[Numpy-discussion] segfault in vdot
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[Numpy-discussion] np.void from 0d array + subclassing
All, * What is the most efficient way to get a np.void object from a 0d structured ndarray ? * Is there any way to subclass np.void ? Thanks a lot in advance ! P. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
A Thursday 17 December 2009 15:16:29 Pierre GM escrigué: All, * What is the most efficient way to get a np.void object from a 0d structured ndarray ? I normally use `PyArray_GETITEM` C macro for general n-d structured arrays. I suppose that this will work with 0-d arrays too. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
On Thu, Dec 17, 2009 at 6:16 AM, Pierre GM pgmdevl...@gmail.com wrote: All, * What is the most efficient way to get a np.void object from a 0d structured ndarray ? * Is there any way to subclass np.void ? The standard way (more or less) works for me: class myvoidclass(np.void): ... pass ... foo = myvoidclass() Traceback (most recent call last): File stdin, line 1, in module TypeError: function takes exactly 1 argument (0 given) foo = myvoidclass(1) dir(foo) ['T', '__abs__', '__add__', '__and__', '__array__', '__array_interface__', '__array_priority__', '__array_struct__', '__ array_wrap__', '__class__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dict__', '__div__', '__divmod__ ', '__doc__', '__eq__', '__float__', '__floordiv__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__hex__', '__init__', '__int__', '__invert__', '__le__', '__len__', '__long__', '__lshift__', '__lt__', '__mod__', '__m odule__', '__mul__', '__ne__', '__neg__', '__new__', '__nonzero__', '__oct__', '__or__', '__pos__', '__pow__', '__radd__ ', '__rand__', '__rdiv__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__ rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__seta ttr__', '__setitem__', '__setstate__', '__str__', '__sub__', '__truediv__', '__weakref__', '__xor__', 'all', 'any', 'arg max', 'argmin', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'cum prod', 'cumsum', 'data', 'diagonal', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', ' item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'prod', 'ptp', 'put', ' ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'v iew'] Is there something more specific you want to do? DG Thanks a lot in advance ! P. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
On Dec 17, 2009, at 10:16 AM, Francesc Alted wrote: A Thursday 17 December 2009 15:16:29 Pierre GM escrigué: All, * What is the most efficient way to get a np.void object from a 0d structured ndarray ? I normally use `PyArray_GETITEM` C macro for general n-d structured arrays. I suppose that this will work with 0-d arrays too. Francesc, you're overestimating my knowledge of C... Can we stick to the Python implementation ? Here's the catch: IIUC, each individual element of a nD structured array is a void, provided the element can be accessed, ie that n0. A 0D array cannot be indexed, so I don't know how capture the object below. The sad trick I found was to do a .reshape(1)[0], but that looks really overkill... The standard way (more or less) works for me: class myvoidclass(np.void): ... pass ... David, what do you do w/ the __new__ of myvoidclass ? Just an empty class doesn't help me much, 'm'fraid. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
On Thu, Dec 17, 2009 at 2:11 PM, Pierre GM pgmdevl...@gmail.com wrote: Francesc, you're overestimating my knowledge of C... Can we stick to the Python implementation ? Here's the catch: IIUC, each individual element of a nD structured array is a void, provided the element can be accessed, ie that n0. A 0D array cannot be indexed, so I don't know how Unless someone can explain why it isn't, this sounds like an API inconsistency, which in turn I would characterize as a bug. But others may disagree and/or explain it away... capture the object below. The sad trick I found was to do a .reshape(1)[0], but that looks really overkill... The standard way (more or less) works for me: class myvoidclass(np.void): ... pass ... David, what do you do w/ the __new__ of myvoidclass ? Just an empty class doesn't help me much, 'm'fraid. Presumably, whatever you want (i.e., override it, calling the base class constructor inside your __new__ if/when needed) - I've never done this, so I have no reason to believe it would/should behave any differently than any other Python subclass; your question merely provoked me to check to see if the normal subclassing syntax did not work for some reason, and since I found that it did, I thought I'd post that result as a data point. Now, if you're generally unfamiliar (but it doesn't sound like you are) with what to do with a subclass' __new__, I'm sure someone else can more easily point you to a reference for that issue. Is there some reason you believe you have to override __new__ differently in your use-case? DG ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
On Thu, Dec 17, 2009 at 16:11, Pierre GM pgmdevl...@gmail.com wrote: On Dec 17, 2009, at 10:16 AM, Francesc Alted wrote: A Thursday 17 December 2009 15:16:29 Pierre GM escrigué: All, * What is the most efficient way to get a np.void object from a 0d structured ndarray ? I normally use `PyArray_GETITEM` C macro for general n-d structured arrays. I suppose that this will work with 0-d arrays too. Francesc, you're overestimating my knowledge of C... Can we stick to the Python implementation ? Here's the catch: IIUC, each individual element of a nD structured array is a void, provided the element can be accessed, ie that n0. A 0D array cannot be indexed, so I don't know how capture the object below. The sad trick I found was to do a .reshape(1)[0], but that looks really overkill... a[()] -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
On Dec 17, 2009, at 6:35 PM, Robert Kern wrote: On Thu, Dec 17, 2009 at 16:11, Pierre GM pgmdevl...@gmail.com wrote: On Dec 17, 2009, at 10:16 AM, Francesc Alted wrote: A Thursday 17 December 2009 15:16:29 Pierre GM escrigué: All, * What is the most efficient way to get a np.void object from a 0d structured ndarray ? I normally use `PyArray_GETITEM` C macro for general n-d structured arrays. I suppose that this will work with 0-d arrays too. Francesc, you're overestimating my knowledge of C... Can we stick to the Python implementation ? Here's the catch: IIUC, each individual element of a nD structured array is a void, provided the element can be accessed, ie that n0. A 0D array cannot be indexed, so I don't know how capture the object below. The sad trick I found was to do a .reshape(1)[0], but that looks really overkill... a[()] Well, that's slick and really neat !!! Typically Robert K's... Thanks a lot !!! And would you have anything as cool for the reverse operation (from np.void to 0d array) ? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
On Thu, Dec 17, 2009 at 17:41, Pierre GM pgmdevl...@gmail.com wrote: On Dec 17, 2009, at 6:35 PM, Robert Kern wrote: On Thu, Dec 17, 2009 at 16:11, Pierre GM pgmdevl...@gmail.com wrote: Here's the catch: IIUC, each individual element of a nD structured array is a void, provided the element can be accessed, ie that n0. A 0D array cannot be indexed, so I don't know how capture the object below. The sad trick I found was to do a .reshape(1)[0], but that looks really overkill... a[()] Well, that's slick and really neat !!! Typically Robert K's... Thanks a lot !!! And would you have anything as cool for the reverse operation (from np.void to 0d array) ? a = np.array(v) -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] np.void from 0d array + subclassing
On Thu, Dec 17, 2009 at 3:46 PM, Robert Kern robert.k...@gmail.com wrote: On Thu, Dec 17, 2009 at 17:41, Pierre GM pgmdevl...@gmail.com wrote: Well, that's slick and really neat !!! Typically Robert K's... Thanks a lot !!! Yeah, sometimes I think the only reason we have a mailing list is so that we're not all just emailing Robert all the time... ;-) DG ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [AstroPy] Rotating and Transforming Vectors--Flight Path of a Celestial Body
It's starting to come back to me. I found a few old graphics books that get into transformation matrices and such. Yes, earth centered. I ground out some code with geometry and trig that at least gets the first point on the path right. I think I can probably apply a rotation around the x-axis several times with a 1/2 degree rotation for each step towards the north to get the job done. I'm still fairly fresh with Python, and found a little bit of info in a Tentative numpy tutorial. I found this on getting started with matrices: from numpy import matrix Apparently matrix is a class in numpy, and there are many others, linalg I think is one. How does one find all the classes, and are there rules that keep them apart. It was tempting to add import numpy in addition to the above, but how is what is in numpy different that the classes? Is numpy solely class driven? That is, if one does a from as above to get all the classes, does it give all the capabilities that just using import numpy does? Anne Archibald wrote: 2009/12/17 Wayne Watson sierra_mtnv...@sbcglobal.net: I'm just getting used to the math and numpy library, and have begun working on a problem of the following sort. Two observing stations are equidistant, 1/2 degree, on either side of a line of longitude of 90 deg west, and both are at 45 deg latitude. Given the earth's circumference as 25020 miles, a meteor is 60 miles above the point between the two sites. That is, if you were standing at long 90deg and lat 45 deg, the meteor would be that high above you. 70 miles along the long line is 1 degree, so the stations are 70 miles apart. I want to know the az and el of the meteor from each station. With some geometry and trig, I've managed to get that first point; however, I can see moving the meteor say, 1/2 deg, along its circular path towards the north pole is going to require more pen and pencil work to get the az/el for it. Long ago in a faraway time, I used to do this stuff. It should be easy to rotate the vector to the first point 1/2 deg northward, and find the vector there, then compute the new az and el from each station. Maybe. I'm just beginning to look at the matrix and vector facilities in numpy. Maybe someone can comment on how this should be done, and steer me towards what I need to know in numpy. You may find that the problem grows drastically easier if you work as much as possible in so-called earth-centered earth-fixed coordinates (sometimes called XYZ) coordinates. These are a rectilinear coordinate system that rotates with the earth, with the Z axis through the north pole and the X axis through the equator at the Greenwich meridian. It's kind of horrible for getting altitudes, since the Earth is sort of pear-shaped, but it makes the 3D geometry much simpler. If you don't go this route, I'd recommend picking one station and defining a rectilinear coordinate system based on its north and vertical vectors. The north and vertical vectors of the other station will be at somewhat funny angles (unless you can get away with treating the Earth as flat between the two), but whatever rectilinear coordinates you choose, a dot product lets you calculate vector lengths and angles between them, and a cross product lets you build vectors orthogonal to a given pair. So, for example, if your station has north vector N and up vector U, you can get its east vector as E=cross(N,U) (then normalize it); if you want to convert an absolute north to a local north (i.e. one that is horizontal) you can do N=cross(E,U) (and normalize it). Then you can get the azimuth of a vector V using dot(N,V)/sqrt(dot(V,V)) and dot(E,V)/sqrt(dot(V,V)). Anne -- Wayne Watson (Watson Adventures, Prop., Nevada City, CA) (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time) Obz Site: 39° 15' 7 N, 121° 2' 32 W, 2700 feet ... humans'innate skills with numbers isn't much better than that of rats and dolphins. -- Stanislas Dehaene, neurosurgeon Web Page: www.speckledwithstars.net/ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [AstroPy] Rotating and Transforming Vectors--Flight Path of a Celestial Body
2009/12/18 Wayne Watson sierra_mtnv...@sbcglobal.net: It's starting to come back to me. I found a few old graphics books that get into transformation matrices and such. Yes, earth centered. I ground out some code with geometry and trig that at least gets the first point on the path right. I think I can probably apply a rotation around the x-axis several times with a 1/2 degree rotation for each step towards the north to get the job done. I'm still fairly fresh with Python, and found a little bit of info in a Tentative numpy tutorial. I found this on getting started with matrices: from numpy import matrix Apparently matrix is a class in numpy, and there are many others, linalg I think is one. How does one find all the classes, and are there rules that keep them apart. It was tempting to add import numpy in addition to the above, but how is what is in numpy different that the classes? Is numpy solely class driven? That is, if one does a from as above to get all the classes, does it give all the capabilities that just using import numpy does? Many things in python are classes; a class is a way of attaching relevant functions to a collection of data (more precisely, a class is a *type*, defining the interpretation of data; usually they also carry a collection of functions to operate on that data). So the central feature of numpy is a class, ndarray, that represents a collection of values of homogeneous type. This may be one, two, or many-dimensional, and there are various operations, including linear algebra, on them available in numpy. The matrix class is a special kind of ndarray in which a number of modifications have been made. In particular, the * operator no longer does element-wise operations, it does matrix multiplication. There are also various rules designed to ensure that matrix objects are always two-dimensional. I avoid matrix objects like the plague, but some people find them useful. numpy.linalg is an entirely different beast. It is a *module*, a collection of functions (and potentially objects and classes). It is like sys or os: you import it and the functions, objects and classes it contains become available. This is a basic feature of python. What is unusual (but not unique) is that rather than having to explicitly import it like: import numpy import numpy.linalg numpy.linalg.svd(numpy.ones((3,2))) numpy automatically imports it for you, every time. This is done for historical reasons and won't change, but is a wart. For your purposes, I recommend simply using numpy arrays - three-element arrays for vectors, three-by-three for matrices - and using the linear algebra functions numpy provides to act on them. For example, dot does matrix-matrix, matrix-vector, and vector-vector multiplication. Anne P.S. you can usually write out a rotation explicitly, e.g. as [[cos(t), sin(t), 0], [-sin(t), cos(t), 0], [0, 0, 1]] but if you want a more general one I believe there's a clever way to make it using two reflections. -A ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
