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 import matplotlib.gridspec as gridspec import_success = True import pdb # for debugging 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_det_noise" mst_cat = "MASTER_READGAIN" ref_cat = "REFERENCE_READGAIN" 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="thresh", group="vimos_ima_det_noise", description="Rejection threshold in sigma above background. [5.0]"), ] 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: print(f.name) 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.mst_cat in self.frames: self.mst_tab = self.frames[self.mst_cat] self.mst_found = True # Read the reference table if self.ref_cat in self.frames: self.ref_tab = self.frames[self.ref_cat] else: self.ref_found = False # table is a list of FITS record arrays, one for each extension # access data by field name: table['COLNAME'] # see help at https://pythonhosted.org/pyfits/users_guide/users_table.html table = self.mst_tab.all_hdu[1].data # check if data in MASTER_READGAIN is valid if ( (False in numpy.isfinite(table['READNOISE'])) or (False in numpy.isfinite(table['GAIN'])) or (False in numpy.isfinite(table['COVAR'])) or ( (True in (table['READNOISE'] <= 0)) or (True in (table['GAIN'] <= 0)) or (True in (table['COVAR'] <= 0)) ) ): # At least one data element is not good, so set the plotting functions to Baddata ones self._add_subplots = self._add_nodata_subplots self._plot = self._baddata_plot else: self._add_subplots = self._add_subplots self._plot = self._data_plot else: # No master data, so 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 _add_subplots(self, figure): # Make 1x2 grid of plots self.img_plot = [] gs = gridspec.GridSpec(1, 2) self.img_plot.append(figure.add_subplot(gs[0,0])) self.img_plot.append(figure.add_subplot(gs[0,1])) def plotProductsGraphics(self): self._plot() def _data_plot(self): title = " " tooltip = "If a REFERENCE value equals a derived MASTER value, the point will lie on the solid blue line" colours = ['red','blue','green','purple'] markers = ["o","s","^","D"] mst_table = self.mst_tab.all_hdu[1].data ref_table = self.ref_tab.all_hdu[1].data for i in range(2): self.img_plot[i].grid(True) self.img_plot[i].set_title(title, fontsize=12, fontweight='semibold') self.img_plot[i].tooltip = tooltip #self.img_plot[i].set_aspect('equal') self.img_plot[i].set_aspect('auto') # Pull out correct numbers in table, be careful to match reference chip to master chip chip_mst = mst_table['EXTNAME'] chip_ref = ref_table['EXTNAME'] x_left=[] y_left=[] x_right=[] y_right=[] chip_label = [] for i in range(len(chip_ref)): for j in range(len(chip_mst)): if ( chip_mst[j] == chip_ref[i]): x_left.append(mst_table['READNOISE'][j]) y_left.append(ref_table['READNOISE'][i]) x_right.append((mst_table['GAIN'][j]) / (mst_table['COVAR'][j])) y_right.append((ref_table['GAIN'][i]) / (ref_table['COVAR'][i])) chip_label.append(chip_mst[j]) self.img_plot[0].set_xlabel("READNOISE [ADU]") self.img_plot[0].set_ylabel("REFERENCE READNOISE [ADU]") for i_row in range(len(mst_table['EXTNAME'])): self.img_plot[0].scatter(x_left[i_row], y_left[i_row], 60, color = colours[i_row],marker = markers[i_row], label = chip_label[i_row]) self.img_plot[0].plot([min(x_left),max(x_left)],[min(x_left),max(x_left)], label="REF == MASTER") self.img_plot[0].legend(loc = 0, scatterpoints = 1) cur_ylim = self.img_plot[0].get_ylim() self.img_plot[0].set_ylim([cur_ylim[0], cur_ylim[1]+0.2*(cur_ylim[1]-cur_ylim[0])]) self.img_plot[1].set_xlabel("GAIN [e-/ADU]") self.img_plot[1].set_ylabel("REFERENCE GAIN [e-/ADU]") for i_row in range(len(mst_table['EXTNAME'])): self.img_plot[1].scatter(x_right[i_row], y_right[i_row], 60, color = colours[i_row],marker = markers[i_row], label = chip_label[i_row]) self.img_plot[1].plot([min(x_right),max(x_right)],[min(x_right),max(x_right)], label="REF == MASTER") self.img_plot[1].legend(loc = 0, scatterpoints = 1) cur_ylim = self.img_plot[1].get_ylim() self.img_plot[1].set_ylim([cur_ylim[0], cur_ylim[1]+0.2*(cur_ylim[1]-cur_ylim[0])]) 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.mst_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 _baddata_plot(self): # could be moved to reflex library? self.img_plot.set_axis_off() text_baddata = "At least one value in MASTER_READGAIN table\n is out of bounds\n" \ "Check the log and input files" self.img_plot.text(0.1, 0.6, text_baddata, color='#11557c', fontsize=18, ha='left', va='center', alpha=1.0) self.img_plot.tooltip = 'Bad data' 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()