 1170 1171 + `UID`: a unique identifier 1172 + `Session`: an identifier for the analytical session 1173 + `Sample`: a sample identifier 1174 + `d45`, `d46`, and at least one of `d47` or `d48`: the working-gas delta values 1175 1176 Independently known oxygen-17 anomalies may be provided as `D17O` (in ‰ relative to 1177 VSMOW, λ = `self.LAMBDA_17`), and are otherwise assumed to be zero. Working-gas deltas `d47`, `d48` 1178 and `d49` are optional, and set to NaN by default. 1179 1180 **Parameters** 1181 1182 + `txt`: the csv string to read 1183 + `sep`: csv separator delimiting the fields. By default, use `,`, `;`, or `\t`, 1184 whichever appers most often in `txt`. 1185 + `session`: set `Session` field to this string for all analyses 1186 ''' 1187 if sep == '': 1188 sep = sorted(',;\t', key = lambda x: - txt.count(x))[0] 1189 txt = [[x.strip() for x in l.split(sep)] for l in txt.splitlines() if l.strip()] 1190 data = [{k: v if k in ['UID', 'Session', 'Sample'] else smart_type(v) for k,v in zip(txt[0], l) if v != ''} for l in txt[1:]] 1191 1192 if session != '': 1193 for r in data: 1194 r['Session'] = session 1195 1196 self += data 1197 self.refresh()
Read txt string in csv format to load analysis data into a D47data object.
In the csv string, spaces before and after field separators (',' by default)
are optional. Each line corresponds to a single analysis.
The required fields are:
UID: a unique identifierSession: an identifier for the analytical sessionSample: a sample identifierd45,d46, and at least one ofd47ord48: the working-gas delta values
Independently known oxygen-17 anomalies may be provided as D17O (in ‰ relative to
VSMOW, λ = self.LAMBDA_17), and are otherwise assumed to be zero. Working-gas deltas d47, d48
and d49 are optional, and set to NaN by default.
Parameters
txt: the csv string to readsep: csv separator delimiting the fields. By default, use,,;, or, whichever appers most often intxt.session: setSessionfield to this string for all analyses
@make_verbal
def
wg(self, samples=None, a18_acid=None):
1200 @make_verbal 1201 def wg(self, samples = None, a18_acid = None): 1202 ''' 1203 Compute bulk composition of the working gas for each session based on 1204 the carbonate standards defined in both `self.Nominal_d13C_VPDB` and 1205 `self.Nominal_d18O_VPDB`. 1206 ''' 1207 1208 self.msg('Computing WG composition:') 1209 1210 if a18_acid is None: 1211 a18_acid = self.ALPHA_18O_ACID_REACTION 1212 if samples is None: 1213 samples = [s for s in self.Nominal_d13C_VPDB if s in self.Nominal_d18O_VPDB] 1214 1215 assert a18_acid, f'Acid fractionation factor should not be zero.' 1216 1217 samples = [s for s in samples if s in self.Nominal_d13C_VPDB and s in self.Nominal_d18O_VPDB] 1218 R45R46_standards = {} 1219 for sample in samples: 1220 d13C_vpdb = self.Nominal_d13C_VPDB[sample] 1221 d18O_vpdb = self.Nominal_d18O_VPDB[sample] 1222 R13_s = self.R13_VPDB * (1 + d13C_vpdb / 1000) 1223 R17_s = self.R17_VPDB * ((1 + d18O_vpdb / 1000) * a18_acid) ** self.LAMBDA_17 1224 R18_s = self.R18_VPDB * (1 + d18O_vpdb / 1000) * a18_acid 1225 1226 C12_s = 1 / (1 + R13_s) 1227 C13_s = R13_s / (1 + R13_s) 1228 C16_s = 1 / (1 + R17_s + R18_s) 1229 C17_s = R17_s / (1 + R17_s + R18_s) 1230 C18_s = R18_s / (1 + R17_s + R18_s) 1231 1232 C626_s = C12_s * C16_s ** 2 1233 C627_s = 2 * C12_s * C16_s * C17_s 1234 C628_s = 2 * C12_s * C16_s * C18_s 1235 C636_s = C13_s * C16_s ** 2 1236 C637_s = 2 * C13_s * C16_s * C17_s 1237 C727_s = C12_s * C17_s ** 2 1238 1239 R45_s = (C627_s + C636_s) / C626_s 1240 R46_s = (C628_s + C637_s + C727_s) / C626_s 1241 R45R46_standards[sample] = (R45_s, R46_s) 1242 1243 for s in self.sessions: 1244 db = [r for r in self.sessions[s]['data'] if r['Sample'] in samples] 1245 assert db, f'No sample from {samples} found in session "{s}".' 1246# dbsamples = sorted({r['Sample'] for r in db}) 1247 1248 X = [r['d45'] for r in db] 1249 Y = [R45R46_standards[r['Sample']][0] for r in db] 1250 x1, x2 = np.min(X), np.max(X) 1251 1252 if x1 < x2: 1253 wgcoord = x1/(x1-x2) 1254 else: 1255 wgcoord = 999 1256 1257 if wgcoord < -.5 or wgcoord > 1.5: 1258 # unreasonable to extrapolate to d45 = 0 1259 R45_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)]) 1260 else : 1261 # d45 = 0 is reasonably well bracketed 1262 R45_wg = np.polyfit(X, Y, 1)[1] 1263 1264 X = [r['d46'] for r in db] 1265 Y = [R45R46_standards[r['Sample']][1] for r in db] 1266 x1, x2 = np.min(X), np.max(X) 1267 1268 if x1 < x2: 1269 wgcoord = x1/(x1-x2) 1270 else: 1271 wgcoord = 999 1272 1273 if wgcoord < -.5 or wgcoord > 1.5: 1274 # unreasonable to extrapolate to d46 = 0 1275 R46_wg = np.mean([y/(1+x/1000) for x,y in zip(X,Y)]) 1276 else : 1277 # d46 = 0 is reasonably well bracketed 1278 R46_wg = np.polyfit(X, Y, 1)[1] 1279 1280 d13Cwg_VPDB, d18Owg_VSMOW = self.compute_bulk_delta(R45_wg, R46_wg) 1281 1282 self.msg(f'Session {s} WG: δ13C_VPDB = {d13Cwg_VPDB:.3f} δ18O_VSMOW = {d18Owg_VSMOW:.3f}') 1283 1284 self.sessions[s]['d13Cwg_VPDB'] = d13Cwg_VPDB 1285 self.sessions[s]['d18Owg_VSMOW'] = d18Owg_VSMOW 1286 for r in self.sessions[s]['data']: 1287 r['d13Cwg_VPDB'] = d13Cwg_VPDB 1288 r['d18Owg_VSMOW'] = d18Owg_VSMOW
Compute bulk composition of the working gas for each session based on
the carbonate standards defined in both self.Nominal_d13C_VPDB and
self.Nominal_d18O_VPDB.
def
compute_bulk_delta(self, R45, R46, D17O=0):
1291 def compute_bulk_delta(self, R45, R46, D17O = 0): 1292 ''' 1293 Compute δ13C_VPDB and δ18O_VSMOW, 1294 by solving the generalized form of equation (17) from 1295 [Brand et al. (2010)](https://doi.org/10.1351/PAC-REP-09-01-05), 1296 assuming that δ18O_VSMOW is not too big (0 ± 50 ‰) and 1297 solving the corresponding second-order Taylor polynomial. 1298 (Appendix A of [Daëron et al., 2016](https://doi.org/10.1016/j.chemgeo.2016.08.014)) 1299 ''' 1300 1301 K = np.exp(D17O / 1000) * self.R17_VSMOW * self.R18_VSMOW ** -self.LAMBDA_17 1302 1303 A = -3 * K ** 2 * self.R18_VSMOW ** (2 * self.LAMBDA_17) 1304 B = 2 * K * R45 * self.R18_VSMOW ** self.LAMBDA_17 1305 C = 2 * self.R18_VSMOW 1306 D = -R46 1307 1308 aa = A * self.LAMBDA_17 * (2 * self.LAMBDA_17 - 1) + B * self.LAMBDA_17 * (self.LAMBDA_17 - 1) / 2 1309 bb = 2 * A * self.LAMBDA_17 + B * self.LAMBDA_17 + C 1310 cc = A + B + C + D 1311 1312 d18O_VSMOW = 1000 * (-bb + (bb ** 2 - 4 * aa * cc) ** .5) / (2 * aa) 1313 1314 R18 = (1 + d18O_VSMOW / 1000) * self.R18_VSMOW 1315 R17 = K * R18 ** self.LAMBDA_17 1316 R13 = R45 - 2 * R17 1317 1318 d13C_VPDB = 1000 * (R13 / self.R13_VPDB - 1) 1319 1320 return d13C_VPDB, d18O_VSMOW
Compute δ13CVPDB and δ18OVSMOW, by solving the generalized form of equation (17) from Brand et al. (2010), assuming that δ18OVSMOW is not too big (0 ± 50 ‰) and solving the corresponding second-order Taylor polynomial. (Appendix A of Daëron et al., 2016)
@make_verbal
def
crunch(self, verbose=''):
1323 @make_verbal 1324 def crunch(self, verbose = ''): 1325 ''' 1326 Compute bulk composition and raw clumped isotope anomalies for all analyses. 1327 ''' 1328 for r in self: 1329 self.compute_bulk_and_clumping_deltas(r) 1330 self.standardize_d13C() 1331 self.standardize_d18O() 1332 self.msg(f"Crunched {len(self)} analyses.")
Compute bulk composition and raw clumped isotope anomalies for all analyses.
def
fill_in_missing_info(self, session='mySession'):
1335 def fill_in_missing_info(self, session = 'mySession'): 1336 ''' 1337 Fill in optional fields with default values 1338 ''' 1339 for i,r in enumerate(self): 1340 if 'D17O' not in r: 1341 r['D17O'] = 0. 1342 if 'UID' not in r: 1343 r['UID'] = f'{i+1}' 1344 if 'Session' not in r: 1345 r['Session'] = session 1346 for k in ['d47', 'd48', 'd49']: 1347 if k not in r: 1348 r[k] = np.nan
Fill in optional fields with default values
def
standardize_d13C(self):
1351 def standardize_d13C(self): 1352 ''' 1353 Perform δ13C standadization within each session `s` according to 1354 `self.sessions[s]['d13C_standardization_method']`, which is defined by default 1355 by `D47data.refresh_sessions()`as equal to `self.d13C_STANDARDIZATION_METHOD`, but 1356 may be redefined abitrarily at a later stage. 1357 ''' 1358 for s in self.sessions: 1359 if self.sessions[s]['d13C_standardization_method'] in ['1pt', '2pt']: 1360 XY = [(r['d13C_VPDB'], self.Nominal_d13C_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d13C_VPDB] 1361 X,Y = zip(*XY) 1362 if self.sessions[s]['d13C_standardization_method'] == '1pt': 1363 offset = np.mean(Y) - np.mean(X) 1364 for r in self.sessions[s]['data']: 1365 r['d13C_VPDB'] += offset 1366 elif self.sessions[s]['d13C_standardization_method'] == '2pt': 1367 a,b = np.polyfit(X,Y,1) 1368 for r in self.sessions[s]['data']: 1369 r['d13C_VPDB'] = a * r['d13C_VPDB'] + b
Perform δ13C standadization within each session s according to
self.sessions[s]['d13C_standardization_method'], which is defined by default
by D47data.refresh_sessions()as equal to self.d13C_STANDARDIZATION_METHOD, but
may be redefined abitrarily at a later stage.
def
standardize_d18O(self):
1371 def standardize_d18O(self): 1372 ''' 1373 Perform δ18O standadization within each session `s` according to 1374 `self.ALPHA_18O_ACID_REACTION` and `self.sessions[s]['d18O_standardization_method']`, 1375 which is defined by default by `D47data.refresh_sessions()`as equal to 1376 `self.d18O_STANDARDIZATION_METHOD`, but may be redefined abitrarily at a later stage. 1377 ''' 1378 for s in self.sessions: 1379 if self.sessions[s]['d18O_standardization_method'] in ['1pt', '2pt']: 1380 XY = [(r['d18O_VSMOW'], self.Nominal_d18O_VPDB[r['Sample']]) for r in self.sessions[s]['data'] if r['Sample'] in self.Nominal_d18O_VPDB] 1381 X,Y = zip(*XY) 1382 Y = [(1000+y) * self.R18_VPDB * self.ALPHA_18O_ACID_REACTION / self.R18_VSMOW - 1000 for y in Y] 1383 if self.sessions[s]['d18O_standardization_method'] == '1pt': 1384 offset = np.mean(Y) - np.mean(X) 1385 for r in self.sessions[s]['data']: 1386 r['d18O_VSMOW'] += offset 1387 elif self.sessions[s]['d18O_standardization_method'] == '2pt': 1388 a,b = np.polyfit(X,Y,1) 1389 for r in self.sessions[s]['data']: 1390 r['d18O_VSMOW'] = a * r['d18O_VSMOW'] + b
Perform δ18O standadization within each session s according to
self.ALPHA_18O_ACID_REACTION and self.sessions[s]['d18O_standardization_method'],
which is defined by default by D47data.refresh_sessions()as equal to
self.d18O_STANDARDIZATION_METHOD, but may be redefined abitrarily at a later stage.
def
compute_bulk_and_clumping_deltas(self, r):
1393 def compute_bulk_and_clumping_deltas(self, r): 1394 ''' 1395 Compute δ13C_VPDB, δ18O_VSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis `r`. 1396 ''' 1397 1398 # Compute working gas R13, R18, and isobar ratios 1399 R13_wg = self.R13_VPDB * (1 + r['d13Cwg_VPDB'] / 1000) 1400 R18_wg = self.R18_VSMOW * (1 + r['d18Owg_VSMOW'] / 1000) 1401 R45_wg, R46_wg, R47_wg, R48_wg, R49_wg = self.compute_isobar_ratios(R13_wg, R18_wg) 1402 1403 # Compute analyte isobar ratios 1404 R45 = (1 + r['d45'] / 1000) * R45_wg 1405 R46 = (1 + r['d46'] / 1000) * R46_wg 1406 R47 = (1 + r['d47'] / 1000) * R47_wg 1407 R48 = (1 + r['d48'] / 1000) * R48_wg 1408 R49 = (1 + r['d49'] / 1000) * R49_wg 1409 1410 r['d13C_VPDB'], r['d18O_VSMOW'] = self.compute_bulk_delta(R45, R46, D17O = r['D17O']) 1411 R13 = (1 + r['d13C_VPDB'] / 1000) * self.R13_VPDB 1412 R18 = (1 + r['d18O_VSMOW'] / 1000) * self.R18_VSMOW 1413 1414 # Compute stochastic isobar ratios of the analyte 1415 R45stoch, R46stoch, R47stoch, R48stoch, R49stoch = self.compute_isobar_ratios( 1416 R13, R18, D17O = r['D17O'] 1417 ) 1418 1419 # Check that R45/R45stoch and R46/R46stoch are undistinguishable from 1, 1420 # and raise a warning if the corresponding anomalies exceed 0.02 ppm. 1421 if (R45 / R45stoch - 1) > 5e-8: 1422 self.vmsg(f'This is unexpected: R45/R45stoch - 1 = {1e6 * (R45 / R45stoch - 1):.3f} ppm') 1423 if (R46 / R46stoch - 1) > 5e-8: 1424 self.vmsg(f'This is unexpected: R46/R46stoch - 1 = {1e6 * (R46 / R46stoch - 1):.3f} ppm') 1425 1426 # Compute raw clumped isotope anomalies 1427 r['D47raw'] = 1000 * (R47 / R47stoch - 1) 1428 r['D48raw'] = 1000 * (R48 / R48stoch - 1) 1429 r['D49raw'] = 1000 * (R49 / R49stoch - 1)
Compute δ13CVPDB, δ18OVSMOW, and raw Δ47, Δ48, Δ49 values for a single analysis r.
def
compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0):
1432 def compute_isobar_ratios(self, R13, R18, D17O=0, D47=0, D48=0, D49=0): 1433 ''' 1434 Compute isobar ratios for a sample with isotopic ratios `R13` and `R18`, 1435 optionally accounting for non-zero values of Δ17O (`D17O`) and clumped isotope 1436 anomalies (`D47`, `D48`, `D49`), all expressed in permil. 1437 ''' 1438 1439 # Compute R17 1440 R17 = self.R17_VSMOW * np.exp(D17O / 1000) * (R18 / self.R18_VSMOW) ** self.LAMBDA_17 1441 1442 # Compute isotope concentrations 1443 C12 = (1 + R13) ** -1 1444 C13 = C12 * R13 1445 C16 = (1 + R17 + R18) ** -1 1446 C17 = C16 * R17 1447 C18 = C16 * R18 1448 1449 # Compute stochastic isotopologue concentrations 1450 C626 = C16 * C12 * C16 1451 C627 = C16 * C12 * C17 * 2 1452 C628 = C16 * C12 * C18 * 2 1453 C636 = C16 * C13 * C16 1454 C637 = C16 * C13 * C17 * 2 1455 C638 = C16 * C13 * C18 * 2 1456 C727 = C17 * C12 * C17 1457 C728 = C17 * C12 * C18 * 2 1458 C737 = C17 * C13 * C17 1459 C738 = C17 * C13 * C18 * 2 1460 C828 = C18 * C12 * C18 1461 C838 = C18 * C13 * C18 1462 1463 # Compute stochastic isobar ratios 1464 R45 = (C636 + C627) / C626 1465 R46 = (C628 + C637 + C727) / C626 1466 R47 = (C638 + C728 + C737) / C626 1467 R48 = (C738 + C828) / C626 1468 R49 = C838 / C626 1469 1470 # Account for stochastic anomalies 1471 R47 *= 1 + D47 / 1000 1472 R48 *= 1 + D48 / 1000 1473 R49 *= 1 + D49 / 1000 1474 1475 # Return isobar ratios 1476 return R45, R46, R47, R48, R49
Compute isobar ratios for a sample with isotopic ratios R13 and R18,
optionally accounting for non-zero values of Δ17O (D17O) and clumped isotope
anomalies (D47, D48, D49), all expressed in permil.
def
split_samples(self, samples_to_split='all', grouping='by_session'):
1479 def split_samples(self, samples_to_split = 'all', grouping = 'by_session'): 1480 ''' 1481 Split unknown samples by UID (treat all analyses as different samples) 1482 or by session (treat analyses of a given sample in different sessions as 1483 different samples). 1484 1485 **Parameters** 1486 1487 + `samples_to_split`: a list of samples to split, e.g., `['IAEA-C1', 'IAEA-C2']` 1488 + `grouping`: `by_uid` | `by_session` 1489 ''' 1490 if samples_to_split == 'all': 1491 samples_to_split = [s for s in self.unknowns] 1492 gkeys = {'by_uid':'UID', 'by_session':'Session'} 1493 self.grouping = grouping.lower() 1494 if self.grouping in gkeys: 1495 gkey = gkeys[self.grouping] 1496 for r in self: 1497 if r['Sample'] in samples_to_split: 1498 r['Sample_original'] = r['Sample'] 1499 r['Sample'] = f"{r['Sample']}__{r[gkey]}" 1500 elif r['Sample'] in self.unknowns: 1501 r['Sample_original'] = r['Sample'] 1502 self.refresh_samples()
Split unknown samples by UID (treat all analyses as different samples) or by session (treat analyses of a given sample in different sessions as different samples).
Parameters
samples_to_split: a list of samples to split, e.g.,['IAEA-C1', 'IAEA-C2']grouping:by_uid|by_session
def
unsplit_samples(self, tables=False):
1505 def unsplit_samples(self, tables = False): 1506 ''' 1507 Reverse the effects of `D47data.split_samples()`. 1508 1509 This should only be used after `D4xdata.standardize()` with `method='pooled'`. 1510 1511 After `D4xdata.standardize()` with `method='indep_sessions'`, one should 1512 probably use `D4xdata.combine_samples()` instead to reverse the effects of 1513 `D47data.split_samples()` with `grouping='by_uid'`, or `w_avg()` to reverse the 1514 effects of `D47data.split_samples()` with `grouping='by_sessions'` (because in 1515 that case session-averaged Δ4x values are statistically independent). 1516 ''' 1517 unknowns_old = sorted({s for s in self.unknowns}) 1518 CM_old = self.standardization.covar[:,:] 1519 VD_old = self.standardization.params.valuesdict().copy() 1520 vars_old = self.standardization.var_names 1521 1522 unknowns_new = sorted({r['Sample_original'] for r in self if 'Sample_original' in r}) 1523 1524 Ns = len(vars_old) - len(unknowns_old) 1525 vars_new = vars_old[:Ns] + [f'D{self._4x}_{pf(u)}' for u in unknowns_new] 1526 VD_new = {k: VD_old[k] for k in vars_old[:Ns]} 1527 1528 W = np.zeros((len(vars_new), len(vars_old))) 1529 W[:Ns,:Ns] = np.eye(Ns) 1530 for u in unknowns_new: 1531 splits = sorted({r['Sample'] for r in self if 'Sample_original' in r and r['Sample_original'] == u}) 1532 if self.grouping == 'by_session': 1533 weights = [self.samples[s][f'SE_D{self._4x}']**-2 for s in splits] 1534 elif self.grouping == 'by_uid': 1535 weights = [1 for s in splits] 1536 sw = sum(weights) 1537 weights = [w/sw for w in weights] 1538 W[vars_new.index(f'D{self._4x}_{pf(u)}'),[vars_old.index(f'D{self._4x}_{pf(s)}') for s in splits]] = weights[:] 1539 1540 CM_new = W @ CM_old @ W.T 1541 V = W @ np.array([[VD_old[k]] for k in vars_old]) 1542 VD_new = {k:v[0] for k,v in zip(vars_new, V)} 1543 1544 self.standardization.covar = CM_new 1545 self.standardization.params.valuesdict = lambda : VD_new 1546 self.standardization.var_names = vars_new 1547 1548 for r in self: 1549 if r['Sample'] in self.unknowns: 1550 r['Sample_split'] = r['Sample'] 1551 r['Sample'] = r['Sample_original'] 1552 1553 self.refresh_samples() 1554 self.consolidate_samples() 1555 self.repeatabilities() 1556 1557 if tables: 1558 self.table_of_analyses() 1559 self.table_of_samples()
Reverse the effects of D47data.split_samples().
This should only be used after D4xdata.standardize() with method='pooled'.
After D4xdata.standardize() with method='indep_sessions', one should
probably use D4xdata.combine_samples() instead to reverse the effects of
D47data.split_samples() with grouping='by_uid', or w_avg() to reverse the
effects of D47data.split_samples() with grouping='by_sessions' (because in
that case session-averaged Δ4x values are statistically independent).
def
assign_timestamps(self):
1561 def assign_timestamps(self): 1562 ''' 1563 Assign a time field `t` of type `float` to each analysis. 1564 1565 If `TimeTag` is one of the data fields, `t` is equal within a given session 1566 to `TimeTag` minus the mean value of `TimeTag` for that session. 1567 Otherwise, `TimeTag` is by default equal to the index of each analysis 1568 in the dataset and `t` is defined as above. 1569 ''' 1570 for session in self.sessions: 1571 sdata = self.sessions[session]['data'] 1572 try: 1573 t0 = np.mean([r['TimeTag'] for r in sdata]) 1574 for r in sdata: 1575 r['t'] = r['TimeTag'] - t0 1576 except KeyError: 1577 t0 = (len(sdata)-1)/2 1578 for t,r in enumerate(sdata): 1579 r['t'] = t - t0
Assign a time field t of type float to each analysis.
If TimeTag is one of the data fields, t is equal within a given session
to TimeTag minus the mean value of TimeTag for that session.
Otherwise, TimeTag is by default equal to the index of each analysis
in the dataset and t is defined as above.
def
report(self):
1582 def report(self): 1583 ''' 1584 Prints a report on the standardization fit. 1585 Only applicable after `D4xdata.standardize(method='pooled')`. 1586 ''' 1587 report_fit(self.standardization)
Prints a report on the standardization fit.
Only applicable after D4xdata.standardize(method='pooled').
def
combine_samples(self, sample_groups):
1590 def combine_samples(self, sample_groups): 1591 ''' 1592 Combine analyses of different samples to compute weighted average Δ4x 1593 and new error (co)variances corresponding to the groups defined by the `sample_groups` 1594 dictionary. 1595 1596 Caution: samples are weighted by number of replicate analyses, which is a 1597 reasonable default behavior but is not always optimal (e.g., in the case of strongly 1598 correlated analytical errors for one or more samples). 1599 1600 Returns a tuplet of: 1601 1602 + the list of group names 1603 + an array of the corresponding Δ4x values 1604 + the corresponding (co)variance matrix 1605 1606 **Parameters** 1607 1608 + `sample_groups`: a dictionary of the form: 1609 ```py 1610 {'group1': ['sample_1', 'sample_2'], 1611 'group2': ['sample_3', 'sample_4', 'sample_5']} 1612 ``` 1613 ''' 1614 1615 samples = [s for k in sorted(sample_groups.keys()) for s in sorted(sample_groups[k])] 1616 groups = sorted(sample_groups.keys()) 1617 group_total_weights = {k: sum([self.samples[s]['N'] for s in sample_groups[k]]) for k in groups} 1618 D4x_old = np.array([[self.samples[x][f'D{self._4x}']] for x in samples]) 1619 CM_old = np.array([[self.sample_D4x_covar(x,y) for x in samples] for y in samples]) 1620 W = np.array([ 1621 [self.samples[i]['N']/group_total_weights[j] if i in sample_groups[j] else 0 for i in samples] 1622 for j in groups]) 1623 D4x_new = W @ D4x_old 1624 CM_new = W @ CM_old @ W.T 1625 1626 return groups, D4x_new[:,0], CM_new
Combine analyses of different samples to compute weighted average Δ4x
and new error (co)variances corresponding to the groups defined by the sample_groups
dictionary.
Caution: samples are weighted by number of replicate analyses, which is a reasonable default behavior but is not always optimal (e.g., in the case of strongly correlated analytical errors for one or more samples).
Returns a tuplet of:
- the list of group names
- an array of the corresponding Δ4x values
- the corresponding (co)variance matrix
Parameters
sample_groups: a dictionary of the form:
{'group1': ['sample_1', 'sample_2'],
'group2': ['sample_3', 'sample_4', 'sample_5']}
@make_verbal
def
standardize( self, method='pooled', weighted_sessions=[], consolidate=True, consolidate_tables=False, consolidate_plots=False, constraints={}):
1629 @make_verbal 1630 def standardize(self, 1631 method = 'pooled', 1632 weighted_sessions = [], 1633 consolidate = True, 1634 consolidate_tables = False, 1635 consolidate_plots = False, 1636 constraints = {}, 1637 ): 1638 ''' 1639 Compute absolute Δ4x values for all replicate analyses and for sample averages. 1640 If `method` argument is set to `'pooled'`, the standardization processes all sessions 1641 in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous, 1642 i.e. that their true Δ4x value does not change between sessions, 1643 ([Daëron, 2021](https://doi.org/10.1029/2020GC009592)). If `method` argument is set to 1644 `'indep_sessions'`, the standardization processes each session independently, based only 1645 on anchors analyses. 1646 ''' 1647 1648 self.standardization_method = method 1649 self.assign_timestamps() 1650 1651 if method == 'pooled': 1652 if weighted_sessions: 1653 for session_group in weighted_sessions: 1654 if self._4x == '47': 1655 X = D47data([r for r in self if r['Session'] in session_group]) 1656 elif self._4x == '48': 1657 X = D48data([r for r in self if r['Session'] in session_group]) 1658 X.Nominal_D4x = self.Nominal_D4x.copy() 1659 X.refresh() 1660 result = X.standardize(method = 'pooled', weighted_sessions = [], consolidate = False) 1661 w = np.sqrt(result.redchi) 1662 self.msg(f'Session group {session_group} MRSWD = {w:.4f}') 1663 for r in X: 1664 r[f'wD{self._4x}raw'] *= w 1665 else: 1666 self.msg(f'All D{self._4x}raw weights set to 1 ‰') 1667 for r in self: 1668 r[f'wD{self._4x}raw'] = 1. 1669 1670 params = Parameters() 1671 for k,session in enumerate(self.sessions): 1672 self.msg(f"Session {session}: scrambling_drift is {self.sessions[session]['scrambling_drift']}.") 1673 self.msg(f"Session {session}: slope_drift is {self.sessions[session]['slope_drift']}.") 1674 self.msg(f"Session {session}: wg_drift is {self.sessions[session]['wg_drift']}.") 1675 s = pf(session) 1676 params.add(f'a_{s}', value = 0.9) 1677 params.add(f'b_{s}', value = 0.) 1678 params.add(f'c_{s}', value = -0.9) 1679 params.add(f'a2_{s}', value = 0., 1680# vary = self.sessions[session]['scrambling_drift'], 1681 ) 1682 params.add(f'b2_{s}', value = 0., 1683# vary = self.sessions[session]['slope_drift'], 1684 ) 1685 params.add(f'c2_{s}', value = 0., 1686# vary = self.sessions[session]['wg_drift'], 1687 ) 1688 if not self.sessions[session]['scrambling_drift']: 1689 params[f'a2_{s}'].expr = '0' 1690 if not self.sessions[session]['slope_drift']: 1691 params[f'b2_{s}'].expr = '0' 1692 if not self.sessions[session]['wg_drift']: 1693 params[f'c2_{s}'].expr = '0' 1694 1695 for sample in self.unknowns: 1696 params.add(f'D{self._4x}_{pf(sample)}', value = 0.5) 1697 1698 for k in constraints: 1699 params[k].expr = constraints[k] 1700 1701 def residuals(p): 1702 R = [] 1703 for r in self: 1704 session = pf(r['Session']) 1705 sample = pf(r['Sample']) 1706 if r['Sample'] in self.Nominal_D4x: 1707 R += [ ( 1708 r[f'D{self._4x}raw'] - ( 1709 p[f'a_{session}'] * self.Nominal_D4x[r['Sample']] 1710 + p[f'b_{session}'] * r[f'd{self._4x}'] 1711 + p[f'c_{session}'] 1712 + r['t'] * ( 1713 p[f'a2_{session}'] * self.Nominal_D4x[r['Sample']] 1714 + p[f'b2_{session}'] * r[f'd{self._4x}'] 1715 + p[f'c2_{session}'] 1716 ) 1717 ) 1718 ) / r[f'wD{self._4x}raw'] ] 1719 else: 1720 R += [ ( 1721 r[f'D{self._4x}raw'] - ( 1722 p[f'a_{session}'] * p[f'D{self._4x}_{sample}'] 1723 + p[f'b_{session}'] * r[f'd{self._4x}'] 1724 + p[f'c_{session}'] 1725 + r['t'] * ( 1726 p[f'a2_{session}'] * p[f'D{self._4x}_{sample}'] 1727 + p[f'b2_{session}'] * r[f'd{self._4x}'] 1728 + p[f'c2_{session}'] 1729 ) 1730 ) 1731 ) / r[f'wD{self._4x}raw'] ] 1732 return R 1733 1734 M = Minimizer(residuals, params) 1735 result = M.least_squares() 1736 self.Nf = result.nfree 1737 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf) 1738 new_names, new_covar, new_se = _fullcovar(result)[:3] 1739 result.var_names = new_names 1740 result.covar = new_covar 1741 1742 for r in self: 1743 s = pf(r["Session"]) 1744 a = result.params.valuesdict()[f'a_{s}'] 1745 b = result.params.valuesdict()[f'b_{s}'] 1746 c = result.params.valuesdict()[f'c_{s}'] 1747 a2 = result.params.valuesdict()[f'a2_{s}'] 1748 b2 = result.params.valuesdict()[f'b2_{s}'] 1749 c2 = result.params.valuesdict()[f'c2_{s}'] 1750 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t']) 1751 1752 1753 self.standardization = result 1754 1755 for session in self.sessions: 1756 self.sessions[session]['Np'] = 3 1757 for k in ['scrambling', 'slope', 'wg']: 1758 if self.sessions[session][f'{k}_drift']: 1759 self.sessions[session]['Np'] += 1 1760 1761 if consolidate: 1762 self.consolidate(tables = consolidate_tables, plots = consolidate_plots) 1763 return result 1764 1765 1766 elif method == 'indep_sessions': 1767 1768 if weighted_sessions: 1769 for session_group in weighted_sessions: 1770 X = D4xdata([r for r in self if r['Session'] in session_group], mass = self._4x) 1771 X.Nominal_D4x = self.Nominal_D4x.copy() 1772 X.refresh() 1773 # This is only done to assign r['wD47raw'] for r in X: 1774 X.standardize(method = method, weighted_sessions = [], consolidate = False) 1775 self.msg(f'D{self._4x}raw weights set to {1000*X[0][f"wD{self._4x}raw"]:.1f} ppm for sessions in {session_group}') 1776 else: 1777 self.msg('All weights set to 1 ‰') 1778 for r in self: 1779 r[f'wD{self._4x}raw'] = 1 1780 1781 for session in self.sessions: 1782 s = self.sessions[session] 1783 p_names = ['a', 'b', 'c', 'a2', 'b2', 'c2'] 1784 p_active = [True, True, True, s['scrambling_drift'], s['slope_drift'], s['wg_drift']] 1785 s['Np'] = sum(p_active) 1786 sdata = s['data'] 1787 1788 A = np.array([ 1789 [ 1790 self.Nominal_D4x[r['Sample']] / r[f'wD{self._4x}raw'], 1791 r[f'd{self._4x}'] / r[f'wD{self._4x}raw'], 1792 1 / r[f'wD{self._4x}raw'], 1793 self.Nominal_D4x[r['Sample']] * r['t'] / r[f'wD{self._4x}raw'], 1794 r[f'd{self._4x}'] * r['t'] / r[f'wD{self._4x}raw'], 1795 r['t'] / r[f'wD{self._4x}raw'] 1796 ] 1797 for r in sdata if r['Sample'] in self.anchors 1798 ])[:,p_active] # only keep columns for the active parameters 1799 Y = np.array([[r[f'D{self._4x}raw'] / r[f'wD{self._4x}raw']] for r in sdata if r['Sample'] in self.anchors]) 1800 s['Na'] = Y.size 1801 CM = linalg.inv(A.T @ A) 1802 bf = (CM @ A.T @ Y).T[0,:] 1803 k = 0 1804 for n,a in zip(p_names, p_active): 1805 if a: 1806 s[n] = bf[k] 1807# self.msg(f'{n} = {bf[k]}') 1808 k += 1 1809 else: 1810 s[n] = 0. 1811# self.msg(f'{n} = 0.0') 1812 1813 for r in sdata : 1814 a, b, c, a2, b2, c2 = s['a'], s['b'], s['c'], s['a2'], s['b2'], s['c2'] 1815 r[f'D{self._4x}'] = (r[f'D{self._4x}raw'] - c - b * r[f'd{self._4x}'] - c2 * r['t'] - b2 * r['t'] * r[f'd{self._4x}']) / (a + a2 * r['t']) 1816 r[f'wD{self._4x}'] = r[f'wD{self._4x}raw'] / (a + a2 * r['t']) 1817 1818 s['CM'] = np.zeros((6,6)) 1819 i = 0 1820 k_active = [j for j,a in enumerate(p_active) if a] 1821 for j,a in enumerate(p_active): 1822 if a: 1823 s['CM'][j,k_active] = CM[i,:] 1824 i += 1 1825 1826 if not weighted_sessions: 1827 w = self.rmswd()['rmswd'] 1828 for r in self: 1829 r[f'wD{self._4x}'] *= w 1830 r[f'wD{self._4x}raw'] *= w 1831 for session in self.sessions: 1832 self.sessions[session]['CM'] *= w**2 1833 1834 for session in self.sessions: 1835 s = self.sessions[session] 1836 s['SE_a'] = s['CM'][0,0]**.5 1837 s['SE_b'] = s['CM'][1,1]**.5 1838 s['SE_c'] = s['CM'][2,2]**.5 1839 s['SE_a2'] = s['CM'][3,3]**.5 1840 s['SE_b2'] = s['CM'][4,4]**.5 1841 s['SE_c2'] = s['CM'][5,5]**.5 1842 1843 if not weighted_sessions: 1844 self.Nf = len(self) - len(self.unknowns) - np.sum([self.sessions[s]['Np'] for s in self.sessions]) 1845 else: 1846 self.Nf = 0 1847 for sg in weighted_sessions: 1848 self.Nf += self.rmswd(sessions = sg)['Nf'] 1849 1850 self.t95 = tstudent.ppf(1 - 0.05/2, self.Nf) 1851 1852 avgD4x = { 1853 sample: np.mean([r[f'D{self._4x}'] for r in self if r['Sample'] == sample]) 1854 for sample in self.samples 1855 } 1856 chi2 = np.sum([(r[f'D{self._4x}'] - avgD4x[r['Sample']])**2 for r in self]) 1857 rD4x = (chi2/self.Nf)**.5 1858 self.repeatability[f'sigma_{self._4x}'] = rD4x 1859 1860 if consolidate: 1861 self.consolidate(tables = consolidate_tables, plots = consolidate_plots)
Compute absolute Δ4x values for all replicate analyses and for sample averages.
If method argument is set to 'pooled', the standardization processes all sessions
in a single step, assuming that all samples (anchors and unknowns alike) are homogeneous,
i.e. that their true Δ4x value does not change between sessions,
(Daëron, 2021). If method argument is set to
'indep_sessions', the standardization processes each session independently, based only
on anchors analyses.
def
standardization_error(self, session, d4x, D4x, t=0):
1864 def standardization_error(self, session, d4x, D4x, t = 0): 1865 ''' 1866 Compute standardization error for a given session and 1867 (δ47, Δ47) composition. 1868 ''' 1869 a = self.sessions[session]['a'] 1870 b = self.sessions[session]['b'] 1871 c = self.sessions[session]['c'] 1872 a2 = self.sessions[session]['a2'] 1873 b2 = self.sessions[session]['b2'] 1874 c2 = self.sessions[session]['c2'] 1875 CM = self.sessions[session]['CM'] 1876 1877 x, y = D4x, d4x 1878 z = a * x + b * y + c + a2 * x * t + b2 * y * t + c2 * t 1879# x = (z - b*y - b2*y*t - c - c2*t) / (a+a2*t) 1880 dxdy = -(b+b2*t) / (a+a2*t) 1881 dxdz = 1. / (a+a2*t) 1882 dxda = -x / (a+a2*t) 1883 dxdb = -y / (a+a2*t) 1884 dxdc = -1. / (a+a2*t) 1885 dxda2 = -x * a2 / (a+a2*t) 1886 dxdb2 = -y * t / (a+a2*t) 1887 dxdc2 = -t / (a+a2*t) 1888 V = np.array([dxda, dxdb, dxdc, dxda2, dxdb2, dxdc2]) 1889 sx = (V @ CM @ V.T) ** .5 1890 return sx
Compute standardization error for a given session and (δ47, Δ47) composition.
@make_verbal
def
summary(self, dir='output', filename=None, save_to_file=True, print_out=True):
1893 @make_verbal 1894 def summary(self, 1895 dir = 'output', 1896 filename = None, 1897 save_to_file = True, 1898 print_out = True, 1899 ): 1900 ''' 1901 Print out an/or save to disk a summary of the standardization results. 1902 1903 **Parameters** 1904 1905 + `dir`: the directory in which to save the table 1906 + `filename`: the name to the csv file to write to 1907 + `save_to_file`: whether to save the table to disk 1908 + `print_out`: whether to print out the table 1909 ''' 1910 1911 out = [] 1912 out += [['N samples (anchors + unknowns)', f"{len(self.samples)} ({len(self.anchors)} + {len(self.unknowns)})"]] 1913 out += [['N analyses (anchors + unknowns)', f"{len(self)} ({len([r for r in self if r['Sample'] in self.anchors])} + {len([r for r in self if r['Sample'] in self.unknowns])})"]] 1914 out += [['Repeatability of δ13C_VPDB', f"{1000 * self.repeatability['r_d13C_VPDB']:.1f} ppm"]] 1915 out += [['Repeatability of δ18O_VSMOW', f"{1000 * self.repeatability['r_d18O_VSMOW']:.1f} ppm"]] 1916 out += [[f'Repeatability of Δ{self._4x} (anchors)', f"{1000 * self.repeatability[f'r_D{self._4x}a']:.1f} ppm"]] 1917 out += [[f'Repeatability of Δ{self._4x} (unknowns)', f"{1000 * self.repeatability[f'r_D{self._4x}u']:.1f} ppm"]] 1918 out += [[f'Repeatability of Δ{self._4x} (all)', f"{1000 * self.repeatability[f'r_D{self._4x}']:.1f} ppm"]] 1919 out += [['Model degrees of freedom', f"{self.Nf}"]] 1920 out += [['Student\'s 95% t-factor', f"{self.t95:.2f}"]] 1921 out += [['Standardization method', self.standardization_method]] 1922 1923 if save_to_file: 1924 if not os.path.exists(dir): 1925 os.makedirs(dir) 1926 if filename is None: 1927 filename = f'D{self._4x}_summary.csv' 1928 with open(f'{dir}/{filename}', 'w') as fid: 1929 fid.write(make_csv(out)) 1930 if print_out: 1931 self.msg('\n' + pretty_table(out, header = 0))
Print out an/or save to disk a summary of the standardization results.
Parameters
dir: the directory in which to save the tablefilename: the name to the csv file to write tosave_to_file: whether to save the table to diskprint_out: whether to print out the table
@make_verbal
def
table_of_sessions( self, dir='output', filename=None, save_to_file=True, print_out=True, output=None):
1934 @make_verbal 1935 def table_of_sessions(self, 1936 dir = 'output', 1937 filename = None, 1938 save_to_file = True, 1939 print_out = True, 1940 output = None, 1941 ): 1942 ''' 1943 Print out an/or save to disk a table of sessions. 1944 1945 **Parameters** 1946 1947 + `dir`: the directory in which to save the table 1948 + `filename`: the name to the csv file to write to 1949 + `save_to_file`: whether to save the table to disk 1950 + `print_out`: whether to print out the table 1951 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 1952 if set to `'raw'`: return a list of list of strings 1953 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 1954 ''' 1955 include_a2 = any([self.sessions[session]['scrambling_drift'] for session in self.sessions]) 1956 include_b2 = any([self.sessions[session]['slope_drift'] for session in self.sessions]) 1957 include_c2 = any([self.sessions[session]['wg_drift'] for session in self.sessions]) 1958 1959 out = [['Session','Na','Nu','d13Cwg_VPDB','d18Owg_VSMOW','r_d13C','r_d18O',f'r_D{self._4x}','a ± SE','1e3 x b ± SE','c ± SE']] 1960 if include_a2: 1961 out[-1] += ['a2 ± SE'] 1962 if include_b2: 1963 out[-1] += ['b2 ± SE'] 1964 if include_c2: 1965 out[-1] += ['c2 ± SE'] 1966 for session in self.sessions: 1967 out += [[ 1968 session, 1969 f"{self.sessions[session]['Na']}", 1970 f"{self.sessions[session]['Nu']}", 1971 f"{self.sessions[session]['d13Cwg_VPDB']:.3f}", 1972 f"{self.sessions[session]['d18Owg_VSMOW']:.3f}", 1973 f"{self.sessions[session]['r_d13C_VPDB']:.4f}", 1974 f"{self.sessions[session]['r_d18O_VSMOW']:.4f}", 1975 f"{self.sessions[session][f'r_D{self._4x}']:.4f}", 1976 f"{self.sessions[session]['a']:.3f} ± {self.sessions[session]['SE_a']:.3f}", 1977 f"{1e3*self.sessions[session]['b']:.3f} ± {1e3*self.sessions[session]['SE_b']:.3f}", 1978 f"{self.sessions[session]['c']:.3f} ± {self.sessions[session]['SE_c']:.3f}", 1979 ]] 1980 if include_a2: 1981 if self.sessions[session]['scrambling_drift']: 1982 out[-1] += [f"{self.sessions[session]['a2']:.1e} ± {self.sessions[session]['SE_a2']:.1e}"] 1983 else: 1984 out[-1] += [''] 1985 if include_b2: 1986 if self.sessions[session]['slope_drift']: 1987 out[-1] += [f"{self.sessions[session]['b2']:.1e} ± {self.sessions[session]['SE_b2']:.1e}"] 1988 else: 1989 out[-1] += [''] 1990 if include_c2: 1991 if self.sessions[session]['wg_drift']: 1992 out[-1] += [f"{self.sessions[session]['c2']:.1e} ± {self.sessions[session]['SE_c2']:.1e}"] 1993 else: 1994 out[-1] += [''] 1995 1996 if save_to_file: 1997 if not os.path.exists(dir): 1998 os.makedirs(dir) 1999 if filename is None: 2000 filename = f'D{self._4x}_sessions.csv' 2001 with open(f'{dir}/{filename}', 'w') as fid: 2002 fid.write(make_csv(out)) 2003 if print_out: 2004 self.msg('\n' + pretty_table(out)) 2005 if output == 'raw': 2006 return out 2007 elif output == 'pretty': 2008 return pretty_table(out)
Print out an/or save to disk a table of sessions.
Parameters
dir: the directory in which to save the tablefilename: the name to the csv file to write tosave_to_file: whether to save the table to diskprint_out: whether to print out the tableoutput: if set to'pretty': return a pretty text table (seepretty_table()); if set to'raw': return a list of list of strings (e.g.,[['header1', 'header2'], ['0.1', '0.2']])
@make_verbal
def
table_of_analyses( self, dir='output', filename=None, save_to_file=True, print_out=True, output=None):
2011 @make_verbal 2012 def table_of_analyses( 2013 self, 2014 dir = 'output', 2015 filename = None, 2016 save_to_file = True, 2017 print_out = True, 2018 output = None, 2019 ): 2020 ''' 2021 Print out an/or save to disk a table of analyses. 2022 2023 **Parameters** 2024 2025 + `dir`: the directory in which to save the table 2026 + `filename`: the name to the csv file to write to 2027 + `save_to_file`: whether to save the table to disk 2028 + `print_out`: whether to print out the table 2029 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 2030 if set to `'raw'`: return a list of list of strings 2031 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2032 ''' 2033 2034 out = [['UID','Session','Sample']] 2035 extra_fields = [f for f in [('SampleMass','.2f'),('ColdFingerPressure','.1f'),('AcidReactionYield','.3f')] if f[0] in {k for r in self for k in r}] 2036 for f in extra_fields: 2037 out[-1] += [f[0]] 2038 out[-1] += ['d13Cwg_VPDB','d18Owg_VSMOW','d45','d46','d47','d48','d49','d13C_VPDB','d18O_VSMOW','D47raw','D48raw','D49raw',f'D{self._4x}'] 2039 for r in self: 2040 out += [[f"{r['UID']}",f"{r['Session']}",f"{r['Sample']}"]] 2041 for f in extra_fields: 2042 out[-1] += [f"{r[f[0]]:{f[1]}}"] 2043 out[-1] += [ 2044 f"{r['d13Cwg_VPDB']:.3f}", 2045 f"{r['d18Owg_VSMOW']:.3f}", 2046 f"{r['d45']:.6f}", 2047 f"{r['d46']:.6f}", 2048 f"{r['d47']:.6f}", 2049 f"{r['d48']:.6f}", 2050 f"{r['d49']:.6f}", 2051 f"{r['d13C_VPDB']:.6f}", 2052 f"{r['d18O_VSMOW']:.6f}", 2053 f"{r['D47raw']:.6f}", 2054 f"{r['D48raw']:.6f}", 2055 f"{r['D49raw']:.6f}", 2056 f"{r[f'D{self._4x}']:.6f}" 2057 ] 2058 if save_to_file: 2059 if not os.path.exists(dir): 2060 os.makedirs(dir) 2061 if filename is None: 2062 filename = f'D{self._4x}_analyses.csv' 2063 with open(f'{dir}/{filename}', 'w') as fid: 2064 fid.write(make_csv(out)) 2065 if print_out: 2066 self.msg('\n' + pretty_table(out)) 2067 return out
Print out an/or save to disk a table of analyses.
Parameters
dir: the directory in which to save the tablefilename: the name to the csv file to write tosave_to_file: whether to save the table to diskprint_out: whether to print out the tableoutput: if set to'pretty': return a pretty text table (seepretty_table()); if set to'raw': return a list of list of strings (e.g.,[['header1', 'header2'], ['0.1', '0.2']])
@make_verbal
def
covar_table( self, correl=False, dir='output', filename=None, save_to_file=True, print_out=True, output=None):
2069 @make_verbal 2070 def covar_table( 2071 self, 2072 correl = False, 2073 dir = 'output', 2074 filename = None, 2075 save_to_file = True, 2076 print_out = True, 2077 output = None, 2078 ): 2079 ''' 2080 Print out, save to disk and/or return the variance-covariance matrix of D4x 2081 for all unknown samples. 2082 2083 **Parameters** 2084 2085 + `dir`: the directory in which to save the csv 2086 + `filename`: the name of the csv file to write to 2087 + `save_to_file`: whether to save the csv 2088 + `print_out`: whether to print out the matrix 2089 + `output`: if set to `'pretty'`: return a pretty text matrix (see `pretty_table()`); 2090 if set to `'raw'`: return a list of list of strings 2091 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2092 ''' 2093 samples = sorted([u for u in self.unknowns]) 2094 out = [[''] + samples] 2095 for s1 in samples: 2096 out.append([s1]) 2097 for s2 in samples: 2098 if correl: 2099 out[-1].append(f'{self.sample_D4x_correl(s1, s2):.6f}') 2100 else: 2101 out[-1].append(f'{self.sample_D4x_covar(s1, s2):.8e}') 2102 2103 if save_to_file: 2104 if not os.path.exists(dir): 2105 os.makedirs(dir) 2106 if filename is None: 2107 if correl: 2108 filename = f'D{self._4x}_correl.csv' 2109 else: 2110 filename = f'D{self._4x}_covar.csv' 2111 with open(f'{dir}/{filename}', 'w') as fid: 2112 fid.write(make_csv(out)) 2113 if print_out: 2114 self.msg('\n'+pretty_table(out)) 2115 if output == 'raw': 2116 return out 2117 elif output == 'pretty': 2118 return pretty_table(out)
Print out, save to disk and/or return the variance-covariance matrix of D4x for all unknown samples.
Parameters
dir: the directory in which to save the csvfilename: the name of the csv file to write tosave_to_file: whether to save the csvprint_out: whether to print out the matrixoutput: if set to'pretty': return a pretty text matrix (seepretty_table()); if set to'raw': return a list of list of strings (e.g.,[['header1', 'header2'], ['0.1', '0.2']])
@make_verbal
def
table_of_samples( self, dir='output', filename=None, save_to_file=True, print_out=True, output=None):
2120 @make_verbal 2121 def table_of_samples( 2122 self, 2123 dir = 'output', 2124 filename = None, 2125 save_to_file = True, 2126 print_out = True, 2127 output = None, 2128 ): 2129 ''' 2130 Print out, save to disk and/or return a table of samples. 2131 2132 **Parameters** 2133 2134 + `dir`: the directory in which to save the csv 2135 + `filename`: the name of the csv file to write to 2136 + `save_to_file`: whether to save the csv 2137 + `print_out`: whether to print out the table 2138 + `output`: if set to `'pretty'`: return a pretty text table (see `pretty_table()`); 2139 if set to `'raw'`: return a list of list of strings 2140 (e.g., `[['header1', 'header2'], ['0.1', '0.2']]`) 2141 ''' 2142 2143 out = [['Sample','N','d13C_VPDB','d18O_VSMOW',f'D{self._4x}','SE','95% CL','SD','p_Levene']] 2144 for sample in self.anchors: 2145 out += [[ 2146 f"{sample}", 2147 f"{self.samples[sample]['N']}", 2148 f"{self.samples[sample]['d13C_VPDB']:.2f}", 2149 f"{self.samples[sample]['d18O_VSMOW']:.2f}", 2150 f"{self.samples[sample][f'D{self._4x}']:.4f}",'','', 2151 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', '' 2152 ]] 2153 for sample in self.unknowns: 2154 out += [[ 2155 f"{sample}", 2156 f"{self.samples[sample]['N']}", 2157 f"{self.samples[sample]['d13C_VPDB']:.2f}", 2158 f"{self.samples[sample]['d18O_VSMOW']:.2f}", 2159 f"{self.samples[sample][f'D{self._4x}']:.4f}", 2160 f"{self.samples[sample][f'SE_D{self._4x}']:.4f}", 2161 f"± {self.samples[sample][f'SE_D{self._4x}'] * self.t95:.4f}", 2162 f"{self.samples[sample][f'SD_D{self._4x}']:.4f}" if self.samples[sample]['N'] > 1 else '', 2163 f"{self.samples[sample]['p_Levene']:.3f}" if self.samples[sample]['N'] > 2 else '' 2164 ]] 2165 if save_to_file: 2166 if not os.path.exists(dir): 2167 os.makedirs(dir) 2168 if filename is None: 2169 filename = f'D{self._4x}_samples.csv' 2170 with open(f'{dir}/{filename}', 'w') as fid: 2171 fid.write(make_csv(out)) 2172 if print_out: 2173 self.msg('\n'+pretty_table(out)) 2174 if output == 'raw': 2175 return out 2176 elif output == 'pretty': 2177 return pretty_table(out)
Print out, save to disk and/or return a table of samples.
Parameters
dir: the directory in which to save the csvfilename: the name of the csv file to write tosave_to_file: whether to save the csvprint_out: whether to print out the tableoutput: if set to'pretty': return a pretty text table (seepretty_table()); if set to'raw': return a list of list of strings (e.g.,[['header1', 'header2'], ['0.1', '0.2']])
def
plot_sessions(self, dir='output', figsize=(8, 8)):
2180 def plot_sessions(self, dir = 'output', figsize = (8,8)): 2181 ''' 2182 Generate session plots and save them to disk. 2183 2184 **Parameters** 2185 2186 + `dir`: the directory in which to save the plots 2187 + `figsize`: the width and height (in inches) of each plot 2188 ''' 2189 if not os.path.exists(dir): 2190 os.makedirs(dir) 2191 2192 for session in self.sessions: 2193 sp = self.plot_single_session(session, xylimits = 'constant') 2194 ppl.savefig(f'{dir}/D{self._4x}_plot_{session}.pdf') 2195 ppl.close(sp.fig)
Generate session plots and save them to disk.
Parameters
dir: the directory in which to save the plotsfigsize: the width and height (in inches) of each plot
@make_verbal
def
consolidate_samples(self):
2198 @make_verbal 2199 def consolidate_samples(self): 2200 ''' 2201 Compile various statistics for each sample. 2202 2203 For each anchor sample: 2204 2205 + `D47` or `D48`: the nominal Δ4x value for this anchor, specified by `self.Nominal_D4x` 2206 + `SE_D47` or `SE_D48`: set to zero by definition 2207 2208 For each unknown sample: 2209 2210 + `D47` or `D48`: the standardized Δ4x value for this unknown 2211 + `SE_D47` or `SE_D48`: the standard error of Δ4x for this unknown 2212 2213 For each anchor and unknown: 2214 2215 + `N`: the total number of analyses of this sample 2216 + `SD_D47` or `SD_D48`: the “sample” (in the statistical sense) standard deviation for this sample 2217 + `d13C_VPDB`: the average δ13C_VPDB value for this sample 2218 + `d18O_VSMOW`: the average δ18O_VSMOW value for this sample (as CO2) 2219 + `p_Levene`: the p-value from a [Levene test](https://en.wikipedia.org/wiki/Levene%27s_test) of equal 2220 variance, indicating whether the Δ4x repeatability this sample differs significantly from 2221 that observed for the reference sample specified by `self.LEVENE_REF_SAMPLE`. 2222 ''' 2223 D4x_ref_pop = [r[f'D{self._4x}'] for r in self.samples[self.LEVENE_REF_SAMPLE]['data']] 2224 for sample in self.samples: 2225 self.samples[sample]['N'] = len(self.samples[sample]['data']) 2226 if self.samples[sample]['N'] > 1: 2227 self.samples[sample][f'SD_D{self._4x}'] = stdev([r[f'D{self._4x}'] for r in self.samples[sample]['data']]) 2228 2229 self.samples[sample]['d13C_VPDB'] = np.mean([r['d13C_VPDB'] for r in self.samples[sample]['data']]) 2230 self.samples[sample]['d18O_VSMOW'] = np.mean([r['d18O_VSMOW'] for r in self.samples[sample]['data']]) 2231 2232 D4x_pop = [r[f'D{self._4x}'] for r in self.samples[sample]['data']] 2233 if len(D4x_pop) > 2: 2234 self.samples[sample]['p_Levene'] = levene(D4x_ref_pop, D4x_pop, center = 'median')[1] 2235 2236 if self.standardization_method == 'pooled': 2237 for sample in self.anchors: 2238 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample] 2239 self.samples[sample][f'SE_D{self._4x}'] = 0. 2240 for sample in self.unknowns: 2241 self.samples[sample][f'D{self._4x}'] = self.standardization.params.valuesdict()[f'D{self._4x}_{pf(sample)}'] 2242 try: 2243 self.samples[sample][f'SE_D{self._4x}'] = self.sample_D4x_covar(sample)**.5 2244 except ValueError: 2245 # when `sample` is constrained by self.standardize(constraints = {...}), 2246 # it is no longer listed in self.standardization.var_names. 2247 # Temporary fix: define SE as zero for now 2248 self.samples[sample][f'SE_D4{self._4x}'] = 0. 2249 2250 elif self.standardization_method == 'indep_sessions': 2251 for sample in self.anchors: 2252 self.samples[sample][f'D{self._4x}'] = self.Nominal_D4x[sample] 2253 self.samples[sample][f'SE_D{self._4x}'] = 0. 2254 for sample in self.unknowns: 2255 self.msg(f'Consolidating sample {sample}') 2256 self.unknowns[sample][f'session_D{self._4x}'] = {} 2257 session_avg = [] 2258 for session in self.sessions: 2259 sdata = [r for r in self.sessions[session]['data'] if r['Sample'] == sample] 2260 if sdata: 2261 self.msg(f'{sample} found in session {session}') 2262 avg_D4x = np.mean([r[f'D{self._4x}'] for r in sdata]) 2263 avg_d4x = np.mean([r[f'd{self._4x}'] for r in sdata]) 2264 # !! TODO: sigma_s below does not account for temporal changes in standardization error 2265 sigma_s = self.standardization_error(session, avg_d4x, avg_D4x) 2266 sigma_u = sdata[0][f'wD{self._4x}raw'] / self.sessions[session]['a'] / len(sdata)**.5 2267 session_avg.append([avg_D4x, (sigma_u**2 + sigma_s**2)**.5]) 2268 self.unknowns[sample][f'session_D{self._4x}'][session] = session_avg[-1] 2269 self.samples[sample][f'D{self._4x}'], self.samples[sample][f'SE_D{self._4x}'] = w_avg(*zip(*session_avg)) 2270 weights = {s: self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 for s in self.unknowns[sample][f'session_D{self._4x}']} 2271 wsum = sum([weights[s] for s in weights]) 2272 for s in weights: 2273 self.unknowns[sample][f'session_D{self._4x}'][s] += [self.unknowns[sample][f'session_D{self._4x}'][s][1]**-2 / wsum] 2274 2275 for r in self: 2276 r[f'D{self._4x}_residual'] = r[f'D{self._4x}'] - self.samples[r['Sample']][f'D{self._4x}']
Compile various statistics for each sample.
For each anchor sample:
D47orD48: the nominal Δ4x value for this anchor, specified byself.Nominal_D4xSE_D47orSE_D48: set to zero by definition
For each unknown sample:
D47orD48: the standardized Δ4x value for this unknownSE_D47orSE_D48: the standard error of Δ4x for this unknown
For each anchor and unknown:
N: the total number of analyses of this sampleSD_D47orSD_D48: the “sample” (in the statistical sense) standard deviation for this sampled13C_VPDB: the average δ13CVPDB value for this sampled18O_VSMOW: the average δ18OVSMOW value for this sample (as CO2)p_Levene: the p-value from a Levene test of equal variance, indicating whether the Δ4x repeatability this sample differs significantly from that observed for the reference sample specified byself.LEVENE_REF_SAMPLE.
def
consolidate_sessions(self):
2280 def consolidate_sessions(self): 2281 ''' 2282 Compute various statistics for each session. 2283 2284 + `Na`: Number of anchor analyses in the session 2285 + `Nu`: Number of unknown analyses in the session 2286 + `r_d13C_VPDB`: δ13C_VPDB repeatability of analyses within the session 2287 + `r_d18O_VSMOW`: δ18O_VSMOW repeatability of analyses within the session 2288 + `r_D47` or `r_D48`: Δ4x repeatability of analyses within the session 2289 + `a`: scrambling factor 2290 + `b`: compositional slope 2291 + `c`: WG offset 2292 + `SE_a`: Model stadard erorr of `a` 2293 + `SE_b`: Model stadard erorr of `b` 2294 + `SE_c`: Model stadard erorr of `c` 2295 + `scrambling_drift` (boolean): whether to allow a temporal drift in the scrambling factor (`a`) 2296 + `slope_drift` (boolean): whether to allow a temporal drift in the compositional slope (`b`) 2297 + `wg_drift` (boolean): whether to allow a temporal drift in the WG offset (`c`) 2298 + `a2`: scrambling factor drift 2299 + `b2`: compositional slope drift 2300 + `c2`: WG offset drift 2301 + `Np`: Number of standardization parameters to fit 2302 + `CM`: model covariance matrix for (`a`, `b`, `c`, `a2`, `b2`, `c2`) 2303 + `d13Cwg_VPDB`: δ13C_VPDB of WG 2304 + `d18Owg_VSMOW`: δ18O_VSMOW of WG 2305 ''' 2306 for session in self.sessions: 2307 if 'd13Cwg_VPDB' not in self.sessions[session]: 2308 self.sessions[session]['d13Cwg_VPDB'] = self.sessions[session]['data'][0]['d13Cwg_VPDB'] 2309 if 'd18Owg_VSMOW' not in self.sessions[session]: 2310 self.sessions[session]['d18Owg_VSMOW'] = self.sessions[session]['data'][0]['d18Owg_VSMOW'] 2311 self.sessions[session]['Na'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.anchors]) 2312 self.sessions[session]['Nu'] = len([r for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns]) 2313 2314 self.msg(f'Computing repeatabilities for session {session}') 2315 self.sessions[session]['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors', sessions = [session]) 2316 self.sessions[session]['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors', sessions = [session]) 2317 self.sessions[session][f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', sessions = [session]) 2318 2319 if self.standardization_method == 'pooled': 2320 for session in self.sessions: 2321 2322 self.sessions[session]['a'] = self.standardization.params.valuesdict()[f'a_{pf(session)}'] 2323 i = self.standardization.var_names.index(f'a_{pf(session)}') 2324 self.sessions[session]['SE_a'] = self.standardization.covar[i,i]**.5 2325 2326 self.sessions[session]['b'] = self.standardization.params.valuesdict()[f'b_{pf(session)}'] 2327 i = self.standardization.var_names.index(f'b_{pf(session)}') 2328 self.sessions[session]['SE_b'] = self.standardization.covar[i,i]**.5 2329 2330 self.sessions[session]['c'] = self.standardization.params.valuesdict()[f'c_{pf(session)}'] 2331 i = self.standardization.var_names.index(f'c_{pf(session)}') 2332 self.sessions[session]['SE_c'] = self.standardization.covar[i,i]**.5 2333 2334 self.sessions[session]['a2'] = self.standardization.params.valuesdict()[f'a2_{pf(session)}'] 2335 if self.sessions[session]['scrambling_drift']: 2336 i = self.standardization.var_names.index(f'a2_{pf(session)}') 2337 self.sessions[session]['SE_a2'] = self.standardization.covar[i,i]**.5 2338 else: 2339 self.sessions[session]['SE_a2'] = 0. 2340 2341 self.sessions[session]['b2'] = self.standardization.params.valuesdict()[f'b2_{pf(session)}'] 2342 if self.sessions[session]['slope_drift']: 2343 i = self.standardization.var_names.index(f'b2_{pf(session)}') 2344 self.sessions[session]['SE_b2'] = self.standardization.covar[i,i]**.5 2345 else: 2346 self.sessions[session]['SE_b2'] = 0. 2347 2348 self.sessions[session]['c2'] = self.standardization.params.valuesdict()[f'c2_{pf(session)}'] 2349 if self.sessions[session]['wg_drift']: 2350 i = self.standardization.var_names.index(f'c2_{pf(session)}') 2351 self.sessions[session]['SE_c2'] = self.standardization.covar[i,i]**.5 2352 else: 2353 self.sessions[session]['SE_c2'] = 0. 2354 2355 i = self.standardization.var_names.index(f'a_{pf(session)}') 2356 j = self.standardization.var_names.index(f'b_{pf(session)}') 2357 k = self.standardization.var_names.index(f'c_{pf(session)}') 2358 CM = np.zeros((6,6)) 2359 CM[:3,:3] = self.standardization.covar[[i,j,k],:][:,[i,j,k]] 2360 try: 2361 i2 = self.standardization.var_names.index(f'a2_{pf(session)}') 2362 CM[3,[0,1,2,3]] = self.standardization.covar[i2,[i,j,k,i2]] 2363 CM[[0,1,2,3],3] = self.standardization.covar[[i,j,k,i2],i2] 2364 try: 2365 j2 = self.standardization.var_names.index(f'b2_{pf(session)}') 2366 CM[3,4] = self.standardization.covar[i2,j2] 2367 CM[4,3] = self.standardization.covar[j2,i2] 2368 except ValueError: 2369 pass 2370 try: 2371 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2372 CM[3,5] = self.standardization.covar[i2,k2] 2373 CM[5,3] = self.standardization.covar[k2,i2] 2374 except ValueError: 2375 pass 2376 except ValueError: 2377 pass 2378 try: 2379 j2 = self.standardization.var_names.index(f'b2_{pf(session)}') 2380 CM[4,[0,1,2,4]] = self.standardization.covar[j2,[i,j,k,j2]] 2381 CM[[0,1,2,4],4] = self.standardization.covar[[i,j,k,j2],j2] 2382 try: 2383 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2384 CM[4,5] = self.standardization.covar[j2,k2] 2385 CM[5,4] = self.standardization.covar[k2,j2] 2386 except ValueError: 2387 pass 2388 except ValueError: 2389 pass 2390 try: 2391 k2 = self.standardization.var_names.index(f'c2_{pf(session)}') 2392 CM[5,[0,1,2,5]] = self.standardization.covar[k2,[i,j,k,k2]] 2393 CM[[0,1,2,5],5] = self.standardization.covar[[i,j,k,k2],k2] 2394 except ValueError: 2395 pass 2396 2397 self.sessions[session]['CM'] = CM 2398 2399 elif self.standardization_method == 'indep_sessions': 2400 pass # Not implemented yet
Compute various statistics for each session.
Na: Number of anchor analyses in the sessionNu: Number of unknown analyses in the sessionr_d13C_VPDB: δ13CVPDB repeatability of analyses within the sessionr_d18O_VSMOW: δ18OVSMOW repeatability of analyses within the sessionr_D47orr_D48: Δ4x repeatability of analyses within the sessiona: scrambling factorb: compositional slopec: WG offsetSE_a: Model stadard erorr ofaSE_b: Model stadard erorr ofbSE_c: Model stadard erorr ofcscrambling_drift(boolean): whether to allow a temporal drift in the scrambling factor (a)slope_drift(boolean): whether to allow a temporal drift in the compositional slope (b)wg_drift(boolean): whether to allow a temporal drift in the WG offset (c)a2: scrambling factor driftb2: compositional slope driftc2: WG offset driftNp: Number of standardization parameters to fitCM: model covariance matrix for (a,b,c,a2,b2,c2)d13Cwg_VPDB: δ13CVPDB of WGd18Owg_VSMOW: δ18OVSMOW of WG
@make_verbal
def
repeatabilities(self):
2403 @make_verbal 2404 def repeatabilities(self): 2405 ''' 2406 Compute analytical repeatabilities for δ13C_VPDB, δ18O_VSMOW, Δ4x 2407 (for all samples, for anchors, and for unknowns). 2408 ''' 2409 self.msg('Computing reproducibilities for all sessions') 2410 2411 self.repeatability['r_d13C_VPDB'] = self.compute_r('d13C_VPDB', samples = 'anchors') 2412 self.repeatability['r_d18O_VSMOW'] = self.compute_r('d18O_VSMOW', samples = 'anchors') 2413 self.repeatability[f'r_D{self._4x}a'] = self.compute_r(f'D{self._4x}', samples = 'anchors') 2414 self.repeatability[f'r_D{self._4x}u'] = self.compute_r(f'D{self._4x}', samples = 'unknowns') 2415 self.repeatability[f'r_D{self._4x}'] = self.compute_r(f'D{self._4x}', samples = 'all samples')
Compute analytical repeatabilities for δ13CVPDB, δ18OVSMOW, Δ4x (for all samples, for anchors, and for unknowns).
@make_verbal
def
consolidate(self, tables=True, plots=True):
2418 @make_verbal 2419 def consolidate(self, tables = True, plots = True): 2420 ''' 2421 Collect information about samples, sessions and repeatabilities. 2422 ''' 2423 self.consolidate_samples() 2424 self.consolidate_sessions() 2425 self.repeatabilities() 2426 2427 if tables: 2428 self.summary() 2429 self.table_of_sessions() 2430 self.table_of_analyses() 2431 self.table_of_samples() 2432 2433 if plots: 2434 self.plot_sessions()
Collect information about samples, sessions and repeatabilities.
@make_verbal
def
rmswd(self, samples='all samples', sessions='all sessions'):
2437 @make_verbal 2438 def rmswd(self, 2439 samples = 'all samples', 2440 sessions = 'all sessions', 2441 ): 2442 ''' 2443 Compute the χ2, root mean squared weighted deviation 2444 (i.e. reduced χ2), and corresponding degrees of freedom of the 2445 Δ4x values for samples in `samples` and sessions in `sessions`. 2446 2447 Only used in `D4xdata.standardize()` with `method='indep_sessions'`. 2448 ''' 2449 if samples == 'all samples': 2450 mysamples = [k for k in self.samples] 2451 elif samples == 'anchors': 2452 mysamples = [k for k in self.anchors] 2453 elif samples == 'unknowns': 2454 mysamples = [k for k in self.unknowns] 2455 else: 2456 mysamples = samples 2457 2458 if sessions == 'all sessions': 2459 sessions = [k for k in self.sessions] 2460 2461 chisq, Nf = 0, 0 2462 for sample in mysamples : 2463 G = [ r for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2464 if len(G) > 1 : 2465 X, sX = w_avg([r[f'D{self._4x}'] for r in G], [r[f'wD{self._4x}'] for r in G]) 2466 Nf += (len(G) - 1) 2467 chisq += np.sum([ ((r[f'D{self._4x}']-X)/r[f'wD{self._4x}'])**2 for r in G]) 2468 r = (chisq / Nf)**.5 if Nf > 0 else 0 2469 self.msg(f'RMSWD of r["D{self._4x}"] is {r:.6f} for {samples}.') 2470 return {'rmswd': r, 'chisq': chisq, 'Nf': Nf}
Compute the χ2, root mean squared weighted deviation
(i.e. reduced χ2), and corresponding degrees of freedom of the
Δ4x values for samples in samples and sessions in sessions.
Only used in D4xdata.standardize() with method='indep_sessions'.
@make_verbal
def
compute_r(self, key, samples='all samples', sessions='all sessions'):
2473 @make_verbal 2474 def compute_r(self, key, samples = 'all samples', sessions = 'all sessions'): 2475 ''' 2476 Compute the repeatability of `[r[key] for r in self]` 2477 ''' 2478 2479 if samples == 'all samples': 2480 mysamples = [k for k in self.samples] 2481 elif samples == 'anchors': 2482 mysamples = [k for k in self.anchors] 2483 elif samples == 'unknowns': 2484 mysamples = [k for k in self.unknowns] 2485 else: 2486 mysamples = samples 2487 2488 if sessions == 'all sessions': 2489 sessions = [k for k in self.sessions] 2490 2491 if key in ['D47', 'D48']: 2492 # Full disclosure: the definition of Nf is tricky/debatable 2493 G = [r for r in self if r['Sample'] in mysamples and r['Session'] in sessions] 2494 chisq = (np.array([r[f'{key}_residual'] for r in G])**2).sum() 2495 Nf = len(G) 2496# print(f'len(G) = {Nf}') 2497 Nf -= len([s for s in mysamples if s in self.unknowns]) 2498# print(f'{len([s for s in mysamples if s in self.unknowns])} unknown samples to consider') 2499 for session in sessions: 2500 Np = len([ 2501 _ for _ in self.standardization.params 2502 if ( 2503 self.standardization.params[_].expr is not None 2504 and ( 2505 (_[0] in 'abc' and _[1] == '_' and _[2:] == pf(session)) 2506 or (_[0] in 'abc' and _[1:3] == '2_' and _[3:] == pf(session)) 2507 ) 2508 ) 2509 ]) 2510# print(f'session {session}: {Np} parameters to consider') 2511 Na = len({ 2512 r['Sample'] for r in self.sessions[session]['data'] 2513 if r['Sample'] in self.anchors and r['Sample'] in mysamples 2514 }) 2515# print(f'session {session}: {Na} different anchors in that session') 2516 Nf -= min(Np, Na) 2517# print(f'Nf = {Nf}') 2518 2519# for sample in mysamples : 2520# X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2521# if len(X) > 1 : 2522# chisq += np.sum([ (x-self.samples[sample][key])**2 for x in X ]) 2523# if sample in self.unknowns: 2524# Nf += len(X) - 1 2525# else: 2526# Nf += len(X) 2527# if samples in ['anchors', 'all samples']: 2528# Nf -= sum([self.sessions[s]['Np'] for s in sessions]) 2529 r = (chisq / Nf)**.5 if Nf > 0 else 0 2530 2531 else: # if key not in ['D47', 'D48'] 2532 chisq, Nf = 0, 0 2533 for sample in mysamples : 2534 X = [ r[key] for r in self if r['Sample'] == sample and r['Session'] in sessions ] 2535 if len(X) > 1 : 2536 Nf += len(X) - 1 2537 chisq += np.sum([ (x-np.mean(X))**2 for x in X ]) 2538 r = (chisq / Nf)**.5 if Nf > 0 else 0 2539 2540 self.msg(f'Repeatability of r["{key}"] is {1000*r:.1f} ppm for {samples}.') 2541 return r
Compute the repeatability of [r[key] for r in self]
def
sample_average(self, samples, weights='equal', normalize=True):
2543 def sample_average(self, samples, weights = 'equal', normalize = True): 2544 ''' 2545 Weighted average Δ4x value of a group of samples, accounting for covariance. 2546 2547 Returns the weighed average Δ4x value and associated SE 2548 of a group of samples. Weights are equal by default. If `normalize` is 2549 true, `weights` will be rescaled so that their sum equals 1. 2550 2551 **Examples** 2552 2553 ```python 2554 self.sample_average(['X','Y'], [1, 2]) 2555 ``` 2556 2557 returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3, 2558 where Δ4x(X) and Δ4x(Y) are the average Δ4x 2559 values of samples X and Y, respectively. 2560 2561 ```python 2562 self.sample_average(['X','Y'], [1, -1], normalize = False) 2563 ``` 2564 2565 returns the value and SE of the difference Δ4x(X) - Δ4x(Y). 2566 ''' 2567 if weights == 'equal': 2568 weights = [1/len(samples)] * len(samples) 2569 2570 if normalize: 2571 s = sum(weights) 2572 if s: 2573 weights = [w/s for w in weights] 2574 2575 try: 2576# indices = [self.standardization.var_names.index(f'D47_{pf(sample)}') for sample in samples] 2577# C = self.standardization.covar[indices,:][:,indices] 2578 C = np.array([[self.sample_D4x_covar(x, y) for x in samples] for y in samples]) 2579 X = [self.samples[sample][f'D{self._4x}'] for sample in samples] 2580 return correlated_sum(X, C, weights) 2581 except ValueError: 2582 return (0., 0.)
Weighted average Δ4x value of a group of samples, accounting for covariance.
Returns the weighed average Δ4x value and associated SE
of a group of samples. Weights are equal by default. If normalize is
true, weights will be rescaled so that their sum equals 1.
Examples
self.sample_average(['X','Y'], [1, 2])
returns the value and SE of [Δ4x(X) + 2 Δ4x(Y)]/3, where Δ4x(X) and Δ4x(Y) are the average Δ4x values of samples X and Y, respectively.
self.sample_average(['X','Y'], [1, -1], normalize = False)
returns the value and SE of the difference Δ4x(X) - Δ4x(Y).
def
sample_D4x_covar(self, sample1, sample2=None):
2585 def sample_D4x_covar(self, sample1, sample2 = None): 2586 ''' 2587 Covariance between Δ4x values of samples 2588 2589 Returns the error covariance between the average Δ4x values of two 2590 samples. If if only `sample_1` is specified, or if `sample_1 == sample_2`), 2591 returns the Δ4x variance for that sample. 2592 ''' 2593 if sample2 is None: 2594 sample2 = sample1 2595 if self.standardization_method == 'pooled': 2596 i = self.standardization.var_names.index(f'D{self._4x}_{pf(sample1)}') 2597 j = self.standardization.var_names.index(f'D{self._4x}_{pf(sample2)}') 2598 return self.standardization.covar[i, j] 2599 elif self.standardization_method == 'indep_sessions': 2600 if sample1 == sample2: 2601 return self.samples[sample1][f'SE_D{self._4x}']**2 2602 else: 2603 c = 0 2604 for session in self.sessions: 2605 sdata1 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample1] 2606 sdata2 = [r for r in self.sessions[session]['data'] if r['Sample'] == sample2] 2607 if sdata1 and sdata2: 2608 a = self.sessions[session]['a'] 2609 # !! TODO: CM below does not account for temporal changes in standardization parameters 2610 CM = self.sessions[session]['CM'][:3,:3] 2611 avg_D4x_1 = np.mean([r[f'D{self._4x}'] for r in sdata1]) 2612 avg_d4x_1 = np.mean([r[f'd{self._4x}'] for r in sdata1]) 2613 avg_D4x_2 = np.mean([r[f'D{self._4x}'] for r in sdata2]) 2614 avg_d4x_2 = np.mean([r[f'd{self._4x}'] for r in sdata2]) 2615 c += ( 2616 self.unknowns[sample1][f'session_D{self._4x}'][session][2] 2617 * self.unknowns[sample2][f'session_D{self._4x}'][session][2] 2618 * np.array([[avg_D4x_1, avg_d4x_1, 1]]) 2619 @ CM 2620 @ np.array([[avg_D4x_2, avg_d4x_2, 1]]).T 2621 ) / a**2 2622 return float(c)
Covariance between Δ4x values of samples
Returns the error covariance between the average Δ4x values of two
samples. If if only sample_1 is specified, or if sample_1 == sample_2),
returns the Δ4x variance for that sample.
def
sample_D4x_correl(self, sample1, sample2=None):
2624 def sample_D4x_correl(self, sample1, sample2 = None): 2625 ''' 2626 Correlation between Δ4x errors of samples 2627 2628 Returns the error correlation between the average Δ4x values of two samples. 2629 ''' 2630 if sample2 is None or sample2 == sample1: 2631 return 1. 2632 return ( 2633 self.sample_D4x_covar(sample1, sample2) 2634 / self.unknowns[sample1][f'SE_D{self._4x}'] 2635 / self.unknowns[sample2][f'SE_D{self._4x}'] 2636 )
Correlation between Δ4x errors of samples
Returns the error correlation between the average Δ4x values of two samples.
def
plot_single_session( self, session, kw_plot_anchors={'ls': 'None', 'marker': 'x', 'mec': (0.75, 0, 0), 'mew': 0.75, 'ms': 4}, kw_plot_unknowns={'ls': 'None', 'marker': 'x', 'mec': (0, 0, 0.75), 'mew': 0.75, 'ms': 4}, kw_plot_anchor_avg={'ls': '-', 'marker': 'None', 'color': (0.75, 0, 0), 'lw': 0.75}, kw_plot_unknown_avg={'ls': '-', 'marker': 'None', 'color': (0, 0, 0.75), 'lw': 0.75}, kw_contour_error={'colors': [[0, 0, 0]], 'alpha': 0.5, 'linewidths': 0.75}, xylimits='free', x_label=None, y_label=None, error_contour_interval='auto', fig='new'):
2638 def plot_single_session(self, 2639 session, 2640 kw_plot_anchors = dict(ls='None', marker='x', mec=(.75, 0, 0), mew = .75, ms = 4), 2641 kw_plot_unknowns = dict(ls='None', marker='x', mec=(0, 0, .75), mew = .75, ms = 4), 2642 kw_plot_anchor_avg = dict(ls='-', marker='None', color=(.75, 0, 0), lw = .75), 2643 kw_plot_unknown_avg = dict(ls='-', marker='None', color=(0, 0, .75), lw = .75), 2644 kw_contour_error = dict(colors = [[0, 0, 0]], alpha = .5, linewidths = 0.75), 2645 xylimits = 'free', # | 'constant' 2646 x_label = None, 2647 y_label = None, 2648 error_contour_interval = 'auto', 2649 fig = 'new', 2650 ): 2651 ''' 2652 Generate plot for a single session 2653 ''' 2654 if x_label is None: 2655 x_label = f'δ$_{{{self._4x}}}$ (‰)' 2656 if y_label is None: 2657 y_label = f'Δ$_{{{self._4x}}}$ (‰)' 2658 2659 out = _SessionPlot() 2660 anchors = [a for a in self.anchors if [r for r in self.sessions[session]['data'] if r['Sample'] == a]] 2661 unknowns = [u for u in self.unknowns if [r for r in self.sessions[session]['data'] if r['Sample'] == u]] 2662 2663 if fig == 'new': 2664 out.fig = ppl.figure(figsize = (6,6)) 2665 ppl.subplots_adjust(.1,.1,.9,.9) 2666 2667 out.anchor_analyses, = ppl.plot( 2668 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors], 2669 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.anchors], 2670 **kw_plot_anchors) 2671 out.unknown_analyses, = ppl.plot( 2672 [r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns], 2673 [r[f'D{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] in self.unknowns], 2674 **kw_plot_unknowns) 2675 out.anchor_avg = ppl.plot( 2676 np.array([ np.array([ 2677 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1, 2678 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1 2679 ]) for sample in anchors]).T, 2680 np.array([ np.array([0, 0]) + self.Nominal_D4x[sample] for sample in anchors]).T, 2681 **kw_plot_anchor_avg) 2682 out.unknown_avg = ppl.plot( 2683 np.array([ np.array([ 2684 np.min([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) - 1, 2685 np.max([r[f'd{self._4x}'] for r in self.sessions[session]['data'] if r['Sample'] == sample]) + 1 2686 ]) for sample in unknowns]).T, 2687 np.array([ np.array([0, 0]) + self.unknowns[sample][f'D{self._4x}'] for sample in unknowns]).T, 2688 **kw_plot_unknown_avg) 2689 if xylimits == 'constant': 2690 x = [r[f'd{self._4x}'] for r in self] 2691 y = [r[f'D{self._4x}'] for r in self] 2692 x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y) 2693 w, h = x2-x1, y2-y1 2694 x1 -= w/20 2695 x2 += w/20 2696 y1 -= h/20 2697 y2 += h/20 2698 ppl.axis([x1, x2, y1, y2]) 2699 elif xylimits == 'free': 2700 x1, x2, y1, y2 = ppl.axis() 2701 else: 2702 x1, x2, y1, y2 = ppl.axis(xylimits) 2703 2704 if error_contour_interval != 'none': 2705 xi, yi = np.linspace(x1, x2), np.linspace(y1, y2) 2706 XI,YI = np.meshgrid(xi, yi) 2707 SI = np.array([[self.standardization_error(session, x, y) for x in xi] for y in yi]) 2708 if error_contour_interval == 'auto': 2709 rng = np.max(SI) - np.min(SI) 2710 if rng <= 0.01: 2711 cinterval = 0.001 2712 elif rng <= 0.03: 2713 cinterval = 0.004 2714 elif rng <= 0.1: 2715 cinterval = 0.01 2716 elif rng <= 0.3: 2717 cinterval = 0.03 2718 elif rng <= 1.: 2719 cinterval = 0.1 2720 else: 2721 cinterval = 0.5 2722 else: 2723 cinterval = error_contour_interval 2724 2725 cval = np.arange(np.ceil(SI.min() / .001) * .001, np.ceil(SI.max() / .001 + 1) * .001, cinterval) 2726 out.contour = ppl.contour(XI, YI, SI, cval, **kw_contour_error) 2727 out.clabel = ppl.clabel(out.contour) 2728 2729 ppl.xlabel(x_label) 2730 ppl.ylabel(y_label) 2731 ppl.title(session, weight = 'bold') 2732 ppl.grid(alpha = .2) 2733 out.ax = ppl.gca() 2734 2735 return out
Generate plot for a single session
def
plot_residuals( self, kde=False, hist=False, binwidth=0.6666666666666666, dir='output', filename=None, highlight=[], colors=None, figsize=None):
2737 def plot_residuals( 2738 self, 2739 kde = False, 2740 hist = False, 2741 binwidth = 2/3, 2742 dir = 'output', 2743 filename = None, 2744 highlight = [], 2745 colors = None, 2746 figsize = None, 2747 ): 2748 ''' 2749 Plot residuals of each analysis as a function of time (actually, as a function of 2750 the order of analyses in the `D4xdata` object) 2751 2752 + `kde`: whether to add a kernel density estimate of residuals 2753 + `hist`: whether to add a histogram of residuals (incompatible with `kde`) 2754 + `histbins`: specify bin edges for the histogram 2755 + `dir`: the directory in which to save the plot 2756 + `highlight`: a list of samples to highlight 2757 + `colors`: a dict of `{<sample>: <color>}` for all samples 2758 + `figsize`: (width, height) of figure 2759 ''' 2760 2761 from matplotlib import ticker 2762 2763 # Layout 2764 fig = ppl.figure(figsize = (8,4) if figsize is None else figsize) 2765 if hist or kde: 2766 ppl.subplots_adjust(left = .08, bottom = .05, right = .98, top = .8, wspace = -0.72) 2767 ax1, ax2 = ppl.subplot(121), ppl.subplot(1,15,15) 2768 else: 2769 ppl.subplots_adjust(.08,.05,.78,.8) 2770 ax1 = ppl.subplot(111) 2771 2772 # Colors 2773 N = len(self.anchors) 2774 if colors is None: 2775 if len(highlight) > 0: 2776 Nh = len(highlight) 2777 if Nh == 1: 2778 colors = {highlight[0]: (0,0,0)} 2779 elif Nh == 3: 2780 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0)])} 2781 elif Nh == 4: 2782 colors = {a: c for a,c in zip(highlight, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])} 2783 else: 2784 colors = {a: hls_to_rgb(k/Nh, .4, 1) for k,a in enumerate(highlight)} 2785 else: 2786 if N == 3: 2787 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0)])} 2788 elif N == 4: 2789 colors = {a: c for a,c in zip(self.anchors, [(0,0,1), (1,0,0), (0,2/3,0), (.75,0,.75)])} 2790 else: 2791 colors = {a: hls_to_rgb(k/N, .4, 1) for k,a in enumerate(self.anchors)} 2792 2793 ppl.sca(ax1) 2794 2795 ppl.axhline(0, color = 'k', alpha = .25, lw = 0.75) 2796 2797 ax1.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'${x:+.0f}$' if x else '$0$')) 2798 2799 session = self[0]['Session'] 2800 x1 = 0 2801# ymax = np.max([1e3 * (r['D47'] - self.samples[r['Sample']]['D47']) for r in self]) 2802 x_sessions = {} 2803 one_or_more_singlets = False 2804 one_or_more_multiplets = False 2805 multiplets = set() 2806 for k,r in enumerate(self): 2807 if r['Session'] != session: 2808 x2 = k-1 2809 x_sessions[session] = (x1+x2)/2 2810 ppl.axvline(k - 0.5, color = 'k', lw = .5) 2811 session = r['Session'] 2812 x1 = k 2813 singlet = len(self.samples[r['Sample']]['data']) == 1 2814 if not singlet: 2815 multiplets.add(r['Sample']) 2816 if r['Sample'] in self.unknowns: 2817 if singlet: 2818 one_or_more_singlets = True 2819 else: 2820 one_or_more_multiplets = True 2821 kw = dict( 2822 marker = 'x' if singlet else '+', 2823 ms = 4 if singlet else 5, 2824 ls = 'None', 2825 mec = colors[r['Sample']] if r['Sample'] in colors else (0,0,0), 2826 mew = 1, 2827 alpha = 0.2 if singlet else 1, 2828 ) 2829 if highlight and r['Sample'] not in highlight: 2830 kw['alpha'] = 0.2 2831 ppl.plot(k, 1e3 * r[f'D{self._4x}_residual'], **kw) 2832 x2 = k 2833 x_sessions[session] = (x1+x2)/2 2834 2835 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000, self.repeatability[f'r_D{self._4x}']*1000, color = 'k', alpha = .05, lw = 1) 2836 ppl.axhspan(-self.repeatability[f'r_D{self._4x}']*1000*self.t95, self.repeatability[f'r_D{self._4x}']*1000*self.t95, color = 'k', alpha = .05, lw = 1) 2837 if not (hist or kde): 2838 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000, f" SD = {self.repeatability['r_D{self._4x}']*1000:.1f} ppm", size = 9, alpha = 1, va = 'center') 2839 ppl.text(len(self), self.repeatability[f'r_D{self._4x}']*1000*self.t95, f" 95% CL = ± {self.repeatability['r_D{self._4x}']*1000*self.t95:.1f} ppm", size = 9, alpha = 1, va = 'center') 2840 2841 xmin, xmax, ymin, ymax = ppl.axis() 2842 for s in x_sessions: 2843 ppl.text( 2844 x_sessions[s], 2845 ymax +1, 2846 s, 2847 va = 'bottom', 2848 **( 2849 dict(ha = 'center') 2850 if len(self.sessions[s]['data']) > (0.15 * len(self)) 2851 else dict(ha = 'left', rotation = 45) 2852 ) 2853 ) 2854 2855 if hist or kde: 2856 ppl.sca(ax2) 2857 2858 for s in colors: 2859 kw['marker'] = '+' 2860 kw['ms'] = 5 2861 kw['mec'] = colors[s] 2862 kw['label'] = s 2863 kw['alpha'] = 1 2864 ppl.plot([], [], **kw) 2865 2866 kw['mec'] = (0,0,0) 2867 2868 if one_or_more_singlets: 2869 kw['marker'] = 'x' 2870 kw['ms'] = 4 2871 kw['alpha'] = .2 2872 kw['label'] = 'other (N$\\,$=$\\,$1)' if one_or_more_multiplets else 'other' 2873 ppl.plot([], [], **kw) 2874 2875 if one_or_more_multiplets: 2876 kw['marker'] = '+' 2877 kw['ms'] = 4 2878 kw['alpha'] = 1 2879 kw['label'] = 'other (N$\\,$>$\\,$1)' if one_or_more_singlets else 'other' 2880 ppl.plot([], [], **kw) 2881 2882 if hist or kde: 2883 leg = ppl.legend(loc = 'upper right', bbox_to_anchor = (1, 1), bbox_transform=fig.transFigure, borderaxespad = 1.5, fontsize = 9) 2884 else: 2885 leg = ppl.legend(loc = 'lower right', bbox_to_anchor = (1, 0), bbox_transform=fig.transFigure, borderaxespad = 1.5) 2886 leg.set_zorder(-1000) 2887 2888 ppl.sca(ax1) 2889 2890 ppl.ylabel(f'Δ$_{{{self._4x}}}$ residuals (ppm)') 2891 ppl.xticks([]) 2892 ppl.axis([-1, len(self), None, None]) 2893 2894 if hist or kde: 2895 ppl.sca(ax2) 2896 X = 1e3 * np.array([r[f'D{self._4x}_residual'] for r in self if r['Sample'] in multiplets or r['Sample'] in self.anchors]) 2897 2898 if kde: 2899 from scipy.stats import gaussian_kde 2900 yi = np.linspace(ymin, ymax, 201) 2901 xi = gaussian_kde(X).evaluate(yi) 2902 ppl.fill_betweenx(yi, xi, xi*0, fc = (0,0,0,.15), lw = 1, ec = (.75,.75,.75,1)) 2903# ppl.plot(xi, yi, 'k-', lw = 1) 2904 ppl.axis([0, None, ymin, ymax]) 2905 elif hist: 2906 ppl.hist( 2907 X, 2908 orientation = 'horizontal', 2909 histtype = 'stepfilled', 2910 ec = [.4]*3, 2911 fc = [.25]*3, 2912 alpha = .25, 2913 bins = np.linspace(-9e3*self.repeatability[f'r_D{self._4x}'], 9e3*self.repeatability[f'r_D{self._4x}'], int(18/binwidth+1)), 2914 ) 2915 ppl.axis([None, None, ymin, ymax]) 2916 ppl.text(0, 0, 2917 f" SD = {self.repeatability[f'r_D{self._4x}']*1000:.1f} ppm\n 95% CL = ± {self.repeatability[f'r_D{self._4x}']*1000*self.t95:.1f} ppm", 2918 size = 7.5, 2919 alpha = 1, 2920 va = 'center', 2921 ha = 'left', 2922 ) 2923 2924 ppl.xticks([]) 2925 ppl.yticks([]) 2926# ax2.spines['left'].set_visible(False) 2927 ax2.spines['right'].set_visible(False) 2928 ax2.spines['top'].set_visible(False) 2929 ax2.spines['bottom'].set_visible(False) 2930 2931 2932 if not os.path.exists(dir): 2933 os.makedirs(dir) 2934 if filename is None: 2935 return fig 2936 elif filename == '': 2937 filename = f'D{self._4x}_residuals.pdf' 2938 ppl.savefig(f'{dir}/{filename}') 2939 ppl.close(fig)
Plot residuals of each analysis as a function of time (actually, as a function of
the order of analyses in the D4xdata object)
kde: whether to add a kernel density estimate of residualshist: whether to add a histogram of residuals (incompatible withkde)histbins: specify bin edges for the histogramdir: the directory in which to save the plothighlight: a list of samples to highlightcolors: a dict of{<sample>: <color>}for all samplesfigsize: (width, height) of figure
def
simulate(self, *args, **kwargs):
2942 def simulate(self, *args, **kwargs): 2943 ''' 2944 Legacy function with warning message pointing to `virtual_data()` 2945 ''' 2946 raise DeprecationWarning('D4xdata.simulate is deprecated and has been replaced by virtual_data()')
Legacy function with warning message pointing to virtual_data()
def
plot_distribution_of_analyses( self, dir='output', filename=None, vs_time=False, figsize=(6, 4), subplots_adjust=(0.02, 0.13, 0.85, 0.8), output=None):
2948 def plot_distribution_of_analyses( 2949 self, 2950 dir = 'output', 2951 filename = None, 2952 vs_time = False, 2953 figsize = (6,4), 2954 subplots_adjust = (0.02, 0.13, 0.85, 0.8), 2955 output = None, 2956 ): 2957 ''' 2958 Plot temporal distribution of all analyses in the data set. 2959 2960 **Parameters** 2961 2962 + `vs_time`: if `True`, plot as a function of `TimeTag` rather than sequentially. 2963 ''' 2964 2965 asamples = [s for s in self.anchors] 2966 usamples = [s for s in self.unknowns] 2967 if output is None or output == 'fig': 2968 fig = ppl.figure(figsize = figsize) 2969 ppl.subplots_adjust(*subplots_adjust) 2970 Xmin = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self)]) 2971 Xmax = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self)]) 2972 Xmax += (Xmax-Xmin)/40 2973 Xmin -= (Xmax-Xmin)/41 2974 for k, s in enumerate(asamples + usamples): 2975 if vs_time: 2976 X = [r['TimeTag'] for r in self if r['Sample'] == s] 2977 else: 2978 X = [x for x,r in enumerate(self) if r['Sample'] == s] 2979 Y = [-k for x in X] 2980 ppl.plot(X, Y, 'o', mec = None, mew = 0, mfc = 'b' if s in usamples else 'r', ms = 3, alpha = .75) 2981 ppl.axhline(-k, color = 'b' if s in usamples else 'r', lw = .5, alpha = .25) 2982 ppl.text(Xmax, -k, f' {s}', va = 'center', ha = 'left', size = 7, color = 'b' if s in usamples else 'r') 2983 ppl.axis([Xmin, Xmax, -k-1, 1]) 2984 ppl.xlabel('\ntime') 2985 ppl.gca().annotate('', 2986 xy = (0.6, -0.02), 2987 xycoords = 'axes fraction', 2988 xytext = (.4, -0.02), 2989 arrowprops = dict(arrowstyle = "->", color = 'k'), 2990 ) 2991 2992 2993 x2 = -1 2994 for session in self.sessions: 2995 x1 = min([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session]) 2996 if vs_time: 2997 ppl.axvline(x1, color = 'k', lw = .75) 2998 if x2 > -1: 2999 if not vs_time: 3000 ppl.axvline((x1+x2)/2, color = 'k', lw = .75, alpha = .5) 3001 x2 = max([r['TimeTag'] if vs_time else j for j,r in enumerate(self) if r['Session'] == session]) 3002# from xlrd import xldate_as_datetime 3003# print(session, xldate_as_datetime(x1, 0), xldate_as_datetime(x2, 0)) 3004 if vs_time: 3005 ppl.axvline(x2, color = 'k', lw = .75) 3006 ppl.axvspan(x1,x2,color = 'k', zorder = -100, alpha = .15) 3007 ppl.text((x1+x2)/2, 1, f' {session}', ha = 'left', va = 'bottom', rotation = 45, size = 8) 3008 3009 ppl.xticks([]) 3010 ppl.yticks([]) 3011 3012 if output is None: 3013 if not os.path.exists(dir): 3014 os.makedirs(dir) 3015 if filename == None: 3016 filename = f'D{self._4x}_distribution_of_analyses.pdf' 3017 ppl.savefig(f'{dir}/{filename}') 3018 ppl.close(fig) 3019 elif output == 'ax': 3020 return ppl.gca() 3021 elif output == 'fig': 3022 return fig
Plot temporal distribution of all analyses in the data set.
Parameters
vs_time: ifTrue, plot as a function ofTimeTagrather than sequentially.
def
plot_bulk_compositions( self, samples=None, dir='output/bulk_compositions', figsize=(6, 6), subplots_adjust=(0.15, 0.12, 0.95, 0.92), show=False, sample_color=(0, 0.5, 1), analysis_color=(0.7, 0.7, 0.7), labeldist=0.3, radius=0.05):
3025 def plot_bulk_compositions( 3026 self, 3027 samples = None, 3028 dir = 'output/bulk_compositions', 3029 figsize = (6,6), 3030 subplots_adjust = (0.15, 0.12, 0.95, 0.92), 3031 show = False, 3032 sample_color = (0,.5,1), 3033 analysis_color = (.7,.7,.7), 3034 labeldist = 0.3, 3035 radius = 0.05, 3036 ): 3037 ''' 3038 Plot δ13C_VBDP vs δ18O_VSMOW (of CO2) for all analyses. 3039 3040 By default, creates a directory `./output/bulk_compositions` where plots for 3041 each sample are saved. Another plot named `__all__.pdf` shows all analyses together. 3042 3043 3044 **Parameters** 3045 3046 + `samples`: Only these samples are processed (by default: all samples). 3047 + `dir`: where to save the plots 3048 + `figsize`: (width, height) of figure 3049 + `subplots_adjust`: passed to `subplots_adjust()` 3050 + `show`: whether to call `matplotlib.pyplot.show()` on the plot with all samples, 3051 allowing for interactive visualization/exploration in (δ13C, δ18O) space. 3052 + `sample_color`: color used for replicate markers/labels 3053 + `analysis_color`: color used for sample markers/labels 3054 + `labeldist`: distance (in inches) from replicate markers to replicate labels 3055 + `radius`: radius of the dashed circle providing scale. No circle if `radius = 0`. 3056 ''' 3057 3058 from matplotlib.patches import Ellipse 3059 3060 if samples is None: 3061 samples = [_ for _ in self.samples] 3062 3063 saved = {} 3064 3065 for s in samples: 3066 3067 fig = ppl.figure(figsize = figsize) 3068 fig.subplots_adjust(*subplots_adjust) 3069 ax = ppl.subplot(111) 3070 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)') 3071 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)') 3072 ppl.title(s) 3073 3074 3075 XY = np.array([[_['d18O_VSMOW'], _['d13C_VPDB']] for _ in self.samples[s]['data']]) 3076 UID = [_['UID'] for _ in self.samples[s]['data']] 3077 XY0 = XY.mean(0) 3078 3079 for xy in XY: 3080 ppl.plot([xy[0], XY0[0]], [xy[1], XY0[1]], '-', lw = 1, color = analysis_color) 3081 3082 ppl.plot(*XY.T, 'wo', mew = 1, mec = analysis_color) 3083 ppl.plot(*XY0, 'wo', mew = 2, mec = sample_color) 3084 ppl.text(*XY0, f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold') 3085 saved[s] = [XY, XY0] 3086 3087 x1, x2, y1, y2 = ppl.axis() 3088 x0, dx = (x1+x2)/2, (x2-x1)/2 3089 y0, dy = (y1+y2)/2, (y2-y1)/2 3090 dx, dy = [max(max(dx, dy), radius)]*2 3091 3092 ppl.axis([ 3093 x0 - 1.2*dx, 3094 x0 + 1.2*dx, 3095 y0 - 1.2*dy, 3096 y0 + 1.2*dy, 3097 ]) 3098 3099 XY0_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(XY0)) 3100 3101 for xy, uid in zip(XY, UID): 3102 3103 xy_in_display_space = fig.dpi_scale_trans.inverted().transform(ax.transData.transform(xy)) 3104 vector_in_display_space = xy_in_display_space - XY0_in_display_space 3105 3106 if (vector_in_display_space**2).sum() > 0: 3107 3108 unit_vector_in_display_space = vector_in_display_space / ((vector_in_display_space**2).sum())**0.5 3109 label_vector_in_display_space = vector_in_display_space + unit_vector_in_display_space * labeldist 3110 label_xy_in_display_space = XY0_in_display_space + label_vector_in_display_space 3111 label_xy_in_data_space = ax.transData.inverted().transform(fig.dpi_scale_trans.transform(label_xy_in_display_space)) 3112 3113 ppl.text(*label_xy_in_data_space, uid, va = 'center', ha = 'center', color = analysis_color) 3114 3115 else: 3116 3117 ppl.text(*xy, f'{uid} ', va = 'center', ha = 'right', color = analysis_color) 3118 3119 if radius: 3120 ax.add_artist(Ellipse( 3121 xy = XY0, 3122 width = radius*2, 3123 height = radius*2, 3124 ls = (0, (2,2)), 3125 lw = .7, 3126 ec = analysis_color, 3127 fc = 'None', 3128 )) 3129 ppl.text( 3130 XY0[0], 3131 XY0[1]-radius, 3132 f'\n± {radius*1e3:.0f} ppm', 3133 color = analysis_color, 3134 va = 'top', 3135 ha = 'center', 3136 linespacing = 0.4, 3137 size = 8, 3138 ) 3139 3140 if not os.path.exists(dir): 3141 os.makedirs(dir) 3142 fig.savefig(f'{dir}/{s}.pdf') 3143 ppl.close(fig) 3144 3145 fig = ppl.figure(figsize = figsize) 3146 fig.subplots_adjust(*subplots_adjust) 3147 ppl.xlabel('$δ^{18}O_{VSMOW}$ of $CO_2$ (‰)') 3148 ppl.ylabel('$δ^{13}C_{VPDB}$ (‰)') 3149 3150 for s in saved: 3151 for xy in saved[s][0]: 3152 ppl.plot([xy[0], saved[s][1][0]], [xy[1], saved[s][1][1]], '-', lw = 1, color = analysis_color) 3153 ppl.plot(*saved[s][0].T, 'wo', mew = 1, mec = analysis_color) 3154 ppl.plot(*saved[s][1], 'wo', mew = 1.5, mec = sample_color) 3155 ppl.text(*saved[s][1], f' {s}', va = 'center', ha = 'left', color = sample_color, weight = 'bold') 3156 3157 x1, x2, y1, y2 = ppl.axis() 3158 ppl.axis([ 3159 x1 - (x2-x1)/10, 3160 x2 + (x2-x1)/10, 3161 y1 - (y2-y1)/10, 3162 y2 + (y2-y1)/10, 3163 ]) 3164 3165 3166 if not os.path.exists(dir): 3167 os.makedirs(dir) 3168 fig.savefig(f'{dir}/__all__.pdf') 3169 if show: 3170 ppl.show() 3171 ppl.close(fig)
Plot δ13C_VBDP vs δ18OVSMOW (of CO2) for all analyses.
By default, creates a directory ./output/bulk_compositions where plots for
each sample are saved. Another plot named __all__.pdf shows all analyses together.
Parameters
samples: Only these samples are processed (by default: all samples).dir: where to save the plotsfigsize: (width, height) of figuresubplots_adjust: passed tosubplots_adjust()show: whether to callmatplotlib.pyplot.show()on the plot with all samples, allowing for interactive visualization/exploration in (δ13C, δ18O) space.sample_color: color used for replicate markers/labelsanalysis_color: color used for sample markers/labelslabeldist: distance (in inches) from replicate markers to replicate labelsradius: radius of the dashed circle providing scale. No circle ifradius = 0.
Inherited Members
- builtins.list
- clear
- copy
- append
- insert
- extend
- pop
- remove
- index
- count
- reverse
- sort
3175class D47data(D4xdata): 3176 ''' 3177 Store and process data for a large set of Δ47 analyses, 3178 usually comprising more than one analytical session. 3179 ''' 3180 3181 Nominal_D4x = { 3182 'ETH-1': 0.2052, 3183 'ETH-2': 0.2085, 3184 'ETH-3': 0.6132, 3185 'ETH-4': 0.4511, 3186 'IAEA-C1': 0.3018, 3187 'IAEA-C2': 0.6409, 3188 'MERCK': 0.5135, 3189 } # I-CDES (Bernasconi et al., 2021) 3190 ''' 3191 Nominal Δ47 values assigned to the Δ47 anchor samples, used by 3192 `D47data.standardize()` to normalize unknown samples to an absolute Δ47 3193 reference frame. 3194 3195 By default equal to (after [Bernasconi et al. (2021)](https://doi.org/10.1029/2020GC009588)): 3196 ```py 3197 { 3198 'ETH-1' : 0.2052, 3199 'ETH-2' : 0.2085, 3200 'ETH-3' : 0.6132, 3201 'ETH-4' : 0.4511, 3202 'IAEA-C1' : 0.3018, 3203 'IAEA-C2' : 0.6409, 3204 'MERCK' : 0.5135, 3205 } 3206 ``` 3207 ''' 3208 3209 3210 @property 3211 def Nominal_D47(self): 3212 return self.Nominal_D4x 3213 3214 3215 @Nominal_D47.setter 3216 def Nominal_D47(self, new): 3217 self.Nominal_D4x = dict(**new) 3218 self.refresh() 3219 3220 3221 def __init__(self, l = [], **kwargs): 3222 ''' 3223 **Parameters:** same as `D4xdata.__init__()` 3224 ''' 3225 D4xdata.__init__(self, l = l, mass = '47', **kwargs) 3226 3227 3228 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'): 3229 ''' 3230 Find all samples for which `Teq` is specified, compute equilibrium Δ47 3231 value for that temperature, and add treat these samples as additional anchors. 3232 3233 **Parameters** 3234 3235 + `fCo2eqD47`: Which CO2 equilibrium law to use 3236 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127); 3237 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)). 3238 + `priority`: if `replace`: forget old anchors and only use the new ones; 3239 if `new`: keep pre-existing anchors but update them in case of conflict 3240 between old and new Δ47 values; 3241 if `old`: keep pre-existing anchors but preserve their original Δ47 3242 values in case of conflict. 3243 ''' 3244 f = { 3245 'petersen': fCO2eqD47_Petersen, 3246 'wang': fCO2eqD47_Wang, 3247 }[fCo2eqD47] 3248 foo = {} 3249 for r in self: 3250 if 'Teq' in r: 3251 if r['Sample'] in foo: 3252 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.' 3253 else: 3254 foo[r['Sample']] = f(r['Teq']) 3255 else: 3256 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.' 3257 3258 if priority == 'replace': 3259 self.Nominal_D47 = {} 3260 for s in foo: 3261 if priority != 'old' or s not in self.Nominal_D47: 3262 self.Nominal_D47[s] = foo[s]
Store and process data for a large set of Δ47 analyses, usually comprising more than one analytical session.
D47data(l=[], **kwargs)
3221 def __init__(self, l = [], **kwargs): 3222 ''' 3223 **Parameters:** same as `D4xdata.__init__()` 3224 ''' 3225 D4xdata.__init__(self, l = l, mass = '47', **kwargs)
Parameters: same as D4xdata.__init__()
Nominal_D4x =
{'ETH-1': 0.2052, 'ETH-2': 0.2085, 'ETH-3': 0.6132, 'ETH-4': 0.4511, 'IAEA-C1': 0.3018, 'IAEA-C2': 0.6409, 'MERCK': 0.5135}
Nominal Δ47 values assigned to the Δ47 anchor samples, used by
D47data.standardize() to normalize unknown samples to an absolute Δ47
reference frame.
By default equal to (after Bernasconi et al. (2021)):
{
'ETH-1' : 0.2052,
'ETH-2' : 0.2085,
'ETH-3' : 0.6132,
'ETH-4' : 0.4511,
'IAEA-C1' : 0.3018,
'IAEA-C2' : 0.6409,
'MERCK' : 0.5135,
}
def
D47fromTeq(self, fCo2eqD47='petersen', priority='new'):
3228 def D47fromTeq(self, fCo2eqD47 = 'petersen', priority = 'new'): 3229 ''' 3230 Find all samples for which `Teq` is specified, compute equilibrium Δ47 3231 value for that temperature, and add treat these samples as additional anchors. 3232 3233 **Parameters** 3234 3235 + `fCo2eqD47`: Which CO2 equilibrium law to use 3236 (`petersen`: [Petersen et al. (2019)](https://doi.org/10.1029/2018GC008127); 3237 `wang`: [Wang et al. (2019)](https://doi.org/10.1016/j.gca.2004.05.039)). 3238 + `priority`: if `replace`: forget old anchors and only use the new ones; 3239 if `new`: keep pre-existing anchors but update them in case of conflict 3240 between old and new Δ47 values; 3241 if `old`: keep pre-existing anchors but preserve their original Δ47 3242 values in case of conflict. 3243 ''' 3244 f = { 3245 'petersen': fCO2eqD47_Petersen, 3246 'wang': fCO2eqD47_Wang, 3247 }[fCo2eqD47] 3248 foo = {} 3249 for r in self: 3250 if 'Teq' in r: 3251 if r['Sample'] in foo: 3252 assert foo[r['Sample']] == f(r['Teq']), f'Different values of `Teq` provided for sample `{r["Sample"]}`.' 3253 else: 3254 foo[r['Sample']] = f(r['Teq']) 3255 else: 3256 assert r['Sample'] not in foo, f'`Teq` is inconsistently specified for sample `{r["Sample"]}`.' 3257 3258 if priority == 'replace': 3259 self.Nominal_D47 = {} 3260 for s in foo: 3261 if priority != 'old' or s not in self.Nominal_D47: 3262 self.Nominal_D47[s] = foo[s]
Find all samples for which Teq is specified, compute equilibrium Δ47
value for that temperature, and add treat these samples as additional anchors.
Parameters
fCo2eqD47: Which CO2 equilibrium law to use (petersen: Petersen et al. (2019);wang: Wang et al. (2019)).priority: ifreplace: forget old anchors and only use the new ones; ifnew: keep pre-existing anchors but update them in case of conflict between old and new Δ47 values; ifold: keep pre-existing anchors but preserve their original Δ47 values in case of conflict.
Inherited Members
- D4xdata
- R13_VPDB
- R18_VSMOW
- LAMBDA_17
- R17_VSMOW
- R18_VPDB
- R17_VPDB
- LEVENE_REF_SAMPLE
- ALPHA_18O_ACID_REACTION
- Nominal_d13C_VPDB
- Nominal_d18O_VPDB
- d13C_STANDARDIZATION_METHOD
- d18O_STANDARDIZATION_METHOD
- make_verbal
- msg
- vmsg
- log
- refresh
- refresh_sessions
- refresh_samples
- read
- input
- wg
- compute_bulk_delta
- crunch
- fill_in_missing_info
- standardize_d13C
- standardize_d18O
- compute_bulk_and_clumping_deltas
- compute_isobar_ratios
- split_samples
- unsplit_samples
- assign_timestamps
- report
- combine_samples
- standardize
- standardization_error
- summary
- table_of_sessions
- table_of_analyses
- covar_table
- table_of_samples
- plot_sessions
- consolidate_samples
- consolidate_sessions
- repeatabilities
- consolidate
- rmswd
- compute_r
- sample_average
- sample_D4x_covar
- sample_D4x_correl
- plot_single_session
- plot_residuals
- simulate
- plot_distribution_of_analyses
- plot_bulk_compositions
- builtins.list
- clear
- copy
- append
- insert
- extend
- pop
- remove
- index
- count
- reverse
- sort
3267class D48data(D4xdata): 3268 ''' 3269 Store and process data for a large set of Δ48 analyses, 3270 usually comprising more than one analytical session. 3271 ''' 3272 3273 Nominal_D4x = { 3274 'ETH-1': 0.138, 3275 'ETH-2': 0.138, 3276 'ETH-3': 0.270, 3277 'ETH-4': 0.223, 3278 'GU-1': -0.419, 3279 } # (Fiebig et al., 2019, 2021) 3280 ''' 3281 Nominal Δ48 values assigned to the Δ48 anchor samples, used by 3282 `D48data.standardize()` to normalize unknown samples to an absolute Δ48 3283 reference frame. 3284 3285 By default equal to (after [Fiebig et al. (2019)](https://doi.org/10.1016/j.chemgeo.2019.05.019), 3286 Fiebig et al. (in press)): 3287 3288 ```py 3289 { 3290 'ETH-1' : 0.138, 3291 'ETH-2' : 0.138, 3292 'ETH-3' : 0.270, 3293 'ETH-4' : 0.223, 3294 'GU-1' : -0.419, 3295 } 3296 ``` 3297 ''' 3298 3299 3300 @property 3301 def Nominal_D48(self): 3302 return self.Nominal_D4x 3303 3304 3305 @Nominal_D48.setter 3306 def Nominal_D48(self, new): 3307 self.Nominal_D4x = dict(**new) 3308 self.refresh() 3309 3310 3311 def __init__(self, l = [], **kwargs): 3312 ''' 3313 **Parameters:** same as `D4xdata.__init__()` 3314 ''' 3315 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
Store and process data for a large set of Δ48 analyses, usually comprising more than one analytical session.
D48data(l=[], **kwargs)
3311 def __init__(self, l = [], **kwargs): 3312 ''' 3313 **Parameters:** same as `D4xdata.__init__()` 3314 ''' 3315 D4xdata.__init__(self, l = l, mass = '48', **kwargs)
Parameters: same as D4xdata.__init__()
Nominal_D4x =
{'ETH-1': 0.138, 'ETH-2': 0.138, 'ETH-3': 0.27, 'ETH-4': 0.223, 'GU-1': -0.419}
Nominal Δ48 values assigned to the Δ48 anchor samples, used by
D48data.standardize() to normalize unknown samples to an absolute Δ48
reference frame.
By default equal to (after Fiebig et al. (2019), Fiebig et al. (in press)):
{
'ETH-1' : 0.138,
'ETH-2' : 0.138,
'ETH-3' : 0.270,
'ETH-4' : 0.223,
'GU-1' : -0.419,
}
Inherited Members
- D4xdata
- R13_VPDB
- R18_VSMOW
- LAMBDA_17
- R17_VSMOW
- R18_VPDB
- R17_VPDB
- LEVENE_REF_SAMPLE
- ALPHA_18O_ACID_REACTION
- Nominal_d13C_VPDB
- Nominal_d18O_VPDB
- d13C_STANDARDIZATION_METHOD
- d18O_STANDARDIZATION_METHOD
- make_verbal
- msg
- vmsg
- log
- refresh
- refresh_sessions
- refresh_samples
- read
- input
- wg
- compute_bulk_delta
- crunch
- fill_in_missing_info
- standardize_d13C
- standardize_d18O
- compute_bulk_and_clumping_deltas
- compute_isobar_ratios
- split_samples
- unsplit_samples
- assign_timestamps
- report
- combine_samples
- standardize
- standardization_error
- summary
- table_of_sessions
- table_of_analyses
- covar_table
- table_of_samples
- plot_sessions
- consolidate_samples
- consolidate_sessions
- repeatabilities
- consolidate
- rmswd
- compute_r
- sample_average
- sample_D4x_covar
- sample_D4x_correl
- plot_single_session
- plot_residuals
- simulate
- plot_distribution_of_analyses
- plot_bulk_compositions
- builtins.list
- clear
- copy
- append
- insert
- extend
- pop
- remove
- index
- count
- reverse
- sort