2. PRTECAN Tutorial#

This tutorial demonstrates how to process Tecan plate reader data using the clophfit.prtecan module.

What you’ll learn:

Tecan file structure and label blocks
Building titrations from multiple files (manually and from list file)
Setting plate scheme, loading additions, background handling
Inspecting and plotting results
Brief overview of fitting methods and quality control

[1]:

# Setup
%load_ext autoreload
%autoreload 2

from pathlib import Path

import arviz as az
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline

from clophfit import prtecan

# Point to the tests data directory shipped with the repo
data_root = Path("../../tests/Tecan")
l1_dir = data_root / "L1"
l2_dir = data_root / "140220"  # second dataset used by the original tutorial
l4_dir = data_root / "L4"

2.1. 1) Understanding Tecan file structure#

Each Tecan file contains global metadata and one or more label blocks (measurement blocks). Blocks with identical key metadata are equivalent; blocks differing only by Integration Time, Flashes, or Gain are almost equivalent after normalization.

[2]:

# Load a single Tecan file and inspect label blocks
tf = prtecan.Tecanfile(l1_dir / "290513_7.2.xls")
lb1, lb2 = tf.labelblocks[1], tf.labelblocks[2]
print("Available label blocks:", list(tf.labelblocks.keys()))
lb1.metadata

Available label blocks: [1, 2]

[2]:

{'Label': Metadata(value='Label1', unit=None),
 'Mode': Metadata(value='Fluorescence Top Reading', unit=None),
 'Excitation Wavelength': Metadata(value=400, unit=['nm']),
 'Emission Wavelength': Metadata(value=535, unit=['nm']),
 'Excitation Bandwidth': Metadata(value=20, unit=['nm']),
 'Emission Bandwidth': Metadata(value=25, unit=['nm']),
 'Gain': Metadata(value=94, unit=['Manual']),
 'Number of Flashes': Metadata(value=10, unit=None),
 'Integration Time': Metadata(value=20, unit=['µs']),
 'Lag Time': Metadata(value='µs', unit=None),
 'Settle Time': Metadata(value='ms', unit=None),
 'Start Time:': Metadata(value='29/05/2013 12.28.10', unit=None),
 'Temperature': Metadata(value=24.9, unit=['°C']),
 'End Time:': Metadata(value='29/05/2013 12.28.50', unit=None)}

[3]:

print("\nSample Data (A01-B06):")
print({k: v for i, (k, v) in enumerate(lb2.data.items()) if i < 18})


Sample Data (A01-B06):
{'A01': 8761.0, 'A02': 15316.0, 'A03': 14346.0, 'A04': 8474.0, 'A05': 16319.0, 'A06': 10912.0, 'A07': 10855.0, 'A08': 12565.0, 'A09': 16695.0, 'A10': 12125.0, 'A11': 10895.0, 'A12': 36597.0, 'B01': 10280.0, 'B02': 11124.0, 'B03': 19612.0, 'B04': 10109.0, 'B05': 11239.0, 'B06': 8556.0}

Load additional files to compare block equivalence and demonstrate normalization across Gain differences.

[4]:

tf1 = prtecan.Tecanfile(l1_dir / "290513_5.5.xls")  # two equivalent blocks
tf2 = prtecan.Tecanfile(
    l1_dir / "290513_8.8.xls"
)  # one equivalent, one almost equivalent

print("tf.lb1 = tf2.lb1 (strict):", lb1 == tf2.labelblocks[1])
print("tf.lb2 = tf2.lb2 (strict):", lb2 == tf2.labelblocks[2])
print("tf.lb2 ~ tf2.lb2 (almost):", lb2.__almost_eq__(tf2.labelblocks[2]))

tf.lb1 = tf2.lb1 (strict): True
tf.lb2 = tf2.lb2 (strict): False
tf.lb2 ~ tf2.lb2 (almost): True

2.2. 2) Grouping files: manual and convenience constructor#

You can group equivalent blocks across files either via TecanfilesGroup or by constructing a Titration directly.

[5]:

# Manual grouping
tfg = prtecan.TecanfilesGroup([tf2, tf, tf1])
lbg1 = tfg.labelblocksgroups[1]
print("Well A01 raw:", lbg1.data["A01"])
print("Well A01 normalized:", lbg1.data_nrm["A01"])

# Same using Titration with explicit x (e.g., pH values)
tit_manual = prtecan.Titration([tf2, tf, tf1], x=np.array([8.8, 7.2, 5.5]), is_ph=True)
print(tit_manual)
print("A01 normalized via Titration:", tit_manual.labelblocksgroups[1].data_nrm["A01"])
tit_manual.labelblocksgroups == tfg.labelblocksgroups

Different LabelblocksGroup across files: ['../../tests/Tecan/L1/290513_8.8.xls', '../../tests/Tecan/L1/290513_7.2.xls', '../../tests/Tecan/L1/290513_5.5.xls'].
Different LabelblocksGroup across files: ['../../tests/Tecan/L1/290513_8.8.xls', '../../tests/Tecan/L1/290513_7.2.xls', '../../tests/Tecan/L1/290513_5.5.xls'].

Well A01 raw: [17123.0, 17088.0, 18713.0]
Well A01 normalized: [910.7978723404256, 908.936170212766, 995.3723404255319]
Titration
        files=["../../tests/Tecan/L1/290513_8.8.xls", ...],
        x=[np.float64(8.8), np.float64(7.2), np.float64(5.5)],
        x_err=[],
        labels=dict_keys([1, 2]),
        params=TitrationConfig(bg=True, bg_adj=False, dil=True, nrm=True, bg_mth='mean', mcmc='None')   pH=True additions=[]
        scheme=PlateScheme(file=None, _buffer=[], _discard=[], _ctrl=[], _names={}))
A01 normalized via Titration: [910.7978723404256, 908.936170212766, 995.3723404255319]

[5]:

True

2.3. 3) Build a titration from a list file#

Using a list file is convenient and less error-prone. The example list/plate files are in tests/Tecan/L1.

[6]:

tit = prtecan.Titration.fromlistfile(l1_dir / "list.pH.csv", is_ph=True)
print("x values (e.g., pH):", tit.x)
lbg1 = tit.labelblocksgroups[1]
lbg2 = tit.labelblocksgroups[2]
print(
    "Temperature in labelblocksgroup 2:",
    [lb.metadata.get("Temperature").value for lb in lbg2.labelblocks],
    lbg2.labelblocks[5].metadata.get("Temperature").unit[0],
)
(lbg1.metadata, lbg2.metadata)

Different LabelblocksGroup across files: ['../../tests/Tecan/L1/290513_8.8.xls', '../../tests/Tecan/L1/290513_8.2.xls', '../../tests/Tecan/L1/290513_7.7.xls', '../../tests/Tecan/L1/290513_7.2.xls', '../../tests/Tecan/L1/290513_6.6.xls', '../../tests/Tecan/L1/290513_6.1.xls', '../../tests/Tecan/L1/290513_5.5.xls'].

x values (e.g., pH): [8.9  8.3  7.7  7.05 6.55 6.   5.5 ]
Temperature in labelblocksgroup 2: [25.1, 24.9, 24.7, 24.7, 25.1, 25.1, 25.1] °C

[6]:

({'Label': Metadata(value='Label1', unit=None),
  'Mode': Metadata(value='Fluorescence Top Reading', unit=None),
  'Excitation Wavelength': Metadata(value=400, unit=['nm']),
  'Emission Wavelength': Metadata(value=535, unit=['nm']),
  'Excitation Bandwidth': Metadata(value=20, unit=['nm']),
  'Emission Bandwidth': Metadata(value=25, unit=['nm']),
  'Number of Flashes': Metadata(value=10, unit=None),
  'Integration Time': Metadata(value=20, unit=['µs']),
  'Lag Time': Metadata(value='µs', unit=None),
  'Settle Time': Metadata(value='ms', unit=None),
  'Gain': Metadata(value=94, unit=None)},
 {'Label': Metadata(value='Label2', unit=None),
  'Mode': Metadata(value='Fluorescence Top Reading', unit=None),
  'Excitation Wavelength': Metadata(value=485, unit=['nm']),
  'Emission Wavelength': Metadata(value=535, unit=['nm']),
  'Excitation Bandwidth': Metadata(value=25, unit=['nm']),
  'Emission Bandwidth': Metadata(value=25, unit=['nm']),
  'Number of Flashes': Metadata(value=10, unit=None),
  'Integration Time': Metadata(value=20, unit=['µs']),
  'Lag Time': Metadata(value='µs', unit=None),
  'Settle Time': Metadata(value='ms', unit=None),
  'Movement': Metadata(value='Move Plate Out', unit=None)})

Within each label-block group, normalized data (by Gain, Flashes, Integration Time) are readily available. In the case of not fully identical labelblock metadata non-normalized data might not exist (empty dict {}).

[7]:

# Inspect raw vs normalized for a sample well
well = "H03"
(lbg1.data[well], lbg2.data, lbg1.data_nrm[well], lbg2.data_nrm[well])

[7]:

([27593.0, 26956.0, 26408.0, 26815.0, 28308.0, 30227.0, 30640.0],
 {},
 [1467.712765957447,
  1433.8297872340427,
  1404.6808510638298,
  1426.3297872340427,
  1505.7446808510638,
  1607.8191489361702,
  1629.787234042553],
 [1456.2121212121212,
  1363.9285714285716,
  1310.357142857143,
  1214.5408163265306,
  1200.9693877551022,
  1224.642857142857,
  1193.8265306122448])

2.4. 4) Load plate scheme and additions#

The plate scheme defines buffer and control wells; additions define dilution steps. After loading these, the processed tit.data[...] arrays reflect background subtraction and optional dilution correction, depending on tit.params.

[8]:

# Load plate scheme and additions (kept to L1 files for consistency)
tit.load_scheme(l1_dir / "scheme.txt")
print(
    f"Titration with {len(tit.tecanfiles)} files and {len(tit.labelblocksgroups)} label groups"
)
print("Buffer wells:", tit.scheme.buffer)
print("Control wells:", tit.scheme.ctrl)
print("Named groups:", tit.scheme.names)

tit.load_additions(l1_dir / "additions.pH")
print("Additions:", tit.additions)
tit.params.bg_adj = True
tit.params.bg_mth = "meansd"
print("Titration Params:", tit.params)
# Example: compare values in data vs normalized groups (after scheme/additions)
(lbg1.data["H12"], tit.data[1]["H12"], lbg1.data_nrm["H12"], tit.bg[1])

Buffer for 'H09:1' was adjusted by 0.67 SD.
Buffer for 'B04:1' was adjusted by 1.78 SD.
Buffer for 'E05:1' was adjusted by 0.44 SD.
Buffer for 'E08:1' was adjusted by 0.59 SD.
Buffer for 'A02:1' was adjusted by 2.10 SD.

Titration with 7 files and 2 label groups
Buffer wells: ['C12', 'D01', 'D12', 'E01', 'E12', 'F01']
Control wells: ['A01', 'F12', 'B01', 'G12', 'B12', 'H01', 'G01', 'A12', 'C01', 'H12']
Named groups: {'E2GFP': {'C01', 'F12', 'B12', 'G01'}, 'V224L': {'H01', 'A01', 'A12', 'H12'}, 'V224Q': {'G12', 'B01'}}
Additions: [100, 2, 2, 2, 2, 2, 2]
Titration Params: TitrationConfig(bg=True, bg_adj=True, dil=True, nrm=True, bg_mth='meansd', mcmc='None')

Buffer for 'C04:1' was adjusted by 2.42 SD.
Buffer for 'F03:1' was adjusted by 0.33 SD.
Buffer for 'F09:1' was adjusted by 0.35 SD.
Buffer for 'G05:1' was adjusted by 0.60 SD.
Buffer for 'C09:1' was adjusted by 0.48 SD.
Buffer for 'F05:1' was adjusted by 0.52 SD.
Buffer for 'F07:1' was adjusted by 0.13 SD.
Buffer for 'C03:1' was adjusted by 4.13 SD.
Buffer for 'F02:1' was adjusted by 2.00 SD.
Buffer for 'A06:1' was adjusted by 0.66 SD.
Buffer for 'D02:1' was adjusted by 3.31 SD.
Buffer for 'E03:1' was adjusted by 2.30 SD.
Buffer for 'B08:1' was adjusted by 0.81 SD.
Buffer for 'A07:1' was adjusted by 1.89 SD.
Buffer for 'D08:1' was adjusted by 0.56 SD.
Buffer for 'E02:1' was adjusted by 1.18 SD.
Buffer for 'H10:1' was adjusted by 0.40 SD.
Buffer for 'D05:1' was adjusted by 0.74 SD.
Buffer for 'C06:1' was adjusted by 1.38 SD.
Buffer for 'G02:1' was adjusted by 1.82 SD.
Buffer for 'B01:1' was adjusted by 1.78 SD.
Buffer for 'E07:1' was adjusted by 0.89 SD.
Buffer for 'D09:1' was adjusted by 0.44 SD.
Buffer for 'E04:1' was adjusted by 1.22 SD.
Buffer for 'C05:1' was adjusted by 0.94 SD.
Buffer for 'B06:1' was adjusted by 1.31 SD.
Buffer for 'A04:1' was adjusted by 1.53 SD.
Buffer for 'E09:1' was adjusted by 0.09 SD.
Buffer for 'D03:1' was adjusted by 2.13 SD.
Buffer for 'D04:1' was adjusted by 1.29 SD.
Buffer for 'C02:1' was adjusted by 5.47 SD.
Buffer for 'A01:1' was adjusted by 3.53 SD.
Buffer for 'A03:1' was adjusted by 1.51 SD.
Buffer for 'B10:1' was adjusted by 0.08 SD.
Buffer for 'H01:1' was adjusted by 2.24 SD.
Buffer for 'B05:1' was adjusted by 0.72 SD.
Buffer for 'H07:1' was adjusted by 0.62 SD.
Buffer for 'C01:1' was adjusted by 6.03 SD.
Buffer for 'G04:1' was adjusted by 0.79 SD.
Buffer for 'D07:1' was adjusted by 0.53 SD.
Buffer for 'B02:1' was adjusted by 1.27 SD.
Buffer for 'G08:1' was adjusted by 0.97 SD.
Buffer for 'B04:2' was adjusted by 2.00 SD.
Buffer for 'E05:2' was adjusted by 1.69 SD.
Buffer for 'E08:2' was adjusted by 1.59 SD.
Buffer for 'A02:2' was adjusted by 1.52 SD.
Buffer for 'C04:2' was adjusted by 2.62 SD.
Buffer for 'G05:2' was adjusted by 0.74 SD.
Buffer for 'C09:2' was adjusted by 1.09 SD.
Buffer for 'C03:2' was adjusted by 5.86 SD.
Buffer for 'F02:2' was adjusted by 1.85 SD.
Buffer for 'A06:2' was adjusted by 1.60 SD.
Buffer for 'D02:2' was adjusted by 2.65 SD.
Buffer for 'E03:2' was adjusted by 3.11 SD.
Buffer for 'B08:2' was adjusted by 1.52 SD.
Buffer for 'A07:2' was adjusted by 2.59 SD.
Buffer for 'D08:2' was adjusted by 1.49 SD.
Buffer for 'E02:2' was adjusted by 1.67 SD.
Buffer for 'D05:2' was adjusted by 1.72 SD.
Buffer for 'C06:2' was adjusted by 1.91 SD.
Buffer for 'G02:2' was adjusted by 1.96 SD.
Buffer for 'B01:2' was adjusted by 2.60 SD.
Buffer for 'E07:2' was adjusted by 1.98 SD.
Buffer for 'D09:2' was adjusted by 0.77 SD.
Buffer for 'E04:2' was adjusted by 1.99 SD.
Buffer for 'C05:2' was adjusted by 1.03 SD.
Buffer for 'B06:2' was adjusted by 1.96 SD.
Buffer for 'A04:2' was adjusted by 2.37 SD.
Buffer for 'D03:2' was adjusted by 2.29 SD.
Buffer for 'D04:2' was adjusted by 1.28 SD.
Buffer for 'C02:2' was adjusted by 7.26 SD.
Buffer for 'A01:2' was adjusted by 3.57 SD.
Buffer for 'A03:2' was adjusted by 2.15 SD.
Buffer for 'B10:2' was adjusted by 0.91 SD.
Buffer for 'H01:2' was adjusted by 2.03 SD.
Buffer for 'B05:2' was adjusted by 1.11 SD.
Buffer for 'H07:2' was adjusted by 1.34 SD.
Buffer for 'C01:2' was adjusted by 8.08 SD.
Buffer for 'D07:2' was adjusted by 0.97 SD.
Buffer for 'B02:2' was adjusted by 1.46 SD.
Buffer for 'G08:2' was adjusted by 1.08 SD.
Buffer for 'B11:2' was adjusted by 0.61 SD.

[8]:

([28309.0, 27837.0, 26511.0, 25771.0, 27048.0, 27794.0, 28596.0],
 array([302.45567376, 321.23670213, 313.92695035, 277.94929078,
        353.65212766, 335.1001773 , 316.34042553]),
 [1505.7978723404256,
  1480.6914893617022,
  1410.159574468085,
  1370.7978723404256,
  1438.723404255319,
  1478.404255319149,
  1521.063829787234],
 array([1203.34219858, 1165.7535461 , 1108.30673759, 1108.58156028,
        1111.2677305 , 1173.7677305 , 1238.61702128]))

Background handling summary:

labelblocksgroups[:].data: unchanged raw block data
labelblocksgroups[:].data_buffersubtracted: background-subtracted
tit.data: background-subtracted and dilution-corrected (if enabled)

The order in which you apply dilution correction and plate scheme can impact your intermediate results, even though the final results might be the same.

Dilution correction adjusts the measured data to account for any dilutions made during sample preparation. This typically involves multiplying the measured values by the dilution factor to estimate the true concentration of the sample.

A plate scheme describes the layout of the samples on a plate (common in laboratory experiments, such as those involving microtiter plates). The plate scheme may involve rearranging or grouping the data in some way based on the physical location of the samples on the plate.

[9]:

# Demonstrate changing background wells and seeing bg estimate
import copy

tit2 = copy.deepcopy(tit)
tit2.params.bg = True
tit2.buffer.wells = ["D01", "E01"]
tit.bg, tit2.bg

[9]:

({1: array([1203.34219858, 1165.7535461 , 1108.30673759, 1108.58156028,
         1111.2677305 , 1173.7677305 , 1238.61702128]),
  2: array([505.62289562, 467.14285714, 473.48639456, 500.29761905,
         524.3622449 , 584.3452381 , 581.8962585 ])},
 {1: array([1016.62234043,  971.56914894,  937.9787234 ,  951.38297872,
          985.74468085, 1005.26595745, 1070.85106383]),
  2: array([420.05050505, 364.69387755, 382.5255102 , 402.19387755,
         430.84183673, 455.07653061, 466.19897959])})

2.5. 5) Quick look at fitting and results#

The tit.results container provides per-label fits; tit.result_global combines multiple labels. Below we only preview access/plotting. For advanced Bayesian/ODR methods, see the dedicated section.

[10]:

tit.bg_err

[10]:

{1: array([85.42617933, 85.42617933, 85.42617933, 85.42617933, 85.42617933,
        85.42617933, 85.42617933]),
 2: array([54.27412064, 54.27412064, 54.27412064, 54.27412064, 54.27412064,
        54.27412064, 54.27412064])}

[11]:

# Access result objects and figures
well = "D10"
single1 = tit.results[1][well]
single2 = tit.results[2][well]
glob = tit.result_global[well]
odr = tit.result_odr[well]

# Display figures inline
print(f"Reduced X2: {single2.result.redchi:.3f}")
single2.figure

Reduced X2: 0.034

[11]:

[12]:

print(f"Reduced X2: {glob.result.redchi:.3f}")
glob.figure

Reduced X2: 2.041

[12]:

[13]:

print(f"Reduced X2: {odr.mini.sum_square:.3f}")
odr.figure

Reduced X2: 0.007

[13]:

[14]:

tit.results[1].dataframe.head()

[14]:

	K	sK	Khdi03	Khdi97	S0_1	sS0_1	S0_1hdi03	S0_1hdi97	S1_1	sS1_1	S1_1hdi03	S1_1hdi97
well
D10	7.353784	0.306455	3	11	173.607145	13.702403	-inf	inf	279.017836	11.34187	-inf	inf

%%time rro = fitting.fit_binding_pymc_odr(glob, n_sd=0.5) rrodf = az.summary(rro) dfaz.plot_trace(rro)

[15]:

tit.result_mcmc

outlier in F11: [1 1 1 0 1 1 1 1 1 1 1 1 1 1].

[15]:

{}

[16]:

tit.result_multi_mcmc

[16]:

{}

[17]:

rp = tit.result_mcmc[well]
rp.figure

Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [K, S0_y1, S1_y1, S0_y2, S1_y2, x_diff, x_start, ye_mag]

/home/runner/work/ClopHfit/ClopHfit/.venv/lib/python3.13/site-packages/pymc/step_methods/hmc/quadpotential.py:316: RuntimeWarning: overflow encountered in dot
  return 0.5 * np.dot(x, v_out)

Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 20 seconds.

[17]:

[18]:

az.plot_trace(
    rp.mini, var_names=["x_true", "K", "x_diff"], divergences=False, combined=True
)

[18]:

array([[<Axes: title={'center': 'x_true'}>,
        <Axes: title={'center': 'x_true'}>],
       [<Axes: title={'center': 'K'}>, <Axes: title={'center': 'K'}>],
       [<Axes: title={'center': 'x_diff'}>,
        <Axes: title={'center': 'x_diff'}>]], dtype=object)

[19]:

# 5.1 Bayesian fitting with PyMC
tit.params.mcmc = "single"
result_mcmc = tit.result_mcmc[well]

print("MCMC Results:")
print(f"Kd: {result_mcmc.result.params['K'].value:.2f}")
print(
    f"95% HDI: [{result_mcmc.result.params['K'].min:.2f}, {result_mcmc.result.params['K'].max:.2f}]"
)

# Plot trace
az.plot_trace(result_mcmc.mini, var_names=["K", "x_true"]);

Buffer for 'H09:1' was adjusted by 0.67 SD.
Buffer for 'B04:1' was adjusted by 1.78 SD.
Buffer for 'E05:1' was adjusted by 0.44 SD.
Buffer for 'E08:1' was adjusted by 0.59 SD.
Buffer for 'A02:1' was adjusted by 2.10 SD.
Buffer for 'C04:1' was adjusted by 2.42 SD.
Buffer for 'F03:1' was adjusted by 0.33 SD.
Buffer for 'F09:1' was adjusted by 0.35 SD.
Buffer for 'G05:1' was adjusted by 0.60 SD.
Buffer for 'C09:1' was adjusted by 0.48 SD.
Buffer for 'F05:1' was adjusted by 0.52 SD.
Buffer for 'F07:1' was adjusted by 0.13 SD.
Buffer for 'C03:1' was adjusted by 4.13 SD.
Buffer for 'F02:1' was adjusted by 2.00 SD.
Buffer for 'A06:1' was adjusted by 0.66 SD.
Buffer for 'D02:1' was adjusted by 3.31 SD.
Buffer for 'E03:1' was adjusted by 2.30 SD.
Buffer for 'B08:1' was adjusted by 0.81 SD.
Buffer for 'A07:1' was adjusted by 1.89 SD.
Buffer for 'D08:1' was adjusted by 0.56 SD.
Buffer for 'E02:1' was adjusted by 1.18 SD.
Buffer for 'H10:1' was adjusted by 0.40 SD.
Buffer for 'D05:1' was adjusted by 0.74 SD.
Buffer for 'C06:1' was adjusted by 1.38 SD.
Buffer for 'G02:1' was adjusted by 1.82 SD.
Buffer for 'B01:1' was adjusted by 1.78 SD.
Buffer for 'E07:1' was adjusted by 0.89 SD.
Buffer for 'D09:1' was adjusted by 0.44 SD.
Buffer for 'E04:1' was adjusted by 1.22 SD.
Buffer for 'C05:1' was adjusted by 0.94 SD.
Buffer for 'B06:1' was adjusted by 1.31 SD.
Buffer for 'A04:1' was adjusted by 1.53 SD.
Buffer for 'E09:1' was adjusted by 0.09 SD.
Buffer for 'D03:1' was adjusted by 2.13 SD.
Buffer for 'D04:1' was adjusted by 1.29 SD.
Buffer for 'C02:1' was adjusted by 5.47 SD.
Buffer for 'A01:1' was adjusted by 3.53 SD.
Buffer for 'A03:1' was adjusted by 1.51 SD.
Buffer for 'B10:1' was adjusted by 0.08 SD.
Buffer for 'H01:1' was adjusted by 2.24 SD.
Buffer for 'B05:1' was adjusted by 0.72 SD.
Buffer for 'H07:1' was adjusted by 0.62 SD.
Buffer for 'C01:1' was adjusted by 6.03 SD.
Buffer for 'G04:1' was adjusted by 0.79 SD.
Buffer for 'D07:1' was adjusted by 0.53 SD.
Buffer for 'B02:1' was adjusted by 1.27 SD.
Buffer for 'G08:1' was adjusted by 0.97 SD.
Buffer for 'B04:2' was adjusted by 2.00 SD.
Buffer for 'E05:2' was adjusted by 1.69 SD.
Buffer for 'E08:2' was adjusted by 1.59 SD.
Buffer for 'A02:2' was adjusted by 1.52 SD.
Buffer for 'C04:2' was adjusted by 2.62 SD.
Buffer for 'G05:2' was adjusted by 0.74 SD.
Buffer for 'C09:2' was adjusted by 1.09 SD.
Buffer for 'C03:2' was adjusted by 5.86 SD.
Buffer for 'F02:2' was adjusted by 1.85 SD.
Buffer for 'A06:2' was adjusted by 1.60 SD.
Buffer for 'D02:2' was adjusted by 2.65 SD.
Buffer for 'E03:2' was adjusted by 3.11 SD.
Buffer for 'B08:2' was adjusted by 1.52 SD.
Buffer for 'A07:2' was adjusted by 2.59 SD.
Buffer for 'D08:2' was adjusted by 1.49 SD.
Buffer for 'E02:2' was adjusted by 1.67 SD.
Buffer for 'D05:2' was adjusted by 1.72 SD.
Buffer for 'C06:2' was adjusted by 1.91 SD.
Buffer for 'G02:2' was adjusted by 1.96 SD.
Buffer for 'B01:2' was adjusted by 2.60 SD.
Buffer for 'E07:2' was adjusted by 1.98 SD.
Buffer for 'D09:2' was adjusted by 0.77 SD.
Buffer for 'E04:2' was adjusted by 1.99 SD.
Buffer for 'C05:2' was adjusted by 1.03 SD.
Buffer for 'B06:2' was adjusted by 1.96 SD.
Buffer for 'A04:2' was adjusted by 2.37 SD.
Buffer for 'D03:2' was adjusted by 2.29 SD.
Buffer for 'D04:2' was adjusted by 1.28 SD.
Buffer for 'C02:2' was adjusted by 7.26 SD.
Buffer for 'A01:2' was adjusted by 3.57 SD.
Buffer for 'A03:2' was adjusted by 2.15 SD.
Buffer for 'B10:2' was adjusted by 0.91 SD.
Buffer for 'H01:2' was adjusted by 2.03 SD.
Buffer for 'B05:2' was adjusted by 1.11 SD.
Buffer for 'H07:2' was adjusted by 1.34 SD.
Buffer for 'C01:2' was adjusted by 8.08 SD.
Buffer for 'D07:2' was adjusted by 0.97 SD.
Buffer for 'B02:2' was adjusted by 1.46 SD.
Buffer for 'G08:2' was adjusted by 1.08 SD.
Buffer for 'B11:2' was adjusted by 0.61 SD.
outlier in F11: [1 1 1 0 1 1 1 1 1 1 1 1 1 1].
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [K, S0_y1, S1_y1, S0_y2, S1_y2, x_diff, x_start, ye_mag]

Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 21 seconds.

MCMC Results:
Kd: 6.88
95% HDI: [6.77, 7.00]

2.6. 6) Quality control and utilities#

A few helper plots are useful to quickly assess experiment consistency (buffer, temperature).

[20]:

# Buffer plot
buf_plot = tit.buffer.plot(nrm=False)
buf_plot.figure

[20]:

[21]:

# Temperature plot
temp_plot = tit.plot_temperature()
temp_plot

[21]:

[22]:

import pandas as pd
import seaborn as sns

df1 = pd.read_csv(l2_dir / "fit1-1.csv", index_col=0)
# merged_df = tit.result_dfs[1][["K", "sK"]].merge(df1, left_index=True, right_index=True)
merged_df = tit.result_global.dataframe[["K", "sK"]].merge(
    df1, left_index=True, right_index=True
)

sns.jointplot(merged_df, x="K_y", y="K_x", ratio=3, space=0.4)

[22]:

<seaborn.axisgrid.JointGrid at 0x7f763a1c2cf0>

If a fit fails in a well, the well key will be anyway present in results list of dict.

tit.results[1].compute_all() conf = prtecan.TecanConfig(Path("jjj"), False, (), "", True, True) tit.export_data_fit(conf)

2.6.1. Posterior#

[23]:

from clophfit.fitting import plotting

np.random.seed(0)  # noqa: NPY002
remcee = glob.mini.emcee(
    burn=100,
    steps=2000,
    workers=8,
    thin=10,
    nwalkers=30,
    progress=False,
    is_weighted=True,
)

f = plotting.plot_emcee(remcee.flatchain)
print(remcee.flatchain.quantile([0.03, 0.97])["K"].to_list())

The chain is shorter than 50 times the integrated autocorrelation time for 5 parameter(s). Use this estimate with caution and run a longer chain!
N/50 = 40;
tau: [58.76969948 53.90983219 52.29509802 54.0327988  52.95252082]
[6.806546839107398, 6.949305454141647]

[24]:

samples = remcee.flatchain[["K"]]
# Convert the dictionary of flatchains to an ArviZ InferenceData object
samples_dict = {key: np.array(val) for key, val in samples.items()}
idata = az.from_dict(posterior=samples_dict)
k_samples = idata.posterior["K"].to_numpy()
percentile_value = np.percentile(k_samples, 3)
print(f"Value at which the probability of being higher is 99%: {percentile_value}")

az.plot_forest(k_samples)

Value at which the probability of being higher is 99%: 6.806546839107398

[24]:

array([<Axes: title={'center': '94.0% HDI'}>], dtype=object)

2.6.2. Combining#

[25]:

tit.result_global.compute_all()

[26]:

with sns.axes_style("darkgrid"):
    g = sns.pairplot(
        tit.result_global.dataframe[["S1_y2", "S0_y2", "K", "S1_y1", "S0_y1"]],
        hue="S1_y1",
        palette="Reds",
        corner=True,
        diag_kind="kde",
    )

[27]:

df_ctr = tit.results[1].dataframe
for name, wells in tit.scheme.names.items():
    for well in wells:
        df_ctr.loc[well, "ctrl"] = name

df_ctr.loc[df_ctr["ctrl"].isna(), "ctrl"] = "U"

sns.set_style("whitegrid")
g = sns.PairGrid(
    df_ctr,
    x_vars=["K", "S1_1", "S0_1"],
    y_vars=["K", "S1_1", "S0_1"],
    hue="ctrl",
    palette="Set1",
    diag_sharey=False,
)
g.map_lower(plt.scatter)
g.map_upper(sns.kdeplot, fill=True)
g.map_diag(sns.kdeplot)
g.add_legend()

/home/runner/work/ClopHfit/ClopHfit/.venv/lib/python3.13/site-packages/seaborn/axisgrid.py:1615: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.
  func(x=x, y=y, **kwargs)
/home/runner/work/ClopHfit/ClopHfit/.venv/lib/python3.13/site-packages/seaborn/axisgrid.py:1615: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.
  func(x=x, y=y, **kwargs)

[27]:

<seaborn.axisgrid.PairGrid at 0x7f7633b8b9d0>

[28]:

tit.result_global["A04"].figure

[28]:

[29]:

keys_unk = tit.fit_keys - set(tit.scheme.ctrl)
res_unk = tit.result_global.dataframe.loc[list(keys_unk)].sort_index()
res_unk["well"] = res_unk.index

f = plt.figure(figsize=(24, 14))
# Make the PairGrida
g = sns.PairGrid(
    res_unk,
    x_vars=["K", "S1_y2", "S0_y2"],
    y_vars="well",
    height=12,
    aspect=0.4,
)
# Draw a dot plot using the stripplot function
g.map(sns.stripplot, size=14, orient="h", palette="Set2", edgecolor="auto")

# Use the same x axis limits on all columns and add better labels
# g.set(xlim=(0, 25), xlabel="Crashes", ylabel="")

# Use semantically meaningful titles for the columns
titles = ["$pK_a$", "B$_{neutral}$", "B$_{anionic}$"]

for ax, title in zip(g.axes.flat, titles, strict=False):
    # Set a different title for each axes
    ax.set(title=title)

    # Make the grid horizontal instead of vertical
    ax.xaxis.grid(False)
    ax.yaxis.grid(True)

sns.despine(left=True, bottom=True)

<Figure size 2400x1400 with 0 Axes>

2.7. 7) Background method comparison#

Different background methods may slightly shift baselines; inspect the impact on a single well.

[30]:

methods = ["mean", "meansd", "fit"]
well = "D10"
fig, axes = plt.subplots(1, len(methods), figsize=(16, 4), sharey=True)
for ax, method in zip(axes, methods, strict=False):
    tit.params.bg_mth = method
    ax.plot(tit.x, tit.data[1][well], "o-", label=method)
    ax.axhline(0, color="gray", ls="--", lw=1)
    ax.set_title(f"method: {method}")
    ax.set_xlabel("pH")
axes[0].set_ylabel("Signal")
plt.tight_layout()

Buffer for 'H09:1' was adjusted by 0.65 SD.
Buffer for 'B04:1' was adjusted by 1.73 SD.
Buffer for 'E05:1' was adjusted by 0.43 SD.
Buffer for 'E08:1' was adjusted by 0.57 SD.
Buffer for 'A02:1' was adjusted by 2.04 SD.
Buffer for 'C04:1' was adjusted by 2.35 SD.
Buffer for 'F03:1' was adjusted by 0.33 SD.
Buffer for 'F09:1' was adjusted by 0.35 SD.
Buffer for 'G05:1' was adjusted by 0.58 SD.
Buffer for 'C09:1' was adjusted by 0.47 SD.
Buffer for 'F05:1' was adjusted by 0.50 SD.
Buffer for 'F07:1' was adjusted by 0.12 SD.
Buffer for 'C03:1' was adjusted by 4.02 SD.
Buffer for 'F02:1' was adjusted by 1.95 SD.
Buffer for 'A06:1' was adjusted by 0.64 SD.
Buffer for 'D02:1' was adjusted by 3.22 SD.
Buffer for 'E03:1' was adjusted by 2.24 SD.
Buffer for 'B08:1' was adjusted by 0.79 SD.
Buffer for 'A07:1' was adjusted by 1.84 SD.
Buffer for 'D08:1' was adjusted by 0.54 SD.
Buffer for 'E02:1' was adjusted by 1.15 SD.
Buffer for 'H10:1' was adjusted by 0.39 SD.
Buffer for 'D05:1' was adjusted by 0.72 SD.
Buffer for 'C06:1' was adjusted by 1.34 SD.
Buffer for 'G02:1' was adjusted by 1.76 SD.
Buffer for 'B01:1' was adjusted by 1.73 SD.
Buffer for 'E07:1' was adjusted by 0.87 SD.
Buffer for 'D09:1' was adjusted by 0.43 SD.
Buffer for 'E04:1' was adjusted by 1.19 SD.
Buffer for 'C05:1' was adjusted by 0.91 SD.
Buffer for 'B06:1' was adjusted by 1.27 SD.
Buffer for 'A04:1' was adjusted by 1.48 SD.
Buffer for 'E09:1' was adjusted by 0.09 SD.
Buffer for 'D03:1' was adjusted by 2.07 SD.
Buffer for 'D04:1' was adjusted by 1.25 SD.
Buffer for 'C02:1' was adjusted by 5.32 SD.
Buffer for 'A01:1' was adjusted by 3.44 SD.
Buffer for 'A03:1' was adjusted by 1.46 SD.
Buffer for 'B10:1' was adjusted by 0.08 SD.
Buffer for 'H01:1' was adjusted by 2.18 SD.
Buffer for 'B05:1' was adjusted by 0.70 SD.
Buffer for 'H07:1' was adjusted by 0.60 SD.
Buffer for 'C01:1' was adjusted by 5.86 SD.
Buffer for 'G04:1' was adjusted by 0.77 SD.
Buffer for 'D07:1' was adjusted by 0.52 SD.
Buffer for 'B02:1' was adjusted by 1.24 SD.
Buffer for 'G08:1' was adjusted by 0.95 SD.
Buffer for 'B04:2' was adjusted by 1.90 SD.
Buffer for 'E05:2' was adjusted by 1.61 SD.
Buffer for 'E08:2' was adjusted by 1.51 SD.
Buffer for 'A02:2' was adjusted by 1.45 SD.
Buffer for 'C04:2' was adjusted by 2.50 SD.
Buffer for 'G05:2' was adjusted by 0.70 SD.
Buffer for 'C09:2' was adjusted by 1.04 SD.
Buffer for 'C03:2' was adjusted by 5.58 SD.
Buffer for 'F02:2' was adjusted by 1.76 SD.
Buffer for 'A06:2' was adjusted by 1.52 SD.
Buffer for 'D02:2' was adjusted by 2.52 SD.
Buffer for 'E03:2' was adjusted by 2.96 SD.
Buffer for 'B08:2' was adjusted by 1.45 SD.
Buffer for 'A07:2' was adjusted by 2.47 SD.
Buffer for 'D08:2' was adjusted by 1.41 SD.
Buffer for 'E02:2' was adjusted by 1.59 SD.
Buffer for 'D05:2' was adjusted by 1.64 SD.
Buffer for 'C06:2' was adjusted by 1.82 SD.
Buffer for 'G02:2' was adjusted by 1.87 SD.
Buffer for 'B01:2' was adjusted by 2.48 SD.
Buffer for 'E07:2' was adjusted by 1.88 SD.
Buffer for 'D09:2' was adjusted by 0.73 SD.
Buffer for 'E04:2' was adjusted by 1.89 SD.
Buffer for 'C05:2' was adjusted by 0.98 SD.
Buffer for 'B06:2' was adjusted by 1.86 SD.
Buffer for 'A04:2' was adjusted by 2.26 SD.
Buffer for 'D03:2' was adjusted by 2.18 SD.
Buffer for 'D04:2' was adjusted by 1.22 SD.
Buffer for 'C02:2' was adjusted by 6.91 SD.
Buffer for 'A01:2' was adjusted by 3.40 SD.
Buffer for 'A03:2' was adjusted by 2.04 SD.
Buffer for 'B10:2' was adjusted by 0.87 SD.
Buffer for 'H01:2' was adjusted by 1.93 SD.
Buffer for 'B05:2' was adjusted by 1.05 SD.
Buffer for 'H07:2' was adjusted by 1.28 SD.
Buffer for 'C01:2' was adjusted by 7.69 SD.
Buffer for 'D07:2' was adjusted by 0.92 SD.
Buffer for 'B02:2' was adjusted by 1.39 SD.
Buffer for 'G08:2' was adjusted by 1.03 SD.
Buffer for 'B11:2' was adjusted by 0.58 SD.
Buffer for 'H09:1' was adjusted by 0.67 SD.
Buffer for 'B04:1' was adjusted by 1.78 SD.
Buffer for 'E05:1' was adjusted by 0.44 SD.
Buffer for 'E08:1' was adjusted by 0.59 SD.
Buffer for 'A02:1' was adjusted by 2.10 SD.
Buffer for 'C04:1' was adjusted by 2.42 SD.
Buffer for 'F03:1' was adjusted by 0.33 SD.
Buffer for 'F09:1' was adjusted by 0.35 SD.
Buffer for 'G05:1' was adjusted by 0.60 SD.
Buffer for 'C09:1' was adjusted by 0.48 SD.
Buffer for 'F05:1' was adjusted by 0.52 SD.
Buffer for 'F07:1' was adjusted by 0.13 SD.
Buffer for 'C03:1' was adjusted by 4.13 SD.
Buffer for 'F02:1' was adjusted by 2.00 SD.
Buffer for 'A06:1' was adjusted by 0.66 SD.
Buffer for 'D02:1' was adjusted by 3.31 SD.
Buffer for 'E03:1' was adjusted by 2.30 SD.
Buffer for 'B08:1' was adjusted by 0.81 SD.
Buffer for 'A07:1' was adjusted by 1.89 SD.
Buffer for 'D08:1' was adjusted by 0.56 SD.
Buffer for 'E02:1' was adjusted by 1.18 SD.
Buffer for 'H10:1' was adjusted by 0.40 SD.
Buffer for 'D05:1' was adjusted by 0.74 SD.
Buffer for 'C06:1' was adjusted by 1.38 SD.
Buffer for 'G02:1' was adjusted by 1.82 SD.
Buffer for 'B01:1' was adjusted by 1.78 SD.
Buffer for 'E07:1' was adjusted by 0.89 SD.
Buffer for 'D09:1' was adjusted by 0.44 SD.
Buffer for 'E04:1' was adjusted by 1.22 SD.
Buffer for 'C05:1' was adjusted by 0.94 SD.
Buffer for 'B06:1' was adjusted by 1.31 SD.
Buffer for 'A04:1' was adjusted by 1.53 SD.
Buffer for 'E09:1' was adjusted by 0.09 SD.
Buffer for 'D03:1' was adjusted by 2.13 SD.
Buffer for 'D04:1' was adjusted by 1.29 SD.
Buffer for 'C02:1' was adjusted by 5.47 SD.
Buffer for 'A01:1' was adjusted by 3.53 SD.
Buffer for 'A03:1' was adjusted by 1.51 SD.
Buffer for 'B10:1' was adjusted by 0.08 SD.
Buffer for 'H01:1' was adjusted by 2.24 SD.
Buffer for 'B05:1' was adjusted by 0.72 SD.
Buffer for 'H07:1' was adjusted by 0.62 SD.
Buffer for 'C01:1' was adjusted by 6.03 SD.
Buffer for 'G04:1' was adjusted by 0.79 SD.
Buffer for 'D07:1' was adjusted by 0.53 SD.
Buffer for 'B02:1' was adjusted by 1.27 SD.
Buffer for 'G08:1' was adjusted by 0.97 SD.
Buffer for 'B04:2' was adjusted by 2.00 SD.
Buffer for 'E05:2' was adjusted by 1.69 SD.
Buffer for 'E08:2' was adjusted by 1.59 SD.
Buffer for 'A02:2' was adjusted by 1.52 SD.
Buffer for 'C04:2' was adjusted by 2.62 SD.
Buffer for 'G05:2' was adjusted by 0.74 SD.
Buffer for 'C09:2' was adjusted by 1.09 SD.
Buffer for 'C03:2' was adjusted by 5.86 SD.
Buffer for 'F02:2' was adjusted by 1.85 SD.
Buffer for 'A06:2' was adjusted by 1.60 SD.
Buffer for 'D02:2' was adjusted by 2.65 SD.
Buffer for 'E03:2' was adjusted by 3.11 SD.
Buffer for 'B08:2' was adjusted by 1.52 SD.
Buffer for 'A07:2' was adjusted by 2.59 SD.
Buffer for 'D08:2' was adjusted by 1.49 SD.
Buffer for 'E02:2' was adjusted by 1.67 SD.
Buffer for 'D05:2' was adjusted by 1.72 SD.
Buffer for 'C06:2' was adjusted by 1.91 SD.
Buffer for 'G02:2' was adjusted by 1.96 SD.
Buffer for 'B01:2' was adjusted by 2.60 SD.
Buffer for 'E07:2' was adjusted by 1.98 SD.
Buffer for 'D09:2' was adjusted by 0.77 SD.
Buffer for 'E04:2' was adjusted by 1.99 SD.
Buffer for 'C05:2' was adjusted by 1.03 SD.
Buffer for 'B06:2' was adjusted by 1.96 SD.
Buffer for 'A04:2' was adjusted by 2.37 SD.
Buffer for 'D03:2' was adjusted by 2.29 SD.
Buffer for 'D04:2' was adjusted by 1.28 SD.
Buffer for 'C02:2' was adjusted by 7.26 SD.
Buffer for 'A01:2' was adjusted by 3.57 SD.
Buffer for 'A03:2' was adjusted by 2.15 SD.
Buffer for 'B10:2' was adjusted by 0.91 SD.
Buffer for 'H01:2' was adjusted by 2.03 SD.
Buffer for 'B05:2' was adjusted by 1.11 SD.
Buffer for 'H07:2' was adjusted by 1.34 SD.
Buffer for 'C01:2' was adjusted by 8.08 SD.
Buffer for 'D07:2' was adjusted by 0.97 SD.
Buffer for 'B02:2' was adjusted by 1.46 SD.
Buffer for 'G08:2' was adjusted by 1.08 SD.
Buffer for 'B11:2' was adjusted by 0.61 SD.
Buffer for 'H09:1' was adjusted by 0.29 SD.
Buffer for 'E06:1' was adjusted by 0.50 SD.
Buffer for 'B04:1' was adjusted by 2.67 SD.
Buffer for 'E05:1' was adjusted by 1.16 SD.
Buffer for 'E08:1' was adjusted by 1.49 SD.
Buffer for 'A02:1' was adjusted by 3.03 SD.
Buffer for 'C04:1' was adjusted by 4.29 SD.
Buffer for 'F03:1' was adjusted by 0.95 SD.
Buffer for 'F09:1' was adjusted by 0.73 SD.
Buffer for 'G05:1' was adjusted by 1.17 SD.
Buffer for 'C09:1' was adjusted by 1.15 SD.
Buffer for 'F05:1' was adjusted by 1.60 SD.
Buffer for 'F07:1' was adjusted by 0.47 SD.
Buffer for 'E11:1' was adjusted by 0.44 SD.
Buffer for 'C03:1' was adjusted by 7.29 SD.
Buffer for 'F02:1' was adjusted by 3.52 SD.
Buffer for 'A06:1' was adjusted by 1.57 SD.
Buffer for 'D02:1' was adjusted by 4.81 SD.
Buffer for 'E03:1' was adjusted by 4.08 SD.
Buffer for 'B08:1' was adjusted by 1.64 SD.
Buffer for 'A07:1' was adjusted by 3.32 SD.
Buffer for 'D08:1' was adjusted by 1.58 SD.
Buffer for 'E02:1' was adjusted by 2.52 SD.
Buffer for 'H10:1' was adjusted by 0.61 SD.
Buffer for 'D05:1' was adjusted by 2.00 SD.
Buffer for 'C06:1' was adjusted by 2.58 SD.
Buffer for 'G02:1' was adjusted by 3.02 SD.
Buffer for 'B01:1' was adjusted by 2.89 SD.
Buffer for 'A08:1' was adjusted by 0.33 SD.
Buffer for 'E07:1' was adjusted by 1.97 SD.
Buffer for 'D09:1' was adjusted by 1.29 SD.
Buffer for 'E04:1' was adjusted by 2.65 SD.
Buffer for 'C05:1' was adjusted by 1.67 SD.
Buffer for 'B06:1' was adjusted by 2.28 SD.
Buffer for 'A04:1' was adjusted by 2.52 SD.
Buffer for 'E09:1' was adjusted by 0.63 SD.
Buffer for 'D03:1' was adjusted by 3.41 SD.
Buffer for 'D04:1' was adjusted by 2.38 SD.
Buffer for 'C02:1' was adjusted by 9.28 SD.
Buffer for 'A01:1' was adjusted by 5.64 SD.
Buffer for 'A03:1' was adjusted by 2.64 SD.
Buffer for 'B10:1' was adjusted by 1.11 SD.
Buffer for 'H01:1' was adjusted by 4.19 SD.
Buffer for 'B05:1' was adjusted by 1.58 SD.
Buffer for 'H07:1' was adjusted by 1.74 SD.
Buffer for 'C01:1' was adjusted by 9.67 SD.
Buffer for 'G04:1' was adjusted by 1.33 SD.
Buffer for 'D07:1' was adjusted by 1.46 SD.
Buffer for 'B02:1' was adjusted by 1.87 SD.
Buffer for 'G08:1' was adjusted by 1.93 SD.
Buffer for 'B04:2' was adjusted by 3.15 SD.
Buffer for 'E05:2' was adjusted by 2.61 SD.
Buffer for 'E08:2' was adjusted by 2.44 SD.
Buffer for 'A02:2' was adjusted by 2.32 SD.
Buffer for 'C04:2' was adjusted by 4.27 SD.
Buffer for 'G05:2' was adjusted by 0.90 SD.
Buffer for 'C09:2' was adjusted by 1.53 SD.
Buffer for 'C03:2' was adjusted by 10.13 SD.
Buffer for 'F02:2' was adjusted by 2.91 SD.
Buffer for 'A06:2' was adjusted by 2.46 SD.
Buffer for 'D02:2' was adjusted by 4.35 SD.
Buffer for 'E03:2' was adjusted by 5.18 SD.
Buffer for 'B08:2' was adjusted by 2.31 SD.
Buffer for 'A07:2' was adjusted by 4.25 SD.
Buffer for 'D08:2' was adjusted by 2.25 SD.
Buffer for 'E02:2' was adjusted by 2.57 SD.
Buffer for 'D05:2' was adjusted by 2.67 SD.
Buffer for 'C06:2' was adjusted by 2.99 SD.
Buffer for 'G02:2' was adjusted by 3.11 SD.
Buffer for 'B01:2' was adjusted by 4.27 SD.
Buffer for 'E07:2' was adjusted by 3.14 SD.
Buffer for 'E04:2' was adjusted by 3.16 SD.
Buffer for 'C05:2' was adjusted by 1.42 SD.
Buffer for 'B06:2' was adjusted by 3.00 SD.
Buffer for 'A04:2' was adjusted by 3.79 SD.
Buffer for 'D03:2' was adjusted by 3.69 SD.
Buffer for 'D04:2' was adjusted by 1.89 SD.
Buffer for 'C02:2' was adjusted by 12.65 SD.
Buffer for 'A01:2' was adjusted by 5.96 SD.
Buffer for 'A03:2' was adjusted by 3.44 SD.
Buffer for 'B10:2' was adjusted by 1.22 SD.
Buffer for 'H01:2' was adjusted by 3.23 SD.
Buffer for 'B05:2' was adjusted by 1.57 SD.
Buffer for 'H07:2' was adjusted by 1.99 SD.
Buffer for 'C01:2' was adjusted by 14.15 SD.
Buffer for 'D07:2' was adjusted by 1.32 SD.
Buffer for 'B02:2' was adjusted by 2.21 SD.
Buffer for 'G08:2' was adjusted by 1.52 SD.
Buffer for 'B11:2' was adjusted by 0.67 SD.

You can decide how to pre-process data with datafit_params:

[bg] subtract background
[dil] apply correction for dilution (when e.g. during a titration you add titrant without protein)
[nrm] normalize for gain, number of flashes and integration time.

[31]:

# 3.1 Accessing processed data
well = "D10"
data = {
    "pH": tit.x,
    "Signal (raw)": tit.labelblocksgroups[1].data_nrm[well],
    "Signal (processed)": tit.data[1][well],
}

plt.figure(figsize=(10, 5))
plt.plot(data["pH"], data["Signal (raw)"], "o-", label="Raw")
plt.plot(data["pH"], data["Signal (processed)"], "s-", label="Processed")
plt.xlabel("pH")
plt.ylabel("Fluorescence")
plt.title(f"Data Processing Pipeline for Well {well}")
plt.legend()
plt.grid(True)

2.8. Cl titration analysis#

[32]:

cl_an = prtecan.Titration.fromlistfile(l2_dir / "list.cl.csv", is_ph=False)
cl_an.load_scheme(l2_dir / "scheme.txt")
cl_an.scheme

 OVER value in Label1: H02 of tecanfile ../../tests/Tecan/140220/pH5.0_200214.xls

[32]:

PlateScheme(file=PosixPath('../../tests/Tecan/140220/scheme.txt'), _buffer=['D01', 'E01', 'D12', 'E12'], _discard=[], _ctrl=['C12', 'A01', 'F12', 'F01', 'G12', 'B01', 'B12', 'H01', 'G01', 'A12', 'C01', 'H12'], _names={'G03': {'A01', 'B12', 'H12'}, 'NTT': {'C12', 'F01', 'F12'}, 'S202N': {'H01', 'G12', 'C01'}, 'V224Q': {'G01', 'B01', 'A12'}})

[33]:

from clophfit import prtecan

cl_an.load_additions(l2_dir / "additions.cl")
print(cl_an.x)
cl_an.x = prtecan.calculate_conc(cl_an.additions, 1000)
cl_an.x

[0 0 0 0 0 0 0 0 0]

[33]:

array([  0.        ,  17.54385965,  34.48275862,  50.84745763,
        66.66666667,  81.96721311,  96.77419355, 138.46153846,
       164.17910448])

[34]:

fres = cl_an.result_global["D10"]
print(fres.is_valid(), fres.result.bic, fres.result.redchi)
fres.figure

outlier in D10: [1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1].

True 5.140358245914923 0.8330868890799472

[34]:

2.9. 8) Batch export (optional)#

You can export processed data and fit results using TecanConfig. Note: adjust paths and toggles (png, fit, comb) as needed.

[35]:

tit.params

[35]:

TitrationConfig(bg=True, bg_adj=True, dil=True, nrm=True, bg_mth='fit', mcmc='single')

[36]:

tit.params.bg_mth = "meansd"
tit.params.mcmc = None
tit.result_global.compute_all()

Buffer for 'H09:1' was adjusted by 0.67 SD.
Buffer for 'B04:1' was adjusted by 1.78 SD.
Buffer for 'E05:1' was adjusted by 0.44 SD.
Buffer for 'E08:1' was adjusted by 0.59 SD.
Buffer for 'A02:1' was adjusted by 2.10 SD.
Buffer for 'C04:1' was adjusted by 2.42 SD.
Buffer for 'F03:1' was adjusted by 0.33 SD.
Buffer for 'F09:1' was adjusted by 0.35 SD.
Buffer for 'G05:1' was adjusted by 0.60 SD.
Buffer for 'C09:1' was adjusted by 0.48 SD.
Buffer for 'F05:1' was adjusted by 0.52 SD.
Buffer for 'F07:1' was adjusted by 0.13 SD.
Buffer for 'C03:1' was adjusted by 4.13 SD.
Buffer for 'F02:1' was adjusted by 2.00 SD.
Buffer for 'A06:1' was adjusted by 0.66 SD.
Buffer for 'D02:1' was adjusted by 3.31 SD.
Buffer for 'E03:1' was adjusted by 2.30 SD.
Buffer for 'B08:1' was adjusted by 0.81 SD.
Buffer for 'A07:1' was adjusted by 1.89 SD.
Buffer for 'D08:1' was adjusted by 0.56 SD.
Buffer for 'E02:1' was adjusted by 1.18 SD.
Buffer for 'H10:1' was adjusted by 0.40 SD.
Buffer for 'D05:1' was adjusted by 0.74 SD.
Buffer for 'C06:1' was adjusted by 1.38 SD.
Buffer for 'G02:1' was adjusted by 1.82 SD.
Buffer for 'B01:1' was adjusted by 1.78 SD.
Buffer for 'E07:1' was adjusted by 0.89 SD.
Buffer for 'D09:1' was adjusted by 0.44 SD.
Buffer for 'E04:1' was adjusted by 1.22 SD.
Buffer for 'C05:1' was adjusted by 0.94 SD.
Buffer for 'B06:1' was adjusted by 1.31 SD.
Buffer for 'A04:1' was adjusted by 1.53 SD.
Buffer for 'E09:1' was adjusted by 0.09 SD.
Buffer for 'D03:1' was adjusted by 2.13 SD.
Buffer for 'D04:1' was adjusted by 1.29 SD.
Buffer for 'C02:1' was adjusted by 5.47 SD.
Buffer for 'A01:1' was adjusted by 3.53 SD.
Buffer for 'A03:1' was adjusted by 1.51 SD.
Buffer for 'B10:1' was adjusted by 0.08 SD.
Buffer for 'H01:1' was adjusted by 2.24 SD.
Buffer for 'B05:1' was adjusted by 0.72 SD.
Buffer for 'H07:1' was adjusted by 0.62 SD.
Buffer for 'C01:1' was adjusted by 6.03 SD.
Buffer for 'G04:1' was adjusted by 0.79 SD.
Buffer for 'D07:1' was adjusted by 0.53 SD.
Buffer for 'B02:1' was adjusted by 1.27 SD.
Buffer for 'G08:1' was adjusted by 0.97 SD.
Buffer for 'B04:2' was adjusted by 2.00 SD.
Buffer for 'E05:2' was adjusted by 1.69 SD.
Buffer for 'E08:2' was adjusted by 1.59 SD.
Buffer for 'A02:2' was adjusted by 1.52 SD.
Buffer for 'C04:2' was adjusted by 2.62 SD.
Buffer for 'G05:2' was adjusted by 0.74 SD.
Buffer for 'C09:2' was adjusted by 1.09 SD.
Buffer for 'C03:2' was adjusted by 5.86 SD.
Buffer for 'F02:2' was adjusted by 1.85 SD.
Buffer for 'A06:2' was adjusted by 1.60 SD.
Buffer for 'D02:2' was adjusted by 2.65 SD.
Buffer for 'E03:2' was adjusted by 3.11 SD.
Buffer for 'B08:2' was adjusted by 1.52 SD.
Buffer for 'A07:2' was adjusted by 2.59 SD.
Buffer for 'D08:2' was adjusted by 1.49 SD.
Buffer for 'E02:2' was adjusted by 1.67 SD.
Buffer for 'D05:2' was adjusted by 1.72 SD.
Buffer for 'C06:2' was adjusted by 1.91 SD.
Buffer for 'G02:2' was adjusted by 1.96 SD.
Buffer for 'B01:2' was adjusted by 2.60 SD.
Buffer for 'E07:2' was adjusted by 1.98 SD.
Buffer for 'D09:2' was adjusted by 0.77 SD.
Buffer for 'E04:2' was adjusted by 1.99 SD.
Buffer for 'C05:2' was adjusted by 1.03 SD.
Buffer for 'B06:2' was adjusted by 1.96 SD.
Buffer for 'A04:2' was adjusted by 2.37 SD.
Buffer for 'D03:2' was adjusted by 2.29 SD.
Buffer for 'D04:2' was adjusted by 1.28 SD.
Buffer for 'C02:2' was adjusted by 7.26 SD.
Buffer for 'A01:2' was adjusted by 3.57 SD.
Buffer for 'A03:2' was adjusted by 2.15 SD.
Buffer for 'B10:2' was adjusted by 0.91 SD.
Buffer for 'H01:2' was adjusted by 2.03 SD.
Buffer for 'B05:2' was adjusted by 1.11 SD.
Buffer for 'H07:2' was adjusted by 1.34 SD.
Buffer for 'C01:2' was adjusted by 8.08 SD.
Buffer for 'D07:2' was adjusted by 0.97 SD.
Buffer for 'B02:2' was adjusted by 1.46 SD.
Buffer for 'G08:2' was adjusted by 1.08 SD.
Buffer for 'B11:2' was adjusted by 0.61 SD.
outlier in F11: [1 1 1 0 1 1 1 1 1 1 1 1 1 1].

[37]:

tit.results[1].compute_all()
tit.results[2].compute_all()

tit.result_odr.compute_all()

[38]:

from tempfile import mkdtemp

out_dir = Path(mkdtemp())
conf = prtecan.TecanConfig(
    out_fp=out_dir, comb=False, lim=None, title="FullAnalysis", fit=True, png=True
)
tit.export_data_fit(conf)
print("Exported to:", out_dir)
# list(out_dir.glob('*'))[:10]
# print("Contents:", *[f.name for f in output_dir.glob("*")], sep="\n- ")

Exported to: /tmp/tmpfjdto758

Tips for development vs tutorial hygiene:

Keep a scratch notebook (e.g., docs/tutorials/prtecan_dev.ipynb) for experiments.
Avoid os.chdir; use Path objects relative to repository root as in this notebook.
When a feature stabilizes, port minimal, clear examples into the main tutorial and keep heavy testing in tests/.

from clophfit.fitting.core import fit_binding_pymc_multi2 trace = fit_binding_pymc_multi2( tit.result_global.results, tit.scheme, tit.buffer.bg_err ) df = az.summary(trace)

[39]:

k = "A05"
for k in tit.fit_keys:
    print(k, np.nanmean(tit.data[1][k]) / tit.data[2][k].mean())

H09 0.42881001531151514
D11 0.6788506005262399
E06 0.3353265046347385
B04 0.5833802245626148
E05 0.0812107981785509
E08 0.16746947038502838
F06 0.5410531893920507
A02 0.2842622849406315
C04 0.669437908962476
F03 0.23188983721050607
H06 0.13254406321561885
F09 0.2324331571256483
G05 0.30630310593756654
H08 0.2979424447611534
B07 0.8385467515801555
C09 0.37203466508284255
F05 0.37327384554892246
F10 0.756279979286702
F07 0.17837992523555274
H05 0.9633277654964443
E11 0.3605343279539651
C10 1.0662740860896185
C03 0.33793406332042025
F02 0.2620887349720975
G03 0.9661608905707901
A06 0.2501571386011681
D02 0.6604721900320528
F11 0.9458359622031078
E03 0.2606745659792658
G09 0.6749695556763955
G12 0.7529363964679259
B08 0.268824876049852
H03 0.44973674048283124
H11 0.3736570389253579
C11 4.017496287431368
A07 0.29060593982591437
D08 0.13563844057959742
E02 0.07393688687110078
D06 0.3124976173255538
H10 0.42557330129992943
B09 0.5170924864289452
D05 0.13292981291280134
C06 0.44882605299112466
G02 0.2782075869502642
B01 0.21435672437989858
C08 0.44360013210616434
F04 0.3665800655936645
A08 0.31392896864685127
F08 1.011779958058933
E07 0.34957321905549554
A12 0.2978940322108694
D09 0.16713985250774538
E04 0.09540287739853831
G06 0.15184952108589214
D10 0.5003410620323786
C05 0.5451889765144525
H12 1.5591731912238007
B06 1.1815666629895112
F12 1.1800394343793166
A04 1.386491819216137
E09 0.38853070034051823
D03 0.25180246140573237
A10 1.7531438919565034
D04 0.2913837885932939
G01 0.23699764930931716
G07 0.3679656630927384
A09 0.4376895267267702
C02 0.43065087030195726
H02 0.6764291091640791
A01 0.6863138605428735
A11 2.0690163836024675
A03 0.1771371804123496
G11 0.7519767148785903
B10 0.1007062293453035
B12 0.446000865869974
H01 0.10203002686903911
G10 0.7205430973792178
B03 1.1796552214433638
C07 1.526137257284391
B05 0.2725077119193997
H07 0.18254930539692082
A05 0.3612897901757064
C01 0.3240597584795503
G04 0.2893076762845823
H04 0.9017487067506514
E10 1.3474816356449948
D07 0.2379344164193609
B02 0.38972030620141984
G08 0.26742763968456523
B11 0.5161712987213453