print 'Good morning, STSCI!'
Good morning, STSCI!
print 'Hello, earthlings!'
print 'Take me to your leader!'
Hello, earthlings! Take me to your leader!
print 'Hubble rocks!'
Hubble rocks!
Put text over here.
emphasize bold
Best equation ever: $E = mc^2$
ls
README.rst Session1_Demo.ipynb Session2_Basics.ipynb Session1_Astro_Demo.ipynb Session1_Introduction.ipynb
cd Music
[Errno 2] No such file or directory: 'Music' /Users/mrdavis/projects/scientific-python-training-2012/lecture_notebooks
pwd
u'/Users/mrdavis/projects/scientific-python-training-2012/lecture_notebooks'
!echo "IPython"
IPython
files = !ls
print files
['README.rst', 'Session1_Astro_Demo.ipynb', 'Session1_Demo.ipynb', 'Session1_Introduction.ipynb', 'Session2_Basics.ipynb']
range?
%pylab inline
Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline]. For more information, type 'help(pylab)'.
import pyfits
data = pyfits.getdata('../data/pix.fits')
imshow(data, vmax=500)
<matplotlib.image.AxesImage at 0x106807110>
import numpy as np
data[data <= 1] = 1
ldata = np.log(data)
imshow(ldata)
gray()
hdr = pyfits.getheader('../data/pix.fits')
hdr
SIMPLE = T / Fits standard BITPIX = 16 / Bits per pixel NAXIS = 2 / Number of axes NAXIS1 = 512 / Axis length NAXIS2 = 512 / Axis length EXTEND = F / File may contain extensions ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator DATE = '2004-07-20T20:35:38' / Date FITS file was generated IRAF-TLM= '17:35:38 (20/07/2004)' / Time of last modification OBJECT = 'm51 B 600s' / Name of the object observed IRAF-MAX= 1.993600E4 / DATA MAX IRAF-MIN= -1.000000E0 / DATA MIN CCDPICNO= 53 / ORIGINAL CCD PICTURE NUMBER ITIME = 600 / REQUESTED INTEGRATION TIME (SECS) TTIME = 600 / TOTAL ELAPSED TIME (SECS) OTIME = 600 / ACTUAL INTEGRATION TIME (SECS) DATA-TYP= 'OBJECT (0)' / OBJECT,DARK,BIAS,ETC. DATE-OBS= '05/04/87' / DATE DD/MM/YY RA = '13:29:24.00' / RIGHT ASCENSION DEC = '47:15:34.00' / DECLINATION EPOCH = 0.00 / EPOCH OF RA AND DEC ZD = '22:14:00.00' / ZENITH DISTANCE UT = ' 9:27:27.00' / UNIVERSAL TIME ST = '14:53:42.00' / SIDEREAL TIME CAM-ID = 1 / CAMERA HEAD ID CAM-TEMP= -106.22 / CAMERA TEMPERATURE, DEG C DEW-TEMP= -180.95 / DEWAR TEMPRATURE, DEG C F1POS = 2 / FILTER BOLT I POSITION F2POS = 0 / FILTER BOLT II POSITION TVFILT = 0 / TV FILTER CMP-LAMP= 0 / COMPARISON LAMP TILT-POS= 0 / TILT POSITION BIAS-PIX= 0 / BI-FLAG = 0 / BIAS SUBTRACT FLAG BP-FLAG = 0 / BAD PIXEL FLAG CR-FLAG = 0 / BAD PIXEL FLAG DK-FLAG = 0 / DARK SUBTRACT FLAG FR-FLAG = 0 / FRINGE FLAG FR-SCALE= 0.00 / FRINGE SCALING PARAMETER TRIM = 'Apr 22 14:11 Trim image section is [3:510,3:510]' BT-FLAG = 'Apr 22 14:11 Overscan correction strip is [515:544,3:510]' FF-FLAG = 'Apr 22 14:11 Flat field image is Flat1.imh with scale=183.9447' CCDPROC = 'Apr 22 14:11 CCD processing done' AIRMASS = 1.08015632629395 / AIRMASS HISTORY 'KPNO-IRAF' HISTORY '24-04-87' HISTORY 'KPNO-IRAF' / HISTORY '08-04-92' /
print hdr
SIMPLE = T / Fits standard BITPIX = 16 / Bits per pixel NAXIS = 2 / Number of axes NAXIS1 = 512 / Axis length NAXIS2 = 512 / Axis length EXTEND = F / File may contain extensions ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator DATE = '2004-07-20T20:35:38' / Date FITS file was generated IRAF-TLM= '17:35:38 (20/07/2004)' / Time of last modification OBJECT = 'm51 B 600s' / Name of the object observed IRAF-MAX= 1.993600E4 / DATA MAX IRAF-MIN= -1.000000E0 / DATA MIN CCDPICNO= 53 / ORIGINAL CCD PICTURE NUMBER ITIME = 600 / REQUESTED INTEGRATION TIME (SECS) TTIME = 600 / TOTAL ELAPSED TIME (SECS) OTIME = 600 / ACTUAL INTEGRATION TIME (SECS) DATA-TYP= 'OBJECT (0)' / OBJECT,DARK,BIAS,ETC. DATE-OBS= '05/04/87' / DATE DD/MM/YY RA = '13:29:24.00' / RIGHT ASCENSION DEC = '47:15:34.00' / DECLINATION EPOCH = 0.00 / EPOCH OF RA AND DEC ZD = '22:14:00.00' / ZENITH DISTANCE UT = ' 9:27:27.00' / UNIVERSAL TIME ST = '14:53:42.00' / SIDEREAL TIME CAM-ID = 1 / CAMERA HEAD ID CAM-TEMP= -106.22 / CAMERA TEMPERATURE, DEG C DEW-TEMP= -180.95 / DEWAR TEMPRATURE, DEG C F1POS = 2 / FILTER BOLT I POSITION F2POS = 0 / FILTER BOLT II POSITION TVFILT = 0 / TV FILTER CMP-LAMP= 0 / COMPARISON LAMP TILT-POS= 0 / TILT POSITION BIAS-PIX= 0 / BI-FLAG = 0 / BIAS SUBTRACT FLAG BP-FLAG = 0 / BAD PIXEL FLAG CR-FLAG = 0 / BAD PIXEL FLAG DK-FLAG = 0 / DARK SUBTRACT FLAG FR-FLAG = 0 / FRINGE FLAG FR-SCALE= 0.00 / FRINGE SCALING PARAMETER TRIM = 'Apr 22 14:11 Trim image section is [3:510,3:510]' BT-FLAG = 'Apr 22 14:11 Overscan correction strip is [515:544,3:510]' FF-FLAG = 'Apr 22 14:11 Flat field image is Flat1.imh with scale=183.9447' CCDPROC = 'Apr 22 14:11 CCD processing done' AIRMASS = 1.08015632629395 / AIRMASS HISTORY 'KPNO-IRAF' HISTORY '24-04-87' HISTORY 'KPNO-IRAF' / HISTORY '08-04-92' / END
hdr['object']
'm51 B 600s'
hdr.keys()
['SIMPLE', 'BITPIX', 'NAXIS', 'NAXIS1', 'NAXIS2', 'EXTEND', 'ORIGIN', 'DATE', 'IRAF-TLM', 'OBJECT', 'IRAF-MAX', 'IRAF-MIN', 'CCDPICNO', 'ITIME', 'TTIME', 'OTIME', 'DATA-TYP', 'DATE-OBS', 'RA', 'DEC', 'EPOCH', 'ZD', 'UT', 'ST', 'CAM-ID', 'CAM-TEMP', 'DEW-TEMP', 'F1POS', 'F2POS', 'TVFILT', 'CMP-LAMP', 'TILT-POS', 'BIAS-PIX', 'BI-FLAG', 'BP-FLAG', 'CR-FLAG', 'DK-FLAG', 'FR-FLAG', 'FR-SCALE', 'TRIM', 'BT-FLAG', 'FF-FLAG', 'CCDPROC', 'AIRMASS', 'HISTORY', 'HISTORY', 'HISTORY', 'HISTORY', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']
plot(data[256])
plot(data[246])
[<matplotlib.lines.Line2D at 0x106812110>]
print data[256, 256]
3666
print data.shape
print data.size
print data.nbytes
print data.dtype
(512, 512) 262144 524288 >i2
print data.dtype.name
int16
data.sum()
28394236
data.sum(axis=1)
array([ 27681, 27913, 28150, 27963, 28152, 28254, 28191, 28120, 28302, 28404, 28521, 28563, 28766, 28937, 29011, 28929, 29222, 29633, 29655, 29809, 30121, 30246, 30487, 30663, 31002, 31479, 31885, 32187, 32487, 32831, 33301, 34232, 34825, 34435, 34158, 34127, 34280, 34399, 34720, 35141, 35663, 36344, 36676, 36831, 37111, 37359, 37590, 38035, 38595, 38890, 39031, 39292, 39654, 39829, 40063, 40236, 40290, 40756, 41265, 42336, 45712, 49010, 47794, 45328, 46463, 51643, 54405, 49559, 44751, 43286, 42736, 42269, 42019, 42073, 42073, 42545, 42502, 41883, 42382, 42340, 42335, 42583, 42893, 43113, 43087, 42961, 43399, 43690, 43870, 44362, 44923, 45443, 45923, 46035, 46737, 47270, 48065, 48699, 49128, 49500, 49971, 49952, 49241, 50261, 49935, 50248, 50668, 51701, 52726, 34947, 53060, 52955, 51924, 51548, 51433, 50980, 50891, 50930, 51194, 51033, 51101, 51521, 52186, 53708, 55735, 56617, 55529, 53970, 53580, 56364, 59592, 57349, 53818, 52638, 51897, 51329, 50784, 50386, 50110, 50093, 50867, 51441, 51706, 52144, 52509, 52563, 52732, 52930, 53018, 53270, 53618, 53887, 54130, 54375, 54679, 55680, 57232, 57372, 55922, 54650, 54298, 54660, 55185, 55301, 55250, 55754, 56552, 57137, 57766, 58826, 60436, 62218, 63698, 64764, 65488, 65934, 66349, 67362, 68720, 69289, 68231, 67000, 65412, 64809, 65944, 69494, 81313, 108129, 121173, 91719, 69963, 64237, 62918, 62735, 62517, 62310, 61895, 61691, 61446, 61563, 61187, 61690, 62183, 62857, 63513, 64290, 64919, 65174, 65003, 64619, 64332, 64044, 63652, 63206, 63411, 63824, 64837, 66011, 67401, 68721, 69217, 69861, 70520, 71151, 73223, 75178, 74951, 74552, 75607, 77173, 79926, 80806, 79107, 78393, 79797, 81487, 83119, 85198, 87629, 89907, 91627, 93310, 94576, 95711, 96857, 98086, 99414, 100612, 101656, 103226, 106495, 110086, 112636, 114786, 117356, 121027, 126775, 134085, 137502, 132594, 124318, 118643, 115461, 113562, 112255, 109878, 106760, 104664, 102846, 100574, 98904, 99661, 104563, 109923, 106648, 97962, 92355, 88825, 86082, 83550, 81805, 80777, 80342, 80644, 81020, 80313, 78249, 75937, 74203, 73775, 72839, 71080, 69998, 69220, 68181, 67612, 67456, 67906, 66872, 67285, 66283, 65545, 64332, 63786, 63655, 63800, 63586, 63618, 63134, 62882, 62858, 63245, 63448, 63334, 63685, 64349, 64780, 61399, 64052, 63166, 62937, 62869, 62521, 62509, 62779, 63193, 63435, 62523, 61252, 60356, 59921, 59629, 59083, 58548, 58651, 59391, 60512, 61381, 62106, 62710, 62674, 61650, 60915, 60587, 60336, 59983, 59779, 59578, 59195, 58953, 58828, 58690, 58794, 59073, 59471, 59863, 59695, 58864, 58164, 57755, 57524, 57555, 57321, 56628, 56138, 55468, 54320, 53844, 53680, 53175, 52979, 52906, 52759, 52575, 52565, 52274, 51950, 51837, 52082, 52510, 52592, 52817, 53012, 52897, 52602, 52273, 52257, 52594, 53087, 53957, 54603, 54331, 53957, 53730, 53864, 54408, 54964, 55917, 56975, 56738, 56424, 56280, 56433, 56228, 56708, 57457, 58169, 60012, 64260, 65278, 59910, 56320, 54712, 53596, 52281, 52264, 50831, 49802, 49052, 49318, 49743, 49357, 48871, 48425, 48721, 48647, 48357, 47907, 47464, 46949, 46821, 47254, 48116, 49403, 49414, 47918, 46749, 46355, 46266, 45138, 43942, 43385, 42957, 42661, 42964, 42435, 40628, 39093, 38538, 37769, 36533, 35351, 34579, 33951, 33388, 33178, 33182, 33097, 33240, 33584, 33591, 33046, 32525, 32277, 31979, 31669, 31383, 31009, 30779, 30543, 30231, 30244, 30147, 30057, 29865, 29484, 29482, 29267, 29172, 28996, 29051, 29013, 28620, 28590, 28392, 28339, 28323, 28274, 28313, 28199, 28159, 27865, 28065, 28048, 27930, 28005, 28015, 27834, 27769, 27975, 27892, 27959, 28116, 28349, 28875, 29642, 29491, 28872, 28568, 28479, 28541, 28533])
data?
help(data)
Help on ndarray object: class ndarray(__builtin__.object) | ndarray(shape, dtype=float, buffer=None, offset=0, | strides=None, order=None) | | An array object represents a multidimensional, homogeneous array | of fixed-size items. An associated data-type object describes the | format of each element in the array (its byte-order, how many bytes it | occupies in memory, whether it is an integer, a floating point number, | or something else, etc.) | | Arrays should be constructed using `array`, `zeros` or `empty` (refer | to the See Also section below). The parameters given here refer to | a low-level method (`ndarray(...)`) for instantiating an array. | | For more information, refer to the `numpy` module and examine the | the methods and attributes of an array. | | Parameters | ---------- | (for the __new__ method; see Notes below) | | shape : tuple of ints | Shape of created array. | dtype : data-type, optional | Any object that can be interpreted as a numpy data type. | buffer : object exposing buffer interface, optional | Used to fill the array with data. | offset : int, optional | Offset of array data in buffer. | strides : tuple of ints, optional | Strides of data in memory. | order : {'C', 'F'}, optional | Row-major or column-major order. | | Attributes | ---------- | T : ndarray | Transpose of the array. | data : buffer | The array's elements, in memory. | dtype : dtype object | Describes the format of the elements in the array. | flags : dict | Dictionary containing information related to memory use, e.g., | 'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc. | flat : numpy.flatiter object | Flattened version of the array as an iterator. The iterator | allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for | assignment examples; TODO). | imag : ndarray | Imaginary part of the array. | real : ndarray | Real part of the array. | size : int | Number of elements in the array. | itemsize : int | The memory use of each array element in bytes. | nbytes : int | The total number of bytes required to store the array data, | i.e., ``itemsize * size``. | ndim : int | The array's number of dimensions. | shape : tuple of ints | Shape of the array. | strides : tuple of ints | The step-size required to move from one element to the next in | memory. For example, a contiguous ``(3, 4)`` array of type | ``int16`` in C-order has strides ``(8, 2)``. This implies that | to move from element to element in memory requires jumps of 2 bytes. | To move from row-to-row, one needs to jump 8 bytes at a time | (``2 * 4``). | ctypes : ctypes object | Class containing properties of the array needed for interaction | with ctypes. | base : ndarray | If the array is a view into another array, that array is its `base` | (unless that array is also a view). The `base` array is where the | array data is actually stored. | | See Also | -------- | array : Construct an array. | zeros : Create an array, each element of which is zero. | empty : Create an array, but leave its allocated memory unchanged (i.e., | it contains "garbage"). | dtype : Create a data-type. | | Notes | ----- | There are two modes of creating an array using ``__new__``: | | 1. If `buffer` is None, then only `shape`, `dtype`, and `order` | are used. | 2. If `buffer` is an object exposing the buffer interface, then | all keywords are interpreted. | | No ``__init__`` method is needed because the array is fully initialized | after the ``__new__`` method. | | Examples | -------- | These examples illustrate the low-level `ndarray` constructor. Refer | to the `See Also` section above for easier ways of constructing an | ndarray. | | First mode, `buffer` is None: | | >>> np.ndarray(shape=(2,2), dtype=float, order='F') | array([[ -1.13698227e+002, 4.25087011e-303], | [ 2.88528414e-306, 3.27025015e-309]]) #random | | Second mode: | | >>> np.ndarray((2,), buffer=np.array([1,2,3]), | ... offset=np.int_().itemsize, | ... dtype=int) # offset = 1*itemsize, i.e. skip first element | array([2, 3]) | | Methods defined here: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | a.__array__(|dtype) -> reference if type unchanged, copy otherwise. | | Returns either a new reference to self if dtype is not given or a new array | of provided data type if dtype is different from the current dtype of the | array. | | __array_prepare__(...) | a.__array_prepare__(obj) -> Object of same type as ndarray object obj. | | __array_wrap__(...) | a.__array_wrap__(obj) -> Object of same type as ndarray object a. | | __contains__(...) | x.__contains__(y) <==> y in x | | __copy__(...) | a.__copy__([order]) | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | If order is 'C' (False) then the result is contiguous (default). | If order is 'Fortran' (True) then the result has fortran order. | If order is 'Any' (None) then the result has fortran order | only if the array already is in fortran order. | | __deepcopy__(...) | a.__deepcopy__() -> Deep copy of array. | | Used if copy.deepcopy is called on an array. | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __delslice__(...) | x.__delslice__(i, j) <==> del x[i:j] | | Use of negative indices is not supported. | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __iand__(...) | x.__iand__(y) <==> x&=y | | __idiv__(...) | x.__idiv__(y) <==> x/=y | | __ifloordiv__(...) | x.__ifloordiv__(y) <==> x//y | | __ilshift__(...) | x.__ilshift__(y) <==> x<<=y | | __imod__(...) | x.__imod__(y) <==> x%=y | | __imul__(...) | x.__imul__(y) <==> x*=y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __ior__(...) | x.__ior__(y) <==> x|=y | | __ipow__(...) | x.__ipow__(y) <==> x**=y | | __irshift__(...) | x.__irshift__(y) <==> x>>=y | | __isub__(...) | x.__isub__(y) <==> x-=y | | __iter__(...) | x.__iter__() <==> iter(x) | | __itruediv__(...) | x.__itruediv__(y) <==> x/y | | __ixor__(...) | x.__ixor__(y) <==> x^=y | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x<<y | | __lt__(...) | x.__lt__(y) <==> x<y | | __mod__(...) | x.__mod__(y) <==> x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | a.__reduce__() | | For pickling. | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y<<x | | __rmod__(...) | x.__rmod__(y) <==> y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | __setslice__(...) | x.__setslice__(i, j, y) <==> x[i:j]=y | | Use of negative indices is not supported. | | __setstate__(...) | a.__setstate__(version, shape, dtype, isfortran, rawdata) | | For unpickling. | | Parameters | ---------- | version : int | optional pickle version. If omitted defaults to 0. | shape : tuple | dtype : data-type | isFortran : bool | rawdata : string or list | a binary string with the data (or a list if 'a' is an object array) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | a.all(axis=None, out=None) | | Returns True if all elements evaluate to True. | | Refer to `numpy.all` for full documentation. | | See Also | -------- | numpy.all : equivalent function | | any(...) | a.any(axis=None, out=None) | | Returns True if any of the elements of `a` evaluate to True. | | Refer to `numpy.any` for full documentation. | | See Also | -------- | numpy.any : equivalent function | | argmax(...) | a.argmax(axis=None, out=None) | | Return indices of the maximum values along the given axis. | | Refer to `numpy.argmax` for full documentation. | | See Also | -------- | numpy.argmax : equivalent function | | argmin(...) | a.argmin(axis=None, out=None) | | Return indices of the minimum values along the given axis of `a`. | | Refer to `numpy.argmin` for detailed documentation. | | See Also | -------- | numpy.argmin : equivalent function | | argsort(...) | a.argsort(axis=-1, kind='quicksort', order=None) | | Returns the indices that would sort this array. | | Refer to `numpy.argsort` for full documentation. | | See Also | -------- | numpy.argsort : equivalent function | | astype(...) | a.astype(t) | | Copy of the array, cast to a specified type. | | Parameters | ---------- | t : str or dtype | Typecode or data-type to which the array is cast. | | Raises | ------ | ComplexWarning : | When casting from complex to float or int. To avoid this, | one should use ``a.real.astype(t)``. | | Examples | -------- | >>> x = np.array([1, 2, 2.5]) | >>> x | array([ 1. , 2. , 2.5]) | | >>> x.astype(int) | array([1, 2, 2]) | | byteswap(...) | a.byteswap(inplace) | | Swap the bytes of the array elements | | Toggle between low-endian and big-endian data representation by | returning a byteswapped array, optionally swapped in-place. | | Parameters | ---------- | inplace: bool, optional | If ``True``, swap bytes in-place, default is ``False``. | | Returns | ------- | out: ndarray | The byteswapped array. If `inplace` is ``True``, this is | a view to self. | | Examples | -------- | >>> A = np.array([1, 256, 8755], dtype=np.int16) | >>> map(hex, A) | ['0x1', '0x100', '0x2233'] | >>> A.byteswap(True) | array([ 256, 1, 13090], dtype=int16) | >>> map(hex, A) | ['0x100', '0x1', '0x3322'] | | Arrays of strings are not swapped | | >>> A = np.array(['ceg', 'fac']) | >>> A.byteswap() | array(['ceg', 'fac'], | dtype='|S3') | | choose(...) | a.choose(choices, out=None, mode='raise') | | Use an index array to construct a new array from a set of choices. | | Refer to `numpy.choose` for full documentation. | | See Also | -------- | numpy.choose : equivalent function | | clip(...) | a.clip(a_min, a_max, out=None) | | Return an array whose values are limited to ``[a_min, a_max]``. | | Refer to `numpy.clip` for full documentation. | | See Also | -------- | numpy.clip : equivalent function | | compress(...) | a.compress(condition, axis=None, out=None) | | Return selected slices of this array along given axis. | | Refer to `numpy.compress` for full documentation. | | See Also | -------- | numpy.compress : equivalent function | | conj(...) | a.conj() | | Complex-conjugate all elements. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | conjugate(...) | a.conjugate() | | Return the complex conjugate, element-wise. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | copy(...) | a.copy(order='C') | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | By default, the result is stored in C-contiguous (row-major) order in | memory. If `order` is `F`, the result has 'Fortran' (column-major) | order. If order is 'A' ('Any'), then the result has the same order | as the input. | | Examples | -------- | >>> x = np.array([[1,2,3],[4,5,6]], order='F') | | >>> y = x.copy() | | >>> x.fill(0) | | >>> x | array([[0, 0, 0], | [0, 0, 0]]) | | >>> y | array([[1, 2, 3], | [4, 5, 6]]) | | >>> y.flags['C_CONTIGUOUS'] | True | | cumprod(...) | a.cumprod(axis=None, dtype=None, out=None) | | Return the cumulative product of the elements along the given axis. | | Refer to `numpy.cumprod` for full documentation. | | See Also | -------- | numpy.cumprod : equivalent function | | cumsum(...) | a.cumsum(axis=None, dtype=None, out=None) | | Return the cumulative sum of the elements along the given axis. | | Refer to `numpy.cumsum` for full documentation. | | See Also | -------- | numpy.cumsum : equivalent function | | diagonal(...) | a.diagonal(offset=0, axis1=0, axis2=1) | | Return specified diagonals. | | Refer to `numpy.diagonal` for full documentation. | | See Also | -------- | numpy.diagonal : equivalent function | | dot(...) | a.dot(b, out=None) | | Dot product of two arrays. | | Refer to `numpy.dot` for full documentation. | | See Also | -------- | numpy.dot : equivalent function | | Examples | -------- | >>> a = np.eye(2) | >>> b = np.ones((2, 2)) * 2 | >>> a.dot(b) | array([[ 2., 2.], | [ 2., 2.]]) | | This array method can be conveniently chained: | | >>> a.dot(b).dot(b) | array([[ 8., 8.], | [ 8., 8.]]) | | dump(...) | a.dump(file) | | Dump a pickle of the array to the specified file. | The array can be read back with pickle.load or numpy.load. | | Parameters | ---------- | file : str | A string naming the dump file. | | dumps(...) | a.dumps() | | Returns the pickle of the array as a string. | pickle.loads or numpy.loads will convert the string back to an array. | | Parameters | ---------- | None | | fill(...) | a.fill(value) | | Fill the array with a scalar value. | | Parameters | ---------- | value : scalar | All elements of `a` will be assigned this value. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.fill(0) | >>> a | array([0, 0]) | >>> a = np.empty(2) | >>> a.fill(1) | >>> a | array([ 1., 1.]) | | flatten(...) | a.flatten(order='C') | | Return a copy of the array collapsed into one dimension. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | Whether to flatten in C (row-major), Fortran (column-major) order, | or preserve the C/Fortran ordering from `a`. | The default is 'C'. | | Returns | ------- | y : ndarray | A copy of the input array, flattened to one dimension. | | See Also | -------- | ravel : Return a flattened array. | flat : A 1-D flat iterator over the array. | | Examples | -------- | >>> a = np.array([[1,2], [3,4]]) | >>> a.flatten() | array([1, 2, 3, 4]) | >>> a.flatten('F') | array([1, 3, 2, 4]) | | getfield(...) | a.getfield(dtype, offset) | | Returns a field of the given array as a certain type. | | A field is a view of the array data with each itemsize determined | by the given type and the offset into the current array, i.e. from | ``offset * dtype.itemsize`` to ``(offset+1) * dtype.itemsize``. | | Parameters | ---------- | dtype : str | String denoting the data type of the field. | offset : int | Number of `dtype.itemsize`'s to skip before beginning the element view. | | Examples | -------- | >>> x = np.diag([1.+1.j]*2) | >>> x | array([[ 1.+1.j, 0.+0.j], | [ 0.+0.j, 1.+1.j]]) | >>> x.dtype | dtype('complex128') | | >>> x.getfield('complex64', 0) # Note how this != x | array([[ 0.+1.875j, 0.+0.j ], | [ 0.+0.j , 0.+1.875j]], dtype=complex64) | | >>> x.getfield('complex64',1) # Note how different this is than x | array([[ 0. +5.87173204e-39j, 0. +0.00000000e+00j], | [ 0. +0.00000000e+00j, 0. +5.87173204e-39j]], dtype=complex64) | | >>> x.getfield('complex128', 0) # == x | array([[ 1.+1.j, 0.+0.j], | [ 0.+0.j, 1.+1.j]]) | | If the argument dtype is the same as x.dtype, then offset != 0 raises | a ValueError: | | >>> x.getfield('complex128', 1) | Traceback (most recent call last): | File "<stdin>", line 1, in <module> | ValueError: Need 0 <= offset <= 0 for requested type but received offset = 1 | | >>> x.getfield('float64', 0) | array([[ 1., 0.], | [ 0., 1.]]) | | >>> x.getfield('float64', 1) | array([[ 1.77658241e-307, 0.00000000e+000], | [ 0.00000000e+000, 1.77658241e-307]]) | | item(...) | a.item(*args) | | Copy an element of an array to a standard Python scalar and return it. | | Parameters | ---------- | \*args : Arguments (variable number and type) | | * none: in this case, the method only works for arrays | with one element (`a.size == 1`), which element is | copied into a standard Python scalar object and returned. | | * int_type: this argument is interpreted as a flat index into | the array, specifying which element to copy and return. | | * tuple of int_types: functions as does a single int_type argument, | except that the argument is interpreted as an nd-index into the | array. | | Returns | ------- | z : Standard Python scalar object | A copy of the specified element of the array as a suitable | Python scalar | | Notes | ----- | When the data type of `a` is longdouble or clongdouble, item() returns | a scalar array object because there is no available Python scalar that | would not lose information. Void arrays return a buffer object for item(), | unless fields are defined, in which case a tuple is returned. | | `item` is very similar to a[args], except, instead of an array scalar, | a standard Python scalar is returned. This can be useful for speeding up | access to elements of the array and doing arithmetic on elements of the | array using Python's optimized math. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.item(3) | 2 | >>> x.item(7) | 5 | >>> x.item((0, 1)) | 1 | >>> x.item((2, 2)) | 3 | | itemset(...) | a.itemset(*args) | | Insert scalar into an array (scalar is cast to array's dtype, if possible) | | There must be at least 1 argument, and define the last argument | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster | than ``a[args] = item``. The item should be a scalar value and `args` | must select a single item in the array `a`. | | Parameters | ---------- | \*args : Arguments | If one argument: a scalar, only used in case `a` is of size 1. | If two arguments: the last argument is the value to be set | and must be a scalar, the first argument specifies a single array | element location. It is either an int or a tuple. | | Notes | ----- | Compared to indexing syntax, `itemset` provides some speed increase | for placing a scalar into a particular location in an `ndarray`, | if you must do this. However, generally this is discouraged: | among other problems, it complicates the appearance of the code. | Also, when using `itemset` (and `item`) inside a loop, be sure | to assign the methods to a local variable to avoid the attribute | look-up at each loop iteration. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.itemset(4, 0) | >>> x.itemset((2, 2), 9) | >>> x | array([[3, 1, 7], | [2, 0, 3], | [8, 5, 9]]) | | max(...) | a.max(axis=None, out=None) | | Return the maximum along a given axis. | | Refer to `numpy.amax` for full documentation. | | See Also | -------- | numpy.amax : equivalent function | | mean(...) | a.mean(axis=None, dtype=None, out=None) | | Returns the average of the array elements along given axis. | | Refer to `numpy.mean` for full documentation. | | See Also | -------- | numpy.mean : equivalent function | | min(...) | a.min(axis=None, out=None) | | Return the minimum along a given axis. | | Refer to `numpy.amin` for full documentation. | | See Also | -------- | numpy.amin : equivalent function | | newbyteorder(...) | arr.newbyteorder(new_order='S') | | Return the array with the same data viewed with a different byte order. | | Equivalent to:: | | arr.view(arr.dtype.newbytorder(new_order)) | | Changes are also made in all fields and sub-arrays of the array data | type. | | | | Parameters | ---------- | new_order : string, optional | Byte order to force; a value from the byte order specifications | above. `new_order` codes can be any of:: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_arr : array | New array object with the dtype reflecting given change to the | byte order. | | nonzero(...) | a.nonzero() | | Return the indices of the elements that are non-zero. | | Refer to `numpy.nonzero` for full documentation. | | See Also | -------- | numpy.nonzero : equivalent function | | prod(...) | a.prod(axis=None, dtype=None, out=None) | | Return the product of the array elements over the given axis | | Refer to `numpy.prod` for full documentation. | | See Also | -------- | numpy.prod : equivalent function | | ptp(...) | a.ptp(axis=None, out=None) | | Peak to peak (maximum - minimum) value along a given axis. | | Refer to `numpy.ptp` for full documentation. | | See Also | -------- | numpy.ptp : equivalent function | | put(...) | a.put(indices, values, mode='raise') | | Set ``a.flat[n] = values[n]`` for all `n` in indices. | | Refer to `numpy.put` for full documentation. | | See Also | -------- | numpy.put : equivalent function | | ravel(...) | a.ravel([order]) | | Return a flattened array. | | Refer to `numpy.ravel` for full documentation. | | See Also | -------- | numpy.ravel : equivalent function | | ndarray.flat : a flat iterator on the array. | | repeat(...) | a.repeat(repeats, axis=None) | | Repeat elements of an array. | | Refer to `numpy.repeat` for full documentation. | | See Also | -------- | numpy.repeat : equivalent function | | reshape(...) | a.reshape(shape, order='C') | | Returns an array containing the same data with a new shape. | | Refer to `numpy.reshape` for full documentation. | | See Also | -------- | numpy.reshape : equivalent function | | resize(...) | a.resize(new_shape, refcheck=True) | | Change shape and size of array in-place. | | Parameters | ---------- | new_shape : tuple of ints, or `n` ints | Shape of resized array. | refcheck : bool, optional | If False, reference count will not be checked. Default is True. | | Returns | ------- | None | | Raises | ------ | ValueError | If `a` does not own its own data or references or views to it exist, | and the data memory must be changed. | | SystemError | If the `order` keyword argument is specified. This behaviour is a | bug in NumPy. | | See Also | -------- | resize : Return a new array with the specified shape. | | Notes | ----- | This reallocates space for the data area if necessary. | | Only contiguous arrays (data elements consecutive in memory) can be | resized. | | The purpose of the reference count check is to make sure you | do not use this array as a buffer for another Python object and then | reallocate the memory. However, reference counts can increase in | other ways so if you are sure that you have not shared the memory | for this array with another Python object, then you may safely set | `refcheck` to False. | | Examples | -------- | Shrinking an array: array is flattened (in the order that the data are | stored in memory), resized, and reshaped: | | >>> a = np.array([[0, 1], [2, 3]], order='C') | >>> a.resize((2, 1)) | >>> a | array([[0], | [1]]) | | >>> a = np.array([[0, 1], [2, 3]], order='F') | >>> a.resize((2, 1)) | >>> a | array([[0], | [2]]) | | Enlarging an array: as above, but missing entries are filled with zeros: | | >>> b = np.array([[0, 1], [2, 3]]) | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple | >>> b | array([[0, 1, 2], | [3, 0, 0]]) | | Referencing an array prevents resizing... | | >>> c = a | >>> a.resize((1, 1)) | Traceback (most recent call last): | ... | ValueError: cannot resize an array that has been referenced ... | | Unless `refcheck` is False: | | >>> a.resize((1, 1), refcheck=False) | >>> a | array([[0]]) | >>> c | array([[0]]) | | round(...) | a.round(decimals=0, out=None) | | Return `a` with each element rounded to the given number of decimals. | | Refer to `numpy.around` for full documentation. | | See Also | -------- | numpy.around : equivalent function | | searchsorted(...) | a.searchsorted(v, side='left') | | Find indices where elements of v should be inserted in a to maintain order. | | For full documentation, see `numpy.searchsorted` | | See Also | -------- | numpy.searchsorted : equivalent function | | setasflat(...) | a.setasflat(arr) | | Equivalent to a.flat = arr.flat, but is generally more efficient. | This function does not check for overlap, so if ``arr`` and ``a`` | are viewing the same data with different strides, the results will | be unpredictable. | | Parameters | ---------- | arr : array_like | The array to copy into a. | | Examples | -------- | >>> a = np.arange(2*4).reshape(2,4)[:,:-1]; a | array([[0, 1, 2], | [4, 5, 6]]) | >>> b = np.arange(3*3, dtype='f4').reshape(3,3).T[::-1,:-1]; b | array([[ 2., 5.], | [ 1., 4.], | [ 0., 3.]], dtype=float32) | >>> a.setasflat(b) | >>> a | array([[2, 5, 1], | [4, 0, 3]]) | | setfield(...) | a.setfield(val, dtype, offset=0) | | Put a value into a specified place in a field defined by a data-type. | | Place `val` into `a`'s field defined by `dtype` and beginning `offset` | bytes into the field. | | Parameters | ---------- | val : object | Value to be placed in field. | dtype : dtype object | Data-type of the field in which to place `val`. | offset : int, optional | The number of bytes into the field at which to place `val`. | | Returns | ------- | None | | See Also | -------- | getfield | | Examples | -------- | >>> x = np.eye(3) | >>> x.getfield(np.float64) | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | >>> x.setfield(3, np.int32) | >>> x.getfield(np.int32) | array([[3, 3, 3], | [3, 3, 3], | [3, 3, 3]]) | >>> x | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323], | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323], | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]]) | >>> x.setfield(np.eye(3), np.int32) | >>> x | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | | setflags(...) | a.setflags(write=None, align=None, uic=None) | | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. | | These Boolean-valued flags affect how numpy interprets the memory | area used by `a` (see Notes below). The ALIGNED flag can only | be set to True if the data is actually aligned according to the type. | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE | can only be set to True if the array owns its own memory, or the | ultimate owner of the memory exposes a writeable buffer interface, | or is a string. (The exception for string is made so that unpickling | can be done without copying memory.) | | Parameters | ---------- | write : bool, optional | Describes whether or not `a` can be written to. | align : bool, optional | Describes whether or not `a` is aligned properly for its type. | uic : bool, optional | Describes whether or not `a` is a copy of another "base" array. | | Notes | ----- | Array flags provide information about how the memory area used | for the array is to be interpreted. There are 6 Boolean flags | in use, only three of which can be changed by the user: | UPDATEIFCOPY, WRITEABLE, and ALIGNED. | | WRITEABLE (W) the data area can be written to; | | ALIGNED (A) the data and strides are aligned appropriately for the hardware | (as determined by the compiler); | | UPDATEIFCOPY (U) this array is a copy of some other array (referenced | by .base). When this array is deallocated, the base array will be | updated with the contents of this array. | | All flags can be accessed using their first (upper case) letter as well | as the full name. | | Examples | -------- | >>> y | array([[3, 1, 7], | [2, 0, 0], | [8, 5, 9]]) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : True | ALIGNED : True | UPDATEIFCOPY : False | >>> y.setflags(write=0, align=0) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : False | ALIGNED : False | UPDATEIFCOPY : False | >>> y.setflags(uic=1) | Traceback (most recent call last): | File "<stdin>", line 1, in <module> | ValueError: cannot set UPDATEIFCOPY flag to True | | sort(...) | a.sort(axis=-1, kind='quicksort', order=None) | | Sort an array, in-place. | | Parameters | ---------- | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'quicksort', 'mergesort', 'heapsort'}, optional | Sorting algorithm. Default is 'quicksort'. | order : list, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. Not all fields need be | specified. | | See Also | -------- | numpy.sort : Return a sorted copy of an array. | argsort : Indirect sort. | lexsort : Indirect stable sort on multiple keys. | searchsorted : Find elements in sorted array. | | Notes | ----- | See ``sort`` for notes on the different sorting algorithms. | | Examples | -------- | >>> a = np.array([[1,4], [3,1]]) | >>> a.sort(axis=1) | >>> a | array([[1, 4], | [1, 3]]) | >>> a.sort(axis=0) | >>> a | array([[1, 3], | [1, 4]]) | | Use the `order` keyword to specify a field to use when sorting a | structured array: | | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) | >>> a.sort(order='y') | >>> a | array([('c', 1), ('a', 2)], | dtype=[('x', '|S1'), ('y', '<i4')]) | | squeeze(...) | a.squeeze() | | Remove single-dimensional entries from the shape of `a`. | | Refer to `numpy.squeeze` for full documentation. | | See Also | -------- | numpy.squeeze : equivalent function | | std(...) | a.std(axis=None, dtype=None, out=None, ddof=0) | | Returns the standard deviation of the array elements along given axis. | | Refer to `numpy.std` for full documentation. | | See Also | -------- | numpy.std : equivalent function | | sum(...) | a.sum(axis=None, dtype=None, out=None) | | Return the sum of the array elements over the given axis. | | Refer to `numpy.sum` for full documentation. | | See Also | -------- | numpy.sum : equivalent function | | swapaxes(...) | a.swapaxes(axis1, axis2) | | Return a view of the array with `axis1` and `axis2` interchanged. | | Refer to `numpy.swapaxes` for full documentation. | | See Also | -------- | numpy.swapaxes : equivalent function | | take(...) | a.take(indices, axis=None, out=None, mode='raise') | | Return an array formed from the elements of `a` at the given indices. | | Refer to `numpy.take` for full documentation. | | See Also | -------- | numpy.take : equivalent function | | tofile(...) | a.tofile(fid, sep="", format="%s") | | Write array to a file as text or binary (default). | | Data is always written in 'C' order, independent of the order of `a`. | The data produced by this method can be recovered using the function | fromfile(). | | Parameters | ---------- | fid : file or str | An open file object, or a string containing a filename. | sep : str | Separator between array items for text output. | If "" (empty), a binary file is written, equivalent to | ``file.write(a.tostring())``. | format : str | Format string for text file output. | Each entry in the array is formatted to text by first converting | it to the closest Python type, and then using "format" % item. | | Notes | ----- | This is a convenience function for quick storage of array data. | Information on endianness and precision is lost, so this method is not a | good choice for files intended to archive data or transport data between | machines with different endianness. Some of these problems can be overcome | by outputting the data as text files, at the expense of speed and file | size. | | tolist(...) | a.tolist() | | Return the array as a (possibly nested) list. | | Return a copy of the array data as a (nested) Python list. | Data items are converted to the nearest compatible Python type. | | Parameters | ---------- | none | | Returns | ------- | y : list | The possibly nested list of array elements. | | Notes | ----- | The array may be recreated, ``a = np.array(a.tolist())``. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.tolist() | [1, 2] | >>> a = np.array([[1, 2], [3, 4]]) | >>> list(a) | [array([1, 2]), array([3, 4])] | >>> a.tolist() | [[1, 2], [3, 4]] | | tostring(...) | a.tostring(order='C') | | Construct a Python string containing the raw data bytes in the array. | | Constructs a Python string showing a copy of the raw contents of | data memory. The string can be produced in either 'C' or 'Fortran', | or 'Any' order (the default is 'C'-order). 'Any' order means C-order | unless the F_CONTIGUOUS flag in the array is set, in which case it | means 'Fortran' order. | | Parameters | ---------- | order : {'C', 'F', None}, optional | Order of the data for multidimensional arrays: | C, Fortran, or the same as for the original array. | | Returns | ------- | s : str | A Python string exhibiting a copy of `a`'s raw data. | | Examples | -------- | >>> x = np.array([[0, 1], [2, 3]]) | >>> x.tostring() | '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tostring('C') == x.tostring() | True | >>> x.tostring('F') | '\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | trace(...) | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) | | Return the sum along diagonals of the array. | | Refer to `numpy.trace` for full documentation. | | See Also | -------- | numpy.trace : equivalent function | | transpose(...) | a.transpose(*axes) | | Returns a view of the array with axes transposed. | | For a 1-D array, this has no effect. (To change between column and | row vectors, first cast the 1-D array into a matrix object.) | For a 2-D array, this is the usual matrix transpose. | For an n-D array, if axes are given, their order indicates how the | axes are permuted (see Examples). If axes are not provided and | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``. | | Parameters | ---------- | axes : None, tuple of ints, or `n` ints | | * None or no argument: reverses the order of the axes. | | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s | `i`-th axis becomes `a.transpose()`'s `j`-th axis. | | * `n` ints: same as an n-tuple of the same ints (this form is | intended simply as a "convenience" alternative to the tuple form) | | Returns | ------- | out : ndarray | View of `a`, with axes suitably permuted. | | See Also | -------- | ndarray.T : Array property returning the array transposed. | | Examples | -------- | >>> a = np.array([[1, 2], [3, 4]]) | >>> a | array([[1, 2], | [3, 4]]) | >>> a.transpose() | array([[1, 3], | [2, 4]]) | >>> a.transpose((1, 0)) | array([[1, 3], | [2, 4]]) | >>> a.transpose(1, 0) | array([[1, 3], | [2, 4]]) | | var(...) | a.var(axis=None, dtype=None, out=None, ddof=0) | | Returns the variance of the array elements, along given axis. | | Refer to `numpy.var` for full documentation. | | See Also | -------- | numpy.var : equivalent function | | view(...) | a.view(dtype=None, type=None) | | New view of array with the same data. | | Parameters | ---------- | dtype : data-type, optional | Data-type descriptor of the returned view, e.g., float32 or int16. | The default, None, results in the view having the same data-type | as `a`. | type : Python type, optional | Type of the returned view, e.g., ndarray or matrix. Again, the | default None results in type preservation. | | Notes | ----- | ``a.view()`` is used two different ways: | | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view | of the array's memory with a different data-type. This can cause a | reinterpretation of the bytes of memory. | | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just | returns an instance of `ndarray_subclass` that looks at the same array | (same shape, dtype, etc.) This does not cause a reinterpretation of the | memory. | | | Examples | -------- | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) | | Viewing array data using a different type and dtype: | | >>> y = x.view(dtype=np.int16, type=np.matrix) | >>> y | matrix([[513]], dtype=int16) | >>> print type(y) | <class 'numpy.matrixlib.defmatrix.matrix'> | | Creating a view on a structured array so it can be used in calculations | | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) | >>> xv = x.view(dtype=np.int8).reshape(-1,2) | >>> xv | array([[1, 2], | [3, 4]], dtype=int8) | >>> xv.mean(0) | array([ 2., 3.]) | | Making changes to the view changes the underlying array | | >>> xv[0,1] = 20 | >>> print x | [(1, 20) (3, 4)] | | Using a view to convert an array to a record array: | | >>> z = x.view(np.recarray) | >>> z.a | array([1], dtype=int8) | | Views share data: | | >>> x[0] = (9, 10) | >>> z[0] | (9, 10) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | T | Same as self.transpose(), except that self is returned if | self.ndim < 2. | | Examples | -------- | >>> x = np.array([[1.,2.],[3.,4.]]) | >>> x | array([[ 1., 2.], | [ 3., 4.]]) | >>> x.T | array([[ 1., 3.], | [ 2., 4.]]) | >>> x = np.array([1.,2.,3.,4.]) | >>> x | array([ 1., 2., 3., 4.]) | >>> x.T | array([ 1., 2., 3., 4.]) | | __array_finalize__ | None. | | __array_interface__ | Array protocol: Python side. | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: C-struct side. | | base | Base object if memory is from some other object. | | Examples | -------- | The base of an array that owns its memory is None: | | >>> x = np.array([1,2,3,4]) | >>> x.base is None | True | | Slicing creates a view, whose memory is shared with x: | | >>> y = x[2:] | >>> y.base is x | True | | ctypes | An object to simplify the interaction of the array with the ctypes | module. | | This attribute creates an object that makes it easier to use arrays | when calling shared libraries with the ctypes module. The returned | object has, among others, data, shape, and strides attributes (see | Notes below) which themselves return ctypes objects that can be used | as arguments to a shared library. | | Parameters | ---------- | None | | Returns | ------- | c : Python object | Possessing attributes data, shape, strides, etc. | | See Also | -------- | numpy.ctypeslib | | Notes | ----- | Below are the public attributes of this object which were documented | in "Guide to NumPy" (we have omitted undocumented public attributes, | as well as documented private attributes): | | * data: A pointer to the memory area of the array as a Python integer. | This memory area may contain data that is not aligned, or not in correct | byte-order. The memory area may not even be writeable. The array | flags and data-type of this array should be respected when passing this | attribute to arbitrary C-code to avoid trouble that can include Python | crashing. User Beware! The value of this attribute is exactly the same | as self._array_interface_['data'][0]. | | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the C-integer corresponding to dtype('p') on this | platform. This base-type could be c_int, c_long, or c_longlong | depending on the platform. The c_intp type is defined accordingly in | numpy.ctypeslib. The ctypes array contains the shape of the underlying | array. | | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the same as for the shape attribute. This ctypes array | contains the strides information from the underlying array. This strides | information is important for showing how many bytes must be jumped to | get to the next element in the array. | | * data_as(obj): Return the data pointer cast to a particular c-types object. | For example, calling self._as_parameter_ is equivalent to | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a | pointer to a ctypes array of floating-point data: | self.data_as(ctypes.POINTER(ctypes.c_double)). | | * shape_as(obj): Return the shape tuple as an array of some other c-types | type. For example: self.shape_as(ctypes.c_short). | | * strides_as(obj): Return the strides tuple as an array of some other | c-types type. For example: self.strides_as(ctypes.c_longlong). | | Be careful using the ctypes attribute - especially on temporary | arrays or arrays constructed on the fly. For example, calling | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory | that is invalid because the array created as (a+b) is deallocated | before the next Python statement. You can avoid this problem using | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will | hold a reference to the array until ct is deleted or re-assigned. | | If the ctypes module is not available, then the ctypes attribute | of array objects still returns something useful, but ctypes objects | are not returned and errors may be raised instead. In particular, | the object will still have the as parameter attribute which will | return an integer equal to the data attribute. | | Examples | -------- | >>> import ctypes | >>> x | array([[0, 1], | [2, 3]]) | >>> x.ctypes.data | 30439712 | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)) | <ctypes.LP_c_long object at 0x01F01300> | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents | c_long(0) | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents | c_longlong(4294967296L) | >>> x.ctypes.shape | <numpy.core._internal.c_long_Array_2 object at 0x01FFD580> | >>> x.ctypes.shape_as(ctypes.c_long) | <numpy.core._internal.c_long_Array_2 object at 0x01FCE620> | >>> x.ctypes.strides | <numpy.core._internal.c_long_Array_2 object at 0x01FCE620> | >>> x.ctypes.strides_as(ctypes.c_longlong) | <numpy.core._internal.c_longlong_Array_2 object at 0x01F01300> | | data | Python buffer object pointing to the start of the array's data. | | dtype | Data-type of the array's elements. | | Parameters | ---------- | None | | Returns | ------- | d : numpy dtype object | | See Also | -------- | numpy.dtype | | Examples | -------- | >>> x | array([[0, 1], | [2, 3]]) | >>> x.dtype | dtype('int32') | >>> type(x.dtype) | <type 'numpy.dtype'> | | flags | Information about the memory layout of the array. | | Attributes | ---------- | C_CONTIGUOUS (C) | The data is in a single, C-style contiguous segment. | F_CONTIGUOUS (F) | The data is in a single, Fortran-style contiguous segment. | OWNDATA (O) | The array owns the memory it uses or borrows it from another object. | WRITEABLE (W) | The data area can be written to. Setting this to False locks | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE | from its base array at creation time, but a view of a writeable | array may be subsequently locked while the base array remains writeable. | (The opposite is not true, in that a view of a locked array may not | be made writeable. However, currently, locking a base object does not | lock any views that already reference it, so under that circumstance it | is possible to alter the contents of a locked array via a previously | created writeable view onto it.) Attempting to change a non-writeable | array raises a RuntimeError exception. | ALIGNED (A) | The data and strides are aligned appropriately for the hardware. | UPDATEIFCOPY (U) | This array is a copy of some other array. When this array is | deallocated, the base array will be updated with the contents of | this array. | | FNC | F_CONTIGUOUS and not C_CONTIGUOUS. | FORC | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test). | BEHAVED (B) | ALIGNED and WRITEABLE. | CARRAY (CA) | BEHAVED and C_CONTIGUOUS. | FARRAY (FA) | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS. | | Notes | ----- | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``), | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag | names are only supported in dictionary access. | | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by | the user, via direct assignment to the attribute or dictionary entry, | or by calling `ndarray.setflags`. | | The array flags cannot be set arbitrarily: | | - UPDATEIFCOPY can only be set ``False``. | - ALIGNED can only be set ``True`` if the data is truly aligned. | - WRITEABLE can only be set ``True`` if the array owns its own memory | or the ultimate owner of the memory exposes a writeable buffer | interface or is a string. | | flat | A 1-D iterator over the array. | | This is a `numpy.flatiter` instance, which acts similarly to, but is not | a subclass of, Python's built-in iterator object. | | See Also | -------- | flatten : Return a copy of the array collapsed into one dimension. | | flatiter | | Examples | -------- | >>> x = np.arange(1, 7).reshape(2, 3) | >>> x | array([[1, 2, 3], | [4, 5, 6]]) | >>> x.flat[3] | 4 | >>> x.T | array([[1, 4], | [2, 5], | [3, 6]]) | >>> x.T.flat[3] | 5 | >>> type(x.flat) | <type 'numpy.flatiter'> | | An assignment example: | | >>> x.flat = 3; x | array([[3, 3, 3], | [3, 3, 3]]) | >>> x.flat[[1,4]] = 1; x | array([[3, 1, 3], | [3, 1, 3]]) | | imag | The imaginary part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.imag | array([ 0. , 0.70710678]) | >>> x.imag.dtype | dtype('float64') | | itemsize | Length of one array element in bytes. | | Examples | -------- | >>> x = np.array([1,2,3], dtype=np.float64) | >>> x.itemsize | 8 | >>> x = np.array([1,2,3], dtype=np.complex128) | >>> x.itemsize | 16 | | nbytes | Total bytes consumed by the elements of the array. | | Notes | ----- | Does not include memory consumed by non-element attributes of the | array object. | | Examples | -------- | >>> x = np.zeros((3,5,2), dtype=np.complex128) | >>> x.nbytes | 480 | >>> np.prod(x.shape) * x.itemsize | 480 | | ndim | Number of array dimensions. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> x.ndim | 1 | >>> y = np.zeros((2, 3, 4)) | >>> y.ndim | 3 | | real | The real part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.real | array([ 1. , 0.70710678]) | >>> x.real.dtype | dtype('float64') | | See Also | -------- | numpy.real : equivalent function | | shape | Tuple of array dimensions. | | Notes | ----- | May be used to "reshape" the array, as long as this would not | require a change in the total number of elements | | Examples | -------- | >>> x = np.array([1, 2, 3, 4]) | >>> x.shape | (4,) | >>> y = np.zeros((2, 3, 4)) | >>> y.shape | (2, 3, 4) | >>> y.shape = (3, 8) | >>> y | array([[ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.]]) | >>> y.shape = (3, 6) | Traceback (most recent call last): | File "<stdin>", line 1, in <module> | ValueError: total size of new array must be unchanged | | size | Number of elements in the array. | | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's | dimensions. | | Examples | -------- | >>> x = np.zeros((3, 5, 2), dtype=np.complex128) | >>> x.size | 30 | >>> np.prod(x.shape) | 30 | | strides | Tuple of bytes to step in each dimension when traversing an array. | | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a` | is:: | | offset = sum(np.array(i) * a.strides) | | A more detailed explanation of strides can be found in the | "ndarray.rst" file in the NumPy reference guide. | | Notes | ----- | Imagine an array of 32-bit integers (each 4 bytes):: | | x = np.array([[0, 1, 2, 3, 4], | [5, 6, 7, 8, 9]], dtype=np.int32) | | This array is stored in memory as 40 bytes, one after the other | (known as a contiguous block of memory). The strides of an array tell | us how many bytes we have to skip in memory to move to the next position | along a certain axis. For example, we have to skip 4 bytes (1 value) to | move to the next column, but 20 bytes (5 values) to get to the same | position in the next row. As such, the strides for the array `x` will be | ``(20, 4)``. | | See Also | -------- | numpy.lib.stride_tricks.as_strided | | Examples | -------- | >>> y = np.reshape(np.arange(2*3*4), (2,3,4)) | >>> y | array([[[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]], | [[12, 13, 14, 15], | [16, 17, 18, 19], | [20, 21, 22, 23]]]) | >>> y.strides | (48, 16, 4) | >>> y[1,1,1] | 17 | >>> offset=sum(y.strides * np.array((1,1,1))) | >>> offset/y.itemsize | 17 | | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) | >>> x.strides | (32, 4, 224, 1344) | >>> i = np.array([3,5,2,2]) | >>> offset = sum(i * x.strides) | >>> x[3,5,2,2] | 813 | >>> offset / x.itemsize | 813 | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = <built-in method __new__ of type object> | T.__new__(S, ...) -> a new object with type S, a subtype of T
data.choose?
data + 'hello there!'
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-30-6ab84f0be10c> in <module>() ----> 1 data + 'hello there!' TypeError: unsupported operand type(s) for +: 'numpy.ndarray' and 'str'
%%file name_of_file.py
x = 5
y = 2
print x + y
Writing name_of_file.py
!python name_of_file.py
7