Speckle Imaging of Spin Fluctuations in a Strongly Interacting Fermi Gas

Spin fluctuations and density fluctuations are studied for a two-component gas of strongly interacting fermions along the BEC-BCS crossover. This is done by in-situ imaging of dispersive speckle patterns. Compressibility and magnetic susceptibility are determined from the measured fluctuations. This new sensitive method easily resolves a tenfold suppression of spin fluctuations below shot noise due to pairing, and can be applied to novel magnetic phases in optical lattices

Suppression of Density Fluctuations in a Quantum Degenerate Fermi Gas

We study density profiles of an ideal Fermi gas and observe Pauli suppression of density fluctuations (atom shot noise) for cold clouds deep in the quantum degenerate regime. Strong suppression is observed for probe volumes containing more than 10,000 atoms. Measuring the level of suppression provides sensitive thermometry at low temperatures. After this method of sensitive noise measurements has been validated with an ideal Fermi gas, it can now be applied to characterize phase transitions in strongly correlated many-body systems.Comment: minor edit: fixed technical problem with arxiv's processing of .eps figur

Seamless Learning als Ansatz zum Umgang mit flexiblem Lehren und Lernen : Erfahrungs-bericht aus dem Seamless Learning Lab

Seamless Learning richtet den Blick auf eine Herausforderung flexiblen Lernens – den Umstand, dass Lernen in verschiedenen Kontexten stattfinden kann. Lernen über Kontexte hinweg bietet Chancen (z. B. die Verknüpfung von formalem Wissen mit der Alltagserfahrung), bringt aber auch Risiken (Fragmentierung der Lernerfahrung) mit sich. In einem laufenden EU-geförderten Projekt werden mittels eines „Design Based Research“-Ansatzes sieben „Seamless Learning“-Konzepte entwickelt, implementiert und erforscht. Diese Konzeption ist sehr beratungsintensiv. Für die langfristige Sicherung der Ergebnisse und eine mögliche Skalierung wird ein frei zugängliches Beratungskonzept inklusive IT-Unterstützung (Beratungs-Framework) entwickelt. In diesem Artikel wird das Projekt kurz präsentiert und theoretisch eingeordnet; die Erfahrungen und Erkenntnisse aus der Entwicklung der „Seamless Learning“-Konzepte vorgestellt, woraus anschließend die Grundlagen für das Beratungskonzept und -tool abgeleitet werden

Array programming with NumPy.

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis

Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Modelling the key drivers of an aerial Phytophthora foliar disease epidemic, from the needles to the whole plant.

Understanding the epidemiology of infectious diseases in a host population is a major challenge in forestry. Radiata pine plantations in New Zealand are impacted by a foliar disease, red needle cast (RNC), caused by Phytophthora pluvialis. This pathogen is dispersed by water splash with polycyclic infection affecting the lower part of the tree canopy. In this study, we extended an SI (Susceptible-Infectious) model presented for RNC to analyse the key epidemiological drivers. We conducted two experiments to empirically fit the extended model: a detached-needle assay and an in vivo inoculation. We used the detached-needle assay data to compare resistant and susceptible genotypes, and the in vivo inoculation data was used to inform sustained infection of the whole plant. We also compared isolations and real-time quantitative PCR (qPCR) to assess P. pluvialis infection. The primary infection rate and the incubation time were similar for susceptible and resistant genotypes. The pathogen death rate was 2.5 times higher for resistant than susceptible genotypes. Further, external proliferation of mycelium and sporangia were only observed on 28% of the resistant ramets compared to 90% of the susceptible ones. Detection methods were the single most important factor influencing parameter estimates of the model, giving qualitatively different epidemic outputs. In the early stages of infection, qPCR proved to be more efficient than isolations but the reverse was true at later points in time. Isolations were not influenced by the presence of lesions in the needles, while 19% of lesioned needle maximized qPCR detection. A primary infection peak identified via qPCR occurred at 4 days after inoculation (dai) with a secondary peak observed 22 dai. Our results have important implications to the management of RNC, by highlighting the main differences in the response of susceptible and resistant genotypes, and comparing the most common assessment methods to detect RNC epidemics

Seamless Learning als Ansatz zum Umgang mit flexiblem Lehren und Lernen : Erfahrungs-bericht aus dem Seamless Learning Lab

Seamless Learning richtet den Blick auf eine Herausforderung flexiblen Lernens – den Umstand, dass Lernen in verschiedenen Kontexten stattfinden kann. Lernen über Kontexte hinweg bietet Chancen (z. B. die Verknüpfung von formalem Wissen mit der Alltagserfahrung), bringt aber auch Risiken (Fragmentierung der Lernerfahrung) mit sich. In einem laufenden EU-geförderten Projekt werden mittels eines „Design Based Research“-Ansatzes sieben „Seamless Learning“-Konzepte entwickelt, implementiert und erforscht. Diese Konzeption ist sehr beratungsintensiv. Für die langfristige Sicherung der Ergebnisse und eine mögliche Skalierung wird ein frei zugängliches Beratungskonzept inklusive IT-Unterstützung (Beratungs-Framework) entwickelt. In diesem Artikel wird das Projekt kurz präsentiert und theoretisch eingeordnet; die Erfahrungen und Erkenntnisse aus der Entwicklung der „Seamless Learning“-Konzepte vorgestellt, woraus anschließend die Grundlagen für das Beratungskonzept und -tool abgeleitet werden

Scipy Lecture Notes: One document to learn numerics, science, and data with Python

International audienceTutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert

statsmodels/statsmodels: Release 0.14.1

<p>This is a bug fix and future-proofing release that contains all bug fixes that have been applied since 0.14.0 was released.</p> <p>There are no enhancements or changes to the statsmdoels API.</p&gt

scipy/scipy: SciPy 1.12.0

<h1>SciPy 1.12.0 Release Notes</h1> <p>SciPy <code>1.12.0</code> is the culmination of <code>6</code> months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with <code>python -Wd</code> and check for <code>DeprecationWarning</code> s). Our development attention will now shift to bug-fix releases on the 1.12.x branch, and on adding new features on the main branch.</p> <p>This release requires Python <code>3.9+</code> and NumPy <code>1.22.4</code> or greater.</p> <p>For running on PyPy, PyPy3 <code>6.0+</code> is required.</p> <h1>Highlights of this release</h1> <ul> <li>Experimental support for the array API standard has been added to part of <code>scipy.special</code>, and to all of <code>scipy.fft</code> and <code>scipy.cluster</code>. There are likely to be bugs and early feedback for usage with CuPy arrays, PyTorch tensors, and other array API compatible libraries is appreciated. Use the <code>SCIPY_ARRAY_API</code> environment variable for testing.</li> <li>A new class, <code>ShortTimeFFT</code>, provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT.</li> <li>Several new constructors have been added for sparse arrays, and many operations now additionally support sparse arrays, further facilitating the migration from sparse matrices.</li> <li>A large portion of the <code>scipy.stats</code> API now has improved support for handling <code>NaN</code> values, masked arrays, and more fine-grained shape-handling. The accuracy and performance of a number of <code>stats</code> methods have been improved, and a number of new statistical tests and distributions have been added.</li> </ul> <h1>New features</h1> <h1><code>scipy.cluster</code> improvements</h1> <ul> <li>Experimental support added for the array API standard; PyTorch tensors, CuPy arrays and array API compatible array libraries are now accepted (GPU support is limited to functions with pure Python implementations). CPU arrays which can be converted to and from NumPy are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the <code>SCIPY_ARRAY_API</code> environment variable before importing <code>scipy</code>. This experimental support is still under development and likely to contain bugs - testing is very welcome.</li> </ul> <h1><code>scipy.fft</code> improvements</h1> <ul> <li>Experimental support added for the array API standard; functions which are part of the <code>fft</code> array API standard extension module, as well as the Fast Hankel Transforms and the basic FFTs which are not in the extension module, now accept PyTorch tensors, CuPy arrays and array API compatible array libraries. CPU arrays which can be converted to and from NumPy arrays are supported module-wide and returned arrays will match the input type. This behaviour is enabled by setting the <code>SCIPY_ARRAY_API</code> environment variable before importing <code>scipy</code>. This experimental support is still under development and likely to contain bugs - testing is very welcome.</li> </ul> <h1><code>scipy.integrate</code> improvements</h1> <ul> <li>Added <code>scipy.integrate.cumulative_simpson</code> for cumulative quadrature from sampled data using Simpson's 1/3 rule.</li> </ul> <h1><code>scipy.interpolate</code> improvements</h1> <ul> <li>New class <code>NdBSpline</code> represents tensor-product splines in N dimensions. This class only knows how to evaluate a tensor product given coefficients and knot vectors. This way it generalizes <code>BSpline</code> for 1D data to N-D, and parallels <code>NdPPoly</code> (which represents N-D tensor product polynomials). Evaluations exploit the localized nature of b-splines.</li> <li><code>NearestNDInterpolator.__call__</code> accepts <code>**query_options</code>, which are passed through to the <code>KDTree.query</code> call to find nearest neighbors. This allows, for instance, to limit the neighbor search distance and parallelize the query using the <code>workers</code> keyword.</li> <li><code>BarycentricInterpolator</code> now allows computing the derivatives.</li> <li>It is now possible to change interpolation values in an existing <code>CloughTocher2DInterpolator</code> instance, while also saving the barycentric coordinates of interpolation points.</li> </ul> <h1><code>scipy.linalg</code> improvements</h1> <ul> <li>Access to new low-level LAPACK functions is provided via <code>dtgsyl</code> and <code>stgsyl</code>.</li> </ul> <h1><code>scipy.optimize</code> improvements</h1> <ul> <li><code>scipy.optimize.isotonic_regression</code> has been added to allow nonparametric isotonic regression.</li> <li><code>scipy.optimize.nnls</code> is rewritten in Python and now implements the so-called fnnls or fast nnls, making it more efficient for high-dimensional problems.</li> <li>The result object of <code>scipy.optimize.root</code> and <code>scipy.optimize.root_scalar</code> now reports the method used.</li> <li>The <code>callback</code> method of <code>scipy.optimize.differential_evolution</code> can now be passed more detailed information via the <code>intermediate_results</code> keyword parameter. Also, the evolution <code>strategy</code> now accepts a callable for additional customization. The performance of <code>differential_evolution</code> has also been improved.</li> <li><code>scipy.optimize.minimize</code> method <code>Newton-CG</code> now supports functions that return sparse Hessian matrices/arrays for the <code>hess</code> parameter and is slightly more efficient.</li> <li><code>scipy.optimize.minimize</code> method <code>BFGS</code> now accepts an initial estimate for the inverse of the Hessian, which allows for more efficient workflows in some circumstances. The new parameter is <code>hess_inv0</code>.</li> <li><code>scipy.optimize.minimize</code> methods <code>CG</code>, <code>Newton-CG</code>, and <code>BFGS</code> now accept parameters <code>c1</code> and <code>c2</code>, allowing specification of the Armijo and curvature rule parameters, respectively.</li> <li><code>scipy.optimize.curve_fit</code> performance has improved due to more efficient memoization of the callable function.</li> </ul> <h1><code>scipy.signal</code> improvements</h1> <ul> <li><code>freqz</code>, <code>freqz_zpk</code>, and <code>group_delay</code> are now more accurate when <code>fs</code> has a default value.</li> <li>The new class <code>ShortTimeFFT</code> provides a more versatile implementation of the short-time Fourier transform (STFT), its inverse (ISTFT) as well as the (cross-) spectrogram. It utilizes an improved algorithm for calculating the ISTFT based on dual windows and provides more fine-grained control of the parametrization especially in regard to scaling and phase-shift. Functionality was implemented to ease working with signal and STFT chunks. A section has been added to the "SciPy User Guide" providing algorithmic details. The functions <code>stft</code>, <code>istft</code> and <code>spectrogram</code> have been marked as legacy.</li> </ul> <h1><code>scipy.sparse</code> improvements</h1> <ul> <li><code>sparse.linalg</code> iterative solvers <code>sparse.linalg.cg</code>, <code>sparse.linalg.cgs</code>, <code>sparse.linalg.bicg</code>, <code>sparse.linalg.bicgstab</code>, <code>sparse.linalg.gmres</code>, and <code>sparse.linalg.qmr</code> are rewritten in Python.</li> <li>Updated vendored SuperLU version to <code>6.0.1</code>, along with a few additional fixes.</li> <li>Sparse arrays have gained additional constructors: <code>eye_array</code>, <code>random_array</code>, <code>block_array</code>, and <code>identity</code>. <code>kron</code> and <code>kronsum</code> have been adjusted to additionally support operation on sparse arrays.</li> <li>Sparse matrices now support a transpose with <code>axes=(1, 0)</code>, to mirror the <code>.T</code> method.</li> <li><code>LaplacianNd</code> now allows selection of the largest subset of eigenvalues, and additionally now supports retrieval of the corresponding eigenvectors. The performance of <code>LaplacianNd</code> has also been improved.</li> <li>The performance of <code>dok_matrix</code> and <code>dok_array</code> has been improved, and their inheritance behavior should be more robust.</li> <li><code>hstack</code>, <code>vstack</code>, and <code>block_diag</code> now work with sparse arrays, and preserve the input sparse type.</li> <li>A new function, <code>scipy.sparse.linalg.matrix_power</code>, has been added, allowing for exponentiation of sparse arrays.</li> </ul> <h1><code>scipy.spatial</code> improvements</h1> <ul> <li>Two new methods were implemented for <code>spatial.transform.Rotation</code>: <code>__pow__</code> to raise a rotation to integer or fractional power and <code>approx_equal</code> to check if two rotations are approximately equal.</li> <li>The method <code>Rotation.align_vectors</code> was extended to solve a constrained alignment problem where two vectors are required to be aligned precisely. Also when given a single pair of vectors, the algorithm now returns the rotation with minimal magnitude, which can be considered as a minor backward incompatible change.</li> <li>A new representation for <code>spatial.transform.Rotation</code> called Davenport angles is available through <code>from_davenport</code> and <code>as_davenport</code> methods.</li> <li>Performance improvements have been added to <code>distance.hamming</code> and <code>distance.correlation</code>.</li> <li>Improved performance of <code>SphericalVoronoi</code> <code>sort_vertices_of_regions</code> and two dimensional area calculations.</li> </ul> <h1><code>scipy.special</code> improvements</h1> <ul> <li>Added <code>scipy.special.stirling2</code> for computation of Stirling numbers of the second kind. Both exact calculation and an asymptotic approximation (the default) are supported via <code>exact=True</code> and <code>exact=False</code> (the default) respectively.</li> <li>Added <code>scipy.special.betaincc</code> for computation of the complementary incomplete Beta function and <code>scipy.special.betainccinv</code> for computation of its inverse.</li> <li>Improved precision of <code>scipy.special.betainc</code> and <code>scipy.special.betaincinv</code>.</li> <li>Experimental support added for alternative backends: functions <code>scipy.special.log_ndtr</code>, <code>scipy.special.ndtr</code>, <code>scipy.special.ndtri</code>, <code>scipy.special.erf</code>, <code>scipy.special.erfc</code>, <code>scipy.special.i0</code>, <code>scipy.special.i0e</code>, <code>scipy.special.i1</code>, <code>scipy.special.i1e</code>, <code>scipy.special.gammaln</code>, <code>scipy.special.gammainc</code>, <code>scipy.special.gammaincc</code>, <code>scipy.special.logit</code>, and <code>scipy.special.expit</code> now accept PyTorch tensors and CuPy arrays. These features are still under development and likely to contain bugs, so they are disabled by default; enable them by setting a <code>SCIPY_ARRAY_API</code> environment variable to <code>1</code> before importing <code>scipy</code>. Testing is appreciated!</li> </ul> <h1><code>scipy.stats</code> improvements</h1> <ul> <li>Added <code>scipy.stats.quantile_test</code>, a nonparametric test of whether a hypothesized value is the quantile associated with a specified probability. The <code>confidence_interval</code> method of the result object gives a confidence interval of the quantile.</li> <li><code>scipy.stats.sampling.FastGeneratorInversion</code> provides a convenient interface to fast random sampling via numerical inversion of distribution CDFs.</li> <li><code>scipy.stats.geometric_discrepancy</code> adds geometric/topological discrepancy metrics for random samples.</li> <li><code>scipy.stats.multivariate_normal</code> now has a <code>fit</code> method for fitting distribution parameters to data via maximum likelihood estimation.</li> <li><code>scipy.stats.bws_test</code> performs the Baumgartner-Weiss-Schindler test of whether two-samples were drawn from the same distribution.</li> <li><code>scipy.stats.jf_skew_t</code> implements the Jones and Faddy skew-t distribution.</li> <li><code>scipy.stats.anderson_ksamp</code> now supports a permutation version of the test using the <code>method</code> parameter.</li> <li>The <code>fit</code> methods of <code>scipy.stats.halfcauchy</code>, <code>scipy.stats.halflogistic</code>, and <code>scipy.stats.halfnorm</code> are faster and more accurate.</li> <li><code>scipy.stats.beta</code> <code>entropy</code> accuracy has been improved for extreme values of distribution parameters.</li> <li>The accuracy of <code>sf</code> and/or <code>isf</code> methods have been improved for several distributions: <code>scipy.stats.burr</code>, <code>scipy.stats.hypsecant</code>, <code>scipy.stats.kappa3</code>, <code>scipy.stats.loglaplace</code>, <code>scipy.stats.lognorm</code>, <code>scipy.stats.lomax</code>, <code>scipy.stats.pearson3</code>, <code>scipy.stats.rdist</code>, and <code>scipy.stats.pareto</code>.</li> <li>The following functions now support parameters <code>axis</code>, <code>nan_policy</code>, and <code>keep_dims</code>: <code>scipy.stats.entropy</code>, <code>scipy.stats.differential_entropy</code>, <code>scipy.stats.variation</code>, <code>scipy.stats.ansari</code>, <code>scipy.stats.bartlett</code>, <code>scipy.stats.levene</code>, <code>scipy.stats.fligner</code>, <code>scipy.stats.circmean</code>, <code>scipy.stats.circvar</code>, <code>scipy.stats.circstd</code>, <code>scipy.stats.tmean</code>, <code>scipy.stats.tvar</code>, <code>scipy.stats.tstd</code>, <code>scipy.stats.tmin</code>, <code>scipy.stats.tmax</code>, and <code>scipy.stats.tsem</code>.</li> <li>The <code>logpdf</code> and <code>fit</code> methods of <code>scipy.stats.skewnorm</code> have been improved.</li> <li>The beta negative binomial distribution is implemented as <code>scipy.stats.betanbinom</code>.</li> <li>Improved performance of <code>scipy.stats.invwishart</code> <code>rvs</code> and <code>logpdf</code>.</li> <li>A source of intermediate overflow in <code>scipy.stats.boxcox_normmax</code> with <code>method='mle'</code> has been eliminated, and the returned value of <code>lmbda</code> is constrained such that the transformed data will not overflow.</li> <li><code>scipy.stats.nakagami</code> <code>stats</code> is more accurate and reliable.</li> <li>A source of intermediate overflow in <code>scipy.norminvgauss.pdf</code> has been eliminated.</li> <li>Added support for masked arrays to <code>scipy.stats.circmean</code>, <code>scipy.stats.circvar</code>, <code>scipy.stats.circstd</code>, and <code>scipy.stats.entropy</code>.</li> <li><code>scipy.stats.dirichlet</code> has gained a new covariance (<code>cov</code>) method.</li> <li>Improved accuracy of <code>entropy</code> method of <code>scipy.stats.multivariate_t</code> for large degrees of freedom.</li> <li><code>scipy.stats.loggamma</code> has an improved <code>entropy</code> method.</li> </ul> <h1>Deprecated features</h1> <ul> <li><p>Error messages have been made clearer for objects that don't exist in the public namespace and warnings sharpened for private attributes that are not supposed to be imported at all.</p> </li> <li><p><code>scipy.signal.cmplx_sort</code> has been deprecated and will be removed in SciPy 1.15. A replacement you can use is provided in the deprecation message.</p> </li> <li><p>Values the the argument <code>initial</code> of <code>scipy.integrate.cumulative_trapezoid</code> other than <code>0</code> and <code>None</code> are now deprecated.</p> </li> <li><p><code>scipy.stats.rvs_ratio_uniforms</code> is deprecated in favour of <code>scipy.stats.sampling.RatioUniforms</code></p> </li> <li><p><code>scipy.integrate.quadrature</code> and <code>scipy.integrate.romberg</code> have been deprecated due to accuracy issues and interface shortcomings. They will be removed in SciPy 1.15. Please use <code>scipy.integrate.quad</code> instead.</p> </li> <li><p>Coinciding with upcoming changes to function signatures (e.g. removal of a deprecated keyword), we are deprecating positional use of keyword arguments for the affected functions, which will raise an error starting with SciPy 1.14. In some cases, this has delayed the originally announced removal date, to give time to respond to the second part of the deprecation. Affected functions are:</p> <ul> <li><code>linalg.{eigh, eigvalsh, pinv}</code></li> <li><code>integrate.simpson</code></li> <li><code>signal.{firls, firwin, firwin2, remez}</code></li> <li><code>sparse.linalg.{bicg, bicgstab, cg, cgs, gcrotmk, gmres, lgmres, minres, qmr, tfqmr}</code></li> <li><code>special.comb</code></li> <li><code>stats.kendalltau</code></li> </ul> </li> <li><p>All wavelet functions have been deprecated, as PyWavelets provides suitable implementations; affected functions are: <code>signal.{daub, qmf, cascade, morlet, morlet2, ricker, cwt}</code></p> </li> <li><p><code>scipy.integrate.trapz</code>, <code>scipy.integrate.cumtrapz</code>, and <code>scipy.integrate.simps</code> have been deprecated in favour of <code>scipy.integrate.trapezoid</code>, <code>scipy.integrate.cumulative_trapezoid</code>, and <code>scipy.integrate.simpson</code> respectively and will be removed in SciPy 1.14.</p&g