Implement reader for PicoQuant cor files

pycorrfit/gui/frontend.py

@@ -1168,8 +1168,8 @@ def OnLoadBatch(self, e=None, dataname=None):
             # the Nth occurence of the keys in Types correspond to the
             # matching curves.
             # Also make sure that number starts at one for each selected file.
-            coords = np.zeros(len(keys), dtype=np.int)
-            Run = np.zeros(len(Curveid), dtype=np.int)
+            coords = np.zeros(len(keys), dtype=np.int64)
+            Run = np.zeros(len(Curveid), dtype=np.int64)
             WorkType = 1*Type
             for fname in np.unique(Filename):
                 # unique returns sorted file names.

pycorrfit/gui/page.py

@@ -87,7 +87,7 @@ def __init__(self, parent, counter, modelid, active_parms, tau=None):
         # This is necessary to prevent "Unsplit" of the SplitterWindow:
         self.spcanvas.SetMinimumPaneSize(1)
         # y difference in pixels between Auocorrelation and Residuals
-        cupsizey = size[1]*4/5
+        cupsizey = (size[1]*4)//5
         # Calculate initial data
         self.calculate_corr()
         # Draw the settings section

pycorrfit/gui/tools/statistics.py

@@ -348,7 +348,7 @@ def OnChooseValues(self, event=None):
         #headlen = len(head)
         # We will sort the checkboxes in more than one column if there
         # are more than *maxitemsincolumn*
-        maxitemsincolumn = np.float(19)
+        maxitemsincolumn = np.float64(19)
         Sizernumber = int(np.ceil(len(Info)/maxitemsincolumn))
         self.boxsizerlist = list()
         for i in np.arange(Sizernumber):

pycorrfit/openfile.py

@@ -226,10 +226,10 @@ def LoadSessionData(sessionfile, parameters_only=False):
             for row in bgtraceread:
                 # Exclude commentaries
                 if (str(row[0])[0:1] != '#'):
-                    bgtrace.append((np.float(row[0]), np.float(row[1])))
+                    bgtrace.append((np.float64(row[0]), np.float64(row[1])))
             bgtrace = np.array(bgtrace)
             newbackground = Trace(trace=bgtrace, name=str(
-                bgrow[1]), countrate=np.float(bgrow[0]))
+                bgrow[1]), countrate=np.float64(bgrow[0]))
             Infodict["Backgrounds"].append(newbackground)
             i = i + 1
         bgfile.close()
@@ -260,7 +260,7 @@ def LoadSessionData(sessionfile, parameters_only=False):
             for row in Wdatareader:
                 # Exclude commentaries
                 if (str(row[0])[0:1] != '#'):
-                    Wdata.append(np.float(row[0]))
+                    Wdata.append(np.float64(row[0]))
             Weightsdict[pageid][Nkey] = np.array(Wdata)
         Infodict["External Weights"] = Weightsdict
     # Preferences

pycorrfit/readfiles/__init__.py

@@ -14,6 +14,7 @@
 # To add a filetype add it here and in the
 # dictionaries at the end of this file.
 from .read_ASC_ALV import openASC
+from .read_cor_PicoQuant import openCOR
 from .read_CSV_PyCorrFit import openCSV
 from .read_SIN_correlator_com import openSIN
 from .read_FCS_Confocor3 import openFCS
@@ -242,6 +243,7 @@ def openZIP(path, filename=None):
 # The wildcards point to the appropriate functions.
 filetypes_dict = {"Correlator.com (*.SIN)|*.SIN;*.sin": openSIN,
                   "ALV (*.ASC)|*.ASC;*.asc": openASC,
+                  "PicoQuant cor (*.cor)|*.cor": openCOR,
                   "PyCorrFit (*.csv)|*.csv": openCSV,
                   "Matlab 'Ries (*.mat)|*.mat": openMAT,
                   "PicoQuant (*.pt3)|*.pt3": openPT3,
@@ -255,6 +257,7 @@ def openZIP(path, filename=None):
 # Dictionary with filetypes we can open that have intensity traces in them.
 filetypes_bg_dict = {"Correlator.com (*.SIN)|*.SIN;*.sin": openSIN,
                      "ALV (*.ASC)|*.ASC": openASC,
+                     "PicoQuant cor (*.cor)|*.cor": openCOR,
                      "PyCorrFit (*.csv)|*.csv": openCSV,
                      "PicoQuant (*.pt3)|*.pt3": openPT3,
                      "Zeiss ConfoCor3 (*.fcs)|*.fcs": openFCS,

pycorrfit/readfiles/read_ASC_ALV.py

@@ -181,7 +181,7 @@ def openASC_old(path):
     for row in readdata:
         for i in np.arange(len(curvelist)):
             if len(row) > 0:
-                data[i].append((np.float(row[0]), np.float(row[i+1])))
+                data[i].append((np.float64(row[0]), np.float64(row[i+1])))
     # Trace
     # Trace is stored in two columns
     # 1st column: time [s]
@@ -196,25 +196,25 @@ def openASC_old(path):
     for row in readtrace:
         # time in ms, countrate
         trace.append(list())
-        trace[0].append((np.float(row[0])*timefactor,
-                         np.float(row[1])))
+        trace[0].append((np.float64(row[0])*timefactor,
+                         np.float64(row[1])))
         # Only trace[0] contains the trace!
         for i in np.arange(len(curvelist)-1):
             trace.append(list())
-            trace[i+1].append((np.float(row[0])*timefactor, 0))
+            trace[i+1].append((np.float64(row[0])*timefactor, 0))
         if not single:
             k = len(curvelist)/2
             if int(k) != k:
                 print("Problem with ALV data. Single mode not recognized.")
             # presumably dual mode. There is a second trace
             # time in ms, countrate
             trace2.append(list())
-            trace2[0].append((np.float(row[0])*timefactor,
-                              np.float(row[2])))
+            trace2[0].append((np.float64(row[0])*timefactor,
+                              np.float64(row[2])))
             # Only trace2[0] contains the trace!
             for i in np.arange(len(curvelist)-1):
                 trace2.append(list())
-                trace2[i+1].append((np.float(row[0])*timefactor, 0))
+                trace2[i+1].append((np.float64(row[0])*timefactor, 0))
 
     # group the resulting curves
     corrlist = list()

pycorrfit/readfiles/read_CSV_PyCorrFit.py

@@ -123,11 +123,11 @@ def openCSV(path, filename=None):
             # separated values as well
             if len(row) == 1:
                 row = row[0].split()
-            data.append((np.float(row[0].strip())*timefactor,
-                         np.float(row[1].strip())))
+            data.append((np.float64(row[0].strip())*timefactor,
+                         np.float64(row[1].strip())))
             if len(row) == 5:
                 # this has to be correlation with weights
-                weights.append(np.float(row[4].strip()))
+                weights.append(np.float64(row[4].strip()))
                 if weightname == "external":
                     try:
                         weightname = "ext. " + \

pycorrfit/readfiles/read_FCS_Confocor3.py

@@ -144,8 +144,8 @@ def openFCS_Multiple(path):
                     for row in readtrace:
                         # tau in ms, trace in kHz
                         # So we need to put some factors here
-                        trace.append((np.float(row[3])*1000,
-                                      np.float(row[4])/1000))
+                        trace.append((np.float64(row[3])*1000,
+                                      np.float64(row[4])/1000))
                     trace = np.array(trace)
                     # If the trace is too big. Wee need to bin it.
                     newtrace = util.downsample_trace(trace)
@@ -172,8 +172,8 @@ def openFCS_Multiple(path):
                     corr = []
                     for row in readcorr:
                         # tau in ms, corr-function
-                        corr.append((np.float(row[3])*1000,
-                                     np.float(row[4])-1))
+                        corr.append((np.float64(row[3])*1000,
+                                     np.float64(row[4])-1))
                     if FoundType[:2] == "AC":
                         ac_correlations.append(np.array(corr))
                     elif FoundType[:2] == "CC":
@@ -360,7 +360,7 @@ def openFCS_Single(path):
                     for row in readtrace:
                         # tau in ms, trace in kHz
                         # So we need to put some factors here
-                        trace.append((np.float(row[0])*1000, np.float(row[1])))
+                        trace.append((np.float64(row[0])*1000, np.float64(row[1])))
                     trace = np.array(trace)
                     # If the trace is too big. Wee need to bin it.
                     newtrace = util.downsample_trace(trace)
@@ -378,7 +378,7 @@ def openFCS_Single(path):
                     corr = []
                     for row in readcorr:
                         # tau in ms, corr-function
-                        corr.append((np.float(row[0]), np.float(row[1])-1))
+                        corr.append((np.float64(row[0]), np.float64(row[1])-1))
                     corr = np.array(corr)
                 fcscurve = False
 

pycorrfit/readfiles/read_SIN_correlator_com.py

@@ -274,29 +274,29 @@ def openSIN_old(path):
         corrdata = []
         for row in readcorr:
             # tau in ms, corr-function minus "1"
-            corrdata.append((np.float(row[0])*timefactor, np.float(row[1])-1))
+            corrdata.append((np.float64(row[0])*timefactor, np.float64(row[1])-1))
         correlations.append(np.array(corrdata))
         trace = []
         for row in readtrace:
             # tau in ms, corr-function minus "1"
-            trace.append((np.float(row[0])*timefactor,
-                          np.float(row[1])/timedivfac))
+            trace.append((np.float64(row[0])*timefactor,
+                          np.float64(row[1])/timedivfac))
         traces.append(np.array(trace))
     elif Mode == "Single Cross":
         curvelist.append("CC")
         corrdata = []
         for row in readcorr:
             # tau in ms, corr-function minus "1"
-            corrdata.append((np.float(row[0])*timefactor, np.float(row[1])-1))
+            corrdata.append((np.float64(row[0])*timefactor, np.float64(row[1])-1))
         correlations.append(np.array(corrdata))
         trace1 = []
         trace2 = []
         for row in readtrace:
             # tau in ms, corr-function minus "1"
-            trace1.append((np.float(row[0])*timefactor,
-                           np.float(row[1])/timedivfac))
-            trace2.append((np.float(row[0])*timefactor,
-                           np.float(row[2])/timedivfac))
+            trace1.append((np.float64(row[0])*timefactor,
+                           np.float64(row[1])/timedivfac))
+            trace2.append((np.float64(row[0])*timefactor,
+                           np.float64(row[2])/timedivfac))
         traces.append([np.array(trace1), np.array(trace2)])
     elif Mode == "Dual Auto":
         curvelist.append("AC1")
@@ -305,18 +305,18 @@ def openSIN_old(path):
         corrdata2 = []
         for row in readcorr:
             # tau in ms, corr-function minus "1"
-            corrdata1.append((np.float(row[0])*timefactor, np.float(row[1])-1))
-            corrdata2.append((np.float(row[0])*timefactor, np.float(row[2])-1))
+            corrdata1.append((np.float64(row[0])*timefactor, np.float64(row[1])-1))
+            corrdata2.append((np.float64(row[0])*timefactor, np.float64(row[2])-1))
         correlations.append(np.array(corrdata1))
         correlations.append(np.array(corrdata2))
         trace1 = []
         trace2 = []
         for row in readtrace:
             # tau in ms, corr-function minus "1"
-            trace1.append((np.float(row[0])*timefactor,
-                           np.float(row[1])/timedivfac))
-            trace2.append((np.float(row[0])*timefactor,
-                           np.float(row[2])/timedivfac))
+            trace1.append((np.float64(row[0])*timefactor,
+                           np.float64(row[1])/timedivfac))
+            trace2.append((np.float64(row[0])*timefactor,
+                           np.float64(row[2])/timedivfac))
         traces.append(np.array(trace1))
         traces.append(np.array(trace2))
     elif Mode == "Dual Cross":
@@ -326,18 +326,18 @@ def openSIN_old(path):
         corrdata2 = []
         for row in readcorr:
             # tau in ms, corr-function minus "1"
-            corrdata1.append((np.float(row[0])*timefactor, np.float(row[1])-1))
-            corrdata2.append((np.float(row[0])*timefactor, np.float(row[2])-1))
+            corrdata1.append((np.float64(row[0])*timefactor, np.float64(row[1])-1))
+            corrdata2.append((np.float64(row[0])*timefactor, np.float64(row[2])-1))
         correlations.append(np.array(corrdata1))
         correlations.append(np.array(corrdata2))
         trace1 = []
         trace2 = []
         for row in readtrace:
             # tau in ms, corr-function minus "1"
-            trace1.append((np.float(row[0])*timefactor,
-                           np.float(row[1])/timedivfac))
-            trace2.append((np.float(row[0])*timefactor,
-                           np.float(row[2])/timedivfac))
+            trace1.append((np.float64(row[0])*timefactor,
+                           np.float64(row[1])/timedivfac))
+            trace2.append((np.float64(row[0])*timefactor,
+                           np.float64(row[2])/timedivfac))
         traces.append([np.array(trace1), np.array(trace2)])
         traces.append([np.array(trace1), np.array(trace2)])
     elif Mode == "Quad":
@@ -351,12 +351,12 @@ def openSIN_old(path):
         corrdata21 = []
         for row in readcorr:
             # tau in ms, corr-function minus "1"
-            corrdata1.append((np.float(row[0])*timefactor, np.float(row[1])-1))
-            corrdata2.append((np.float(row[0])*timefactor, np.float(row[2])-1))
+            corrdata1.append((np.float64(row[0])*timefactor, np.float64(row[1])-1))
+            corrdata2.append((np.float64(row[0])*timefactor, np.float64(row[2])-1))
             corrdata12.append(
-                (np.float(row[0])*timefactor, np.float(row[3])-1))
+                (np.float64(row[0])*timefactor, np.float64(row[3])-1))
             corrdata21.append(
-                (np.float(row[0])*timefactor, np.float(row[4])-1))
+                (np.float64(row[0])*timefactor, np.float64(row[4])-1))
         correlations.append(np.array(corrdata1))
         correlations.append(np.array(corrdata2))
         correlations.append(np.array(corrdata12))
@@ -365,10 +365,10 @@ def openSIN_old(path):
         trace2 = []
         for row in readtrace:
             # tau in ms, corr-function minus "1"
-            trace1.append((np.float(row[0])*timefactor,
-                           np.float(row[1])/timedivfac))
-            trace2.append((np.float(row[0])*timefactor,
-                           np.float(row[2])/timedivfac))
+            trace1.append((np.float64(row[0])*timefactor,
+                           np.float64(row[1])/timedivfac))
+            trace2.append((np.float64(row[0])*timefactor,
+                           np.float64(row[2])/timedivfac))
         traces.append(np.array(trace1))
         traces.append(np.array(trace2))
         traces.append([np.array(trace1), np.array(trace2)])

pycorrfit/readfiles/read_cor_PicoQuant.py

@@ -0,0 +1,97 @@
+"""PicoQuant .cor files"""
+import csv
+import pathlib
+import warnings
+
+import numpy as np
+
+class LoadCORError(BaseException):
+    pass
+
+def get_header_index(L, elem):
+    try:
+        index = L.index(elem)
+    except ValueError:
+        raise ValueError(f'Expected {elem} in the data header, but found only {L}')
+
+    return index
+
+def openCOR(path, filename=None):
+    """
+    Read data from a PicoQuant .cor file.
+
+    This format consists of a metadata header, a blank line, then a
+    whitespace-delimited data header followed by a data array.
+    ------------------------------------------------------------
+    TTTR Correlator Export
+    PicoHarp Software version 3.0.0.3 format version 3.0
+    Raw data: c:\\users\\baker lab 432-a\\desktop\\grant fcs\\default_013.ptu
+    Recorded: 16/12/22 17:40:13
+    Mode: T2
+    Routing Mask A: 0 1 0 0 0
+    Routing Mask B: 1 0 0 0 0
+    Start time [s]: 0.000000
+    Time span [s]: 7.545534
+    Counts A: 382989
+    Counts B: 523316
+    Tau resolution [s]: 0.00000002500000
+
+     taustep       tau/s        G(A,A)    G(B,B)    G(A,B)
+          6     0.0000001500    0.8375    0.3556    0.1708
+          7     0.0000001750    0.6503    0.2652    0.1445
+          9     0.0000002250    0.5680    0.2173    0.1264
+         11     0.0000002750    0.4836    0.1594    0.1614
+         13     0.0000003250    0.3241    0.1528    0.1486
+         15     0.0000003750    0.2819    0.1236    0.1641
+         17     0.0000004250    0.3591    0.1467    0.1102
+    [...]
+    ------------------------------------------------------------
+
+    Returns:
+    A dictionary containing:
+       Trace: list of 2d np.array containing two columns:
+              1st: tau in ms
+              2nd: corresponding correlation signal
+       Type: list of strings indictating the type of each correlation
+       Filename: the basename for each file loaded
+       Trace: blank list, since cor does not store intensity traces
+    """
+    path = pathlib.Path(path)
+    if filename is not None:
+        warnings.warn("Using `filename` is deprecated.", DeprecationWarning)
+        path = path / filename
+    filename = path.name
+
+    s_to_ms = 1000 # cor provides seconds, we want ms
+
+    corfile = path.open('r', encoding='utf-8')
+    header = list()
+    for line in corfile:
+        header.append(line.strip())
+        if header[-1] == '':
+            break
+
+    if header[0] != 'TTTR Correlator Export':
+        raise ValueError(f'Error while reading {path.name}. Expected the first line to be "TTTR Correlator Export"')        
+
+    data_header = next(corfile).split()
+    data = np.loadtxt(corfile)
+
+    tau = data[:, get_header_index(data_header, 'tau/s')]*s_to_ms
+    correlation_names = ['G(A,A)', 'G(A,B)', 'G(B,B)']
+    correlation_types = ['AC',     'CC',     'AC'    ]
+    correlations = list()
+
+    for correlation_name in correlation_names:
+        correlation = data[:, get_header_index(data_header, correlation_name)]
+        cor = np.zeros((len(correlation), 2), dtype=correlation.dtype)
+        cor[:, 0] = tau
+        cor[:, 1] = correlation
+        correlations.append(cor)
+
+    dictionary = dict()
+    dictionary['Correlation'] = correlations
+    dictionary['Trace'] = [[] for _ in dictionary['Correlation']]
+    dictionary['Type'] = correlation_types
+    dictionary['Filename'] = [path.name for _ in dictionary['Correlation']]
+    return dictionary