notebook.community

Edit and run 





In [1]:



    


%matplotlib inline



    











In [1]:



    


from iuvs import meta



    


















    






/Users/klay6683/miniconda3/lib/python3.4/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.
  "You should import from ipykernel or jupyter_client instead.", ShimWarning)

















In [2]:



    


from pathlib import Path
# root = Path('/home/klay6683/to_keep')
root = Path('/Users/klay6683/data/iuvs')
# df = pd.read_hdf('/home/klay6683/to_keep/l1a_dark_scan.h5', 'df')
df = pd.read_hdf(str(root / 'l1a_dark_scan.h5'), 'df')

using previously defined clean up function





In [3]:



    


df = meta.clean_up_dark_scan(df)

creating little checking function:





In [4]:



    


def check_values(col):
    return df[col.upper()].value_counts(dropna=False)

filter further down

remove cruise and transition and only consider 3 int_times for now





In [5]:



    


df = df[df.COLLECTION_ID!='cruise']

df = df[df.COLLECTION_ID!='transition']

df = df[df.INT_TIME.isin([14400, 4200, 1400])]

load HK data





In [6]:



    


from iuvs import hk

hkdb = pd.read_hdf(str(root / 'HK_DB.h5'), 'df')

pick a time resolution





In [7]:



    


timeres = '1s'

resample





In [8]:



    


newind = pd.DatetimeIndex(start=hkdb.index[0].replace(microsecond=0),
                 end=hkdb.index[-1], freq='1s')



    











In [9]:



    


snew=pd.Series(1, newind)
sold = pd.Series(2, hkdb.index)
smerged=pd.concat([snew,sold])
smerged = smerged.sort_index()
reindexed = hkdb.reindex(smerged.index)
reindexed.head()



    


















    
Out[9]:










  
    

      
      FUV_CHIP_TEMP_C
      FUV_DET_TEMP_C
      FUV_INT_TEMP_C
      GRAT_MOT_TEMP_C
      HV_POWR_TEMP_C
      IUVS_1_TEMP_C
      IUVS_2_TEMP_C
      IUVS_3_TEMP_C
      IUVS_4_TEMP_C
      IUVS_5_TEMP_C
      ...
      MUV_DET_TEMP_C
      MUV_INT_TEMP_C
      OP_1_HTR_TEMP_C
      OP_2_HTR_TEMP_C
      POWER_BD1_TEMP_C
      POWER_BD2_TEMP_C
      PROC_BD_TEMP_C
      SCAN_MOT_TEMP_C
      ZONE_1_HTR_TEMP_C
      ZONE_2_HTR_TEMP_C
    

  
  
    

      2013-12-16 00:00:27.000000000
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      2013-12-16 00:00:27.110313951
      6.7534
      7.743806
      5.947079
      27.915505
      30.65817
      24.283262
      28.417406
      24.945137
      27.340724
      -57.437199
      ...
      7.829424
      5.908708
      2.655265
      -11.086761
      -7.396871
      -9.342501
      -9.712488
      24.21937
      26.494905
      26.26658
    

    

      2013-12-16 00:00:28.000000000
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      2013-12-16 00:00:29.000000000
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

    

      2013-12-16 00:00:30.000000000
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
    

  



5 rows × 21 columns



















In [10]:



    


hkfuvtemp = reindexed.FUV_DET_TEMP_C.interpolate(method='time')
hkmuvtemp = reindexed.MUV_DET_TEMP_C.interpolate(method='time')



    











In [11]:



    


hkfuvtemp.tail()



    


















    
Out[11]:








2015-08-11 19:30:54.000000000   -20.355654
2015-08-11 19:30:55.000000000   -20.355654
2015-08-11 19:30:56.000000000   -20.355654
2015-08-11 19:30:57.000000000   -20.355654
2015-08-11 19:30:57.949607491   -20.355654
Name: FUV_DET_TEMP_C, dtype: float64

















In [12]:



    


g = df.groupby(['CHANNEL', 'INT_TIME', 'activity', 'BINNING_SET'])



    











In [13]:



    


size = g.size()



    











In [14]:



    


i = size[size>4000].index



    











In [15]:



    


i.get_level_values('BINNING_SET')



    


















    
Out[15]:








Index([  (0, 4, 89, 115),   (52, 8, 89, 80),   (0, 4, 96, 115),
       (140, 4, 96, 115), (140, 8, 101, 80)],
      dtype='object', name='BINNING_SET')

















In [16]:



    


size[size>4000]



    


















    
Out[16]:








CHANNEL  INT_TIME  activity  BINNING_SET      
FUV      4200      periapse  (0, 4, 89, 115)      8484
         14400     apoapse   (52, 8, 89, 80)      5468
MUV      4200      periapse  (0, 4, 96, 115)      4030
                             (140, 4, 96, 115)    4830
         14400     apoapse   (140, 8, 101, 80)    5334
dtype: int64

















In [31]:



    


%matplotlib inline



    











In [32]:



    


for key, content in g:
    resampled = content.resample(timeres).dropna(how='all')
    if key[0]=='FUV':
        resampled['HK_TEMP'] = hkfuvtemp
    else:
        resampled['HK_TEMP'] = hkmuvtemp
    title = '_'.join([str(i) for i in key])
    print(title)
    fig, ax = plt.subplots(nrows=2)
    resampled.plot(kind='scatter', x='DET_TEMP', y='mean', ax=ax[0],
                 xlim=(-25, -11), ylim=(0.1, 1.5), title=title+' vs DET_TEMP')

    resampled.plot(kind='scatter', x='HK_TEMP', y='mean', ax=ax[1],
                 xlim=(-25, -11), ylim=(0.1, 1.5), title=title+' vs HK_TEMP')
    fig.tight_layout()
    fig.savefig('plots/'+title+'.png', dpi=150)
    plt.close(fig)



    


















    






FUV_1400_outbound_(0, 3, 880, 3)
FUV_1400_outbound_(52, 4, 89, 115)
FUV_1400_outbound_(52, 8, 89, 115)
FUV_1400_outlimb_(0, 4, 89, 115)
FUV_1400_outlimb_(52, 4, 89, 115)
FUV_1400_outlimb_(52, 8, 89, 115)
FUV_4200_outboundhifi_(0, 1, 319, 11)
FUV_4200_outdiskhifi_(0, 1, 319, 11)
FUV_4200_periapse_(0, 1, 0, 1)
FUV_4200_periapse_(0, 4, 89, 115)
FUV_4200_periapse_(1, 4, 89, 115)
FUV_4200_periapsehifi_(0, 1, 319, 11)
FUV_14400_apoapse_(0, 1, 0, 1)
FUV_14400_apoapse_(0, 4, 89, 80)
FUV_14400_apoapse_(1, 4, 89, 80)
FUV_14400_apoapse_(52, 8, 89, 80)
FUV_14400_apoapse_(65, 4, 89, 80)
FUV_14400_inbound_(0, 1, 0, 1)
FUV_14400_inbound_(0, 4, 89, 115)
FUV_14400_inbound_(52, 4, 89, 115)
FUV_14400_inbound_(52, 8, 89, 115)
FUV_14400_outbound_(0, 1, 0, 1)
FUV_14400_outbound_(52, 4, 89, 115)
FUV_14400_outbound_(52, 8, 89, 115)
FUV_14400_outboundhifi_(0, 1, 319, 11)
FUV_14400_outdisk_(0, 1, 0, 1)
FUV_14400_outdisk_(0, 4, 89, 115)
FUV_14400_outdisk_(52, 4, 89, 115)
FUV_14400_outdisk_(52, 8, 89, 115)
MUV_1400_apoapse_(0, 16, 0, 16)
MUV_1400_inbound_(0, 16, 0, 16)
MUV_1400_outbound_(0, 3, 880, 3)
MUV_1400_outbound_(0, 4, 96, 115)
MUV_1400_outbound_(2, 34, 96, 115)
MUV_1400_outbound_(140, 8, 96, 115)
MUV_1400_outlimb_(0, 4, 96, 115)
MUV_1400_outlimb_(140, 8, 96, 115)
MUV_4200_inbound_(0, 16, 0, 16)
MUV_4200_outboundhifi_(0, 1, 441, 11)
MUV_4200_outdiskhifi_(0, 1, 441, 11)
MUV_4200_periapse_(0, 1, 0, 1)
MUV_4200_periapse_(0, 4, 96, 115)
MUV_4200_periapse_(140, 4, 96, 115)
MUV_4200_periapsehifi_(0, 1, 441, 11)
MUV_14400_apoapse_(0, 1, 0, 1)
MUV_14400_apoapse_(0, 4, 101, 80)
MUV_14400_apoapse_(0, 16, 0, 16)
MUV_14400_apoapse_(2, 34, 101, 80)
MUV_14400_apoapse_(140, 8, 101, 80)
MUV_14400_inbound_(0, 1, 0, 1)
MUV_14400_inbound_(0, 16, 0, 16)
MUV_14400_inbound_(2, 34, 101, 80)
MUV_14400_outbound_(0, 1, 0, 1)
MUV_14400_outbound_(0, 4, 96, 115)
MUV_14400_outbound_(0, 16, 0, 16)
MUV_14400_outbound_(2, 34, 96, 115)
MUV_14400_outbound_(140, 8, 96, 115)
MUV_14400_outboundhifi_(0, 1, 441, 11)
MUV_14400_outdisk_(0, 1, 0, 1)
MUV_14400_outdisk_(0, 4, 96, 115)
MUV_14400_outdisk_(140, 8, 96, 115)

















In [64]:



    


for key, content in g:
    resampled = content.resample(timeres).dropna(how='all')
    selector = (df.CHANNEL == key[0]) & (df.INT_TIME==key[1]) & (df.activity==key[2])
    subdf = df[selector]
    subdf = subdf.resample(timeres).dropna(how='all')
    if key[0]=='FUV':
        resampled['HK_TEMP'] = hktemps.FUV_DET_TEMP_C
        subdf['HK_TEMP'] = hktemps.FUV_DET_TEMP_C
    else:
        resampled['HK_TEMP'] = hktemps.MUV_DET_TEMP_C
        subdf['HK_TEMP'] = hktemps.MUV_DET_TEMP_C
    title = '_'.join([str(i) for i in key])
    lesser_title = '_'.join([str(i) for i in key[:-1]])
    print(title)
    fig, ax = plt.subplots(nrows=2)
    subdf.plot(kind='scatter', x='HK_TEMP', y='mean', ax=ax[0],
                 xlim=(-25, -11), ylim=(0.1, 1.5), title=lesser_title+' all BINNING SETS')

    resampled.plot(kind='scatter', x='HK_TEMP', y='mean', ax=ax[1],
                 xlim=(-25, -11), ylim=(0.1, 1.5), title=title+' ')
    fig.tight_layout()
    fig.savefig('plots/'+title+'_vs_all_bins_'+'.png', dpi=150)
    plt.close(fig)



    


















    






FUV_1400_outbound_(0, 3, 880, 3)
FUV_1400_outbound_(52, 4, 89, 115)
FUV_1400_outbound_(52, 8, 89, 115)
FUV_1400_outlimb_(0, 4, 89, 115)
FUV_1400_outlimb_(52, 4, 89, 115)
FUV_1400_outlimb_(52, 8, 89, 115)
FUV_4200_outboundhifi_(0, 1, 319, 11)
FUV_4200_outdiskhifi_(0, 1, 319, 11)
FUV_4200_periapse_(0, 1, 0, 1)
FUV_4200_periapse_(0, 4, 89, 115)
FUV_4200_periapse_(1, 4, 89, 115)
FUV_4200_periapsehifi_(0, 1, 319, 11)
FUV_14400_apoapse_(0, 1, 0, 1)
FUV_14400_apoapse_(0, 4, 89, 80)
FUV_14400_apoapse_(1, 4, 89, 80)
FUV_14400_apoapse_(52, 8, 89, 80)
FUV_14400_apoapse_(65, 4, 89, 80)
FUV_14400_inbound_(0, 1, 0, 1)
FUV_14400_inbound_(0, 4, 89, 115)
FUV_14400_inbound_(52, 4, 89, 115)
FUV_14400_inbound_(52, 8, 89, 115)
FUV_14400_outbound_(0, 1, 0, 1)
FUV_14400_outbound_(52, 4, 89, 115)
FUV_14400_outbound_(52, 8, 89, 115)
FUV_14400_outboundhifi_(0, 1, 319, 11)
FUV_14400_outdisk_(0, 1, 0, 1)
FUV_14400_outdisk_(0, 4, 89, 115)
FUV_14400_outdisk_(52, 4, 89, 115)
FUV_14400_outdisk_(52, 8, 89, 115)
MUV_1400_apoapse_(0, 16, 0, 16)
MUV_1400_inbound_(0, 16, 0, 16)
MUV_1400_outbound_(0, 3, 880, 3)
MUV_1400_outbound_(0, 4, 96, 115)
MUV_1400_outbound_(2, 34, 96, 115)
MUV_1400_outbound_(140, 8, 96, 115)
MUV_1400_outlimb_(0, 4, 96, 115)
MUV_1400_outlimb_(140, 8, 96, 115)
MUV_4200_inbound_(0, 16, 0, 16)
MUV_4200_outboundhifi_(0, 1, 441, 11)
MUV_4200_outdiskhifi_(0, 1, 441, 11)
MUV_4200_periapse_(0, 1, 0, 1)
MUV_4200_periapse_(0, 4, 96, 115)
MUV_4200_periapse_(140, 4, 96, 115)
MUV_4200_periapsehifi_(0, 1, 441, 11)
MUV_14400_apoapse_(0, 1, 0, 1)
MUV_14400_apoapse_(0, 4, 101, 80)
MUV_14400_apoapse_(0, 16, 0, 16)
MUV_14400_apoapse_(2, 34, 101, 80)
MUV_14400_apoapse_(140, 8, 101, 80)
MUV_14400_inbound_(0, 1, 0, 1)
MUV_14400_inbound_(0, 16, 0, 16)
MUV_14400_inbound_(2, 34, 101, 80)
MUV_14400_outbound_(0, 1, 0, 1)
MUV_14400_outbound_(0, 4, 96, 115)
MUV_14400_outbound_(0, 16, 0, 16)
MUV_14400_outbound_(2, 34, 96, 115)
MUV_14400_outbound_(140, 8, 96, 115)
MUV_14400_outboundhifi_(0, 1, 441, 11)
MUV_14400_outdisk_(0, 1, 0, 1)
MUV_14400_outdisk_(0, 4, 96, 115)
MUV_14400_outdisk_(140, 8, 96, 115)

















In [51]:



    


%matplotlib nbagg



    











In [40]:



    


from scipy.interpolate import UnivariateSpline as Spline



    











In [41]:



    


Spline?



    











In [68]:



    


for key, content in g:
    if key != ('MUV', 14400, 'apoapse', (2, 34, 101, 80)):
        continue
    print(key)
    resampled = content[['mean']].resample(timeres).dropna(how='all')
    if key[0]=='FUV':
        resampled['HK_TEMP'] = hkfuvtemp
    else:
        resampled['HK_TEMP'] = hkmuvtemp

    both_there = resampled['HK_TEMP'].notnull() & (resampled['mean'].notnull())
    y = resampled[both_there]['mean']
    x = resampled[both_there]['HK_TEMP']

    print(len(x), len(y))
    spline = UnivariateSpline(x.values, y.values)
    xs = np.linspace(-23, -12, 1000)
    ys = spline(xs)
    
    plt.figure()
    plt.scatter(x, y)
    plt.plot(xs, ys, color='green', lw=2)
    title = '_'.join([str(i) for i in key])
    plt.title(title)
    plt.xlim(-24, -10)
    plt.ylim(0.2, 0.55)
    plt.xlabel('HK_TEMP')
    plt.ylabel('dark mean')
#     print(title)
#     fig, ax = plt.subplots(nrows=2)
#     resampled.plot(kind='scatter', x='DET_TEMP', y='mean', ax=ax[0],
#                  xlim=(-25, -11), ylim=(0.1, 1.5), title=title+' vs DET_TEMP')

#     resampled.plot(kind='scatter', x='HK_TEMP', y='mean', ax=ax[1],
#                  xlim=(-25, -11), ylim=(0.1, 1.5), title=title+' vs HK_TEMP')
#     fig.tight_layout()
#     fig.savefig('plots/'+title+'.png', dpi=150)
#     plt.close(fig)



    


















    






('MUV', 14400, 'apoapse', (2, 34, 101, 80))
2732 2732










    




















    























In [69]:



    


for key, content in g:
    if key != ('MUV', 14400, 'apoapse', (140, 8, 101, 80)):
        continue
    print(key)
    resampled = content[['mean']].resample(timeres).dropna(how='all')
    if key[0]=='FUV':
        resampled['HK_TEMP'] = hkfuvtemp
    else:
        resampled['HK_TEMP'] = hkmuvtemp

    y = resampled['mean'].dropna()
    x = resampled[resampled['mean'].notnull()]['HK_TEMP']
    plt.figure()
    plt.scatter(x, y)
    plt.plot(xs, ys, color='green', lw=2)
    print(len(x), len(y))
    title = '_'.join([str(i) for i in key])
    plt.title(title)
    plt.xlim(-24, -10)
    plt.ylim(0.2, 0.55)
    plt.xlabel('HK_TEMP')
    plt.ylabel('dark mean')



    


















    






('MUV', 14400, 'apoapse', (140, 8, 101, 80))










    




















    
















    






5334 5334

















In [70]:



    


for key, content in g:
    if key != ('MUV', 14400, 'apoapse', (0, 4, 101, 80)):
        continue
    print(key)
    resampled = content[['mean']].resample(timeres).dropna(how='all')
    if key[0]=='FUV':
        resampled['HK_TEMP'] = hkfuvtemp
    else:
        resampled['HK_TEMP'] = hkmuvtemp

    y = resampled['mean'].dropna()
    x = resampled[resampled['mean'].notnull()]['HK_TEMP']
    plt.figure()
    plt.scatter(x, y)
    plt.plot(xs, ys, color='green', lw=2)
    print(len(x), len(y))
    title = '_'.join([str(i) for i in key])
    plt.title(title)
    plt.xlim(-24, -10)
    plt.ylim(0.2, 0.55)
    plt.xlabel('HK_TEMP')
    plt.ylabel('dark mean')



    


















    






('MUV', 14400, 'apoapse', (0, 4, 101, 80))










    




















    
















    






380 380

















In [83]:



    


from scipy.optimize import curve_fit
def func(x, a, b, c):
    return a * np.exp(b * x) + c



    











In [88]:



    


yexp = func(xs, 10, 1, 0.2)



    











In [89]:



    


plt.figure()
plt.plot(xs, yexp)



    


















    




















    
















    
Out[89]:








[<matplotlib.lines.Line2D at 0x116365c18>]

















In [79]:



    


for key, content in g:
    if key != ('FUV', 14400, 'inbound', (52, 4, 89, 115)):
        continue
    print(key)
    resampled = content[['mean']].resample(timeres).dropna(how='all')
    if key[0]=='FUV':
        resampled['HK_TEMP'] = hkfuvtemp
    else:
        resampled['HK_TEMP'] = hkmuvtemp

    both_there = resampled['HK_TEMP'].notnull() & (resampled['mean'].notnull())
    y = resampled[both_there]['mean']
    x = resampled[both_there]['HK_TEMP']

    print(len(x), len(y))
    spline = UnivariateSpline(x.values, y.values)
    xs = np.linspace(-23, -12, 1000)
    ys = spline(xs)
    popt, pcov = curve_fit(func, x, y)
    plt.figure()
    plt.scatter(x, y)
    plt.plot(xs, ys, color='green', lw=2)
    title = '_'.join([str(i) for i in key])
    plt.title(title)
    plt.xlim(-24, -10)
    plt.ylim(0.2, 0.55)
    plt.xlabel('HK_TEMP')
    plt.ylabel('dark mean')
    print(popt, pcov)
#     print(title)
#     fig, ax = plt.subplots(nrows=2)
#     resampled.plot(kind='scatter', x='DET_TEMP', y='mean', ax=ax[0],
#                  xlim=(-25, -11), ylim=(0.1, 1.5), title=title+' vs DET_TEMP')

#     resampled.plot(kind='scatter', x='HK_TEMP', y='mean', ax=ax[1],
#                  xlim=(-25, -11), ylim=(0.1, 1.5), title=title+' vs HK_TEMP')
#     fig.tight_layout()
#     fig.savefig('plots/'+title+'.png', dpi=150)
#     plt.close(fig)



    


















    






('FUV', 14400, 'inbound', (52, 4, 89, 115))
701 701










    






---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-79-fa2d9bd86947> in <module>()
     17     xs = np.linspace(-23, -12, 1000)
     18     ys = spline(xs)
---> 19     popt, pcov = curve_fit(func, x, y)
     20     plt.figure()
     21     plt.scatter(x, y)

/Users/klay6683/miniconda3/lib/python3.4/site-packages/scipy/optimize/minpack.py in curve_fit(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, **kw)
    584     if ier not in [1, 2, 3, 4]:
    585         msg = "Optimal parameters not found: " + errmsg
--> 586         raise RuntimeError(msg)
    587 
    588     warn_cov = False

RuntimeError: Optimal parameters not found: Number of calls to function has reached maxfev = 800.
















In [74]:



    


for key, content in g:
    if key != ('FUV', 14400, 'inbound', (52, 8, 89, 115)):
        continue
    print(key)
    resampled = content[['mean']].resample(timeres).dropna(how='all')
    if key[0]=='FUV':
        resampled['HK_TEMP'] = hkfuvtemp
    else:
        resampled['HK_TEMP'] = hkmuvtemp

    y = resampled['mean'].dropna()
    x = resampled[resampled['mean'].notnull()]['HK_TEMP']
    plt.figure()
    plt.scatter(x, y)
    plt.plot(xs, ys, color='green', lw=2)
    print(len(x), len(y))
    title = '_'.join([str(i) for i in key])
    plt.title(title)
    plt.xlim(-24, -10)
    plt.ylim(0.2, 0.55)
    plt.xlabel('HK_TEMP')
    plt.ylabel('dark mean')



    


















    






('FUV', 14400, 'inbound', (52, 8, 89, 115))










    




















    
















    






1090 1090

















In [31]:



    


type(x)



    


















    
Out[31]:








pandas.core.series.Series

















In [12]:



    


import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('notebook')



    


















    






/Users/klay6683/miniconda3/lib/python3.4/site-packages/matplotlib-1.5.dev1-py3.4-macosx-10.5-x86_64.egg/matplotlib/__init__.py:866: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))

















In [57]:



    


g = sns.FacetGrid(df, row='INT_TIME', hue='CHANNEL', size=1.7, aspect=4,)
g.map(sns.distplot, 'mean')



    


















    




















    
















    
Out[57]:








<seaborn.axisgrid.FacetGrid at 0x7fd189b57240>

















In [187]:



    


g = sns.PairGrid(df, x_vars=['INT_TIME'], hue='CHANNEL',
                y_vars=['mean','std'], size=3.5)
g.map(sns.violinplot)



    


















    






WARNING:py.warnings:/usr/local/python3/miniconda/lib/python3.4/site-packages/matplotlib-1.5.dev1-py3.4-linux-x86_64.egg/matplotlib/pyplot.py:507: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)











    




















    
















    
Out[187]:








<seaborn.axisgrid.PairGrid at 0x7f543c184ba8>

















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In [33]:



    


df.sort_index(inplace=True)



    











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In [ ]:

Odd things

Difference between MIRROR_DEG and MIR_DEG





In [80]:



    


df['mirdeg_delta'] = df.MIRROR_DEG - df.MIR_DEG



    











In [81]:



    


plt.figure()
df.mirdeg_delta.plot(style='.')



    


















    




















    
















    
Out[81]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f0d0843f198>

















In [ ]:

Content source: michaelaye/iuvs

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