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from __future__ import with_statement
from __future__ import absolute_import
from __future__ import print_function
import sys
try:
import numpy
import reflex
from pipeline_product import PipelineProduct
import pipeline_display
import reflex_plot_widgets
from matplotlib import gridspec, pylab, pyplot
import pdb # for debugging
import_success = True
except ImportError:
import_success = False
print("Error importing modules pyfits, wx, matplotlib, numpy")
# Median absolute deviation function; used to scale the images
def MAD(x):
x=numpy.array(x)
return numpy.median(numpy.abs(x-numpy.median(x)))
def paragraph(text, width=None):
""" wrap text string into paragraph
text: text to format, removes leading space and newlines
width: if not None, wraps text, not recommended for tooltips as
they are wrapped by wxWidgets by default
"""
import textwrap
if width is None:
return textwrap.dedent(text).replace('\n', ' ').strip()
else:
return textwrap.fill(textwrap.dedent(text), width=width)
class DataPlotterManager(object):
"""
This class must be added to the PipelineInteractiveApp with setPlotManager
It must have following member functions which will be called by the app:
- setInteractiveParameters(self)
- readFitsData(self, fitsFiles):
- addSubplots(self, figure):
- plotProductsGraphics(self, figure, canvas)
Following members are optional:
- setWindowHelp(self)
- setWindowTitle(self)
"""
# static members
recipe_name = "vimos_ima_dark"
img_cat = "MASTER_DARK"
diff_img_cat = "DIFFIMG_DARK"
diff_stats_cat = "DIFFIMG_STATS_DARK"
def setWindowTitle(self):
return self.recipe_name+"_interactive"
def setInteractiveParameters(self):
"""
This function specifies which are the parameters that should be presented
in the window to be edited. Note that the parameter has to also be in the
in_sop port (otherwise it won't appear in the window). The descriptions are
used to show a tooltip. They should match one to one with the parameter
list.
"""
return [
reflex.RecipeParameter(recipe=self.recipe_name, displayName="combtype",
group="vimos_ima_dark", description="Combination algorithm. <median | mean> [median]"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="scaletype",
group="vimos_ima_dark", description="Scaling algorithm. <none | additive | multiplicative | exptime> [exptime]"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="xrej",
group="vimos_ima_dark", description="True if using extra rejection cycle. [TRUE]"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="thresh",
group="vimos_ima_dark", description="Rejection threshold in sigma above background. [5.0]"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="ncells",
group="vimos_ima_dark", description="Number of cells per data channel to evaluate stats. < 1 | 2 | 4 | 8 | 16 | 32 | 64 > [8]")
]
def readFitsData(self, fitsFiles):
"""
This function should be used to read and organize the raw fits files
produced by the recipes.
It receives as input a list of reflex.FitsFiles
"""
# organize the files into a dictionary, here we assume we only have
# one file per category if there are more, one must use a
# dictionary of lists
self.frames = dict()
for f in fitsFiles:
self.frames[f.category] = PipelineProduct(f)
# we only have two states, we have data or we don't
# define the plotting functions we want to use for each
if self.img_cat in self.frames:
dark_img = self.frames[self.img_cat]
self.n_extn = len(dark_img.hdulist())-1 # number of extensions
self.dark_img_name = dark_img.fits_file.name
self.dark_img_hdu = dark_img.all_hdu
self.img_found = True
# Read the dark images
self.dark_images = []
for i in range(self.n_extn):
dark_img.readImage(i+1)
self.dark_images.append(dark_img.image)
# Read the difference images
if self.diff_img_cat in self.frames:
diff_img = self.frames[self.diff_img_cat]
self.diff_img_name = diff_img.fits_file.name
self.diff_img_hdu = diff_img.all_hdu[0]
self.diff_img_found = True
self.diff_images = []
for i in range(self.n_extn):
diff_img.readImage(i+1)
self.diff_images.append(diff_img.image)
else:
self.diff_img_found = False
# Read in statistics FITS table
if self.diff_stats_cat in self.frames:
self.stats_found = True
self.stats = self.frames[self.diff_stats_cat]
# stats_table is a list of FITS record arrays, one for each extension
# access data by field name: stats_table[i_ext]['COLNAME']
# see help at https://pythonhosted.org/pyfits/users_guide/users_table.html
self.stats_table = []
for i in range(self.n_extn):
self.stats_table.append(self.stats.all_hdu[i+1].data)
else:
self.stats_found = False
# re-define plotting functions to enable callbacks
self._add_subplots = self._add_subplots
self._plot = self._data_plot
# Define radio button options as a dict; this is handy for assessing which option the user selected
if ((self.diff_img_found is True) and (self.stats_found is True)):
self.radio_button_opts = {'Master DARK Image':0,'Histogram of DARK Image':1,'Diff Image (DARK-REF)':2,'Stats on Diff Image':3}
else:
self.radio_button_opts = {'Master DARK Image':0,'Histogram of DARK Image':1}
# Set the initial radio button selections (value 0)
self.radio_button_label = [key for key, value in iter(self.radio_button_opts.items()) if value == 0][0]
else:
# Set the plotting functions to NODATA ones
self._add_subplots = self._add_nodata_subplots
self._plot = self._nodata_plot
def addSubplots(self, figure):
self._add_subplots(figure)
def plotProductsGraphics(self):
self._plot()
def plotWidgets(self) :
widgets = list()
# Radio button
self.radiobutton = reflex_plot_widgets.InteractiveRadioButtons(self.axradiobutton,
self.setRadioCallback,
[key for key,value in sorted(self.radio_button_opts.items(),key= lambda k: k[1])],
self.radio_button_opts.get(self.radio_button_label),
title='Select item to display')
widgets.append(self.radiobutton)
return widgets
def setRadioCallback(self, label) :
# Only do something if user changes the button
if (label != self.radio_button_label):
self.radio_button_label = label
self._plot()
def _add_subplots(self, figure):
self.img_plot = []
gs = gridspec.GridSpec(7, 2)
self.img_plot.append(figure.add_subplot(gs[1:4,0]))
self.img_plot.append(figure.add_subplot(gs[1:4,1]))
self.img_plot.append(figure.add_subplot(gs[4:8,0]))
self.img_plot.append(figure.add_subplot(gs[4:8,1]))
self.axradiobutton = figure.add_subplot(gs[0,0])
def _data_plot(self):
#clock_pattern = self.dark_img_hdu[0].header['HIERARCH ESO DET READ CLOCK']
#binx = self.dark_img_hdu[0].header['HIERARCH ESO DET WIN1 BINX']
#biny = self.dark_img_hdu[0].header['HIERARCH ESO DET WIN1 BINY']
for i in range(self.n_extn):
chip_name = self.dark_img_hdu[i+1].header['EXTNAME']
imgdisp = pipeline_display.ImageDisplay()
imgdisp.setAspect('equal')
if ( (self.radio_button_opts[self.radio_button_label] == 0) or
(self.radio_button_opts[self.radio_button_label] == 2)): # show master dark image or diff image
self.img_plot[i].cla()
if ( (i==0) or (i==1) ):
pylab.setp(self.img_plot[i].get_xticklabels(), visible = False)
imgdisp.setLabels('','Y')
else:
pylab.setp(self.img_plot[i].get_xticklabels(), visible = True)
imgdisp.setLabels('X','Y')
if (self.radio_button_opts[self.radio_button_label] == 0):
title = "Master DARK Chip:{}".format(chip_name)
temp_image = self.dark_images[i]
tool_tip = "Dark"
# Set z-limit using 1 iteration of sigma clipping using MED and MAD
temp_image = temp_image[numpy.isfinite(temp_image)]
try:
med = self.dark_img_hdu[i+1].header['HIERARCH ESO QC DARKMED']
except:
med = numpy.median(temp_image)
try:
sig = self.dark_img_hdu[i+1].header['HIERARCH ESO QC DARKRMS']
except:
sig = 1.48 * MAD(temp_image)
temp_image = temp_image[numpy.abs(temp_image-med) < 3*sig]
new_med = numpy.median(temp_image)
new_sig = 1.48 * MAD(temp_image)
# Set limits to median-1sigma, median+3sigma for darks
imgdisp.z_lim = new_med-new_sig, new_med+3*new_sig
imgdisp.display(self.img_plot[i], title, tool_tip, self.dark_images[i])
elif (self.radio_button_opts[self.radio_button_label] == 2):
title = "Diff Image Chip: {}".format(chip_name)
temp_image = self.diff_images[i]
tool_tip = "DARK - REF"
temp_image = temp_image[numpy.isfinite(temp_image)]
try:
med = self.diff_img_hdu[i+1].header['HIERARCH ESO QC DARKDIFF_MED']
except:
med = numpy.median(temp_image)
try:
sig = self.diff_img_hdu[i+1].header['HIERARCH ESO QC DARKDIFF_RMS']
except:
sig = 1.48 * MAD(temp_image)
temp_image = temp_image[numpy.abs(temp_image-med) < 3*sig]
new_med = numpy.median(temp_image)
new_sig = 1.48*MAD(temp_image)
# Set limits to median-3sigma, median+3sigma for diff image
imgdisp.z_lim = new_med-3*new_sig, new_med+3*new_sig
imgdisp.display(self.img_plot[i], title, tool_tip, self.diff_images[i])
elif (self.radio_button_opts[self.radio_button_label] == 1): # show histogram
self.img_plot[i].cla()
self.img_plot[i].set_title("Histogram Chip: {}".format(chip_name), fontsize=12, fontweight='semibold')
pylab.setp(self.img_plot[i].get_xticklabels(), visible = True)
temp_image = self.dark_images[i]
x = temp_image[numpy.isfinite(temp_image)]
try:
med = self.dark_img_hdu[i+1].header['HIERARCH ESO QC DARKMED']
except:
med = numpy.median(x)
try:
sig = self.dark_img_hdu[i+1].header['HIERARCH ESO QC DARKRMS']
except:
sig = 1.48 * MAD(x)
n, bins, patches = self.img_plot[i].hist(x,normed=True,range=(med-5.0*sig,med+5.0*sig))
self.img_plot[i].axis('tight') # change aspect ratio to show all data, has to be placed after .hist()
if ( (i==0) or (i==1)):
self.img_plot[i].set_xlabel('')
if ( (i==2) or (i==3)):
self.img_plot[i].set_xlabel('Pixel Value [ADU]')
self.img_plot[i].set_ylabel('Normalised PDF')
self.img_plot[i].tooltip = '10 bins over a range Median'+u"\u00B1"+'5*sigma'
self.img_plot[i].text(0.05,0.9,'Med: {:4.2e}'.format(med), transform=self.img_plot[i].transAxes)
self.img_plot[i].text(0.05,0.8,'Mean: {:4.2e}'.format(numpy.mean(x)), transform=self.img_plot[i].transAxes)
self.img_plot[i].text(0.05,0.7,'MAD: {:4.2e}'.format(sig/1.48), transform=self.img_plot[i].transAxes)
self.img_plot[i].text(0.05,0.6,'RMS: {:4.2e}'.format(numpy.std(x)), transform=self.img_plot[i].transAxes)
elif (self.radio_button_opts[self.radio_button_label] == 3): # show statistics
self.img_plot[i].cla()
x = numpy.linspace(1,len(self.stats_table[0]['xmin']), num = len(self.stats_table[0]['xmin']))
y = self.stats_table[i]['median']
err = 1.483*(self.stats_table[i]['mad'])
title = "Stats {} ".format(chip_name)
tool_tip = ("X axis: Index of small cell/box on chip \nY axis: Median value of (DARK-REF) pixels in box \n\t (Err bars = 1.48*Median Abs Deviation)")
scatter_display = pipeline_display.ScatterDisplay()
self.img_plot[i].axis('tight') # change aspect ratio to show all data
if (min(y) != max(y)):
self.img_plot[i].set_ylim(min(y),max(y))
else:
self.img_plot[i].set_ylim(min(y)-2*max(err),min(y)+2*max(err))
if (max(x) != max(x)):
self.img_plot[i].set_xlim(min(x),max(x))
else:
self.img_plot[i].set_xlim(min(x)-1,max(x)+1)
if ( (i==0) or (i==1) ):
pylab.setp(self.img_plot[i].get_xticklabels(), visible = False)
xtitle = ""
else:
pylab.setp(self.img_plot[i].get_xticklabels(), visible = True)
xtitle = "Index of Cell on Chip"
scatter_display.setLabels(xtitle,"DARK - REF")
scatter_display.display(self.img_plot[i],
title, tool_tip,
x, y, err)
def _add_nodata_subplots(self, figure):
self.img_plot = figure.add_subplot(1,1,1)
def _nodata_plot(self):
# could be moved to reflex library?
self.img_plot.set_axis_off()
text_nodata = "Data not found. Input files should contain this" \
" type:\n%s" % self.img_cat
self.img_plot.text(0.1, 0.6, text_nodata, color='#11557c',
fontsize=18, ha='left', va='center', alpha=1.0)
self.img_plot.tooltip = 'No data found'
def setWindowHelp(self):
help_text = """
This is an interactive window which help asses the quality of the execution of a recipe.
"""
return help_text
#This is the 'main' function
if __name__ == '__main__':
from reflex_interactive_app import PipelineInteractiveApp
# Create interactive application
interactive_app = PipelineInteractiveApp()
# get inputs from the command line
interactive_app.parse_args()
#Check if import failed or not
if not import_success:
interactive_app.setEnableGUI(False)
#Open the interactive window if enabled
if interactive_app.isGUIEnabled():
#Get the specific functions for this window
dataPlotManager = DataPlotterManager()
interactive_app.setPlotManager(dataPlotManager)
interactive_app.showGUI()
else:
interactive_app.set_continue_mode()
#Print outputs. This is parsed by the Reflex python actor to
#get the results. Do not remove
interactive_app.print_outputs()
sys.exit()
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