Search Results for "google-visualization-python" - Page 17
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Showing 626 open source projects for "google-visualization-python"
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TF Quant Finance
High-performance TensorFlow library for quantitative finance
TF Quant Finance is a high-performance library of quantitative finance components built on TensorFlow, aimed at research and production workloads. It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The... -
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MLBox
MLBox is a powerful Automated Machine Learning python library
MLBox is a powerful Automated Machine Learning python library. Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the-art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,...) Prediction with model interpretation. -
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captcha_break
Identification codes
This project will use Keras to build a deep convolutional neural network to identify the captcha verification code. It is recommended to use a graphics card to run the project. The following visualization codes are jupyter notebookall done in . If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. It supports image verification codes and voice verification codes. We use its function of generating image verification codes. ... -
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PyQtRPT
Pyside QtRPT
Pyside QtRPT Python Binding for QtRPT (PySide Shiboken) QtRPT Report Engine Python Report License LGPL3 compatible with QtRPT (see license) """QtRPT is the easy-to-use print report engine written in C++ QtToolkit. It allows combining several reports in one XML file. For separately taken field, you can specify some condition depending on which this field will display in different font and background color, etc. -
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pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners... -
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pyCraft
Minecraft-client networking library in Python
Minecraft Python Client Library. This project aims to be a modern, Python3-compatible, well-documented library for communication with a Minecraft server. Although pyCraft is compatible any supported server, only a subset of all packets are currently decoded or encoded by the library: those necessary to remain connected to the server, those used for chat, and some others. -
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pytkapp
Python package for develop an SDI/MDI application and set of widgets.
=========== pytkapp =========== Python package for develop an application that provide multi-documents/single-document interface with using of tkinter library and set of additional tkinter widgets. See available demos: pytkapp/demo/run_ptaoptionsdemo.py - run GUI demo for option's container (available widgets, rules) pytkapp/demo/run_ptamdidemo.py - run demo of MDI application pytkapp/demo/run_ptasdidemo.py - run demo of SDI application pytkapp/demo/run_tkwbasicdemo.py - run demo of basic widgets pytkapp/demo/run_tkwtldemo.py - run demo of tablelist-based widgets pytkapp/demo/run_diademo.py - run demo for dialog widgets (selector, xmessage) Notes ========= 1) PyTkApp package was tested on python 2.7, 3.1 2) If you planning to use tablelist-based widgets then you need to download tcl Tablelist package from http://www.nemethi.de/ -
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backtrader
Python Backtesting library for trading strategies
backtrader is a Python framework for developing, backtesting, and running trading strategies. Its Cerebro engine coordinates strategies, data feeds, brokers, indicators, analyzers, and execution. Developers can combine multiple strategies, instruments, and timeframes while resampling or replaying market data. The framework includes a large indicator library, custom indicator support, analyzers, position sizing, commissions, and trading calendars. -
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AeroPython
Classical Aerodynamics of potential flow using Python
The AeroPython series of lessons is the core of a university course (Aerodynamics-Hydrodynamics, MAE-6226) by Prof. Lorena A. Barba at the George Washington University. The first version ran in Spring 2014 and these Jupyter Notebooks were prepared for that class, with assistance from Barba-group PhD student Olivier Mesnard. In Spring 2015, we revised and extended the collection, adding student assignments to strengthen the learning experience. The course is also supported by an open learning... -
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Django REST Pandas
Serves up Pandas dataframes via the Django REST Framework
Django REST Pandas (DRP) provides a simple way to generate and serve pandas DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. This capability can often be leveraged by sending users to the same URL that your visualization code uses internally to load the data. ... -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. -
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tkFileBrowser
File browser for Tkinter
tkFileBrowser is an alternative to tkinter.filedialog that allows the user to select files or directories. The GUI is written with tkinter but the look is closer to GTK and the application uses GTK bookmarks (the one displayed in nautilus or thunar for instance). This filebrowser supports new directory creation and filtype filtering. -
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tkColorPicker
Color picker dialog for Tkinter, alternative to tkinter.colorchooser
The tkcolorpicker module contains a `ColorPicker` class which implements the color picker dialog and an `askcolor` function that displays the color picker and returns the chosen color in RGB and HTML formats. -
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Easy Web automation library
Easy Web automation library
This library has been designed to work with selenium for web automation. It has incorporated functions and handled exception from selenium. It uses selenium library for web interfaces. -
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numpy_exercises
Numpy exercises
numpy_exercises is a collection of short practice problems designed to teach numerical computing through individual NumPy functions. The material is organized as Jupyter notebooks so learners can work interactively and compare their answers with companion solution notebooks. Exercises cover array creation, manipulation, string operations, help utilities, input and output, and linear algebra. Additional sections address discrete Fourier transforms, logic functions, mathematics, random... -
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Maya
Datetimes for Humans
Maya is a Python library that simplifies working with datetime objects. It provides a human-friendly API for parsing, formatting, and manipulating dates and times, addressing common frustrations with Python's built-in datetime module. -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
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ComplexEventExtraction
Expression pattern collection of Chinese compound event extraction
ComplexEventExtraction is a Chinese NLP project for identifying relationships between events expressed in compound sentences. It defines patterns for causal, conditional, sequential, contrastive, and parallel event structures. The system uses explicit connective words and phrase combinations to split text into linked event pairs. Extracted results can support event graphs that represent how situations develop, conflict, or depend on one another. The repository catalogs hundreds of linguistic... -
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Autologging
Easier logging and tracing of Python functions and class methods.
..."autologging.traced" decorates a class to provide automatic CALL/RETURN tracing for all class, static, and instance methods, as well as the special __init__ method (by default) "autologging.TRACE" is a custom log level (lower than logging.DEBUG) that is registered with the Python logging module when autologging is imported -
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FilterPy
Python Kalman filtering and optimal estimation library
...NumPy and SciPy handle the numerical work, with Matplotlib commonly used for visualization. The library accompanies the book Kalman and Bayesian Filters in Python and is useful for learning, prototyping, tracking, navigation, and estimation experiments. -
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Teach Me Quantum
Practical Course on Quantum Information Science and Quantum Computing
A university-level course on Quantum Computing and Quantum Information Science that incorporates IBM Q Experience and Qiskit. This course is adequate for general audiences without prior knowledge on Quantum Mechanics and Quantum Computing (see prior knowledge), has an estimated average duration of 10 weeks at 3h/week (see duration), and is meant to be the entrypoint into the Quantum World. -
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WIP
Create Siri Shortcuts with Python
python-shortcuts is a library to create Siri Shortcuts on your laptop with your favorite text editor. It uses toml to represent shortcuts. The library is in a very early development state (PR welcome!), so it does not support all actions from the Shortcuts app. -
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SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with... -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. ... -
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more...
