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deepCR-0.2.1rc0
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-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepCR-0.2.1rc0 |
| نام | deepCR |
| نسخه کتابخانه | 0.2.1rc0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Keming Zhang |
| ایمیل نویسنده | kemingz@berkeley.edu |
| آدرس صفحه اصلی | http://pypi.python.org/pypi/deepCR/ |
| آدرس اینترنتی | https://pypi.org/project/deepCR/ |
| مجوز | BSD 3-Clause |
[](https://travis-ci.com/profjsb/deepCR) [](https://codecov.io/gh/profjsb/deepCR)
[](https://deepcr.readthedocs.io/en/latest/?badge=latest)
## deepCR: Deep Learning Based Cosmic Ray Removal for Astronomical Images
Identify and remove cosmic rays from astronomical images using trained convolutional neural networks.
Documentation and tutorials: https://deepcr.readthedocs.io/
This is the installable package which implements the methods described in the paper: Zhang & Bloom (2019), submitted.
Code to reproduce benchmarking results in the paper is at: https://github.com/kmzzhang/deepCR-paper
If you use this package, please cite Zhang & Bloom (2019): https://arxiv.org/abs/1907.09500 and consider including a
link to this repository.
Note: the current release includes only model for HST ACS/WFC.
<img src="https://raw.githubusercontent.com/profjsb/deepCR/master/imgs/postage-sm.jpg" wdith="90%">
### Installation
```bash
pip install deepCR
```
Or you can install from source:
```bash
git clone https://github.com/profjsb/deepCR.git
cd deepCR/
python setup.py install
```
### Quick Start
Quick download of a HST ACS/WFC image
```bash
wget -O jdba2sooq_flc.fits https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:HST/product/jdba2sooq_flc.fits
```
With Python >=3.5:
For smaller sized images
```python
from deepCR import deepCR
from astropy.io import fits
image = fits.getdata("jdba2sooq_flc.fits")[:512,:512]
# create an instance of deepCR with specified model configuration
mdl = deepCR(mask="ACS-WFC-F606W-2-32",
inpaint="ACS-WFC-F606W-2-32",
device="CPU")
# apply to input image
mask, cleaned_image = mdl.clean(image, threshold = 0.5)
# best threshold is highest value that generate mask covering full extent of CR
# choose threshold by visualizing outputs.
# note that deepCR-inpaint would overestimate if mask does not fully cover CR.
# if you only need CR mask you may skip image inpainting for shorter runtime
mask = mdl.clean(image, threshold = 0.5, inpaint=False)
# if you want probabilistic cosmic ray mask instead of binary mask
prob_mask = mdl.clean(image, binary=False)
```
For WFC full size images (4k * 2k), you should specify **segment = True** to tell deepCR to segment the input image into 256*256 patches, and process one patch at a time.
Otherwise this would take up > 10gb memory. We recommended you use segment = True for images larger than 1k * 1k on CPU. GPU memory limits may be more strict.
```python
image = fits.getdata("jdba2sooq_flc.fits")
mask, cleaned_image = mdl.clean(image, threshold = 0.5, segment = True)
```
(CPU only) In place of **segment = True**, you can also specify **parallel = True** and invoke the multi-threaded version of segment mode. This will speed things up. You don't have to specify segment = True again.
```python
image = fits.getdata("jdba2sooq_flc.fits")
mask, cleaned_image = mdl.clean(image, threshold = 0.5, parallel = True, n_jobs=-1)
```
**n_jobs=-1** makes use of all your CPU cores.
Note that this won't speed things up if you're using GPU!
### Currently available models
mask:
ACS-WFC-F606W-2-4
ACS-WFC-F606W-2-32(*)
inpaint:
ACS-WFC-F606W-2-32(*)
ACS-WFC-F606W-3-32
Recommended models are marked in (*). Larger number indicate larger capacity.
Input images should come from *_flc.fits* files which are in units of electrons.
### Limitations and Caveats
The currently included models are trained and benchmarked on HST ACS/WFC images in the F606W filter.
Visual inspection shows that these models also work well on filters from F435W to F814W. However, users should use a higher threshold (e.g. 0.9) for short wavelength filters to minimize false detections, if any.
### Contributing
We are very interested in getting bug fixes, new functionality, and new trained models from the community (especially for ground-based imaging and spectroscopy). Please fork this repo and issue a PR with your changes. It will be especially helpful if you add some tests for your changes.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.5 | Python |
نحوه نصب
نصب پکیج whl deepCR-0.2.1rc0:
pip install deepCR-0.2.1rc0.whl
نصب پکیج tar.gz deepCR-0.2.1rc0:
pip install deepCR-0.2.1rc0.tar.gz