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Correlation of sliced TTTR objects¶
Introduction¶
Fluorescence Correlation Spectroscopy (FCS) in cells often suffers from artifacts caused by bright aggregates or vesicles, depletion of fluorophores or bleaching of a fluorescent background. A common practice is to record multiple FCS curves and reject manually FCS curves. When recording TTTR data that is later correlated the processing of the data, i.e., the computation of FCS curves and the sorting based on certain criteria [Ries2010] can be automated to greatly simplify and accelerate the data analysis.
Here, we demonstrate how to use tttrlib to slice data into subsets and correlate the subsets individually. The output of such a procedure can be used as an input to select and discriminate perturbed FCS curves for automated suppression of sample-related artifacts in Fluorescence Correlation Spectroscopy.
Implementation¶
First, we import libraries to read the data into a new TTTR container. We inspect the header to find header tags that inform on the macro time calibration. We print the content of the data header as is can be useful for asserting correct correlation parameters in later steps.
import pylab as plt
import tttrlib
import numpy as np
data = tttrlib.TTTR('../../tttr-data/pq/ptu/pq_ptu_hh_t2.ptu')
print(data.header.get_json())
{
"MeasDesc_ContainerType": 0,
"MeasDesc_RecordType": 1,
"Tag Version": "1.0.00",
"tags": [
{
"idx": -1,
"name": "File_GUID",
"type": 1073872895,
"value": "{BFCC4594-FEC0-4514-ACBD-CD9307163BF6}"
},
{
"idx": -1,
"name": "File_CreatingTime",
"type": 553648136,
"value": 1539707502.9450002
},
{
"idx": -1,
"name": "Measurement_SubMode",
"type": 268435464,
"value": 1
},
{
"idx": -1,
"name": "File_Comment",
"type": 1073872895,
"value": ""
},
{
"idx": -1,
"name": "TTResult_StopReason",
"type": 268435464,
"value": 1
},
{
"idx": -1,
"name": "Fast_Load_End",
"type": 4294901768,
"value": null
},
{
"idx": -1,
"name": "CreatorSW_Name",
"type": 1073872895,
"value": "SymPhoTime 64"
},
{
"idx": -1,
"name": "CreatorSW_Version",
"type": 1073872895,
"value": "2.3"
},
{
"idx": -1,
"name": "CreatorSW_SVNBuild",
"type": 268435464,
"value": 4724
},
{
"idx": -1,
"name": "CreatorSW_Modules",
"type": 268435464,
"value": 0
},
{
"idx": -1,
"name": "ImgHdr_Dimensions",
"type": 268435464,
"value": 1
},
{
"idx": -1,
"name": "ImgHdr_Ident",
"type": 268435464,
"value": 3
},
{
"idx": -1,
"name": "Measurement_Mode",
"type": 268435464,
"value": 2
},
{
"idx": -1,
"name": "HW_Type",
"type": 1073872895,
"value": "HydraHarp"
},
{
"idx": -1,
"name": "HW_SerialNo",
"type": 1073872895,
"value": "1011034"
},
{
"idx": -1,
"name": "HW_Version",
"type": 1073872895,
"value": "2.0"
},
{
"idx": -1,
"name": "HW_ExternalRefClock",
"type": 8,
"value": false
},
{
"idx": -1,
"name": "HW_Modules",
"type": 268435464,
"value": 4
},
{
"idx": 0,
"name": "HWModule_TypeCode",
"type": 268435464,
"value": 1000
},
{
"idx": 0,
"name": "HWModule_VersCode",
"type": 268435464,
"value": 18153480
},
{
"idx": 1,
"name": "HWModule_TypeCode",
"type": 268435464,
"value": 1010
},
{
"idx": 1,
"name": "HWModule_VersCode",
"type": 268435464,
"value": 33620490
},
{
"idx": 2,
"name": "HWModule_TypeCode",
"type": 268435464,
"value": 1040
},
{
"idx": 2,
"name": "HWModule_VersCode",
"type": 268435464,
"value": 18219530
},
{
"idx": 3,
"name": "HWModule_TypeCode",
"type": 268435464,
"value": 1040
},
{
"idx": 3,
"name": "HWModule_VersCode",
"type": 268435464,
"value": 18153994
},
{
"idx": -1,
"name": "HW_Markers",
"type": 268435464,
"value": 4
},
{
"idx": 0,
"name": "HWMarkers_Enabled",
"type": 8,
"value": true
},
{
"idx": 0,
"name": "HWMarkers_RisingEdge",
"type": 8,
"value": true
},
{
"idx": 1,
"name": "HWMarkers_Enabled",
"type": 8,
"value": true
},
{
"idx": 1,
"name": "HWMarkers_RisingEdge",
"type": 8,
"value": false
},
{
"idx": 2,
"name": "HWMarkers_Enabled",
"type": 8,
"value": true
},
{
"idx": 2,
"name": "HWMarkers_RisingEdge",
"type": 8,
"value": false
},
{
"idx": 3,
"name": "HWMarkers_Enabled",
"type": 8,
"value": true
},
{
"idx": 3,
"name": "HWMarkers_RisingEdge",
"type": 8,
"value": false
},
{
"idx": -1,
"name": "HWMarkers_HoldOff",
"type": 268435464,
"value": 0
},
{
"idx": -1,
"name": "HW_BaseResolution",
"type": 536870920,
"value": 9.999999960041972e-13
},
{
"idx": -1,
"name": "MeasDesc_BinningFactor",
"type": 268435464,
"value": 4
},
{
"idx": -1,
"name": "MeasDesc_Resolution",
"type": 536870920,
"value": 3.999999984016789e-12
},
{
"idx": -1,
"name": "HWSync_CFDLevel",
"type": 268435464,
"value": 300
},
{
"idx": -1,
"name": "HWSync_CFDZeroCross",
"type": 268435464,
"value": 10
},
{
"idx": -1,
"name": "HWSync_Offset",
"type": 268435464,
"value": 0
},
{
"idx": -1,
"name": "HWSync_Divider",
"type": 268435464,
"value": 1
},
{
"idx": -1,
"name": "HW_InpChannels",
"type": 268435464,
"value": 5
},
{
"idx": 0,
"name": "HWInpChan_CFDLevel",
"type": 268435464,
"value": 20
},
{
"idx": 0,
"name": "HWInpChan_CFDZeroCross",
"type": 268435464,
"value": 10
},
{
"idx": 0,
"name": "HWInpChan_Offset",
"type": 268435464,
"value": 0
},
{
"idx": 0,
"name": "HWInpChan_Enabled",
"type": 8,
"value": true
},
{
"idx": 0,
"name": "HWInpChan_ModuleIdx",
"type": 268435464,
"value": 2
},
{
"idx": 1,
"name": "HWInpChan_CFDLevel",
"type": 268435464,
"value": 180
},
{
"idx": 1,
"name": "HWInpChan_CFDZeroCross",
"type": 268435464,
"value": 10
},
{
"idx": 1,
"name": "HWInpChan_Offset",
"type": 268435464,
"value": 0
},
{
"idx": 1,
"name": "HWInpChan_Enabled",
"type": 8,
"value": true
},
{
"idx": 1,
"name": "HWInpChan_ModuleIdx",
"type": 268435464,
"value": 2
},
{
"idx": 2,
"name": "HWInpChan_CFDLevel",
"type": 268435464,
"value": 20
},
{
"idx": 2,
"name": "HWInpChan_CFDZeroCross",
"type": 268435464,
"value": 10
},
{
"idx": 2,
"name": "HWInpChan_Offset",
"type": 268435464,
"value": -3900
},
{
"idx": 2,
"name": "HWInpChan_Enabled",
"type": 8,
"value": true
},
{
"idx": 2,
"name": "HWInpChan_ModuleIdx",
"type": 268435464,
"value": 3
},
{
"idx": 3,
"name": "HWInpChan_CFDLevel",
"type": 268435464,
"value": 180
},
{
"idx": 3,
"name": "HWInpChan_CFDZeroCross",
"type": 268435464,
"value": 10
},
{
"idx": 3,
"name": "HWInpChan_Offset",
"type": 268435464,
"value": 0
},
{
"idx": 3,
"name": "HWInpChan_Enabled",
"type": 8,
"value": true
},
{
"idx": 3,
"name": "HWInpChan_ModuleIdx",
"type": 268435464,
"value": 3
},
{
"idx": -1,
"name": "TTResult_SyncRate",
"type": 268435464,
"value": 0
},
{
"idx": 0,
"name": "TTResult_InputRate",
"type": 268435464,
"value": 114240
},
{
"idx": 1,
"name": "TTResult_InputRate",
"type": 268435464,
"value": 0
},
{
"idx": 2,
"name": "TTResult_InputRate",
"type": 268435464,
"value": 94520
},
{
"idx": 3,
"name": "TTResult_InputRate",
"type": 268435464,
"value": 0
},
{
"idx": -1,
"name": "MeasDesc_GlobalResolution",
"type": 536870920,
"value": 1e-12
},
{
"idx": -1,
"name": "TTResult_NumberOfRecords",
"type": 268435464,
"value": 4135302
},
{
"idx": -1,
"name": "MeasDesc_AcquisitionTime",
"type": 268435464,
"value": 36000000
},
{
"idx": -1,
"name": "TTResult_StopAfter",
"type": 268435464,
"value": 17938
},
{
"idx": -1,
"name": "TTResultFormat_TTTRRecType",
"type": 268435464,
"value": 16843268
},
{
"idx": -1,
"name": "TTResultFormat_BitsPerRecord",
"type": 268435464,
"value": 32
},
{
"idx": 1,
"name": "MeasDesc_NumberMicrotimes",
"type": 268435464,
"value": 8192
}
]
}
Here, we manually compute the calibration. This may not be necessary in many cases.
time_calibration = data.header.tag('MeasDesc_GlobalResolution')['value']
Next, we plan to split the TTTR data into separate data chunks. Here, chunk
the data into at least 5 seconds long pieces. The method get_ranges_by_time_window
returns a one-dimensional array with the beginning and the end index of each
chunk. Later, we will use these start/stop indices to define TTTR slices
that will be correlated. To slice the data into time windows we use the
macro time calibration we computed previously. Note, the last chunk can
be shorter than the specified time.
minimum_window_length = 5.0 # in seconds
time_windows = data.get_ranges_by_time_window(
minimum_window_length, macro_time_calibration=time_calibration)
start_stop = time_windows.reshape((len(time_windows)//2, 2))
print(start_stop)
[[ 0 1006022]
[1006022 2020788]
[2020788 3029947]
[3029947 3630609]]
Before correlating the data, we define the number of bins and the number of coarsening steps for the multi-tau correlation algorithm and create a new correlator instance.
corr_settings = {
"n_bins": 9,
"n_casc": 30
}
correlator = tttrlib.Correlator(**corr_settings)
We print the used routing channels and define the routing channels that should be used in the correlation as a first and second correlation channel.
print(data.used_routing_channels)
ch1 = [0]
ch2 = [2]
# use start-stop to create new TTTR objects that are correlated
correlations = list()
for start, stop in start_stop:
indices = np.arange(start, stop, dtype=np.int64)
tttr_slice = data[indices]
tttr_ch1 = tttr_slice[tttr_slice.get_selection_by_channel(ch1)]
tttr_ch2 = tttr_slice[tttr_slice.get_selection_by_channel(ch2)]
correlator.set_tttr(
tttr_1=tttr_ch1,
tttr_2=tttr_ch2
)
correlations.append(
(correlator.x_axis, correlator.correlation)
)
print(correlations)
[0 2]
[(array([0.00000000e+00, 1.00000000e-12, 2.00000000e-12, 3.00000000e-12,
4.00000000e-12, 5.00000000e-12, 6.00000000e-12, 7.00000000e-12,
8.00000000e-12, 9.00000000e-12, 1.10000000e-11, 1.30000000e-11,
1.50000000e-11, 1.70000000e-11, 1.90000000e-11, 2.10000000e-11,
2.30000000e-11, 2.50000000e-11, 2.70000000e-11, 3.10000000e-11,
3.50000000e-11, 3.90000000e-11, 4.30000000e-11, 4.70000000e-11,
5.10000000e-11, 5.50000000e-11, 5.90000000e-11, 6.30000000e-11,
7.10000000e-11, 7.90000000e-11, 8.70000000e-11, 9.50000000e-11,
1.03000000e-10, 1.11000000e-10, 1.19000000e-10, 1.27000000e-10,
1.35000000e-10, 1.51000000e-10, 1.67000000e-10, 1.83000000e-10,
1.99000000e-10, 2.15000000e-10, 2.31000000e-10, 2.47000000e-10,
2.63000000e-10, 2.79000000e-10, 3.11000000e-10, 3.43000000e-10,
3.75000000e-10, 4.07000000e-10, 4.39000000e-10, 4.71000000e-10,
5.03000000e-10, 5.35000000e-10, 5.67000000e-10, 6.31000000e-10,
6.95000000e-10, 7.59000000e-10, 8.23000000e-10, 8.87000000e-10,
9.51000000e-10, 1.01500000e-09, 1.07900000e-09, 1.14300000e-09,
1.27100000e-09, 1.39900000e-09, 1.52700000e-09, 1.65500000e-09,
1.78300000e-09, 1.91100000e-09, 2.03900000e-09, 2.16700000e-09,
2.29500000e-09, 2.55100000e-09, 2.80700000e-09, 3.06300000e-09,
3.31900000e-09, 3.57500000e-09, 3.83100000e-09, 4.08700000e-09,
4.34300000e-09, 4.59900000e-09, 5.11100000e-09, 5.62300000e-09,
6.13500000e-09, 6.64700000e-09, 7.15900000e-09, 7.67100000e-09,
8.18300000e-09, 8.69500000e-09, 9.20700000e-09, 1.02310000e-08,
1.12550000e-08, 1.22790000e-08, 1.33030000e-08, 1.43270000e-08,
1.53510000e-08, 1.63750000e-08, 1.73990000e-08, 1.84230000e-08,
2.04710000e-08, 2.25190000e-08, 2.45670000e-08, 2.66150000e-08,
2.86630000e-08, 3.07110000e-08, 3.27590000e-08, 3.48070000e-08,
3.68550000e-08, 4.09510000e-08, 4.50470000e-08, 4.91430000e-08,
5.32390000e-08, 5.73350000e-08, 6.14310000e-08, 6.55270000e-08,
6.96230000e-08, 7.37190000e-08, 8.19110000e-08, 9.01030000e-08,
9.82950000e-08, 1.06487000e-07, 1.14679000e-07, 1.22871000e-07,
1.31063000e-07, 1.39255000e-07, 1.47447000e-07, 1.63831000e-07,
1.80215000e-07, 1.96599000e-07, 2.12983000e-07, 2.29367000e-07,
2.45751000e-07, 2.62135000e-07, 2.78519000e-07, 2.94903000e-07,
3.27671000e-07, 3.60439000e-07, 3.93207000e-07, 4.25975000e-07,
4.58743000e-07, 4.91511000e-07, 5.24279000e-07, 5.57047000e-07,
5.89815000e-07, 6.55351000e-07, 7.20887000e-07, 7.86423000e-07,
8.51959000e-07, 9.17495000e-07, 9.83031000e-07, 1.04856700e-06,
1.11410300e-06, 1.17963900e-06, 1.31071100e-06, 1.44178300e-06,
1.57285500e-06, 1.70392700e-06, 1.83499900e-06, 1.96607100e-06,
2.09714300e-06, 2.22821500e-06, 2.35928700e-06, 2.62143100e-06,
2.88357500e-06, 3.14571900e-06, 3.40786300e-06, 3.67000700e-06,
3.93215100e-06, 4.19429500e-06, 4.45643900e-06, 4.71858300e-06,
5.24287100e-06, 5.76715900e-06, 6.29144700e-06, 6.81573500e-06,
7.34002300e-06, 7.86431100e-06, 8.38859900e-06, 8.91288700e-06,
9.43717500e-06, 1.04857510e-05, 1.15343270e-05, 1.25829030e-05,
1.36314790e-05, 1.46800550e-05, 1.57286310e-05, 1.67772070e-05,
1.78257830e-05, 1.88743590e-05, 2.09715110e-05, 2.30686630e-05,
2.51658150e-05, 2.72629670e-05, 2.93601190e-05, 3.14572710e-05,
3.35544230e-05, 3.56515750e-05, 3.77487270e-05, 4.19430310e-05,
4.61373350e-05, 5.03316390e-05, 5.45259430e-05, 5.87202470e-05,
6.29145510e-05, 6.71088550e-05, 7.13031590e-05, 7.54974630e-05,
8.38860710e-05, 9.22746790e-05, 1.00663287e-04, 1.09051895e-04,
1.17440503e-04, 1.25829111e-04, 1.34217719e-04, 1.42606327e-04,
1.50994935e-04, 1.67772151e-04, 1.84549367e-04, 2.01326583e-04,
2.18103799e-04, 2.34881015e-04, 2.51658231e-04, 2.68435447e-04,
2.85212663e-04, 3.01989879e-04, 3.35544311e-04, 3.69098743e-04,
4.02653175e-04, 4.36207607e-04, 4.69762039e-04, 5.03316471e-04,
5.36870903e-04, 5.70425335e-04, 6.03979767e-04, 6.71088631e-04,
7.38197495e-04, 8.05306359e-04, 8.72415223e-04, 9.39524087e-04,
1.00663295e-03, 1.07374182e-03, 1.14085068e-03, 1.20795954e-03,
1.34217727e-03, 1.47639500e-03, 1.61061273e-03, 1.74483045e-03,
1.87904818e-03, 2.01326591e-03, 2.14748364e-03, 2.28170137e-03,
2.41591909e-03, 2.68435455e-03, 2.95279001e-03, 3.22122546e-03,
3.48966092e-03, 3.75809638e-03, 4.02653183e-03, 4.29496729e-03,
4.56340274e-03, 4.83183820e-03, 5.36870911e-03, 5.90558002e-03,
6.44245093e-03, 6.97932185e-03, 7.51619276e-03, 8.05306367e-03,
8.58993458e-03, 9.12680549e-03, 9.66367641e-03]), array([ nan, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 19.93621689,
19.93621689, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
4.98405422, 0. , 0. , 0. , 0. ,
2.49202711, 0. , 0. , 0. , 0. ,
2.49202711, 2.49202711, 0. , 0. , 1.24601356,
1.24601356, 2.49202711, 2.49202711, 0. , 0. ,
0. , 0. , 0.62300678, 1.86902033, 0.62300678,
1.24601356, 0. , 1.86902033, 0.62300678, 1.24601356,
0.62300678, 1.24601356, 1.55751694, 0.31150339, 1.86902033,
1.86902033, 2.49202711, 0.93451017, 0. , 1.24601356,
1.40176525, 1.71326864, 1.09026186, 1.71326864, 1.24601356,
0.77875847, 1.71326864, 1.40176525, 2.49202711, 1.55751695,
2.10264788, 2.33627542, 1.71326864, 1.86902033, 1.3238894 ,
2.25839957, 1.71326864, 1.71326864, 2.06370995, 2.21946165,
1.79114449, 1.90795826, 2.60884089, 1.98583411, 1.75220657,
2.56990296, 2.43362023, 2.27786854, 2.16105477, 2.58937193,
2.66724777, 2.72565466, 2.58937193, 2.31680646, 2.19999269,
2.14158581, 2.49202712, 2.39468231, 2.69645122, 2.46282368,
2.21946166, 2.61857538, 2.12211685, 2.48229265, 2.42388576,
2.67698227, 2.26813407, 2.3557444 , 2.42875301, 2.50662886,
2.18052375, 2.56016851, 2.52123059, 2.56016852, 2.57963749,
2.52366422, 2.4360539 , 2.39224874, 2.46769097, 2.53583234,
2.49932804, 2.33870911, 2.42510263, 2.42631945, 2.43240351,
2.3581781 , 2.3691294 , 2.41780181, 2.40198329, 2.43118674,
2.31315616, 2.31011415, 2.42084389, 2.41780188, 2.34479329,
2.33749244, 2.38068922, 2.37825561, 2.40745908, 2.41354314,
2.4050255 , 2.35969935, 2.33414637, 2.33658002, 2.3432725 ,
2.27817318, 2.33718851, 2.29825061, 2.28456153, 2.29247085,
2.26418007, 2.22721951, 2.22478595, 2.19390944, 2.17702625,
2.14797495, 2.16911709, 2.14234732, 2.13238479, 2.12059705,
2.06097343, 2.07785678, 2.0505547 , 1.99762352, 1.99009461,
1.98210939, 1.97891536, 1.95461737, 1.9174668 , 1.89012676,
1.86092348, 1.85997304, 1.82073105, 1.79437965, 1.79380946,
1.77046206, 1.7461642 , 1.7177786 , 1.69612349, 1.67005742,
1.65114012, 1.62579651, 1.6153969 , 1.61070109, 1.57381673,
1.55776114, 1.53287411, 1.51226492, 1.48727328, 1.47018143,
1.44577914, 1.43554136, 1.41456135, 1.39870527, 1.38512653,
1.35848596, 1.3365035 , 1.30981055, 1.28976257, 1.27576541,
1.26028046, 1.24195305, 1.22931999, 1.22211614, 1.20714306,
1.18915162, 1.1746205 , 1.16096869, 1.15181343, 1.14041935,
1.12945777, 1.12323759, 1.11588797, 1.10647534, 1.09582916,
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1.97584533, 1.95014977, 1.93786895, 1.90949056, 1.8748016 ,
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1.77039515, 1.74418969, 1.70679904, 1.68185945, 1.65230973,
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1.55102179, 1.51637095, 1.49563548, 1.47505114, 1.462714 ,
1.42974464, 1.42289617, 1.4048908 , 1.38748057, 1.37052436,
1.35003021, 1.32662162, 1.31037857, 1.28214247, 1.26768957,
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1.12748505, 1.12447564, 1.11524709, 1.10137279, 1.08987321,
1.08166831, 1.07394753, 1.07277611, 1.06766039, 1.0632272 ,
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1.00445297, 1.00763238, 1.00530159, 1.00439547, 1.00083133,
1.00240098, 1.0072293 , 1.00660229, 1.00515576, 1.00662854,
1.00284228, 1.0011055 , 1.00147621, 1.00434522, 1.0046919 ,
1.00097375, 1.00208634, 1.00113971, 0.99898307, 1.00135092,
1.00410835])), (array([0.00000000e+00, 1.00000000e-12, 2.00000000e-12, 3.00000000e-12,
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3.48966092e-03, 3.75809638e-03, 4.02653183e-03, 4.29496729e-03,
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6.44245093e-03, 6.97932185e-03, 7.51619276e-03, 8.05306367e-03,
8.58993458e-03, 9.12680549e-03, 9.66367641e-03]), array([ nan, 32.85659916, 0. , 0. , 0. ,
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2.50274878, 2.80757075, 2.29418639, 2.40648922, 2.03749421,
2.82361402, 2.3744027 , 2.74339772, 2.2941864 , 2.51879206,
2.58296511, 2.65515979, 2.28616478, 2.33429457, 2.47868392,
2.64713817, 2.50274882, 2.45461904, 2.36237029, 2.49873803,
2.32627297, 2.40247847, 2.40247847, 2.67922475, 2.4064893 ,
2.47467317, 2.42253258, 2.44659749, 2.37640823, 2.54285709,
2.50876517, 2.25808921, 2.48269489, 2.38041911, 2.38844075,
2.37841373, 2.39144891, 2.43255979, 2.41651656, 2.41350847,
2.24806234, 2.33028409, 2.39545988, 2.35535175, 2.40147616,
2.29518958, 2.37540596, 2.33178838, 2.32577221, 2.28416004,
2.28416009, 2.28315744, 2.33680217, 2.23552909, 2.26009545,
2.25157256, 2.23928953, 2.15405976, 2.17511665, 2.14177682,
2.18138374, 2.14202769, 2.06857967, 2.10141843, 2.09239427,
2.07183901, 2.03022694, 2.02157879, 2.00841846, 1.99425543,
1.9853566 , 1.94775532, 1.93497117, 1.91598278, 1.88377119,
1.85143425, 1.83714602, 1.81978699, 1.79434363, 1.78544493,
1.7537973 , 1.73950935, 1.72174324, 1.67850207, 1.67637191,
1.64861001, 1.62301018, 1.59546758, 1.59333739, 1.56541875,
1.54774708, 1.52189703, 1.49112738, 1.48340442, 1.45521981,
1.43278512, 1.42264931, 1.40007355, 1.38442277, 1.36705735,
1.35069461, 1.32328622, 1.30498842, 1.27871579, 1.27337497,
1.26224495, 1.25566635, 1.23584857, 1.22531949, 1.20515469,
1.18503678, 1.17333238, 1.15997887, 1.1449919 , 1.13509693,
1.12927168, 1.11450776, 1.11060297, 1.10618303, 1.09617729,
1.09366682, 1.08453049, 1.07849841, 1.07615622, 1.06949139,
1.06206654, 1.05729668, 1.05381453, 1.04732474, 1.03678521,
1.02982695, 1.03301873, 1.03359564, 1.02927755, 1.02359016,
1.01870417, 1.01929504, 1.02250112, 1.01681796, 1.01246129,
1.00964768, 1.01105145, 1.01628692, 1.00698772, 1.00788262,
1.00871123, 1.01104185, 1.00617445, 1.00715962, 1.00765396,
1.0058421 , 1.0052472 , 1.00780469, 1.00302651, 0.99956998,
0.99862306, 0.9983719 , 0.99414985, 0.99562462, 0.99457819,
0.99816575, 1.00227135, 1.0038968 , 1.00154566, 0.99922897,
1.00064833, 0.99775634, 0.99972016, 1.00335871, 1.00158416,
1.00225922]))]
Finally, we compute the correlation for all data and plot all correlations for all subsets.
correlator = tttrlib.Correlator(
tttr=data,
channels=(ch1, ch2),
**corr_settings
)
fig, ax = plt.subplots(nrows=1, ncols=2, sharey=True, sharex=True)
ax[0].semilogx(
correlator.x_axis,
correlator.correlation
)
ax[0].set_title('Correlation all data')
ax[1].set_title('Correlation of slices')
for x, y in correlations[:-1]:
ax[1].semilogx(x, y)
ax[0].set_ylim([0.9, 3])
ax[0].set_xlabel(r'corr. time (s) ')
ax[1].set_xlabel(r'corr. time (s) ')
ax[0].set_ylabel(r'corr. amplitude')
plt.show()
Slicing data and correlating photon traces of data subsets can be useful to discriminate artifacts or to estimate the noise of experimental correlation curves.
Total running time of the script: (0 minutes 3.071 seconds)