Scientific Visualization
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(2) Outline ✓ Motivation. ✓ Scientific visualization software. ✓ Visualization with Python. ✓ Python performance optimizations. ✓ Development tools. ✓ Conclusion. ✓ Future plans. ✓ Discussion May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 2.
(3) Motivation We need easy-to-use methods for:. ✓ visualizing and analyzing two- and threedimensional data sets, perhaps with a dynamic component. ✓ creating publication-quality graphics. ✓ making glossy figures for high impact journals or press releases . May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 3.
(4) Scientific plotting methods ✓ line / bar graphs, curve plots. ✓ scatter plots. ✓ surface plots, mesh rendering with iso-surface generation. ✓ contour plots. ✓ vector / streamline plots. ✓ volume graphics. ✓ molecule plots May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 4.
(5) Scientific visualization tools ✓ Gnuplot. ✓ Xmgrace. ✓ OpenDX. ✓ ParaView. ✓ Mayavi2. ✓ MATLAB. ✓ Mathematica. ✓ Octave, Scilab, Freemat May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 5.
(6) … drawbacks ✓ Gnuplot — limited functionality. ✓ Xmgrace — too old, requires OSF/Motif (X11). ✓ OpenDX — no longer maintained (2007). ✓ ParaView — not very intuitive. ✓ Mayavi2 — not very responsive. ✓ MATLAB — 5 floating, 3 user licenses (16K €/year). ✓ Mathematica — expensive (~2.500 €/user). ✓ Octave, Scilab, Freemat — no syntax compatibility May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 6.
(7) Scientific visualization APIs ✓ Matplotlib. ✓ mlab,VTK. ✓ OpenGL. ✓ pgplot. ✓ PyQtGraph. ✓ PyQwt / PyQwt3D. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 7.
(8) Scientific visualization APIs ✓ Matplotlib — de-facto standard (“workhorse”). ✓ mlab,VTK — versatile, but difficult to learn; slow. ✓ OpenGL — large and complex. ✓ pgplot — too old. ✓ PyQtGraph — no yet mature. ✓ PyQwt / PyQwt3D — currently unmaintained. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 8.
(9) Remaining solutions GUI + API: ✓ ParaView. ✓ Mayavi2. API:. }. both based on VTK. ✓ matplotlib. ✓ n.n. ← Let’s talk about this later …. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 9.
(10) ParaView. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 10.
(11) Mayavi2. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 11.
(12) matplotlib. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 12.
(13) Problems so far ✓ separated 2D and (hardware accelerated) 3D world . ✓ some graphics backends "only" produce pictures ➟ no presentation of continuous data streams. ✓ bare minimum level of interoperability ➟ limited user interaction . ✓ poor performance on large data sets. ✓ APIs are partly device- and platform-dependent May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 13.
(14) Isn’t there an all-in-one solution?. All these components provide powerful APIs for Python !. !. There must be a reason for that … May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 14.
(15) … so let’s push for Python ✓ free and open. ✓ dynamic typed language, easy to understand. ✓ powerful modules for science, technology, engineering and mathematics (STEM): NumPy, SciPy, Pandas, SymPy. ✓ great visualization libraries: Matplotlib, MayaVi,VTK, PyOpenGL. ✓ techniques to boost execution speed: PyPy, Cython, PyOpenCL, PyCUDA, Numba. ✓ wrapper for GUI toolkits: PyQt4, PyGTK, wxWidgets May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 15.
(16) … get it up and running. IPython + NumPy + SciPy + Matplotlib What else do we need?. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 16.
(17) … achieve more Python performance Numba: compiles annotated Python and NumPy code to LLVM (through decorators). ✓ just-in-time compilation. ✓ vectorization. ✓ parallelization. NumbaPro: adds support for multicore and GPU architectures (* Numba (Pro) is part of Anaconda (Accelerate), a (commercial) Python distribution from Continuum Analytics. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 17.
(18) … achieve more graphics performance and interop GR framework: a universal framework for crossplatform visualization (*. ✓ procedural graphics backend ➟ presentation of continuous data streams. ✓ coexistent 2D and 3D world. ✓ interoperability with GUI toolkits ➟ good user interaction (* The GR framework is an in-house project initiated by the group Scientific IT Systems. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 18.
(19) “Our” Scientific Python distribution. IPython + NumPy + SciPy + Numba + GR framework +. PyOpenGL + PyOpenCL + PyCUDA + PyQt4/PyGTK/wxWidgets ➟ more performance and interoperability May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 19.
(20) How can we use it? ✓ GR framework (and other mentioned packages) available on all Linux and OS X machines (Python and IPython) at PGI / JCNS: % gr % igr" ✓ GR framework can also be used with Anaconda: % anaconda. ✓ Windows version(s) on request. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 20.
(21) Batteries included ✓ NumPy — package for numerical computation. ✓ SciPy — collection of numerical algorithms and specific toolboxes. ✓ Matplotlib — popular plotting package. ✓ Pandas — provides high-performance, easy to use data structures. ✓ SymPy — symbolic mathematics and computer algebra. ✓ IPython — rich interactive interface (including IPython notebook). ✓ Mayavi2 — 3D visualization framework, based on VTK. ✓ scikit-image — algorithms for image processing. ✓ h5py, PyTables — managing hierarchical datasets (HDF5). May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 21.
(22) Visualization with Python IPython / PyPy/ Anaconda. Qt / wx event loop. glgr / iGR App. C / C++. GR. .... GR3 off-screen rendering. socket communication. direct rendering. JavaScript generation. POV-Ray generation. C / ObjC. OpenGL. ES. Browser WebGL GKS. OpenGL (WGL / CGL / GLX). POV-Ray. GKS logical device drivers Qt. gksqt. May 22, 2014. wx. X11. GLUT. wxGLCanvas. QGLWidget. Quartz. GKSTerm. Win32. Java. gksweb. PDF MOV. PS. 0MQ SVG. OpenGL .... HTML5. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. More logical device drivers / plugins:. – CGM, GKSM, GIF, RF, UIL. – WMF, Xfig. – GS (BMP, JPEG, PNG, TIFF). Highlights:. – simultaneous output to multiple output devices. – direct generation of MPEG4 image sequences. – flicker-free display ("double buffering"). 22.
(23) Presentation of continuous data streams in 2D ... from numpy import sin, cos, sqrt, pi, array import gr. !. def rk4(x, k1 = h k2 = h k3 = h k4 = h return. !. h, y, f): * f(x, y) * f(x + 0.5 * h, y + 0.5 * k1) * f(x + 0.5 * h, y + 0.5 * k2) * f(x + h, y + k3) x + h, y + (k1 + 2 * (k2 + k3) + k4) / 6.0. def damped_pendulum_deriv(t, state): theta, omega = state return array([omega, -gamma * omega - 9.81 / L * sin(theta)]). !. def pendulum(t, theta, omega) gr.clearws() ... # draw pendulum (pivot point, rod, bob, ...) gr.updatews(). !. theta = 70.0 # initial angle gamma = 0.1 # damping coefficient L = 1 # pendulum length t = 0 dt = 0.04 state = array([theta * pi / 180, 0]). !. while t < 30: t, state = rk4(t, dt, state, damped_pendulum_deriv) theta, omega = state pendulum(t, theta, omega). May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 23.
(24) ... with full 3D functionality from numpy import sin, cos, array import gr, gr3. !. def rk4(x, k1 = h k2 = h k3 = h k4 = h return. !. h, y, f): * f(x, y) * f(x + 0.5 * h, y + 0.5 * k1) * f(x + 0.5 * h, y + 0.5 * k2) * f(x + h, y + k3) x + h, y + (k1 + 2 * (k2 + k3) + k4) / 6.0. def pendulum_derivs(t, state): t1, w1, t2, w2 = state a = (m1 + m2) * l1 b = m2 * l2 * cos(t1 - t2) c = m2 * l1 * cos(t1 - t2) d = m2 * l2 e = -m2 * l2 * w2**2 * sin(t1 - t2) - 9.81 * (m1 + m2) * sin(t1) f = m2 * l1 * w1**2 * sin(t1 - t2) - m2 * 9.81 * sin(t2) return array([w1, (e*d-b*f) / (a*d-c*b), w2, (a*f-c*e) / (a*d-c*b)]). !. def double_pendulum(theta, length, mass): gr.clearws() gr3.clear(). ! !. May 22, 2014. .... # draw pivot point, rods, bobs (using 3D meshes). gr3.drawimage(0, 1, 0, 1, 500, 500, gr3.GR3_Drawable.GR3_DRAWABLE_GKS) gr.updatews(). Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 24.
(25) ... in real-time import wave, pyaudio import numpy import gr. !. SAMPLES=1024 FS=44100. ! f !. # Sampling frequency. = [FS/float(SAMPLES)*t for t in range(1, SAMPLES/2+1)]. wf = wave.open('Monty_Python.wav', 'rb') pa = pyaudio.PyAudio() stream = pa.open(format=pa.get_format_from_width(wf.getsampwidth()), channels=wf.getnchannels(), rate=wf.getframerate(), output=True). ! ... !. data = wf.readframes(SAMPLES) while data != '' and len(data) == SAMPLES * wf.getsampwidth(): stream.write(data) amplitudes = numpy.fromstring(data, dtype=numpy.short) power = abs(numpy.fft.fft(amplitudes / 65536.0))[:SAMPLES/2]. !. gr.clearws() ... gr.polyline(SAMPLES/2, f, power) gr.updatews() data = wf.readframes(SAMPLES). May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 25.
(26) ... with user interaction import gr3 from OpenGL.GLUT import * # ... Read MRI data width = height = 1000 isolevel = 100 angle = 0. !. def display(): vertices, normals = gr3.triangulate(data, (1.0/160, 1.0/160, 1.0/200), (-0.5, -0.5, -0.5), isolevel) mesh = gr3.createmesh(len(vertices)*3, vertices, normals, np.ones(vertices.shape)) gr3.drawmesh(mesh, 1, (0,0,0), (0,0,1), (0,1,0), (1,1,1), (1,1,1)) gr3.cameralookat(-2*math.cos(angle), -2*math.sin(angle), -0.25, 0, 0, -0.25, 0, 0, -1) gr3.drawimage(0, width, 0, height, width, height, gr3.GR3_Drawable.GR3_DRAWABLE_OPENGL) glutSwapBuffers() gr3.clear() gr3.deletemesh(ctypes.c_int(mesh.value)) def motion(x, y): isolevel = 256*y/height angle = -math.pi + 2*math.pi*x/width glutPostRedisplay() glutInit() glutInitWindowSize(width, height) glutCreateWindow("Marching Cubes Demo"). !. glutDisplayFunc(display) glutMotionFunc(motion) glutMainLoop(). May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 26.
(27) ... with Qt. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 27.
(28) ... and wxWidgets. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 28.
(29) Scalable graphics in Web browsers. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 29.
(30) Import PDF import gr (w, h, data) = gr.readimage("fs.pdf") if w < h: r = float(w)/h gr.setviewport(0.5*(1-r), 0.5*(1+r), 0, 1); else: r = float(h)/w gr.setviewport(0, 1, 0.5*(1-r), 0.5*(1+r)); gr.drawimage(0, 1, 0, 1, w, h, data) gr.updatews(). May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 30.
(31) Success stories (I) World’s most powerful laboratory small-angle X-ray scattering facility at Forschungszentrum Jülich. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 31.
(32) Success stories (II) Nframes = 100 radius = 1 height = 4 distance = 5. !. def RunSimulation(): # defining materials mAir = HomogeneousMaterial("Air", 0.0, 0.0) mSubstrate = HomogeneousMaterial("Substrate", 6e-6, 2e-8) mParticle = HomogeneousMaterial("Particle", 6e-4, 2e-8) # collection of particles cylinder_ff = FormFactorCylinder(radius, height) cylinder = Particle(mParticle, cylinder_ff) particle_layout = ParticleLayout() particle_layout.addParticle(cylinder) # interference function interference = InterferenceFunction1DParaCrystal(distance, 3 * nanometer) particle_layout.addInterferenceFunction(interference) # air layer with particles and substrate form multi layer air_layer = Layer(mAir) air_layer.setLayout(particle_layout) substrate_layer = Layer(mSubstrate) multi_layer = MultiLayer() multi_layer.addLayer(air_layer) multi_layer.addLayer(substrate_layer) # build and run experiment simulation = Simulation() simulation.setDetectorParameters(250, -4*degree, 4*degree, 250, 0*degree, 8*degree) simulation.setBeamParameters(1.0 * angstrom, 0.2 * degree, 0.0 * degree) simulation.setSample(multi_layer) simulation.runSimulation() return simulation.getIntensityData().getArray(). BornAgain. A software to simulate and fit neutron and x-ray scattering at grazing incidence (GISANS and GISAXS), using distortedwave Born approximation (DWBA). def SetParameters(i): radius = (1. + (3.0/Nframes)*i) * nanometer height = (1. + (4.0/Nframes)*i) * nanometer distance = (10. - (1.0/Nframes)*i) * nanometer. !. for i in range(100): SetParameters(i) result = RunSimulation() gr.pygr.imshow(numpy.log10(numpy.rot90(result, 1)), cmap=gr.COLORMAP_PILATUS). May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 32.
(33) Success stories (III) NICOS. a network-based control system written for neutron scattering instruments at the FRM II. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 33.
(34) Coming soon: Python moldyn package …. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 34.
(35) … with video output. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 35.
(36) … and POV-Ray output. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 36.
(37) … in highest resolution. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 37.
(38) Performance optimizations ✓ ✓. ✓. May 22, 2014. NumPy module for handling multi-dimensional arrays (ndarray). Numba (Anaconda) ✓ just-in-time compilation driven by @autojit- or @jitdecorators (LLVM). ✓ vectorization of ndarray based functions (ufuncs). Numba Pro (Anaconda Accelerate) ✓ parallel loops and ufuncs. ✓ execution of ufunfs on GPUs. ✓ “Python” GPU kernels. ✓ GPU optimized libraries (cuBLAS, cuFFT, cuRAND) Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 38.
(39) Realization. ✓ ✓. May 22, 2014. NumPy vector operations on ndarrays instead of loops ➟ works in any NumPy Python environment. Numba (Anaconda) add @jit and @autojit decorators ➟ useful for “many” function calls with “big” arrays . Numba Pro (Anaconda Accelerate) add @vectorize decorators implementation of multi-core / GPU kernels in "Python" switch to GPU-optimized features ➟ useful only for "large" arrays . performance. ✓. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 39.
(40) Particle simulation import numpy as np ! ! numba.decorators import autojit from ! N # number of particles ! = 300 M # masses ! = 0.05 * np.ones(N) size # particle size ! = 0.04 ! ! @autojit def ! step(dt, size, a): # update positions ! a[0] += dt * a[1] ! ! n = a.shape[1] ! D = np.empty((n, n), dtype=np.float) ! for i in range(n): for j in range(n): ! dx = a[0, i, 0] - a[0, j, 0] ! dy = a[0, i, 1] - a[0, j, 1] ! D[i, j] = np.sqrt(dx*dx + dy*dy) ! ! ! ... # find pairs of particles undergoing a collision ! ... # check for crossing boundary ! return a ... ! ! !a[0, :] = -0.5 + np.random.random((N, 2)) # positions a[1, ! :] = -0.5 + np.random.random((N, 2)) # velocities a[0, ! :] *= (4 - 2*size) dt ! = 1. / 30 ! while True: ! ! a = step(dt, size, a) ..... May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 40.
(41) Diffusion ! import numpy ! numba.decorators import jit from ! ! = 0.005 dx ! = 0.005 dy ! = 0.5 a ! = dx*dx*dy*dy/(2*a*(dx*dx+dy*dy)) dt ! timesteps = 300 ! ! = int(1/dx) nx ! = int(1/dy) ny ! = numpy.zeros([nx,ny]) ui ! = numpy.zeros([nx,ny]) u ! ! ! diff_step(u, ui): def ! for i in range(1,nx-1): ! for j in range(1,ny-1): ! uxx = ( ui[i+1,j] - 2*ui[i,j] + ui[i-1, j] )/(dx*dx) ! uyy = ( ui[i,j+1] - 2*ui[i,j] + ui[i, j-1] )/(dy*dy) ! u[i,j] = ui[i,j]+dt*a*(uxx+uyy) ! ! diff_step_numba = jit('void(f8[:,:], f8[:,:])')(diff_step) ! ! m in range(timesteps): for diff_step_numba(u, diff_step (u, ui) ! ui = numpy.copy(u) .... May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 41.
(42) Mandelbrot set from numbapro import vectorize import numpy as np. !. @vectorize(['uint8(uint32, f8, f8, f8, f8, uint32, uint32, uint32)'], target='gpu') def mandel(tid, min_x, max_x, min_y, max_y, width, height, iters): pixel_size_x = (max_x - min_x) / width pixel_size_y = (max_y - min_y) / height. ! ! ! ! ! ! !. x = tid % width y = tid / width real = min_x + x * pixel_size_x imag = min_y + y * pixel_size_y c = complex(real, imag) z = 0.0j for i in range(iters): z = z * z + c if (z.real * z.real + z.imag * z.imag) >= 4: return i return 255. def create_fractal(min_x, max_x, min_y, max_y, width, height, iters): tids = np.arange(width * height, dtype=np.uint32) return mandel(tids, np.float64(min_x), np.float64(max_x), np.float64(min_y), np.float64(max_y), np.uint32(height), np.uint32(width), np.uint32(iters)). May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 42.
(43) Performance comparison Calculation of Mandelbrot set. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 43.
(44) Numba (Pro) review ✓ ✓ ✓ ✓. functions with numerical code can be compiled with little effort and lead to impressive results. numerical code should be separated from logic statements (and processing of lists, dictionaries). advanced Technologie due to LLVM intermediate language (LLVM IR). easy installation and maintenance Download link (Continuum Analytics): http://www.continuum.io/downloads. % bash Anaconda-1.x.x-[Linux|MacOSX]-x86[_64].sh % conda update conda % conda update anaconda. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 44.
(45) Development tools You can use your favorite editor and start Python in a shell. But the impatient user should chose a development environment:. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 45.
(46) IPython console. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 46.
(47) IPython notebook. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 47.
(48) Spyder. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 48.
(49) PyCharm. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 49.
(50) Bokeh. import numpy as np from scipy.integrate import odeint from bokeh.plotting import *. !. sigma = 10 rho = 28 beta = 8.0/3 theta = 3 * np.pi / 4. !. def lorenz(xyz, t): x, y, z = xyz x_dot = sigma * (y - x) y_dot = x * rho - x * z - y z_dot = x * y - beta* z return [x_dot, y_dot, z_dot]. !. initial = (-10, -7, 35) t = np.arange(0, 100, 0.006). ! solution = odeint(lorenz, initial, t) !. x = solution[:, 0] y = solution[:, 1] z = solution[:, 2] xprime = np.cos(theta) * x - np.sin(theta) * y. !. colors = ["#C6DBEF", "#9ECAE1", "#6BAED6", “#4292C6", "#2171B5", "#08519C", "#08306B",]. ! output_file("lorenz.html", title="lorenz.py example") !. multi_line(np.array_split(xprime, 7), np.array_split(z, 7), line_color=colors, line_alpha=0.8, line_width=1.5, tools=“pan,wheel_zoom,box_zoom,reset,previewsave", title="lorenz example", name="lorenz_example"). !. show() May 22, 2014. # open a browser Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 50.
(51) Resources. ✓ ✓ ✓ ✓ ✓. May 22, 2014. Website: http://gr-framework.org. PyPI: https://pypi.python.org/pypi/gr. Git Repository: http://github.com/jheinen/gr. Binstar: https://binstar.org/jheinen/gr. Talk: Scientific Visualization Workshop (PDF, HTML). Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 51.
(52) Website. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 52.
(53) Git-Repo. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 53.
(54) PyPI. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 54.
(55) Binstar. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 55.
(56) Conclusion ✓ ✓ ✓. May 22, 2014. The use of Python with the GR framework and Numba (Pro) extensions allows the realization of high-performance visualization applications in scientific and technical environments. The GR framework can seamlessly be integrated into "foreign" Python environments, e.g. Anaconda, by using the ctypes mechanism. Anaconda (Accelerate) is an easy to manage (commercial) Python distribution that can be enhanced by the use of the GR framework with its functions for real-time or 3D visualization applications. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 56.
(57) Future plans ✓ ✓ ✓. ✓ ✓. May 22, 2014. implement your(!) feature requests. moldyn package for Python. more. ✓ tutorials. ✓ convenience functions. ✓ documentation. ✓ examples (gallery). IPython notebook integration. Bokeh integration. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 57.
(58) Thank you for your attention References: Numba, A Dynamic Python compiler for Science: http://lanyrd.com/2013/pycon/scdyzh Continuum Analytics: http://www.continuum.io. ! Contact: [email protected] @josef_heinen". ! Thanks to: Florian Rhiem, Ingo Heimbach, Christian Felder, David Knodt, Jörg Winkler, Fabian Beule, Marcel Dück, Marvin Goblet, et al.. May 22, 2014. Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems. 58.
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