Re: Trying to understand Distributions

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

On Tue, Jul 13, 2021 at 12:46 AM Keith Sloan <[email protected]> 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 <[email protected]>
> 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 <[email protected]>
>> 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 [email protected]://mail.python.org/mailman/listinfo/scipy-user
>>
>> --
>> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>>
>>
>
> --
> Robert Kern
>
> --
> ========== Art & Ceramics ===========https://www.instagram.com/ksloan1952/
>
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-- 
Robert Kern

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