2. Getting started with prtecan#
[1]:
import hashlib
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from clophfit import prtecan
%load_ext autoreload
%autoreload 2
os.chdir("../../tests/Tecan/140220/")
2.1. To parse a Tecan file#
[2]:
tf = prtecan.Tecanfile("../290212_6.38.xls")
tf.__dict__
[2]:
{'path': '../290212_6.38.xls',
'metadata': {'Device: infinite 200': Metadata(value='Serial number: 810002712', unit=['Serial number of connected stacker:']),
'Firmware: V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)': Metadata(value='MAI, V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)', unit=None),
'Date:': Metadata(value='29/02/2012', unit=None),
'Time:': Metadata(value='15.57.05', unit=None),
'System': Metadata(value='TECANROBOT', unit=None),
'User': Metadata(value='TECANROBOT\\Administrator', unit=None),
'Plate': Metadata(value='PE 96 Flat Bottom White [PE.pdfx]', unit=None),
'Plate-ID (Stacker)': Metadata(value='Plate-ID (Stacker)', unit=None),
'Shaking (Linear) Duration:': Metadata(value=50, unit=['s']),
'Shaking (Linear) Amplitude:': Metadata(value=2, unit=['mm'])},
'labelblocks': [Labelblock(path='../290212_6.38.xls', metadata={'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=81, 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='29/02/2012 15.57.55', unit=None), 'Temperature': Metadata(value=26.0, unit=['°C']), 'End Time:': Metadata(value='29/02/2012 15.58.35', unit=None)}, data={'A01': 30072.0, 'A02': 27276.0, 'A03': 22249.0, 'A04': 30916.0, 'A05': 27943.0, 'A06': 25130.0, 'A07': 26765.0, 'A08': 27836.0, 'A09': 23084.0, 'A10': 31370.0, 'A11': 16890.0, 'A12': 22136.0, 'B01': 22336.0, 'B02': 31327.0, 'B03': 24855.0, 'B04': 32426.0, 'B05': 30066.0, 'B06': 27018.0, 'B07': 28269.0, 'B08': 27570.0, 'B09': 31310.0, 'B10': 24358.0, 'B11': 22595.0, 'B12': 20355.0, 'C01': 23232.0, 'C02': 32241.0, 'C03': 28309.0, 'C04': 26642.0, 'C05': 28818.0, 'C06': 26638.0, 'C07': 26423.0, 'C08': 29441.0, 'C09': 28541.0, 'C10': 29656.0, 'C11': 29841.0, 'C12': 25738.0, 'D01': 26578.0, 'D02': 22280.0, 'D03': 36219.0, 'D04': 25735.0, 'D05': 35433.0, 'D06': 27376.0, 'D07': 22497.0, 'D08': 35681.0, 'D09': 26154.0, 'D10': 32311.0, 'D11': 27495.0, 'D12': 22459.0, 'E01': 27576.0, 'E02': 26058.0, 'E03': 28882.0, 'E04': 26188.0, 'E05': 27531.0, 'E06': 31269.0, 'E07': 26757.0, 'E08': 26427.0, 'E09': 27764.0, 'E10': 27184.0, 'E11': 26556.0, 'E12': 18494.0, 'F01': 22120.0, 'F02': 26642.0, 'F03': 25432.0, 'F04': 23801.0, 'F05': 23932.0, 'F06': 28471.0, 'F07': 27151.0, 'F08': 30243.0, 'F09': 29908.0, 'F10': 27429.0, 'F11': 23624.0, 'F12': 20150.0, 'G01': 18084.0, 'G02': 23496.0, 'G03': 25347.0, 'G04': 30926.0, 'G05': 32714.0, 'G06': 29026.0, 'G07': 33351.0, 'G08': 25353.0, 'G09': 29849.0, 'G10': 25687.0, 'G11': 20913.0, 'G12': 20991.0, 'H01': 25504.0, 'H02': 23601.0, 'H03': 24436.0, 'H04': 27613.0, 'H05': 28201.0, 'H06': 29740.0, 'H07': 26272.0, 'H08': 28696.0, 'H09': 29984.0, 'H10': 25136.0, 'H11': 22063.0, 'H12': 20888.0}),
Labelblock(path='../290212_6.38.xls', metadata={'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']), 'Gain': Metadata(value=93, 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='29/02/2012 15.58.39', unit=None), 'Temperature': Metadata(value=25.9, unit=['°C']), 'End Time:': Metadata(value='29/02/2012 15.59.19', unit=None), 'Movement': Metadata(value='Move Plate Out', unit=None)}, data={'A01': 9165.0, 'A02': 11213.0, 'A03': 5417.0, 'A04': 10530.0, 'A05': 6730.0, 'A06': 6030.0, 'A07': 11485.0, 'A08': 7145.0, 'A09': 5500.0, 'A10': 7513.0, 'A11': 5483.0, 'A12': 11319.0, 'B01': 8135.0, 'B02': 8635.0, 'B03': 7166.0, 'B04': 9435.0, 'B05': 7788.0, 'B06': 7041.0, 'B07': 11493.0, 'B08': 13986.0, 'B09': 8769.0, 'B10': 7816.0, 'B11': 10934.0, 'B12': 5036.0, 'C01': 7856.0, 'C02': 8268.0, 'C03': 6798.0, 'C04': 6419.0, 'C05': 6559.0, 'C06': 5926.0, 'C07': 6156.0, 'C08': 7566.0, 'C09': 6630.0, 'C10': 7443.0, 'C11': 7497.0, 'C12': 5361.0, 'D01': 6188.0, 'D02': 7004.0, 'D03': 8403.0, 'D04': 5852.0, 'D05': 7990.0, 'D06': 6614.0, 'D07': 5928.0, 'D08': 9109.0, 'D09': 7397.0, 'D10': 8205.0, 'D11': 7020.0, 'D12': 5478.0, 'E01': 6370.0, 'E02': 6065.0, 'E03': 6670.0, 'E04': 6027.0, 'E05': 6572.0, 'E06': 7354.0, 'E07': 6854.0, 'E08': 7054.0, 'E09': 6590.0, 'E10': 6576.0, 'E11': 6234.0, 'E12': 4677.0, 'F01': 5681.0, 'F02': 6748.0, 'F03': 6182.0, 'F04': 5725.0, 'F05': 5665.0, 'F06': 6637.0, 'F07': 6474.0, 'F08': 6975.0, 'F09': 7234.0, 'F10': 6897.0, 'F11': 5845.0, 'F12': 5588.0, 'G01': 4130.0, 'G02': 6065.0, 'G03': 5765.0, 'G04': 6861.0, 'G05': 7485.0, 'G06': 6812.0, 'G07': 7820.0, 'G08': 6503.0, 'G09': 7202.0, 'G10': 6193.0, 'G11': 5567.0, 'G12': 4991.0, 'H01': 5881.0, 'H02': 9284.0, 'H03': 5867.0, 'H04': 6682.0, 'H05': 6656.0, 'H06': 6938.0, 'H07': 7399.0, 'H08': 6705.0, 'H09': 7031.0, 'H10': 6965.0, 'H11': 5454.0, 'H12': 5849.0})]}
When KEYS are identical labelblocks are considered eq equal i.e. they contain the same measurement.
[3]:
tf.labelblocks[0].metadata
[3]:
{'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=81, 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='29/02/2012 15.57.55', unit=None),
'Temperature': Metadata(value=26.0, unit=['°C']),
'End Time:': Metadata(value='29/02/2012 15.58.35', unit=None)}
[4]:
print(tf.labelblocks[0].data)
{'A01': 30072.0, 'A02': 27276.0, 'A03': 22249.0, 'A04': 30916.0, 'A05': 27943.0, 'A06': 25130.0, 'A07': 26765.0, 'A08': 27836.0, 'A09': 23084.0, 'A10': 31370.0, 'A11': 16890.0, 'A12': 22136.0, 'B01': 22336.0, 'B02': 31327.0, 'B03': 24855.0, 'B04': 32426.0, 'B05': 30066.0, 'B06': 27018.0, 'B07': 28269.0, 'B08': 27570.0, 'B09': 31310.0, 'B10': 24358.0, 'B11': 22595.0, 'B12': 20355.0, 'C01': 23232.0, 'C02': 32241.0, 'C03': 28309.0, 'C04': 26642.0, 'C05': 28818.0, 'C06': 26638.0, 'C07': 26423.0, 'C08': 29441.0, 'C09': 28541.0, 'C10': 29656.0, 'C11': 29841.0, 'C12': 25738.0, 'D01': 26578.0, 'D02': 22280.0, 'D03': 36219.0, 'D04': 25735.0, 'D05': 35433.0, 'D06': 27376.0, 'D07': 22497.0, 'D08': 35681.0, 'D09': 26154.0, 'D10': 32311.0, 'D11': 27495.0, 'D12': 22459.0, 'E01': 27576.0, 'E02': 26058.0, 'E03': 28882.0, 'E04': 26188.0, 'E05': 27531.0, 'E06': 31269.0, 'E07': 26757.0, 'E08': 26427.0, 'E09': 27764.0, 'E10': 27184.0, 'E11': 26556.0, 'E12': 18494.0, 'F01': 22120.0, 'F02': 26642.0, 'F03': 25432.0, 'F04': 23801.0, 'F05': 23932.0, 'F06': 28471.0, 'F07': 27151.0, 'F08': 30243.0, 'F09': 29908.0, 'F10': 27429.0, 'F11': 23624.0, 'F12': 20150.0, 'G01': 18084.0, 'G02': 23496.0, 'G03': 25347.0, 'G04': 30926.0, 'G05': 32714.0, 'G06': 29026.0, 'G07': 33351.0, 'G08': 25353.0, 'G09': 29849.0, 'G10': 25687.0, 'G11': 20913.0, 'G12': 20991.0, 'H01': 25504.0, 'H02': 23601.0, 'H03': 24436.0, 'H04': 27613.0, 'H05': 28201.0, 'H06': 29740.0, 'H07': 26272.0, 'H08': 28696.0, 'H09': 29984.0, 'H10': 25136.0, 'H11': 22063.0, 'H12': 20888.0}
2.2. To group list of files into a titration#
[5]:
tit = prtecan.Titration.fromlistfile("../list.pH")
[6]:
tit.__dict__
[6]:
{'tecanfiles': [Tecanfile(path=PosixPath('../290212_5.78.xls'), metadata={'Device: infinite 200': Metadata(value='Serial number: 810002712', unit=['Serial number of connected stacker:']), 'Firmware: V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)': Metadata(value='MAI, V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)', unit=None), 'Date:': Metadata(value='29/02/2012', unit=None), 'Time:': Metadata(value='16.05.07', unit=None), 'System': Metadata(value='TECANROBOT', unit=None), 'User': Metadata(value='TECANROBOT\\Administrator', unit=None), 'Plate': Metadata(value='PE 96 Flat Bottom White [PE.pdfx]', unit=None), 'Plate-ID (Stacker)': Metadata(value='Plate-ID (Stacker)', unit=None), 'Shaking (Linear) Duration:': Metadata(value=50, unit=['s']), 'Shaking (Linear) Amplitude:': Metadata(value=2, unit=['mm'])}, labelblocks=[Labelblock(path=PosixPath('../290212_5.78.xls'), metadata={'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=81, 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='29/02/2012 16.05.57', unit=None), 'Temperature': Metadata(value=25.9, unit=['°C']), 'End Time:': Metadata(value='29/02/2012 16.06.37', unit=None)}, data={'A01': 30344.0, 'A02': 26474.0, 'A03': 22354.0, 'A04': 30250.0, 'A05': 27578.0, 'A06': 25706.0, 'A07': 25897.0, 'A08': 27827.0, 'A09': 23665.0, 'A10': 30974.0, 'A11': 16450.0, 'A12': 21338.0, 'B01': 22952.0, 'B02': 31339.0, 'B03': 25076.0, 'B04': 32899.0, 'B05': 30582.0, 'B06': 28196.0, 'B07': 28263.0, 'B08': 27802.0, 'B09': 32203.0, 'B10': 25775.0, 'B11': 23928.0, 'B12': 21080.0, 'C01': 24226.0, 'C02': 33054.0, 'C03': 29478.0, 'C04': 28194.0, 'C05': 30022.0, 'C06': 27844.0, 'C07': 28843.0, 'C08': 31429.0, 'C09': 29932.0, 'C10': 31076.0, 'C11': 31071.0, 'C12': 23717.0, 'D01': 27495.0, 'D02': 22802.0, 'D03': 35939.0, 'D04': 27076.0, 'D05': 35748.0, 'D06': 28582.0, 'D07': 23785.0, 'D08': 36154.0, 'D09': 27057.0, 'D10': 32703.0, 'D11': 27950.0, 'D12': 21254.0, 'E01': 28662.0, 'E02': 26591.0, 'E03': 30660.0, 'E04': 27358.0, 'E05': 29174.0, 'E06': 32520.0, 'E07': 27738.0, 'E08': 26825.0, 'E09': 28313.0, 'E10': 28646.0, 'E11': 27914.0, 'E12': 18474.0, 'F01': 22773.0, 'F02': 29533.0, 'F03': 26615.0, 'F04': 25283.0, 'F05': 24544.0, 'F06': 29848.0, 'F07': 29557.0, 'F08': 31794.0, 'F09': 31517.0, 'F10': 29393.0, 'F11': 24909.0, 'F12': 20339.0, 'G01': 18756.0, 'G02': 25256.0, 'G03': 27042.0, 'G04': 32966.0, 'G05': 38739.0, 'G06': 29929.0, 'G07': 34599.0, 'G08': 28187.0, 'G09': 32818.0, 'G10': 26181.0, 'G11': 21687.0, 'G12': 21627.0, 'H01': 27580.0, 'H02': 23914.0, 'H03': 24818.0, 'H04': 28229.0, 'H05': 29399.0, 'H06': 34307.0, 'H07': 26146.0, 'H08': 29867.0, 'H09': 30602.0, 'H10': 25947.0, 'H11': 22218.0, 'H12': 21287.0}), Labelblock(path=PosixPath('../290212_5.78.xls'), metadata={'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']), 'Gain': Metadata(value=93, 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='29/02/2012 16.06.41', unit=None), 'Temperature': Metadata(value=26.2, unit=['°C']), 'End Time:': Metadata(value='29/02/2012 16.07.22', unit=None), 'Movement': Metadata(value='Move Plate Out', unit=None)}, data={'A01': 6289.0, 'A02': 8775.0, 'A03': 5121.0, 'A04': 8084.0, 'A05': 6307.0, 'A06': 5646.0, 'A07': 7352.0, 'A08': 5447.0, 'A09': 4838.0, 'A10': 7255.0, 'A11': 4474.0, 'A12': 6574.0, 'B01': 5306.0, 'B02': 7310.0, 'B03': 5847.0, 'B04': 6649.0, 'B05': 6141.0, 'B06': 5625.0, 'B07': 6645.0, 'B08': 5785.0, 'B09': 7071.0, 'B10': 5874.0, 'B11': 5351.0, 'B12': 4052.0, 'C01': 4904.0, 'C02': 6810.0, 'C03': 6127.0, 'C04': 5040.0, 'C05': 5587.0, 'C06': 4836.0, 'C07': 5585.0, 'C08': 6313.0, 'C09': 6370.0, 'C10': 5996.0, 'C11': 5844.0, 'C12': 4235.0, 'D01': 5498.0, 'D02': 4545.0, 'D03': 7800.0, 'D04': 5238.0, 'D05': 6528.0, 'D06': 5362.0, 'D07': 4773.0, 'D08': 7662.0, 'D09': 5807.0, 'D10': 6675.0, 'D11': 5483.0, 'D12': 3908.0, 'E01': 5946.0, 'E02': 4780.0, 'E03': 5962.0, 'E04': 4892.0, 'E05': 5464.0, 'E06': 5780.0, 'E07': 5184.0, 'E08': 4973.0, 'E09': 5961.0, 'E10': 5100.0, 'E11': 5161.0, 'E12': 3390.0, 'F01': 4229.0, 'F02': 5121.0, 'F03': 5061.0, 'F04': 4552.0, 'F05': 4519.0, 'F06': 5208.0, 'F07': 5361.0, 'F08': 5876.0, 'F09': 6376.0, 'F10': 5793.0, 'F11': 4799.0, 'F12': 3790.0, 'G01': 3573.0, 'G02': 4668.0, 'G03': 5056.0, 'G04': 5661.0, 'G05': 7013.0, 'G06': 5170.0, 'G07': 7033.0, 'G08': 5312.0, 'G09': 6208.0, 'G10': 5309.0, 'G11': 4261.0, 'G12': 4205.0, 'H01': 5335.0, 'H02': 5430.0, 'H03': 5557.0, 'H04': 6033.0, 'H05': 5740.0, 'H06': 6189.0, 'H07': 5746.0, 'H08': 5871.0, 'H09': 6459.0, 'H10': 5455.0, 'H11': 5079.0, 'H12': 4477.0})]),
Tecanfile(path=PosixPath('../290212_6.38.xls'), metadata={'Device: infinite 200': Metadata(value='Serial number: 810002712', unit=['Serial number of connected stacker:']), 'Firmware: V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)': Metadata(value='MAI, V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)', unit=None), 'Date:': Metadata(value='29/02/2012', unit=None), 'Time:': Metadata(value='15.57.05', unit=None), 'System': Metadata(value='TECANROBOT', unit=None), 'User': Metadata(value='TECANROBOT\\Administrator', unit=None), 'Plate': Metadata(value='PE 96 Flat Bottom White [PE.pdfx]', unit=None), 'Plate-ID (Stacker)': Metadata(value='Plate-ID (Stacker)', unit=None), 'Shaking (Linear) Duration:': Metadata(value=50, unit=['s']), 'Shaking (Linear) Amplitude:': Metadata(value=2, unit=['mm'])}, labelblocks=[Labelblock(path=PosixPath('../290212_6.38.xls'), metadata={'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=81, 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='29/02/2012 15.57.55', unit=None), 'Temperature': Metadata(value=26.0, unit=['°C']), 'End Time:': Metadata(value='29/02/2012 15.58.35', unit=None)}, data={'A01': 30072.0, 'A02': 27276.0, 'A03': 22249.0, 'A04': 30916.0, 'A05': 27943.0, 'A06': 25130.0, 'A07': 26765.0, 'A08': 27836.0, 'A09': 23084.0, 'A10': 31370.0, 'A11': 16890.0, 'A12': 22136.0, 'B01': 22336.0, 'B02': 31327.0, 'B03': 24855.0, 'B04': 32426.0, 'B05': 30066.0, 'B06': 27018.0, 'B07': 28269.0, 'B08': 27570.0, 'B09': 31310.0, 'B10': 24358.0, 'B11': 22595.0, 'B12': 20355.0, 'C01': 23232.0, 'C02': 32241.0, 'C03': 28309.0, 'C04': 26642.0, 'C05': 28818.0, 'C06': 26638.0, 'C07': 26423.0, 'C08': 29441.0, 'C09': 28541.0, 'C10': 29656.0, 'C11': 29841.0, 'C12': 25738.0, 'D01': 26578.0, 'D02': 22280.0, 'D03': 36219.0, 'D04': 25735.0, 'D05': 35433.0, 'D06': 27376.0, 'D07': 22497.0, 'D08': 35681.0, 'D09': 26154.0, 'D10': 32311.0, 'D11': 27495.0, 'D12': 22459.0, 'E01': 27576.0, 'E02': 26058.0, 'E03': 28882.0, 'E04': 26188.0, 'E05': 27531.0, 'E06': 31269.0, 'E07': 26757.0, 'E08': 26427.0, 'E09': 27764.0, 'E10': 27184.0, 'E11': 26556.0, 'E12': 18494.0, 'F01': 22120.0, 'F02': 26642.0, 'F03': 25432.0, 'F04': 23801.0, 'F05': 23932.0, 'F06': 28471.0, 'F07': 27151.0, 'F08': 30243.0, 'F09': 29908.0, 'F10': 27429.0, 'F11': 23624.0, 'F12': 20150.0, 'G01': 18084.0, 'G02': 23496.0, 'G03': 25347.0, 'G04': 30926.0, 'G05': 32714.0, 'G06': 29026.0, 'G07': 33351.0, 'G08': 25353.0, 'G09': 29849.0, 'G10': 25687.0, 'G11': 20913.0, 'G12': 20991.0, 'H01': 25504.0, 'H02': 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unit=None)}, data={'A01': 21781.0, 'A02': 19837.0, 'A03': 6518.0, 'A04': 15502.0, 'A05': 7404.0, 'A06': 6548.0, 'A07': 21295.0, 'A08': 11259.0, 'A09': 5946.0, 'A10': 8219.0, 'A11': 7864.0, 'A12': 21495.0, 'B01': 15989.0, 'B02': 14683.0, 'B03': 10798.0, 'B04': 16903.0, 'B05': 12414.0, 'B06': 11957.0, 'B07': 26895.0, 'B08': 42803.0, 'B09': 17662.0, 'B10': 15413.0, 'B11': 24685.0, 'B12': 7572.0, 'C01': 18788.0, 'C02': 13054.0, 'C03': 8753.0, 'C04': 9629.0, 'C05': 7190.0, 'C06': 6934.0, 'C07': 6767.0, 'C08': 12480.0, 'C09': 7122.0, 'C10': 13614.0, 'C11': 12529.0, 'C12': 5942.0, 'D01': 7306.0, 'D02': 14689.0, 'D03': 8747.0, 'D04': 6358.0, 'D05': 9221.0, 'D06': 9806.0, 'D07': 8833.0, 'D08': 13679.0, 'D09': 12220.0, 'D10': 12491.0, 'D11': 9737.0, 'D12': 9106.0, 'E01': 6761.0, 'E02': 7196.0, 'E03': 7318.0, 'E04': 6413.0, 'E05': 8943.0, 'E06': 10226.0, 'E07': 11244.0, 'E08': 13281.0, 'E09': 7071.0, 'E10': 10103.0, 'E11': 7061.0, 'E12': 9701.0, 'F01': 9828.0, 'F02': 11799.0, 'F03': 9161.0, 'F04': 7971.0, 'F05': 7443.0, 'F06': 10464.0, 'F07': 8841.0, 'F08': 7708.0, 'F09': 9704.0, 'F10': 10569.0, 'F11': 8842.0, 'F12': 15387.0, 'G01': 4542.0, 'G02': 10097.0, 'G03': 6949.0, 'G04': 7642.0, 'G05': 9072.0, 'G06': 12862.0, 'G07': 8432.0, 'G08': 11197.0, 'G09': 10102.0, 'G10': 8671.0, 'G11': 9859.0, 'G12': 5350.0, 'H01': 6390.0, 'H02': 21322.0, 'H03': 6283.0, 'H04': 7940.0, 'H05': 7611.0, 'H06': 7873.0, 'H07': 11582.0, 'H08': 7259.0, 'H09': 7456.0, 'H10': 11454.0, 'H11': 6264.0, 'H12': 9338.0}), Labelblock(path=PosixPath('../290212_9.31.xls'), metadata={'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']), 'Gain': Metadata(value=93, unit=['Optimal']), '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='29/02/2012 15.09.54', unit=None), 'Temperature': Metadata(value=25.6, unit=['°C']), 'End Time:': Metadata(value='29/02/2012 15.10.32', unit=None), 'Movement': Metadata(value='Move Plate Out', unit=None)}, data={'A01': 22534.0, 'A02': 20637.0, 'A03': 6619.0, 'A04': 16075.0, 'A05': 7514.0, 'A06': 6607.0, 'A07': 22410.0, 'A08': 11433.0, 'A09': 6076.0, 'A10': 8355.0, 'A11': 8132.0, 'A12': 22112.0, 'B01': 16390.0, 'B02': 15242.0, 'B03': 10962.0, 'B04': 17372.0, 'B05': 12636.0, 'B06': 12262.0, 'B07': 27865.0, 'B08': 44737.0, 'B09': 18520.0, 'B10': 15948.0, 'B11': 25542.0, 'B12': 7894.0, 'C01': 19337.0, 'C02': 13287.0, 'C03': 8811.0, 'C04': 9814.0, 'C05': 7217.0, 'C06': 6855.0, 'C07': 6631.0, 'C08': 13014.0, 'C09': 7213.0, 'C10': 13935.0, 'C11': 12791.0, 'C12': 5970.0, 'D01': 7353.0, 'D02': 15032.0, 'D03': 8787.0, 'D04': 6322.0, 'D05': 9351.0, 'D06': 9890.0, 'D07': 8963.0, 'D08': 13843.0, 'D09': 12461.0, 'D10': 12549.0, 'D11': 9855.0, 'D12': 9223.0, 'E01': 6838.0, 'E02': 7232.0, 'E03': 7435.0, 'E04': 6425.0, 'E05': 9026.0, 'E06': 10137.0, 'E07': 11509.0, 'E08': 13770.0, 'E09': 7051.0, 'E10': 10308.0, 'E11': 7123.0, 'E12': 9913.0, 'F01': 10060.0, 'F02': 12050.0, 'F03': 9297.0, 'F04': 8090.0, 'F05': 7611.0, 'F06': 10571.0, 'F07': 8968.0, 'F08': 7733.0, 'F09': 9854.0, 'F10': 10762.0, 'F11': 9047.0, 'F12': 16064.0, 'G01': 4573.0, 'G02': 10354.0, 'G03': 7076.0, 'G04': 7819.0, 'G05': 9170.0, 'G06': 13204.0, 'G07': 8531.0, 'G08': 11287.0, 'G09': 10206.0, 'G10': 8615.0, 'G11': 10207.0, 'G12': 5355.0, 'H01': 6457.0, 'H02': 22046.0, 'H03': 6326.0, 'H04': 7962.0, 'H05': 7785.0, 'H06': 8084.0, 'H07': 11921.0, 'H08': 7392.0, 'H09': 7647.0, 'H10': 11908.0, 'H11': 6326.0, 'H12': 9303.0})], allequal=True, metadata={'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), 'Gain': Metadata(value=93, unit=None)})],
'conc': [5.78, 6.38, 6.83, 7.24, 7.67, 8.23, 8.82, 9.31],
'metadata': {'Device: infinite 200': Metadata(value='Serial number: 810002712', unit=['Serial number of connected stacker:']),
'Firmware: V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)': Metadata(value='MAI, V_2.11_04/08_InfiniTe (Apr 4 2008/14.37.11)', unit=None),
'Date:': Metadata(value='29/02/2012', unit=None),
'System': Metadata(value='TECANROBOT', unit=None),
'User': Metadata(value='TECANROBOT\\Administrator', unit=None),
'Plate': Metadata(value='PE 96 Flat Bottom White [PE.pdfx]', unit=None),
'Plate-ID (Stacker)': Metadata(value='Plate-ID (Stacker)', unit=None),
'Shaking (Linear) Duration:': Metadata(value=50, unit=['s']),
'Shaking (Linear) Amplitude:': Metadata(value=2, unit=['mm'])},
'_additions': None,
'_data_dilutioncorrected': None,
'_data_dilutioncorrected_norm': None,
'_buffer_wells': None,
'_dil_corr': Field(name=None,type=None,default=<dataclasses._MISSING_TYPE object at 0x7f6d87124880>,default_factory=<dataclasses._MISSING_TYPE object at 0x7f6d87124880>,init=False,repr=False,hash=None,compare=True,metadata=mappingproxy({}),kw_only=<dataclasses._MISSING_TYPE object at 0x7f6d87124880>,_field_type=None)}
[7]:
tit.labelblocksgroups[0].labelblocks[0].metadata["Temperature"], tit.labelblocksgroups[
1
].labelblocks[0].metadata["Temperature"]
[7]:
(Metadata(value=25.9, unit=['°C']), Metadata(value=26.2, unit=['°C']))
[8]:
tit.labelblocksgroups[0].metadata
[8]:
{'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=81, unit=None)}
[9]:
tit.labelblocksgroups[1].metadata
[9]:
{'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),
'Gain': Metadata(value=93, unit=None)}
[10]:
tit.labelblocksgroups[0].data["A01"], tit.labelblocksgroups[0].data["H12"]
[10]:
([30344.0, 30072.0, 31010.0, 32678.0, 33731.0, 36506.0, 37967.0, 37725.0],
[21287.0, 20888.0, 21209.0, 21711.0, 22625.0, 23397.0, 24791.0, 25045.0])
2.3. pH titration analysis#
[11]:
ph = prtecan.Titration.fromlistfile("list.pH")
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH7.6_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH7.1_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:A06 of tecanfile pH6.5_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH6.5_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:A06 of tecanfile pH5.8_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH5.8_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH5.0_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
[12]:
ph_an = prtecan.TitrationAnalysis.fromlistfile("./list.pH")
ph_an.load_scheme("./scheme.txt")
print(ph_an.scheme)
ph_an.labelblocksgroups[1].data["D01"]
PlateScheme(file='./scheme.txt', buffer=['D01', 'E01', 'D12', 'E12'], ctrl=['B01', 'C12', 'H01', 'A01', 'G12', 'F12', 'G01', 'B12', 'F01', 'A12', 'C01', 'H12'], names={'G03': {'H12', 'B12', 'A01'}, 'NTT': {'F01', 'F12', 'C12'}, 'S202N': {'C01', 'H01', 'G12'}, 'V224Q': {'A12', 'G01', 'B01'}})
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH7.6_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH7.1_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:A06 of tecanfile pH6.5_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH6.5_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:A06 of tecanfile pH5.8_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH5.8_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH5.0_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
[12]:
[51.0, 52.0, 50.0, 51.0, 55.0, 58.0, 57.0]
[13]:
g = ph_an.plot_buffer()
[14]:
ph_an.load_additions("additions.pH")
g = ph_an.plot_buffer()
[15]:
ph_an.data_dilutioncorrected_norm[1]["D11"]
[15]:
[188.5044642857143,
146.82991071428572,
72.77678571428571,
32.84107142857143,
16.899107142857147,
13.234375000000002,
10.650000000000002]
2.4. Cl titration analysis#
[16]:
cl_an = prtecan.TitrationAnalysis.fromlistfile("list.cl")
cl_an.load_scheme("scheme.txt")
cl_an.scheme
/home/dan/workspace/ClopHfit/src/clophfit/prtecan/prtecan.py:312: UserWarning: OVER
Overvalue in Label1:H02 of tecanfile pH5.0_200214.xls
self.data: dict[str, float] = self._extract_data(lines[15:23])
[16]:
PlateScheme(file='scheme.txt', buffer=['D01', 'E01', 'D12', 'E12'], ctrl=['B01', 'C12', 'H01', 'A01', 'G12', 'F12', 'G01', 'B12', 'F01', 'A12', 'C01', 'H12'], names={'G03': {'H12', 'B12', 'A01'}, 'NTT': {'F01', 'F12', 'C12'}, 'S202N': {'C01', 'H01', 'G12'}, 'V224Q': {'A12', 'G01', 'B01'}})
[17]:
cl_an.load_additions("additions.cl")
print(cl_an.conc)
cl_an.conc = prtecan.calculate_conc(cl_an.additions, 1000)
cl_an.conc
[0, 0, 0, 0, 0, 0, 0, 0, 0]
[17]:
array([ 0. , 17.54385965, 34.48275862, 50.84745763,
66.66666667, 81.96721311, 96.77419355, 138.46153846,
164.17910448])
[18]:
lbg = cl_an.labelblocksgroups[1]
x = cl_an.conc
plt.plot(x, lbg.data["D01"])
plt.plot(x, lbg.data["E01"])
plt.plot(x, lbg.data["D12"])
plt.plot(x, lbg.data["E12"])
plt.plot(x, cl_an.labelblocksgroups[1].data["D12"])
plt.plot(x, cl_an.labelblocksgroups[1].data["E12"])
plt.plot(x, lbg.data["A11"])
plt.plot(x, np.array(cl_an.labelblocksgroups[1].data["A11"]) / 2, "--")
[18]:
[<matplotlib.lines.Line2D at 0x7f6d28a08d60>]
[19]:
lbg.labelblocks[1].buffer
[19]:
62.5
[20]:
f = cl_an.plot_buffer()
2.5. Fitting#
TitrationAnalysis class need API changes to better rationalize its use.
[21]:
ph_an.fit("pH", fin=-1, tval=0.95)
type(ph_an.fittings)
[21]:
list
[22]:
# ph_an.fittings[1].to_csv('fit1-1.csv')
ph_an.fittings[1]
[22]:
| ss | msg | df | tval | chisqr | K | SA | SB | sK | sSA | sSB | ctrl | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| F10 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 6693.437017 | 7.392060 | 173.191371 | 2397.601964 | 0.253098 | 230.970124 | 227.175160 | NaN |
| F05 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 71.339265 | 7.967075 | 286.493656 | 4436.350293 | 0.015118 | 18.673010 | 33.912982 | NaN |
| C02 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 1189.845980 | 7.111161 | 372.490817 | 2498.509163 | 0.114808 | 115.910030 | 84.615790 | NaN |
| G03 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 18685.315353 | 8.000562 | 264.242815 | 7200.728074 | 0.147956 | 298.850672 | 564.906314 | NaN |
| G11 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 100.323661 | 8.057604 | 120.616011 | 1566.668760 | 0.053034 | 21.496166 | 43.572261 | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| C03 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 4585.873884 | 6.904054 | 1125.893478 | 6158.368236 | 0.100773 | 269.585029 | 154.139517 | NaN |
| E02 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 4809.652367 | 8.012438 | 430.617399 | 7776.125864 | 0.071165 | 151.030696 | 289.614135 | NaN |
| F01 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 6563.935327 | 7.540270 | 1095.373842 | 7939.778918 | 0.081497 | 212.126197 | 242.899640 | NTT |
| A12 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 79295.720807 | 8.108638 | 372.770924 | 10999.586294 | 0.206807 | 594.724249 | 1285.714864 | V224Q |
| F03 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 13.456577 | 7.966524 | 135.586744 | 1521.217494 | 0.019661 | 8.111441 | 14.721964 | NaN |
92 rows × 12 columns
[23]:
ph_an.fittings[1].loc[["H06", "A01"]]
[23]:
| ss | msg | df | tval | chisqr | K | SA | SB | sK | sSA | sSB | ctrl | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| H06 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 85.730623 | 8.044499 | 583.667709 | 9807.482415 | 0.007650 | 19.955025 | 39.796818 | NaN |
| A01 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 1912.411207 | 7.939952 | 308.491030 | 5053.296317 | 0.067897 | 97.573993 | 171.649842 | G03 |
[24]:
key = "H06"
lbg = ph_an.labelblocksgroups[1]
x = ph_an.conc
y = lbg.data[key]
dd = prtecan.prtecan.fit_titration("pH", x[:-1], y[:-1])
dd
[24]:
| ss | msg | df | tval | chisqr | K | SA | SB | sK | sSA | sSB | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Both actual and predicted relative reductions ... | 3 | 3.182446 | 85.730623 | 8.044499 | 583.667709 | 9807.482415 | 0.00765 | 19.955025 | 39.796818 |
[25]:
ph_an.plot_well("H06")
[25]:
[26]:
f = ph_an.plot_k(1, title="2014-12-23")
[27]:
cl_an.fit("Cl", fin=None)
[ ]:
cl_an.plot_all_wells("cl.pdf")
[28]:
ph_an.print_fitting(2)
K sK SA sSA SB sSB SA2 sSA2 SB2 sSB2
G03
A01 8.12 0.07 22500 318 13976 400 624 277 5215 257
B12 8.15 0.03 18666 77 14000 33 522 1640 2845 331
H12 7.71 0.02 23432 236 14741 11 353 81 5584 146
NTT
C12 7.80 0.11 22069 648 14721 575 957 294 2554 26
F12 7.72 0.09 23474 567 15716 613 586 253 2842 55
F01 7.64 0.04 47972 33 23856 375 1573 1038 7997 54
S202N
H01 7.15 0.00 36727 0 24994 8 1197 1081 5148 14
G12 7.34 0.15 22702 346 16628 178 817 189 2384 141
C01 7.39 0.30 43803 220 30991 1494 3709 2136 7038 390
V224Q
B01 8.00 0.03 40086 263 23554 346 1523 900 15426 91
G01 8.19 0.03 42427 521 25191 49 2863 8260 18045 1209
A12 8.41 0.01 28827 53 16747 17 396 512 11943 1436
K sK SA sSA SB sSB SA2 sSA2 SB2 sSB2
UNK
A02 7.44 0.09 29201 711 22082 87 2081 386 3894 29
A03 7.19 0.18 54837 1357 36866 421 2380 1291 10085 115
A04 6.94 0.01 29194 38 20916 24 545 547 3302 469
A05 7.01 0.27 46100 1902 32024 331 896 3322 7150 19
A06 7.67 nan nan nan 43675 nan 2374 nan 12625 nan
A07 7.73 0.17 36210 682 25416 680 4365 746 6252 120
A08 7.93 0.03 37481 416 26108 39 4886 715 6906 261
A09 7.51 0.22 32032 1830 23874 716 1469 960 5483 167
A10 7.24 0.15 45481 2112 30590 341 2685 1058 7070 24
A11 7.40 0.27 32893 523 23381 527 1723 626 5171 138
B02 7.04 0.03 32438 167 19461 45 739 1391 3184 955
B03 7.15 0.06 37821 202 27571 342 2556 576 5875 17
B04 6.98 0.16 45928 968 32056 183 1226 1420 8236 48
B05 7.71 0.04 30801 479 22783 68 2007 302 5117 17
B06 6.96 0.16 44717 1014 30759 161 1029 1237 7094 24
B07 7.40 0.32 59690 1401 40533 1298 4277 3135 12369 260
B08 6.99 0.27 26001 1187 16256 223 254 2966 2166 971
B09 6.93 0.16 34448 1187 24399 111 830 815 4600 33
B10 7.14 0.14 38115 1490 26696 165 1850 545 5711 38
B11 6.93 0.11 33743 724 24301 76 485 667 4601 26
C02 7.70 0.11 20866 43 16612 476 1844 265 2508 52
C03 6.89 0.11 40925 633 29239 158 905 854 6173 22
C04 7.00 0.06 40127 374 22583 176 794 4949 4127 2648
C05 7.33 0.12 47759 341 34636 1008 3330 3006 8302 56
C06 7.02 0.30 25100 615 20488 621 220 1099 3462 18
C07 7.04 0.04 33204 193 24653 482 926 280 4566 3
C08 7.16 0.14 49277 2316 33471 739 2325 1392 8749 48
C09 7.74 0.33 46356 4863 32300 360 5320 1279 8211 377
C10 6.88 0.08 28919 280 20436 269 -152 477 3416 12
C11 6.93 0.04 24635 256 18272 20 699 246 2634 7
D02 6.98 0.00 31973 18 24485 18 348 116 4623 18
D03 7.99 0.02 39247 15 19960 242 816 98 14099 171
D04 8.03 0.13 29378 361 16236 421 822 814 9097 157
D05 8.21 0.03 29852 9 16649 713 1699 107 9761 111
D06 7.47 0.02 16634 1313 16151 335 169 105 3349 2
D07 8.19 0.03 20952 16 13716 267 748 196 4945 50
D08 8.02 0.02 17065 13 12511 197 246 81 2944 4
D09 8.18 0.08 17813 65 12479 256 881 240 3244 22
D10 8.13 0.09 24432 152 14530 232 690 841 6683 162
D11 8.04 0.08 16191 118 12328 152 179 9 2354 50
E02 8.07 0.00 27972 11 16118 16 570 2791 7813 480
E03 7.86 0.06 18338 28 13495 127 -40 161 2704 23
E04 8.17 0.03 32087 109 17079 258 1593 944 10611 185
E05 8.12 0.03 19836 116 13671 76 582 362 4022 68
E06 8.18 0.01 30349 133 16307 41 1131 2016 10354 332
E07 7.90 0.03 22743 30 14371 347 -198 949 5322 97
E08 8.21 0.13 19485 116 12905 404 474 1790 4017 313
E09 8.47 0.01 15294 1 11592 16 514 82 2236 53
E10 8.07 0.08 17668 186 13614 172 254 119 2209 26
E11 8.07 0.09 17401 243 11642 215 333 351 3607 63
F02 8.14 0.09 25613 166 16190 1394 2180 749 7728 77
F03 7.97 0.02 15030 250 12090 438 136 12 1524 6
F04 7.92 0.02 26097 93 15984 355 415 116 7003 14
F05 7.99 0.04 21195 244 13882 178 410 247 4443 10
F06 8.13 0.01 19658 4 14245 98 528 379 3574 68
F07 7.89 0.03 20514 138 14070 182 308 35 4083 26
F08 7.92 0.08 19911 149 13363 755 268 18 3530 55
F09 7.91 0.07 19372 298 13238 374 316 206 4002 38
F10 8.10 0.05 15485 76 13012 25 1133 546 2700 904
F11 7.90 0.04 16929 56 12290 130 209 14 2720 28
G02 8.11 0.02 21881 195 14406 12 891 275 5440 115
G03 8.23 0.21 24629 664 14908 568 1342 1101 7349 320
G04 8.03 0.00 23948 51 16044 2 885 2588 6129 1261
G05 8.14 0.04 21675 136 14442 127 967 887 5783 163
G06 7.82 0.04 16165 10 12718 169 -81 1292 1983 151
G07 7.86 0.09 15957 24 12457 47 24 1128 1839 121
G08 8.26 0.06 18384 11 12291 139 721 3053 3694 531
G09 7.72 0.23 15209 166 12746 601 81 122 1091 42
G10 7.95 0.10 13394 54 11240 102 74 490 884 87
G11 7.92 0.03 14276 56 10983 60 109 9 1525 25
H02 8.32 nan nan nan 45581 nan 2929 nan 40617 nan
H03 8.04 0.00 34444 42 17801 12 1298 134 10968 12
H04 7.97 0.03 23258 22 14384 421 206 225 5265 19
H05 8.03 0.03 36111 134 18130 201 1339 538 12496 32
H06 8.04 0.00 31683 36 16373 328 549 22 9798 3
H07 8.11 0.03 31464 115 16234 97 800 1648 10197 217
H08 8.18 0.01 64241 17 26105 217 1953 289 27051 767
H09 8.04 0.02 31934 100 16786 66 563 1870 10268 298
H10 8.01 0.04 18238 49 12508 48 160 514 3145 132
H11 7.99 0.01 21640 126 13407 15 191 149 5008 23
2.5.1. selection#
[29]:
f = ph_an.plot_ebar(0)
[30]:
f = ph_an.plot_ebar(2, xmin=7.7, ymin=25)
[31]:
import seaborn as sb
sb.set_style("whitegrid")
g = sb.PairGrid(
ph_an.fittings[0],
x_vars=["K", "SA", "SB"],
y_vars=["K", "SA", "SB"],
# hue='SB',
palette="Blues",
diag_sharey=False,
)
g.map_lower(plt.scatter)
g.map_upper(sb.kdeplot, fill=True)
g.map_diag(sb.kdeplot)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
[31]:
<seaborn.axisgrid.PairGrid at 0x7f6d21fcb6d0>
Fit first (0) and second (1) dataset and global (2).
[32]:
with sb.axes_style("darkgrid"):
g = sb.pairplot(
ph_an.fittings[2][["SA", "SB", "K", "SA2", "SB2"]],
# hue='SB',
palette="Reds",
corner=True,
diag_kind="kde",
)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=vector, **plot_kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
/home/dan/workspace/ClopHfit/.hatch/clophfit/lib/python3.10/site-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
func(x=x, y=y, **kwargs)
[33]:
from pandas.plotting import scatter_matrix
def plot_matrix(tit, lb):
df = tit.fittings[lb].loc[tit.keys_unk]
try:
f = scatter_matrix(
df[["K", "SA", "SB", "SA2", "SB2"]],
figsize=(14, 14),
diagonal="kde",
alpha=0.8,
)
except KeyError:
f = scatter_matrix(
df[["K", "SA", "SB"]], figsize=(10, 10), diagonal="kde", alpha=0.8
)
return f
f = plot_matrix(ph_an, 2)
2.5.2. combining#
[34]:
res_unk = ph_an.fittings[1].loc[ph_an.keys_unk].sort_index()
res_unk["well"] = res_unk.index
[35]:
# g = sns.FacetGrid(data=res2, row='well', margin_titles=True)
# g.map(plt.errorbar, 'K', range(1), xerr='sK', palette='Oranges_d') # x, y,
f = plt.figure(figsize=(24, 14))
# Make the PairGrid
g = sb.PairGrid(
res_unk,
x_vars=["K", "SA", "SB"],
y_vars="well",
height=12,
aspect=0.4,
)
# Draw a dot plot using the stripplot function
g.map(sb.stripplot, size=14, orient="h", palette="Set2", edgecolor="gray")
# 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):
# 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)
sb.despine(left=True, bottom=True)
<Figure size 2400x1400 with 0 Axes>
