[yt-users] Normalized axis

Nathan Goldbaum nathan12343 at gmail.com
Fri Jun 9 07:10:49 PDT 2017


Try to use plot.set_axes_unit("unitary").
On Fri, Jun 9, 2017 at 9:08 AM Sushilkumar <sushil.sush19us at gmail.com>
wrote:

> Dear yt
>
> How do I label the axis of a plot in normalized values?
>
> On my plot I want to label my x and y axis in terms of the vertical line.
>
> Do I use convert_to_plot under streamlines?
>
> Link to the plot and script is below
>
> https://drive.google.com/open?id=0B4g8shg4DL7oak5PLWVVdG5UMHc
>
> Thanks in advance
>
>
>
> On Thu, Jun 8, 2017 at 1:01 PM, <yt-users-request at lists.spacepope.org>
> wrote:
>
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>> Today's Topics:
>>
>>    1. Re: selecting data by refinement level (Klaus Weide)
>>    2. Re: selecting data by refinement level (Matthew Turk)
>>    3. Re: selecting data by refinement level (Nathan Goldbaum)
>>    4. Re: selecting data by refinement level (Klaus Weide)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Wed, 7 Jun 2017 13:53:49 -0500 (CDT)
>> From: Klaus Weide <klaus at flash.uchicago.edu>
>> To: Discussion of the yt analysis package
>>         <yt-users at lists.spacepope.org>
>> Subject: Re: [yt-users] selecting data by refinement level
>> Message-ID: <Pine.LNX.4.64.1706071350010.1322 at flash.uchicago.edu>
>> Content-Type: TEXT/PLAIN; charset=US-ASCII
>>
>> On Wed, 7 Jun 2017, Matthew Turk wrote:
>>
>> > Hi Klaus,
>> >
>> > Whoops, I said "grid_collection" but I meant "data_collection".
>>
>> Thanks Matt. I have not tried the 'collection of grids' approach yet, but
>> the following does what I want:
>>
>>         ds = yt.load(dump_path)
>>
>>         ad = ds.all_data()
>>         if fine_only:
>>             maxreflevel = ad.index.grid_levels.max()
>>             print '*** Filtering data so that only FLASH refinement level
>> {} cells are used'.format(maxreflevel+1)
>>             ad.min_level = maxreflevel
>>             ad.max_level = maxreflevel
>>             print 'These cover a volume of %s' % ad['cell_volume'].sum()
>>         # .....
>>         data = {}
>>         data['dens'] = ad['dens'].to_ndarray()
>>         # .....
>>
>>
>> Klaus
>>
>>
>> ------------------------------
>>
>> Message: 2
>> Date: Wed, 7 Jun 2017 13:56:51 -0500
>> From: Matthew Turk <matthewturk at gmail.com>
>> To: Discussion of the yt analysis package
>>         <yt-users at lists.spacepope.org>
>> Subject: Re: [yt-users] selecting data by refinement level
>> Message-ID:
>>         <CALO3=5HbBWfPj8hf5KEwMq1CiU932Q_u+=
>> hHcBzYirQzGYCm3A at mail.gmail.com>
>> Content-Type: text/plain; charset="UTF-8"
>>
>> Great!  As a quick note, you can use `.d` as shorthand for
>> `to_ndarray()`.  The YTArray it returns is a subclass of ndarray, so
>> should in general be usable in both ways; as you probably figured out,
>> if you do any manipulation of individual elements it adds overhead and
>> (often frustrating, but hopefully ultimately helpful), restrictions on
>> combining incorrect units unless you look at it as a strict ndarray.
>>
>> On Wed, Jun 7, 2017 at 1:53 PM, Klaus Weide <klaus at flash.uchicago.edu>
>> wrote:
>> > On Wed, 7 Jun 2017, Matthew Turk wrote:
>> >
>> >> Hi Klaus,
>> >>
>> >> Whoops, I said "grid_collection" but I meant "data_collection".
>> >
>> > Thanks Matt. I have not tried the 'collection of grids' approach yet,
>> but
>> > the following does what I want:
>> >
>> >         ds = yt.load(dump_path)
>> >
>> >         ad = ds.all_data()
>> >         if fine_only:
>> >             maxreflevel = ad.index.grid_levels.max()
>> >             print '*** Filtering data so that only FLASH refinement
>> level {} cells are used'.format(maxreflevel+1)
>> >             ad.min_level = maxreflevel
>> >             ad.max_level = maxreflevel
>> >             print 'These cover a volume of %s' % ad['cell_volume'].sum()
>> >         # .....
>> >         data = {}
>> >         data['dens'] = ad['dens'].to_ndarray()
>> >         # .....
>> >
>> >
>> > Klaus
>> > _______________________________________________
>> > yt-users mailing list
>> > yt-users at lists.spacepope.org
>> > http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
>>
>>
>> ------------------------------
>>
>> Message: 3
>> Date: Wed, 7 Jun 2017 12:00:44 -0700
>> From: Nathan Goldbaum <nathan12343 at gmail.com>
>> To: Discussion of the yt analysis package
>>         <yt-users at lists.spacepope.org>
>> Subject: Re: [yt-users] selecting data by refinement level
>> Message-ID:
>>         <CAJXewO=
>> gk4Fqrh_fKxmgKVu9vgu53TVQd0P0-Ra5wBBSPm-_Gg at mail.gmail.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> On Wed, Jun 7, 2017 at 11:56 AM, Matthew Turk <matthewturk at gmail.com>
>> wrote:
>>
>> > Great!  As a quick note, you can use `.d` as shorthand for
>> > `to_ndarray()`.  The YTArray it returns is a subclass of ndarray, so
>> > should in general be usable in both ways; as you probably figured out,
>> > if you do any manipulation of individual elements it adds overhead and
>> > (often frustrating, but hopefully ultimately helpful), restrictions on
>> > combining incorrect units unless you look at it as a strict ndarray.
>> >
>>
>> One tiny note:
>>
>> YTArray.d is short for YTArray.ndarray_view(), which returns a view onto
>> the array. This doesn't copy the underlying data.
>>
>> YTArray.to_ndarray() is equivalend to YTArray.value or YTArray.v, whcih
>> returns an ndarray containing a copy of the underlying array data. Getting
>> a copy sometimes adds overhead.
>>
>> Choosing whether or now you want a copy comes down to what exactly you are
>> doing, since copying adds overhead, but it also prevents silently
>> overwriting data in an unexpected, nonlocal fashion.
>>
>>
>> >
>> > On Wed, Jun 7, 2017 at 1:53 PM, Klaus Weide <klaus at flash.uchicago.edu>
>> > wrote:
>> > > On Wed, 7 Jun 2017, Matthew Turk wrote:
>> > >
>> > >> Hi Klaus,
>> > >>
>> > >> Whoops, I said "grid_collection" but I meant "data_collection".
>> > >
>> > > Thanks Matt. I have not tried the 'collection of grids' approach yet,
>> but
>> > > the following does what I want:
>> > >
>> > >         ds = yt.load(dump_path)
>> > >
>> > >         ad = ds.all_data()
>> > >         if fine_only:
>> > >             maxreflevel = ad.index.grid_levels.max()
>> > >             print '*** Filtering data so that only FLASH refinement
>> > level {} cells are used'.format(maxreflevel+1)
>> > >             ad.min_level = maxreflevel
>> > >             ad.max_level = maxreflevel
>> > >             print 'These cover a volume of %s' %
>> ad['cell_volume'].sum()
>> > >         # .....
>> > >         data = {}
>> > >         data['dens'] = ad['dens'].to_ndarray()
>> > >         # .....
>> > >
>> > >
>> > > Klaus
>> > > _______________________________________________
>> > > yt-users mailing list
>> > > yt-users at lists.spacepope.org
>> > > http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
>> > _______________________________________________
>> > yt-users mailing list
>> > yt-users at lists.spacepope.org
>> > http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org
>> >
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>> ------------------------------
>>
>> Message: 4
>> Date: Wed, 7 Jun 2017 14:45:52 -0500 (CDT)
>> From: Klaus Weide <klaus at flash.uchicago.edu>
>> To: Discussion of the yt analysis package
>>         <yt-users at lists.spacepope.org>
>> Subject: Re: [yt-users] selecting data by refinement level
>> Message-ID: <Pine.LNX.4.64.1706071443510.1322 at flash.uchicago.edu>
>> Content-Type: TEXT/PLAIN; charset=US-ASCII
>>
>> On Wed, 7 Jun 2017, Nathan Goldbaum wrote:
>> > On Wed, Jun 7, 2017 at 11:56 AM, Matthew Turk <matthewturk at gmail.com>
>> wrote:
>> >
>> > > Great!  As a quick note, you can use `.d` as shorthand for
>> > > `to_ndarray()`.
>>
>> > One tiny note:
>> >
>> > YTArray.d is short for YTArray.ndarray_view(), which returns a view onto
>> > the array. This doesn't copy the underlying data.
>>
>> Matt and Nathan,
>>
>> Thanks for the additional info, I think it's very useful to know.
>>
>> Klaus
>>
>>
>> ------------------------------
>>
>> Subject: Digest Footer
>>
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>> ------------------------------
>>
>> End of yt-users Digest, Vol 112, Issue 5
>> ****************************************
>>
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