1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
|
from __future__ import with_statement
import sys
try:
import numpy
import re
import reflex
from pipeline_product import PipelineProduct
import pipeline_display
import reflex_plot_widgets
import matplotlib.gridspec as gridspec
import_success = True
except ImportError:
import_success = False
print "Error importing modules pyfits, wx, matplotlib, numpy"
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):
# static members
recipe_name = "kmos_std_star"
star_spec_cat = "STAR_SPEC"
telluric_cat = "TELLURIC"
std_image_cat = "STD_IMAGE"
y_scalefactor = 1000
def setWindowTitle(self):
return self.recipe_name+"_interactive"
def setInteractiveParameters(self):
return [
reflex.RecipeParameter(recipe=self.recipe_name, displayName="imethod",
group="Recons.", description="Interpolation Method (NN, lwNN, swNN, MS, CS)"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="xcal_interpolation",
group="Recons.", description="Interpolate xcal between rotator angles"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="mask_method",
group="Extr.", description="Extraction Method (optimal/integrated)"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="centre",
group="Extr.", description="Centre (integrated only)"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="radius",
group="Extr.", description="Radius (integrated only)"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="fmethod",
group="Extr.", description="Fitting Method (gauss, moffat)"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="neighborhoodRange",
group="Extr.", description="Range for Neighbors"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="flux",
group="Extr.", description="Apply flux conservation"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="cmethod",
group="Comb.", description="Combination Method (average, median, sum, min_max, ksigma)"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="cpos_rej",
group="Comb.", description="The positive rejection threshold"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="cneg_rej",
group="Comb.", description="The negative rejection threshold"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="citer",
group="Comb.", description="The number of iterations"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="cmax",
group="Comb.", description="The number of maximum pixel values"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="cmin",
group="Comb.", description="The number of minimum pixel values"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="startype",
group="Star", description="The star spectral type (O, B, A, F, G)"),
reflex.RecipeParameter(recipe=self.recipe_name, displayName="magnitude",
group="Star", description="Star Magnitude"),
]
def readFitsData(self, fitsFiles):
self.frames = dict()
for f in fitsFiles:
self.frames[f.category] = PipelineProduct(f)
# Two cases: the file category is found or not found.
# Define the plotting functions in both cases
if self.std_image_cat in self.frames and self.star_spec_cat in self.frames and self.telluric_cat in self.frames:
# Get the wished files
std_image = self.frames[self.std_image_cat]
star_spec = self.frames[self.star_spec_cat]
telluric = self.frames[self.telluric_cat]
# Initialise
self.star_data_extnames = []
self.qc_std_trace = dict()
self.qc_spat_res = dict()
self.image_std = dict()
self.image_std_avg = dict()
self.image_std_stdev = dict()
self.qc_thruput = dict()
self.qc_zpoint = dict()
self.crpix1 = dict()
self.crval1 = dict()
self.cdelt1 = dict()
self.spec_data = dict()
self.spec_noise = dict()
# READ data
self.qc_nr_std_stars = star_spec.all_hdu[0].header['ESO QC NR STD STARS']
self.qc_thruput_mean = star_spec.all_hdu[0].header['ESO QC THRUPUT MEAN']
self.qc_thruput_sdv = star_spec.all_hdu[0].header['ESO QC THRUPUT SDV']
# Loop on all extensions of std_image to find the std stars extensions
for std_image_ext in std_image.all_hdu:
naxis = std_image_ext.header['NAXIS']
# NAXIS is 2 if there is an image, 0 otherwise
if (naxis == 2):
# extname is like IFU.3.DATA
extname = std_image_ext.header['EXTNAME']
self.star_data_extnames.append(extname)
self.qc_std_trace[extname] = std_image_ext.header['ESO QC STD TRACE']
self.qc_spat_res[extname] = std_image_ext.header['ESO QC SPAT RES']
self.image_std[extname] = std_image_ext.data
self.image_std_avg[extname] = numpy.average(std_image_ext.data)
self.image_std_stdev[extname] = numpy.std(std_image_ext.data)
# Get infos from star_spec using the extname
self.qc_thruput[extname] = star_spec.all_hdu[extname].header['ESO QC THRUPUT']
self.qc_zpoint[extname] = star_spec.all_hdu[extname].header['ESO QC ZPOINT']
self.crpix1[extname] = star_spec.all_hdu[extname].header['CRPIX1']
self.crval1[extname] = star_spec.all_hdu[extname].header['CRVAL1']
self.cdelt1[extname] = star_spec.all_hdu[extname].header['CDELT1']
self.spec_data[extname] = star_spec.all_hdu[extname].data
# noise_extname is like IFU.3.NOISE
noise_extname = re.sub("DATA", "NOISE", extname)
self.spec_noise[extname] = star_spec.all_hdu[noise_extname].data
# Set the plotting functions
self._add_subplots = self._add_subplots
self._plot = self._data_plot
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,
self.star_data_extnames, 0, title='Standard star selection')
widgets.append(self.radiobutton)
return widgets
def setRadioCallback(self, label) :
# Setup the image display
imgdisp = pipeline_display.ImageDisplay()
imgdisp.setAspect('equal')
imgdisp.z_lim = (self.image_std_avg[label] - self.image_std_stdev[label], self.image_std_avg[label] + 2 * self.image_std_stdev[label])
imgdisp.display(self.img_plot, "Median collapsed cube (STD_IMAGE)\nFWHM "+ r"$\simeq$ " +str( numpy.round(self.qc_spat_res[label], 2) )+ " [arcsec]", self._data_plot_get_tooltip(label), self.image_std[label])
self.img_plot.set_xlabel("pixels")
self.img_plot.set_ylabel("pixels")
# Define wave
pix = numpy.arange(len(self.spec_data[label]))
wave = self.crval1[label] + pix * self.cdelt1[label]
# Plot Spectrum
specdisp = pipeline_display.SpectrumDisplay()
self.spec_plot.clear()
specdisp.setLabels(r"$\lambda$["+"$\mu$m]", "Flux (ADU) [x"+str(self.y_scalefactor)+"]" )
specdisp.display(self.spec_plot, "Extracted Standard Star Spectrum (STAR_SPEC)", self._data_plot_get_tooltip(label),
wave, self.spec_data[label]/self.y_scalefactor) # TO DO: Unify plotting in one function.
if (self.spec_noise[label] is not None):
# Overplot the Noise spectrum
specdisp.overplot(self.spec_plot, wave, self.spec_noise[label]/self.y_scalefactor, 'red') # TO DO: Unify plotting in one function.
self.spec_plot.legend(('Flux', 'Noise'))
def _add_subplots(self, figure):
gs = gridspec.GridSpec(2, 2)
self.axradiobutton = figure.add_subplot(gs[0,0])
self.img_plot = figure.add_subplot(gs[0,1])
self.spec_plot = figure.add_subplot(gs[1,:])
def _data_plot_get_tooltip(self, extname):
# Create the tooltip
tooltip = " \
ESO QC NR STD STARS : %f \n \
ESO QC THRUPUT MEAN : %f \n \
ESO QC THRUPUT SDV : %f \n \
ESO QC STD TRACE : %f \n \
ESO QC SPAT RES : %f \n \
ESO QC THRUPUT : %f \n \
ESO QC ZPOINT : %f \
" % (self.qc_nr_std_stars, self.qc_thruput_mean, self.qc_thruput_sdv, self.qc_std_trace[extname], self.qc_spat_res[extname], self.qc_thruput[extname], self.qc_zpoint[extname])
return tooltip
def _data_plot(self):
extname = self.star_data_extnames[0]
imgdisp = pipeline_display.ImageDisplay()
imgdisp.setAspect('equal')
imgdisp.z_lim = (self.image_std_avg[extname] - self.image_std_stdev[extname], self.image_std_avg[extname] + 2 * self.image_std_stdev[extname])
imgdisp.display(self.img_plot, "Median collapsed cube (STD_IMAGE)\nFWHM "+ r"$\simeq$ " +str( numpy.round(self.qc_spat_res[extname], 2) )+ " [arcsec]", self._data_plot_get_tooltip(extname), self.image_std[extname])
#Please alert if it shuld be latex "\approx" instead of "\simeq".
self.img_plot.set_xlabel("pixels")
self.img_plot.set_ylabel("pixels")
# Define wave
pix = numpy.arange(len(self.spec_data[extname]))
wave = self.crval1[extname] + pix * self.cdelt1[extname]
# Plot Spectrum
specdisp = pipeline_display.SpectrumDisplay()
specdisp.setLabels(r"$\lambda$["+"$\mu$m]", "Flux (ADU) [x"+str(self.y_scalefactor)+"]" )
specdisp.display(self.spec_plot, "Extracted Standard Star Spectrum (STAR_SPEC)", self._data_plot_get_tooltip(extname), wave, self.spec_data[extname]/self.y_scalefactor) # TO DO: Unify plotting in one function.
if (self.spec_noise[extname] is not None):
# Overplot the Noise spectrum
specdisp.overplot(self.spec_plot, wave, self.spec_noise[extname]/self.y_scalefactor, 'red') # TO DO: Unify plotting in one function.
self.spec_plot.legend(('Flux', 'Noise'))
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"
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 = text_nodata
#This is the 'main' function
if __name__ == '__main__':
from reflex_interactive_app import PipelineInteractiveApp
# Create interactive application
interactive_app = PipelineInteractiveApp(enable_init_sop=True)
# 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()
|
