jnburchett / veeper Goto Github PK

Sometimes I need to use convertAnnotations to produce an input file for VPmeasure with all of the correct formatting. However, convertAnnotations crashes if these is only a single line in the annotations file. This problem is solved by adding a single line to the annotations file (i.e., it works with only two lines in the annotations, but not one). Here is the error message:

Traceback (most recent call last):
File "../../lines_together/convertAnnotations.py", line 82, in
for lam in restwaves:
TypeError: iteration over a 0-d array

After completing a fit, it would be useful to have the option of saving the model to a fits file so that the model can be used for other purposes.

As stated. We need a way to keep track of reliability and comments for each component. There are at least two approaches:

1. Create new mandatory columns in the output that are filled with the respective values when given in input, or filled with a default value when not given in the input.

2. Columns that are given in the input and are not mandatory, are still written in the output.

Option 2 is more general, option 1 more structured.

While inspecting quality of fits by clicking on various windows with the cursor, the code crashed out of the blue with the following error messages:

TypeError Traceback (most recent call last)
/Users/toddtripp/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_qt5.pyc in mousePressEvent(self, event)
257 if button is not None:
258 FigureCanvasBase.button_press_event(self, x, y, button,
--> 259 guiEvent=event)
260 if DEBUG:
261 print('button pressed:', event.button())

/Users/toddtripp/anaconda/lib/python2.7/site-packages/matplotlib/backend_bases.pyc in button_press_event(self, x, y, button, dblclick, guiEvent)
1901 mouseevent = MouseEvent(s, self, x, y, button, self._key,
1902 dblclick=dblclick, guiEvent=guiEvent)
-> 1903 self.callbacks.process(s, mouseevent)
1904
1905 def button_release_event(self, x, y, button, guiEvent=None):

/Users/toddtripp/anaconda/lib/python2.7/site-packages/matplotlib/cbook.pyc in process(self, s, _args, *_kwargs)
561 for cid, proxy in list(six.iteritems(self.callbacks[s])):
562 try:
--> 563 proxy(_args, *_kwargs)
564 except ReferenceError:
565 self._remove_proxy(proxy)

/Users/toddtripp/anaconda/lib/python2.7/site-packages/matplotlib/cbook.pyc in call(self, _args, *_kwargs)
428 mtd = self.func
429 # invoke the callable and return the result
--> 430 return mtd(_args, *_kwargs)
431
432 def eq(self, other):

/Users/toddtripp/python_code/joebvp/joebvp/VPmeasure.pyc in on_click(self, event)
524 def on_click(self, event):
525 self.lastclick=event.xdata
--> 526 self.sideplot(self.lastclick)
527
528 def addmpl(self, fig):

/Users/toddtripp/python_code/joebvp/joebvp/VPmeasure.pyc in sideplot(self, cenwave, wavebuf)
415 self.sideax.plot(self.wave, self.normsig, linestyle='steps-mid', color='red', lw=0.5)
416 self.sideax.plot(self.wave, -self.normsig, linestyle='steps-mid', color='red', lw=0.5)
--> 417 self.sideax.set_xlim(cenwave-wavebuf,cenwave+wavebuf)
418 self.sideax.set_ylim(cfg.ylim)
419 self.changesidefig(self.sidefig)

TypeError: unsupported operand type(s) for -: 'NoneType' and 'int'

The parameter "lowblim" in the cfg.py file is being overridden somewhere in the code. I have noticed this happening before, and I just did a test to verify that the problem is occurring. I currently have lowblim set to 3.0, but when I just did a run with only H I lines, the code did not allow b to go below 5. Since 5 was the original default value, I infer that it is hardcoded somewhere, and the cfg.py file is being trumped.

Is it possible that this is specific to H I? I believe that the H I lines have a different value for the upper b limit than the metal lines, so I suppose that the parameters in the cfg.py file might only apply to the metals. However, I would like to be able to adjust lowblim for the H I line fitting as well.

Need to add near-UV line-spread functions.

Currently the code prints the values for all of the parameters in a fit for each iteration. This is not very useful because there are typically too many parameters and too many iterations to enable any inferences to be drawn from all of the parameter values that are spit out with each iteration.

However, it would be helpful to see the chi2 value and the DOF after each iteration. This type of output format would be better:

Iter 142 CHI-SQUARE = 2365.906138 DOF = 1497
Iter 143 CHI-SQUARE = 2365.90612 DOF = 1497
Iter 144 CHI-SQUARE = 2365.906102 DOF = 1497
etc.