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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Master Class for Exoplanet Time Series Observation Photometry
Step1:  As an example, Spitzer data is expected to be store in the directory structure
Step2:  Load Wanderer Class
Necessary Constants Spitzer
Step3:  Load Stored Instance from Save Files
If this is not your time, then you can load a saved file in the loadfitsdir directory that has the filename
Step4:  Double check for NaNs
Step5:  Identifier Strong Outliers
Step6:  TEST
Try column-wise background subtraction (and row-wise) to model the read pattern
Step7:  Create Beta Variable Radius
Gaussian_Fit_AnnularMask_rad_betaRad_0.0_0.0
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Python Code:
from wanderer import wanderer
def clipOutlier2D(arr2D, nSig=10):
    arr2D     = arr2D.copy()
    medArr2D  = median(arr2D,axis=0)
    sclArr2D  = np.sqrt(((scale.mad(arr2D)**2.).sum()))
    outliers  = abs(arr2D - medArr2D) >  nSig*sclArr2D
    inliers   = abs(arr2D - medArr2D) <= nSig*sclArr2D
    arr2D[outliers] = median(arr2D[inliers],axis=0)
    return arr2D
rcParams['figure.dpi'] = 150
rcParams['image.interpolation'] = 'None'
rcParams['image.cmap']          = 'Blues_r'
rcParams['axes.grid']           = False
Explanation: Master Class for Exoplanet Time Series Observation Photometry
End of explanation
from os import environ
planetName      = 'GJ1214'
planetDirectory = '/Research/Planets/'
channel = 'ch2'
# channel = 'ch2/'
dataSub = 'bcd/'
dataDir     = environ['HOME'] + planetDirectory + planetName + '/data/raw/' + channel + '/big/'
AORs = []
for dirNow in glob(dataDir + '/*'):
    AORs.append(dirNow.split('/')[-1])
fileExt = '*bcd.fits'
uncsExt = '*bunc.fits'
print(dataDir)
len(AORs)
iAOR        = 0
AORNow      = AORs[iAOR]
loadfitsdir = dataDir + AORNow + '/' + channel + '/' + dataSub
print(loadfitsdir)
nCores = cpu_count()
fitsFilenames = glob(loadfitsdir + fileExt);print(len(fitsFilenames))
uncsFilenames = glob(loadfitsdir + uncsExt);print(len(uncsFilenames))
header_test = fits.getheader(fitsFilenames[0])
print('AORLABEL:\t{}\nNum Fits Files:\t{}\nNum Unc Files:\t{}'.format\
          (header_test['AORLABEL'], len(fitsFilenames), len(uncsFilenames)))
Explanation: As an example, Spitzer data is expected to be store in the directory structure:
$HOME/PLANET_DIRECTORY/PLANETNAME/data/raw/AORDIR/CHANNEL/bcd/
EXAMPLE:
On a Linux machine
With user tempuser,
And all Spitzer data is store in Research/Planets
The planet named Happy-5b
Observed during AOR r11235813
In CH2 (4.5 microns)
The loadfitsdir should read as: /home/tempuser/Research/Planets/HAPPY5/data/raw/r11235813/ch2/bcd/
End of explanation
ppm             = 1e6
y,x             = 0,1
yguess, xguess  = 15., 15.   # Specific to Spitzer circa 2010 and beyond
filetype        = 'bcd.fits' # Specific to Spitzer Basic Calibrated Data
Explanation: Load Wanderer Class
Necessary Constants Spitzer
End of explanation
method = 'median'
print('Initialize an instance of `wanderer` as `example_wanderer_median`\n')
example_wanderer_median = wanderer(fitsFileDir=loadfitsdir, filetype=filetype, telescope='Spitzer', 
                                            yguess=yguess, xguess=xguess, method=method, nCores=nCores)
example_wanderer_median.AOR        = AORNow
example_wanderer_median.planetName = planetName
example_wanderer_median.channel    = channel
print('Load Data From Fits Files in ' + loadfitsdir + '\n')
# exarymple_wanderer_median.load_data_from_fits_files()
example_wanderer_median.spitzer_load_fits_file(outputUnits='electrons')#(outputUnits='muJ_per_Pixel')
print('Skipping Load Data From Save Files in ' + loadfitsdir + '\n')
# example_wanderer_median.load_data_from_save_files(savefiledir='./SaveFiles/', \
# saveFileNameHeader='Example_Wanderer_Median_', saveFileType='.pickle.save')
Explanation: Load Stored Instance from Save Files
If this is not your time, then you can load a saved file in the loadfitsdir directory that has the filename:
PLANETNAME_Wanderer_METHOD_DATE.pickle.save 
```python
method = 'mean'
example_wanderer_mean = wanderer(loadfitsdir=loadfitsdir, filetype=filetype, 
                                            yguess=yguess, xguess=xguess, method=method)
example_wanderer_mean.load_data_from_save_files(savefiledir='./SaveFiles/', 
                        saveFileNameHeader=planetName+'Wanderer_Mean', saveFileType='.pickle.save')
```
```python
method = 'median'
example_wanderer_median = wanderer(loadfitsdir=loadfitsdir_ModA, filetype=filetype, 
                                            yguess=yguess, xguess=xguess, method=method)
example_wanderer_median.load_data_from_save_files(savefiledir='./SaveFiles/', saveFileNameHeader='Example_Wanderer_Median_', saveFileType='.pickle.save')
```
Start a New Instance with Median for the Metric
End of explanation
example_wanderer_median.imageCube[np.where(isnan(example_wanderer_median.imageCube))] = \
                                                    np.nanmedian(example_wanderer_median.imageCube)
Explanation: Double check for NaNs
End of explanation
print('Find, flag, and NaN the "Bad Pixels" Outliers' + '\n')
example_wanderer_median.find_bad_pixels()
print('Fit for All Centers: Flux Weighted, Gaussian Fitting, Gaussian Moments, Least Asymmetry' + '\n')
# example_wanderer_median.fit_gaussian_centering()
example_wanderer_median.fit_flux_weighted_centering()
# example_wanderer_median.fit_least_asymmetry_centering()
# example_wanderer_median.fit_all_centering() # calling this calls least_asymmetry, which does not work :(
# start = time()
# example_wanderer_median.mp_fit_gaussian_centering(subArray=6)
# print('Operation took {} seconds with {} cores'.format(time()-start, example_wanderer_median.nCores))
# centering_df_copy = example_wanderer_median.centering_df
# example_wanderer_median.centering_df = {}
start = time()
example_wanderer_median.mp_lmfit_gaussian_centering(subArraySize=6, recheckMethod=None, median_crop=False)
print('Operation took {} seconds with {} cores'.format(time()-start, example_wanderer_median.nCores))
nSig       = 10.1
medY       = median(example_wanderer_median.centering_GaussianFit.T[y])
medX       = median(example_wanderer_median.centering_GaussianFit.T[x])
stdY       = std(example_wanderer_median.centering_GaussianFit.T[y])
stdX       = std(example_wanderer_median.centering_GaussianFit.T[x])
ySig = 4
xSig = 4
outliers   = (((example_wanderer_median.centering_GaussianFit.T[y] - medY)/(ySig*stdY))**2 + \
              ((example_wanderer_median.centering_GaussianFit.T[x] - medX)/(xSig*stdX))**2) > 1
ax = figure().add_subplot(111)
cx, cy = example_wanderer_median.centering_GaussianFit.T[x],example_wanderer_median.centering_GaussianFit.T[y]
ax.plot(cx,cy,'.',ms=1)
ax.plot(cx[outliers],cy[outliers],'.',ms=1)
# ax.plot(median(cx), median(cy),'ro',ms=1)
ax.set_xlim(medX-nSig*stdX,medX+nSig*stdX)
ax.set_ylim(medY-nSig*stdY,medY+nSig*stdY)
from sklearn.cluster import DBSCAN
dbs     = DBSCAN(n_jobs=-1, eps=0.2, leaf_size=10)
dbsPred = dbs.fit_predict(example_wanderer_median.centering_GaussianFit)
dbs_options = [k for k in range(-1,100) if (dbsPred==k).sum()]
dbs_options
fig = figure(figsize=(6,6))
ax  = fig.add_subplot(111)
medGaussCenters   = median(example_wanderer_median.centering_GaussianFit,axis=0)
sclGaussCenters   = scale.mad(example_wanderer_median.centering_GaussianFit)
sclGaussCenterAvg = np.sqrt(((sclGaussCenters**2.).sum()))
yctrs = example_wanderer_median.centering_GaussianFit.T[y]
xctrs = example_wanderer_median.centering_GaussianFit.T[x]
nSigmas         = 5
for nSig in linspace(1,10,10):
    CircularAperture(medGaussCenters[::-1],nSig*sclGaussCenterAvg).plot(ax=ax)
for dbsOpt in dbs_options:
    ax.plot(xctrs[dbsPred==dbsOpt], yctrs[dbsPred==dbsOpt],'.',zorder=0, ms=1)
npix = 3
stillOutliers = np.where(abs(example_wanderer_median.centering_GaussianFit - medGaussCenters) > 4*sclGaussCenterAvg)[0]
print(len(stillOutliers))
# for o in stillOutliers:
#     figure()
#     imshow(example_wanderer_median.imageCube[o][16-npix:16+npix+1,16-npix:16+npix+1])
dbsClean  = 0
dbsKeep   = (dbsPred == dbsClean)
Explanation: Identifier Strong Outliers
End of explanation
nCores = example_wanderer_median.nCores
start = time()
example_wanderer_median.mp_measure_background_circle_masked()
print('CircleBG took {} seconds with {} cores'.format(time() - start, nCores))
start = time()
example_wanderer_median.mp_measure_background_annular_mask()
print('AnnularBG took {} seconds with {} cores'.format(time() - start, nCores))
start = time()
example_wanderer_median.mp_measure_background_KDE_Mode()
print('KDEUnivBG took {} seconds with {} cores'.format(time() - start, nCores))
start = time()
example_wanderer_median.mp_measure_background_median_masked()
print('MedianBG took {} seconds with {} cores'.format(time() - start, nCores))
fig = figure(figsize=(20,10))
ax  = fig.add_subplot(111)
ax.plot(example_wanderer_median.timeCube, example_wanderer_median.background_CircleMask,'.',alpha=0.2)
ax.plot(example_wanderer_median.timeCube, example_wanderer_median.background_Annulus,'.',alpha=0.2)
ax.plot(example_wanderer_median.timeCube, example_wanderer_median.background_MedianMask,'.',alpha=0.2)
ax.plot(example_wanderer_median.timeCube, example_wanderer_median.background_KDEUniv,'.',alpha=0.2)
ax.axvline(example_wanderer_median.timeCube.min()-.01+0.02)
ax.set_ylim(-25,100)
# ax.set_xlim(example_wanderer_median.timeCube.min()-.01,example_wanderer_median.timeCube.min() + .05)
example_wanderer_median.measure_effective_width()
print(example_wanderer_median.effective_widths.mean(), sqrt(example_wanderer_median.effective_widths).mean())
vrad_dist = example_wanderer_median.quadrature_widths - np.median(example_wanderer_median.quadrature_widths)
vrad_dist = clipOutlier2D(vrad_dist, nSig=5)
ax = figure().add_subplot(111)
ax.hist(vrad_dist, bins=example_wanderer_median.nFrames//100);
ax.hist(0.75*vrad_dist, bins=example_wanderer_median.nFrames//100);
ax.hist(0.5*vrad_dist, bins=example_wanderer_median.nFrames//100);
ax.hist(0.25*vrad_dist, bins=example_wanderer_median.nFrames//100);
# ax.set_xlim(-.25,.25);
print('Pipeline took {} seconds thus far'.format(time() - startFull))
print('Iterating over Background Techniques, Centering Techniques, Aperture Radii' + '\n')
centering_choices  = ['Gaussian_Fit']#, 'Gaussian_Mom', 'FluxWeighted']#, 'LeastAsymmetry']
background_choices = ['AnnularMask']#example_wanderer_median.background_df.columns
staticRads         = np.arange(1, 6,0.5)#[1.0 ]# aperRads = np.arange(1, 6,0.5)
varRads            = [0.0, 0.25, 0.50, 0.75, 1.0, 1.25, 1.50]#[None]# 
vrad_dist = example_wanderer_median.quadrature_widths - np.median(example_wanderer_median.quadrature_widths)
vrad_dist = clipOutlier2D(vrad_dist, nSig=5)
for staticRad in tqdm_notebook(staticRads, total=len(staticRads), desc='Static'):
    for varRad in tqdm_notebook(varRads, total=len(varRads), desc='Variable'):
        startMPFlux = time()
        example_wanderer_median.mp_compute_flux_over_time_varRad(staticRad, varRad, centering_choices[0], \
                                                          background_choices[0], useTheForce=True)
        
        # print('Flux Measurements took {} seconds for sRad {} and vRad {}'.format(time()-startMPFlux,staticRad,varRad))
Explanation: TEST
Try column-wise background subtraction (and row-wise) to model the read pattern
End of explanation
example_wanderer_median.mp_compute_flux_over_time_betaRad()
print('Entire Pipeline took {} seconds'.format(time() - startFull))
example_wanderer_median.mp_DBScan_Flux_All()
inlier_master = array(list(example_wanderer_median.inliers_Phots.values())).mean(axis=0) == 1.0
((~inlier_master).sum() / inlier_master.size)*100
example_wanderer_median.extract_PLD_components()
example_wanderer_median.mp_DBScan_PLD_All()
print('Saving `example_wanderer_median` to a set of pickles for various Image Cubes and the Storage Dictionary')
savefiledir         = environ['HOME']+'/Research/Planets/'+planetName+'/ExtracedData/' + channel 
saveFileNameHeader  = planetName+'_'+ AORNow +'_Median'
saveFileType        = '.joblib.save'
if not path.exists(environ['HOME']+'/Research/Planets/'+planetName+'/ExtracedData/'):
    mkdir(environ['HOME']+'/Research/Planets/'+planetName+'/ExtracedData/')
if not path.exists(savefiledir):
    print('Creating ' + savefiledir)
    mkdir(savefiledir)
print()
print('Saving to ' + savefiledir + saveFileNameHeader + saveFileType)
print()
example_wanderer_median.save_data_to_save_files(savefiledir=savefiledir, \
                                                saveFileNameHeader=saveFileNameHeader, \
                                                saveFileType=saveFileType)
quad_width= example_wanderer_median.quadrature_widths.values
vrad_dist = quad_width - np.median(quad_width)
vrad_dist = clipOutlier2D(vrad_dist, nSig=5)
vrad_dist_med = np.median(vrad_dist)
color_cycle = rcParams['axes.prop_cycle'].by_key()['color']
ax = figure().add_subplot(111)
for key in example_wanderer_median.flux_TSO_df.keys():
    staticRad = float(key.split('_')[-2])
    varRad    = float(key.split('_')[-1])
    aperRad   = staticRad + varRad*vrad_dist_med
    colorNow  = color_cycle[int(varRad*4)]
    # if aperRad > 1.5 and aperRad < 3.5:
    ax.scatter(aperRad, scale.mad(np.diff(example_wanderer_median.flux_TSO_df[key])), \
                   color=colorNow, zorder=int(varRad*4))
for varRad in [0.,0.25, 0.5, 0.75, 1.0, 1.25, 1.5]:
    colorNow  = color_cycle[int(varRad*4)]
    ax.scatter([],[], color=colorNow, label=varRad)
ax.set_xlabel('StaticRad + Average(varRad)')
ax.set_ylabel('MAD( Diff ( Flux ) )')
ax.legend(loc=0)
# ax.set_ylim(292.5,294)
# ax.set_xlim(2.9, 3.1)
print('Entire Pipeline took {} seconds'.format(time() - startFull))
Explanation: Create Beta Variable Radius
Gaussian_Fit_AnnularMask_rad_betaRad_0.0_0.0
End of explanation
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