395 skills found · Page 10 of 14
bijonai / IdeajsA powerful mathematical visualization library for interactive geometry and function plotting.
ytdai / Gvmap[R package] An Improved Function to Plot Heatmap of Genomic Data
sfcheung / SemptoolsHelper functions for customizing plots by semPlot::semPaths
wp-xyz / DataloggerReads measured values from a digital voltmeter and plots them as a function of time.
sigbertklinke / Plot.matrixVisualizes a matrix object plainly as heatmap. It provides a single S3 function plot for matrices.
elliotwaite / Softmax Logit PathsPlots how the logit values that are passed into the softmax function change over time as the model is trained.
fennstef / GraphplotPlotting Utility Functions for Canvas
CollinErickson / ContourFunctionsContour plot functions for R
RMI-PACTA / R2dii.plotA package containing functions to create standard PACTA plots using ggplot, together with data processing functions needed for the charts.
mikolalysenko / Implicit StudioA simple implicit function plotting application
dewittpe / QwrapsWrapper functions for producing base R plots using ggplot2. Included functions are qacf(), qroc(), qsurvplot(), with more to come. There are several other wrapper functions in this package which have been useful for me when working on data analysis reports.
bjkomer / Spatial Semantic PointersHelper functions and plots for using Spatial Semantic Pointers
rose3fa / RTACalcMatLab thin film reflection, transmission, absorption calculator based on transfer matrix method. Includes ability to plot dispersion for arbitrary number of films and wavelength-dependent complex dielectric functions.
danielfleischer / Git Commits Lines GraphPlot project lines of code as a function of time.
mankoff / Gdb Fortran ToolsTools to help debug Fortran code with gdb - plotting, saving, and Numpy functions.
brettonsimpson / DarkplotterDark Plotter is a Python package for visualizing velocity as a function of radius for the Milky Way galaxy in an interactive way.
linusromer / Bezierplotbezierplot is a Lua program that approximates function plots by cubic bézier splines (paths are output in TikZ)
StevenMedvetz / Stock AnalysisThis repository is for a personal python library of mine for stock analysis on both an asset and portfolio level. It contains many standard visualization and computational functions for data analysis of stocks. It is built off of the yfinance and plotly libraries.
orbeckst / RecSQLRecSQL is a hack that allows one to load table-like data records into an in-memory sqlite database for quick and dirty analysis via SQL. The SQLarray class has additional SQL functions such as sqrt or histogram defined. SQL tables can always be returned as numpy record arrays so that data can be easily handled in other packages such as numpy or plotted via matplotlib. Selections produce new SQLarray objects.
RameshOswal / Classify Bio Images For Protein Localization Using ALMost proteins localize to specific regions where they perform their biological function. Fluorescent microscopy can reveal the subcellular localization patterns of tagged proteins. The goal of this project is to use active learning to build a classifier that capable of classifying bioimages (encoded as feature vectors) according to subcellular localization patterns. There are three data pools: Easy: A low-noise data pool Moderate: This pool has some noise (labels and features) Difficult: The points in this pool have a larger number of features than those in the easy and moderate pools. Some of these features are irrelevant. Your algorithm will need to perform active learning and feature selection. Each data pool consists of 4120 training images and 1000 test images. Each image is represented as a feature vector (you do not need to do feature extraction yourself). There are 8 subcellular localization patterns: (i) Endosomes; (ii) Lysosomes; (iii) Mitochondria; (iv) Peroxisomes; (v) Actin; (vi) Plasma Membrane; (vii) Microtubules; and (viii) Endoplasmic Reticulum. The data are based on those released by Dr. Nicholas Hamilton for his paper Statistical and visual differentiation of high throughput subcellular imaging, N. Hamilton, J. Wang, M.C. Kerr and R.D. Teasdale, BMC Bioinformatics 2009, 10:94. Select and implement a suitable active learning algorithm and apply it to the training data. Additionally, implement a random learner that selects random images in the training data. Using a budget of 2,500 calls to the oracle, compute and plot the test errors for each algorithm as a function of the number of calls to the oracle. Use the test data to compute the test errors. Repeat this for the easy and moderate data pools. If you are working on a team, or want extra credit, apply your algorithm to the difficult pool as well.
