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passing self-test for load and download--major API rework to use xarr…
…ay.Dataset
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| image: | ||
| - Visual Studio 2017 | ||
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| stack: python 3 | ||
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| environment: | ||
| PY_DIR: C:\Python36-x64 | ||
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| clone_depth: 3 | ||
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| before_build: | ||
| - cmd: set PATH=%PY_DIR%;%PY_DIR%\Scripts;%PATH% | ||
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| build_script: | ||
| - pip install -e .[tests] | ||
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| after_build: | ||
| - pytest -v | ||
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| [run] | ||
| cover_pylib = false | ||
| omit = | ||
| /home/travis/virtualenv/* | ||
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| [report] | ||
| exclude_lines = | ||
| pragma: no cover | ||
| def __repr__ | ||
| RuntimeError | ||
| NotImplementedError | ||
| ImportError | ||
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python | ||
| """ | ||
| Reads DASC allsky cameras images in FITS formats into GeoData. | ||
| Run standalone from PlayDASC.py | ||
| """ | ||
| from pathlib import Path | ||
| import warnings # corrupt FITS files let off a flood of AstroPy warnings | ||
| from astropy.io.fits.verify import VerifyWarning | ||
| import logging | ||
| from astropy.io import fits | ||
| import numpy as np | ||
| from datetime import timedelta | ||
| from dateutil.parser import parse | ||
| import xarray | ||
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| def load(flist:list, azfn:Path=None, elfn:Path=None, minmax:tuple=None, treq:list=None) -> xarray.Dataset: | ||
| """ | ||
| reads FITS images and spatial az/el calibration for allsky camera | ||
| Bdecl is in degrees, from IGRF model | ||
| """ | ||
| warnings.filterwarnings('ignore', category=VerifyWarning) | ||
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| if not flist: | ||
| raise FileNotFoundError('no files of this wavelength') | ||
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| if isinstance(flist,(str,Path)): | ||
| flist = [flist] | ||
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| #%% read one file mode | ||
| if treq is not None: | ||
| expstart = [] | ||
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| for i,fn in enumerate(flist): | ||
| try: | ||
| with fits.open(fn, mode='readonly') as h: | ||
| expstart.append(parse(h[0].header['OBSDATE'] + ' ' + h[0].header['OBSSTART'])) | ||
| except IOError: #many corrupted files, accounted for by preallocated vectors | ||
| pass | ||
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| expstart = np.array(expstart) | ||
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| if isinstance(treq,float) or len(treq) == 1: # single frame | ||
| fi = np.nanargmin(abs(expstart-treq)) #index number in flist desired | ||
| elif len(treq)==2: #frames within bounds | ||
| if treq[0] is not None: | ||
| fi = (treq[0] <= expstart) | ||
| if treq[1] is not None: | ||
| fi &= (expstart < treq[1]) | ||
| elif treq[1] is not None: | ||
| fi = (expstart < treq[1]) | ||
| else: | ||
| fi = slice(None) | ||
| else: | ||
| fi = slice(None) | ||
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| flist = flist[fi] | ||
| if len(flist)==0: | ||
| raise FileNotFoundError('no files found within time limits') | ||
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| if isinstance(flist,Path): # so that we can iterate | ||
| flist = [flist] | ||
| #%% iterate over image files | ||
| time = []; img= []; wavelen = [] | ||
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| for i,fn in enumerate(flist): | ||
| try: | ||
| with fits.open(fn, mode='readonly') as h: | ||
| assert h[0].header['BITPIX']==16,'this function assumes unsigned 16-bit data' | ||
| expstart = parse(h[0].header['OBSDATE'] + 'T' + h[0].header['OBSSTART']) | ||
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| time.append((expstart, expstart + timedelta(seconds=h[0].header['EXPTIME']))) #EXPTIME is in seconds | ||
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| wavelen.append(int(h[0].header['FILTWAV'])) | ||
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| sensorloc={'lat':h[0].header['GLAT'], | ||
| 'lon':h[0].header['GLON'], | ||
| 'alt_m':200.} # TODO use real altitude | ||
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| """ | ||
| DASC iKon cameras are/were 14-bit at least through 2015. So what they did was | ||
| just write unsigned 14-bit data into signed 16-bit integers, which doesn't overflow | ||
| since 14-bit \in {0,16384}. | ||
| These files do not have a BZERO value. Someday when they're written correctly this | ||
| code may need updating. | ||
| Further, there was a RAID failure that filled the data files with random values. | ||
| Don Hampton says about 90% of data OK, but 10% NOK. | ||
| """ | ||
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| I = np.rot90(h[0].data,-1) #NOTE: rotation to match online AVIs from UAF website. It's not transpose, and the cal file seems off. | ||
| if not 'BZERO' in h[0].header.keys(): | ||
| I[I>16384] = 0 #extreme, corrupted data | ||
| I = I.clip(0,16384).astype(np.uint16) #discard bad values for 14-bit cameras. | ||
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| img.append(I) | ||
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| except (IOError,TypeError) as e: | ||
| logging.warning(f'{fn} has error {e}') | ||
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| # %% collect output | ||
| img = np.array(img) | ||
| time = np.array(time) | ||
| wavelen = np.array(wavelen) | ||
| wavelengths = np.unique(wavelen) | ||
| # %% deal with corrupted data | ||
| if minmax is not None: | ||
| img[(img<minmax[0]) | (img>minmax[1])] = 1 #instead of 0 for lognorm | ||
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| ds = {} | ||
| for w in wavelengths: | ||
| ds[w] = (('time','y','x'),img[wavelen==w,...]) | ||
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| data = xarray.Dataset(ds, | ||
| coords={'time':time[:,0]}, | ||
| attrs={'timeend':time[:,1], | ||
| 'sensorloc':sensorloc}) | ||
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| if azfn is not None and elfn is not None: | ||
| with fits.open(Path(azfn).expanduser(),mode='readonly') as h: | ||
| data['az'] = (('y','x'),h[0].data) | ||
| with fits.open(Path(elfn).expanduser(),mode='readonly') as h: | ||
| data['el'] = (('y','x'),h[0].data) | ||
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| return data |
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