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Commit 1a81229e authored by Yi Hu's avatar Yi Hu
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修改： README.md 

	修改：     crmask.py
                   修改注释格式，sphinx生成文档不报警
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README.md
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......@@ -60,4 +60,4 @@ If you would like use the native training model of deepCR, which are obtained fr
# Bugs
Please contact **_HU Yi_** (huyi.naoc@gmail.com) to report bugs.
Bugs can be reported and filed at https://csst-tb.bao.ac.cn/code/huyi/csst-commom-crmask/-/issues. Alternatively, you can contact **_HU Yi_** (huyi.naoc@gmail.com) to report bugs.
crmask.py
View file @ 1a81229e
......@@ -23,51 +23,51 @@ from astropy.nddata import CCDData
from pathlib import Path
import ccdproc
import datetime
import sys
__all__ = ['CRMask']
class CRMask:
def __init__(self, obj, flag = None, mask = None, clean = None, sky = None, save_flag = True, update_flag = True, save_name = None, flag_suffix = 'flg', clean_suffix = 'crclean', append_flag = False, mask_suffix = 'crmask', model = 'deepCR', fill_flag = True, fill_method = 'inpainting', gpu_flag = False, config_path = 'crmask.ini', **kwargs):
"""
Instantiation of CRMask with specified model configuration.
Parameters
----------
obj : string, Path, astropy.io.fits.HDUList, numpy.ndarray, astropy.nddata.CCDData or list of string
if model is ``deepCR``, ``lacosmic``, obj is input image to be cosmic-ray masked
if model is ``deepCR_train``, obj is input training and validating images
flag : (optional) string, Path, astropy.io.fits.HDUList, numpy.ndarray or astropy.nddata.CCDData
flag image(s), default is None
mask : (optional) string or list of string
mask image(s), default is None, necessary when model is ``deepCR_train`` or ``benchmark``, otherwise, let it alone
sky : (optional) string or list of string
sky image(s), default is None, optional when model is ``deepCR_train``, otherwise, let it alone
save_flag : (optional) boolean
whether save CR mask (and cleaned) image, default is True
update_flag : (optional) boolean
whether update flag image, default is True
save_name : (optional) string
output mask, cleaned and flag filename. default is None. If save_name is None, use the filename as the input. And if save_flag is True, save_name is None, and obj is a numpy.ndarray or a astropy.nddata.CCDData, cr_mask will raise a ValueError exception
flag_suffix : (optional) string
suffix name of flag file, if flag is a numpy.ndarray or a astropy.nddata.CCDData, default is ``flg``
mask_suffix : (optional) string
suffix name of mask file, default is ``crmask``
clean_suffix : (optional) string
suffix name of cleaned file, default is ``crclean``
model : (optional) string
model type, can be ``deepCR``, ``lacosmic``, ``deepCR_train`` or ``benchmark`` default is ``deepCR``
fill_flag : (optional) boolean
whehter generate cleaned image, default is True
fill_method : (optional) string
fill method for CR contaminated pixel, can be ``inpainting``, ``meanmask``, ``meanmed``, default is ``inpainting``
gpu_flag : (optional) boolean
whether use GPU, default is False
config_path : (optional) string
configuration file path, default is ``./crmask.ini``
Instantiation of CRMask with specified model configuration.
Parameters
__________
obj : string, Path, astropy.io.fits.HDUList, numpy.ndarray, astropy.nddata.CCDData or list of string
if model is ``deepCR``, ``lacosmic``, obj is input image to be cosmic-ray masked, if model is ``deepCR_train``, obj is input training and validating images
flag : (optional) string, Path, astropy.io.fits.HDUList, numpy.ndarray or astropy.nddata.CCDData
flag image(s), default is None
mask : (optional) string or list of string
mask image(s), default is None, necessary when model is ``deepCR_train`` or ``benchmark``, otherwise, let it alone
sky : (optional) string or list of string
sky image(s), default is None, optional when model is ``deepCR_train``, otherwise, let it alone
save_flag : (optional) boolean
whether save CR mask (and cleaned) image, default is True
update_flag : (optional) boolean
whether update flag image, default is True
save_name : (optional) string
output mask, cleaned and flag filename. default is None. If save_name is None, use the filename as the input. And if save_flag is True, save_name is None, and obj is a numpy.ndarray or a astropy.nddata.CCDData, cr_mask will raise a ValueError exception
flag_suffix : (optional) string
suffix name of flag file, if flag is a numpy.ndarray or a astropy.nddata.CCDData, default is ``flg``
mask_suffix : (optional) string
suffix name of mask file, default is ``crmask``
clean_suffix : (optional) string
suffix name of cleaned file, default is ``crclean``
model : (optional) string
model type, can be ``deepCR``, ``lacosmic``, ``deepCR_train`` or ``benchmark`` default is ``deepCR``
fill_flag : (optional) boolean
whehter generate cleaned image, default is True
fill_method : (optional) string
fill method for CR contaminated pixel, can be ``inpainting``, ``meanmask``, ``meanmed``, default is ``inpainting``
gpu_flag : (optional) boolean
whether use GPU, default is False
config_path : (optional) string
configuration file path, default is ``./crmask.ini``
"""
self.model = model
if model == 'deepCR_train':
self.image_sets = obj
......@@ -440,18 +440,19 @@ class CRMask:
def cr_mask(self):
"""
Cosmic ray detection and mask.
Cosmic ray detection and mask.
Returns
-------
Returns
-------
masked : numpy.ndarray
cosmic ray masked image.
cleaned : numpy.ndarray, optional
Only returned if `fill_flag` is True
cosmic ray cleaned image.
Examples
-------
Examples
--------
>>> from crmask import CRMask
>>> crobj = CRMask('xxxx.fits', 'deepCR')
>>> crobj.cr_mask()
......@@ -710,21 +711,44 @@ class CRMask:
def cr_train(self):
"""
Training models, only support ``deepCR_train``. It will generate pytorch's *.pth file.
The train is very painful and time consuming, do NOT use it in pipelines.
Training models, only support ``deepCR_train``. It will generate pytorch's \*.pth file. The train is very painful and time consuming, do NOT use it in pipelines.
Returns
-------
Returns
-------
No returns
Examples
-------
Examples
--------
>>> from crmask import CRMask
>>> imglist = ['MSC_MS_210525170000_100000010_23_sci.fits', 'MSC_MS_210525171000_100000011_23_sci.fits', 'MSC_MS_210525172000_100000012_23_sci.fits', 'MSC_MS_210525173000_100000013_23_sci.fits', 'MSC_MS_210525174000_100000014_23_sci.fits', 'MSC_MS_210525175000_100000015_23_sci.fits', 'MSC_MS_210525180000_100000016_23_sci.fits', 'MSC_MS_210525181000_100000017_23_sci.fits', 'MSC_MS_210525182000_100000018_23_sci.fits', 'MSC_MS_210525183000_100000019_23_sci.fits']
>>> masklist = ['MSC_CRM_210525170000_100000010_23_raw.fits', 'MSC_CRM_210525171000_100000011_23_raw.fits', 'MSC_CRM_210525172000_100000012_23_raw.fits', 'MSC_CRM_210525173000_100000013_23_raw.fits', 'MSC_CRM_210525174000_100000014_23_raw.fits', 'MSC_CRM_210525175000_100000015_23_raw.fits', 'MSC_CRM_210525180000_100000016_23_raw.fits', 'MSC_CRM_210525181000_100000017_23_raw.fits', 'MSC_CRM_210525182000_100000018_23_raw.fits', 'MSC_CRM_210525183000_100000019_23_raw.fits']
>>> trainobj = CRMask(imglist, mask = masklist, model = 'deepCR_train')
>>> imglist = [
>>> 'MSC_MS_210525170000_100000010_23_sci.fits',
>>> 'MSC_MS_210525171000_100000011_23_sci.fits',
>>> 'MSC_MS_210525172000_100000012_23_sci.fits',
>>> 'MSC_MS_210525173000_100000013_23_sci.fits',
>>> 'MSC_MS_210525174000_100000014_23_sci.fits',
>>> 'MSC_MS_210525175000_100000015_23_sci.fits',
>>> 'MSC_MS_210525180000_100000016_23_sci.fits',
>>> 'MSC_MS_210525181000_100000017_23_sci.fits',
>>> 'MSC_MS_210525182000_100000018_23_sci.fits',
>>> 'MSC_MS_210525183000_100000019_23_sci.fits'
>>> ]
>>> masklist = [
>>> 'MSC_CRM_210525170000_100000010_23_raw.fits',
>>> 'MSC_CRM_210525171000_100000011_23_raw.fits',
>>> 'MSC_CRM_210525172000_100000012_23_raw.fits',
>>> 'MSC_CRM_210525173000_100000013_23_raw.fits',
>>> 'MSC_CRM_210525174000_100000014_23_raw.fits',
>>> 'MSC_CRM_210525175000_100000015_23_raw.fits',
>>> 'MSC_CRM_210525180000_100000016_23_raw.fits',
>>> 'MSC_CRM_210525181000_100000017_23_raw.fits',
>>> 'MSC_CRM_210525182000_100000018_23_raw.fits',
>>> 'MSC_CRM_210525183000_100000019_23_raw.fits'
>>> ]
>>> trainobj = CRMask(imglist, mask = masklist,
>>> model = 'deepCR_train')
>>> trainobj.cr_train()
"""
if self.model == 'deepCR_train':
CRMask.cr_train_deepCR(self)
else:
......
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