Hi,
I have written a script to try to combine five images into a three channels
RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…>
.
The resulting RGB image looks like a grey scale image instead of a color
one.
I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array:
*rgb = np.zeros((shape[0],shape[1],3),dtype=float)
mxr=np.max(R)
mxg=np.max(G)
mxb=np.max(B)
Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array
rgb[:,:,0]=Rnorm
rgb[:,:,1]=Gnorm
rgb[:,:,2]=Bnorm*
*pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[])
pylab.imshow(rgb)
pylab.show()*
Any advice?
Thank you
Jean-Patrick
Hello,
I have looked at both these answers from SO -
https://stackoverflow.com/questions/34428886/discrete-fourier-transformatio…
and
https://stackoverflow.com/questions/25735153/plotting-a-fast-fourier-transf…
My question is somewhat similar. I have data from a CSV file that has
measurements of a mean sea level pressure. The data is available every 5
minutes. That means 8928 sample points over a month. But during a
hurricane event there was a power failure and only 8867 data points are
available. I am short by 61 points to get a uniformly spaced sample. I am
wanting to take an FFT of the data in order to check for periodicity, waves
and frequencies there of. What are my best options ?
Best regards,
Ashwin.
Hi All,
On behalf of the NumPy team I am pleased to announce the release of NumPy
1.21.6. NumPy 1.21.6 is a very small release that achieves two things:
- Backs out the mistaken backport of C++ code into 1.21.5.
- Provides a 32 bit Windows wheel for Python 3.10.
The provision of the 32 bit wheel is intended to make life easier for
oldest-supported-numpy.
The Python versions supported in this release are 3.7-3.10. If you want to
compile your own version using gcc-11 you will need to use gcc-11.2+ to
avoid problems. Wheels can be downloaded from PyPI
<https://pypi.org/project/numpy/1.21.6/>; source archives, release notes,
and wheel hashes are available on Github
<https://github.com/numpy/numpy/releases/tag/v1.21.6>. Linux users will
need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014
wheels. A recent version of pip is needed to install the universal2
macos wheels.
Cheers,
Charles Harris
I am pleased to announce the release of SfePy 2022.1.
Description
-----------
SfePy (simple finite elements in Python) is a software for solving systems of
coupled partial differential equations by finite element methods. It is
distributed under the new BSD license.
Home page: https://sfepy.org
Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/
Git (source) repository, issue tracker: https://github.com/sfepy/sfepy
Highlights of this release
--------------------------
- new handling of state variables data and State class removal
- many new sensitivity analysis terms based on multi-linear term implementation
For full release notes see [1].
Cheers,
Robert Cimrman
[1] http://docs.sfepy.org/doc/release_notes.html#id1
---
Contributors to this release in alphabetical order:
Robert Cimrman
Robert T. McGibbon
Vladimir Lukes
Dear Pythonistas and solar power enthusiasts,
On behalf of the maintainers, we're happy to announce a new release of
pvlib python:
software for simulating performance of photovoltaic solar energy systems.
*See what's new for v0.9.1:*
* https://pvlib-python.readthedocs.io/en/stable/whatsnew.html
*Releases are available from PyPI and the conda-forge channel:*
* https://pypi.org/project/pvlib/
* https://anaconda.org/conda-forge/pvlib-python
*Read the Documentation:*
* https://pvlib-python.readthedocs.io/en/stable/index.html
*Report issues & contribute:*
* https://github.com/pvlib/pvlib-python
*Highlights:*
** *Beautifully updated documentation theme that is easier to browse and
navigate. Please share your feedback!
* Implementation of pvlib.temperature.prilliman
<https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.tem…>,
a transient weighted moving-average temperature model by Prilliman, *et al.*,
to calculate back of module temperature from high-frequency sub-hourly
irradiance input to account for different characteristic timescales between
heat transfer and power conversion.
* New infinite sheds model for either bifacial or monofacial modules, that
calculates row-to-row shading and view factors for sky and ground reflected
diffuse irradiance. Use pvlib.bifacial.infinite_sheds.get_irradiance
<https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.bif…>
to calculate the plane of array irradiance, all its components on both
front and back sides, and the total from both sides.
*The maintainers thank you for using pvlib python!*
--
Mark Mikofski, PhD (2005)
*Fiat Lux*
