/usr/lib64/python2.6/site-packages/matplotlib
Edit: /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyo (99824B)
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Numerical python functions written for compatability with matlab(TM)
commands with the same names.
Matlab(TM) compatible functions
-------------------------------
:func:`cohere`
Coherence (normalized cross spectral density)
:func:`csd`
Cross spectral density uing Welch's average periodogram
:func:`detrend`
Remove the mean or best fit line from an array
:func:`find`
Return the indices where some condition is true;
numpy.nonzero is similar but more general.
:func:`griddata`
interpolate irregularly distributed data to a
regular grid.
:func:`prctile`
find the percentiles of a sequence
:func:`prepca`
Principal Component Analysis
:func:`psd`
Power spectral density uing Welch's average periodogram
:func:`rk4`
A 4th order runge kutta integrator for 1D or ND systems
:func:`specgram`
Spectrogram (power spectral density over segments of time)
Miscellaneous functions
-------------------------
Functions that don't exist in matlab(TM), but are useful anyway:
:meth:`cohere_pairs`
Coherence over all pairs. This is not a matlab function, but we
compute coherence a lot in my lab, and we compute it for a lot of
pairs. This function is optimized to do this efficiently by
caching the direct FFTs.
:meth:`rk4`
A 4th order Runge-Kutta ODE integrator in case you ever find
yourself stranded without scipy (and the far superior
scipy.integrate tools)
:meth:`contiguous_regions`
return the indices of the regions spanned by some logical mask
:meth:`cross_from_below`
return the indices where a 1D array crosses a threshold from below
:meth:`cross_from_above`
return the indices where a 1D array crosses a threshold from above
record array helper functions
-------------------------------
A collection of helper methods for numpyrecord arrays
.. _htmlonly:
See :ref:`misc-examples-index`
:meth:`rec2txt`
pretty print a record array
:meth:`rec2csv`
store record array in CSV file
:meth:`csv2rec`
import record array from CSV file with type inspection
:meth:`rec_append_fields`
adds field(s)/array(s) to record array
:meth:`rec_drop_fields`
drop fields from record array
:meth:`rec_join`
join two record arrays on sequence of fields
:meth:`rec_groupby`
summarize data by groups (similar to SQL GROUP BY)
:meth:`rec_summarize`
helper code to filter rec array fields into new fields
For the rec viewer functions(e rec2csv), there are a bunch of Format
objects you can pass into the functions that will do things like color
negative values red, set percent formatting and scaling, etc.
Example usage::
r = csv2rec('somefile.csv', checkrows=0)
formatd = dict(
weight = FormatFloat(2),
change = FormatPercent(2),
cost = FormatThousands(2),
)
rec2excel(r, 'test.xls', formatd=formatd)
rec2csv(r, 'test.csv', formatd=formatd)
scroll = rec2gtk(r, formatd=formatd)
win = gtk.Window()
win.set_size_request(600,800)
win.add(scroll)
win.show_all()
gtk.main()
Deprecated functions
---------------------
The following are deprecated; please import directly from numpy (with
care--function signatures may differ):
:meth:`load`
load ASCII file - use numpy.loadtxt
:meth:`save`
save ASCII file - use numpy.savetxt
i( t divisionN( t verbosec C s. t i t i t i | t i | | S( N( t npt expt linspacet log( t xmint xmaxt N( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt logspace s c C s t i t i | | S( s return sqrt(x dot x)( R t sqrtt dot( t x( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt _norm s c C s t i t | | S( s+ return x times the hanning window of len(x)( R t hanningt len( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt window_hanning s c C s | S( s# No window function; simply return x( ( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt window_none s c C sA | d j p
| d j o t | S| d j o t | Sd S( Nt constantt linear( t Nonet detrend_meant detrend_linear( R t key( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt detrend s
i c C s t i | } | d j p | d j p | i d j o | | i | St d g | i } t i | | <| | i | | S( s0 Return x minus its mean along the specified axisi i N( R t asarrayR t ndimt meant slicet newaxis( R t axist ind( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt demean s *
c C s | | i S( s Return x minus the mean(x)( R ( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyR s c C s | S( s Return x: no detrending( ( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt detrend_none s c C sr t i t | d t i } t i | | d d } | d | d } | i | | i } | | | | S( s2 Return y minus best fit line; 'linear' detrending t dtypet biasi i ( i i ( i i ( R t arangeR t float_t covR ( t yR t Ct bt a( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyR s
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o
| } nB | | | | | | !} | | | } t i i | d
| } t i | | | | | d d | f coherence vector for
that pair. I.e., ``Cxy[(i,j) = cohere(X[:,i], X[:,j])``.
Number of dictionary keys is ``len(ij)``.
- *Phase*: dictionary of phases of the cross spectral density at
each frequency for each pair. Keys are (*i*, *j*).
- *freqs*: vector of frequencies, equal in length to either the
coherence or phase vectors for any (*i*, *j*) key.
Eg., to make a coherence Bode plot::
subplot(211)
plot( freqs, Cxy[(12,19)])
subplot(212)
plot( freqs, Phase[(12,19)])
For a large number of pairs, :func:`cohere_pairs` can be much more
efficient than just calling :func:`cohere` for each pair, because
it caches most of the intensive computations. If :math:`N` is the
number of pairs, this function is :math:`O(N)` for most of the
heavy lifting, whereas calling cohere for each pair is
:math:`O(N^2)`. However, because of the caching, it is also more
memory intensive, making 2 additional complex arrays with
approximately the same number of elements as *X*.
See :file:`test/cohere_pairs_test.py` in the src tree for an
example script that shows that this :func:`cohere_pairs` and
:func:`cohere` give the same results for a given pair.
.. seealso::
:func:`psd`
For information about the methods used to compute
:math:`P_{xy}`, :math:`P_{xx}` and :math:`P_{yy}`.
Ni i i s
Cacheing FFTsR" i
s Computing coherences( RY R R6 R" t sett addR R1 R3 R4 R5 R8 t linalgt normR7 R9 R: R\ R R; t arctan2t imagRV R$ ($ t Xt ijR? R@ R RA RB t preferSpeedOverMemoryt progressCallbackt returnPxxt numRowst numColst tmpt
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-
*&c C s t i | | \ } } | i t i } t i | t i | d } t i | t | } | d | d } d t i | t | t | } | S( s
Return the entropy of the data in *y*.
.. math::
\sum p_i \log_2(p_i)
where :math:`p_i` is the probability of observing *y* in the
:math:`i^{th}` bin of *bins*. *bins* can be a number of bins or a
range of bins; see :func:`numpy.histogram`.
Compare *S* with analytic calculation for a Gaussian::
x = mu + sigma * randn(200000)
Sanalytic = 0.5 * ( 1.0 + log(2*pi*sigma**2.0) )
i i g (
R t histogramt astypeR% t taket nonzeroR\ R R< R ( R' t binsR. t pt deltat S( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt entropy s 'c G sI | \ } } d t i d t i | t i d d | | | d S( sC Return the normal pdf evaluated at *x*; args provides *mu*, *sigma*g ?i g ( R R
t piR ( R Rb t mut sigma( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt normpdf s c
C s t | } | d d j o t d d n | d | d } d | | t i | d | d t i } t i t i | d | t t i d | d t g } | d | d } t | t i d t | | } t i
i
t i | | | i t i } t i | | S( s@ Returm the levy pdf evaluated at *x* for params *gamma*, *alpha*i i s% x must be an event length array; try
s/ x = np.linspace(minx, maxx, N), where N is eveni (
R R2 R R$ R% R> t intR R] R R9 R R (
R t gammat alphaR t dxR^ R t dft cflt px( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt levypdf s - &.c C s t i t i | \ } | S( s1 Return the indices where ravel(condition) is true( R R t ravel( t conditiont res( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt find s c C sD t i | } t | d j o t i g S| d j i d } t | d j o t i t | St | t | j o t i g St i t | d f | i } | | d d +t i | } | d j i d } | d j i d } | | t | | j i d d } t i | | | | } | S( s
Return the indices of the longest stretch of contiguous ones in *x*,
assuming *x* is a vector of zeros and ones. If there are two
equally long stretches, pick the first.
i i i i(
R R R t arrayR R$ R6 R" t difft max( R R R' t dift upt dnRL ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt longest_contiguous_ones s "
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t | S( s! alias for longest_contiguous_ones( R ( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt longest_ones s c C s t i i | \ } } } | d | i d } | i } | | } t | | j i } | d d | f i } t i | | }
|
| | | f S( s
Compute the principal components of *P*. *P* is a (*numVars*,
*numObs*) array. *frac* is the minimum fraction of variance that a
component must contain to be included.
Return value is a tuple of the form (*Pcomponents*, *Trans*,
*fracVar*) where:
- *Pcomponents* : a (numVars, numObs) array
- *Trans* : the weights matrix, ie, *Pcomponents* = *Trans* *
*P*
- *fracVar* : the fraction of the variance accounted for by each
component returned
A similar function of the same name was in the Matlab (TM)
R13 Neural Network Toolbox but is not found in later versions;
its successor seems to be called "processpcs".
i i N( R Rf t svdRY R< R t transposeR ( t Pt fract Ut st vt varEacht totVart fracVarR t Transt Pcomponents( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt prepca s
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| | j | d | } | i | S( sS
Return the percentiles of *x*. *p* can either be a sequence of
percentile values or a scalar. If *p* is a sequence, the ith
element of the return sequence is the *p*(i)-th percentile of *x*.
If *p* is a scalar, the largest value of *x* less than or equal to
the *p* percentage point in the sequence is returned.
g Y@i ( R R R t sortR R3 R4 R R R t whereR ( R R t NxR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt prctile s
c C s t i | p! t i d | d d | } n t i | } | i d j p& | i d j p | i d j o t d n t | | } t i | | S( s
Return the rank for each element in *x*, return the rank
0..len(*p*). Eg if *p* = (25, 50, 75), the return value will be a
len(*x*) array with values in [0,1,2,3] where 0 indicates the
value is less than the 25th percentile, 1 indicates the value is
>= the 25th and < 50th percentile, ... and 3 indicates the value
is above the 75th percentile cutoff.
*p* is either an array of percentiles in [0..100] or a scalar which
indicates how many quantiles of data you want ranked.
g Y@i i id s/ percentiles should be in range 0..100, not 0..1(
R3 R4 R R$ R R t minR2 R t searchsorted( R R t ptiles( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt prctile_rank/ s
!9c C s t i | t i } | o* | | i d d | i d d } nS | | i d d d d t i f } | | i d d d d t i f } | S( s
Return the matrix *M* with each row having zero mean and unit std.
If *dim* = 1 operate on columns instead of rows. (*dim* is
opposite to the numpy axis kwarg.)
R i i N( R R R% R t stdR ( t Mt dim( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt
center_matrixG s *))c
C s y t | } Wn1 t j
o% t i t | f t i } n# Xt i t | | f t i } | | d `_
at mathworld.
i i ( R R R
R ( Rj t Yt sigmaxt sigmayt muxt muyt sigmaxyt Xmut Ymut rhot zt denom( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt bivariate_normal s
:*c C s1 t i | i \ } } | | | | | | f S( s.
*Z* and *Cond* are *M* x *N* matrices. *Z* are data and *Cond* is
a boolean matrix where some condition is satisfied. Return value
is (*x*, *y*, *z*) where *x* and *y* are the indices into *Z* and
*z* are the values of *Z* at those indices. *x*, *y*, and *z* are
1D arrays.
( R t indicesRY ( t Zt CondRj R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt
get_xyz_where s g?c C s t i | | f d } xm t t | | | D]Q } t i i d | d } t i i d | d } t i i | | | f `_.
.. note::
What the function here calculates may not be what you really want;
*caveat emptor*.
It also seems that this function's name is badly misspelled.
s6 This does not belong in matplotlib and will be removed( t warningst warnt DeprecationWarningR R R R] ( R t fprime( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt liaupunov s t
FIFOBufferc B sD e Z d Z d Z d Z d Z d Z d Z d Z RS( s
A FIFO queue to hold incoming *x*, *y* data in a rotating buffer
using numpy arrays under the hood. It is assumed that you will
call asarrays much less frequently than you add data to the queue
-- otherwise another data structure will be faster.
This can be used to support plots where data is added from a real
time feed and the plot object wants to grab data from the buffer
and plot it to screen less freqeuently than the incoming.
If you set the *dataLim* attr to
:class:`~matplotlib.transforms.BBox` (eg
:attr:`matplotlib.Axes.dataLim`), the *dataLim* will be updated as
new data come in.
TODO: add a grow method that will extend nmax
.. note::
mlab seems like the wrong place for this class.
c C s t i | f t i | _ t i | f t i | _ t i | f t i | _ t i | f t i | _ d | _ | | _ d | _
h | _ d S( s-
Buffer up to *nmax* points.
i N( R R6 R% t _xat _yat _xst _yst _indt _nmaxR t dataLimt callbackd( t selft nmax( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt __init__F s c C s | i i | g i | d S( sk
Call *func* every time *N* events are passed; *func* signature
is ``func(fifo)``.
N( R t
setdefaultt append( R t funcR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt registerS s c C s | i d j o/ t i | | f g } | i i | d n | i | i } | | i | <| | i | g } t |
|
D] \ } } | | | | q{~ } t | } | i | qx Wt
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Load ASCII data from *fname* into an array and return the array.
Deprecated: use numpy.loadtxt.
The data must be regular, same number of values in every row
*fname* can be a filename or a file handle. Support for gzipped
files is automatic, if the filename ends in '.gz'.
matfile data is not supported; for that, use :mod:`scipy.io.mio`
module.
Example usage::
X = load('test.dat') # data in two columns
t = X[:,0]
y = X[:,1]
Alternatively, you can do the same with "unpack"; see below::
X = load('test.dat') # a matrix of data
x = load('test.dat') # a single column of data
- *comments*: the character used to indicate the start of a comment
in the file
- *delimiter* is a string-like character used to seperate values
in the file. If *delimiter* is unspecified or *None*, any
whitespace string is a separator.
- *converters*, if not *None*, is a dictionary mapping column number to
a function that will convert that column to a float (or the optional
*dtype* if specified). Eg, if column 0 is a date string::
converters = {0:datestr2num}
- *skiprows* is the number of rows from the top to skip.
- *usecols*, if not *None*, is a sequence of integer column indexes to
extract where 0 is the first column, eg ``usecols=[1,4,5]`` to extract
just the 2nd, 5th and 6th columns
- *unpack*, if *True*, will transpose the matrix allowing you to unpack
into named arguments on the left hand side::
t,y = load('test.dat', unpack=True) # for two column data
x,y,z = load('somefile.dat', usecols=[3,5,7], unpack=True)
- *dtype*: the array will have this dtype. default: ``numpy.float_``
.. seealso::
See :file:`examples/pylab_examples/load_converter.py` in the source tree
Exercises many of these options.
s use numpy.loadtxtR c S s
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to_filehandlet enumerateR4 t stripR t getR= R R R RY R R ( R* t commentsR, t
converterst skiprowst usecolst unpackR" R- Rj R5 t converterseqRL t lineR0 Rs R1 t valst _[2]R/ t _[3]t thisLent rt c( ( R, s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt load sB ;
<
36 g gc C sA t | t i j o t t i | t t St i | Sd S( s
Compute exponentials which safely underflow to zero.
Slow, but convenient to use. Note that numpy provides proper
floating point exception handling with access to the underlying
hardware.
N( t typeR t ndarrayR t clipt exp_safe_MINt exp_safe_MAXt math( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt exp_safee s c G s t i t | | S( s
amap(function, sequence[, sequence, ...]) -> array.
Works like :func:`map`, but it returns an array. This is just a
convenient shorthand for ``numpy.array(map(...))``.
( R R t map( t fnRb ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt amaps s c C s# t i t i t i | d S( sP
Return the root mean square of all the elements of *a*, flattened out.
i ( R R
R R] ( R* ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt rms_flat} s c C s t i t i | S( s
Return the *l1* norm of *a*, flattened out.
Implemented as a separate function (not a call to :func:`norm` for speed).
( R R< R] ( R* ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt l1norm s c C s# t i t i t i | d S( s
Return the *l2* norm of *a*, flattened out.
Implemented as a separate function (not a call to :func:`norm` for speed).
i ( R R
R< R] ( R* ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt l2norm s c C sJ | d j o t i t i | St i t i | | d | Sd S( s
norm(a,p=2) -> l-p norm of a.flat
Return the l-p norm of *a*, considered as a flat array. This is NOT a true
matrix norm, since arrays of arbitrary rank are always flattened.
*p* can be a number or the string 'Infinity' to get the L-infinity norm.
t Infinityg ?N( R t amaxR] R< ( R* R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt norm_flat s
c K s | i d d | d d j } | d j o | d } d } n | d j o
d } n y& | d } | | t | | } Wn1 t j
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frange([start,] stop[, step, keywords]) -> array of floats
Return a numpy ndarray containing a progression of floats. Similar to
:func:`numpy.arange`, but defaults to a closed interval.
``frange(x0, x1)`` returns ``[x0, x0+1, x0+2, ..., x1]``; *start*
defaults to 0, and the endpoint *is included*. This behavior is
different from that of :func:`range` and
:func:`numpy.arange`. This is deliberate, since :func:`frange`
will probably be more useful for generating lists of points for
function evaluation, and endpoints are often desired in this
use. The usual behavior of :func:`range` can be obtained by
setting the keyword *closed* = 0, in this case, :func:`frange`
basically becomes :func:numpy.arange`.
When *step* is given, it specifies the increment (or
decrement). All arguments can be floating point numbers.
``frange(x0,x1,d)`` returns ``[x0,x0+d,x0+2d,...,xfin]`` where
*xfin* <= *x1*.
:func:`frange` can also be called with the keyword *npts*. This
sets the number of points the list should contain (and overrides
the value *step* might have been given). :func:`numpy.arange`
doesn't offer this option.
Examples::
>>> frange(3)
array([ 0., 1., 2., 3.])
>>> frange(3,closed=0)
array([ 0., 1., 2.])
>>> frange(1,6,2)
array([1, 3, 5]) or 1,3,5,7, depending on floating point vagueries
>>> frange(1,6.5,npts=5)
array([ 1. , 2.375, 3.75 , 5.125, 6.5 ])
t closedi i g g ?t nptsN( R
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| g } n t | t | j o4 t | d j o | t | } q/t d n t i | i i
t | | } t i | i
d | } x" | i i D] } | | | | Wt i i | d | S( sL
*r* is a numpy record array
*summaryfuncs* is a list of (*attr*, *func*, *outname*) tuples
which will apply *func* to the the array *r*[attr] and assign the
output to a new attribute name *outname*. The returned record
array is identical to *r*, with extra arrays for each element in
*summaryfuncs*.
R ( R R" R R R R R R ( RE t summaryfuncsR R0 R R R R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt
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t } n xo t | i i D][ \ } } | i | } | i | } | d j o t i | i t } n | | | 7 s ( RH R R t str_t string0R R R t itemsizeR t int16t int32t int64t int8t int_RO R= t float32t float64R' R= R% ( t colnamet columnR' t ntypet length( Rl ( R+ s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt get_justify s @`0`9i i c s | \ } } } } | d j o | d t | i | S | t j o | t | } n( | t j o | t | } n | i | Sd S( Ni R ( R t ljustR= R t rjust( R t just_pad_prec_spacert justt padt prect spacer( R< ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyR4 O s
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i g } t | | | | D]% \ } } } } | | | | | q~ q9W| o | i n d S( s3
Save the data from numpy recarray *r* into a
comma-/space-/tab-delimited file. The record array dtype names
will be used for column headers.
*fname*: can be a filename or a file handle. Support for gzipped
files is automatic, if the filename ends in '.gz'
*withheader*: if withheader is False, do not write the attribute
names in the first row
.. seealso::
:func:`csv2rec`
For information about *missing* and *missingd*, which can
be used to fill in masked values into your CSV file.
c s f d } | S( Nc s | o | S | Sd S( N( ( R1 R t mval( R ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyR s ( ( R R ( ( R s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt with_mask~ s R t
return_openedR, i t
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withheaderRe R RL R R- t openedRh RZ t mvalst ismaskedR/ t rowmaskR0 R R1 R Rd ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt rec2csvg s<
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d n t | d o@ | i | i t j } | i | i t j } | i } n | ot| d
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i | } | | | } nO| d j o5| d d d f | d d d f } | d d d f | d d d f } t i! | i" i# } t i! | i" i# } | i$ | i | j p | i$ | i | j o t d n |
i% | } | | i | i$ t& d | i d | i | i$ t& d | i d f } n
t d t i' t i( | o" t i) i* t i( | | } n | S( s
``zi = griddata(x,y,z,xi,yi)`` fits a surface of the form *z* =
*f*(*x*, *y*) to the data in the (usually) nonuniformly spaced
vectors (*x*, *y*, *z*). :func:`griddata` interpolates this
surface at the points specified by (*xi*, *yi*) to produce
*zi*. *xi* and *yi* must describe a regular grid, can be either 1D
or 2D, but must be monotonically increasing.
A masked array is returned if any grid points are outside convex
hull defined by input data (no extrapolation is done).
If interp keyword is set to '`nn`' (default),
uses natural neighbor interpolation based on Delaunay
triangulation. By default, this algorithm is provided by the
:mod:`matplotlib.delaunay` package, written by Robert Kern. The
triangulation algorithm in this package is known to fail on some
nearly pathological cases. For this reason, a separate toolkit
(:mod:`mpl_tookits.natgrid`) has been created that provides a more
robust algorithm fof triangulation and interpolation. This
toolkit is based on the NCAR natgrid library, which contains code
that is not redistributable under a BSD-compatible license. When
installed, this function will use the :mod:`mpl_toolkits.natgrid`
algorithm, otherwise it will use the built-in
:mod:`matplotlib.delaunay` package.
If the interp keyword is set to '`linear`', then linear interpolation
is used instead of natural neighbor. In this case, the output grid
is assumed to be regular with a constant grid spacing in both the x and
y directions. For regular grids with nonconstant grid spacing, you
must use natural neighbor interpolation. Linear interpolation is only valid if
:mod:`matplotlib.delaunay` package is used - :mod:`mpl_tookits.natgrid`
only provides natural neighbor interpolation.
The natgrid matplotlib toolkit can be downloaded from
http://sourceforge.net/project/showfiles.php?group_id=80706&package_id=142792
i( t _natgridt __version__N( Rr s using natgrid version %ss using delaunay version %ss= inputs xi and yi must have same number of dimensions (1 or 2)i i s" inputs xi and yi must be 1D or 2D.s5 inputs x,y,z must all be 1D arrays of the same lengthR Rp sR only natural neighor interpolation allowed when using natgrid toolkit in griddata.i t extt nuls8 output grid defined by xi,yi must be monotone increasingR sA output grid must have constant spacing when using interp='linear's interp keyword must be one of 'linear' (for linear interpolation) or 'nn' (for natural neighbor interpolation). Default is 'nn'.(+ t mpl_toolkits.natgridRq Rr R0 R t matplotlib.delaunayt delaunayR t griddatat _reportedR t reportR R R R' t compressR t
compressedR2 t setit setrR R
R R= R R RY t natgriddt meshgridt
Triangulationt nn_interpolatort finfoR" t
resolutionR t linear_interpolatort complexR R t mat masked_where( R R' R t xit yit interpRq Rr t _use_natgridRw t xot yot zot triR t dyt epsxt epsy( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyRx s~ %
0
D
)
**:+3 "c
C s t i | o
| g } n t i | } t i | } t i | } t | i } t | | d x[-1]``, the routine
tries an extrapolation. The relevance of the data obtained from
this, of course, is questionable...
Original implementation by Halldor Bjornsson, Icelandic
Meteorolocial Office, March 2006 halldor at vedur.is
Completely reworked and optimized for Python by Norbert Nemec,
Institute of Theoretical Physics, University or Regensburg, April
2006 Norbert.Nemec at physik.uni-regensburg.de
i ii g N( R R R% R R R R6 RY R R t chooseR t signRC ( R R R' R R R R R R Re t sidxt xidxt yidxt xidxp1R t dy1t dy2t dy1dy2( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt stineman_interpo
s2 +
+ c C s" t i t i | | \ } | S( s
*points* is a sequence of *x*, *y* points.
*verts* is a sequence of *x*, *y* vertices of a polygon.
Return value is a sequence of indices into points for the points
that are inside the polygon.
( R R t nxutilst points_inside_poly( t pointst vertsR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt inside_poly
s c C s t i | p t i | o
t } n t } | i | } | i | } t | } t | } | | i d | } | i d | } | | | *| | | *| d d d | | )| | f S( s,
Given a sequence of *xs* and *ys*, return the vertices of a
polygon that has a horizontal base at *xmin* and an upper bound at
the *ys*. *xmin* is a scalar.
Intended for use with :meth:`matplotlib.axes.Axes.fill`, eg::
xv, yv = poly_below(0, x, y)
ax.fill(xv, yv)
i Ni( R t
isMaskedArrayR R R R5 ( R t xst yst nxR R R R' ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt
poly_below
s
c C s t i | p t i | p t i | o
t } n t } t | } t i | p | | i | } n t i | p | | i | } n | i | | d d d f } | i | | d d d f } | | f S( sC
Given a sequence of *x*, *ylower* and *yupper*, return the polygon
that fills the regions between them. *ylower* or *yupper* can be
scalar or iterable. If they are iterable, they must be equal in
length to *x*.
Return value is *x*, *y* arrays for use with
:meth:`matplotlib.axes.Axes.fill`.
Ni( R R R R R3 R4 R5 R> ( R t ylowert yupperR R R' ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt poly_between
s
0
""c C s t i | d | d j S( s
Tests whether first and last object in a sequence are the same. These are
presumably coordinates on a polygonal curve, in which case this function
tests if that curve is closed.
i i( R t all( Rj ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt is_closed_polygon s c C s d } g } xj t | D]\ \ } } | d j o | o
| } q | d j o% | o | i | | f d } q q W| d j o | i | | d f n | S( s
return a list of (ind0, ind1) such that mask[ind0:ind1].all() is
True and we cover all such regions
TODO: this is a pure python implementation which probably has a much faster numpy impl
i N( R R7 R ( R t in_regiont
boundariesRL R1 ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt contiguous_regions s
c C s^ t i | } | } t i | d | j | d | j @ d } t | o | d S| Sd S( s
return the indices into *x* where *x* crosses some threshold from
below, eg the i's where::
x[i-1]=threshold
Example code::
import matplotlib.pyplot as plt
t = np.arange(0.0, 2.0, 0.1)
s = np.sin(2*np.pi*t)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(t, s, '-o')
ax.axhline(0.5)
ax.axhline(-0.5)
ind = cross_from_below(s, 0.5)
ax.vlines(t[ind], -1, 1)
ind = cross_from_above(s, -0.5)
ax.vlines(t[ind], -1, 1)
plt.show()
.. seealso::
:func:`cross_from_above` and :func:`contiguous_regions`
ii i N( R R R R ( R t thresholdR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt cross_from_below/ s !+
c C sX t i | } t i | d | j | d | j @ d } t | o | d S| Sd S( s
return the indices into *x* where *x* crosses some threshold from
below, eg the i's where::
x[i-1]>threshold and x[i]<=threshold
.. seealso::
:func:`cross_from_below` and :func:`contiguous_regions`
ii i N( R R R R ( R R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt cross_from_aboveV s
+
c C s. t i | } t i | | d | d | S( s
Finds the length of a set of vectors in *n* dimensions. This is
like the :func:`numpy.norm` function for vectors, but has the ability to
work over a particular axis of the supplied array or matrix.
Computes ``(sum((x_i)^P))^(1/P)`` for each ``{x_i}`` being the
elements of *X* along the given axis. If *axis* is *None*,
compute over all elements of *X*.
R g ?( R R R< ( Rj R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt vector_lengthsj s
c C s% t i | d d } t | d d S( s
Computes the distance between a set of successive points in *N* dimensions.
Where *X* is an *M* x *N* array or matrix. The distances between
successive rows is computed. Distance is the standard Euclidean
distance.
R i i ( R R R ( Rj ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt distances_along_curvew s c C s1 t | } t i t i d t i | f S( s
Computes the distance travelled along a polygonal curve in *N* dimensions.
Where *X* is an *M* x *N* array or matrix. Returns an array of
length *M* consisting of the distance along the curve at each point
(i.e., the rows of *X*).
i ( R R R> R6 t cumsum( Rj ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt path_length s c
C sf | d | | | d | | } } | d | | | d | | } } | | | | | | | | f S( s
Converts a quadratic Bezier curve to a cubic approximation.
The inputs are the *x* and *y* coordinates of the three control
points of a quadratic curve, and the output is a tuple of *x* and
*y* coordinates of the four control points of the cubic curve.
g @g @g ?gUUUUUU?gUUUUUU?gUUUUUU?gUUUUUU?( (
t q0xt q0yt q1xt q1yt q2xt q2yt c1xt c1yt c2xt c2y( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt
quad2cubic s %%( R t
__future__R R R R6 RX t numpyR R t
matplotlibR t matplotlib.nxutilsR t matplotlib.cbookR3 R R
R R R R R R R! R RS R t kwdocdRX RU RZ R[ Ra Rc R0 R R R R R R R R R R R R R R R R R R R R R R R R2 R% RG Rs RM R RK RL RN RQ RR RS RT RW R` Rf Ri R| R R R R R R R R R R R R R R$ R% R& R) R* R, R- R. R/ R1 t bool_RB RC RD RG RH t object_R R2 R5 R9 Rc Ro Rx Ry R R R R R R R R R R R R R R ( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/mlab.pyt s 0 X :
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