Pipeline to Get 1D Spectra from Raw Data (IRD Stream)
=====================================================
This tutorial demonstrates how to reduce raw data to
wavelength-calibrated 1D spectra. By using the ``Stream2D`` framework,
you can apply functions to multiple FITS files efficiently.
The same pipeline is available in ``examples/python/IRD_stream.py``. For
REACH data, refer the script available in
``examples/python/REACH_stream.py``.
- :ref:`step0`
- :ref:`step1`
- :ref:`step1-1`
- :ref:`step1-2`
- :ref:`step1-3`
- :ref:`step1-4`
- :ref:`step2`
- :ref:`step2-1`
- :ref:`step2-2`
- :ref:`step2-3`
- :ref:`step2-4`
- :ref:`step2-5`
.. _step0:
Step 0: Settings
----------------
Directory Structure
~~~~~~~~~~~~~~~~~~~
First, create a ``datadir`` for the raw data and ``anadir`` for storing
the output.
This tutorial assumes the following directory structure:
::
.
笏披楳笏 pyird/
笏披楳笏 data/
笏披楳笏 20210317/
笏懌楳笏 flat
笏懌楳笏 thar
笏懌楳笏 target
笏懌楳笏 dark
笏披楳笏 reduc
In this structure, the flat, thar, target, and dark directories are part
of the ``datadir``, each containing raw data for 窶炉lat窶�, 窶狼hAr窶�,
窶狼arget窶�, and optionally 窶魯ark窶� frames. The reduc directory is used as
``anadir`` for storing processed data.
.. code:: ipython3
import pathlib
basedir = pathlib.Path('~/pyird/data/20210317/').expanduser()
datadir_flat = basedir/'flat/'
datadir_dark = basedir/'dark/'
datadir_thar = basedir/'thar'
datadir_target = basedir/'target/'
anadir = basedir/'reduc/'
Specify the Data to be Analyzed
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Please change the following variables based on the data you want to
analyze.
.. code:: ipython3
band = 'h' #'h' or 'y'
mmf = 'mmf2' #'mmf1' (comb fiber) or 'mmf2' (star fiber)
readout_noise_mode = 'default'
# file numbers of fits files (last five digits)
fitsid_flat_comb = list(range(41704,41804,2))
fitsid_flat_star = list(range(41804,41904,2))
fitsid_dark = [41504]
fitsid_thar = list(range(14632,14732))
fitsid_target = [41510]
**Note**:
Ensure that the ``readout_noise_mode`` is set to either 窶腕eal窶� or
窶賄efault窶�.
- ``readout_noise_mode = 'real'``: Need to reduce the dataset with
``band = 'y'`` and ``mmf = 'mmf1'`` at first.
- With this setting, uncertainties and signal-to-noise ratio at each
wavelength will be included in the output files (**nw窶ｦ_m?.dat**
and **ncw窶ｦ_m?.dat**).
- Those values are based on the readout noise (RN) calculated using
the comb spectrum (in mmf1) of the Y/J band.
- ``readout_noise_mode = 'default'``: Uses a default readout noise (RN)
value (RN=12 :math:`e^{-}`).
.. _step1:
Step 1: Preprocessing the Calibration Dataset
---------------------------------------------
.. _step1-1:
Step 1-1: Identifying Apertures
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- The ``aptrace`` function is used to identify apertures.
- Number of apertures (``nap``): 42 for H band, 102 for Y/J band.
- ``cutrow`` sets the initial row number on the detector image for
searching apertures.
- These apertures are identified in the FLAT_COMB data.
- If your observation was performed with a single fiber, set ``nap`` to
half the default value.
.. code:: ipython3
from pyird.utils import irdstream
## FLAT_COMB
# settings
flat_comb = irdstream.Stream2D("flat_comb", datadir_flat, anadir, fitsid=fitsid_flat_comb, band=band)
# aperture extraction
if band=='h':
trace_mmf=flat_comb.aptrace(cutrow = 1300,nap=42)
elif band=='y':
trace_mmf=flat_comb.aptrace(cutrow = 1000,nap=102)
.. parsed-literal::
fitsid: [41704, 41706, 41708, 41710, 41712, 41714, 41716, 41718, 41720, 41722, 41724, 41726, 41728, 41730, 41732, 41734, 41736, 41738, 41740, 41742, 41744, 41746, 41748, 41750, 41752, 41754, 41756, 41758, 41760, 41762, 41764, 41766, 41768, 41770, 41772, 41774, 41776, 41778, 41780, 41782, 41784, 41786, 41788, 41790, 41792, 41794, 41796, 41798, 41800, 41802]
fitsid incremented: [41705, 41707, 41709, 41711, 41713, 41715, 41717, 41719, 41721, 41723, 41725, 41727, 41729, 41731, 41733, 41735, 41737, 41739, 41741, 41743, 41745, 41747, 41749, 41751, 41753, 41755, 41757, 41759, 41761, 41763, 41765, 41767, 41769, 41771, 41773, 41775, 41777, 41779, 41781, 41783, 41785, 41787, 41789, 41791, 41793, 41795, 41797, 41799, 41801, 41803]
median combine:
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 50/50 [00:00<00:00, 1054.97it/s]
.. parsed-literal::
default nap value
cross-section: row 1301
.. image:: IRD_stream_files/IRD_stream_11_3.png
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 42/42 [00:06<00:00, 6.55it/s]
.. image:: IRD_stream_files/IRD_stream_11_5.png
- Define 窶�trace_mask窶� to mask light from both fibers.
- Aperture width is 6 pixels (from -2 to +4) for IRD data and 5 pixels
(from -2 to 3) for REACH data by default. You can change it ``.width``
instance of trace_mmf.
.. code:: ipython3
trace_mask = trace_mmf.mask()
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎| 42/42 [00:00<00:00, 147.27it/s]
- Reduce apertures in the mask to extract the spectrum from the desired
fiber
.. code:: ipython3
trace_mmf.choose_aperture(fiber=mmf)
.. _step1-2:
Step 1-2: Removing hotpixels
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- There are two options for creating the hotpixel mask.
- This tutorial introduces one method using dark data.
- Refer to
`pyird.io.read_hotpix <https://secondearths.sakura.ne.jp/pyird/pyird/pyird.io.html#module-pyird.io.read_hotpix>`_
module for an alternative approach without dark data.
.. code:: ipython3
from pyird.image.bias import bias_subtract_image
from pyird.image.hotpix import identify_hotpix_sigclip
## HOTPIXEL MASK:
## DARK
dark = irdstream.Stream2D('dark', datadir_dark, anadir, fitsid=fitsid_dark, band=band) # Multiple file is ok
median_image = dark.immedian()
im_subbias = bias_subtract_image(median_image)
hotpix_mask = identify_hotpix_sigclip(im_subbias)
.. parsed-literal::
fitsid: [41504]
fitsid incremented: [41505]
median combine:
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎| 1/1 [00:00<00:00, 133.92it/s]
.. parsed-literal::
hotpix mask = 0.58 percent
.. _step1-3:
Step 1-3: Wavelength Calibration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Wavelength calibration is performed by using reference frames
(Thrium-Argon).
- You do not need to manually identify emission lines;
``calibrate_wavelength`` automatically references the line list!
.. code:: ipython3
## THAR (ThAr-ThAr)
# Settings
if band=='h':
rawtag='IRDAD000'
elif band=='y':
rawtag='IRDBD000'
thar=irdstream.Stream2D("thar", datadir_thar, anadir, rawtag=rawtag, fitsid=fitsid_thar, band=band)
thar.trace = trace_mmf
# removing noise pattern
thar.clean_pattern(trace_mask=trace_mask,extin='', extout='_cp', hotpix_mask=hotpix_mask)
# wavelength calibration
thar.calibrate_wavelength()
.. parsed-literal::
fitsid: [14632, 14633, 14634, 14635, 14636, 14637, 14638, 14639, 14640, 14641, 14642, 14643, 14644, 14645, 14646, 14647, 14648, 14649, 14650, 14651, 14652, 14653, 14654, 14655, 14656, 14657, 14658, 14659, 14660, 14661, 14662, 14663, 14664, 14665, 14666, 14667, 14668, 14669, 14670, 14671, 14672, 14673, 14674, 14675, 14676, 14677, 14678, 14679, 14680, 14681, 14682, 14683, 14684, 14685, 14686, 14687, 14688, 14689, 14690, 14691, 14692, 14693, 14694, 14695, 14696, 14697, 14698, 14699, 14700, 14701, 14702, 14703, 14704, 14705, 14706, 14707, 14708, 14709, 14710, 14711, 14712, 14713, 14714, 14715, 14716, 14717, 14718, 14719, 14720, 14721, 14722, 14723, 14724, 14725, 14726, 14727, 14728, 14729, 14730, 14731]
clean_pattern: output extension=_cp
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 100/100 [01:17<00:00, 1.29it/s]
.. parsed-literal::
median combine: _cp
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 100/100 [00:00<00:00, 2181.84it/s]
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎| 21/21 [00:00<00:00, 120.21it/s]
.. parsed-literal::
H band
standard deviation of residuals (1st identification) = 0.00903
Start iterations of ThAr fitting:
# 1 standard dev= 0.013397134666936214
# 2 standard dev= 0.007742066126647391
# 3 standard dev= 0.004204915254553077
# 4 standard dev= 0.002751270962171607
# 5 standard dev= 0.0020364380977693844
# 6 standard dev= 0.0015237661131787936
# 7 standard dev= 0.0012786796163100497
# 8 standard dev= 0.0009973943461107947
.. image:: IRD_stream_files/IRD_stream_19_5.png
.. _step1-4:
Step 1-4: Creating a Normalized Flat
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- This process similar to
`hdsis_ecf <https://github.com/chimari/hds_iraf>`_ for HDS/Subaru
data to reduce the fringe appearing in a spectrum.
- In the preparation of this process, we create the normalized flat by
using ``apnormalize``.
- After applying ``flatten``, **窶�{stream_id}_{band}_{mmf}.fits窶�**
(e.g., flat_star_h_m2.fits) is created in anadir, containing the
extracted spectrum of flat data.
.. code:: ipython3
## FLAT
if mmf=='mmf2': # Star fiber -> FLAT_STAR
# Settings
flat_star=irdstream.Stream2D("flat_star", datadir_flat, anadir, fitsid=fitsid_flat_star, band=band)
flat_star.trace = trace_mmf
# Removing noise pattern
flat_star.clean_pattern(trace_mask=trace_mask, extin='', extout='_cp', hotpix_mask=hotpix_mask)
flat_star.imcomb = True # median combine
# Extract 1D spectrum
flat_star.flatten(hotpix_mask=hotpix_mask)
# Flat spectrum normalized in each pixel within an aperture
df_flatn = flat_star.apnormalize()
elif mmf=='mmf1': # Comb fiber -> FLAT_COMB
flat_comb.trace = trace_mmf
# Removing noise pattern
flat_comb.clean_pattern(trace_mask=trace_mask, extin='', extout='_cp', hotpix_mask=hotpix_mask)
flat_comb.imcomb = True # median combine
# Extract 1D spectrum
flat_comb.flatten(hotpix_mask=hotpix_mask)
# Flat spectrum normalized in each pixel within an aperture
df_flatn = flat_comb.apnormalize()
.. parsed-literal::
fitsid: [41804, 41806, 41808, 41810, 41812, 41814, 41816, 41818, 41820, 41822, 41824, 41826, 41828, 41830, 41832, 41834, 41836, 41838, 41840, 41842, 41844, 41846, 41848, 41850, 41852, 41854, 41856, 41858, 41860, 41862, 41864, 41866, 41868, 41870, 41872, 41874, 41876, 41878, 41880, 41882, 41884, 41886, 41888, 41890, 41892, 41894, 41896, 41898, 41900, 41902]
fitsid incremented: [41805, 41807, 41809, 41811, 41813, 41815, 41817, 41819, 41821, 41823, 41825, 41827, 41829, 41831, 41833, 41835, 41837, 41839, 41841, 41843, 41845, 41847, 41849, 41851, 41853, 41855, 41857, 41859, 41861, 41863, 41865, 41867, 41869, 41871, 41873, 41875, 41877, 41879, 41881, 41883, 41885, 41887, 41889, 41891, 41893, 41895, 41897, 41899, 41901, 41903]
clean_pattern: output extension=_cp
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 50/50 [00:35<00:00, 1.41it/s]
0%| | 0/1 [00:00<?, ?it/s]
.. parsed-literal::
median combine: _cp
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎| 50/50 [00:00<00:00, 743.29it/s]
0%| | 0/21 [00:00<?, ?it/s][A
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎| 21/21 [00:00<00:00, 121.76it/s]
0%| | 0/21 [00:00<?, ?it/s][A
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎| 21/21 [00:00<00:00, 120.32it/s]
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 1/1 [00:09<00:00, 9.37s/it]
.. parsed-literal::
flatten (+ hotpix mask): output extension=_hp_m2
continuum is fitted with order_fit = 23.
median combine: _cp
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 50/50 [00:00<00:00, 1211.51it/s]
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 21/21 [00:03<00:00, 6.13it/s]
.. _step2:
Step 2: Extracting the Target 1D Spectrum
-----------------------------------------
From here, we will extract target spectrum.
.. code:: ipython3
#--------FOR TARGET--------#
# Settings
target = irdstream.Stream2D('targets', datadir_target, anadir, fitsid=fitsid_target, band=band)
target.info = True # show detailed info
target.trace = trace_mmf
.. parsed-literal::
fitsid: [41510]
fitsid incremented: [41511]
.. _step2-1:
Step 2-1: Removing Noise Pattern on the Detector
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. code:: ipython3
target.clean_pattern(trace_mask=trace_mask, extin='', extout='_cp', hotpix_mask=hotpix_mask)
.. parsed-literal::
clean_pattern: output extension=_cp
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 1/1 [00:00<00:00, 1.31it/s]
.. _step2-2:
Step 2-2: Aperture Extraction & Flat Fielding
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- The ``apext_flatfield`` function extracts each order while applying
flat fielding.
- This process requires the flat spectrum normalized in each pixel
within an aperture (i.e., df_flatn).
- After this process, **窶僮RDA000窶ｦ_flnhp.fits窶�** (when
``hotpix_mask`` is set) or **窶僮RDA000窶ｦ_fln.fits窶�** (when
``hotpix_mask = None``) is created.
.. code:: ipython3
target.apext_flatfield(df_flatn, hotpix_mask=hotpix_mask)
.. parsed-literal::
0%| | 0/1 [00:00<?, ?it/s]
0%| | 0/21 [00:00<?, ?it/s][A
5%|笆遺毎笆遺毎笆遺毎笆� | 1/21 [00:00<00:02, 7.82it/s][A
10%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 2/21 [00:00<00:02, 6.40it/s][A
14%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺慢 | 3/21 [00:00<00:02, 6.03it/s][A
19%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 4/21 [00:00<00:02, 5.96it/s][A
24%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺慢 | 5/21 [00:00<00:02, 5.89it/s][A
29%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 6/21 [00:00<00:02, 5.87it/s][A
33%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺鮪 | 7/21 [00:01<00:02, 5.88it/s][A
38%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 8/21 [00:01<00:02, 5.87it/s][A
43%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺慢 | 9/21 [00:01<00:02, 5.87it/s][A
48%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 10/21 [00:01<00:01, 5.85it/s][A
52%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 11/21 [00:01<00:01, 5.85it/s][A
57%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 12/21 [00:02<00:01, 5.86it/s][A
62%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 13/21 [00:02<00:01, 5.86it/s][A
67%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 14/21 [00:02<00:01, 5.86it/s][A
71%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 15/21 [00:02<00:01, 5.86it/s][A
76%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 16/21 [00:02<00:00, 5.86it/s][A
81%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 17/21 [00:02<00:00, 5.87it/s][A
86%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 18/21 [00:03<00:00, 5.88it/s][A
90%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 19/21 [00:03<00:00, 5.87it/s][A
95%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 20/21 [00:03<00:00, 5.87it/s][A
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 21/21 [00:03<00:00, 5.90it/s]
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 1/1 [00:03<00:00, 3.62s/it]
.. parsed-literal::
pixel = -2, Mean = 0.84721
pixel = -1, Mean = 1.35783
pixel = 0, Mean = 1.46715
pixel = 1, Mean = 1.34046
pixel = 2, Mean = 0.77173
pixel = 3, Mean = 0.17888
.. parsed-literal::
.. _step2-3:
Step 2-3: Assigning Wavelength to the Extracted Spectrum
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- The ``dispcor`` function assigns wavelength solution to the extracted
spectrum.
- Please change the ``extin`` option to ``extin='_flnhp'`` or
``extin='_fln'`` depending on the previous process.
- After this process, **窶冽窶ｦ_m?.dat窶�** is created, with data format:
``$1: Wavelength [nm]``, ``$2: Order``, ``$3: Counts``.
.. code:: ipython3
target.dispcor(master_path=thar.anadir, extin='_flnhp')
.. parsed-literal::
dispcor: output spectrum= w41511_m2.dat
.. image:: IRD_stream_files/IRD_stream_30_1.png
.. _step2-4:
Step 2-4: Creating the Blaze Function
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- The blaze function is created from FLAT spectrum to 窶倪冢ormalize窶吮� the
spectra.
- After this process, **窶冽blaze_{band}_{mmf}.dat窶�** is created.
.. code:: ipython3
# blaze function
if mmf=='mmf2':
flat_star.apext_flatfield(df_flatn, hotpix_mask=hotpix_mask)
flat_star.dispcor(master_path=thar.anadir)
elif mmf=='mmf1':
flat_comb.apext_flatfield(df_flatn, hotpix_mask=hotpix_mask)
flat_comb.dispcor(master_path=thar.anadir)
.. parsed-literal::
0%| | 0/1 [00:00<?, ?it/s]
.. parsed-literal::
median combine: _cp
.. parsed-literal::
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 50/50 [00:00<00:00, 1064.24it/s]
0%| | 0/21 [00:00<?, ?it/s][A
10%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 2/21 [00:00<00:02, 8.69it/s][A
14%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺慢 | 3/21 [00:00<00:02, 7.28it/s][A
19%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 4/21 [00:00<00:02, 6.68it/s][A
24%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺慢 | 5/21 [00:00<00:02, 6.40it/s][A
29%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 6/21 [00:00<00:02, 6.21it/s][A
33%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺鮪 | 7/21 [00:01<00:02, 6.11it/s][A
38%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 8/21 [00:01<00:02, 6.05it/s][A
43%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺慢 | 9/21 [00:01<00:01, 6.00it/s][A
48%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 10/21 [00:01<00:01, 5.96it/s][A
52%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 11/21 [00:01<00:01, 5.93it/s][A
57%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 12/21 [00:01<00:01, 5.93it/s][A
62%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 13/21 [00:02<00:01, 5.92it/s][A
67%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 14/21 [00:02<00:01, 5.91it/s][A
71%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 15/21 [00:02<00:01, 5.89it/s][A
76%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 16/21 [00:02<00:00, 5.90it/s][A
81%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 17/21 [00:02<00:00, 5.90it/s][A
86%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 18/21 [00:02<00:00, 5.91it/s][A
90%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆� | 19/21 [00:03<00:00, 5.90it/s][A
95%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎 | 20/21 [00:03<00:00, 5.90it/s][A
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 21/21 [00:03<00:00, 6.08it/s]
100%|笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆遺毎笆�| 1/1 [00:12<00:00, 12.14s/it]
.. parsed-literal::
pixel = -2, Mean = 0.84721
pixel = -1, Mean = 1.35783
pixel = 0, Mean = 1.46715
pixel = 1, Mean = 1.34046
pixel = 2, Mean = 0.77173
pixel = 3, Mean = 0.17888
dispcor: output spectrum= wblaze_h_m2.dat
.. image:: IRD_stream_files/IRD_stream_32_4.png
.. _step2-5:
Step 2-5: Normalizing the Spectra
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Normalize the target spectrum by dividing it by the blaze function.
- After ``normalize1D``, the normalized spectrum (**nw窶ｦ_m?.dat**)
and the order-combined spectrum (**ncw窶ｦ_m?.dat**) are created.
- Data formats are:
- Normalized (**nw**): ``$1: Wavelength [nm]``, ``$2: Order``,
``$3: Counts``, ``$4: S/N``, ``$5: Uncertainties``
- Order-combined (**ncw**): ``$1: Wavelength [nm]``, ``$2: Counts``,
``$3: S/N``, ``$4: Uncertainties``
- For the order-combined spectra: There are overlapping wavelengths at
the edges of orders, so we 窶從ormalize窶� by summing up the flux in these
regions to improve the signal-to-noise ratio.
.. code:: ipython3
# combine & normalize
if mmf=='mmf2':
target.normalize1D(master_path=flat_star.anadir, readout_noise_mode=readout_noise_mode)
elif mmf=='mmf1':
target.normalize1D(master_path=flat_comb.anadir, readout_noise_mode=readout_noise_mode)
.. parsed-literal::
Using default readout Noise : 12
readout noise of IRD detectors: ~12e- (10min exposure)
normalize1D: output normalized 1D spectrum= nw41511_m2.dat
.. image:: IRD_stream_files/IRD_stream_34_1.png
.. image:: IRD_stream_files/IRD_stream_34_2.png
This concludes the data reduction process!
See ``TUTORIAL: Output Spectra`` page for detailed explanations of
output spectra.