Exemplo n.º 1
0
def preliminary():
    """Creates a preliminary template"""
    stf.set_peak_start(209900)
    stf.set_peak_end(210500)
    stf.set_fit_start(209900)
    stf.set_fit_end(210400)
    stf.set_peak_mean(3)
    stf.set_base_start(209600)
    stf.set_base_end(209900)
    stf.measure()
    return stf.leastsq(5)
Exemplo n.º 2
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def batch_cursors():
    """Sets appropriate cursor positions for analysing
    the extracted events."""
    stf.set_peak_start(100)
    stf.set_peak_end(598)
    stf.set_fit_start(120)
    stf.set_fit_end(598)
    stf.set_peak_mean(3)
    stf.set_base_start(0)
    stf.set_base_end(100)
    stf.measure()
Exemplo n.º 3
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def final():
    """Creates a final template"""
    stf.set_peak_start(100)
    stf.set_peak_end(599)
    stf.set_fit_start(100)
    stf.set_fit_end(599)
    stf.set_peak_mean(3)
    stf.set_base_start(0)
    stf.set_base_end(100)
    stf.measure()
    return stf.leastsq(5)
Exemplo n.º 4
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def preliminary():
    """Creates a preliminary template"""
    stf.set_peak_start(209900)
    stf.set_peak_end(210500)
    stf.set_fit_start(209900)
    stf.set_fit_end(210400)
    stf.set_peak_mean(3)
    stf.set_base_start(209600)
    stf.set_base_end(209900)
    stf.measure()
    return stf.leastsq(5)
Exemplo n.º 5
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def batch_cursors():
    """Sets appropriate cursor positions for analysing
    the extracted events."""
    stf.set_peak_start(100)
    stf.set_peak_end(598)
    stf.set_fit_start(120)
    stf.set_fit_end(598)
    stf.set_peak_mean(3)
    stf.set_base_start(0)
    stf.set_base_end(100)
    stf.measure()
Exemplo n.º 6
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def final():
    """Creates a final template"""
    stf.set_peak_start(100)
    stf.set_peak_end(599)
    stf.set_fit_start(100)
    stf.set_fit_end(599)
    stf.set_peak_mean(3)
    stf.set_base_start(0)
    stf.set_base_end(100)
    stf.measure()
    return stf.leastsq(5)
Exemplo n.º 7
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def yvalue(origin, interval):

    stf.set_fit_start(origin, True)
    stf.set_fit_end(origin + interval, True)
    stf.measure()
    x = int(stf.get_fit_end(False))
    y = []
    for i in range(stf.get_size_channel()):
        stf.set_trace(i)
        y.append(stf.get_trace(i)[x])

    return y
Exemplo n.º 8
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def fit_experiment(params, pulse_length, function_to_fit):

    num_sweeps = stf.get_size_channel()
    stf.set_channel(0)
    stf.set_trace(0)

    #jjm_analysis.set_params(params);
    #stf.measure();
    #this is in samples
    #peak_index = stf.peak_index();
    #stf.set_fit_start(peak_index, is_time=False);
    #fit_start_time = peak_index*stf.get_sampling_interval();
    #stf.set_fit_end(fit_start_time+pulse_length-(10*stf.get_sampling_interval()), is_time=True);
    #fit_func = stf.leastsq(function_to_fit);
    #fit_func['Baseline(pA)']=stf.get_base();
    #fit_df = pd.DataFrame(fit_func, index=[0]);

    fits = []
    traces = []
    for x in range(0, num_sweeps):
        stf.set_trace(x)
        jjm_analysis.set_params(params)
        stf.measure()
        #this is in samples
        peak_index = stf.peak_index()
        stf.set_fit_start(peak_index, is_time=False)
        fit_start_time = peak_index * stf.get_sampling_interval()
        stf.set_fit_end(fit_start_time + pulse_length -
                        (10 * stf.get_sampling_interval()),
                        is_time=True)
        sweep_fit = stf.leastsq(function_to_fit)
        sweep_fit['Baseline(pA)'] = stf.get_base()
        fits.append(sweep_fit)
        traces.append(x)

    fit_df = pd.DataFrame(fits)
    return (fit_df)
Exemplo n.º 9
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def EPSPtrains(latency=200,
               numStim=4,
               intvlList=[1, 0.8, 0.6, 0.4, 0.2, 0.1, 0.08, 0.06, 0.04, 0.02]):

    # Initialize
    numTrains = len(intvlList)  # Number of trains
    intvlArray = np.array(intvlList) * 1000  # Units in ms
    si = stf.get_sampling_interval()  # Units in ms

    # Background subtraction
    traceBaselines = []
    subtractedTraces = []
    k = 1e-4
    x = [i * stf.get_sampling_interval() for i in range(stf.get_size_trace())]
    for i in range(numTrains):
        stf.set_trace(i)
        z = x
        y = stf.get_trace()
        traceBaselines.append(y)
        ridx = []
        if intvlArray[i] > 500:
            for j in range(numStim):
                ridx += range(
                    int(round(((intvlArray[i] * j) + latency - 1) / si)),
                    int(round(
                        ((intvlArray[i] * (j + 1)) + latency - 1) / si)) - 1)
        else:
            ridx += range(
                int(round((latency - 1) / si)),
                int(
                    round(((intvlArray[i] *
                            (numStim - 1)) + latency + 500) / si)) - 1)
        ridx += range(int(round(4999 / si)), int(round(5199 / si)))
        z = np.delete(z, ridx, 0)
        y = np.delete(y, ridx, 0)
        yi = np.interp(x, z, y)
        yf = signal.symiirorder1(yi, (k**2), 1 - k)
        traceBaselines.append(yf)
        subtractedTraces.append(stf.get_trace() - yf)
    stf.new_window_list(traceBaselines)
    stf.new_window_list(subtractedTraces)

    # Measure depolarization
    # Initialize variables
    a = []
    b = []

    # Set baseline start and end cursors
    stf.set_base_start(np.round(
        (latency - 50) / si))  # Average during 50 ms period before stimulus
    stf.set_base_end(np.round(latency / si))

    # Set fit start cursor
    stf.set_fit_start(np.round(latency / si))
    stf.set_fit_end(
        np.round(((intvlArray[1] * (numStim - 1)) + latency + 1000) /
                 si))  # Include a 1 second window after last stimulus

    # Start AUC calculations
    for i in range(numTrains):
        stf.set_trace(i)
        stf.measure()
        b.append(stf.get_base())
        n = int(stf.get_fit_end() + 1 - stf.get_fit_start())
        x = np.array([k * stf.get_sampling_interval() for k in range(n)])
        y = stf.get_trace()[int(stf.get_fit_start()):int(stf.get_fit_end() +
                                                         1)]
        a.append(np.trapz(y - b[i], x))  # Units in V.s

    return a
Exemplo n.º 10
0
def wcp(V_step=-5, step_start=10, step_duration=20):
    """
    Measures whole cell properties. Specifically, this function returns the
    voltage clamp step estimates of series resistance, input resistance, cell
    membrane resistance, cell membrane capacitance, cell surface area and
    specific membrane resistance.
    
    The series (or access) resistance is obtained my dividing the voltage step
    by the peak amplitude of the current transient (Ogden, 1994): Rs = V / Ip
    
    The input resistance is obtained by dividing the voltage step by the average
    amplitude of the steady-state current (Barbour, 2014): Rin = V / Iss
    
    The cell membrane resistance is calculated by subtracting the series
    resistance from the input resistance (Barbour, 1994): Rm = Rin - Rs
    
    The cell membrane capacitance is estimated by dividing the transient charge
    by the size of the voltage-clamp step (Taylor et al. 2012): Cm = Q / V
    
    The cell surface area is estimated by dividing the cell capacitance by the
    specific cell capacitance, c (1.0 uF/cm^2; Gentet et al. 2000; Niebur, 2008):
    Area = Cm / c
    
    The specific membrane resistance is calculated by multiplying the cell
    membrane resistance with the cell surface area: rho = Rm * Area

    Users should be aware of the approximate nature of determining cell
    capacitance and derived parameters from the voltage-clamp step method
    (Golowasch, J. et al., 2009)

    References:
    Barbour, B. (2014) Electronics for electrophysiologists. Microelectrode
     Techniques workshop tutorial.
     www.biologie.ens.fr/~barbour/electronics_for_electrophysiologists.pdf
    Gentet, L.J., Stuart, G.J., and Clements, J.D. (2000) Direct measurement
     of specific membrane capacitance in neurons. Biophys J. 79(1):314-320
    Golowasch, J. et al. (2009) Membrane Capacitance Measurements Revisited:
     Dependence of Capacitance Value on Measurement Method in Nonisopotential
     Neurons. J Neurophysiol. 2009 Oct; 102(4): 2161-2175.
    Niebur, E. (2008), Scholarpedia, 3(6):7166. doi:10.4249/scholarpedia.7166
     www.scholarpedia.org/article/Electrical_properties_of_cell_membranes
     (revision #13938, last accessed 30 April 2018)
    Ogden, D. Chapter 16: Microelectrode electronics, in Ogden, D. (ed.)
     Microelectrode Techniques. 1994. 2nd Edition. Cambridge: The Company
     of Biologists Limited.
    Taylor, A.L. (2012) What we talk about when we talk about capacitance
     measured with the voltage-clamp step method J Comput Neurosci.
     32(1):167-175
    """

    # Error checking
    if stf.get_yunits() != "pA":
        raise ValueError('The recording is not voltage clamp')

    # Prepare variables from input arguments
    si = stf.get_sampling_interval()
    t0 = step_start / si
    l = step_duration / si

    # Set cursors and update measurements
    stf.set_base_start((step_start - 1) / si)
    stf.set_base_end(t0 - 1)
    stf.set_peak_start(t0)
    stf.set_peak_end((step_start + 1) / si)
    stf.set_fit_start(t0)
    stf.set_fit_end(t0 + l - 1)
    stf.set_peak_direction("both")
    stf.measure()

    # Calculate series resistance (Rs) from initial transient
    b = stf.get_base()
    Rs = 1000 * V_step / (stf.get_peak() - b)  # in Mohm

    # Calculate charge delivered during the voltage clamp step
    n = int(stf.get_fit_end() + 1 - stf.get_fit_start())
    x = [i * stf.get_sampling_interval() for i in range(n)]
    y = stf.get_trace()[int(stf.get_fit_start()):int(stf.get_fit_end() + 1)]
    Q = np.trapz(y - b, x)

    # Set cursors and update measurements
    stf.set_base_start(t0 + l - 1 - (step_duration / 4) / si)
    stf.set_base_end(t0 + l - 1)
    stf.measure()

    # Measure steady state current and calculate input resistance
    I = stf.get_base() - b
    Rin = 1000 * V_step / I  # in Mohm

    # Calculate cell membrane resistance
    Rm = Rin - Rs  # in Mohm

    # Calculate voltage-clamp step estimate of the cell capacitance
    t = x[-1] - x[0]
    Cm = (Q - I * t) / V_step  # in pF

    # Estimate membrane surface area, where the capacitance per unit area is 1.0 uF/cm^2
    A = Cm * 1e-06 / 1.0  # in cm^2

    # Calculate specific membrane resistance
    rho = 1e+03 * Rm * A  # in kohm.cm^2; usually 10 at rest

    # Create table of results
    retval = []
    retval += [("Holding current (pA)", b)]
    retval += [("Series resistance (Mohm)", Rs)]
    retval += [("Input resistance (Mohm)", Rin)]
    retval += [("Cell resistance (Mohm)", Rm)]
    retval += [("Cell capacitance (pF)", Cm)]
    retval += [("Surface area (um^2)", A * 1e+04**2)]
    retval += [("Membrane resistivity (kohm.cm^2)", rho)]
    retval = dict(retval)
    stf.show_table(retval, "Whole-cell properties")

    return retval
Exemplo n.º 11
0
def glu_iv(pulses=13, subtract_base=True):
    """Calculates an iv from a repeated series of fast application and
    voltage pulses. 

    Keyword arguments:
    pulses        -- Number of pulses for the iv.
    subtract_base -- If True (default), baseline will be subtracted.
    
    Returns:
    True if successful.
    """

    # Some ugly definitions for the time being
    # Cursors are in ms here.
    gFitEnd = 330.6  # fit end cursor is variable
    gFSelect = 0  # Monoexp
    gDictSize = stf.leastsq_param_size(
        gFSelect) + 2  # Parameters, chisqr, peak value
    gBaseStart = 220.5  # Start and end of the baseline before the control pulse, in ms
    gBaseEnd = 223.55
    gPeakStart = 223.55  # Start and end of the peak cursors for the control pulse, in ms
    gPeakEnd = 253.55

    if (gDictSize < 0):
        print('Couldn\'t retrieve function id=%d, aborting now.' % gFSelect)
        return False

    if (not (stf.check_doc())):
        print('Couldn\'t find an open file; aborting now.')
        return False

    # analyse iv, subtract baseline if requested:
    ivtools.analyze_iv(pulses)
    if (subtract_base == True):
        if (not (stf.set_base_start(gBaseStart, True))): return False
        if (not (stf.set_base_end(gBaseEnd, True))): return False
        stf.measure()
        stf.select_all()
        stf.subtract_base()

    # set cursors:
    if (not (stf.set_peak_start(gPeakStart, True))): return False
    if (not (stf.set_peak_end(gPeakEnd, True))): return False
    if (not (stf.set_base_start(gBaseStart, True))): return False
    if (not (stf.set_base_end(gBaseEnd, True))): return False
    if (not (stf.set_fit_end(gFitEnd, True))): return False

    if (not (stf.set_peak_mean(3))): return False
    if (not (stf.set_peak_direction("both"))): return False

    # A list for dictionary keys and values:
    dict_keys = []
    dict_values = np.empty((gDictSize, stf.get_size_channel()))
    firstpass = True
    for n in range(0, stf.get_size_channel()):
        if (stf.set_trace(n) == False):
            print('Couldn\'t set a new trace; aborting now.')
            return False

        print('Analyzing trace %d of %d' % (n + 1, stf.get_size_channel()))
        # set the fit window cursors:
        if (not (stf.set_fit_start(stf.peak_index()))): return False

        # Least-squares fitting:
        p_dict = stf.leastsq(gFSelect)

        if (p_dict == 0):
            print('Couldn\'t perform a fit; aborting now.')
            return False

        # Create an empty list:
        tempdict_entry = []
        row = 0
        for k, v in p_dict.iteritems():
            if (firstpass == True):
                dict_keys.append(k)
            dict_values[row][n] = v
            row = row + 1

        if (firstpass):
            dict_keys.append("Peak amplitude")
        dict_values[row][n] = stf.get_peak() - stf.get_base()

        firstpass = False

    retDict = dict()
    # Create the dictionary for the table:
    entry = 0
    for elem in dict_keys:
        retDict[elem] = dict_values[entry].tolist()
        entry = entry + 1

    return stf.show_table_dictlist(retDict)
Exemplo n.º 12
0
def glu_iv( pulses = 13, subtract_base=True ):
    """Calculates an iv from a repeated series of fast application and
    voltage pulses. 

    Keyword arguments:
    pulses        -- Number of pulses for the iv.
    subtract_base -- If True (default), baseline will be subtracted.
    
    Returns:
    True if successful.
    """

    # Some ugly definitions for the time being
    # Cursors are in ms here.
    gFitEnd = 330.6 # fit end cursor is variable
    gFSelect  =  0 # Monoexp
    gDictSize =  stf.leastsq_param_size( gFSelect ) + 2 # Parameters, chisqr, peak value
    gBaseStart  = 220.5 # Start and end of the baseline before the control pulse, in ms
    gBaseEnd    = 223.55
    gPeakStart  = 223.55 # Start and end of the peak cursors for the control pulse, in ms
    gPeakEnd = 253.55 
    
    if ( gDictSize < 0 ):
        print('Couldn\'t retrieve function id=%d, aborting now.'%gFSelect)
        return False        
    
    if ( not(stf.check_doc()) ):
        print('Couldn\'t find an open file; aborting now.')
        return False
    
    # analyse iv, subtract baseline if requested:
    ivtools.analyze_iv( pulses )
    if ( subtract_base == True ):
        if ( not(stf.set_base_start( gBaseStart, True )) ): return False
        if ( not(stf.set_base_end( gBaseEnd, True )) ): return False
        stf.measure()
        stf.select_all()
        stf.subtract_base()
    
    # set cursors:
    if ( not(stf.set_peak_start( gPeakStart, True )) ): return False
    if ( not(stf.set_peak_end( gPeakEnd, True )) ): return False
    if ( not(stf.set_base_start( gBaseStart, True )) ): return False
    if ( not(stf.set_base_end( gBaseEnd, True )) ): return False
    if ( not(stf.set_fit_end( gFitEnd, True )) ): return False
    
    if ( not(stf.set_peak_mean( 3 )) ): return False
    if ( not(stf.set_peak_direction( "both" )) ): return False

    # A list for dictionary keys and values:
    dict_keys = []
    dict_values = np.empty( (gDictSize, stf.get_size_channel()) )
    firstpass = True
    for n in range( 0, stf.get_size_channel() ):
        if ( stf.set_trace( n ) == False ):
            print('Couldn\'t set a new trace; aborting now.')
            return False
        
        print('Analyzing trace %d of %d'%( n+1, stf.get_size_channel() ) )
        # set the fit window cursors:
        if ( not(stf.set_fit_start( stf.peak_index() )) ): return False
        
        # Least-squares fitting:
        p_dict = stf.leastsq( gFSelect )
        
        if ( p_dict == 0 ):
            print('Couldn\'t perform a fit; aborting now.')
            return False
            
        # Create an empty list:
        tempdict_entry = []
        row = 0
        for k, v in p_dict.iteritems():
            if ( firstpass == True ):
                dict_keys.append( k )
            dict_values[row][n] = v 
            row = row+1
        
        if ( firstpass ):
            dict_keys.append( "Peak amplitude" )
        dict_values[row][n] = stf.get_peak()-stf.get_base()
        
        firstpass = False
    
    retDict = dict()
    # Create the dictionary for the table:
    entry = 0
    for elem in dict_keys:
        retDict[ elem ] = dict_values[entry].tolist()
        entry = entry+1
   
    return stf.show_table_dictlist( retDict )