from IPython.display import IFrame, Image, display, HTML def myiframe(url): return IFrame(url, width=700, height=350) def myiframe2(url): return HTML(''.format(url)) def myimgdisplay(url): i = Image(filename=url) return display(i)

Diviner radiometer

Mapping the surface temperature of the 8
Constantly taking data (raw approx 2 TB so far)
in 9 wavelengths bands, each with 21 detector
after producing lots of standard surface maps: 150 TB

Postdoc task:

Recalibration of complete dataset
Previous implementation: professional progger, C++
- fast, but unflexible, hard to grok
Open hand how to do it:
- Python, with a lot of Pandas!



In [2]:

    
myimgdisplay('./teaser1.jpg')



In [3]:

    
myimgdisplay('./Data_workshop_LPSC2014_Aye/Data_workshop_LPSC2014_Aye.001.jpg')



In [4]:

    
myimgdisplay('./Data_workshop_LPSC2014_Aye/Data_workshop_LPSC2014_Aye.002.jpg')



In [5]:

    
myimgdisplay('./Data_workshop_LPSC2014_Aye/Data_workshop_LPSC2014_Aye.003.jpg')



In [9]:

    
def get_sv_selector(df):
    "Create dataframe selector for pointing limits of divconstants 'c' file"
    return (df.last_az_cmd >= c.SV_AZ_MIN) & \
           (df.last_az_cmd <= c.SV_AZ_MAX) & \
           (df.last_el_cmd >= c.SV_EL_MIN) & \
           (df.last_el_cmd <= c.SV_EL_MAX)



In [ ]:

    
def define_sdtype(df):
    df['sdtype'] = 0
    df.loc[get_sv_selector(df), 'sdtype'] = 1
    df.loc[get_bb_selector(df), 'sdtype'] = 2
    df.loc[get_st_selector(df), 'sdtype'] = 3
    df.loc[get_stowed_selector(df), 'sdtype'] = -2
    # the following defines the sequential list of calibration blocks inside
    # the dataframe. nd.label provides an ID for each sequential part where
    # the given condition is true.
    # this still includes the moving areas, because i want the sv and bbv
    # attached to each other to deal with them later as a separate calibration
    # block
    # DECISION: block labels contain moving data as well
    # WARNING: But not all moving data is contained in block labels!
    # The end of calib block has pointing commands set to nadir.
    # below defined "is_xxx" do NOT contain moving data.
    df['calib_block_labels'] = nd.label((df.sdtype == 1) |
                                        (df.sdtype == 2) |
                                        (df.sdtype == 3))[0]



In [ ]:

    
df.loc[df.moving == 1, 'sdtype'] = -1

    df['space_block_labels'] = nd.label(df.sdtype == 1)[0]
    df['bb_block_labels'] = nd.label(df.sdtype == 2)[0]
    df['st_block_labels'] = nd.label(df.sdtype == 3)[0]

    # now I don't need to check for moving anymore, the sdtypes are clean
    df['is_spaceview'] = (df.sdtype == 1)
    df['is_bbview'] = (df.sdtype == 2)
    df['is_stview'] = (df.sdtype == 3)
    df['is_moving'] = (df.sdtype == -1)
    df['is_stowed'] = (df.sdtype == -2)
    df['is_calib'] = df.is_spaceview | df.is_bbview | df.is_stview