Merge pull request #26 from Lucelenie/master

Added xml files from experiments run on December 15, 16, 17 and 18

examples/Fluorescence_Inhibitor_Spectra/2015-12-15/xml2png4scans-spectra.py

@@ -0,0 +1,165 @@
+# This script takes xml data file output from the Tecan Infinite m1000 Pro plate reader
+# and makes quick and dirty images of the raw data.
+
+# But with scans and not just singlet reads.
+# This script specifically combines four spectrum scripts (AB, CD, EF, GH) into a single dataframe and plot.
+
+# The same procedure can be used to make matrices suitable for analysis using
+# matrix = dataframe.values
+
+# Made by Sonya Hanson, with some help from things that worked in xml2png.py and xml2png4scans.py
+# Friday, November 18,2015
+
+# Usage: python xml2png4scans-spectra.py *.xml
+
+############ For future to combine with xml2png.py
+#
+#    for i, sect in enumerate(Sections):
+#        reads = sect.xpath("*/Well")
+#        parameters = root.xpath(path)[0]
+#        if reads[0].attrib['Type'] == "Scan":
+#
+##############
+
+import matplotlib.pyplot as plt
+from lxml import etree
+import pandas as pd
+import matplotlib.cm as cm
+import seaborn
+import sys
+import os
+
+### Define xml files.
+
+xml_files = sys.argv[1:]
+
+so_many = len(xml_files)
+print "****This script is about to make png files for %s xml files. ****"  % so_many
+
+### Define extract function that extracts parameters
+
+def extract(taglist):
+    result = []
+    for p in taglist:
+        print "Attempting to extract tag '%s'..." % p
+        try:
+            param = parameters.xpath("*[@Name='" + p + "']")[0]
+            result.append( p + '=' + param.attrib['Value'])
+        except:
+            ### tag not found
+            result.append(None)
+
+    return result
+
+### Define an initial set of dataframes, one per each section
+
+large_dataframe0 = pd.DataFrame()
+large_dataframe1 = pd.DataFrame()
+large_dataframe2 = pd.DataFrame()
+
+for file in xml_files:
+
+    ### Parse XML file.
+
+    root = etree.parse(file)
+
+    ### Remove extension from xml filename.
+
+    file_name = os.path.splitext(file)[0]
+
+    ### Extract plate type and barcode.
+
+    plate = root.xpath("/*/Header/Parameters/Parameter[@Name='Plate']")[0]
+    plate_type = plate.attrib['Value']
+
+    try:
+        bar = root.xpath("/*/Plate/BC")[0]
+        barcode = bar.text
+    except:
+        barcode = 'no barcode'
+
+    ### Define Sections.
+
+    Sections = root.xpath("/*/Section")
+    much = len(Sections)
+    print "****The xml file " + file + " has %s data sections:****" % much
+    for sect in Sections:
+        print sect.attrib['Name']
+
+    for i, sect in enumerate(Sections):
+
+        ### Extract Parameters for this section.
+
+        path = "/*/Section[@Name='" + sect.attrib['Name'] + "']/Parameters"
+        parameters = root.xpath(path)[0]
+
+        ### Parameters are extracted slightly differently depending on Absorbance or Fluorescence read.
+        # Attach these to title1, title2, or title3, depending on section which will be the same for all 4 files.
+
+        if  parameters[0].attrib['Value'] == "Absorbance":
+            result = extract(["Mode", "Wavelength Start", "Wavelength End", "Wavelength Step Size"])
+            globals()["title"+str(i)] = '%s, %s, %s, %s' % tuple(result)
+
+        else:
+            result = extract(["Gain", "Excitation Wavelength", "Emission Wavelength", "Part of Plate", "Mode"])
+            globals()["title"+str(i)] = '%s, %s, %s, \n %s, %s' % tuple(result)
+
+        print "****The %sth section has the parameters:****" %i
+        print globals()["title"+str(i)]
+
+        ### Extract Reads for this section.
+
+        Sections = root.xpath("/*/Section")
+
+        reads = root.xpath("/*/Section[@Name='" + sect.attrib['Name'] + "']/*/Well")
+
+        wellIDs = [read.attrib['Pos'] for read in reads]
+
+        data = [(s.text, float(s.attrib['WL']), r.attrib['Pos'])
+                 for r in reads
+                 for s in r]
+
+        dataframe = pd.DataFrame(data, columns=['fluorescence','wavelength (nm)','Well'])
+
+        ### dataframe_rep replaces 'OVER' (when fluorescence signal maxes out) with '3289277', an arbitrarily high number
+
+        dataframe_rep = dataframe.replace({'OVER':'3289277'})
+
+        dataframe_rep[['fluorescence']] = dataframe_rep[['fluorescence']].astype('float')
+
+        ### Create large_dataframe1, large_dataframe2, and large_dataframe3 that collect data for each section
+        ### as we run through cycle through sections and files.
+
+        globals()["dataframe_pivot"+str(i)] = pd.pivot_table(dataframe_rep, index = 'wavelength (nm)', columns= ['Well'])
+
+        print 'The max fluorescence value in this dataframe is %s'% globals()["dataframe_pivot"+str(i)].values.max()
+
+        globals()["large_dataframe"+str(i)] = pd.concat([globals()["large_dataframe"+str(i)],globals()["dataframe_pivot"+str(i)]])
+
+### Plot, making a separate png for each section.
+
+for i, sect in enumerate(Sections):
+
+    section_name = sect.attrib['Name']
+
+    path = "/*/Section[@Name='" + sect.attrib['Name'] + "']/Parameters"
+    parameters = root.xpath(path)[0]
+
+    if  parameters[0].attrib['Value'] == "Absorbance":
+        section_ylim = [0,0.2]
+    else:
+        section_ylim = [0,40000]
+
+    Alphabet = ['A','B','C','D','E','F','G','H']
+
+    fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))
+    for j,A in enumerate(Alphabet):
+        for k in range(1,12):
+            try:
+                globals()["large_dataframe"+str(i)].fluorescence.get(A + str(k)).plot(ax=axes[(j/3)%3,j%3], title=A, c=cm.hsv(k*15), ylim=section_ylim, xlim=[240,800])
+            except:
+                print "****No row %s.****" %A
+
+    fig.suptitle('%s \n %s \n Barcode = %s' % (globals()["title"+str(i)], plate_type, barcode), fontsize=14)
+    fig.subplots_adjust(hspace=0.3)
+    plt.savefig('%s_%s.png' % (file_name, section_name))