Interactive visualization of climate model data via Python or GUI with psyplot

2021 ◽  
Author(s):  
Philipp S. Sommer
Keyword(s):  
Climate Model ◽  
Basic Structure ◽  
Special Focus ◽  
Command Line ◽  
Software Packages ◽  
Open Source Data ◽  
Source Data ◽  
Different Types ◽  
Flexible Framework ◽  
High Level

<div> <p><span data-contrast="auto">psyplot (</span><span data-contrast="none">https://psyplot.github.io</span><span data-contrast="auto">) is an open-source data visualization framework that integrates rich computational and mathematical software packages (such as xarray and matplotlib) into a flexible framework for visualization. It differs from most of the visual analytic software such that it focuses on extensibility in order to flexibly tackle the different types of analysis questions that arise in pioneering research. The design of the high-level API of the framework enables a simple and standardized usage from the command-line, python scripts or Jupyter notebooks. A modular plugin framework enables a flexible development of the framework that can potentially go into many different directions. The additional enhancement with a graphical user interface (GUI) makes it the only visualization framework that can be handled from the convenient command-line or scripts, as well as via point-click handling. It additionally allows to build further desktop applications on top of the existing framework.</span><span data-ccp-props="{"201341983":0,"335559739":160,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">In this presentation, I will show the main functionalities of psyplot, with a special focus on the visualization of unstructured grids (such as the ICON model by the German Weather Service (DWD)), and the usage of psyplot on the HPC facilities of the DKRZ (mistral, jupyterhub, remote desktop, etc.). My demonstration will cover the basic structure of the psyplot framework and how to use psyplot in python scripts (and Jupyter notebooks). I will demonstrate a quick demo of to the psyplot GUI and psy-view, a ncview-like interface built upon psyplot, and talk about different features such as reusing plot configurations and exporting figures.</span></p> </div>

scholarly journals Free and (Mostly) Open Source Data Analysis Software for Academic Research

2021 ◽  
Author(s):  
Chinchu C.
Keyword(s):  
Data Analysis ◽  
Knowledge Creation ◽  
Statistical Data ◽  
Academic Research ◽  
Research Process ◽  
Ease Of Use ◽  
Statistical Data Analysis ◽  
Software Packages ◽  
Open Source Data ◽  
Source Data

Data analysis is a crucial task in knowledge creation in social sciences. Free resources for data analysis provide researchers with greater freedom and make the research process more accessible and democratic. This article lists some free software which can perform basic and advanced statistical data analysis tasks. Some software which can perform other tasks such as text mining are also introduced. Ease of use and functionality are the major criteria for selecting these software packages.

Using Histological and Chemical Methods for Detection of Unauthorized Tissues Addition in Emulsion Type Meat Product

2020 ◽  
Keyword(s):  
Vascular Tissue ◽  
Meat Product ◽  
Calcium Content ◽  
Processed Meat ◽  
Low Protein ◽  
Significant Difference ◽  
Different Types ◽  
Plant Stem ◽  
High Level ◽  
Reliable Methods

Total twenty different processed meat plant producing emulsion type sausage were histologically and chemically examined for detection of adulteration with unauthorized tissues. Results revealed that samples were adulterated with different types of animal tissues included; hyaline cartilage, tendon, spongy bone, peripheral nerve trunk, basophilic matrix, lymphatic tissue, fascia, fibrocartilage and vascular tissue. Moreover, these samples were adulterated Also, adulterated with plant tissue included; plant stem, leaves and root. Chemical analysis showed a significant difference in their chemical composition (moisture, fat, protein, ash and calcium) content. Moisture and fat content varied around the permissible limit of E.S.S. while low protein, high ash and calcium content was detected in the examined samples. Therefore, Histological and chemical examinations can be used as reliable methods to detect adultration using unauthorized addition of both animal and plant tissues in processed meat product samples which revealed a high level of falsification.

scholarly journals The Insect Type 1 Tyramine Receptors: From Structure to Behavior

Insects ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 315
Author(s):  
Luca Finetti ◽  
Thomas Roeder ◽  
Girolamo Calò ◽  
Giovanni Bernacchia
Keyword(s):  
Intracellular Signaling ◽  
Physiological Role ◽  
Basic Structure ◽  
Special Focus ◽  
Behavioral Traits ◽  
Physiological Relevance ◽  
Transduction Mechanisms ◽  
Octopamine Receptor ◽  
Available Information ◽  
First Time

Tyramine is a neuroactive compound that acts as neurotransmitter, neuromodulator, and neurohormone in insects. Three G protein-coupled receptors, TAR1-3, are responsible for mediating the intracellular pathway in the complex tyraminergic network. TAR1, the prominent player in this system, was initially classified as an octopamine receptor which can also be activated by tyramine, while it later appeared to be a true tyramine receptor. Even though TAR1 is currently considered as a well-defined tyramine receptor and several insect TAR1s have been characterized, a defined nomenclature is still inconsistent. In the last years, our knowledge on the structural, biochemical, and functional properties of TAR1 has substantially increased. This review summarizes the available information on TAR1 from different insect species in terms of basic structure, its regulation and signal transduction mechanisms, and its distribution and functions in the brain and the periphery. A special focus is given to the TAR1-mediated intracellular signaling pathways as well as to their physiological role in regulating behavioral traits. Therefore, this work aims to correlate, for the first time, the physiological relevance of TAR1 functions with the tyraminergic system in insects. In addition, pharmacological studies have shed light on compounds with insecticidal properties having TAR1 as a target and on the emerging trend in the development of novel strategies for pest control.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

scholarly journals Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

2014 ◽  
Vol 112 (6) ◽  
pp. 1584-1598 ◽  
Author(s):  
Marino Pagan ◽  
Nicole C. Rust
Keyword(s):  
Task Performance ◽  
Single Neuron ◽  
Weighted Sums ◽  
Neural Responses ◽  
Signal Modulation ◽  
Neural Data ◽  
Match To Sample ◽  
Neural Populations ◽  
Different Types ◽  
High Level

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance ( d′).

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