.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/correlation/plot_sliced_correlation.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_correlation_plot_sliced_correlation.py: ================================== 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 :cite:`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. .. GENERATED FROM PYTHON SOURCE LINES 25-32 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. .. GENERATED FROM PYTHON SOURCE LINES 32-39 .. code-block:: Python 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()) .. rst-class:: sphx-glr-script-out .. code-block:: none { "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 } ] } .. GENERATED FROM PYTHON SOURCE LINES 40-42 Here, we manually compute the calibration. This may not be necessary in many cases. .. GENERATED FROM PYTHON SOURCE LINES 42-44 .. code-block:: Python time_calibration = data.header.tag('MeasDesc_GlobalResolution')['value'] .. GENERATED FROM PYTHON SOURCE LINES 45-52 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. .. GENERATED FROM PYTHON SOURCE LINES 52-58 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none [[ 0 1006022] [1006022 2020788] [2020788 3029947] [3029947 3630609]] .. GENERATED FROM PYTHON SOURCE LINES 59-62 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. .. GENERATED FROM PYTHON SOURCE LINES 62-68 .. code-block:: Python corr_settings = { "n_bins": 9, "n_casc": 30 } correlator = tttrlib.Correlator(**corr_settings) .. GENERATED FROM PYTHON SOURCE LINES 69-72 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. .. GENERATED FROM PYTHON SOURCE LINES 72-92 .. code-block:: Python 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) .. rst-class:: sphx-glr-script-out .. code-block:: none [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, 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GENERATED FROM PYTHON SOURCE LINES 93-95 Finally, we compute the correlation for all data and plot all correlations for all subsets. .. GENERATED FROM PYTHON SOURCE LINES 95-117 .. code-block:: Python 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() .. image-sg:: /auto_examples/correlation/images/sphx_glr_plot_sliced_correlation_001.png :alt: Correlation all data, Correlation of slices :srcset: /auto_examples/correlation/images/sphx_glr_plot_sliced_correlation_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 118-121 Slicing data and correlating photon traces of data subsets can be useful to discriminate artifacts or to estimate the noise of experimental correlation curves. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.071 seconds) .. _sphx_glr_download_auto_examples_correlation_plot_sliced_correlation.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_sliced_correlation.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_sliced_correlation.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_