1,720,963 research outputs found
bdestombe/flopymetascript: Version 1.0.2
No full text<p>Converts a zip with MODFLOW input files to a zip containing Flopy script</p>
bdestombe/python-dts-calibration: v0.5.0
No full text<ul>
<li>More efficient calculation of the variance</li>
</ul>
bdestombe/python-dts-calibration: v0.6.0
No full text<ul>
<li>Reworked the double-ended calibration routine and the routine for confidence intervals. The integrated differential attenuation is not zero at x=0 anymore.</li>
<li>Verbose commands carpentry</li>
<li>Bug fixed that would make the read_silixa routine crash if there are copies of the same file in the same folder</li>
<li>Routine to read sensornet files. Only single-ended configurations supported for now. Anyone has double-ended measurements?</li>
<li>Lazy calculation of the confidence intervals</li>
<li>Bug solved. The x-coordinates where not calculated correctly. The bug only appeared for measurements along long cables.</li>
<li>Example notebook of importing a timeseries. For example, importing measurments from an external temperature sensor for calibration.</li>
<li>Updated documentation</li>
</ul>
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Python DTS Calibration
No full text<p>A Python package to load raw DTS files, perform a calibration, and plot the result. In addition, confidence boundaries are calculated for the temperature measurements along the entire cable.</p>Development: https://github.com/bdestombe/python-dts-calibration/ ; Documentation: https://python-dts-calibration.readthedocs.io
bdestombe/python-dts-calibration: v0.5.1
No full text<p>0.5.1 (2018-10-19)</p>
<ul>
<li>dts-calibration is now citable</li>
<li>Refractored the MC confidence interval routine</li>
<li>MC confidence interval routine speed up, with full dask support</li>
<li>Link to mybinder.org to try the example notebooks online</li>
<li>Added a few missing dependencies</li>
<li>The routine to read the Silixa files is completely refractored. Faster, smarter. Supports both the path to a directory and a list of file paths.</li>
<li>Changed imports from dtscalibration to be relative</li>
</ul>
Correction: des Tombe, B., et al. Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. <i>Sensors</i> 2020, <i>20</i>, 2235
No full textThe authors wish to make the following two corrections to this paper [...
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