NumPy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
OpenChrom
Chromeleon
Xcalibur
Analyst
Empower
Clarity
YouNote.io
tryAround
Data from different systems and vendors can be imported and analyzed. It runs under Windows, macOS and Linux. It started out as a ChemStation alternative, but grew larger over time. Its strength is to handle GC/MS and GC/FID measurements. Methods for peak detection, integration, identification, quantitation and reporting are supported. Using internal (ISTD) and external standards (ESTD) for quantitation purposes is supported as well. Additional filter help to optimize the measurements and classifier calculate key values of the chromatographic data and help to point out problems like shifted retention times or degraded columns.
OpenChromBased on our record, NumPy seems to be a lot more popular than OpenChrom. While we know about 107 links to NumPy, we've tracked only 4 mentions of OpenChrom. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
See if they can automate an existing workflow with https://openchrom.net/ There are special discounts for students. Source: 12 months ago
OpenChrom can be a free alternative for some things. Source: about 1 year ago
The question is which .raw file format. I'd contact https://lablicate.com/ as https://openchrom.net/ seems to support both Agilent .D and several .raw files. Source: about 1 year ago
Http://openchrom.net/ has initial HPLC support. Right-click menu and Chromatogram Filter: Zeroset and Chromatogram Substract may be what you need. Source: over 1 year ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Chromeleon - This chromatography data system (CDS) software performs analytical processes for stand-alone ion, liquid and gas chromatography and mass spectrometry, or for an enterprise-wide solution.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Xcalibur - Control, and process data from Thermo Scientific LC-MS systems and related instruments
OpenCV - OpenCV is the world's biggest computer vision library
Analyst - Instrument control, data analysis, reporting, and audit trail for SCIEX Mass Spectrometer systems.