Attachment 226758 Details for Bug 257476 – shar

shar

py-nifty7.shar (text/plain), 4.46 KB, created by Yuri Victorovich on 2021-07-28 20:04:35 UTC

(hide)

Description:

Filename:

MIME Type:

Creator: Yuri Victorovich

Created: 2021-07-28 20:04:35 UTC

Size: 4.46 KB

patch

obsolete

># This is a shell archive.  Save it in a file, remove anything before
># this line, and then unpack it by entering "sh file".  Note, it may
># create directories; files and directories will be owned by you and
># have default permissions.
>#
># This archive contains:
>#
>#	math/py-nifty7
>#	math/py-nifty7/pkg-descr
>#	math/py-nifty7/Makefile
>#	math/py-nifty7/distinfo
>#	math/py-nifty7/example.py
>#
>echo c - math/py-nifty7
>mkdir -p math/py-nifty7 > /dev/null 2>&1
>echo x - math/py-nifty7/pkg-descr
>sed 's/^X//' >math/py-nifty7/pkg-descr << '711c023102c7c58fd8bab0489c9949cd'
>XNIFTy, "Numerical Information Field Theory", is a versatile library designed to
>Xenable the development of signal inference algorithms that operate regardless of
>Xthe underlying grids (spatial, spectral, temporal, ...) and their resolutions.
>XIts object-oriented framework is written in Python, although it accesses
>Xlibraries written in C++ and C for efficiency.
>X
>XWWW: https://ift.pages.mpcdf.de/nifty
>711c023102c7c58fd8bab0489c9949cd
>echo x - math/py-nifty7/Makefile
>sed 's/^X//' >math/py-nifty7/Makefile << '5ec986f84aa65ebee4dab0ed8250ddb3'
>XPORTNAME=	nifty7
>XDISTVERSION=	7.2
>XCATEGORIES=	math python
>XMASTER_SITES=	CHEESESHOP
>XPKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
>X
>XMAINTAINER=	yuri@FreeBSD.org
>XCOMMENT=	Numerical Information Field Theory
>X
>XLICENSE=	GPLv3
>XLICENSE_FILE=	${WRKSRC}/COPYING
>X
>XPY_DEPENDS=	${PYNUMPY} \
>X		${PYTHON_PKGNAMEPREFIX}scipy>0:science/py-scipy@${PY_FLAVOR}
>XBUILD_DEPENDS=	${PY_DEPENDS}
>XRUN_DEPENDS=	${PY_DEPENDS}
>X
>XUSES=		python:3.6+
>XUSE_PYTHON=	distutils autoplist
>X
>XNO_ARCH=	yes
>X
>X.include <bsd.port.mk>
>5ec986f84aa65ebee4dab0ed8250ddb3
>echo x - math/py-nifty7/distinfo
>sed 's/^X//' >math/py-nifty7/distinfo << '5646c4dc451b6000fdcc42f00fe8c1e1'
>XTIMESTAMP = 1627500537
>XSHA256 (nifty7-7.2.tar.gz) = c40ed1d64f2160b323af559f4aef5904138bdc1bb6d01c1362464691c1954127
>XSIZE (nifty7-7.2.tar.gz) = 183825
>5646c4dc451b6000fdcc42f00fe8c1e1
>echo x - math/py-nifty7/example.py
>sed 's/^X//' >math/py-nifty7/example.py << 'f009dafa1dad638a2ff0e074eb0c1492'
>X#%matplotlib inline
>Ximport numpy as np
>Ximport nifty7 as ift
>Ximport matplotlib.pyplot as plt
>Xplt.rcParams['figure.dpi'] = 100
>Xplt.style.use("seaborn-notebook")
>X
>Xdef Curvature(R, N, Sh):
>X    IC = ift.GradientNormController(iteration_limit=50000,
>X                                    tol_abs_gradnorm=0.1)
>X    # WienerFilterCurvature is (R.adjoint*N.inverse*R + Sh.inverse) plus some handy
>X    # helper methods.
>X    return ift.WienerFilterCurvature(R,N,Sh,iteration_controller=IC,iteration_controller_sampling=IC)
>X
>X
>Xs_space = ift.RGSpace(N_pixels)
>Xh_space = s_space.get_default_codomain()
>XHT = ift.HarmonicTransformOperator(h_space, target=s_space)
>X
>X# Operators
>XSh = ift.create_power_operator(h_space, power_spectrum=pow_spec)
>XR = HT # @ ift.create_harmonic_smoothing_operator((h_space,), 0, 0.02)
>X
>X# Fields and data
>Xsh = Sh.draw_sample_with_dtype(dtype=np.float64)
>Xnoiseless_data=R(sh)
>Xnoise_amplitude = np.sqrt(0.2)
>XN = ift.ScalingOperator(s_space, noise_amplitude**2)
>X
>Xn = ift.Field.from_random(domain=s_space, random_type='normal',
>X                          std=noise_amplitude, mean=0)
>Xd = noiseless_data + n
>Xj = R.adjoint_times(N.inverse_times(d))
>Xcurv = Curvature(R=R, N=N, Sh=Sh)
>XD = curv.inverse
>X
>X# Get signal data and reconstruction data
>Xs_data = HT(sh).val
>Xm_data = HT(m).val
>Xd_data = d.val
>X
>Xplt.plot(s_data, 'r', label="Signal", linewidth=2)
>Xplt.plot(d_data, 'k.', label="Data")
>Xplt.plot(m_data, 'k', label="Reconstruction",linewidth=2)
>Xplt.title("Reconstruction")
>Xplt.legend()
>Xplt.show()
>X
>Xplt.plot(s_data - s_data, 'r', label="Signal", linewidth=2)
>Xplt.plot(d_data - s_data, 'k.', label="Data")
>Xplt.plot(m_data - s_data, 'k', label="Reconstruction",linewidth=2)
>Xplt.axhspan(-noise_amplitude,noise_amplitude, facecolor='0.9', alpha=.5)
>Xplt.title("Residuals")
>Xplt.legend()
>Xplt.show()
>X
>Xs_power_data = ift.power_analyze(sh).val
>Xm_power_data = ift.power_analyze(m).val
>Xplt.loglog()
>Xplt.xlim(1, int(N_pixels/2))
>Xymin = min(m_power_data)
>Xplt.ylim(ymin, 1)
>Xxs = np.arange(1,int(N_pixels/2),.1)
>Xplt.plot(xs, pow_spec(xs), label="True Power Spectrum", color='k',alpha=0.5)
>Xplt.plot(s_power_data, 'r', label="Signal")
>Xplt.plot(m_power_data, 'k', label="Reconstruction")
>Xplt.axhline(noise_amplitude**2 / N_pixels, color="k", linestyle='--', label="Noise level", alpha=.5)
>Xplt.axhspan(noise_amplitude**2 / N_pixels, ymin, facecolor='0.9', alpha=.5)
>Xplt.title("Power Spectrum")
>Xplt.legend()
>Xplt.show()
>f009dafa1dad638a2ff0e074eb0c1492
>exit
>

Actions: View

Attachments on bug 257476: 226758