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Numerical python functions written for compatability with MATLAB
commands with the same names.
MATLAB 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, 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:`recs_join`
a simple join of multiple recarrays using a single column as a key
: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
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Keyword arguments:
*NFFT*: integer
The number of data points used in each block for the FFT.
Must be even; a power 2 is most efficient. The default value is 256.
*Fs*: scalar
The sampling frequency (samples per time unit). It is used
to calculate the Fourier frequencies, freqs, in cycles per time
unit. The default value is 2.
*detrend*: callable
The function applied to each segment before fft-ing,
designed to remove the mean or linear trend. Unlike in
MATLAB, where the *detrend* parameter is a vector, in
matplotlib is it a function. The :mod:`~matplotlib.pylab`
module defines :func:`~matplotlib.pylab.detrend_none`,
:func:`~matplotlib.pylab.detrend_mean`, and
:func:`~matplotlib.pylab.detrend_linear`, but you can use
a custom function as well.
*window*: callable or ndarray
A function or a vector of length *NFFT*. To create window
vectors see :func:`window_hanning`, :func:`window_none`,
:func:`numpy.blackman`, :func:`numpy.hamming`,
:func:`numpy.bartlett`, :func:`scipy.signal`,
:func:`scipy.signal.get_window`, etc. The default is
:func:`window_hanning`. If a function is passed as the
argument, it must take a data segment as an argument and
return the windowed version of the segment.
*noverlap*: integer
The number of points of overlap between blocks. The default value
is 0 (no overlap).
*pad_to*: integer
The number of points to which the data segment is padded when
performing the FFT. This can be different from *NFFT*, which
specifies the number of data points used. While not increasing
the actual resolution of the psd (the minimum distance between
resolvable peaks), this can give more points in the plot,
allowing for more detail. This corresponds to the *n* parameter
in the call to fft(). The default is None, which sets *pad_to*
equal to *NFFT*
*sides*: [ 'default' | 'onesided' | 'twosided' ]
Specifies which sides of the PSD to return. Default gives the
default behavior, which returns one-sided for real data and both
for complex data. 'onesided' forces the return of a one-sided PSD,
while 'twosided' forces two-sided.
*scale_by_freq*: boolean
Specifies whether the resulting density values should be scaled
by the scaling frequency, which gives density in units of Hz^-1.
This allows for integration over the returned frequency values.
The default is True for MATLAB compatibility.
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The power spectral density by Welch's average periodogram method.
The vector *x* is divided into *NFFT* length blocks. Each block
is detrended by the function *detrend* and windowed by the function
*window*. *noverlap* gives the length of the overlap between blocks.
The absolute(fft(block))**2 of each segment are averaged to compute
*Pxx*, with a scaling to correct for power loss due to windowing.
If len(*x*) < *NFFT*, it will be zero padded to *NFFT*.
*x*
Array or sequence containing the data
%(PSD)s
Returns the tuple (*Pxx*, *freqs*).
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
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The cross power spectral density by Welch's average periodogram
method. The vectors *x* and *y* are divided into *NFFT* length
blocks. Each block is detrended by the function *detrend* and
windowed by the function *window*. *noverlap* gives the length
of the overlap between blocks. The product of the direct FFTs
of *x* and *y* are averaged over each segment to compute *Pxy*,
with a scaling to correct for power loss due to windowing.
If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero
padded to *NFFT*.
*x*, *y*
Array or sequence containing the data
%(PSD)s
Returns the tuple (*Pxy*, *freqs*).
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
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Compute a spectrogram of data in *x*. Data are split into *NFFT*
length segements and the PSD of each section is computed. The
windowing function *window* is applied to each segment, and the
amount of overlap of each segment is specified with *noverlap*.
If *x* is real (i.e. non-complex) only the spectrum of the positive
frequencie is returned. If *x* is complex then the complete
spectrum is returned.
%(PSD)s
Returns a tuple (*Pxx*, *freqs*, *t*):
- *Pxx*: 2-D array, columns are the periodograms of
successive segments
- *freqs*: 1-D array of frequencies corresponding to the rows
in Pxx
- *t*: 1-D array of times corresponding to midpoints of
segments.
.. seealso::
:func:`psd`
:func:`psd` differs in the default overlap; in returning
the mean of the segment periodograms; and in not returning
times.
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length segments. Your signal is too short for your choice of *NFFT*.
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The coherence between *x* and *y*. Coherence is the normalized
cross spectral density:
.. math::
C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}}
*x*, *y*
Array or sequence containing the data
%(PSD)s
The return value is the tuple (*Cxy*, *f*), where *f* are the
frequencies of the coherence vector. For cohere, scaling the
individual densities by the sampling frequency has no effect,
since the factors cancel out.
.. seealso::
:func:`psd` and :func:`csd`
For information about the methods used to compute
:math:`P_{xy}`, :math:`P_{xx}` and :math:`P_{yy}`.
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Call signature::
Cxy, Phase, freqs = cohere_pairs( X, ij, ...)
Compute the coherence and phase for all pairs *ij*, in *X*.
*X* is a *numSamples* * *numCols* array
*ij* is a list of tuples. Each tuple is a pair of indexes into
the columns of X for which you want to compute coherence. For
example, if *X* has 64 columns, and you want to compute all
nonredundant pairs, define *ij* as::
ij = []
for i in range(64):
for j in range(i+1,64):
ij.append( (i,j) )
*preferSpeedOverMemory* is an optional bool. Defaults to true. If
False, limits the caching by only making one, rather than two,
complex cache arrays. This is useful if memory becomes critical.
Even when *preferSpeedOverMemory* is False, :func:`cohere_pairs`
will still give significant performace gains over calling
:func:`cohere` for each pair, and will use subtantially less
memory than if *preferSpeedOverMemory* is True. In my tests with
a 43000,64 array over all nonredundant pairs,
*preferSpeedOverMemory* = True delivered a 33% performance boost
on a 1.7GHZ Athlon with 512MB RAM compared with
*preferSpeedOverMemory* = False. But both solutions were more
than 10x faster than naively crunching all possible pairs through
:func:`cohere`.
Returns::
(Cxy, Phase, freqs)
where:
- *Cxy*: dictionary of (*i*, *j*) tuples -> 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}`.
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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
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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) )
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WARNING: this function is deprecated -- please see class PCA instead
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
R13 Neural Network Toolbox but is not found in later versions;
its successor seems to be called "processpcs".
s4 This function is deprecated -- see class PCA insteadi i N(
t warningst warnR Rg t svdRZ R= R t transposeR ( t Pt fract Ut st vt varEacht totVart fracVarR t Transt Pcomponents( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt prepca s
t PCAc B s5 e Z d � Z d d � Z d � Z e d � � Z RS( c C s� | j \ } } | | k r* t d � � n | | | _ | _ | j d d � | _ | j d d � | _ | j | � } | | _ t
j j | d t
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j | | j � j } | d t t | � � } | | j � | _ | | _ | | _ d S( s
compute the SVD of a and store data for PCA. Use project to
project the data onto a reduced set of dimensions
Inputs:
*a*: a numobservations x numdims array
Attrs:
*a* a centered unit sigma version of input a
*numrows*, *numcols*: the dimensions of a
*mu* : a numdims array of means of a
*sigma* : a numdims array of atandard deviation of a
*fracs* : the proportion of variance of each of the principal components
*Wt* : the weight vector for projecting a numdims point or array into PCA space
*Y* : a projected into PCA space
The factor loadings are in the Wt factor, ie the factor
loadings for the 1st principal component are given by Wt[0]
s5 we assume data in a is organized with numrows>numcolsR i t
full_matricesi N( RZ t RuntimeErrort numrowst numcolsR R� t stdR� t centerR+ R Rg R� t FalseR t TR> R R= t fracst Wtt Y( t selfR+ R/ t mR� R� t VhR� t vars( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt __init__& s ! g c C s� t j | � } t | j � } | j d | j k rJ t d | j � � n t j | j | j | � j � j } | j
| k } | d k r� | d d � | f } n
| | } | S( sW project x onto the principle axes, dropping any axes where fraction of variance<minfraci����s# Expected an array with dims[-1]==%di N( R R R RZ R� R3 R R� R� R� R� ( R� R
t minfract ndimsR� t maskt Yreduced( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt project] s $
c C s | | j | j S( s< center the data using the mean and sigma from training set a( R� R� ( R� R
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d d d
d d d d d d d g � } t j d d d d d d d d d d d d d! d" d# d$ d% d& d' d( g � } | d) | } d* | d+ | } t j | | | | g � j } | S(, Ng uy���?g��9��?gK�'z�j�?g�ypaz�?g��{b�?g��L��?g�Ҙ9�z�?g�H;Ey��?g�8��?g� �l|�?g.Ta�w�?g/`v
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_get_colinearw s ( t __name__t
__module__R� R� R� t staticmethodR� ( ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR� % s 7 g g 9@g I@g �R@g Y@c C s d � } t } t j | � r' t } n t j | � } t j | � j � } | j � | d | j d d } | j t j
� } | d } | d } | t | � k }
| r� |
r| d 8} | d 8} | d 7} qn0 | |
c d 8<| |
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c d 7<| | | | | | � 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.
c S s | | | | S( ss Returns the point at the given fraction between a and b, where
'fraction' must be between 0 and 1.
( ( R+ R* t fraction( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt _interpolate� s g Y@i i ( R1 R4 R5 R� R R� R� t sortRZ R� R� R ( R
R� R� t scalart pert valuest idxst ait biR� t cond( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt prctile� s*
c C s� t j | � s/ t j d | d d | � } n t j | � } | j � d k st | j � d k st | j � d k r� t d � � n t | | � } t j | | � 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(
R4 R5 R R% R R� t minR3 R� t searchsorted( R
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6c C s� t j | t j � } | rD | | j d d � | j d d � } nR | | j d d � d d � t j f } | | j d d � d d � t j 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 R� R ( t Mt dim( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt
center_matrix� s )))c
C s} y t | � } Wn/ t k
rA t j t | � f t j � } n" Xt j t | � | f t j � } | | d <d } xt j t | � d � D]� } | | } | | d | } | d } | | } t j | | | � � } t j | | | | | | � � }
t j | | | |
| | � � } t j | | | | | | � � } | | d | d |
d | | | | d <q� W| S( s�
Integrate 1D or ND system of ODEs using 4-th order Runge-Kutta.
This is a toy implementation which may be useful if you find
yourself stranded on a system w/o scipy. Otherwise use
:func:`scipy.integrate`.
*y0*
initial state vector
*t*
sample times
*derivs*
returns the derivative of the system and has the
signature ``dy = derivs(yi, ti)``
Example 1 ::
## 2D system
def derivs6(x,t):
d1 = x[0] + 2*x[1]
d2 = -3*x[0] + 4*x[1]
return (d1, d2)
dt = 0.0005
t = arange(0.0, 2.0, dt)
y0 = (1,2)
yout = rk4(derivs6, y0, t)
Example 2::
## 1D system
alpha = 2
def derivs(x,t):
return -alpha*x + exp(-t)
y0 = 1
yout = rk4(derivs, y0, t)
If you have access to scipy, you should probably be using the
scipy.integrate tools rather than this function.
i i g @g @i ( R t TypeErrorR R7 R& R% R (
t derivst y0RR t Nyt youtRM t thistt dtt dt2t k1t k2t k3t k4( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt rk4� s$ .
"!
$$$2g �?c C s� | | } | | } | | | } | d | d | d | d d | | | | | }
d t j | | t j d | d � } t j |
d d | d � | S( s�
Bivariate Gaussian distribution for equal shape *X*, *Y*.
See `bivariate normal
<http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_
at mathworld.
i i ( R R� R R ( Rk R� t sigmaxt sigmayt muxt muyt sigmaxyt Xmut Ymut rhot zt denom( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt bivariate_normal$ s
:*c C s1 t j | j � \ } } | | | | | | 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 indicesRZ ( t Zt CondRk R� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt
get_xyz_where5 s g�������?c C s� t j | | f � d } xm t t | | | � � D]Q } t j j d | d � } t j j d | d � } t j j � | | | f <q4 W| S( sT
Return a *M* x *N* sparse matrix with *frac* elements randomly
filled.
g i i ( R R7 R9 R� t randomt randintt rand( R� R R� t dataRM R
R( ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt get_sparse_matrix@ s ! c C s# | | } t j t j | | � � S( s1
Return the distance between two points.
( R R R ( R
R( t d( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt distL s
c C s� t j | t j � } t j | t j � } t j | t j � } | | } | | } t j | | � } | d k r~ t | | � St j | | � } | | k r� t | | � S| | } | | | } t | | � S( s�
Get the distance of a point to a segment.
*p*, *s0*, *s1* are *xy* sequences
This algorithm from
http://softsurfer.com/Archive/algorithm_0102/algorithm_0102.htm#Distance%20to%20Ray%20or%20Segment
i ( R R R& R R ( R� t s0t s1R� t wR� R� R* t pb( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt dist_point_to_segmentS s
c C s� | \ \ } } \ } } | \ \ } } \ } } | | | | | | | | }
| | | | | | | | } | | | | | | | | } |
d k r� t S| |
}
| |
} d |
k o� d k n o� d | k o� d k SS( s�
Return *True* if *s1* and *s2* intersect.
*s1* and *s2* are defined as::
s1: (x1, y1), (x2, y2)
s2: (x3, y3), (x4, y4)
i g g �?( R� ( R t s2t x1t y1t x2t y2t x3t y3t x4t y4t dent n1t n2t u1t u2( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt segments_intersectn s """
c C s� t j | � r" | | | � } n | | | � � } t j j | � } d t j d } | t j j t | � � } | t j | � } t j j | � j
S( s;
Compute an FFT phase randomized surrogate of *x*.
g @y �?( R4 R5 R R: R� R� R R R t ifftRW ( R
R RB R� R+ t phase( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt fftsurr� s c C s5 t j d t � t j t j t j | | � � � � S( s�
*x* is a very long trajectory from a map, and *fprime* returns the
derivative of *x*.
This function will be removed from matplotlib.
Returns :
.. math::
\lambda = \frac{1}{n}\sum \ln|f^'(x_i)|
.. seealso::
Lyapunov Exponent
Sec 10.5 Strogatz (1994) "Nonlinear Dynamics and Chaos".
`Wikipedia article on Lyapunov Exponent
<http://en.wikipedia.org/wiki/Lyapunov_exponent>`_.
.. 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( R� R� t DeprecationWarningR R R R^ ( R
t fprime( ( sB /opt/alt/python27/lib64/python2.7/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 j | f t j � | _ t j | f t j � | _ t j | f t j � | _ t j | f t j � | _ d | _ | | _ d | _
i | _ d S( s-
Buffer up to *nmax* points.
i N( R R7 R& t _xat _yat _xst _yst _indt _nmaxR t dataLimt callbackd( R� t nmax( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR� � s c C s | j j | g � j | � d S( sk
Call *func* every time *N* events are passed; *func* signature
is ``func(fifo)``.
N( R' t
setdefaultt append( R� t funcR ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt register� s c C s� | j d k r= t j | | f g � } | j j | d � n | j | j } | | j | <| | j | <xN | j j
� D]= \ } } | j | d k rw x | D] } | | � q� Wqw qw W| j d 7_ d S( s6
Add scalar *x* and *y* to the queue.
i i N( R& R R R t update_from_data_xyR$ R% R" R# R' t items( R� R
R( t xyR R t funcsR+ ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyRf � s
c C s? | j d k r d S| j d | j } | j | | j | f S( sJ
Get the last *x*, *y* or *None*. *None* if no data set.
i i N( NN( R$ R R% R" R# ( R� R ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt last� s c C s� | j | j k r0 | j | j | j | j f S| j | j } | j | | j | j | *| j | | j | j | )| j | | j | j | *| j | | j | j | )| j | j f S( sm
Return *x* and *y* as arrays; their length will be the len of
data added or *nmax*.
( R$ R% R" R# R R! ( R� R ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt asarrays� s c C s` | j d k r t d � � n | j � \ } } | j j | | t � | j j | | t � d S( sG
Update the *datalim* in the current data in the fifo.
s# You must first set the dataLim attrN( R& R R3 R2 t update_from_dataR1 t update_numerix( R� R
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( R� R� t __doc__R� R, Rf R1 R2 R5 ( ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR � s
c C s< t j | f d t j �} d | | (t j | | d d �S( s5
Compute the len(*n*) moving average of *x*.
R# g �?t modet valid( R t emptyR& t convolve( R
R/ R ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt movavg s s %.18et c C s( t j d t � t j | � ra | j d � rO d d l } | j | d � } q� t | d � } n$ t | d � ry | } n t
d � � t j | � } d } | j d k r� | j } t | � d f | _ n x? | D]7 } | j | j g | D] } | | ^ q� � d
� q� W| d k r$| | _ n d S( s�
Save the data in *X* to file *fname* using *fmt* string to convert the
data to strings.
Deprecated. Use numpy.savetxt.
*fname* can be a filename or a file handle. If the filename ends
in '.gz', the file is automatically saved in compressed gzip
format. The :func:`load` function understands gzipped files
transparently.
Example usage::
save('test.out', X) # X is an array
save('test1.out', (x,y,z)) # x,y,z equal sized 1D arrays
save('test2.out', x) # x is 1D
save('test3.out', x, fmt='%1.4e') # use exponential notation
*delimiter* is used to separate the fields, eg. *delimiter* ','
for comma-separated values.
s use numpy.savetxts .gzi����Nt wbR t seeks% fname must be a string or file handlei s
( R� R� R R4 t is_string_liket endswitht gzipt opent filet hasattrR3 R R R R RZ R t writet join( t fnameRk t fmtt delimiterRA t fht origShapet rowt val( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt save s$
5 t #c s t j d t � | d k r% i } n t j | � } g } � d k rR d � }
n � f d � }
d } x0t | � D]"\ } }
| | k r� qt n |
j | d � d j � }
t |
� s� qt n | d k rg t |
|
� � D] \ } } | j
| t � ^ q� } n | d k rH|
|
� } g | D] } | | | | � ^ q%} n5 g t |
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� � D] \ } } | | | � ^ q[} t | � } | j | � qt Wt
j | | � } | j \ } } | d k s�| d k r�t | | � f | _ n | r�| j � S| Sd S( s
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
| j � S( N( t split( R
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to_filehandlet enumerateRP t stripR t getR> R* R R� RZ R� R� ( RG t commentsRI t
converterst skiprowst usecolst unpackR# RJ Rk RQ t converterseqRM t lineRt RM t valsRL t thisLent rt c( ( RI sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt loadQ s@ ; 7 *2
g g�������c C s? t | � t j k r. t t j | t t � � St j | � 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
( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt exp_safe� s c G s t j 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 fnRc ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt amap� s c C s# t j t j t j | � d � � S( sP
Return the root mean square of all the elements of *a*, flattened out.
i ( R R R R^ ( R+ ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt rms_flat s c C s t j t j | � � 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+ ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt l1norm s c C s# t j t j t j | � 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+ ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt l2norm s c C sH | d k r" t j t j | � � St j t j | � | � 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� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt norm_flat s c K s� | j d d � | d d k } | d k r? | d } d } n | d k rT d } n y& | d } | | t | | � } Wn/ t k
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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) R R> t KeyErrorR� t roundR R% ( t xinit xfinR� t kwt endpointRs ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt frange( s )
"t lc C s` | d k r | } n t j | f | | � } x+ t | � D] } | f | } d | | <q; W| S( s
Returns the identity matrix of shape (*n*, *n*, ..., *n*) (rank *r*).
For ranks higher than 2, this object is simply a multi-index Kronecker
delta::
/ 1 if i0=i1=...=iR,
id[i0,i1,...,iR] = -|
\ 0 otherwise.
Optionally a *dtype* (or typecode) may be given (it defaults to 'l').
Since rank defaults to 2, this function behaves in the default case (when
only *n* is given) like ``numpy.identity(n)`` -- but surprisingly, it is
much faster.
i N( R R R7 R9 ( R/ t rankR# t typecodet idenRM t idx( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt identityl s
c C s� d } | | k r0 | d | d | t | � St t j | � t j | � � } t | � | } t | | � } | | t | | | | t | d | � � S( sF
Return the representation of a *number* in any given *base*.
t$ 0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZi i ( R� Rg R t longt base_reprR� ( t numbert baset paddingt charst max_exponentt max_powert
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Return the binary representation of the input *number* as a
string.
This is more efficient than using :func:`base_repr` with base 2.
Increase the value of max_length for very large numbers. Note that
on 32-bit machines, 2**1023 is the largest integer power of 2
which can be converted to a Python float.
i i����i i t t L(
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r; t j | � | SXd | k rY t j | � | St | � Sd S( s�
Return the log(*x*) in base 2.
This is a _slow_ function but which is guaranteed to return the correct
integer value if the input is an integer exact power of 2.
i t 1N( R� t AssertionErrorR� Rg R R ( R
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Returns the log base 2 of *n* if *n* is a power of 2, zero otherwise.
Note the potential ambiguity if *n* == 1: 2**0 == 1, interpret accordingly.
i R� i N( R� R ( R/ R� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt ispower2� s c C s" t j | j � t j | j � k S( sZ
Like the MATLAB function with the same name, returns *True*
if the supplied numpy array or matrix *X* looks like a vector,
meaning it has a one non-singleton axis (i.e., it can have
multiple axes, but all must have length 1, except for one of
them).
If you just want to see if the array has 1 axis, use X.ndim == 1.
( R t prodRZ R� ( Rk ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt isvector� s
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rK t SX| Sd S( s' :func:`numpy.isnan` for arbitrary typesN( R4 R? R� R t isnant NotImplementedErrorR� ( R
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c
C s� t j | � re t j | � re t | � re t j | d � re t | � t | � k rw t d � qw n | g } | g } t t j | � } | d k r� g | D] } | j ^ q� } n t j | � s� | g } n t | � t | � k rt | � d k r | t | � } qt d � n t j | j j
t | | � � } t j | j
d | �} x" | j j D] } | | | | <q_Wx' t | | � D] \ } } | | | <q�W| S( s�
Return a new record array with field names populated with data
from arrays in *arrs*. If appending a single field, then *names*,
*arrs* and *dtypes* do not have to be lists. They can just be the
values themselves.
i s- number of arrays do not match number of namesi s- dtypes must be None, a single dtype or a listR# N( R4 R? R5 R R3 Ri R R R R# t descrt zipt recarrayRZ t fields(
t rect namest arrst dtypesR+ t newdtypet newrect fieldt namet arr( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt rec_append_fields� s, " c C s� t | � } t | � } t j g | j j D]% } | | k r+ | | j | f ^ q+ � } t j | j d | �} x | j D] } | | | | <q{ W| S( sI
Return a new numpy record array with fields in *names* dropped.
R# ( Re R R R# R� R� RZ ( R� R� t NrR� R� R� R� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt rec_drop_fields
s (c C s_ t j | � r! | j d � } n g } x | D] } | j | | � q. Wt j j | d | �S( sJ
Return a new numpy record array with only fields listed in names
t ,R� ( R4 R? RP R* R R� t
fromarrays( R� R� t arraysR� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt rec_keep_fields s
c C s3 t � } xV t | � D]H \ } } t g | D] } | | ^ q, � } | j | g � j | � q W| j � } | j � g } xn | D]f } t | � } | | }
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j j | d | �S( s�
*r* is a numpy record array
*groupby* is a sequence of record array attribute names that
together form the grouping key. eg ('date', 'productcode')
*stats* is a sequence of (*attr*, *func*, *outname*) tuples which
will call ``x = func(attr)`` and assign *x* to the record array
output with attribute *outname*. For example::
stats = ( ('sales', len, 'numsales'), ('sales', np.mean, 'avgsale') )
Return record array has *dtype* names for each attribute name in
the the *groupby* argument, with the associated group values, and
for each outname name in the *stats* argument, with the associated
stat summary output.
R� (
t dictRS t tupleR) R* t keysR� t listt extendR� R R� t fromrecords( R_ t groupbyt statst rowdRM RL t attrR R� t rowsR t thisrR+ t outnamet attrsR0 t outnamesR� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt rec_groupby+ s" #
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c C s� t | j j � } g | D] } | | ^ q } xD | D]< \ } } } | j | � | j t j | | | � � � q6 Wt j j | 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� ( R_ t summaryfuncsR� R� R� R� R+ R� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt
rec_summarizeZ s
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s; t j � � r � f � n xX � D]P } | � j j k rM t d | � � n | � j j k r" t d | � � q" q" W� f d � } t g t � � D] \ } }
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g | D] } | | ^ q� } t | � } n � f d � } g � D] } | | � ^ qE} � � � f d � } � � � f d � } g � j j
D]0 } | d � k r�| | d � | d
f ^ q�} g � j j
D]0 } | d � k r�| | d � | d
f ^ q�} t j | | | � }! t j | | | f d |! �}" | d k r�x= | D]2 }# |# |! j k rWt j d |# |! j f � qWqWWn x7 |! j D], } |! | }$ |$ j d k r�d |" | <q�q�W| d k r-| d k r-|" j j j � }% x6 | j � D]% \ } }& | |% k r|& |" | <qqWn x} � j j D]o }' | |' � }( | rk� |' | |" |( | *n | d k s�| d k r:| r:� |' | |" |( | | | +q:q:Wxw � j j D]i }' | |' � }( |' � k r�| r�� |' | |" |( | *n | d k r�| r�� |' | |" |( | )q�q�W|" j d � � |" S( s
Join record arrays *r1* and *r2* on *key*; *key* is a tuple of
field names -- if *key* is a string it is assumed to be a single
attribute name. If *r1* and *r2* have equal values on all the keys
in the *key* tuple, then their fields will be merged into a new
record array containing the intersection of the fields of *r1* and
*r2*.
*r1* (also *r2*) must not have any duplicate keys.
The *jointype* keyword can be 'inner', 'outer', 'leftouter'. To
do a rightouter join just reverse *r1* and *r2*.
The *defaults* keyword is a dictionary filled with
``{column_name:default_value}`` pairs.
The keywords *r1postfix* and *r2postfix* are postfixed to column names
(other than keys) that are both in *r1* and *r2*.
s r1 does not have key field %ss r2 does not have key field %sc s! t g � D] } | | ^ q
� S( N( R� ( RL R� ( R ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt makekey� s i t outert leftouterc s� � j | } | j t j k r4 | | j d d f S� j | } | j | j k rh | | j d d f S| | j d d f Sd S( sG if name is a string key, use the larger size of r1 or r2 before mergingi i N( R# Rb R t string_R� t num( R� t dt1R� ( t r1( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt key_desc� s
c s. | � k s | � j j k r" | S| � Sd S( sU
The column name in *newrec* that corresponds to the column in *r1*.
N( R# R� ( R� ( R t r1postfixt r2( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt mapped_r1field� s c s. | � k s | � j j k r" | S| � Sd S( sU
The column name in *newrec* that corresponds to the column in *r2*.
N( R# R� ( R� ( R R� t r2postfix( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt mapped_r2field� s i R# s6 rec_join defaults key="%s" not in new dtype names "%s"R_ RM R� t orderN( R_ RM ( R4 R? R# R� R3 R� RS Re R� R R� R t
differenceR� R� R R� R� t kindR� R. R� () R R� R� t jointypet defaultsR� R� R� R� RM RL t r1dt r2dt r1keyst r2keyst common_keyst kt r1indt r2indt
common_lent left_lent right_lent left_keyst left_indt
right_keyst right_indR� t keydescR� R� t desct r1desct r2descR� R� t thiskeyR� t
newrec_fieldsR� R� t newfield( ( R R� R� R� R� sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt rec_joinp sz
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$ R� c sb g } t j t j | � g | D] } t | � ^ q � } � � f d � } | d k r� x� | D]) \ }
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| g g | D] }
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Join a sequence of record arrays on single column key.
This function only joins a single column of the multiple record arrays
*key*
is the column name that acts as a key
*name*
is the name of the column that we want to join
*recs*
is a list of record arrays to join
*jointype*
is a string 'inner' or 'outer'
*missing*
is what any missing field is replaced by
*postfixes*
if not None, a len recs sequence of postfixes
returns a record array with columns [rowkey, name0, name1, ... namen-1].
or if postfixes [PF0, PF1, ..., PFN-1] are supplied,
[rowkey, namePF0, namePF1, ... namePFN-1].
Example::
r = recs_join("date", "close", recs=[r0, r1], missing=0.)
c s | d k r � S| � Sd S( N( R ( R_ ( t missingR� ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt extract s R� R� s %dR� s %s%sR� N( R4 t align_iteratorsR� t
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f d � � i d d 6d d 6d d 6} � � � � � � f d � } � d k } | r8x: | D]2 } t | � r | d j | � r q�n | } Pq�Wt d � } | j d � g � t � } x t | � D]� \ } } | j � j � j d d � } d j g | D] } | | k r�| ^ q�� } t | � s�d | } n | j | | � } | j | d � } | d k r� j | d | � n
� j | � | d | | <q]Wn: t j � � rrg � j d � D] } | j � ^ qW� n | | � } | d k r�t d � � n � j d � t j � d | �} | | � | rx8 | j � } t | � r�| d j | � r�q�n Pq�n g } g } x� t | � D]� \ } } t | � s=q n | d j | � rVq n | j g t | � | � D] \ } } }! | | |! � ^ qo� | j g t � | � D] \ } }! � | |! � ^ q�� q W� j! � t | � s�d S| rQt j" | � rQy d d l# m$ }" Wn t% k
r2t& d � � qiX|" j' | d � d! | �}# n t j( j' | d � �}# |# S(# su
Load data from comma/space/tab delimited file in *fname* into a
numpy record array and return the record array.
If *names* is *None*, a header row is required to automatically
assign the recarray names. The headers will be lower cased,
spaces will be converted to underscores, and illegal attribute
name characters removed. If *names* is not *None*, it is a
sequence of names to use for the column names. In this case, it
is assumed there is no header row.
- *fname*: can be a filename or a file handle. Support for gzipped
files is automatic, if the filename ends in '.gz'
- *comments*: the character used to indicate the start of a comment
in the file
- *skiprows*: is the number of rows from the top to skip
- *checkrows*: is the number of rows to check to validate the column
data type. When set to zero all rows are validated.
- *converterd*: if not *None*, is a dictionary mapping column number or
munged column name to a converter function.
- *names*: if not None, is a list of header names. In this case, no
header will be read from the file
- *missingd* is a dictionary mapping munged column names to field values
which signify that the field does not contain actual data and should
be masked, e.g. '0000-00-00' or 'unused'
- *missing*: a string whose value signals a missing field regardless of
the column it appears in
- *use_mrecords*: if True, return an mrecords.fromrecords record array if any of the data are missing
If no rows are found, *None* is returned -- see :file:`examples/loadrec.py`
i����Nt FHc B sD e Z d Z d � Z d � Z d � Z d � Z d � Z d � Z RS( sC
For space-delimited files, we want different behavior than
comma or tab. Generally, we want multiple spaces to be
treated as a single separator, whereas with comma and tab we
want multiple commas to return multiple (empty) fields. The
join/strip trick below effects this.
c S s
| | _ d S( N( RJ ( R� RJ ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR� g s c S s | j j � d S( N( RJ t close( R� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR j s c S s | j j | � d S( N( RJ R> ( R� t arg( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR> m s c S s d j | j � � S( NR< ( RF RP ( R� R� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt fixp s c S s | j | j j � � S( N( R RJ t next( R� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR t s c s s& x | j D] } | j | � Vq
Wd S( N( RJ R ( R� R\ ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt __iter__w s ( R� R� R6 R� R R> R R R ( ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyR _ s R< RI c sD � r: x1 t | � D] \ } } | � d k r Pq q Wn � | f S( Ni ( RS ( t readerRM RL ( RJ RX ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt process_skiprows s
c s9 | � k s- | � j | � k s- | d k r1 t St Sd S( s. Should the value val in column name be masked?R� N( RU R1 R� ( R� RM ( R� t missingd( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt ismissing� s -c s � � � f d � } | S( Nc s! � | | � r � S� | � Sd S( N( ( R� RM ( R, R+ R
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i R� c sR � | � } | j d k s9 | j d k s9 | j d k rH t d � � n | j � S( Ni s
not a date( t hourt minutet secondR3 t date( R
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dateparser( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt mydate� s -c s{ i � � 6� � 6� � 6� � 6� � 6} y | | | � Wn6 | � k r[ t d � � qw � | | | | � Sn X| Sd S( Ns, Could not find a working conversion function( R3 ( R� t itemR+ t funcmap( t get_funcR
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Returns a textual representation of a record array.
*r*: numpy recarray
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*padding*: space between each column
*precision*: number of decimal places to use for floats.
Set to an integer to apply to all floats. Set to a
list of integers to apply precision individually.
Precision for non-floats is simply ignored.
*fields* : if not None, a list of field names to print. fields
can be a list of strings like ['field1', 'field2'] or a single
comma separated string like 'field1,field2'
Example::
precision=[0,2,3]
Output::
ID Price Return
ABC 12.54 0.234
XYZ 6.32 -0.076
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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
for formatd type FormatFloat, we override the precision to store
full precision floats in the CSV file
.. seealso::
:func:`csv2rec`
For information about *missing* and *missingd*, which can
be used to fill in masked values into your CSV file.
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``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( R� 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� R� 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.natgridR� R� R1 R1 t matplotlib.delaunayt delaunayR� t griddatat _reportedR t reportR R� R RD R� t compresst
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| � r� t j | � \ } | | d | | <q� | | d k r| r�| d | | <q�q� | | d k r+| r�| d | | <q�q� t j | | k � \ } t | � } | | | | | | | d | | | | d | | | | <q� W| S( s�
This function provides simple (but somewhat less so than
:func:`cbook.simple_linear_interpolation`) linear interpolation.
:func:`simple_linear_interpolation` will give a list of point
between a start and an end, while this does true linear
interpolation at an arbitrary set of points.
This is very inefficient linear interpolation meant to be used
only for a small number of points in relatively non-intensive use
cases. For real linear interpolation, use scipy.
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:func:`slopes` calculates the slope *y*'(*x*)
The slope is estimated using the slope obtained from that of a
parabola through any three consecutive points.
This method should be superior to that described in the appendix
of A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russel
W. Stineman (Creative Computing July 1980) in at least one aspect:
Circles for interpolation demand a known aspect ratio between
*x*- and *y*-values. For many functions, however, the abscissa
are given in different dimensions, so an aspect ratio is
completely arbitrary.
The parabola method gives very similar results to the circle
method for most regular cases but behaves much better in special
cases.
Norbert Nemec, Institute of Theoretical Physics, University or
Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de
(inspired by a original implementation by Halldor Bjornsson,
Icelandic Meteorological Office, March 2006 halldor at vedur.is)
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Given data vectors *x* and *y*, the slope vector *yp* and a new
abscissa vector *xi*, the function :func:`stineman_interp` uses
Stineman interpolation to calculate a vector *yi* corresponding to
*xi*.
Here's an example that generates a coarse sine curve, then
interpolates over a finer abscissa::
x = linspace(0,2*pi,20); y = sin(x); yp = cos(x)
xi = linspace(0,2*pi,40);
yi = stineman_interp(xi,x,y,yp);
plot(x,y,'o',xi,yi)
The interpolation method is described in the article A
CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russell
W. Stineman. The article appeared in the July 1980 issue of
Creative Computing with a note from the editor stating that while
they were:
not an academic journal but once in a while something serious
and original comes in adding that this was
"apparently a real solution" to a well known problem.
For *yp* = *None*, the routine automatically determines the slopes
using the :func:`slopes` routine.
*x* is assumed to be sorted in increasing order.
For values ``xi[j] < x[0]`` or ``xi[j] > 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
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+ c C s" t j t j | | � � \ } | 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� ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt inside_poly� s c C s� t j | � s t j | � r' t } n t } | j | � } | j | � } t | � } t | � } | | j d | � } | j 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)
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c C s� t j | � s- t j | � s- t j | � r6 t } n t } t | � } t j | � sm | | j | � } n t j | � s� | | j | � } n | j | | d d d � f � } | j | | 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`.
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- ""c C s t j | d | d k � 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( Rk ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt is_closed_polygon� s c C s� d } g } xd t | � D]V \ } } | d k r@ | r@ | } q | d k r | r | j | | f � d } q q W| d k r� | j | | 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 RS R* ( R� t in_regiont
boundariesRM RM ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt contiguous_regions� s
c C s\ t j | � } | } t j | d | k | d | k @� d } t | � rT | 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
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`
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t thresholdR ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt cross_from_below� s !+ c C sV t j | � } t j | d | k | d | k @� d } t | � rN | 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`
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+ c C s. t j | � } t j | | 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= ( Rk R� R ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt vector_lengths. s
c C s% t j | 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� ( Rk ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt distances_along_curve; s c C s1 t | � } t j t j d � t j | � 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? R7 t cumsum( Rk ( ( sB /opt/alt/python27/lib64/python2.7/site-packages/matplotlib/mlab.pyt path_lengthF 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.
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