dataesr/scientific-paragraphs-categorization huggingface dataset:
Geophysical analyses are often performed in spherical geometry and require the use of spherical harmonic functions to express observables or physical quantities. When expanded to high degree, the accuracy and speed of the spherical harmonic transforms and reconstructions are of paramount importance. SHTools is a time and user-tested open-source archive of both Fortran 95 and Python routines for performing spherical harmonic analyses. The routines support all spherical-harmonic normalization conventions used in the geosciences, including 4p-normalized, Schmidt seminormalized, orthonormalized, and unnormalized harmonics, along with the option of employing the Condon-Shortley phase factor of ð21Þ m. Data on the sphere can be sampled on a variety of grid formats, including equally spaced cylindrical grids and grids appropriate for integration by Gauss-Legendre quadrature.
Over the past two decades, a number of highly accurate and optimized software packages have been written to meet these needs. These include the C-based codes SHTns  (Schaeffer, 2013) , Libsharp  (Reinecke & Seljebotn, 2013) , and SpharmonicKit (http://www. cs. dartmouth. edu/geelong/sphere/), the Fortran-based code harmonic_synth  (Holmes & Pavlis, 2006) , and the MATLAB-based code GrafLab  (Bucha & Jan ak, 2013). In addition to these generic codes, more specialized software packages exist such as the HEALpix package  (G orski et al. , 2005)  that can be used with high-resolution pixelated data, the Fortran-based SPHEREPACK  (Adams & Swarztrauber, 1999)  that can be used with differential equations involving spherical harmonic functions, and a suite of MATLAB-based code that can be used for localized spectral analyses  (Harig et al.
, 2015). Though these software packages have many similarities, they each differ in their ease of use, their graphical visualization capabilities, the completness of the documentation, the manner that they are maintained, the programming languages they support, the conventions used for the gridded data and spherical harmonic functions, and the number of additional tools they provide for specialized applications. SHTools is an archive of Fortran 95 and Python software that can be used to perform spherical harmonic transforms and reconstructions, rotations of data expressed in spherical harmonics, and multitaper spectral analyses on the sphere. SHTools is extremely versatile and easy to use. The software is open source, versioned by git, and released with an unrestrictive BSD 3-clause license.
Though many of the routines are generic and could be used in any of the physical sciences, this package is tailored to the specific needs of the geosciences community. As examples, it can accommodate any standard normalization of the spherical harmonic functions, including 4p-normalized, Schmidt seminormalized, orthonormalized, and unnormalized harmonics. One can choose to use or exclude the Condon-Shortley phase factor of ð21Þ m with the associated Legendre functions, and both real and complex spherical harmonics are supported. Besides providing basic routines for working with data expressed in spherical harmonics, several specialized tools are provided by SHTools for common geophysical problems. As examples, routines are provided for performing multitaper spectral analyses localized to arbitrarily shaped domains.
The Fourier transforms are computed by the software FFTW  (Frigo & Johnson, 2005) , and the Fortran routines are OpenMP thread-safe allowing for their use in parallelized programs. In this document, we provide all information that is necessary to start using the SHTools software package. In section 2, we start by providing the definitions of the spherical harmonic functions for both real and complex fields. This includes descriptions of all the normalization conventions that can be found in the geosciences. Next, in section 3, we provide some of the implementation details regarding how the spherical harmonic expansions and reconstructions are performed. In sections 4 and 5, the Fortran 95 and Python components are described separately. This includes simple installation instructions and the definitions of common variables.
Making use of two component vectors a m ðhÞ; b m ðhÞ ð Þ 5 X L l5m C lm ; S lm ð Þ P lm ðcos hÞ; (28) Equation (  27 ) can be written more simply as f h; / ð Þ5 X L m50 a m ðhÞ cos m/1b m ðhÞ sin m/ ð Þ : (29) For a given latitude band, the function f can thus be evaluated on a series of grid nodes all at the same time using an inverse fast Fourier transform. For this operation, SHTools makes use of the highly optimized software package FFTW  (Frigo & Johnson, 2005)  that supports grids of arbitrary length and both real and complex data. To adequately sample the function in longitude, a minimum of 2L11 data points should be employed. The slowest part of reconstructing the function f involves the computation of the coefficients a m and b m. These coefficients depend upon the associated Legendre functions, which are calculated efficiently using standard three-term recursion relations over adjacent spherical harmonic degrees (e.
e. , when the spherical harmonic degree is limited to a maximum value L. As seen in equation (  32 ), the functions c lm and s lm can be approximated as polynomials of maximum degree L, and when multiplied by the associated Legendre function, the integrand is approximately a polynomial of maximum degree 2L. For the case of Gauss-Legendre quadrature, the quadrature is exact when the function f is sampled in latitude at the ðL11Þ zeros of the Legendre Polynomial of degree ðL11Þ. Since the function also needs to be sampled on ð2L11Þ equally space grid nodes for the Fourier transforms in longitude, the function f is sampled on a grid of size ðL11Þ3ð2L11Þ. The second type of quadrature used in SHTools is appropriate for data that are sampled on regular grids. We make use of the work of  Driscoll and Healy (1994)  who showed that an exact quadrature exists when the function f is sampled at N equally spaced nodes in latitude and N equally spaced nodes in longitude.
For this sampling, the grids make use of the latitude band at 908N, but not 908S, and the number of samples is 2ðL11Þ, which is always even. Given that the sampling in latitude was imposed a priori, it should not be a surprise that these grids contain almost twice as many samples in latitude as the grids used with Gauss-Legendre quadrature. The convenience of using a regularly spaced grid comes with the drawback of being computationally slower by about a factor of two. In the geosciences, it is common to work with grids that are equally spaced in degrees latitude and longitude, such as with data sets expressed in cylindrical projections, and SHTools provides the option of using grids of size N32N. For this case, when performing the Fourier transform in equation (  31 ), the coefficients c lm and s lm with m > L are simply discarded.
SHTools for Fortran 95 Installation of the Fortran 95 components of SHTools is straightforward and has been tested on multiple platforms using multiple compilers. Before attempting an installation, it will first be necessary to install both the FFTW  (Frigo & Johnson, 2005)  and LAPACK software packages. After having done this, the most generic way to install the SHTools archive is by use of the unix command-line utility make. To build both the standard library, as well as the thread-safe library that is OpenMP compatible, it is only necessary to execute make fortran fortran-mp in a unix terminal. By default, the archive will be built using the gfortran compiler, but alternative compilers and compiler flags can be used by specifying optional arguments to the make variables F95 and F95FLAGS.
Tested compilers include Absoft ProFortran (f95), Intel fortran (ifort) and g95. A series of tests programs can be built and executed using make fortran-tests and the compiled library, modules, man pages, and web documentation can be made available on a system level by executing. Time to perform the reconstruction of a function from its spherical harmonic coefficients (solid lines) and the spherical harmonic transform of the function (dashed lines). Plotted are timing results as a function of spherical harmonic bandwidth using the real and complex Gauss-Legendre and  Driscoll and Healy (1994)  quadrature implementations. Calculations were performed on a Mac Pro 2. 7 GHz 12 Core Intel Xeon E5 using 64 bit executables and level 3 optimizations. Geochemistry, Geophysics, Geosystems 10. 1029/2018GC007529 make install-fortran Alternatively, if using the macOS operating system, the Fortran 95 components can be installed using the brew package manager by executing brew tap shtools/shtools brew install shtools Additional installation instructions can be found in the web documentation.
Almost all capabilities of SHTools can be accessed directly from within any Fortran 95 program. To call an SHTools routine, it is necessary to make use of the shtools and/or planetsconstants modules by including the commands use shtools use planetsconstants in the source file. These should be placed after the program, subroutine, or function declaration, but before any statements such as implicit none. The second use statement is only necessary if one needs to access the constants defined by SHTools, such as the mean radii and GM of the planets. When compiling the program, it is necessary to ensure that the SHTools library file libSHTOOLS. a and the directory containing the module files are accessible to the compiler. For a standard installation, these should be found in/usr/local/lib and/usr/local/include, respectively.
pip install pyshtools In the Python environment, the command import pyshtools will load several packages into the pyshtools namespace. These packages are very similar to the seven highlevel groupings of functions in the SHTOOLS library, and include Geochemistry, Geophysics, Geosystems 10. 1029/2018GC007529 shclasses: All pyshtools classes and subclasses, shtools: All Fortran-95 wrapped SHTools routines, legendre: Legendre functions, expand: Spherical harmonic expansion routines, shio: Spherical harmonic input/output, storage, and conversion routines, spectralanalysis: Global and local spectral analysis routines, rotate: Spherical harmonic rotation routines, gravmag: Gravity and magnetics routines, constant: Planetary constants, utils: Utilities. In addition to loading these packages, three high-level class structures will be loaded into the primary namespace that provided easy access to most operations on spherical harmonic coefficients, grids, and localization windows: SHCoeffs: A high-level class for spherical harmonic coefficients, SHGrid: A high-level class for global grids, SHWindow: A high-level class for localization windows.
The vast majority of the pyshtools functionality can be accessed via the three high-level classes. Using these classes, operations are exceedingly simple to perform, as all required metadata are stored as class attributes. These attributes include the spherical harmonic normalization, Condon-Shortley phase convention, maximum spherical harmonic degree, data type, grid type, and grid size. Once the classes are initialized, it is only necessary to execute one of its methods, usually without any arguments, to perform basic operations. As an example, if clm is an SHCoeffs instance, then reconstructing these coefficients on a grid, and reexpanding this grid into spherical harmonics is as easy as grid 5 clm. expand() clm2 5 grid. expand() Tables (4-6) provide all the attributes and methods that are implemented for these three classes.
pyshtools. [tab] where [tab] is the tab key. To read the documentation of a class, method, or routine in IPython, such as SHCoeffs, it is only necessary to enter pyshtools. SHCoeffs? To access an SHTools constant and its associated info string, such as the mass of Mars, one would use the commands pyshtools. constant. mass_mars pyshtools. constant. mass_mars. info() Documentation for the Python functions used in SHTools can also be accessed by their unix man pages, by appending py to the name and using all lower case letters.
Examples The Python pyshtools package provides three classes for interacting with spherical harmonic coefficients, grids, and localization windows: SHCoeffs, SHGrid, and SHWindow. These classes provide simple methods for initializing class instances, visualizing spectra and grids, basic data transformations, and output to arrays and files. In this section, we first provide simple demonstrations of basic operations involving Geochemistry, Geophysics, Geosystems 10. 1029/2018GC007529 spherical harmonic coefficients and grids. Following this, the basic techniques for performing localized spectral analyses are demonstrated using both spherical cap and arbitrarily shaped localization windows.
The spectrum of the coefficients, which is independent of the chosen normalization, can be output as a numpy array using the spectrum() method. By default, the output spectrum corresponds to the power per degree of the function with unit 5 'per_l. ' The energy spectrum can be output by specifying convention5 'energy,' and the unit of the spectrum can be set alternatively to either per_lm or per_dlogl. Two methods are provided for visualizing the spectrum graphically: plot_spectrum() plots the spectrum as a function of spherical harmonic degree, and plot_spectrum2d() plots the spectrum as a function of degree and angular order. The following two lines of code demonstrate how to calculate and plot the power per coefficient spectrum: power 5 clm. spectrum(convention5 'power,' unit5 'per_lm') fig, ax 5 clm.
The method set_coeffs() allows one to modify the value of one or more degrees and orders by providing a list of values for the degrees ls and orders ms. To obtain a numpy array of the coefficients, with the option of using a different normalization, one would use the method to_array(). Finally, the method to_file() outputs the coefficients either to a numpy formatted file or to an shtools asciiformatted list of degrees, orders, and cosine and sine coefficients. The following example lines of code show how to set the degree 2 coefficients to zero and to output the spherical-harmonic coefficients to an array and file: clm. set_coeffs(values 5 0, ls 5 2, ms 5 [-2, 21, 0, 1, 2]) coeffs 5 clm. to_array() clm. to_file(filename, lmax 5 40) It is further noted that one can apply arithmetic operations to SHCoeffs class instances, including addition, subtraction, multiplication, division, and powers.
Development Roadmap The SHTools software package started development in 2004. It is time and user-tested, and new features have been continuously implemented based on real-life needs of scientists working in the geosciences. Initially started as a Fortran 95 project, full Python capabilities were added in 2015, and the pyshtools package is now being actively developed and expanded upon. In its current state (version 4. 2), pyshtools supports all basic operations involving spherical harmonic coefficients, grids, and localization windows. In coming releases, four different development activities will be emphasized. First, even though basic plotting routines are provided for inspecting SHCoeffs, SHGrid, and SHWindow class instances, these graphical routines will be improved upon by adding map projection capabilities from the generic mapping tools (GMT) package  (Wessel & Smith, 1991).
Acknowledgments SHTools is an open-source software project, and we are indebted to all those who have contributed, including  Elliott Sales de Andrade ,  Ilya Oshchepkov ,  Andrew Walker ,  Benda X , and xoviat. The most recent release of SHTools can always be downloaded from GitHub (https://github. com/SHTOOLS/SHTOOLS/releases), and the web documentation can be accessed at https://shtools. github. io/SHTOOLS/. This work was supported by  CNES.
This modeling provides many observables: prompt neutron and gamma fission spectra, multiplicities and also fine decompositions: number of neutrons emitted as a function of the fragment mass, spectra per fragments, etc. This model relies on nuclear structure databases and on several basic nuclear models describing for instance gamma strength functions or level densities. Additionally some free parameters are still to be determined, namely two parameters describing the excitation energy repartition law, the spin cutoff of the heavy and light fragments and a rescaling parameter for the rotational inertia momentum of the fragments with respect of the rigid-body model. In the present work we investigate the impact of this latter parameter. For this we mainly substitute the corrected rigid-body value by a quantity obtained from a microscopic description of the fission fragment.
g. cross sections. In a first section we recall the usual classical and semi-classical models for nuclear moment of inertia. Then in a second section we present the cranking model based on the Finite-Range Liquid-Drop-Model  [1]  that we have implemented to provide moments of inertia carrying a more refined mark of the nuclear structure. In a third section we a e-mail: pierre. tamagno@cea. fr show where these phenomenological "ingredients" are used in practical modeling of fission observables. The modeling scheme of the FIFRELIN code  [2]  will be used as an exemple. Finally the impact of the choice of model for nuclear moments of inertia will be illustrated on selected fission observables provided by the FIFRELIN code.
Energies E ν and occupation probability v 2 ν are obtained from the independent particle states by applying either a BCS  [9]  or a Lipkin-Nogami  [10, 11]  pairing correlation correction. These developments where originally designed for the CONRAD evaluation code  [12]  but are now shared with FIFRELIN. Before we investigate the impact of different models for nuclear moments of inertia, we make a comparison with experimental moments of inertia that can be extracted from spectroscopy measurements. To obtain experimental moments of inertia, the ENSDF database  [13]  has been automatically explored to find low-lying 0 + , 2 + , 4 + patterns in level schemes. If the ratio of the two transition energies is close to 2(2 + 1)/4(4 + 1) then a moment of inertia is extracted from the lower transition energy.
OCT-Angiography System Setup The Spectralis OCT2 (Heidelberg Engineering GmbH, Heidelberg, Germany) was the 85 kHz Spectral Domain OCT device used to obtain OCT-angiograms. Detailed features of the OCT-A system have been previously described. 34 A high resolution OCT-A volume scan of 15 3 108 (approximately 4. 5 3 3 mm) centered in foveal area was acquired in each enrolled subject. The volume scan was made by 261 B-scans, and the automated averaging process was set at 35 frames per scan (distance between two consecutive B-scans of 11 lm). The OCT-A software (Heyex Software Version 1. 9. 201. 0; Heidelberg Engineering) allowed visualization of angiograms both in Cscan (''en-face'') or B-scan projections. For our purpose only, Cscans were assessed. A maximum intensity projection algorithm was chosen to highlight the highest value voxel along a line of sight.
A quantitative analysis of OCT-angiograms was performed to detect flow, flow-void areas, and blood flow changes at the level of the CC. The first step of this analysis was thresholding the image with the Phansalkar method (radius of 15 pixels), a specifically designed automated local thresholding algorithm for low-contrast images. 35 This process was obtained by importing OCT-angiograms in FIJI, an available for free imaging software (expanded version of ImageJ, Version 1. 51h; Wayne Rasband, National Institute of Health, Bethesda, MD, USA). The thresholded images were then analyzed with the Analyze Particles FIJI tool, which measured and computed all areas greater or equal to 1 pixel where there was flow (decorrelated pixels) information. 36 The CC vascular flow density (VD) was then computed as the ratio (expressed as percentage) between the number of decorrelated pixels and the total number of pixels composing the image.
Quantitative data were obtained from a 4 3 3 mm area (original image 4. 5 3 3 mm area, 158 3 108) to minimize the effects of artifacts at the edge of the Cscan. Each OCT-angiogram had a resolution of 784 3 520 pixels. A qualitative assessment of the CC on baseline and HGT OCT-angiograms was also performed to localize the perfusion changes. The potential stress-induced changes were better visualized with a subtraction protocol in the image calculator tool of FIJI. The two OCT-A imaging sessions (baseline and under stress test) were performed by the same experienced operator (ML), within a single visit. All scans were independently reviewed by two investigators (FCP and ML) to ensure correct segmentation and sufficient image quality for post hoc analysis.
Statistical Analysis Quantitative variables were described by their mean and standard deviation (SD). The absolute and the relative (ratio of absolute difference and baseline value) differences between baseline and under stress quantitative data were also reported. The Shapiro-Wilk test was used to check whether the sample came from a normally distributed population. Depending on the statistical distribution, comparisons of means of independent variables were performed using the Student's t-test or the Mann-Whitney test. The paired t-test or Wilcoxon-Mann-Whitney test was used to compare paired data. The linear regression analysis and the Pearson's ''r'' or Spearman's rank correlation coefficient were used to measure the statistical dependence of quantitative variables. A P value of <0. 05 was considered as statistically significant.
Our analysis initially identified six major genetic clusters corresponding to different geographic macro-areas along the Mediterranean range. Long-distance migrants among wild populations, due to translocations, were found and removed from the reference database. Assignment tests allowed us to allocate approximately 70% of confiscated individuals of unknown origin to one of the six Mediterranean macro-areas. Most of the assigned tortoises belonged to the genetic cluster corresponding to the area where the respective captivity center was located. However, we also found evidence of long-distance origin of confiscated individuals, especially in centers along the Adriatic coast and facing the Balkan regions, a well-known source of illegally traded individuals. Our results clearly show the role for reintroduction projects of the microsatellite panel, which was useful to re-assign most of the confiscated individuals to the respective macro-area of origin.
Initially, all individuals were genotyped at 9 microsatellite loci (Test10, Test56, Test71, Test76, Test88, Gal136, Gal75, Gal73, and Gal263) as in  [28]  (see also  [32, 33] ). However, two loci (Test88 and Gal73) yielded inconsistent reactions and were discarded from downstream analyses. Detailed protocols are provided as Supplementary Material. In order to combine our dataset with the dataset from Perez et al. [28]  avoiding mislabelling of alleles, we re-genotyped selected samples from  [28] , and we recalibrated binning set and allele nomenclature to match their dataset. Fragment analysis of PCR products was performed by Macrogen Inc. on an ABI 3730xl Genetic Analyser (Applied Biosystems) with a 400HD size standard. Allele calling was performed with GENEMAPPER® 4. 1 checking electropherograms by eye.
Genetic structure and reference database As the first step to assess the area of origin of confiscated individuals, we carried out a population structure analysis of individuals that can be confidently considered as belonging to natural populations (hereon 'wild'), in order to define possible source populations and to compile a reference database of individuals genuinely belonging to each identified population. The multilocus genotypes of the 154 newly collected wild tortoises were joined to the dataset from  [28] , excluding from the latter all the individuals that were reported to be migrant or 1 st and 2 nd generation hybrids, and the admixed population of Bosco Nordio. The joint wild dataset consisted of 461 individuals (Fig  1 ). We performed the cluster analyses on the wild dataset using the Bayesian method implemented in STRUCTURE 2.