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- 2024-11-16
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QtiPlot 1.1.3 Multilingual (Win / macOS / Linux) | 472 MB
Languages: English, 中文, Čeština, Deutsch, Español, Français,
Ελληνικά, Italiano, 日本語, Português, Română, Русский, Svenska QtiPlot是一个用于数据分析和可视化的跨平台科学应用程序。由于其多语言支持，QtiPlot被积极用于世界各地的学术机构的教学。许多研究科学家信任QtiPlot来分析他们的数据并发布他们的工作结果。来自各个科学领域和行业的数千名注册用户已经选择了QtiPlot来帮助他们进行日常工作。QtiPlot is a cross-platform scientific application for data analysis and visualisation. Thanks to its multilingual support, QtiPlot is actively used for teaching in academic institutions all over the world. Numerous research scientists trust QtiPlot for analysing their data and publishing the results of their work. Thousands of registered users from various scientific fields and industries have already chosen QtiPlot to help them in their daily work. Data Analysis and Scientific Visualization
Signal ProcessingThe Fourier transform decomposes a function (often a function of time) or a signal into its constituent frequencies. The fast Fourier transform (FFT) is an algorithm for computing the discrete Fourier transform. The correlation function, also known as the covariance function, is used to test the similarity of two signals x(t) and y(t). The correlation of a signal with itself, called autocorrelation, is frequently used in spectral analysis. Data interpolation creates a new data curve with a higher number of points generated by interpolation of your data. Three interpolation methods are available in QtiPlot. There are four methods of curve smoothing available in QtiPlot: moving window average, Savitzki-Golay, LOWESS (locally weighted scatterplot smoothing) and FFT filtering. The FFT method calculates a cut-off frequency and applies a low pass filter to the data. Data fittingLinear data fitting is the process of constructing a straight line that has the best fit to a series of data points. Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an Nth degree polynomial. Boltzmann and logistic models can be used to fit curves having a sigmoidal shape. The Gauss model can be used to fit a curve which has a bell shape. In spectroscopy, the Lorentz distribution is used to describe the shape of spectral lines which are subject to homogeneous broadening in which all atoms interact in the same way with the frequency range contained in the line shape. The Pseudo-Voigt fit model is often used for calculations of experimental spectral line shapes. It is a linear combination of Gaussian and Lorentzian functions with different FWHM. Using the fit wizard tool it is possible to define your own custom fit model. The multi-peak fit tool in QtiPlot allows to fit your data points to a sum of Gaussian, Lorentzian or Pseudo-Voigt functions. Image AnalysisThe image profiles graph creates a gray scale matrix plot of the analysed image and lets you see the X and Y intensity profiles of the data pixels. The ability to control the exact position of the horizontal and vertical lines along each axis is provided. The View Pixel Line Profile tool can be used to retrieve the pixel intensities from an image in a 2D plot layer. It opens an integer value dialog allowing to select the number of pixels to be averaged when calculating the pixel intensity. The selection of the pixel range from the image is done manually by drawing a line whose end points must be situated inside the image. This handy image analysis tool creates a new plot window displaying the intensity profile curve. Descriptive StatisticsThe following moments and quantiles can be computed by QtiPlot on table columns or rows: mean, standard deviation, standard error, variance, sum, skewness, kurtosis, minimum and maximum values, median, first and third quantile and the interquartile range. It is also possible to define a list of custom percentiles values between 1 and 99 that can be calculated on table columns/rows. QtiPlot provides a frequency count dialog that can be used in order to calculate the frequency distribution of the data values from a table column. The normality test (Shapiro-Wilk method) can be used to verify the null hypothesis that a data sample came from a normally distributed population. Parametric TestsA t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribution under the null hypothesis. QtiPlot can perform several forms of the parametric Student's t-test: one sample, two sample and paired sample t-test. A chi-squared test is any statistical hypothesis test where the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. QtiPlot can perform the chi-square test for variance. Non-parametric TestsQtiPlot can perform the following non-parametric tests: Mann-Whitney, Kolmogorov-Smirnov, Wilcoxon signed rank test, the paired sample sign test, Mood's median test, Kruskal-Wallis ANOVA and Friedman ANOVA. The Mann-Whitney test can be used in order to determine whether two independent samples were selected from populations having the same distribution. The Kolmogorov-Smirnov test can be used in order to estimate the statistical likelihood that two samples are drawn from the same distribution. The one sample Wilcoxon signed rank test can be used as a non-parametric alternative to a one sample t-test. This test determines whether the median of the data sample is equal to a specific value. The paired sample Wilcoxon signed rank test can be used in order to determine whether two dependent samples were selected from populations having the same distribution. It can be use as an alternative to the paired sample Student's t-test. The paired sample sign test is a statistical method that can be used in order to investigate for consistent differences between pairs of observations. QtiPlot computes the test statistics and a probability using the cumulative distribution function of the binomial distribution. The null hypothesis is rejected if the probability is lower than the value of the user defined significance level. Mood's median test can be used in order to test whether several samples originate from the same distribution. This test uses the chi-square statistic (χ2). QtiPlot computes an approximated p-value, which is the probability of a chi-squared value of at least χ2 being observed, using the lower tail of the cumulative distribution function for the chi-squared distribution. The null hypothesis is rejected if the approximated probability is lower than the value of input significance level. Kruskal-Wallis ANOVA can be used in order to test whether several samples originate from the same distribution. QtiPlot computes the test statistic (H) and an approximated p-value. The approximation is based on the fact that when the size of the input samples (Ni) or the number of samples (k) is large (i.e. Ni > 5 or k ≥ 3), the probability distribution of H is that of a chi-squared distribution whith k-1 degrees of freedom. The p-value computed by QtiPlot is the probability of a chi-squared value of at least H being observed. The null hypothesis is rejected if the approximated probability is lower than the value of the significance level. Friedman ANOVA is a non-parametric statistical test developed by Milton Friedman that can be used in order to test whether several samples originate from the same distribution. QtiPlot computes the test statistic (Q) and an approximated p-value. The approximation is based on the fact that when the size of the input samples (N) or the number of samples (k) is large (i.e. N > 15 or k > 4), the probability distribution of Q is that of a chi-squared distribution whith k-1 degrees of freedom. The p-value computed by QtiPlot is the probability of a chi-squared value of at least Q being observed. The null hypothesis is rejected if the approximated probability is lower than the value of the significance level. ANOVAThe one-way analysis of variance (abbreviated one-way ANOVA) can be used to compare the means of two or more samples using the F distribution. QtiPlot can perform several post-hoc analysis tests that can determine which level means or sample means are significantly different from each other. The folowing post-hoc tests are currently implemented: Tukey, Bonferroni, Dunn-Sidak, Fisher's Least Significant Difference (LSD), Scheffe, Holm-Bonferroni and Holm-Sidak. The two-way analysis of variance (abbreviated two-way ANOVA) can be used to compare the means of two or more samples using the F distribution. In the QtiPlot implementation of two-way ANOVA there are two factors defined, A and B, with LA and LB levels respectively, for a total number of samples N = LALB. QtiPlot can perform several post-hoc analysis tests that can determine which level means or sample means are significantly different from each other. The folowing post-hoc tests are currently implemented: Tukey, Bonferroni, Dunn-Sidak, Fisher's Least Significant Difference (LSD), Scheffe, Holm-Bonferroni and Holm-Sidak. Interoperability with OriginLabQtiPlot can import *.opj project files created with OriginLab versions ranging from 3.5 to 10.0 (Origin 2023). The new project format (*.opju) introduced in Origin 2018 is only supported on Windows operating systems. On Windows, *.opju projects can also be converted to *.opj files, that can be afterwards imported directly by QtiPlot on macOS and Linux operating systems. QtiPlot can also export individual project windows, projects folders or entire projects as Origin C files that can be compiled and imported by OriginLab. Microsoft ExcelQtiPlot can import data from workbooks stored in binary *.xls or *xlsx files, as well as from Microsoft Excel *.xml files, using different methods. On Windows operating systems where Microsoft Excel is locally installed, QtiPlot can also import the charts from workbooks. QtiPlot can export data from table and matrix windows as binary *.xls files. Excel workbooks can be embedded into QtiPlot as OLE instances on Windows operating systems where Microsoft Excel is locally available. When an Excel workbook is created or imported in QtiPlot as an OLE instance, a new menu becomes available in the menu bar of the application. From this menu you can perform various operations with the data in the workbook: you can convert a data selection or entire worksheets to QtiPlot tables or export data to ASCII files. You can also convert Excel charts to QtiPlot graph windows or export them as image files. LibreOffice & OpenOfficeQtiPlot can import both data and charts from spreadsheets stored in binary *.ods files as well as from flat XML *.fods files. QtiPlot can export data from worksheets as binary *.ods files if either LibreOffice or Apache OpenOffice are installed on your computer. Import of LabVIEW TDMS filesWhen importing LabVIEW TDMS files QtiPlot opens a dialog that gives the possibility to preview the raw data. The dialog displays the structure of the TDMS file in a tree widget allowing the selection of the groups and channels to be imported. It is possible to enable/disable the import of an entire group or of individual channels by checking/unchecking the corresponding box situated on the right side of the selection tree. Database ImportQtiPlot can import SQL databases (MySQL, PostgreSQL or SQLite). It is also possible to perform an SQL query on the database, prior to the import operation. QtiPlot can import Microsoft Access databases (.mdb) on all supported operating systems (Windows, Linux and macOS). Serial portQtiPlot provides a serial port monitor which makes possible to both read and write data through a serial port. Using this QtiPlot feature data acquisition from serial port devices like Arduino boards, for example, is straightforward. The communication with the serial port devices can be fully customized via a user friendly dialog. Import of Matlab files (*.mat)When importing Matlab format files (*.mat) QtiPlot opens a user friendly dialog that allows total control over the import process. The dialog displays the structure of the Matlab file in a tree widget allowing the selection of the variables to be imported. QtiPlot creates a new table for each variable imported from the Matlab file. Third-party ExtensionsRunning Python scripts from QtiPlot opens the possibility to use powerfull existing scientific tools, like SymPy, SciPy or rpy2, thus bringing unlimited data analysis power. Export graphics to TeXQtiPlot can be easily integrated with LaTeX, the high-quality typesetting system which is the de facto standard for the communication and publication of scientific documents: all worksheets can be exported as .tex tables and all plots can be exported as .tex figures using the TikZ/Pgf graphic systems. Equation editorBy default the equation editor renders equation markup via a built-in LaTeX parser. QtiPlot can also make use of a LaTeX compiler web service, all you need is an internet connection. QtiPlot can be easily integrated with a locally installed LaTeX compiler making possible the compilation of complete documents and not just of equation markup, so that you can fully customize the image output in terms of font size, text and background color. 本部分内容设定了隐藏，需要回复后才能看到
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