From keith at sloan-home.co.uk  Sat Jul  3 13:17:31 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Sat, 3 Jul 2021 18:17:31 +0100
Subject: [SciPy-User] Trying to understand Distributions
Message-ID: <d55c999c-0b55-0f98-c40c-578ec824134f@sloan-home.co.uk>

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-gamma-distribution-from-data 
<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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From robert.kern at gmail.com  Sat Jul  3 13:39:04 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Sat, 3 Jul 2021 13:39:04 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <d55c999c-0b55-0f98-c40c-578ec824134f@sloan-home.co.uk>
References: <d55c999c-0b55-0f98-c40c-578ec824134f@sloan-home.co.uk>
Message-ID: <CAF6FJiv28rntH7DZNrzq5Xbvu+W_L0XZMupG4Bx1ADcH1kx8sA@mail.gmail.com>

Instead of making your own bar chart from the results of `np.histogram()`,
I recommend using `plt.hist(data, bins=binCount, density=True)`. The
`density=True` argument is important to make the histogram Y axis
commensurable with the PDF Y axis.

On Sat, Jul 3, 2021 at 1:19 PM Keith Sloan <keith at sloan-home.co.uk> wrote:

> 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 follow
> https://stackoverflow.com/questions/40979643/python-how-to-fit-a-gamma-distribution-from-data
> <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, joinimport numpy as npimport matplotlib.pyplot as pltfrom 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 = 30alphaVal = .3
> ##### uminusrfig = 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 statsfrom scipy import statsxfield = '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 distributionxx = 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')---> 46 plt.bar(x[1:], counts)     47 plt.show()     48
> /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/matplotlib/pyplot.py in bar(x, height, width, bottom, align, data, **kwargs)   2649         x, height, width=0.8, bottom=None, *, align='center',   2650         data=None, **kwargs):-> 2651     return 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 in inner(ax, data, *args, **kwargs)   1359     def inner(ax, *args, data=None, **kwargs):   1360         if data is None:-> 1361             return 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 in bar(self, x, height, width, bottom, align, **kwargs)   2302                 yerr = self._convert_dx(yerr, y0, y, self.convert_yunits)   2303 -> 2304         x, 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> in broadcast_arrays(*args, **kwargs)
> /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/numpy/lib/stride_tricks.py in broadcast_arrays(subok, *args)    536     args = [np.array(_m, copy=False, subok=subok) for _m in args]    537 --> 538     shape = _broadcast_shape(*args)    539     540     if all(array.shape == shape for array in 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--> 420     b = np.broadcast(*args[:32])    421     # unfortunately, it cannot handle 32 or more arguments directly    422     for pos in range(32, len(args), 31):
> ValueError: shape mismatch: objects cannot be broadcast to a single shape
>
>
>
> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>


-- 
Robert Kern
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From mikofski at berkeley.edu  Mon Jul  5 01:35:37 2021
From: mikofski at berkeley.edu (Dr. Mark Alexander Mikofski PhD)
Date: Sun, 4 Jul 2021 22:35:37 -0700
Subject: [SciPy-User] [ANN] Software job opportunity in clean energy
Message-ID: <CAEqRcW2m2pQJd-i2azFE3bYUbsAZJgr=sneF+WC7JZuevGzdXw@mail.gmail.com>

Dear Pythonistas,

DNV Energy USA is looking for an experienced software engineer to help
accelerate the renewable energy transition. Do you know any software
engineers interested in clean energy?
Would you mind sharing the following link with your network?

https://www.linkedin.com/jobs/view/2574048777

Thank you!
Mark A. Mikofski
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From andreas.schuldei at th-luebeck.de  Mon Jul 12 10:03:15 2021
From: andreas.schuldei at th-luebeck.de (Schuldei, Andreas)
Date: Mon, 12 Jul 2021 14:03:15 +0000
Subject: [SciPy-User] coordinate and vector transformations between
 cartesian and cylindrical coordinate system
Message-ID: <8248fc6c39e04397877eb3ce0947fafe@th-luebeck.de>

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.transform.Rotation.__len__.html>, 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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From guillaume at damcb.com  Mon Jul 12 10:46:33 2021
From: guillaume at damcb.com (Guillaume Gay)
Date: Mon, 12 Jul 2021 16:46:33 +0200
Subject: [SciPy-User] coordinate and vector transformations between
 cartesian and cylindrical coordinate system
In-Reply-To: <8248fc6c39e04397877eb3ce0947fafe@th-luebeck.de>
References: <8248fc6c39e04397877eb3ce0947fafe@th-luebeck.de>
Message-ID: <43e08f19-5d50-1f1b-87b9-72a621508804@damcb.com>

Hi Andreas?

What exactly do you have in mind? If your positions are stored as a (n, 
3) array of points, Cartesian to cylindrical is (for example):

|rho = np.linalg.norm(pos[:, :2], axis=1) theta = np.arctan2(pos[:, 1], 
pos[:, 0]) z = pos[:, 2] |

Conversely if you have three cylindrical coordinates (rho, theta, z), 
you can get the Cartesian with:

|x = pos[:, 0] * np.cos(pos[:, 1]) y = pos[:, 0] * np.sin(pos[:, 1]) z = 
pos[:, 2] |

Hope this helps,

Best regards,
Guillaume

On 12/07/2021 16:03, Schuldei, Andreas wrote:

> 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.transform.Rotation.__len__.html>, 
> 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
>
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user

?
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From andreas.schuldei at th-luebeck.de  Mon Jul 12 11:40:16 2021
From: andreas.schuldei at th-luebeck.de (Schuldei, Andreas)
Date: Mon, 12 Jul 2021 15:40:16 +0000
Subject: [SciPy-User] coordinate and vector transformations between
 cartesian and cylindrical coordinate system
In-Reply-To: <43e08f19-5d50-1f1b-87b9-72a621508804@damcb.com>
References: <8248fc6c39e04397877eb3ce0947fafe@th-luebeck.de>,
 <43e08f19-5d50-1f1b-87b9-72a621508804@damcb.com>
Message-ID: <64dd568c93fb4381b76206833dbad17c@th-luebeck.de>

Hi Guillaume,


I had come up with this


# coordinate transformation from cylindrical to cartesian coordinates
def coordinate_transform_cy2ca(r, theta, z):
    x = np.cos(theta) * r
    y = np.sin(theta) * r
    return x, y, z


# coordinate transformation back
def coordinate_transform_ca2cy(x: np.ndarray, y: np.ndarray, z: np.ndarray):
    r = np.sqrt(np.square(x) + np.square(y))
    theta = np.arctan2(x, y)
    return r, theta, z


# vector transformation from cylindrical to cartesian coordinate system
def vector_transform_cy2ca(v_r, v_theta, v_z, R, T, Z):
    x = v_r * np.cos(T) - R * np.sin(T) * v_theta
    y = v_r * np.sin(T) + R * np.cos(T) * v_theta
    return x, y, v_z


# vector transformation back
def vector_transform_ca2cy(v_x, v_y, v_z, X, Y, Z):
    sq_devisor = np.square(X) + np.square(Y)
    devisor = np.sqrt(sq_devisor)
    r = np.divide((v_x * X + v_y * Y), devisor, out=np.zeros_like(X, dtype=float), where=devisor != 0)
    theta = np.divide((-Y * v_x + v_y * X), sq_devisor, out=np.zeros_like(Y, dtype=float), where=sq_devisor != 0)
    return r, theta, v_z


# translate (aka move origin) of coordinates (in a meshgrid or otherwise) by vector.
# axis orientation and scaling remains the same
def coordinate_translate_ca_by_vector(x: np.ndarray, y: np.ndarray, z: np.ndarray, vector: np.ndarray) -> \
        Tuple[np.ndarray, np.ndarray, np.ndarray]:
    x_t: np.ndarray = x - vector[0]
    y_t: np.ndarray = y - vector[1]
    z_t: np.ndarray = z - vector[2]
    return x_t, y_t, z_t



based on https://mathepedia.de/Zylinderkoordinaten.html


However, as you can guess from my gymnastics with the !=0 in the vector_transform_ca2cy(), there are issues if you need to transform vectors on the z-axis. Incidentally, those are quite important to me and I had hoped that a well-done library would have found a better solution than what I came up with -- which frankly does not work all that well.


Furthermore, here http://www.uobabylon.edu.iq/eprints/paper_11_24775_76.pdf (e.g. page 36/37) I find confirmation that vector transformation is not the same as coordinate transformation. However, it disagrees a little with my vector transformation based on https://mathepedia.de/Zylinderkoordinaten.html. So I would have liked a battle-tested lib like scipy that I can rely on.


Is anyone familiar with the topic who can share some light on this?


Andreas

________________________________
Von: SciPy-User <scipy-user-bounces+andreas.schuldei=th-luebeck.de at python.org> im Auftrag von Guillaume Gay <guillaume at damcb.com>
Gesendet: Montag, 12. Juli 2021 16:46:33
An: scipy-user at python.org
Betreff: Re: [SciPy-User] coordinate and vector transformations between cartesian and cylindrical coordinate system


Hi Andreas?

What exactly do you have in mind? If your positions are stored as a (n, 3) array of points, Cartesian to cylindrical is (for example):

rho = np.linalg.norm(pos[:, :2], axis=1)
theta = np.arctan2(pos[:, 1], pos[:, 0])
z = pos[:, 2]


Conversely if you have three cylindrical coordinates (rho, theta, z), you can get the Cartesian with:


x = pos[:, 0] * np.cos(pos[:, 1])
y = pos[:, 0] * np.sin(pos[:, 1])
z = pos[:, 2]


Hope this helps,

Best regards,
Guillaume

On 12/07/2021 16:03, Schuldei, Andreas wrote:

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.transform.Rotation.__len__.html>, 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



_______________________________________________
SciPy-User mailing list
SciPy-User at python.org<mailto:SciPy-User at python.org>
https://mail.python.org/mailman/listinfo/scipy-user


?
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From keith at sloan-home.co.uk  Mon Jul 12 16:00:19 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Mon, 12 Jul 2021 21:00:19 +0100
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJiv28rntH7DZNrzq5Xbvu+W_L0XZMupG4Bx1ADcH1kx8sA@mail.gmail.com>
References: <CAF6FJiv28rntH7DZNrzq5Xbvu+W_L0XZMupG4Bx1ADcH1kx8sA@mail.gmail.com>
Message-ID: <1cec9370-c452-92bd-2cf8-abaae513a309@sloan-home.co.uk>

Okay I changed the plot histogram as advised by Robert Kern ax2 as 
opposed to ax1

but how do I get the correct gamma plot as I am not understanding 
np.linespace other the first parameter is a minimum and second maximum

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)
ax2.hist(RErange1[xfield].data, bins=binCount, density=True)
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')

-- 
========== Art & Ceramics ===========
https://www.instagram.com/ksloan1952/

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From robert.kern at gmail.com  Mon Jul 12 16:36:09 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Mon, 12 Jul 2021 16:36:09 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <1cec9370-c452-92bd-2cf8-abaae513a309@sloan-home.co.uk>
References: <CAF6FJiv28rntH7DZNrzq5Xbvu+W_L0XZMupG4Bx1ADcH1kx8sA@mail.gmail.com>
 <1cec9370-c452-92bd-2cf8-abaae513a309@sloan-home.co.uk>
Message-ID: <CAF6FJisXL8DpLGVtHVUujcyD-M895kjoWgdK8nbMFKvNfVhzbg@mail.gmail.com>

On Mon, Jul 12, 2021 at 4:06 PM Keith Sloan <keith at sloan-home.co.uk> wrote:

> Okay I changed the plot histogram as advised by Robert Kern ax2 as opposed
> to ax1
>
> but how do I get the correct gamma plot as I am not understanding
> np.linespace other the first parameter is a minimum and second maximum
>
> 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)
>

You don't use fit() on histogram counts. Use it on the data itself.

You probably want to fix the `loc` parameter when you do the fitting. It is
not typical to allow a zero-shifted gamma distribution (though I am told
that astronomy is a place where that does happen). We can use floc=0 to fix
that value and we can ignore it when it comes out of the output.

gam_shape, _, gam_scale = stats.gamma.fit(RErange1[xfield].data, floc=0.0)

Here's a quick demonstration with 100 points that I randomly draw from a
gamma distribution:

|3> data = stats.gamma.rvs(3, scale=2.0, size=100)


|4> data
array([ 3.07534256,  4.57813927, 13.8711741 ,  6.57534761,  5.28268719,
 7.7073074 ,  2.95929127,  7.58245554,  6.10107878,  3.52840919,
 2.91885749,  4.05885291,  5.03287215, 10.76342819,
        8.6441407 ,  8.64011327,  5.461707  ,  6.15034854,  2.84274024,
 8.16282873,  5.111948  , 11.0312346 ,  0.50150262,  4.31496838,
 5.78209971,  9.0395057 ,  3.97753423,  2.81673685,
        1.71633995,  9.24401038,  7.50455745,  3.90409068,  4.56853541,
16.55915147,  2.33004283,  2.89232249, 10.0872901 ,  3.67701972,
 9.15203612,  3.33052913,  5.38745436,  3.95826717,
        3.69810183,  5.52858701,  6.04013903,  1.41587988,  4.53861925,
 7.65106151,  4.05856947,  6.02678829,  6.08308182,  4.22620438,
 9.93515377,  9.45789334,  3.17088024, 10.59471748,
       12.19263839,  8.12720946,  8.05421344,  5.31204559,  4.65193752,
 6.83243734,  0.85212066, 16.05784841,  1.97212606,  6.28613507,
 8.00692823,  4.0366943 ,  5.07313536,  9.36507581,
        7.28824511,  6.38240736,  3.4051997 ,  6.24772202,  3.11340298,
 9.00223885,  6.87387583,  4.75206877,  3.68490716,  6.71069272,
 2.43124401, 11.01701724,  5.33665806,  4.36158851,
        5.89422855,  6.60390376,  8.24682823,  4.60149744,  5.11966798,
 1.72012758,  3.85308752,  4.13263933,  6.71598009,  5.47673371,
 2.89124835,  4.88451718,  6.69019804,  2.82428072,
        6.06868802, 11.30380543])

|5> stats.gamma.fit(data, floc=0)
(3.6909888310977736, 0, 1.6193634011500304)

ax2 = fig.add_subplot(3, 1, 2)
> ax2.hist(RErange1[xfield].data, bins=binCount, density=True)
> 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')
>
Now be sure to use all of the fitted parameters, not just the shape
parameter (we can ignore the loc= parameter since we fixed it to 0).

x0, x1 = stats.gamma.ppf([0.1, 0.99], gam_shape, scale=gam_scale)
x = np.linspace(x0, x1, 243)
# Though this might be better:
# x = np.linspace(0, RErange1[xfield].data.max(), 243)
ax2.plot(x, stats.gamma.pdf(x, gam_shape, scale=gam_scale), ...)

-- 
Robert Kern
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From keith at sloan-home.co.uk  Mon Jul 12 23:41:09 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Tue, 13 Jul 2021 04:41:09 +0100
Subject: [SciPy-User] Trying to understand distributions
Message-ID: <1be97ef7-475a-0c5d-db27-d7a5c6888a5e@sloan-home.co.uk>

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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From keith at sloan-home.co.uk  Tue Jul 13 00:14:54 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Tue, 13 Jul 2021 05:14:54 +0100
Subject: [SciPy-User] Trying to understand Distributions
Message-ID: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>

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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From robert.kern at gmail.com  Tue Jul 13 00:19:51 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 13 Jul 2021 00:19:51 -0400
Subject: [SciPy-User] Trying to understand distributions
In-Reply-To: <1be97ef7-475a-0c5d-db27-d7a5c6888a5e@sloan-home.co.uk>
References: <1be97ef7-475a-0c5d-db27-d7a5c6888a5e@sloan-home.co.uk>
Message-ID: <CAF6FJivSfQ=596BQ7AzepukJxAeLscP34bVqfniBcBVYvn8EWw@mail.gmail.com>

On Mon, Jul 12, 2021 at 11:42 PM Keith Sloan <keith at sloan-home.co.uk> wrote:

> And can get what looks like a gamma fit with
>
> ax3 = fig.add_subplot(4, 1, 3)
> ag1, bg1, cg1 =stats.gamma.fit(counts)
>
Again, do not pass `counts` to `fit()`. `fit()` works on the raw data. Omit
this plot.

> 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')
>
Again, you are omitting bg2 and cg2  from the `ppf()` and `pdf()` calls.
You need to pass all of them.

-- 
Robert Kern
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From robert.kern at gmail.com  Tue Jul 13 00:23:17 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 13 Jul 2021 00:23:17 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
Message-ID: <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>

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 at 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
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From keith at sloan-home.co.uk  Tue Jul 13 00:05:13 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Tue, 13 Jul 2021 05:05:13 +0100
Subject: [SciPy-User] Trying to understand distributions
Message-ID: <c18f477d-5c03-5b43-00a1-6611dae9e723@sloan-home.co.uk>

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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From keith at sloan-home.co.uk  Tue Jul 13 00:45:17 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Tue, 13 Jul 2021 05:45:17 +0100
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
Message-ID: <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>

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 at sloan-home.co.uk 
> <mailto:keith at 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 at sloan-home.co.uk <mailto:keith at 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 at python.org  <mailto:SciPy-User at 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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From robert.kern at gmail.com  Tue Jul 13 00:54:34 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Tue, 13 Jul 2021 00:54:34 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
Message-ID: <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>

x = np.linspace(x0, x1, 100)

On Tue, Jul 13, 2021 at 12:46 AM Keith Sloan <keith at sloan-home.co.uk> wrote:

> 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()
> [image: 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 at 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()
>> [image: 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 at 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 listSciPy-User at python.orghttps://mail.python.org/mailman/listinfo/scipy-user
>>
>> --
>> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>>
>>
>
> --
> Robert Kern
>
> --
> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>


-- 
Robert Kern
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From pmhobson at gmail.com  Tue Jul 13 13:22:53 2021
From: pmhobson at gmail.com (Paul Hobson)
Date: Tue, 13 Jul 2021 10:22:53 -0700
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
Message-ID: <CADT3MEAMxARmOAUo+jQ1W9jxjeuD-fRw1uwDFh2BfKLJQBg77g@mail.gmail.com>

Keith,

Robert's given you great advice. My library (paramnormal) might help you
out here if you're still struggling. It's basically a wrapper around select
scipy distributions to make the parameterization a little more conventional.
http://phobson.github.io/paramnormal/tutorial/fitting.html

The gamma distribution is one of the ones whose wrapper has be implemented
http://phobson.github.io/paramnormal/api/paramnormal.html#paramnormal.dist.gamma

Good luck,
-Paul






On Mon, Jul 12, 2021 at 9:55 PM Robert Kern <robert.kern at gmail.com> wrote:

> x = np.linspace(x0, x1, 100)
>
> On Tue, Jul 13, 2021 at 12:46 AM Keith Sloan <keith at sloan-home.co.uk>
> wrote:
>
>> 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()
>> [image: 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 at 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()
>>> [image: 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 at 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 listSciPy-User at python.orghttps://mail.python.org/mailman/listinfo/scipy-user
>>>
>>> --
>>> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>>>
>>>
>>
>> --
>> Robert Kern
>>
>> --
>> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>>
>> _______________________________________________
>> SciPy-User mailing list
>> SciPy-User at python.org
>> https://mail.python.org/mailman/listinfo/scipy-user
>>
>
>
> --
> Robert Kern
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From keith at sloan-home.co.uk  Wed Jul 14 10:05:56 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Wed, 14 Jul 2021 15:05:56 +0100
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
Message-ID: <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>

Well thanks to advice from Robert I managed to overlay a gamma plot on 
the histogram, except the gamma looks more like a normal distribution to me.

overlay

And when I try an add a normal to the mix I have a problem with x axis 
etc and it looks like a normal distribution

w
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user

-- 
========== Art & Ceramics ===========
https://www.instagram.com/ksloan1952/

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From robert.kern at gmail.com  Wed Jul 14 10:31:27 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 14 Jul 2021 10:31:27 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
Message-ID: <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>

On Wed, Jul 14, 2021 at 10:07 AM Keith Sloan <keith at sloan-home.co.uk> wrote:

> And when I try an add a normal to the mix I have a problem with x axis etc
> and it looks like a normal distribution
> [image: w]
>
> You'll notice that your shape parameter is very high. The Gamma
distribution does asymptotically approach the normal distribution as the
shape parameter increases. Your shape parameter is very large.

One issue is that it looks like your `uminusr` data is truncated
("censored") to omit any values less than 1.8. The fitting procedure in
`fit()` assumes that you are giving it uncensored data. That's driving it
to make the left tail as low as possible and driving the Gamma to look more
normal.


-- 
Robert Kern
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From keith at sloan-home.co.uk  Wed Jul 14 10:45:46 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Wed, 14 Jul 2021 15:45:46 +0100
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
Message-ID: <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>

Thanks for all the help but still experiencing further problems, maybe 
it is just me but there seems inconsistencies between distributions and 
fit, ppt and pdf calls.

1) Trying to fit exponential, fit does not give scale but pdf seems to 
accept a scale value

2) Also a gamma plot of a second distribution is not showing curve

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 = 100
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(4, 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)

ag, bg, cg =stats.gamma.fit(RErange1[xfield].data)
print(ag, bg, cg)
xg0, xg1 = stats.gamma.ppf([0.01, 0.99], ag, loc = bg, scale=cg)
xg = np.linspace(xg0,xg1, int(ag))

an, bn =stats.norm.fit(RErange1[xfield].data)
print(an, bn)
xn0, xn1 = stats.norm.ppf([0.01, 0.99])
xn = np.linspace(xn0,xn1,100)

ax1.plot(xg, stats.gamma.pdf(xg, ag, loc=bg, scale = cg),'r-', lw=2, 
alpha=0.6, label='gamma pdf')
ax1.plot(xn, stats.norm.pdf(xn, an, bn),'y-', lw=2, alpha=0.6, 
label='normal pdf')
ax1.hist(RErange1[xfield].data, bins=binCount, density=True)

xfield = 'CountInCyl'
#counts, bins = np.histogram(RErange1[xfield].data,bins=binCount)
#print(counts)

ax2 = fig.add_subplot(4, 1, 2)
ax2.set_ylabel('Count In Cyl')
ax2.set_xlabel(xfield)
#ae, be, ce = stats.expon.fit(RErange1[xfield].data)
ae, be = stats.expon.fit(RErange1[xfield].data)

xe0, xe1 = stats.expon.ppf([0.01, 0.99])
xe = np.linspace(xe0,xe1,100)
#ax2.plot(xg, stats.expon.pdf(xe, ae, loc=be, scale = ce),'r-', lw=2, 
alpha=0.6, label='gamma pdf')
ax2.hist(RErange1[xfield].data, bins=binCount, density=True)

xfield = 'DistanceTo5nn'
ax3 = fig.add_subplot(4, 1, 3)
ax3.set_ylabel('Distance to 5nn')
ax3.set_xlabel(xfield)

ag, bg, cg =stats.gamma.fit(RErange1[xfield].data)
print(ag, bg, cg)
xg0, xg1 = stats.gamma.ppf([0.01, 0.99], ag, loc = bg, scale=cg)
xg = np.linspace(xg0,xg1, int(ag))

ax3.hist(RErange1[xfield].data, bins=binCount, density=True)
ax3.plot(xg, stats.gamma.pdf(xg, ag, loc=bg, scale = cg),'r-', lw=2, 
alpha=0.6, label='gamma pdf')

plt.show()




> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user

-- 
========== Art & Ceramics ===========
https://www.instagram.com/ksloan1952/

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From robert.kern at gmail.com  Wed Jul 14 10:56:25 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 14 Jul 2021 10:56:25 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
 <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
Message-ID: <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>

On Wed, Jul 14, 2021 at 10:47 AM Keith Sloan <keith at sloan-home.co.uk> wrote:

> Thanks for all the help but still experiencing further problems, maybe it
> is just me but there seems inconsistencies between distributions and fit,
> ppt and pdf calls.
>
> 1) Trying to fit exponential, fit does not give scale but pdf seems to
> accept a scale value
>
> ae, be = stats.expon.fit(RErange1[xfield].data)

loc=ae, scale=be

The last two parameters in the fit() results are always loc and scale.

> xe0, xe1 = stats.expon.ppf([0.01, 0.99])

Don't forget to pass in the fitted parameters.

2) Also a gamma plot of a second distribution is not showing curve
>

Stop using int(ag) for the number of points in the linspace(). It's usually
an inappropriate value. In that second case, int(ag)==1.

-- 
Robert Kern
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From keith at sloan-home.co.uk  Wed Jul 14 13:24:31 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Wed, 14 Jul 2021 18:24:31 +0100
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
 <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
 <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>
Message-ID: <6fdac486-171d-03da-e0f7-150359eeeafe@sloan-home.co.uk>

Still confused by expon.fit only returning two ( same for norm) where as 
gamma returns a value for the shape as well as loc and scale
i.e. get error with

ax2 = fig.add_subplot(4, 1, 2)
ax2.set_ylabel('Count In Cyl')
ax2.set_xlabel(xfield)
#ae, be, ce = stats.expon.fit(RErange1[xfield].data)
ae, be = stats.expon.fit(RErange1[xfield].data)

xe0, xe1 = stats.expon.ppf([0.01, 0.99], loc=ae, scale=be)
xe = np.linspace(xe0,xe1,100)
ax2.plot(xg, stats.expon.pdf(xe, loc=ae, scale=be),'r-', lw=2, 
alpha=0.6, label='gamma pdf')
ax2.hist(RErange1[xfield].data, bins=binCount, density=True)

Does not like the ax2.plot

On 14/07/2021 15:56, Robert Kern wrote:
> On Wed, Jul 14, 2021 at 10:47 AM Keith Sloan <keith at sloan-home.co.uk 
> <mailto:keith at sloan-home.co.uk>> wrote:
>
>     Thanks for all the help but still experiencing further problems,
>     maybe it is just me but there seems inconsistencies between
>     distributions and fit, ppt and pdf calls.
>
>     1) Trying to fit exponential, fit does not give scale but pdf
>     seems to accept a scale value
>
> > ae, be = stats.expon.fit(RErange1[xfield].data)
>
> loc=ae, scale=be
>
> The last two parameters in the fit() results are always loc and scale.
>
> > xe0, xe1 = stats.expon.ppf([0.01, 0.99])
>
> Don't forget to pass in the fitted parameters.
>
>     2) Also a gamma plot of a second distribution is not showing curve
>
>
> Stop using int(ag) for the number of points in the linspace(). It's 
> usually an inappropriate value. In that second case, int(ag)==1.
>
> -- 
> Robert Kern
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user

-- 
========== Art & Ceramics ===========
https://www.instagram.com/ksloan1952/

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From robert.kern at gmail.com  Wed Jul 14 13:42:47 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Wed, 14 Jul 2021 13:42:47 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <6fdac486-171d-03da-e0f7-150359eeeafe@sloan-home.co.uk>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
 <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
 <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>
 <6fdac486-171d-03da-e0f7-150359eeeafe@sloan-home.co.uk>
Message-ID: <CAF6FJisbbw7RBdNGrN0-c=PXDDRrJZ1++3HKR2_PfWnayUPBKA@mail.gmail.com>

On Wed, Jul 14, 2021 at 1:25 PM Keith Sloan <keith at sloan-home.co.uk> wrote:

> Still confused by expon.fit only returning two ( same for norm) where as
> gamma returns a value for the shape as well as loc and scale
>
The exponential and normal distributions just don't have shape parameters.

> i.e. get error with
>
> ax2 = fig.add_subplot(4, 1, 2)
> ax2.set_ylabel('Count In Cyl')
> ax2.set_xlabel(xfield)
> #ae, be, ce = stats.expon.fit(RErange1[xfield].data)
> ae, be = stats.expon.fit(RErange1[xfield].data)
>
> xe0, xe1 = stats.expon.ppf([0.01, 0.99], loc=ae, scale=be)
> xe = np.linspace(xe0,xe1,100)
> ax2.plot(xg, stats.expon.pdf(xe, loc=ae, scale=be),'r-', lw=2, alpha=0.6,
> label='gamma pdf')
> ax2.hist(RErange1[xfield].data, bins=binCount, density=True)
>
> Does not like the ax2.plot
>
Because you used ax2.plot(xg, ...) instead of ax2.plot(xe, ...)?

-- 
Robert Kern
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From keith at sloan-home.co.uk  Thu Jul 15 11:05:26 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Thu, 15 Jul 2021 16:05:26 +0100
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJisbbw7RBdNGrN0-c=PXDDRrJZ1++3HKR2_PfWnayUPBKA@mail.gmail.com>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
 <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
 <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>
 <6fdac486-171d-03da-e0f7-150359eeeafe@sloan-home.co.uk>
 <CAF6FJisbbw7RBdNGrN0-c=PXDDRrJZ1++3HKR2_PfWnayUPBKA@mail.gmail.com>
Message-ID: <d74dc9bd-f84b-24d4-2e34-15a4c37fce56@sloan-home.co.uk>

Okay I now have three fits

I got rid of the clipping on Galaxy Count but gamma and norm still look 
very similar. Is there another distribution like
gamma? but instead of being fatter before the peak is fatter after the 
peak and would be a better match? i.e in the area
between 2.3 and 2.4

I know there are similar distributions to expon but nor familiar with 
them. 'Count In Cyl' are integer counts so bin set accordingly
but would prefer a distribution where the red line went through the 
midpoint of each bar of the histogram

Happy with 'Distance to 5nn' and gamma fit.

Thanks in anticipation.


-- 
========== Art & Ceramics ===========
https://www.instagram.com/ksloan1952/

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From robert.kern at gmail.com  Thu Jul 15 13:08:25 2021
From: robert.kern at gmail.com (Robert Kern)
Date: Thu, 15 Jul 2021 13:08:25 -0400
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <d74dc9bd-f84b-24d4-2e34-15a4c37fce56@sloan-home.co.uk>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
 <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
 <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>
 <6fdac486-171d-03da-e0f7-150359eeeafe@sloan-home.co.uk>
 <CAF6FJisbbw7RBdNGrN0-c=PXDDRrJZ1++3HKR2_PfWnayUPBKA@mail.gmail.com>
 <d74dc9bd-f84b-24d4-2e34-15a4c37fce56@sloan-home.co.uk>
Message-ID: <CAF6FJismhG0t+nCgNSaWzRG=Jtx2ePXKqWfhYG8wySrLYV1+rA@mail.gmail.com>

On Thu, Jul 15, 2021 at 11:06 AM Keith Sloan <keith at sloan-home.co.uk> wrote:

> Okay I now have three fits
>
> I got rid of the clipping on Galaxy Count but gamma and norm still look
> very similar. Is there another distribution like
> gamma  but instead of being fatter before the peak is fatter after the
> peak and would be a better match? i.e in the area
> between 2.3 and 2.4
>
Sure, `johnsonsu` will probably fit. Depending on what you actually want to
do with the fitted distribution, you might be better served with a
nonparametric KDE instead.

> I know there are similar distributions to expon but nor familiar with
> them. 'Count In Cyl' are integer counts so bin set accordingly
> but would prefer a distribution where the red line went through the
> midpoint of each bar of the histogram
>
If the data is discrete, you probably want to fit one of the discrete
distributions instead.


https://docs.scipy.org/doc/scipy/reference/stats.html#discrete-distributions

You can start with `geom`, but that first bin looks pretty tall for that.
Maybe `zipf` or `logser` will work.

--
Robert Kern
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From keith at sloan-home.co.uk  Fri Jul 16 10:05:18 2021
From: keith at sloan-home.co.uk (Keith Sloan)
Date: Fri, 16 Jul 2021 15:05:18 +0100
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <CAF6FJismhG0t+nCgNSaWzRG=Jtx2ePXKqWfhYG8wySrLYV1+rA@mail.gmail.com>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
 <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
 <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>
 <6fdac486-171d-03da-e0f7-150359eeeafe@sloan-home.co.uk>
 <CAF6FJisbbw7RBdNGrN0-c=PXDDRrJZ1++3HKR2_PfWnayUPBKA@mail.gmail.com>
 <d74dc9bd-f84b-24d4-2e34-15a4c37fce56@sloan-home.co.uk>
 <CAF6FJismhG0t+nCgNSaWzRG=Jtx2ePXKqWfhYG8wySrLYV1+rA@mail.gmail.com>
Message-ID: <603c4615-14ac-fa24-6d8d-666ac6b79ee5@sloan-home.co.uk>

Thanks

On 15/07/2021 18:08, Robert Kern wrote:
> On Thu, Jul 15, 2021 at 11:06 AM Keith Sloan <keith at sloan-home.co.uk 
> <mailto:keith at sloan-home.co.uk>> wrote:
>
>     Okay I now have three fits
>
>     I got rid of the clipping on Galaxy Count but gamma and norm still
>     look very similar. Is there another distribution like
>     gamma? but instead of being fatter before the peak is fatter after
>     the peak and would be a better match? i.e in the area
>     between 2.3 and 2.4
>
> Sure, `johnsonsu` will probably fit. Depending on what you actually 
> want to do with the fitted distribution, you might be better served 
> with a nonparametric KDE instead.


johnsonsu seems to work just fine.

>     I know there are similar distributions to expon but nor familiar
>     with them. 'Count In Cyl' are integer counts so bin set accordingly
>     but would prefer a distribution where the red line went through
>     the midpoint of each bar of the histogram
>
> If the data is discrete, you probably want to fit one of the discrete 
> distributions instead.
>
> https://docs.scipy.org/doc/scipy/reference/stats.html#discrete-distributions 
> <https://docs.scipy.org/doc/scipy/reference/stats.html#discrete-distributions>
>
> You can start with `geom`, but that first bin looks pretty tall for 
> that. Maybe `zipf` or `logser` will work.

Looked at geom and did not see a fit function

Not sure I do want a discrete distribution as what I want to 
subsequently perform is fits for n sets of the three distributions
and see if I can find any correlation between the distribution fit 
parameters.

>
> --
> Robert Kern
>
> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user

-- 
========== Art & Ceramics ===========
https://www.instagram.com/ksloan1952/

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From chris.barker at noaa.gov  Fri Jul 16 14:21:16 2021
From: chris.barker at noaa.gov (Chris Barker)
Date: Fri, 16 Jul 2021 11:21:16 -0700
Subject: [SciPy-User] Trying to understand Distributions
In-Reply-To: <603c4615-14ac-fa24-6d8d-666ac6b79ee5@sloan-home.co.uk>
References: <6fcc14c8-d349-5666-11a0-72dc0d645743@sloan-home.co.uk>
 <CAF6FJivGhw8a-pG54wrkksyACtUEsA+iG-UfwDLmfHw-OHOYoA@mail.gmail.com>
 <ff79c3f1-ca1f-2231-5918-5d033257640b@sloan-home.co.uk>
 <CAF6FJivtkTvpK5YrfKGcQbJP6pq6sZDq-MxbPzZqPiCgvm+28g@mail.gmail.com>
 <b5528ce7-6bfa-a4c1-b21c-d010060068a2@sloan-home.co.uk>
 <CAF6FJitWtzbicGAEYhF3YFUjmNbBLLt0mWGRMJeq_e9_xeHaRQ@mail.gmail.com>
 <9ace18af-37eb-47a2-f459-cf92e5f32c45@sloan-home.co.uk>
 <CAF6FJiuOKycHLPq+oWYd7zeQkVvBpk81Gk6D1HeFTX-9N_Yt1Q@mail.gmail.com>
 <f0a467a5-5205-984f-aaab-cab861dee3e0@sloan-home.co.uk>
 <CAF6FJiv+8aXiLBS28s-L9V7+eDT0L_tthGyrRA3n4eoypXmeiQ@mail.gmail.com>
 <6fdac486-171d-03da-e0f7-150359eeeafe@sloan-home.co.uk>
 <CAF6FJisbbw7RBdNGrN0-c=PXDDRrJZ1++3HKR2_PfWnayUPBKA@mail.gmail.com>
 <d74dc9bd-f84b-24d4-2e34-15a4c37fce56@sloan-home.co.uk>
 <CAF6FJismhG0t+nCgNSaWzRG=Jtx2ePXKqWfhYG8wySrLYV1+rA@mail.gmail.com>
 <603c4615-14ac-fa24-6d8d-666ac6b79ee5@sloan-home.co.uk>
Message-ID: <CALGmxEJUcc_Axe+7wHFx6orOy6TbvTVC+zFAcrLuemVFJTC--Q@mail.gmail.com>

These data are clearly skewed -- one option is to transform the data with a
power transformation:

log normal is a famous transformation (skewed the other way), but you
can use pretty much anything :-) an exponent > 1 would likely match this
data well.

Here's one random reference I found with a quick Google:

http://seismo.berkeley.edu/~kirchner/eps_120/Toolkits/Toolkit_03.pdf

Coincidentally enough -- that was one of the first hits on Google -- but I
took that class 25 years ago!

-CHB



On Fri, Jul 16, 2021 at 7:06 AM Keith Sloan <keith at sloan-home.co.uk> wrote:

> Thanks
> On 15/07/2021 18:08, Robert Kern wrote:
>
> On Thu, Jul 15, 2021 at 11:06 AM Keith Sloan <keith at sloan-home.co.uk>
> wrote:
>
>> Okay I now have three fits
>>
>> I got rid of the clipping on Galaxy Count but gamma and norm still look
>> very similar. Is there another distribution like
>> gamma  but instead of being fatter before the peak is fatter after the
>> peak and would be a better match? i.e in the area
>> between 2.3 and 2.4
>>
> Sure, `johnsonsu` will probably fit. Depending on what you actually want
> to do with the fitted distribution, you might be better served with a
> nonparametric KDE instead.
>
>
> johnsonsu seems to work just fine.
>
> I know there are similar distributions to expon but nor familiar with
>> them. 'Count In Cyl' are integer counts so bin set accordingly
>> but would prefer a distribution where the red line went through the
>> midpoint of each bar of the histogram
>>
> If the data is discrete, you probably want to fit one of the discrete
> distributions instead.
>
>
> https://docs.scipy.org/doc/scipy/reference/stats.html#discrete-distributions
>
> You can start with `geom`, but that first bin looks pretty tall for that.
> Maybe `zipf` or `logser` will work.
>
> Looked at geom and did not see a fit function
>
> Not sure I do want a discrete distribution as what I want to subsequently
> perform is fits for n sets of the three distributions
> and see if I can find any correlation between the distribution fit
> parameters.
>
>
> --
> Robert Kern
>
> _______________________________________________
> SciPy-User mailing listSciPy-User at python.orghttps://mail.python.org/mailman/listinfo/scipy-user
>
> --
> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>
> _______________________________________________
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> https://mail.python.org/mailman/listinfo/scipy-user
>


-- 

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From charlesr.harris at gmail.com  Sun Jul 18 16:13:08 2021
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Sun, 18 Jul 2021 14:13:08 -0600
Subject: [SciPy-User] (no subject)
Message-ID: <CAB6mnxJYbwj3n55r0jcaM27d8J5WsQcLk8OHsfPB9bqOFq_X9w@mail.gmail.com>

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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