pyDSF/pydsf.py

#! /usr/bin/env python2
# -*- coding: utf-8 -*-
import os
import csv

try:
    import matplotlib as mpl
    mpl.use('Qt4Agg')
    import matplotlib.ticker as ticker
    import matplotlib.pyplot as plt
except ImportError:
    raise ImportError('----- Matplotlib must be installed. -----')

from peakdetect import *

try:
    import numpy as np
except ImportError:
    raise ImportError('----- NumPy must be installed. -----')

try:
    from scipy.signal import filtfilt,  butter
    from scipy import interpolate
    from scipy import optimize
except ImportError:
    raise ImportError('----- SciPy must be installed. -----')

class Well:
    def __init__(self,  owner):
        self.owner = owner
        self.name = None
        self.raw = np.zeros(self.owner.reads,  dtype=np.float)
        self.filtered = np.zeros(self.owner.reads,  dtype=np.float)
        self.derivatives = np.zeros((4,  self.owner.reads))
        self.splines = {"raw": None,  
                                 "filtered": None,  
                                 "derivative1": None}
        self.tm = None
        self.tm_sd = None
        
    def filter_raw(self):
        """
        Apply a filter to the raw data
        """
        b, a = butter(3, 0.3)
        self.filtered = filtfilt(b, a, self.raw)
        
    def calc_spline(self, y):
        """
        Calculate a spline that represents the smoothed data points
        """
        spline = interpolate.InterpolatedUnivariateSpline(self.owner.temprange, y)
        return spline 
        
    def calc_derivatives(self, spline='filtered'):
        for t in self.owner.temprange:
            temp = self.splines[spline].derivatives(t)
            for i in range(4):
                self.derivatives[i,  t - self.owner.t1] = temp[i]
                
    def calc_tm(self):
        """
        Calculate the Tm of the well. Returns either the Tm or 'None'.
        """
        
        # Check if the well has already been flagged as denatured
        if self in self.owner.denatured_wells:
            return None # Return 'None' if true
        
        # First assume that the well is denatured
        self.owner.denatured_wells.append(self)

        x = self.owner.temprange
        y = self.derivatives[1]
        lookahead = len(x)/4    # Amount of data points to look around potential peaks to verify
        try:
            peaks = peakdetect(y, x,  lookahead) # Run peak finding algorithm
        except:
            return None # In case of error, return no peak
        
        try:
            for i in peaks[0]: # Iterate over all peaks
                tm = i[0] # Highest peak found is the first in list
                # Check if the peak is within cutoff range
                if tm and tm >= self.owner.tm_cutoff_low and tm <= self.owner.tm_cutoff_high:
                    self.owner.denatured_wells.remove(self) # If everything is fine, remove the denatured flag
                    return tm # and return the Tm
                else:
                    return None # otherwise, return no Tm
        except:
            return None # In case of error, return nothing
            
    def is_denatured(self):
        """
        Check if the well is denatured. Returns true if the well has been already flagged as
        denatured, no Tm was found, or if the initial signal intensity is above a user definded
        threshold.
        """
        denatured = True # Assumption is that the well is denatured
        
        if self in self.owner.denatured_wells: # check if the well is already flagged as denatured
            return denatured # return true if it is
            
        if self.tm and (self.tm <= self.owner.tm_cutoff_low or self.tm >= self.owner.tm_cutoff_high):
            denatured = True
            return denatured
            
        for i in self.derivatives[1]: # Iterate over all points in the first derivative
            if i > 0: # If a positive slope is found
                denatured = False # set denatured flag to False
        
        reads = int(round(self.owner.reads/10)) # How many values should be checked against the signal threshold:
                                                # 1/10 of the total number of data point
        read = 0 # Initialize running variable representing the current data point
        
        if not denatured:
            for j in self.filtered: # Iterate over the filtered data
                if self.owner.signal_threshold:                           # If a signal threshold was defined
                    if j > self.owner.signal_threshold and read <= reads: # iterate over 1/10 of all data points 
                                                                          # and check for values larger than the threshold.
                        denatured = True # Set flag to True if a match is found
                        print("{}: {}".format(self.name, j))
                        return denatured # and return
            read += 1
            
        return denatured
                
    def analyze(self):
        self.filter_raw()
        self.splines["raw"] = self.calc_spline(self.raw)
        self.splines["filtered"] = self.calc_spline(self.filtered)
        
        self.calc_derivatives()
        if self.is_denatured():
            self.owner.denatured_wells.append(self)
        
        self.splines["derivative1"] = self.calc_spline(self.derivatives[1])
        
        self.tm = self.calc_tm()
        #print(self.tm)
        
class Experiment:
    def __init__(self, type, gui=None, files=None, replicates=None, t1=25, t2=95, dt=1, cols=12, rows=8,  cutoff_low=None,  cutoff_high=None,  signal_threshold=None,  color_range=None):
        self.replicates = replicates
        self.cols = cols
        self.rows = rows
        self.t1 = t1
        self.t2 = t2
        self.dt = dt
        self.temprange = np.arange(self.t1, self.t2 + 1, self.dt,  dtype=float)
        self.reads = int(round((t2 + 1 - t1) / dt))
        self.wellnum = self.cols * self.rows
        self.files = files
        self.type = type
        self.wells = []
        self.max_tm = None
        self.min_tm = None
        self.replicates = None
        self.gui=gui
        self.signal_threshold = signal_threshold
        self.avg_plate = None
        if cutoff_low:
            self.tm_cutoff_low = cutoff_low
        else:
            self.tm_cutoff_low = self.t1
        if cutoff_high:
            self.tm_cutoff_high = cutoff_high
        else:
            self.tm_cutoff_high = self.t2
        if color_range:
            self.color_range = color_range
        else:
            self.color_range = None
            
        self.plates = []
            
        i = 1
        for file in files:
            plate = Plate(type=self.type, filename=file, t1=self.t1, t2=self.t2, dt=self.dt, cols=self.cols,  rows=self.rows,  cutoff_low=self.tm_cutoff_low,  cutoff_high=self.tm_cutoff_high,  signal_threshold=self.signal_threshold,  color_range=self.color_range)
            plate.id = i
            self.plates.append(plate)
            i += 1
        if len(files) > 1:
            self.avg_plate = Plate(type=self.type, filename=None, t1=self.t1, t2=self.t2, dt=self.dt, cols=self.cols,  rows=self.rows,  cutoff_low=self.tm_cutoff_low,  cutoff_high=self.tm_cutoff_high,  signal_threshold=self.signal_threshold,  color_range=self.color_range)
            self.avg_plate.id = 'average'
 
    def analyze(self):
        for plate in self.plates:
            plate.analyze(gui=self.gui)
            
        if len(self.plates) > 1:
        
            #self.tm_replicates = np.zeros( self.wellnum, dtype=float )
            #self.tm_replicates_sd = np.zeros( self.wellnum, dtype=float )
        
        
            for i in range(self.wellnum):
                tmp =  []
                for plate in self.plates:
                    tm = plate.wells[i].tm
                    self.avg_plate.wells[i].name = plate.wells[i].name
                    if plate.wells[i] not in plate.denatured_wells:
                        tmp.append(tm)
                if len(tmp) > 0:
                    #self.avg_plate.wells[i].tm = (sum(tmp)/len(tmp))
                    self.avg_plate.wells[i].tm = np.mean(tmp)
                    self.avg_plate.wells[i].tm_sd = np.std(tmp)
                    #self.tm_replicates[i] = (sum(tmp)/len(tmp))
                else:
                    self.avg_plate.denatured_wells.append(self.avg_plate.wells[i])


class Plate:
    def __init__(self,  type, id=None, filename=None, replicates=None, t1=25, t2=95, dt=1, cols=12, rows=8,  cutoff_low=None,  cutoff_high=None,  signal_threshold=None,  color_range=None):
        self.cols = cols
        self.rows = rows
        self.t1 = t1
        self.t2 = t2
        self.dt = dt
        self.temprange = np.arange(self.t1, self.t2 + 1, self.dt,  dtype=float)
        self.reads = int(round((t2 + 1 - t1) / dt))
        self.wellnum = self.cols * self.rows
        self.filename = filename
        self.type = type
        self.wells = []
        self.max_tm = None
        self.min_tm = None
        self.replicates = None
        self.signal_threshold = signal_threshold
        self.id = id
        if cutoff_low:
            self.tm_cutoff_low = cutoff_low
        else:
            self.tm_cutoff_low = self.t1
        if cutoff_high:
            self.tm_cutoff_high = cutoff_high
        else:
            self.tm_cutoff_high = self.t2
        if color_range:
            self.color_range = color_range
        else:
            self.color_range = None
        
        self.denatured_wells = []
        self.tms = []
        
        for i in range(self.wellnum):
            well = Well(owner = self)
            self.wells.append(well)
                  
        #self.analyze()

    
    def analytikJena(self):
        """
        Data processing for Analytik Jena qTower 2.0 export files
        """
        with open(self.filename,  'r') as f:
            reader = csv.reader(f, delimiter=';',  quoting=csv.QUOTE_NONE)
            
            i = 0
            for row in reader:
                temp = np.zeros(self.reads,  dtype=float)
                for read in range(self.reads+1):
                    if read > 0:
                        try:
                            temp[read - 1] = row[read]
                        except:
                            temp[read - 1] = 0.0
                    elif read == 0:
                        self.wells[i].name = row[read]
                self.wells[i].raw = temp
                i += 1
                
    def analyze(self,  gui=None):
        try:
            # Try to access data file in the given path
            with open(self.filename) as f: pass
        except IOError as e:
            # If the file is not found, or not accessible: abort
            print('Error accessing file: {}'.format(e))
        
        
        if self.type == 'Analytik Jena qTOWER 2.0/2.2':
            self.analytikJena()
            if gui:
                update_progress_bar(gui.pb, 1)
        else:
            # Raise exception, if the instrument's name is unknown
            raise NameError('Unknown instrument type: {}'.format(self.type))
        
        for well in self.wells:
            well.analyze()
            if gui:
                update_progress_bar(gui.pb, 15)
            
            self.tms.append(well.tm)
            
        if self.replicates:
            if self.replicates == 'rows':
                print "rows"
            if self.replicates == 'cols':
                print "cols"
                
        self.max_tm = max(self.tms)
        self.min_tm = min(self.tms)
        
    def write_tm_table(self, filename):
        with open(filename, 'w') as f:
            f.write('#{:<4s}{:>13s}\n'.format('ID', '"Tm [°C]"'))
            for well in self.wells:
                if well.tm == None or well in self.denatured_wells:
                    f.write('{:<5s}{:>12s}\n'.format(well.name, 'NaN'))
                else:
                    f.write('{:<5s}{:>12s}\n'.format(well.name, str(well.tm)))
                    
    def write_avg_tm_table(self, filename):
        with open(filename, 'w') as f:
            f.write('#{:<4s}{:>13s}{:>13s}\n'.format('"ID"', '"Tm [°C]"', '"SD"'))
            for well in self.wells:
                if well.tm == None or well in self.denatured_wells:
                    f.write('{:<5s}{:>12s}{:>12s}\n'.format(well.name, 'NaN', 'NaN'))
                else:
                    f.write('{:<5s}{:>12s}{:>12s}\n'.format(well.name, str(well.tm), str(well.tm_sd)))
                    
    def write_raw_table(self, filename):
        with open(filename, 'w') as f:
            f.write('#"Raw data"\n')
            f.write('#{:<10s}'.format('"T [°C]"'))
            for well in self.wells:
                f.write('{:>15s}'.format(well.name))
            f.write('\n')
            
            i = 0
            for t in self.temprange:
                f.write('{:<10s}'.format(str(t)))
                for well in self.wells:
                    d = well.raw[i]
                    f.write('{:>-15.3f}'.format(float(np.round(d,  decimals=3))))
                f.write('\n')
                i += 1
                
    def write_filtered_table(self, filename):
        with open(filename, 'w') as f:
            f.write('#"Filtered data" \n')
            f.write('#{:<10s}'.format('"T [°C]"'))
            for well in self.wells:
                f.write('{:>15s}'.format(well.name))
            f.write('\n')
            
            i = 0
            for t in self.temprange:
                f.write('{:<10s}'.format(str(t)))
                for well in self.wells:
                    d = well.filtered[i]
                    f.write('{:>-15.3f}'.format(float(np.round(d,  decimals=3))))
                f.write('\n')
                i += 1
                    
    def write_derivative_table(self, filename):
        with open(filename, 'w') as f:
            f.write('#"Derivative dI/dT"\n')
            f.write('#{:<10s}'.format('"T [°C]"'))
            for well in self.wells:
                f.write('{:>15s}'.format(well.name))
            f.write('\n')
            
            i = 0
            for t in self.temprange:
                f.write('{:<10s}'.format(str(t)))
                for well in self.wells:
                    d = well.derivatives[1][i]
                    f.write('{:>-15.3f}'.format(float(np.round(d,  decimals=3))))
                f.write('\n')
                i += 1
        
        
def update_progress_bar(bar, value):
    bar.setValue(value)
    
def plot_tm_heatmap_average(experiment, gui=None):
    """
    Plot Tm heatmap (Fig. 1)
    """
    x = 1               # Position in columns
    y = 1               # Position in rows
    x_values = []       # Array holding the columns
    y_values = []       # Array holding the rows
    c_values = []       # Array holding the color values aka Tm

#    c = well.tm # If not, set color to Tm
#    if c < experiment.tm_cutoff_low:     # Check if Tm is lower that the cutoff
#        c = experiment.tm_cutoff_low     # If it is, set color to cutoff
#    elif c > experiment.tm_cutoff_high:  # Check if Tm is higher that the cutoff
#        c = experiment.tm_cutoff_high    # If it is, set color to cutoff
#    else:                       # If the plate is denatured
#        c = experiment.tm_cutoff_low # Set its color to the low cutoff
    for c in experiment.tm_replicates:

        x_values.append(x)  # Add values to the respective arrays
        y_values.append(y)
        c_values.append(c)
        x += 1              # Increase column by one
        if x > experiment.cols:  # If maximum column per row is reached
            x = 1           # reset column to one
            y += 1          # and increase row by one

    fig1 = plt.figure() # new figure 
    ax1 = fig1.add_subplot(1, 1, 1) # A single canvas
    ax1.autoscale(tight=True) # Scale plate size
    ax1.xaxis.set_major_locator(ticker.MaxNLocator(experiment.cols + 1))  # n columns
    ax1.yaxis.set_major_locator(ticker.MaxNLocator(experiment.rows + 1))  # n rows
    if experiment.color_range:
        cax = ax1.scatter(x_values, y_values, s=300, c=c_values, marker='s',  vmin=experiment.color_range[0],  vmax=experiment.color_range[1]) # plot wells and color using the colormap
    else:
        cax = ax1.scatter(x_values, y_values, s=300, c=c_values, marker='s') # plot wells and color using the colormap
    ax1.invert_yaxis() # invert y axis to math plate layout
    cbar = fig1.colorbar(cax) # show colorbar
    ax1.set_xlabel('Columns') # set axis and colorbar label
    ax1.set_ylabel('Rows')
    ax1.set_title('$T_m$ heatmap (average)')
    cbar.set_label(u"Temperature [°C]")
        
        
def plot_tm_heatmap_single(plate, gui=None):
    """
    Plot Tm heatmap (Fig. 1)
    """
    x = 1               # Position in columns
    y = 1               # Position in rows
    x_values = []       # Array holding the columns
    y_values = []       # Array holding the rows
    c_values = []       # Array holding the color values aka Tm
    for well in plate.wells: # Iterate over all wells
        if well not in plate.denatured_wells: # Check if well is denatured (no Tm found)
            c = well.tm # If not, set color to Tm
            if c < plate.tm_cutoff_low:     # Check if Tm is lower that the cutoff
                c = plate.tm_cutoff_low     # If it is, set color to cutoff
            elif c > plate.tm_cutoff_high:  # Check if Tm is higher that the cutoff
                c = plate.tm_cutoff_high    # If it is, set color to cutoff
        else:                       # If the plate is denatured
            c = plate.tm_cutoff_low # Set its color to the low cutoff
        x_values.append(x)  # Add values to the respective arrays
        y_values.append(y)
        c_values.append(c)
        x += 1              # Increase column by one
        if x > plate.cols:  # If maximum column per row is reached
            x = 1           # reset column to one
            y += 1          # and increase row by one

    fig1 = plt.figure() # new figure 
    ax1 = fig1.add_subplot(1, 1, 1) # A single canvas
    ax1.autoscale(tight=True) # Scale plate size
    ax1.xaxis.set_major_locator(ticker.MaxNLocator(plate.cols + 1))  # n columns
    ax1.yaxis.set_major_locator(ticker.MaxNLocator(plate.rows + 1))  # n rows
    if plate.color_range:
        cax = ax1.scatter(x_values, y_values, s=300, c=c_values, marker='s',  vmin=plate.color_range[0],  vmax=plate.color_range[1]) # plot wells and color using the colormap
    else:
        cax = ax1.scatter(x_values, y_values, s=300, c=c_values, marker='s') # plot wells and color using the colormap
    ax1.invert_yaxis() # invert y axis to math plate layout
    cbar = fig1.colorbar(cax) # show colorbar
    ax1.set_xlabel('Columns') # set axis and colorbar label
    ax1.set_ylabel('Rows')
    if str(plate.id) == 'average':
        title = '$T_m$ heatmap (average)'
    else:
        title = '$T_m$ heatmap (plate #{})'.format(str(plate.id))
    ax1.set_title(title)
    cbar.set_label(u"Temperature [°C]")
    
#    if gui:
#        update_progress_bar(gui.pb, 50)
    
def plot_derivative(plate, gui=None):
    """
    Plot derivatives (Fig. 2)
    """
    fig2 = plt.figure() # new figure
    fig2.suptitle('Individual Derivatives (plate #{})'.format(str(plate.id))) # set title
    
    for plot_num in range(1, plate.wellnum + 1): # iterate over all wells
        well = plate.wells[plot_num - 1] # get single well based on current plot number
        ax = fig2.add_subplot(plate.rows,  plate.cols,  plot_num) # add new subplot
        ax.autoscale(tight=True) # scale to data
        ax.set_title(well.name,  size='xx-small') # set title of current subplot to well identifier
        
        if well in plate.denatured_wells:
            ax.patch.set_facecolor('#FFD6D6')
        
        if plot_num == plate.wellnum - plate.cols + 1: # add axis label to the subplot in the bottom left corner of the figure
            ax.set_xlabel(u'T [°C]',  size='xx-small')
            ax.set_ylabel('dI/dT',  size='xx-small')

        x = plate.temprange     # set values for the x axis to the given temperature range
        y = well.derivatives[1] # grab y values from the first derivative of the well
        
        ax.xaxis.set_major_locator(ticker.MaxNLocator(4)) # only show three tickmarks on both axes
        ax.yaxis.set_major_locator(ticker.MaxNLocator(4))
        if well not in plate.denatured_wells: # check if well is denatured (without determined Tm)
            tm = well.tm # if not, grab its Tm
        else:
            tm = None # else set Tm to None
        if tm:
            ax.axvline(x=tm) # plot vertical line at the Tm
        ax.axvspan(plate.t1, plate.tm_cutoff_low, facecolor='0.8',  alpha=0.5)   # shade lower cutoff area
        ax.axvspan(plate.tm_cutoff_high, plate.t2,  facecolor='0.8',  alpha=0.5) # shade higher cutoff area 
        for label in ax.get_xticklabels() + ax.get_yticklabels(): # set fontsize for all tick labels to xx-small
            label.set_fontsize('xx-small')
         
        cax = ax.plot(x, y) # plot data to the current subplot
#    if gui:    
#        update_progress_bar(gui.pb, 75)
 
def plot_raw(plate, gui=None): 
    """
    Plot raw data (Fig. 3)
    """    
    fig3 = plt.figure() # new figure
    fig3.suptitle('Raw Data (plate #{})'.format(str(plate.id))) # set title
    
    for plot_num in range(1, plate.wellnum + 1): # iterate over all wells
        well = plate.wells[plot_num - 1] # get single well based on current plot number
        ax = fig3.add_subplot(plate.rows,  plate.cols,  plot_num) # add new subplot
        ax.autoscale(tight=True) # scale to data
        ax.set_title(well.name,  size='xx-small') # set title of current subplot to well identifier
        
        if well in plate.denatured_wells:
            ax.patch.set_facecolor('#FFD6D6')
        
        if plot_num == plate.wellnum - plate.cols + 1: # add axis label to the subplot in the bottom left corner of the figure
            ax.set_xlabel(u'T [°C]',  size='xx-small')
            ax.set_ylabel('I',  size='xx-small')
        
        x = plate.temprange # set values for the x axis to the given temperature range
        y = well.raw        # grab y values from the raw data of the well
        
        ax.xaxis.set_major_locator(ticker.MaxNLocator(4)) # only show three tickmarks on both axes
        ax.yaxis.set_major_locator(ticker.MaxNLocator(4))
        ax.axvspan(plate.t1, plate.tm_cutoff_low, facecolor='0.8',  alpha=0.5)   # shade lower cutoff area
        ax.axvspan(plate.tm_cutoff_high, plate.t2,  facecolor='0.8',  alpha=0.5) # shade higher cutoff area 
        for label in ax.get_xticklabels() + ax.get_yticklabels(): # set fontsize for all tick labels to xx-small
            label.set_fontsize('xx-small')
        
        cax = ax.plot(x, y) # plot data to the current subplot
    
#    if gui:
#        update_progress_bar(gui.pb, 100)
#        gui.pb.hide()

def plot(experiment, gui=None):
    for plate in experiment.plates:
        plot_raw(plate)
        plot_derivative(plate)
        plot_tm_heatmap_single(plate)
        
    if len(experiment.plates) > 1:
        plot_tm_heatmap_single(experiment.avg_plate)
        #plot_tm_heatmap_average(experiment)
    
    plt.show()
        

#plate = Plate('/home/alex/Dokumente/Universitaet/Promotion/Denaturierung/26092012/26092012-MG.csv', type='analytikJena', cutoff_low=35.0,  cutoff_high=60.0,  signal_threshold=50000,  color_range=(42, 50))
#plot(plate)