@header@

matplotlib.pylab (version 1.1.0.dev5176)

index
/home/jdhunter/dev/lib64/python2.5/site-packages/matplotlib/pylab.py

This is a procedural interface to the matplotlib object-oriented plotting library. The following plotting commands are provided; the majority have Matlab(TM) analogs and similar argument. _Plotting commands acorr - plot the autocorrelation function annotate - annotate something in the figure arrow - add an arrow to the axes axes - Create a new axes axhline - draw a horizontal line across axes axvline - draw a vertical line across axes axhspan - draw a horizontal bar across axes axvspan - draw a vertical bar across axes axis - Set or return the current axis limits bar - make a bar chart barh - a horizontal bar chart broken_barh - a set of horizontal bars with gaps box - set the axes frame on/off state boxplot - make a box and whisker plot cla - clear current axes clabel - label a contour plot clf - clear a figure window clim - adjust the color limits of the current image close - close a figure window colorbar - add a colorbar to the current figure cohere - make a plot of coherence contour - make a contour plot contourf - make a filled contour plot csd - make a plot of cross spectral density delaxes - delete an axes from the current figure draw - Force a redraw of the current figure errorbar - make an errorbar graph figlegend - make legend on the figure rather than the axes figimage - make a figure image figtext - add text in figure coords figure - create or change active figure fill - make filled polygons gca - return the current axes gcf - return the current figure gci - get the current image, or None getp - get a handle graphics property grid - set whether gridding is on hist - make a histogram hold - set the axes hold state ioff - turn interaction mode off ion - turn interaction mode on isinteractive - return True if interaction mode is on imread - load image file into array imshow - plot image data ishold - return the hold state of the current axes legend - make an axes legend loglog - a log log plot matshow - display a matrix in a new figure preserving aspect pcolor - make a pseudocolor plot pcolormesh - make a pseudocolor plot using a quadrilateral mesh pie - make a pie chart plot - make a line plot plot_date - plot dates plotfile - plot column data from an ASCII tab/space/comma delimited file pie - pie charts polar - make a polar plot on a PolarAxes psd - make a plot of power spectral density quiver - make a direction field (arrows) plot rc - control the default params rgrids - customize the radial grids and labels for polar savefig - save the current figure scatter - make a scatter plot setp - set a handle graphics property semilogx - log x axis semilogy - log y axis show - show the figures specgram - a spectrogram plot spy - plot sparsity pattern using markers or image stem - make a stem plot subplot - make a subplot (numrows, numcols, axesnum) subplots_adjust - change the params controlling the subplot positions of current figure subplot_tool - launch the subplot configuration tool table - add a table to the plot text - add some text at location x,y to the current axes thetagrids - customize the radial theta grids and labels for polar title - add a title to the current axes xcorr - plot the autocorrelation function of x and y xlim - set/get the xlimits ylim - set/get the ylimits xticks - set/get the xticks yticks - set/get the yticks xlabel - add an xlabel to the current axes ylabel - add a ylabel to the current axes autumn - set the default colormap to autumn bone - set the default colormap to bone cool - set the default colormap to cool copper - set the default colormap to copper flag - set the default colormap to flag gray - set the default colormap to gray hot - set the default colormap to hot hsv - set the default colormap to hsv jet - set the default colormap to jet pink - set the default colormap to pink prism - set the default colormap to prism spring - set the default colormap to spring summer - set the default colormap to summer winter - set the default colormap to winter spectral - set the default colormap to spectral _Event handling connect - register an event handler disconnect - remove a connected event handler _Matrix commands cumprod - the cumulative product along a dimension cumsum - the cumulative sum along a dimension detrend - remove the mean or besdt fit line from an array diag - the k-th diagonal of matrix diff - the n-th differnce of an array eig - the eigenvalues and eigen vectors of v eye - a matrix where the k-th diagonal is ones, else zero find - return the indices where a condition is nonzero fliplr - flip the rows of a matrix up/down flipud - flip the columns of a matrix left/right linspace - a linear spaced vector of N values from min to max inclusive logspace - a log spaced vector of N values from min to max inclusive meshgrid - repeat x and y to make regular matrices ones - an array of ones rand - an array from the uniform distribution [0,1] randn - an array from the normal distribution rot90 - rotate matrix k*90 degress counterclockwise squeeze - squeeze an array removing any dimensions of length 1 tri - a triangular matrix tril - a lower triangular matrix triu - an upper triangular matrix vander - the Vandermonde matrix of vector x svd - singular value decomposition zeros - a matrix of zeros _Probability levypdf - The levy probability density function from the char. func. normpdf - The Gaussian probability density function rand - random numbers from the uniform distribution randn - random numbers from the normal distribution _Statistics corrcoef - correlation coefficient cov - covariance matrix amax - the maximum along dimension m mean - the mean along dimension m median - the median along dimension m amin - the minimum along dimension m norm - the norm of vector x prod - the product along dimension m ptp - the max-min along dimension m std - the standard deviation along dimension m asum - the sum along dimension m _Time series analysis bartlett - M-point Bartlett window blackman - M-point Blackman window cohere - the coherence using average periodiogram csd - the cross spectral density using average periodiogram fft - the fast Fourier transform of vector x hamming - M-point Hamming window hanning - M-point Hanning window hist - compute the histogram of x kaiser - M length Kaiser window psd - the power spectral density using average periodiogram sinc - the sinc function of array x _Dates date2num - convert python datetimes to numeric representation drange - create an array of numbers for date plots num2date - convert numeric type (float days since 0001) to datetime _Other angle - the angle of a complex array load - load ASCII data into array polyfit - fit x, y to an n-th order polynomial polyval - evaluate an n-th order polynomial roots - the roots of the polynomial coefficients in p save - save an array to an ASCII file trapz - trapezoidal integration __end

Modules

numpy.add_newdocs
numpy.core.defchararray
matplotlib.cm
numpy.ctypeslib
numpy.lib.scimath
numpy.fft.fftpack
numpy.fft.fftpack_lite
numpy.fft.helper
numpy.linalg.info
numpy.linalg.lapack_lite
numpy.linalg.linalg
numpy.ma
math
matplotlib
matplotlib.mlab
matplotlib.mpl
numpy
matplotlib.pyplot
numpy.core.records
sys
warnings

Functions


add_docstring(...)
docstring(obj, docstring) Add a docstring to a built-in obj if possible. If the obj already has a docstring raise a RuntimeError If this routine does not know how to add a docstring to the object raise a TypeError

arange(...)
arange([start,] stop[, step,], dtype=None) For integer arguments, just like range() except it returns an array whose type can be specified by the keyword argument dtype.  If dtype is not specified, the type of the result is deduced from the type of the arguments. For floating point arguments, the length of the result is ceil((stop - start)/step).  This rule may result in the last element of the result being greater than stop.

array(...)
array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0) Return an array from object with the specified data-type. Parameters ---------- object : array-like     an array, any object exposing the array interface, any     object whose __array__ method returns an array, or any     (nested) sequence. dtype : data-type     The desired data-type for the array.  If not given, then     the type will be determined as the minimum type required     to hold the objects in the sequence.  This argument can only     be used to 'upcast' the array.  For downcasting, use the     .astype(t) method. copy : bool     If true, then force a copy.  Otherwise a copy will only occur     if __array__ returns a copy, obj is a nested sequence, or     a copy is needed to satisfy any of the other requirements order : {'C', 'F', 'A' (None)}     Specify the order of the array.  If order is 'C', then the     array will be in C-contiguous order (last-index varies the     fastest).  If order is 'FORTRAN', then the returned array     will be in Fortran-contiguous order (first-index varies the     fastest).  If order is None, then the returned array may     be in either C-, or Fortran-contiguous order or even     discontiguous. subok : bool     If True, then sub-classes will be passed-through, otherwise     the returned array will be forced to be a base-class array ndmin : int     Specifies the minimum number of dimensions that the resulting     array should have.  1's will be pre-pended to the shape as     needed to meet this requirement.

beta(...)
Beta distribution over [0, 1]. beta(a, b, size=None) -> random values

bincount(...)
bincount(x,weights=None) Return the number of occurrences of each value in x. x must be a list of non-negative integers.  The output, b[i], represents the number of times that i is found in x.  If weights is specified, every occurrence of i at a position p contributes weights[p] instead of 1. See also: histogram, digitize, unique.

binomial(...)
Binomial distribution of n trials and p probability of success. binomial(n, p, size=None) -> random values

bytes(...)
Return random bytes. bytes(length) -> str

can_cast(...)
can_cast(from=d1, to=d2) Returns True if data type d1 can be cast to data type d2 without losing precision.

chisquare(...)
Chi^2 distribution. chisquare(df, size=None) -> random values

compare_chararrays(...)

concatenate(...)
concatenate((a1, a2, ...), axis=0) Join arrays together. The tuple of sequences (a1, a2, ...) are joined along the given axis (default is the first one) into a single numpy array. Examples -------- >>> concatenate( ([0,1,2], [5,6,7]) ) array([0, 1, 2, 5, 6, 7])

digitize(...)
digitize(x,bins) Return the index of the bin to which each value of x belongs. Each index i returned is such that bins[i-1] <= x < bins[i] if bins is monotonically increasing, or bins [i-1] > x >= bins[i] if bins is monotonically decreasing. Beyond the bounds of the bins 0 or len(bins) is returned as appropriate.

dot(...)

empty(...)
empty(shape, dtype=float, order='C') Return a new array of given shape and type with all entries uninitialized. This can be faster than zeros. Parameters ---------- shape : tuple of integers     Shape of the new array dtype : data-type     The desired data-type for the array. order : {'C', 'F'}     Whether to store multidimensional data in C or Fortran order.

exponential(...)
Exponential distribution. exponential(scale=1.0, size=None) -> random values

f(...)
F distribution. f(dfnum, dfden, size=None) -> random values

fastCopyAndTranspose = _fastCopyAndTranspose(...)
_fastCopyAndTranspose(a)

frombuffer(...)
frombuffer(buffer=, dtype=float, count=-1, offset=0) Returns a 1-d array of data type dtype from buffer. Parameters ---------- buffer     An object that exposes the buffer interface dtype : data-type     Data type of the returned array. count : int     Number of items to read. -1 means all data in the buffer. offset : int     Number of bytes to jump from the start of the buffer before reading Notes ----- If the buffer has data that is not in machine byte-order, then use a proper data type descriptor. The data will not be byteswapped, but the array will manage it in future operations.

fromfile(...)
fromfile(file=, dtype=float, count=-1, sep='') Return an array of the given data type from a text or binary file. Data written using the tofile() method can be conveniently recovered using this function. Parameters ---------- file : file or string     Open file object or string containing a file name. dtype : data-type     Data type of the returned array.     For binary files, it is also used to determine the size and order of     the items in the file. count : int     Number of items to read. -1 means all data in the whole file. sep : string     Separator between items if file is a text file.     Empty ("") separator means the file should be treated as binary. See also -------- loadtxt : load data from text files Notes ----- WARNING: This function should be used sparingly as the binary files are not platform independent. In particular, they contain no endianess or datatype information. Nevertheless it can be useful for reading in simply formatted or binary data quickly.

fromiter(...)
fromiter(iterable, dtype, count=-1) Return a new 1d array initialized from iterable. Parameters ---------- iterable     Iterable object from which to obtain data dtype : data-type     Data type of the returned array. count : int     Number of items to read. -1 means all data in the iterable. Returns ------- new_array : ndarray

frompyfunc(...)
frompyfunc(func, nin, nout) take an arbitrary python function that takes nin objects as input and returns nout objects and return a universal function (ufunc).  This ufunc always returns PyObject arrays

fromstring(...)
fromstring(string, dtype=float, count=-1, sep='') Return a new 1d array initialized from the raw binary data in string. If count is positive, the new array will have count elements, otherwise its size is determined by the size of string.  If sep is not empty then the string is interpreted in ASCII mode and converted to the desired number type using sep as the separator between elements (extra whitespace is ignored). ASCII integer conversions are base-10; octal and hex are not supported.

gamma(...)
Gamma distribution. gamma(shape, scale=1.0, size=None) -> random values

geometric(...)
Geometric distribution with p being the probability of "success" on an individual trial. geometric(p, size=None)

get_state(...)
Return a tuple representing the internal state of the generator. get_state() -> ('MT19937', int key[624], int pos, int has_gauss, float cached_gaussian)

getbuffer(...)
getbuffer(obj [,offset[, size]]) Create a buffer object from the given object referencing a slice of length size starting at offset.  Default is the entire buffer. A read-write buffer is attempted followed by a read-only buffer.

geterrobj(...)
geterrobj() Used internally by `geterr`. Returns ------- errobj : list     Internal numpy buffer size, error mask, error callback function.

gumbel(...)
Gumbel distribution. gumbel(loc=0.0, scale=1.0, size=None)

hypergeometric(...)
Hypergeometric distribution. Consider an urn with ngood "good" balls and nbad "bad" balls. If one were to draw nsample balls from the urn without replacement, then the hypergeometric distribution describes the distribution of "good" balls in the sample. hypergeometric(ngood, nbad, nsample, size=None)

inner(...)
inner(a,b) Returns the dot product of two arrays, which has shape a.shape[:-1] + b.shape[:-1] with elements computed by the product of the elements from the last dimensions of a and b.

int_asbuffer(...)

laplace(...)
Laplace distribution. laplace(loc=0.0, scale=1.0, size=None)

lexsort(...)
lexsort(keys=, axis=-1) -> array of indices. Argsort with list of keys. Perform an indirect sort using a list of keys. The first key is sorted, then the second, and so on through the list of keys. At each step the previous order is preserved when equal keys are encountered. The result is a sort on multiple keys.  If the keys represented columns of a spreadsheet, for example, this would sort using multiple columns (the last key being used for the primary sort order, the second-to-last key for the secondary sort order, and so on). Parameters ---------- keys : (k,N) array or tuple of (N,) sequences     Array containing values that the returned indices should sort, or     a sequence of things that can be converted to arrays of the same shape. axis : integer     Axis to be indirectly sorted.  Default is -1 (i.e. last axis). Returns ------- indices : (N,) integer array     Array of indices that sort the keys along the specified axis. See Also -------- argsort : indirect sort sort : inplace sort Examples -------- >>> a = [1,5,1,4,3,6,7] >>> b = [9,4,0,4,0,4,3] >>> ind = lexsort((b,a)) >>> print ind [2 0 4 3 1 5 6] >>> print take(a,ind) [1 1 3 4 5 6 7] >>> print take(b,ind) [0 9 0 4 4 4 3]

loads(...)
loads(string) -- Load a pickle from the given string

logistic(...)
Logistic distribution. logistic(loc=0.0, scale=1.0, size=None)

lognormal(...)
Log-normal distribution. Note that the mean parameter is not the mean of this distribution, but the underlying normal distribution.     lognormal(mean, sigma) <=> exp(normal(mean, sigma)) lognormal(mean=0.0, sigma=1.0, size=None)

logseries(...)
Logarithmic series distribution. logseries(p, size=None)

multinomial(...)
Multinomial distribution. multinomial(n, pvals, size=None) -> random values pvals is a sequence of probabilities that should sum to 1 (however, the last element is always assumed to account for the remaining probability as long as sum(pvals[:-1]) <= 1).

multivariate_normal(...)
Return an array containing multivariate normally distributed random numbers with specified mean and covariance. multivariate_normal(mean, cov) -> random values multivariate_normal(mean, cov, [m, n, ...]) -> random values mean must be a 1 dimensional array. cov must be a square two dimensional array with the same number of rows and columns as mean has elements. The first form returns a single 1-D array containing a multivariate normal. The second form returns an array of shape (m, n, ..., cov.shape[0]). In this case, output[i,j,...,:] is a 1-D array containing a multivariate normal.

negative_binomial(...)
Negative Binomial distribution. negative_binomial(n, p, size=None) -> random values

newbuffer(...)
newbuffer(size) Return a new uninitialized buffer object of size bytes

noncentral_chisquare(...)
Noncentral Chi^2 distribution. noncentral_chisquare(df, nonc, size=None) -> random values

noncentral_f(...)
Noncentral F distribution. noncentral_f(dfnum, dfden, nonc, size=None) -> random values

normal(...)
Normal distribution (mean=loc, stdev=scale). normal(loc=0.0, scale=1.0, size=None) -> random values

packbits(...)
out = numpy.packbits(myarray, axis=None) myarray : an integer type array whose elements should be packed to bits This routine packs the elements of a binary-valued dataset into a NumPy array of type uint8 ('B') whose bits correspond to the logical (0 or nonzero) value of the input elements. The dimension over-which bit-packing is done is given by axis. The shape of the output has the same number of dimensions as the input (unless axis is None, in which case the output is 1-d).    Example:    >>> a = array([[[1,0,1],    ...             [0,1,0]],    ...            [[1,1,0],    ...             [0,0,1]]])    >>> b = numpy.packbits(a,axis=-1)    >>> b    array([[[160],[64]],[[192],[32]]], dtype=uint8)    Note that 160 = 128 + 32              192 = 128 + 64

pareto(...)
Pareto distribution. pareto(a, size=None)

permutation(...)
Given an integer, return a shuffled sequence of integers >= 0 and < x; given a sequence, return a shuffled array copy. permutation(x)

poisson(...)
Poisson distribution. poisson(lam=1.0, size=None) -> random values

power(...)
Power distribution. power(a, size=None)

putmask(...)
putmask(a, mask, values) Sets a.flat[n] = values[n] for each n where mask.flat[n] is true. If values is not the same size as `a` and `mask` then it will repeat. This gives behavior different from a[mask] = values. Parameters ---------- a : {array_like}     Array to put data into mask : {array_like}     Boolean mask array values : {array_like}     Values to put

rand(...)
Return an array of the given dimensions which is initialized to random numbers from a uniform distribution in the range [0,1). rand(d0, d1, ..., dn) -> random values Note:  This is a convenience function. If you want an             interface that takes a tuple as the first argument             use numpy.random.random_sample(shape_tuple).

randint(...)
Return random integers x such that low <= x < high. randint(low, high=None, size=None) -> random values If high is None, then 0 <= x < low.

randn(...)
Returns zero-mean, unit-variance Gaussian random numbers in an array of shape (d0, d1, ..., dn). randn(d0, d1, ..., dn) -> random values Note:  This is a convenience function. If you want an             interface that takes a tuple as the first argument             use numpy.random.standard_normal(shape_tuple).

random = random_sample(...)
Return random floats in the half-open interval [0.0, 1.0). random_sample(size=None) -> random values

random_integers(...)
Return random integers x such that low <= x <= high. random_integers(low, high=None, size=None) -> random values. If high is None, then 1 <= x <= low.

random_sample(...)
Return random floats in the half-open interval [0.0, 1.0). random_sample(size=None) -> random values

ranf = random_sample(...)
Return random floats in the half-open interval [0.0, 1.0). random_sample(size=None) -> random values

rayleigh(...)
Rayleigh distribution. rayleigh(scale=1.0, size=None)

sample = random_sample(...)
Return random floats in the half-open interval [0.0, 1.0). random_sample(size=None) -> random values

seed(...)
Seed the generator. seed(seed=None) seed can be an integer, an array (or other sequence) of integers of any length, or None. If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.

set_numeric_ops(...)
set_numeric_ops(op=func, ...) Set some or all of the number methods for all array objects.  Do not forget **dict can be used as the argument list.  Return the functions that were replaced, which can be stored and set later.

set_state(...)
Set the state from a tuple. state = ('MT19937', int key[624], int pos, int has_gauss, float cached_gaussian) For backwards compatibility, the following form is also accepted although it is missing some information about the cached Gaussian value. state = ('MT19937', int key[624], int pos) set_state(state)

set_string_function(...)
set_string_function(f, repr=1) Set the python function f to be the function used to obtain a pretty printable string version of an array whenever an array is printed. f(M) should expect an array argument M, and should return a string consisting of the desired representation of M for printing.

seterrobj(...)
seterrobj() Used internally by `seterr`. Parameters ---------- errobj : list     [buffer_size, error_mask, callback_func] See Also -------- seterrcall

shuffle(...)
Modify a sequence in-place by shuffling its contents. shuffle(x)

standard_cauchy(...)
Standard Cauchy with mode=0. standard_cauchy(size=None)

standard_exponential(...)
Standard exponential distribution (scale=1). standard_exponential(size=None) -> random values

standard_gamma(...)
Standard Gamma distribution. standard_gamma(shape, size=None) -> random values

standard_normal(...)
Standard Normal distribution (mean=0, stdev=1). standard_normal(size=None) -> random values

standard_t(...)
Standard Student's t distribution with df degrees of freedom. standard_t(df, size=None)

triangular(...)
Triangular distribution starting at left, peaking at mode, and ending at right (left <= mode <= right). triangular(left, mode, right, size=None)

uniform(...)
Uniform distribution over [low, high). uniform(low=0.0, high=1.0, size=None) -> random values

unpackbits(...)
out = numpy.unpackbits(myarray, axis=None) myarray - array of uint8 type where each element represents a bit-field    that should be unpacked into a boolean output array    The shape of the output array is either 1-d (if axis is None) or    the same shape as the input array with unpacking done along the    axis specified.

vonmises(...)
von Mises circular distribution with mode mu and dispersion parameter kappa on [-pi, pi]. vonmises(mu, kappa, size=None)

wald(...)
Wald (inverse Gaussian) distribution. wald(mean, scale, size=None)

weibull(...)
Weibull distribution. weibull(a, size=None)

where(...)
where(condition, x, y) or where(condition) Return elements from `x` or `y`, depending on `condition`. Parameters ---------- condition : array of bool     When True, yield x, otherwise yield y. x,y : 1-dimensional arrays     Values from which to choose. Notes ----- This is equivalent to     [xv if c else yv for (c,xv,yv) in zip(condition,x,y)] The result is shaped like `condition` and has elements of `x` or `y` where `condition` is respectively True or False. In the special case, where only `condition` is given, the tuple condition.nonzero() is returned, instead. Examples -------- >>> where([True,False,True],[1,2,3],[4,5,6]) array([1, 5, 3])

zeros(...)
zeros(shape, dtype=float, order='C') Return a new array of given shape and type, filled zeros. Parameters ---------- shape : tuple of integers     Shape of the new array dtype : data-type     The desired data-type for the array. order : {'C', 'F'}     Whether to store multidimensional data in C or Fortran order.

zipf(...)
Zipf distribution. zipf(a, size=None)

Data

ALLOW_THREADS = 1
BUFSIZE = 10000
CLIP = 0
DAILY = 3
ERR_CALL = 3
ERR_DEFAULT = 0
ERR_DEFAULT2 = 2084
ERR_IGNORE = 0
ERR_LOG = 5
ERR_PRINT = 4
ERR_RAISE = 2
ERR_WARN = 1
FLOATING_POINT_SUPPORT = 1
FPE_DIVIDEBYZERO = 1
FPE_INVALID = 8
FPE_OVERFLOW = 2
FPE_UNDERFLOW = 4
FR = FR
False_ = False
HOURLY = 4
Inf = inf
Infinity = inf
MAXDIMS = 32
MINUTELY = 5
MO = MO
MONTHLY = 1
NAN = nan
NINF = -inf
NZERO = -0.0
NaN = nan
PINF = inf
PZERO = 0.0
RAISE = 2
SA = SA
SECONDLY = 6
SHIFT_DIVIDEBYZERO = 0
SHIFT_INVALID = 9
SHIFT_OVERFLOW = 3
SHIFT_UNDERFLOW = 6
SU = SU
ScalarType = (<type 'int'>, <type 'float'>, <type 'complex'>, <type 'long'>, <type 'bool'>, <type 'str'>, <type 'unicode'>, <type 'buffer'>, <type 'numpy.int64'>, <type 'numpy.int16'>, <type 'numpy.uint64'>, <type 'numpy.complex64'>, <type 'numpy.int64'>, <type 'numpy.void'>, <type 'numpy.int32'>, <type 'numpy.complex128'>, <type 'numpy.float32'>, <type 'numpy.float64'>, <type 'numpy.bool_'>, <type 'numpy.uint64'>, ...)
TH = TH
TU = TU
True_ = True
UFUNC_BUFSIZE_DEFAULT = 10000
UFUNC_PYVALS_NAME = 'UFUNC_PYVALS'
WE = WE
WEEKLY = 2
WRAP = 1
YEARLY = 0
__version__ = '1.1.0.dev5176'
absolute = <ufunc 'absolute'>
add = <ufunc 'add'>
arccos = <ufunc 'arccos'>
arccosh = <ufunc 'arccosh'>
arcsin = <ufunc 'arcsin'>
arcsinh = <ufunc 'arcsinh'>
arctan = <ufunc 'arctan'>
arctan2 = <ufunc 'arctan2'>
arctanh = <ufunc 'arctanh'>
bitwise_and = <ufunc 'bitwise_and'>
bitwise_not = <ufunc 'invert'>
bitwise_or = <ufunc 'bitwise_or'>
bitwise_xor = <ufunc 'bitwise_xor'>
c_ = <numpy.lib.index_tricks.CClass object at 0xc48510>
cast = {<type 'numpy.int64'>: <function <lambda> at 0xa...mpy.complex64'>: <function <lambda> at 0xa9d500>}
ceil = <ufunc 'ceil'>
colorbar_doc = '\nAdd a colorbar to a plot.\n\nFunction signatures ...e, do not use any of the\naxes properties kwargs.\n'
conj = <ufunc 'conjugate'>
conjugate = <ufunc 'conjugate'>
cos = <ufunc 'cos'>
cosh = <ufunc 'cosh'>
degrees = <ufunc 'degrees'>
divide = <ufunc 'divide'>
e = 2.7182818284590451
equal = <ufunc 'equal'>
exp = <ufunc 'exp'>
expm1 = <ufunc 'expm1'>
fabs = <ufunc 'fabs'>
floor = <ufunc 'floor'>
floor_divide = <ufunc 'floor_divide'>
fmod = <ufunc 'fmod'>
frexp = <ufunc 'frexp'>
greater = <ufunc 'greater'>
greater_equal = <ufunc 'greater_equal'>
hypot = <ufunc 'hypot'>
index_exp = <numpy.lib.index_tricks.IndexExpression object at 0xc48610>
inf = inf
infty = inf
invert = <ufunc 'invert'>
isfinite = <ufunc 'isfinite'>
isinf = <ufunc 'isinf'>
isnan = <ufunc 'isnan'>
ldexp = <ufunc 'ldexp'>
left_shift = <ufunc 'left_shift'>
less = <ufunc 'less'>
less_equal = <ufunc 'less_equal'>
little_endian = True
log = <ufunc 'log'>
log10 = <ufunc 'log10'>
log1p = <ufunc 'log1p'>
logical_and = <ufunc 'logical_and'>
logical_not = <ufunc 'logical_not'>
logical_or = <ufunc 'logical_or'>
logical_xor = <ufunc 'logical_xor'>
maximum = <ufunc 'maximum'>
mgrid = <numpy.lib.index_tricks.nd_grid object at 0xc48390>
minimum = <ufunc 'minimum'>
mod = <ufunc 'remainder'>
modf = <ufunc 'modf'>
multiply = <ufunc 'multiply'>
nan = nan
nbytes = {<type 'numpy.int64'>: 8, <type 'numpy.int16'>: ...type 'numpy.uint8'>: 1, <type 'numpy.uint16'>: 2}
negative = <ufunc 'negative'>
newaxis = None
not_equal = <ufunc 'not_equal'>
ogrid = <numpy.lib.index_tricks.nd_grid object at 0xc483d0>
ones_like = <ufunc 'ones_like'>
pi = 3.1415926535897931
r_ = <numpy.lib.index_tricks.RClass object at 0xc48490>
radians = <ufunc 'radians'>
rcParams = {'figure.subplot.right': 0.90000000000000002, 'm...persize': 'letter', 'svg.embed_char_paths': True}
rcParamsDefault = {'figure.subplot.right': 0.90000000000000002, 'm...persize': 'letter', 'svg.embed_char_paths': True}
reciprocal = <ufunc 'reciprocal'>
remainder = <ufunc 'remainder'>
right_shift = <ufunc 'right_shift'>
rint = <ufunc 'rint'>
s_ = <numpy.lib.index_tricks.IndexExpression object at 0xc48690>
sctypeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2: <type 'numpy.uint8'>, 3: <type 'numpy.int16'>, 4: <type 'numpy.uint16'>, 5: <type 'numpy.int32'>, 6: <type 'numpy.uint32'>, 7: <type 'numpy.int64'>, 8: <type 'numpy.uint64'>, 9: <type 'numpy.int64'>, ...}
sctypeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bool_'>, 'Complex128': <type 'numpy.complex256'>, 'Complex32': <type 'numpy.complex64'>, 'Complex64': <type 'numpy.complex128'>, 'D': 'Complex64', 'F': 'Complex32', 'Float128': <type 'numpy.float128'>, 'Float32': <type 'numpy.float32'>, ...}
sctypes = {'complex': [<type 'numpy.complex64'>, <type 'numpy.complex128'>, <type 'numpy.complex256'>], 'float': [<type 'numpy.float32'>, <type 'numpy.float64'>, <type 'numpy.float128'>], 'int': [<type 'numpy.int8'>, <type 'numpy.int16'>, <type 'numpy.int32'>, <type 'numpy.int64'>], 'others': [<type 'bool'>, <type 'object'>, <type 'str'>, <type 'unicode'>, <type 'numpy.void'>], 'uint': [<type 'numpy.uint8'>, <type 'numpy.uint16'>, <type 'numpy.uint32'>, <type 'numpy.uint64'>]}
sign = <ufunc 'sign'>
signbit = <ufunc 'signbit'>
sin = <ufunc 'sin'>
sinh = <ufunc 'sinh'>
sqrt = <ufunc 'sqrt'>
square = <ufunc 'square'>
subtract = <ufunc 'subtract'>
tan = <ufunc 'tan'>
tanh = <ufunc 'tanh'>
true_divide = <ufunc 'true_divide'>
typeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2: <type 'numpy.uint8'>, 3: <type 'numpy.int16'>, 4: <type 'numpy.uint16'>, 5: <type 'numpy.int32'>, 6: <type 'numpy.uint32'>, 7: <type 'numpy.int64'>, 8: <type 'numpy.uint64'>, 9: <type 'numpy.int64'>, ...}
typeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bool_'>, 'Complex128': <type 'numpy.complex256'>, 'Complex32': <type 'numpy.complex64'>, 'Complex64': <type 'numpy.complex128'>, 'D': 'Complex64', 'F': 'Complex32', 'Float128': <type 'numpy.float128'>, 'Float32': <type 'numpy.float32'>, ...}
typecodes = {'All': '?bhilqpBHILQPfdgFDGSUVO', 'AllFloat': 'fdgFDG', 'AllInteger': 'bBhHiIlLqQpP', 'Character': 'c', 'Complex': 'FDG', 'Float': 'fdg', 'Integer': 'bhilqp', 'UnsignedInteger': 'BHILQP'}
@footer@

Data
		ALLOW_THREADS = 1 BUFSIZE = 10000 CLIP = 0 DAILY = 3 ERR_CALL = 3 ERR_DEFAULT = 0 ERR_DEFAULT2 = 2084 ERR_IGNORE = 0 ERR_LOG = 5 ERR_PRINT = 4 ERR_RAISE = 2 ERR_WARN = 1 FLOATING_POINT_SUPPORT = 1 FPE_DIVIDEBYZERO = 1 FPE_INVALID = 8 FPE_OVERFLOW = 2 FPE_UNDERFLOW = 4 FR = FR False_ = False HOURLY = 4 Inf = inf Infinity = inf MAXDIMS = 32 MINUTELY = 5 MO = MO MONTHLY = 1 NAN = nan NINF = -inf NZERO = -0.0 NaN = nan PINF = inf PZERO = 0.0 RAISE = 2 SA = SA SECONDLY = 6 SHIFT_DIVIDEBYZERO = 0 SHIFT_INVALID = 9 SHIFT_OVERFLOW = 3 SHIFT_UNDERFLOW = 6 SU = SU ScalarType = (<type 'int'>, <type 'float'>, <type 'complex'>, <type 'long'>, <type 'bool'>, <type 'str'>, <type 'unicode'>, <type 'buffer'>, <type 'numpy.int64'>, <type 'numpy.int16'>, <type 'numpy.uint64'>, <type 'numpy.complex64'>, <type 'numpy.int64'>, <type 'numpy.void'>, <type 'numpy.int32'>, <type 'numpy.complex128'>, <type 'numpy.float32'>, <type 'numpy.float64'>, <type 'numpy.bool_'>, <type 'numpy.uint64'>, ...) TH = TH TU = TU True_ = True UFUNC_BUFSIZE_DEFAULT = 10000 UFUNC_PYVALS_NAME = 'UFUNC_PYVALS' WE = WE WEEKLY = 2 WRAP = 1 YEARLY = 0 __version__ = '1.1.0.dev5176' absolute = <ufunc 'absolute'> add = <ufunc 'add'> arccos = <ufunc 'arccos'> arccosh = <ufunc 'arccosh'> arcsin = <ufunc 'arcsin'> arcsinh = <ufunc 'arcsinh'> arctan = <ufunc 'arctan'> arctan2 = <ufunc 'arctan2'> arctanh = <ufunc 'arctanh'> bitwise_and = <ufunc 'bitwise_and'> bitwise_not = <ufunc 'invert'> bitwise_or = <ufunc 'bitwise_or'> bitwise_xor = <ufunc 'bitwise_xor'> c_ = <numpy.lib.index_tricks.CClass object at 0xc48510> cast = {<type 'numpy.int64'>: <function <lambda> at 0xa...mpy.complex64'>: <function <lambda> at 0xa9d500>} ceil = <ufunc 'ceil'> colorbar_doc = '\nAdd a colorbar to a plot.\n\nFunction signatures ...e, do not use any of the\naxes properties kwargs.\n' conj = <ufunc 'conjugate'> conjugate = <ufunc 'conjugate'> cos = <ufunc 'cos'> cosh = <ufunc 'cosh'> degrees = <ufunc 'degrees'> divide = <ufunc 'divide'> e = 2.7182818284590451 equal = <ufunc 'equal'> exp = <ufunc 'exp'> expm1 = <ufunc 'expm1'> fabs = <ufunc 'fabs'> floor = <ufunc 'floor'> floor_divide = <ufunc 'floor_divide'> fmod = <ufunc 'fmod'> frexp = <ufunc 'frexp'> greater = <ufunc 'greater'> greater_equal = <ufunc 'greater_equal'> hypot = <ufunc 'hypot'> index_exp = <numpy.lib.index_tricks.IndexExpression object at 0xc48610> inf = inf infty = inf invert = <ufunc 'invert'> isfinite = <ufunc 'isfinite'> isinf = <ufunc 'isinf'> isnan = <ufunc 'isnan'> ldexp = <ufunc 'ldexp'> left_shift = <ufunc 'left_shift'> less = <ufunc 'less'> less_equal = <ufunc 'less_equal'> little_endian = True log = <ufunc 'log'> log10 = <ufunc 'log10'> log1p = <ufunc 'log1p'> logical_and = <ufunc 'logical_and'> logical_not = <ufunc 'logical_not'> logical_or = <ufunc 'logical_or'> logical_xor = <ufunc 'logical_xor'> maximum = <ufunc 'maximum'> mgrid = <numpy.lib.index_tricks.nd_grid object at 0xc48390> minimum = <ufunc 'minimum'> mod = <ufunc 'remainder'> modf = <ufunc 'modf'> multiply = <ufunc 'multiply'> nan = nan nbytes = {<type 'numpy.int64'>: 8, <type 'numpy.int16'>: ...type 'numpy.uint8'>: 1, <type 'numpy.uint16'>: 2} negative = <ufunc 'negative'> newaxis = None not_equal = <ufunc 'not_equal'> ogrid = <numpy.lib.index_tricks.nd_grid object at 0xc483d0> ones_like = <ufunc 'ones_like'> pi = 3.1415926535897931 r_ = <numpy.lib.index_tricks.RClass object at 0xc48490> radians = <ufunc 'radians'> rcParams = {'figure.subplot.right': 0.90000000000000002, 'm...persize': 'letter', 'svg.embed_char_paths': True} rcParamsDefault = {'figure.subplot.right': 0.90000000000000002, 'm...persize': 'letter', 'svg.embed_char_paths': True} reciprocal = <ufunc 'reciprocal'> remainder = <ufunc 'remainder'> right_shift = <ufunc 'right_shift'> rint = <ufunc 'rint'> s_ = <numpy.lib.index_tricks.IndexExpression object at 0xc48690> sctypeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2: <type 'numpy.uint8'>, 3: <type 'numpy.int16'>, 4: <type 'numpy.uint16'>, 5: <type 'numpy.int32'>, 6: <type 'numpy.uint32'>, 7: <type 'numpy.int64'>, 8: <type 'numpy.uint64'>, 9: <type 'numpy.int64'>, ...} sctypeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bool_'>, 'Complex128': <type 'numpy.complex256'>, 'Complex32': <type 'numpy.complex64'>, 'Complex64': <type 'numpy.complex128'>, 'D': 'Complex64', 'F': 'Complex32', 'Float128': <type 'numpy.float128'>, 'Float32': <type 'numpy.float32'>, ...} sctypes = {'complex': [<type 'numpy.complex64'>, <type 'numpy.complex128'>, <type 'numpy.complex256'>], 'float': [<type 'numpy.float32'>, <type 'numpy.float64'>, <type 'numpy.float128'>], 'int': [<type 'numpy.int8'>, <type 'numpy.int16'>, <type 'numpy.int32'>, <type 'numpy.int64'>], 'others': [<type 'bool'>, <type 'object'>, <type 'str'>, <type 'unicode'>, <type 'numpy.void'>], 'uint': [<type 'numpy.uint8'>, <type 'numpy.uint16'>, <type 'numpy.uint32'>, <type 'numpy.uint64'>]} sign = <ufunc 'sign'> signbit = <ufunc 'signbit'> sin = <ufunc 'sin'> sinh = <ufunc 'sinh'> sqrt = <ufunc 'sqrt'> square = <ufunc 'square'> subtract = <ufunc 'subtract'> tan = <ufunc 'tan'> tanh = <ufunc 'tanh'> true_divide = <ufunc 'true_divide'> typeDict = {0: <type 'numpy.bool_'>, 1: <type 'numpy.int8'>, 2: <type 'numpy.uint8'>, 3: <type 'numpy.int16'>, 4: <type 'numpy.uint16'>, 5: <type 'numpy.int32'>, 6: <type 'numpy.uint32'>, 7: <type 'numpy.int64'>, 8: <type 'numpy.uint64'>, 9: <type 'numpy.int64'>, ...} typeNA = {'?': 'Bool', 'B': 'UInt8', 'Bool': <type 'numpy.bool_'>, 'Complex128': <type 'numpy.complex256'>, 'Complex32': <type 'numpy.complex64'>, 'Complex64': <type 'numpy.complex128'>, 'D': 'Complex64', 'F': 'Complex32', 'Float128': <type 'numpy.float128'>, 'Float32': <type 'numpy.float32'>, ...} typecodes = {'All': '?bhilqpBHILQPfdgFDGSUVO', 'AllFloat': 'fdgFDG', 'AllInteger': 'bBhHiIlLqQpP', 'Character': 'c', 'Complex': 'FDG', 'Float': 'fdg', 'Integer': 'bhilqp', 'UnsignedInteger': 'BHILQP'}