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NAMI.py
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NAMI.py
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# Copyright (c) 2014, Durham University
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the Durham University nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL DURHAM UNIVERSITY BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# Please cite: Groftehauge, M. K., Hajizadeh, N. R., Swann, M. J. & Pohl, E. (2015). Acta Cryst. D71, 36-44.
from __future__ import unicode_literals
import sys, os, numpy, csv, math, re, ntpath, itertools, decimal
from functools import partial
from collections import Counter
from PyQt4 import QtGui, QtCore
from PyQt4.QtCore import pyqtSlot, SIGNAL, SLOT
from PyQt4.Qt import *
#from PyQt4.Qt import WindowStaysOnTopHint
#from numpy import genfromtxt, NaN, Inf, arange, isscalar, array
# MATPLOTLIB
#need to check whether I neeed this one, don't dare to remove it yet
#import matplotlib as mpl
#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
# These are needed to plot the waterfall plot, which is a hack of the polygon plots.
# See: http://matplotlib.org/examples/mplot3d/polys3d_demo.html
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
from matplotlib.backend_bases import FigureManagerBase, KeyEvent, MouseEvent
from matplotlib.collections import PolyCollection, LineCollection
from matplotlib.colors import colorConverter
from matplotlib.font_manager import FontProperties
import matplotlib.colors as col
import matplotlib.cm as cm
from mpl_toolkits.mplot3d import Axes3D
# SCIPY
from scipy.interpolate import interp1d
import scipy.signal
from pylab import * #imports matplotlib as mpl and numpy as np and makes it behave like MatLab
from scipy.optimize import curve_fit
from scipy import asarray as ar,exp
from scipy import stats
# PRETTYPLOTLIB
#import prettyplotlib as ax
#from prettyplotlib import brewer2mpl
print "NAMI was developed using the following Python and library versions."
print "Fortunately, an exact match is not necessarily required."
print "Python version:", "2.7.6"
print "PyQt4 version:", "4.8.1"
print "Matplotlib version:", "1.3.1"
print "Scipy version:", "0.9.0"
print "Numpy version:", "1.6.1"
print ""
print "These are the installed versions on this system."
print "Python version:", (sys.version)
print "PyQt4 version:", QtCore.QT_VERSION_STR
print "Matplotlib version:", mpl.__version__
print "Scipy version:", scipy.__version__
print "Numpy version:", np.__version__
###################################################################################################################################
progname = os.path.basename(sys.argv[0])
progversion = "0.1"
class Dictionaries():
def namedictionary(self):
''' creates a dictionary that relates well number to well names'''
# WELL NAMES
labelc = ['1', '2', '3', '4', '5', '6', '7', '8', '9',
'10', '11', '12']
labelr = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H' ]
names = []
for c in labelc:
for r in labelr:
names.append(r+c)
convert = lambda text: int(text) if text.isdigit() else text
alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
a = sorted(names, key = alphanum_key)
# WELL NUMBERS
wells = []
for w in range(1,97):
wells.append(('Well %s' % w))
self.dict_numb_name = dict(zip(wells, a))
return self.dict_numb_name
def solutionsfile(self, *args):
''' Reads in the solution file into a dictionary. The key name is the well number and the values are all the different variables in the solution file. '''
try:
args = list(args)[0]
fsolution = numpy.genfromtxt(args, delimiter=',', dtype=None)
adict = {}
for w in range(1,97):
if w == fsolution[w-1][0]:
adict[('Well %s' % (w))] = (round(fsolution[w-1][1],2),round(fsolution[w-1][2],4),fsolution[w-1][3],fsolution[w-1][4],fsolution[w-1][5],round(fsolution[w-1][6],2),int(fsolution[w-1][7]),int(fsolution[w-1][8]))
self.dict_sol_numb = adict
#print adict['Well 44']
except:
self.dict_sol_numb = {}
return self.dict_sol_numb
class ApplicationWindow(QtGui.QMainWindow):
''' This is the main body of the program: contains functions for doing the calculations as well as the main window of the GUI. Coordinates signal transfer between classes (and other windows)'''
def __init__(self):
QtGui.QMainWindow.__init__(self)
self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
#self.resize()
self.create_main_frame()
self._generator = None
self._timerId = None
self.th_data = []
def create_main_frame(self):
#self.main_frame = QWidget()
self.main_widget = QtGui.QWidget(self)
#self.setFixedSize(950, 800)
##############################################
# FOR THE TABLE
self.widgetT = TablePopup()
self.widgetT.setGeometry(QRect(100, 100, 400, 300))
self.connect(self.widgetT, SIGNAL('mySigT'), self.tableParameters)
self.connect(self.widgetT, SIGNAL('Table'), self.tempTable)
#############################################
# QUIT BAR
self.file_menu = QtGui.QMenu('&File', self)
self.file_menu.addAction('&Quit', self.fileQuit,
QtCore.Qt.CTRL + QtCore.Qt.Key_Q)
self.menuBar().addMenu(self.file_menu)
##############################################
# OPEN BAR, in the old version they were only on the menu button
self.open_menu = QtGui.QMenu('&Open', self)
self.file_menu.addAction('&Open Dataset', self.fileOpen)
self.menuBar().addMenu(self.file_menu)
self.open_menu = QtGui.QMenu('&Open', self)
self.file_menu.addAction('&Open solutionsfile', self.openSolFile)
self.menuBar().addMenu(self.file_menu)
self.open_menu = QtGui.QMenu('&Open', self)
self.file_menu.addAction('&Open Results', self.openResults)
self.menuBar().addMenu(self.file_menu)
##############################################
# CALLING ANOTHER CLASS: THE PLOT MENU
# this forms the window where we get the extra plots. it calls the class that contains the data handling
self.plot_menu = QtGui.QMenu('&Data', self)
self.menuBar().addSeparator()
self.plot_menu.addAction('Plots for Analysis', self.plotMenu)
self.plot_menu.addAction('Load table (from previous results)', self.widgetT.showTable)
self.plot_menu.addAction('Re-input parameters', self.resultsinputwindow)
self.menuBar().addMenu(self.plot_menu)
self.widgetplot = AnalysisPlotPopup()
self.widgetplot.setGeometry(400,200,950,900)
############################################
# HELP BAR
# adding details about what to cite and how to use the program should be inserted here. (connect it to self.help_menu)
self.help_menu = QtGui.QMenu('&Help', self)
self.menuBar().addSeparator()
self.menuBar().addMenu(self.help_menu)
# ABOUT
self.help_menu.addAction('&About', self.about)
##############################################
# might want to make this into a gridlayout to be able to position the buttons more specifically
l = QtGui.QVBoxLayout(self.main_widget)
#############################################
#DATA ANALYSIS: buttons that coordinate into the input file parameters and the data analysis process.
# START LOOP
self.button1 = QtGui.QPushButton('Set Input Parameters')
self.button1.clicked.connect(self.start)
l.addWidget(self.button1)
self.widget = InputParametersPopup()
#self.widget.setGeometry(QRect(1000, 1000, 1000, 1000))
# read in data emitted from the other class
self.connect(self.widget, SIGNAL('mySig'), self.parameters)
# CONTINUE LOOP
self.button2 = QtGui.QPushButton('Begin Data Analysis')
self.button2.clicked.connect(self.continues)
l.addWidget(self.button2)
##############################################
# FIGURE: Creating a canvas for matplotlib etc.
self.figure = plt.figure(dpi=100)
self.canvas = mpl.backends.backend_qt4agg.FigureCanvasQTAgg(self.figure)
self.canvas.setMinimumSize(800,500)
l.addWidget(self.canvas)
self.canvas.setParent(self.main_widget) # main_widget parent instead of main_frame
self.canvas.setFocusPolicy(Qt.ClickFocus)
self.canvas.setFocus()
########################################
# Calling classes containing dictionaries of well names and numbers
D = Dictionaries()
self.b = D.namedictionary()
########################################
# NAVIGATION TOOLBAR
# this is the little tiny toolbar at the bottom, where you can save individual figures etc. Don't think most buttons work on it though...
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
l.addWidget(self.mpl_toolbar)
self.canvas.mpl_connect('button_press_event', self.on_key_press)
#self.main_widget.setFocus()
self.setCentralWidget(self.main_widget)
########################################
# EXTRAS: Checkboxes, etc.
# CHECKBOX FOR AUTOMATIC MODE
self.chick = QtGui.QCheckBox('Automatic', self)
l.addWidget(self.chick)
#self.connect(self.chick, QtCore.SIGNAL('toggled(bool)'), self.continues)
# CHECKBOX FOR SAVEFILE
self.savfil = QtGui.QCheckBox('Save Graphs', self)
l.addWidget(self.savfil)
self.connect(self.savfil, QtCore.SIGNAL('toggled(bool)'), self.updategraph)
# COMMENTS TO TXT FILE
self.edit1 = QtGui.QLineEdit()
l.addWidget(self.edit1)
self.edit1.setPlaceholderText("Insert comments for the text file ")
# STATUSBAR
self.statusBar().showMessage("All hail Queen Nelly!", 2000)
###########################################
# FOR SIGNALLING: CONNECTING BUTTONS
self.connect(self, SIGNAL('datadict'), self.widgetplot.graphdata)
self.connect(self, SIGNAL('colordict'), self.widgetplot.colouring)
self.connect(self.widgetT, SIGNAL('mySigT'), self.widgetplot.values)
self.connect(self, SIGNAL('colordict1'), self.widgetplot.colouring)
self.connect(self, SIGNAL('results'), self.widgetplot.values)
self.connect(self, SIGNAL('solvs'), self.widgetplot.canvastime)
self.connect(self.widget, SIGNAL('opensolution'), self.openSolFile)
self.connect(self.widget, SIGNAL('openrawdata'), self.fileOpen)
self.connect(self.widget, SIGNAL('openresults'), self.openResults)
self.connect(self, SIGNAL('opens'), self.widget.reciever_s)
self.connect(self, SIGNAL('openr'), self.widget.reciever_r)
self.connect(self, SIGNAL('open_rawdata'), self.widget.reciever_rawdata)
##########################################
def fileQuit(self):
# closes the window
self.close()
def plotMenu(self):
self.emit(QtCore.SIGNAL('solvs'), self.solvfilename)
self.widgetplot.show()
def on_key_press(self, event):
''' This is for the table. It divides up the matplotwindow into squares where the contents of the solution file will show when you hover over it. For instance, where you to click and hover
over a normal denaturation plot, you would see the contents of a certain well. This is a slight bug, but I think its OK as long as no one notices. As you can see, it is a function that reacts to an event, it does not need to be passed any variables. The event that I have defined it to react to is obviously buttonpress.'''
l_h = self.figure.get_window_extent().height
l_w = self.figure.get_window_extent().width
# we need to use those above to scale it
i_w = 800
i_h = 426
scale_w = i_w/l_w
scale_h = i_h/l_h
# need to fix these in case the table moves in pixels
width = 51/scale_w
height = 35/scale_h
start_x = 61/scale_w
start_y = 376/scale_h
coords = []
# Building up the coordinate system, relating coordinates with the well number
for s in range(1,13,1):
for d in range(1,9,1):
coords.append((s,d))
coords = [(x,y) for y,x in coords] # (r,c) structure
grid = sorted(coords)
adict = {}
for w in range(1,97):
adict[grid[w-1]] = ('Well %s' % (w))
try:
D = Dictionaries()
sdict = D.solutionsfile(self.solvfilename)
# populate an array
n = []
for i in range(0,96):
nh = grid[i][0]
nw = grid[i][1]
n.append([(start_x+(nw*width), start_y-(nh*height)), (start_x+((nw+1)*width), start_y-((nh+1)*height))])
#if (start_x+(nw*width), start_y-(nh*height)) <= (event.x, event.y) <= ((start_x+((nw+1)*width), start_y-((nh+1)*height))):
#if (start_x+(nw*width)) <= event.x <= (start_x+((nw+1)*width)):
#print (start_y-(nh*height)), (start_y-((nh+1)*height))
# detecting where the cursor is when the button is pressed
for i in range(len(n)):
if n[i][0][0] < event.x < n[i][1][0]:
if n[i][1][1] <= event.y <= n[i][0][1]:
string = str(sdict[adict[grid[i]]]) # just showing contents
self.mpl_toolbar.set_message(str(adict[grid[i]]))
self.canvas.setToolTip(string)
print adict[grid[i]]
except: # when no sol file has been submitted, it wont crash
pass
#button_press_handler(event, self.canvas, self.mpl_toolbar)
def openSolFile(self):
''' for opening the solutionfile'''
self.solvfilename = str(QtGui.QFileDialog.getOpenFileName(self, 'Open File', os.curdir))
self.emit(QtCore.SIGNAL('opens'), self.solvfilename) # emit solutionfilename so it can be shown in the window (when you have selected it)
if self.solvfilename.endswith('.sol'): #making sure we only open .sol files
pass
else:
QtGui.QMessageBox.critical(self, "Error", "It's not a .sol file, double check please.")
return self.solvfilename
def openResults(self):
''' opening the resultsfile, similar to above function '''
self.resultsfile = str(QtGui.QFileDialog.getOpenFileName(self, 'Open File', os.curdir))
self.emit(QtCore.SIGNAL('openr'), self.resultsfile)
if self.resultsfile.endswith('.csv'):
self.resultsinputwindow()
else:
QtGui.QMessageBox.critical(self, "Error", "That does not look like a .csv file.")
print self.resultspopulation()
self.emit(QtCore.SIGNAL('colordict1'), self.th_data)
#self.emit(QtCore.SIGNAL('results'), float(self.referencet),float(self.sigfigchange))
def resultsinputwindow(self):
'''This is the window that asks the user for two values: the signficant change in temperature and the reference temperature. It is
used when the user re-reads in old results and want to tweak the table/plots without having to restart the program. '''
(text,truth)=QInputDialog.getText(self,"Get text","Reference temperature",QLineEdit.Normal,"50")
(text1,truth1)=QInputDialog.getText(self,"Get text","Signficant change in temperature",QLineEdit.Normal,"2")
if truth and truth1:
self.referencet = text
self.sigfigchange = text1
else:
pass
# it then emits the signal afterwards
self.emit(QtCore.SIGNAL('results'), float(self.referencet),float(self.sigfigchange))
def resultspopulation(self): # NEED TO MAKE THIS TAKE INTO CONSIDERATION IF USER SAID SOME OTHER VALUE
''' pretty sure this function processes the resultsfile into an array. '''
if len(self.resultsfile) > 0:
fresults = numpy.genfromtxt(self.resultsfile, delimiter=',', dtype=None)
for w in range(3,99):
if len(fresults[w][2]) == 0:
self.th_data.append(float(fresults[w][1]))
else:
self.th_data.append((float(fresults[w][1]),float(fresults[w][2])))
self.parsefile(int(fresults[1][0]),int(fresults[1][1]),int(fresults[1][2]), int(fresults[1][3]))
#print "self, thdaa", self.th_data
def fileOpen(self):
self.openFile()
def openFile(self):
self.filename = str(QtGui.QFileDialog.getOpenFileName(self, 'Open File', os.curdir))
self.emit(QtCore.SIGNAL('open_rawdata'), self.filename)
if self.filename.endswith('.csv'):
pass
else:
QtGui.QMessageBox.critical(self, "Error", "That does not look like a .csv file")
return self.filename
def parameters(self, *args):
''' Really stupid way of accepting parameters send from another class, in the end i never figured out the alternative '''
self.args = list(args) # now its a list, so I can access the values easier
return self.args
def parsefile(self, column_temp=0, offset_temp=23, yval=3, columnwell=1, wells=97, increment=1):
''' The data from the machine is processed in this function, inserted into a dictionary of dictionaries. It needs specification of which column all the data is. Default values have been given, based on our machine.
returns the dictionary of dictionaries (the data)
and an array containing the well numbers, this is used in the iteration later'''
my_data = genfromtxt(self.filename, delimiter=',')
# !!! It currently reads in all the wells, but sometime in the future it would be nice to be able to tweak this, especially if you only loaded a fraction of the wells.
#wells=40 # user needs to be able to modify it
self.array = []
for i in xrange(1,wells):
self.array.append('Well %s' % i)
# creating a dictionary of dictionaries
self.data = {}
for x in xrange(1,wells): #USER INPUT = HOW MANY WELLS.
self.data['Well %s' % x] = {}
# reading in the relevant data and manipulating it
for i in xrange(1, my_data.shape[0]): # ASSUMPTION: header is one line.
#print i, my_data[i,column_temp], round((my_data[i,column_temp]*1)+offset_temp, 2)
my_data[i,column_temp] = (my_data[i,column_temp]*increment)+offset_temp
# inserting into dictionary of dictionaries
for values in xrange(1, my_data.shape[0]): # iterating over the array. (my_data.shape[0] = nr of rows in the array)
if my_data[values,columnwell] in xrange(1,wells):
self.data['Well %s' % int(my_data[values,columnwell])][my_data[values,column_temp]]=my_data[values,yval]
#sorted(self.data['Well %s' % int(my_data[values,columnwell])])
self.emit(QtCore.SIGNAL('datadict'), self.data)
return self.data, self.array
#################################################################################################################################
# ANALYSIS PART
def slopetime(self):
'''This function fits a sliding window of varying window size along the data, and evaluates where along the data we have a steepest increase by fitting
a line to each window and determining the corresponding r_value*slope value (so its normalized).
It starts with a window size 7, and then slides along the data with 1 increment and evaluates where we have the largest slope*r value. It then takes this
max value, and appends to a list, and simultaneously also makes a note of the window size. It does this for different windowsizes, with increment 2, up
until 35. We then get two lists, each windowsize has a corresponding r value which matches the max(slope*r) value.
ASSUMPTION: max(slope*r) is where we will find the melting temperature, as a result of the nature of the experiment. '''
topr = []
wsize = []
for w_size in range(7,35,2): # We are trying different window sizes
# Clear these for each new window size
intercep = []
slopes = []
r = []
wsize.append(w_size) # make note of which window size we are currently investigating
for index in range(int((w_size-1)/2),int((len(yvalues)-(w_size-1)/2))): # And sliding these different window sizes along the array
ys = yvalues[index-((w_size-1)/2):index+((w_size-1)/2)]
xs = temperature[index-((w_size-1)/2):index+((w_size-1)/2)]
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(xs,ys) # Calculating the slope at each window point
# Appending to arrays
slopes.append(slope)
intercep.append(intercept)
r.append(r_value)
multiply = [a*(b**2) for a,b in zip(slopes,r)]
# by definition, where the melting temperature can be found, the slope*r will be greatest, so for all the windows of size w_size, make note
# of the r_value which corresponds to the highest value of (slope*r). This will be where the melting temperature can be found. Also, before
# we made a note of the w_size. W_size and topr works as keys, to determine which windowsize will give the best melting temperature (using topr)
topr.append(r[multiply.index(max(multiply))])
return wsize, topr
def rtime(self):
''' This function uses the topr and wsize arrays from slopetime. Empirically it has been determined that for a value of
r that roughly corresponds to 0.996 gives the best value for the melting temperature. However, this is an approximate number, so we need to find
the windowsize that roughly correspondsself.canvas.draw() to an r value of 0.996, or at least the transition between 0.997 to something lower. The r value does not
drop linearly, i.e. it could be a drop down to 0.996 and then it would increase, and then later drop again. We want this "latest" drop, because
this tends to be the most accurate. Hence, we extract all the values between 0.9954 and 0.9971 and then choose the windowsize that is the largest,
this will from empirical considerations be the best one for analysis. '''
window, r = self.slopetime()
(a, b) = self.slopetime()
c = zip(a,b)
try:
dough = [(a,b) for (a,b) in c if 0.9954<= b <= 0.9971] # Extract all rvalues with the corresponding wsize that have an rvalue between 0.9954 & 0.9971
aa = [a for (a,b) in dough]
#print "Value of r is:", max(aa)
windowsize = max(aa) # From the previous extraction, choose the one with the biggest windowsize.
except ValueError: # means there is no value between 0.9954 and 0.9971. Just find the closest one.
hello = min(enumerate(r), key=lambda x: abs(x[1]-0.996))
#print "The value of r:", hello[1]
windowsize = window[hello[0]]
#print "optimal window size is then:", windowsize
return windowsize
def windowtime(self):
'''Optimal windowsize has been determined by the previous 2 functions, here we will implement the results from previous analysis, i.e. the
optimal windowsize and determine the melting temperature. The window function will "calculate the derivative" by taking subsequent and previous
indices and subtract them from the current y-value. To enhance the analysis, we then fill in the data using a quadratic spline (to avoid singularities)
We define the melting temperature to be at the absolute peak of this spline curve. '''
windowz = self.rtime() # obtain window size from dynamic window function
lw = (windowz-1)/2
rw = (windowz-1)/2
# arrays that will contain the values of the window calculation
wwindow = []
xxwindow = []
# for a given window size, calculate the difference between each points
for index in range(int(lw),len(yvalues)-(int(rw)+1)):
# do left and right window separartely. enables you to define an asymmetric window if needed
leftfraction = []
rightfraction = []
for n in range(lw):
leftfraction.append((yvalues[index]-yvalues[index-(n+1)]))
for d in range(rw):
rightfraction.append((yvalues[index]-yvalues[index+(d+1)]))
wwindow.append((sum(leftfraction)-sum(rightfraction)))
xxwindow.append(temperature[index])
#print temperature[index],type(temperature[index])
# SPLINE OF WINDOW
window_spl = scipy.interpolate.UnivariateSpline(xxwindow, wwindow, k=2)
xwindow = linspace(min(xxwindow),max(xxwindow),1000)
#max_array = scipy.signal.argrelmax(numpy.array(window_spl(xwindow)),order=15)
#print max_array
lists = list(window_spl(xwindow)) # make the spline array into a list
maxis = lists.index(max(lists)) # find absolute maxima
#print "The melting temperature is suggested to be: ", xwindow[maxis]
return xwindow, window_spl(xwindow), xwindow[maxis], maxis, max(window_spl(xwindow)) # returns spline wvalues, spline yvalues, the melting temperature, index of melting temperature, and the maximum of the spline yvalues for normalization later
def peaktime(self):
''' Sometimes, the denaturation is a 2-step process, this manifests as two peaks in the window-spline function.
To facilitate subsequent analysis, this function will detect the two peaks and return the temperature corresponding to the two peaks. '''
x, vector, th, b, c = self.windowtime()
scaling = len(x)/len(temperature)
yheight = 0.02 # tweak this depening on window size?
if self.rtime() > 11:
windows = 6
else:
windows = 4
maxima = []
vector = list(vector/c)
for yval in range(len(vector)):
try:
if vector[yval] > 0.3: # needs to be larger than 0.3, all other peaks are insignificant as small peaks
if vector[yval] > (vector[yval+(windows*scaling)]+yheight) and vector[yval] > (vector[yval-(windows*scaling)]+yheight):
maxima.append(vector[yval])
#elif 0 <= vector[yval] <= 0.3: # detecting unusual activity
except IndexError: # means we came to an end
pass
maxima.sort()
good_indices = [ vector.index(fit) for fit in maxima ] # for the maxima, it finds the maximum index but also those around it.
groups = self.group_consecutives(sorted(good_indices), step=1)
#print groups # for the sake of hewl-gi data
#print "nr of peaks", len(groups)
try:
peak_1 = groups[0]
pe_y1=[]
pe_x1=[]
for i in peak_1:
pe_y1.append(vector[i])
pe_x1.append(x[i])
a= pe_x1[pe_y1.index(max(pe_y1))]
if len(groups) > 1:
peak_2 = groups[1]
pe_y2=[]
pe_x2=[]
for i in peak_2:
pe_y2.append(vector[i])
pe_x2.append(x[i])
f= pe_x2[pe_y2.index(max(pe_y2))]
else:
f = ''
except ValueError: # means we have a peak really at the end
a = ''
f = ''
return a, f
def group_consecutives(self, vals, step=1):
"""Return list of consecutive lists of numbers from vals (number list)."""
run = []
result = [run]
expect = None
for v in vals:
if (v == expect) or (expect is None):
run.append(v)
else:
run = [v]
result.append(run)
expect = v + step
return result
def find_nearest(self,array,value):
idx = (numpy.abs(array-value)).argmin()
return array[idx]
def peakanalysis(self, yarray, index_th, scaling, treshold):
''' For analysing whether peaks identified are true peaks.'''
try:
a = index_th-(treshold*scaling)
b = index_th+(treshold*scaling)
if a<0: # means its so close to the edge it will go around
print "Too close to the edge?"
return "WARNING, Tm might not be true"
else:
# evaluation of the peak
before = yarray[index_th]-yarray[a]
after = yarray[index_th]-yarray[b]
if before > 0 and after >0: # true peak, values are smaller on either side
return ''
elif before <0 or after <0:
print "Too close to the edge?"
return "WARNING, Tm might not be true"
except IndexError: # peak top is too close to the end of the array (high T), i think when b is a number longer than the array length
print "NOOO"
return "WARNING, Tm might not be true"
#################################################################################################################################
def updategraph(self, status, *args):
''' This is where it all happens. The dictionary is iterated over (i.e. each well is processed) and the data-analysis functions are called, denaturation/two peak analysis is conducted and finally plotted. Also writes the resultsfile as it is iterating.
as you can see, it calls the function that inserts the data into a dictionary. The parsefile function returns the dictonary of dictionaries (i.e. full data) and an array of all the wells, it is used the in iteration below. '''
data, array = self.parsefile(column_temp=self.args[0]-1,offset_temp=self.args[1], yval=self.args[2]-1, columnwell=self.args[3]-1, wells=self.args[4]+1, increment=self.args[5]) # reading in the data, using the user comments into the correct datastructure (dictionary of dictionaries)
#print data['Well %s' % 2]
#############################################
# RESULTS FILE, INITIATE AND WRITE FIRST ROW
name = os.path.basename(self.filename)
f = open("results.csv", "w")
c = csv.writer(f)
c.writerow(["#", 'Columntemperature', 'Offset_temperature', 'Filter','Columnwell'])
c.writerow(["#", self.args[0]-1, self.args[1] ,self.args[2]-1 ,self.args[3]-1] )
c.writerow(['Well Number', 'Calculated_Th', 'Peak_2/Warning', 'User_Input'])
############################################
#f = open(self.filename+'_results'+'.txt', 'a')
#f.write('%s\t %s\t %s\t %s\n' % ('Well', 'Calculated_Th', 'Peak_2/Warning','User_Input'))
for i in array:
#################################################################################################
# EXTRACTING DATA
global yvalues, temperature # assignign global values
yvalues1 = data[i].values() #yvalues
temperature1 = data[i].keys() # Temperature
# IDK, BUT YOU NEED THIS IN ORDER TO GET THE DICTIONARY IN ORDER WHEN YOU HAVE INSERTED FLOATS AS A VALUE FOR INCREMENT OR OFFSET TEMPERATURE
zipped = zip(temperature1, yvalues1)
zipped.sort(key = lambda t: t[0])
a = [list(t) for t in zip(*zipped)]
temperature = a[0]
yvalues = a[1]
#print min(temperature), max(temperature)
#################################################################################################
# SPLINE
spl = scipy.interpolate.UnivariateSpline(temperature, yvalues)
x = linspace(min(temperature),max(temperature),1000)
#################################################################################################
# EXTRACING VALUES FROM ANALYSIS
x, y, th, index_th , xth = self.windowtime()
print i, ", The melting temperature is suggested to be: ", th
#################################################################################################
# NEED TO WARN USER WHEN THERE IS NO BASELINE (UNSTABLE PROTEIN)
# either within a certain percentage of the end, or evaluate peak analysis, like done in peak_2!! But change treshold
# to 2 degrees
self.comment = ''
#################################################################################################
# DENATURATION
count = 0
for points in y/xth:
if points > 1.5 or points < (-1.0):
count = count + 1
#################################################################################################
# TWO STAGE DECAY
peak_1, peak_2 = self.peaktime()
# just make sure whatever peak that comes out of there (usually named Peak_1) is taken care of
if peak_1 != '':
if (th-3) <= peak_1 <= (th+3):
peak_1 = ''
if peak_2 != '':
if (th-3) <= peak_2 <= (th+3):
peak_2 = ''
########################################################################################################
# FOR THE TABLE LATER
if peak_2 != '' and peak_1 == '':
#print tuple (sorted(round(th,1),round(peak_2,1)) )
self.th_data.append( tuple (sorted( [round(th,1),round(peak_2,1)] ) ) )
elif peak_1 != '' and peak_2 == '':
#print tuple( sorted(round(th,1),round(peak_1,1)) )
self.th_data.append( tuple( sorted( [round(th,1),round(peak_1,1)] ) ))
elif count > 500:
self.th_data.append('N/S')
elif len(self.comment) > 1:
self.th_data.append(((round(th,1),'ATTN')))
else:
self.th_data.append(round(th,1))
#################################################################################################
# PLOTTING AND WRITING DATA TO FILE
####################################################
# COLOURS
scattercolor = [0,99.0/255.0,136.0/255.0]
windowcolor = [159.0/255.0,161.0/255.0,97.0/255.0]
derivativecolor = [126.0/255.0,49.0/255.0,123.0/255.0]
peaks = [170.0/255.0,43.0/255.0,74.0/255.0]
###################################################
# MAIN PLOT
ax = self.figure.add_subplot(1,1,1)
ax.scatter(temperature, yvalues/(max(yvalues)), alpha=0.6, color=scattercolor)
ax.plot(x, y/xth, alpha = 0.5, color=derivativecolor)
axvline(th, color=peaks, alpha=0.7, linewidth=1.5) # MELTING TEMPERATURE()
axvline(th - 0.5 * self.rtime(), color=windowcolor, alpha=0.7) # window begin
axvline(th + 0.5 * self.rtime(), color=windowcolor, alpha=0.7) # window end
txt = "T:"+str( '%1.1f' % th)+", "+self.comment
# EXTRA PEAK DETECTION
if peak_2 != '' and peak_1 == '':
axvline(peak_2, color=peaks, alpha=0.7, linewidth=1.5)
txt = "T:"+str(round(peak_2,1))+", "+str('%1.1f' % th)+self.comment
elif peak_1 != '' and peak_2 == '':
axvline(peak_1, color=peaks, alpha=0.7, linewidth=1.5)
txt = "T: "+str(round(peak_1,1))+", "+str('%1.1f' % th)+self.comment
# DENATURED
if count > 500: # means more that 50% of data looks FANIIIII
txt = 'NO SIGNAL FOUND'#, or maybe it is:'+str(round(th,2))
# AXES LABELS
ylabel('Normalized Intensity')
xlabel('Temperature ($^o$C)')
text(min(temperature),1.1 , ("%s, " % i)+txt)
ylim(-0.1,1.2)
#ticklabel_format(style='sci', axis='y', scilimits=(0,0)
# show figure
self.canvas.draw()
#################################################################################################
# SAVING THE GRAPH
if self.savfil.isChecked() == True:
savefig('Well '+str(self.b[i])+'.png')
self.canvas.figure.clf()
else:
self.canvas.figure.clf()
#################################################################################################
# AUTOMATIC MODE OR NOT
if status == 0:
yield
#write to file only after they pressed continue!
c.writerow([i, round(th,1), str(peak_2)+self.comment, self.edit1.text()])
self.edit1.setText('')
# continue
#######################################################################################################################
# LAST MINUTE FIX
# wait, do I need this?
self.canvas.figure.clf()
f.close()
# CALL TABLE FUNCTION TO DISPLAY TABLE, first we need to show the popup for the insertion of values
self.widgetT.show() # display popup window
def tempTable(self):
''' This function first considers the suggestions by the user (the new temperature), creates a new dictionary based on this (again, the well is the key and the temperatures are the values). It then creates a colour coded table based on the reference temperature and what is considered a signficant change.
I think it has 5 different shades of change. It's a dynamic range, so it will adjust for each dataset (i.e. the darkest blue is always the largest increase) '''
resultsfile = open("results.csv", 'r')
datareader = csv.reader(resultsfile)
next(datareader)
count = 0
# We will be modifying the self.meltingt data, instead of creating a whole new array. Userinputs will be replacing the program suggestions
for row in datareader:
#print row
if count > 2:
try:
if len([ elem for elem in row[3].split(",") ]) > 1:
print tuple([ float(elem) for elem in row[3].split(",") ])
self.th_data[count] = tuple([ float(elem) for elem in row[3].split(",") ])
else:
self.th_data[count] = [ float(elem) for elem in row[3].split(",") ][0]
print [ float(elem) for elem in row[3].split(",") ]
except ValueError:
pass
count = count + 1
self.emit(QtCore.SIGNAL('colordict'), self.th_data)
# reference t and sig fig comes from the user input from the popup window
#################################################################
# OBTAINING REFERENCE AND SIG.CHANGE TEMPERATURE
try:
ref = self.tableargs[0] # reference temperature, from userinput
minsigdelta = self.tableargs[1] # smallest significant change
except: # this is the instance when you call the table from the menu instead, when re-reading in the results. Then it fetches the values for these parameters from an alterative window
ref = float(self.referencet)
minsigdelta = float(self.sigfigchange)
################################################################
#print "OK THIS IS THE TEMPTS THAT GO INTO THE TABLE", self.th_data
# LABEL FOR COLUMNS AND ROWS
labelc = ['1', '2', '3', '4', '5', '6', '7', '8', '9',
'10', '11', '12']
labelr = ['',' A ', ' B ', ' C ', ' D ', ' E ', ' F ', ' G ', ' H ' ]
cpool = [ (0.97,0.54,0.54),(0.83,0.25,0.25) , (0.70,0.08,0.08), (0.51,0.02,0.02), (0.43,0.05,0.05) ,
(0.3,0.02,0.02), (0.0078,0.22,0.35), (0.015,0.352,0.55) ,(0.019,0.439,0.70) ,(0.211,0.56,0.75) , (0.65,0.66,0.811) ,
(0.65,0.74,0.86) ,(0.65,0.74,0.86)]
cmap3 = col.ListedColormap(cpool[0:12], 'indexed')
cm.register_cmap(cmap=cmap3)
# IF the user has only loaded <96 wells, we need to populate them so that we do not get an error in the table
poo = [1]*40
print "the length is", len(self.th_data)
print "the length of test is", len(poo)
if len(self.th_data) < 96:
96-len(self.th_data)
for addintegers in range(1,97-len(self.th_data)):
self.th_data.append('N/A')
else:
pass