Mailman 3 July 2021 - SciPy-User

How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

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

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn July 28, 2022

July 28, 2022

LinkedIn ------------ Jelle Feringa requested to add you as a connection on LinkedIn: ------------------------------------------ Jose, I'd like to add you to my professional network on LinkedIn. - Jelle Accept invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… View invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… ------------------------------------------ DID YOU KNOW your LinkedIn profile helps you control your public image when people search for you? Setting your profile as public means your LinkedIn profile will come up when people enter your name in leading search engines. Take control of your image! http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/ewp/inv-22/ -- (c) 2011, LinkedIn Corporation

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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(no subject)
by Charles R Harris July 18, 2021

July 18, 2021

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.1. The NumPy 1.21.1 is a maintenance release that fixes bugs discovered after the 1.21.0 release. OpenBLAS has also been updated to v0.3.17 to deal with arm64 problems. The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with development Python 3.10 and Python 3.10 will be officially supported after it is released. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.21.1/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.1>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 11 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Ganesh Kathiresan - Gregory R. Lee - Hugo Defois + - Kevin Sheppard - Matti Picus - Ralf Gommers - Sayed Adel - Sebastian Berg - Thomas J. Fan *Pull requests merged* A total of 26 pull requests were merged for this release. - #19311: REV,BUG: Replace `NotImplemented` with `typing.Any` - #19324: MAINT: Fixed the return-dtype of `ndarray.real` and `imag` - #19330: MAINT: Replace `"dtype[Any]"` with `dtype` in the definiton of\... - #19342: DOC: Fix some docstrings that crash pdf generation. - #19343: MAINT: bump scipy-mathjax - #19347: BUG: Fix arr.flat.index for large arrays and big-endian machines - #19348: ENH: add `numpy.f2py.get_include` function - #19349: BUG: Fix reference count leak in ufunc dtype handling - #19350: MAINT: Annotate missing attributes of `np.number` subclasses - #19351: BUG: Fix cast safety and comparisons for zero sized voids - #19352: BUG: Correct Cython declaration in random - #19353: BUG: protect against accessing base attribute of a NULL subarray - #19365: BUG, SIMD: Fix detecting AVX512 features on Darwin - #19366: MAINT: remove `print()`\'s in distutils template handling - #19390: ENH: SIMD architectures to show\_config - #19391: BUG: Do not raise deprecation warning for all nans in unique\... - #19392: BUG: Fix NULL special case in object-to-any cast code - #19430: MAINT: Use arm64-graviton2 for testing on travis - #19495: BUILD: update OpenBLAS to v0.3.17 - #19496: MAINT: Avoid unicode characters in division SIMD code comments - #19499: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11 - #19500: BUG: fix a numpy.npiter leak in npyiter\_multi\_index\_set - #19501: TST: Fix a `GenericAlias` test failure for python 3.9.0 - #19502: MAINT: Start testing with Python 3.10.0b3. - #19503: MAINT: Add missing dtype overloads for object- and ctypes-based\... - #19510: REL: Prepare for NumPy 1.21.1 release. Cheers, Charles Harris

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Re: [SciPy-User] Trying to understand Distributions
by Keith Sloan July 16, 2021

July 16, 2021

Thanks for all your help, but still not getting it right ax4 = fig.add_subplot(4, 1, 4) ag2, bg2, cg2 =stats.gamma.fit(RErange1[xfield].data) print(ag2, bg2, cg2) x0, x1 = stats.gamma.ppf([0.1, 0.99], ag2, loc = bg2, scale=cg2) #x = np.linspace(stats.gamma.ppf(x0,x1), int(ag2)) x = np.linspace(stats.gamma.ppf(x0,x1), 100) ax4.plot(x, stats.gamma.pdf(x, ag2, loc= bg2, scale = cg2),'r-', lw=5, alpha=0.6, label='gamma pdf') plt.show() blank On 13/07/2021 05:34, Robert Kern wrote: > Sorry, please reply, but to the mailing list, not just me personally. > The mailing list is set up so that most mail software will default to > replying to the mailing list. > > Don't use `int(ag2)` for the number of steps in the linspace(). It's > probably not a suitable value. Try 100. > > On Tue, Jul 13, 2021 at 12:30 AM Keith Sloan <keith(a)sloan-home.co.uk > <mailto:keith@sloan-home.co.uk>> wrote: > > Still doing something wrong > > ax4 = fig.add_subplot(4, 1, 4) > ag2, bg2, cg2 =stats.gamma.fit(RErange1[xfield].data) > print(ag2, bg2, cg2) > x0, x1 = stats.gamma.ppf([0.1, 0.99], ag2, loc = bg2, scale=cg2) > x = np.linspace(stats.gamma.ppf(x0,x1, int(ag2))) > ax4.plot(x, stats.gamma.pdf(x, ag2, loc= bg2, scale = cg2),'r-', > lw=5, alpha=0.6, label='gamma pdf') > > plt.show() > > blank > On 13/07/2021 05:23, Robert Kern wrote: >> Please try to respond to these messages so that the email thread >> remains intact. You are making a new thread with each email, >> which makes the discussion difficult to follow. It would help me >> refer back to what we've gone over previously. >> >> On Tue, Jul 13, 2021 at 12:16 AM Keith Sloan >> <keith(a)sloan-home.co.uk <mailto:keith@sloan-home.co.uk>> wrote: >> >> Again another unsuccessful attempt trying to follow Roberts >> advice >> >> Data is astronomic so loc = 0 is not okay >> >> Okay, then don't omit it. >> >> ax4 = fig.add_subplot(4, 1, 4) >> >> ag2, bg2, cg2 =stats.gamma.fit(RErange1[xfield].data) >> print(ag2, bg2, cg2) >> x0, x1 = stats.gamma.ppf([0.1, 0.99], ag2, scale=cg2) >> x = np.linspace(stats.gamma.ppf(x0,x1, int(ag2))) >> ax4.plot(x, stats.gamma.pdf(x, ag2, scale = cg2),'r-', lw=5, >> alpha=0.6, label='gamma pdf') >> >> x0, x1 = stats.gamma.ppf([0.1, 0.99], ag2, loc=bg2, scale=cg2) >> x = np.linspace(stats.gamma.ppf(x0,x1, int(ag2))) >> ax4.plot(x, stats.gamma.pdf(x, ag2, loc=bg2, scale = cg2),'r-', >> lw=5, alpha=0.6, label='gamma pdf') >> >> -- >> Robert Kern >> >> _______________________________________________ >> SciPy-User mailing list >> SciPy-User(a)python.org <mailto:SciPy-User@python.org> >> https://mail.python.org/mailman/listinfo/scipy-user <https://mail.python.org/mailman/listinfo/scipy-user> > > -- > ========== Art & Ceramics =========== > https://www.instagram.com/ksloan1952/ <https://www.instagram.com/ksloan1952/> > > > > -- > Robert Kern -- ========== Art & Ceramics =========== https://www.instagram.com/ksloan1952/

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Trying to understand Distributions
by Keith Sloan July 13, 2021

July 13, 2021

Again another unsuccessful attempt trying to follow Roberts advice Data is astronomic so loc = 0 is not okay ax4 = fig.add_subplot(4, 1, 4) ag2, bg2, cg2 =stats.gamma.fit(RErange1[xfield].data) print(ag2, bg2, cg2) x0, x1 = stats.gamma.ppf([0.1, 0.99], ag2, scale=cg2) x = np.linspace(stats.gamma.ppf(x0,x1, int(ag2))) ax4.plot(x, stats.gamma.pdf(x, ag2, scale = cg2),'r-', lw=5, alpha=0.6, label='gamma pdf') plt.show() -- ========== Art & Ceramics =========== https://www.instagram.com/ksloan1952/

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Trying to understand distributions
by Keith Sloan July 13, 2021

July 13, 2021

Okay I can produce a histogram of my data with either ax1, ax2 from astropy.table import Table, join import numpy as np import matplotlib.pyplot as plt from matplotlib.pyplot import plot RawMassEClassEmeasure = Table.read('../../GAMA_Data/REMassEClassEmeasure.fits') #print(RawMassEClassEmeasure.colnames) # CLEAN DATA #REMassEClassEmeasure = RawMassEClassEmeasure[RawMassEClassEmeasure['CountInCyl']> -500] RErange = RawMassEClassEmeasure[RawMassEClassEmeasure['CountInCyl']> -500] RErange1 = RErange[RErange['SurfaceDensity']< 50] binCount = 30 alphaVal = .3 ##### uminusr fig = plt.figure(figsize=(12, 6), dpi=200) fig.suptitle('Plot - Histogram Red Galaxies for Elliptical Galaxies') #fig.legend(loc="upper right") #import scipy.stats as stats from scipy import stats xfield = 'uminusr' counts, bins = np.histogram(RErange1[xfield].data,bins=binCount) print(counts) ax1 = fig.add_subplot(3, 1, 1) ax1.set_ylabel('Galaxy Count') ax1.set_xlabel(xfield) counts, bins = np.histogram(RErange1[xfield].data,bins=binCount) ax1.hist(bins[:-1],bins, weights=counts) ax2 = fig.add_subplot(3, 1, 2) ax2.hist(RErange1[xfield].data, bins=binCount, density=True) And get Histograms And can get what looks like a gamma fit with ax3 = fig.add_subplot(4, 1, 3) ag1, bg1, cg1 =stats.gamma.fit(counts) print(ag1, bg1, cg1) x = np.linspace(stats.gamma.ppf(0.1, ag1),stats.gamma.ppf(0.99, ag1), int(ag1)) ax3.plot(x, stats.gamma.pdf(x, ag1),'r-', lw=5, alpha=0.6, label='gamma pdf') ax4 = fig.add_subplot(4, 1, 4) ag2, bg2, cg2 =stats.gamma.fit(RErange1[xfield].data) print(ag2, bg2, cg2) x = np.linspace(stats.gamma.ppf(0.1, ag2),stats.gamma.ppf(0.99, ag2), int(ag2)) ax4.plot(x, stats.gamma.pdf(x, ag2),'r-', lw=5, alpha=0.6, label='gamma pdf') plt.show() gamma But I have a problem if I try and combine the histogram and gamma plots as they have different x and y scales, what am I missing? -- ========== Art & Ceramics =========== https://www.instagram.com/ksloan1952/

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Trying to understand distributions
by Keith Sloan July 13, 2021

July 13, 2021

If I try to follow Robert Kern's advice to us use all of the fitted parameters but without success ax3 = fig.add_subplot(4, 1, 3) ag1, bg1, cg1 =stats.gamma.fit(counts) print(ag1, bg1, cg1) x = np.linspace(stats.gamma.ppf(0.1, ag1),stats.gamma.ppf(0.99, ag1), int(ag1)) ax3.plot(x, stats.gamma.pdf(x, ag1, scale = cg1),'r-', lw=5, alpha=0.6, label='gamma pdf') ax4 = fig.add_subplot(4, 1, 4) ag2, bg2, cg2 =stats.gamma.fit(RErange1[xfield].data) print(ag2, bg2, cg2) x = np.linspace(stats.gamma.ppf(0.1, ag2),stats.gamma.ppf(0.99, ag2), int(ag2)) ax4.plot(x, stats.gamma.pdf(x, ag2, scale = cg2),'r-', lw=5, alpha=0.6, label='gamma pdf') RK -- ========== Art & Ceramics =========== https://www.instagram.com/ksloan1952/

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Trying to understand Distributions
by Keith Sloan July 12, 2021

July 12, 2021

I have a histogram that I would like to fit a gamma and rayleigh curve ideally with a measure of fit. I am struggling to have the histogram and distribution with the same scales. I tried to followhttps://stackoverflow.com/questions/40979643/python-how-to-fit-a-gamm… <https://github.com/scipy/scipy/issues/url> and don't really know what I am doing I would like to plot the histogram and distribution in one figure i.e. one for gamma + histogram and another for rayleigh + histogram and as I said some quantitative measure of the fit. Thanks from astropy.table import Table, join import numpy as np import matplotlib.pyplot as plt from matplotlib.pyplot import plot RawMassEClassEmeasure = Table.read('../../GAMA_Data/REMassEClassEmeasure.fits') #print(RawMassEClassEmeasure.colnames) # CLEAN DATA #REMassEClassEmeasure = RawMassEClassEmeasure[RawMassEClassEmeasure['CountInCyl']> -500] RErange = RawMassEClassEmeasure[RawMassEClassEmeasure['CountInCyl']> -500] RErange1 = RErange[RErange['SurfaceDensity']< 50] binCount = 30 alphaVal = .3 ##### uminusr fig = plt.figure(figsize=(12, 6), dpi=200) fig.suptitle('Plot - Histogram Red Galaxies for Elliptical Galaxies') #fig.legend(loc="upper right") #import scipy.stats as stats from scipy import stats xfield = 'uminusr' counts, bins = np.histogram(RErange1[xfield].data,bins=binCount) print(counts) ag, bg, cg =stats.gamma.fit(counts) print(ag, bg, cg) ax1 = fig.add_subplot(3, 1, 1) ax1.set_ylabel('Galaxy Count') ax1.set_xlabel(xfield) counts, bins = np.histogram(RErange1[xfield].data,bins=binCount) ax1.hist(bins[:-1],bins, weights=counts) ax2 = fig.add_subplot(3, 1, 2) x = np.linspace(stats.gamma.ppf(0.1, ag),stats.gamma.ppf(0.99, ag), 243) ax2.plot(x, stats.gamma.pdf(x, ag),'r-', lw=5, alpha=0.6, label='gamma pdf') param = stats.rayleigh.fit(counts) # distribution fitting # fitted distribution xx = np.linspace(0,45,1000) pdf_fitted = stats.rayleigh.pdf(xx,loc=param[0],scale=param[1]) pdf = stats.rayleigh.pdf(xx,loc=0,scale=8.5) ax3 = fig.add_subplot(3, 1, 3) plot(xx,pdf,'r-', lw=5, alpha=0.6, label='rayleigh pdf') plot(xx,pdf,'k-', label='Data') plt.bar(x[1:], counts) plt.show() [ 55 77 61 80 94 87 102 115 133 133 123 121 133 122 118 152 142 140 120 96 84 71 39 26 19 9 8 3 0 4] 216.37114598925467 -636.6370861665209 3.3226700375837455 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /var/folders/cj/z259fmwd41dgzl8mq8nppwd40000gn/T/ipykernel_8820/1690958570.py in<module> 44 plot(xx,pdf,'r-', lw=5, alpha=0.6, label='rayleigh pdf') 45 plot(xx,pdf,'k-', label='Data') ---> 46plt.bar(x[1:], counts) 47 plt.show() 48 /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/matplotlib/pyplot.py inbar(x, height, width, bottom, align, data, **kwargs) 2649 x, height, width=0.8, bottom=None, *, align='center', 2650 data=None, **kwargs): -> 2651return gca().bar( 2652 x, height, width=width, bottom=bottom, align=align, 2653 **({"data": data} if data is not None else {}), **kwargs) /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/matplotlib/__init__.py ininner(ax, data, *args, **kwargs) 1359 def inner(ax, *args, data=None, **kwargs): 1360 if datais None: -> 1361return func(ax, *map(sanitize_sequence, args), **kwargs) 1362 1363 bound= new_sig.bind(ax, *args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/matplotlib/axes/_axes.py inbar(self, x, height, width, bottom, align, **kwargs) 2302 yerr= self._convert_dx(yerr, y0, y, self.convert_yunits) 2303 -> 2304x, height, width, y, linewidth, hatch = np.broadcast_arrays( 2305 # Make args iterable too. 2306 np.atleast_1d(x), height, width, y, linewidth, hatch) <__array_function__ internals> inbroadcast_arrays(*args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/numpy/lib/stride_tricks.py inbroadcast_arrays(subok, *args) 536 args= [np.array(_m, copy=False, subok=subok) for _min args] 537 --> 538shape= _broadcast_shape(*args) 539 540 if all(array.shape== shapefor arrayin args): /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/numpy/lib/stride_tricks.py in_broadcast_shape(*args) 418 # use the old-iterator because np.nditer does not handle size 0 arrays 419 # consistently --> 420b= np.broadcast(*args[:32]) 421 # unfortunately, it cannot handle 32 or more arguments directly 422 for posin range(32, len(args), 31): ValueError: shape mismatch: objects cannot be broadcast to a single shape ========== Art & Ceramics =========== https://www.instagram.com/ksloan1952/

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coordinate and vector transformations between cartesian and cylindrical coordinate system
by Schuldei, Andreas July 12, 2021

July 12, 2021

Hi, I am looking for ways to do coordinate and vector transformations between cartesian and cylindrical coordinates. Scipy has scipy.spatial.transform<https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transfor…>, but no ready (easy?) way to do the transformations I look for. Is there a way to use spatial.transform for this that I don't see? Transformations this common surely are done frequently, but I fail to find examples that I can learn from or reuse. Kind regards, Andreas

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