The Far-Reaching Impact of MATLAB and Simulink Explore the wide range of product capabilities, and find the solution that is right for your application or industry. Matlab crashes frequently on Arch Linux. Learn more about crash, linux, 2015a MATLAB. Matlab crash low graphic issue-(although i. Learn more about matlab, crash, low graphics MATLAB.
- Matlab 2015a 8.5.0.197613 Code
- Matlab 2015a 8.5.0.197613 Download
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- Matlab 2015a Download
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- Matlab problem.version 2015a. Learn more about MATLAB. Skip to content. Matlab R2015a (build 8.5.0.197613). Update of JAVA is not the solution. I think its the version of Matlab or windows 0 Comments. Show Hide all comments. Sign in to comment. PRIYA ALAGARSAMY on 8 Mar 2016.
- Jun 29, 2016 MATLAB 2015a crashes upon start or upon attempt to run a.m file. I've notices several other threads stating similar problems, but most of them lead to now non-existent mathworks page. Any help resolving this issue would be appreciated.
Akvis natureart 10.0 download. Name: Matlab 2015a
Version: 8.5.0.197613
Mac Platform: Intel
Includes: K
OS version: Mac OS X 10.9.5 or later
Processor type(s) & speed: 64-bit processor
RAM minimum: 2 GB
Video RAM: No specific
What’s New
Version 8.5.0 (R2015a):
New Features, Bug Fixes, Compatibility Considerations
Desktop
Documentation: Integrate documentation for custom toolboxes into the MATLAB Help Browser
Documentation: Determine when feature introduced
Array Size Limit: Limit maximum array size to prevent unintended creation of very large matrices
Tab Completion: Complete class properties and methods while editing class definition files
User Interface Preferences: Control user interface language
Language and Programming
repelem Function: Repeat copies of array elements to create a larger array
sort Function: Now preserves shape of cell array of string inputs
isenum Function: Determine if variable is enumeration
milliseconds Function: Convert duration to number of milliseconds
Publishing Markup: Include external file content
fullfile Function: Maintain all double-dot symbols
Python Objects: Indexing Support
Python Version 3.4: MATLAB Support
MATLAB Engine for Python: Support for startup options
MATLAB Engine for Python: Support for Unicode in Python 2.7
Conversion of Character Arrays to Java Strings: Preserve null characters
WSDL Web Services Documents: Limitations
Unit Testing Framework: Tag tests for categorization and selection
Unit Testing Framework: Run tests in parallel
Unit Testing Framework: Share variables between tests in scripts
Unit Testing Framework: Use prebuilt test fixtures
Unit Testing Framework: Compare objects using isequaln
Unit Testing Framework: Use homogeneous expected causes with Throws constraint
Git Source Control Integration: View branch details and delete branches
C Matrix Library: New functions
Functionality being removed or changed
Mathematics
discretize Function: Group numeric data into bins or categories
Descriptive Statistics: Omit NaN values in basic statistical calculations, including max, min, mean, median, sum, var, std, and cov
ismembertol and uniquetol Functions: Perform set comparisons using a tolerance
Random Numbers: Generate random numbers using the double-precision, SIMD-oriented Fast Mersenne Twister (dSFMT) algorithm
nearestNeighbor Function: Determine nearest alphaShape boundary point
Functionality being removed or changed
Data Import and Export
Datastore: Read one complete file with ‘file’ option for ReadSize property
Datastore: Read data in parallel from a datastore with partition function using Parallel Computing Toolbox
webwrite Function: Send data to RESTful Web services using HTTP POST method
webread and websave Functions: Request data from RESTful Web services using HTTP POST method
xlsread and readtable Functions: Read larger spreadsheet files from Excel software
textscan and readtable Functions: Return consistent results when reading quoted strings
Scientific File Format Libraries: Upgrades
Functionality being removed or changed
Graphics
drawnow Function: Improve performance in animation loops with new option
Functionality being removed or changed
Performance
MapReduce: Run mapreduce algorithms on any computer cluster that supports parallel pools using MATLAB Distributed Computing Server
Interpolation Functions: Execute faster with multithreaded calculations
Hardware Support
IP camera: Acquire video directly from Internet Protocol cameras
BeagleBone Black Hardware: Access BeagleBone Black hardware with the MATLAB Support Package for BeagleBone Black Hardware
Arduino Hardware: Access to Arduino Leonardo and other boards with the MATLAB Support Package for Arduino Hardware
Arduino Hardware: New configurePin function
Functionality being removed or changed
↑ Info
Explore new ideas
MATLAB® is the high-level language and interactive environment used by millions of engineers and scientists worldwide. It lets you explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
Put your ideas into action
You can use MATLAB in projects such as modeling energy consumption to build smart power grids, developing control algorithms for hypersonic vehicles, analyzing weather data to visualize the track and intensity of hurricanes, and running millions of simulations to pinpoint optimal dosing for antibiotics.
System requirements
Intel Mac OS X 10.9.5 or later
More info: http://www.mathworks.com/products/matlab/index.html?s_tid=gn_loc_drop
Version: 8.5.0.197613
Mac Platform: Intel
Includes: K
OS version: Mac OS X 10.9.5 or later
Processor type(s) & speed: 64-bit processor
RAM minimum: 2 GB
Video RAM: No specific
What’s New
Version 8.5.0 (R2015a):
New Features, Bug Fixes, Compatibility Considerations
Desktop
Documentation: Integrate documentation for custom toolboxes into the MATLAB Help Browser
Documentation: Determine when feature introduced
Array Size Limit: Limit maximum array size to prevent unintended creation of very large matrices
Tab Completion: Complete class properties and methods while editing class definition files
User Interface Preferences: Control user interface language
Language and Programming
repelem Function: Repeat copies of array elements to create a larger array
sort Function: Now preserves shape of cell array of string inputs
isenum Function: Determine if variable is enumeration
milliseconds Function: Convert duration to number of milliseconds
Publishing Markup: Include external file content
fullfile Function: Maintain all double-dot symbols
Python Objects: Indexing Support
Python Version 3.4: MATLAB Support
MATLAB Engine for Python: Support for startup options
MATLAB Engine for Python: Support for Unicode in Python 2.7
Conversion of Character Arrays to Java Strings: Preserve null characters
WSDL Web Services Documents: Limitations
Unit Testing Framework: Tag tests for categorization and selection
Unit Testing Framework: Run tests in parallel
Unit Testing Framework: Share variables between tests in scripts
Unit Testing Framework: Use prebuilt test fixtures
Unit Testing Framework: Compare objects using isequaln
Unit Testing Framework: Use homogeneous expected causes with Throws constraint
Git Source Control Integration: View branch details and delete branches
C Matrix Library: New functions
Functionality being removed or changed
Mathematics
discretize Function: Group numeric data into bins or categories
Descriptive Statistics: Omit NaN values in basic statistical calculations, including max, min, mean, median, sum, var, std, and cov
ismembertol and uniquetol Functions: Perform set comparisons using a tolerance
Random Numbers: Generate random numbers using the double-precision, SIMD-oriented Fast Mersenne Twister (dSFMT) algorithm
nearestNeighbor Function: Determine nearest alphaShape boundary point
Functionality being removed or changed
Data Import and Export
Datastore: Read one complete file with ‘file’ option for ReadSize property
Datastore: Read data in parallel from a datastore with partition function using Parallel Computing Toolbox
webwrite Function: Send data to RESTful Web services using HTTP POST method
webread and websave Functions: Request data from RESTful Web services using HTTP POST method
xlsread and readtable Functions: Read larger spreadsheet files from Excel software
textscan and readtable Functions: Return consistent results when reading quoted strings
Scientific File Format Libraries: Upgrades
Functionality being removed or changed
Graphics
drawnow Function: Improve performance in animation loops with new option
Functionality being removed or changed
Performance
MapReduce: Run mapreduce algorithms on any computer cluster that supports parallel pools using MATLAB Distributed Computing Server
Interpolation Functions: Execute faster with multithreaded calculations
Hardware Support
IP camera: Acquire video directly from Internet Protocol cameras
BeagleBone Black Hardware: Access BeagleBone Black hardware with the MATLAB Support Package for BeagleBone Black Hardware
Arduino Hardware: Access to Arduino Leonardo and other boards with the MATLAB Support Package for Arduino Hardware
Arduino Hardware: New configurePin function
Functionality being removed or changed
↑ Info
Explore new ideas
MATLAB® is the high-level language and interactive environment used by millions of engineers and scientists worldwide. It lets you explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
Put your ideas into action
You can use MATLAB in projects such as modeling energy consumption to build smart power grids, developing control algorithms for hypersonic vehicles, analyzing weather data to visualize the track and intensity of hurricanes, and running millions of simulations to pinpoint optimal dosing for antibiotics.
System requirements
Intel Mac OS X 10.9.5 or later
More info: http://www.mathworks.com/products/matlab/index.html?s_tid=gn_loc_drop
Related Posts:
Code for the paper Variation of outdoor illumination as a function of solar elevation and light pollution by Spitschan, Aguirre, Brainard & Sweeney (2016). The code provided here performs calibration of the spectrometers, pre-processing of the raw, uncalibrated spectra and figure generation for the figures in the paper.
Authors
The code provided here was written by Manuel Spitschan. The scientific work was performed by Manuel Spitschan, Geoffrey K. Aguirre, David H. Brainard and Alison Sweeney, University of Pennsylvania.
Citation
If you use this code or data set, please cite it as Nuke studio 11.1v3 video.
Spitschan M, Aguirre GK, Brainard DH & Sweeney AM (2016). Variation of outdoor illumination as a function of solar elevation and light pollution. Scientific Reports 6, 26756. doi:10.1038/srep26756.
Matlab 2015a 8.5.0.197613 Code
Clone the repository
The URL for the repository is https://github.com/spitschan/IlluminationSpectraDataset.git. Use your favorite git client to clone the repository or type the following in the command line:
Dependencies, third party software and requirements
The code provided here relies on functions from the Psychtoolbox. A small number of functions in
IlluminationSpectraDataset/code/helpers
were obtained from third party sources and have been attributed in IlluminationSpectraDataset/code/Contents.m
. The software provided here was developed and tested on MATLAB 2015a (8.5.0.197613, 64-bit, maci64) on Mac OS X 10.11.3 (El Capitan).
The raw data are available on the FigShare repository (doi:10.6084/m9.figshare.2009070.v1). Graphicriver investment plan 16 pages business brochure template.
Setting up paths
- In MATLAB, go to the folder where you have cloned in the repository.
- Add it recursively to the path:
Spectrometer calibration
The spectrometer give us uncalibrated spectra but we have produced a pipeline to calibrate these to convert the spectra to absolute downwelling vector irradiance.
- Download the spectrometer calibration data (300.55 MB) from the FigShare repository (doi:10.6084/m9.figshare.2009070.v1). This tarball contains measurements of the spectrometer properties needed reproduce the calibration steps described in the paper.
- Unpack the repository into
IlluminationSpectraDataset/calibration
. The resulting folder should be calledIlluminationSpectraDataset/calibration/calibrationdata
. Note that this folder is not subject to git versioning as it is included in.gitignore
. - Run the calibration for the spectrometers:
- Note that the low-sensitivity spectrometer is called 'a' and the high-sensitivity spectrometer is called 'b'.
- The calibration will produce two calibration files in
IlluminationSpectraDataset/calibration/cal
calledOO_USB2000+a.mat
(for the 'a' spectrometer) andOO_USB2000+b.mat
(for the 'b' spectrometer). - There will also be plots produced in
IlluminationSpectraDataset/calibration/cal/plots
which form the basis for supplementary figures S5-S7 of Spitschan et al. (2016).
Data preprocessing
- Download the raw data (669.38 MB) from the FigShare repository (doi:10.6084/m9.figshare.2009070.v1). This tarball contains the raw, uncalibrated spectra from the paper.
- Unpack the repository into directly into the root repository
IlluminationSpectraDataset/
. The resulting folder should be calledIlluminationSpectraDataset/dataraw
. Note that this folder is not subject to git versioning as it is included in.gitignore
. - Run
IlluminationSpectraDataset_Analysis_Preprocess
to pre-process the raw, uncalibrated spectra. - This will produce two CSV files with the calibrated data in
IlluminationSpectraDataset/data
:CSSP_spectra.csv
(Rural data) andDRL_spectra.csv
(City data). CSSP_quality.csv
andDRL_quality.csv
are also produced by this function and contain information about the included spectra.
Data analysis
- To run the full analysis, run
IlluminationSpectraDataset_Analysis_FullAnalysis
. This script will generate the Figures 1-6 and S1-S4 from the paper. Note that figures S5-S7 are generated by the calibration step above.
Figures and tables
The code provided here reproduces all elements from Figures 1-6, Supplementary Figures S1-S7 and Tables S1-S7. Note that the figures produced are raw versions of the ones produced in the paper, which have been edited in Adobe Illustrator. The correspondences of the figures in the paper and supplementary material and the figures produced here are as follows.
Figure/table in paper | Path to figure/table produced here |
---|---|
Figure 1 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure1A.pdf IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure1B.pdf Color maps in IlluminationSpectraDataset/analysis/results/ColorMaps |
Figure 2 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure2.pdf |
Figure 3 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure3.pdf |
Figure 4 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure4a.png IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure4b.png |
Figure 5 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure5.pdf |
Figure 6 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_Figure6.pdf |
Figure S1 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_FigureS1A.pdf IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_FigureS1B.pdf |
Figure S2 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_FigureS2.pdf |
Figure S3 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_FigureS3.pdf |
Figure S4 | IlluminationSpectraDataset/analysis/results/IlluminationSpectraDataset_Analysis_FigureS4.pdf |
Figure S5 | IlluminationSpectraDataset/calibration/cals/plots/SpectralStatsAnalysis_FigureS5_DarkNoiseSpectra_a.pdf IlluminationSpectraDataset/calibration/cals/plots/SpectralStatsAnalysis_FigureS5_DarkNoiseSpectra_b.pdf |
Figure S6 | IlluminationSpectraDataset/calibration/cals/plots/IlluminationSpectraDataset_Calibration_FigureS1_WlCorrection_a.pdf IlluminationSpectraDataset/calibration/cals/plots/IlluminationSpectraDataset_Calibration_FigureS1_WlCorrection_b.pdf IlluminationSpectraDataset/calibration/cals/plots/IlluminationSpectraDataset_Calibration_FigureS1_WlCorrectionInset_a.pdf IlluminationSpectraDataset/calibration/cals/plots/IlluminationSpectraDataset_Calibration_FigureS1_WlCorrectionInset_b.pdf |
Figure S7 | IlluminationSpectraDataset/calibration/cals/plots/IlluminationSpectraDataset_Calibration_FigureS3_RelativeSensitivityCalibration_a.pdf IlluminationSpectraDataset/calibration/cals/plots/IlluminationSpectraDataset_Calibration_FigureS3_RelativeSensitivityCalibration_b.pdf |
Table S1 | (Column vector 280-840 nm with 1 nm spacing.) |
Table S2 | IlluminationSpectraDataset/data/CSSP_spectra.csv |
Table S3 | IlluminationSpectraDataset/data/DRL_spectra.csv |
Table S4 | IlluminationSpectraDataset/analysis/data/B_CIE3x/B_CIE3R.csv |
Table S5 | IlluminationSpectraDataset/analysis/data/B_CIE3x/B_CIE3C.csv |
Table S6 | IlluminationSpectraDataset/analysis/data/B_CIE3x/w_CIE3R.csv |
Table S7 | IlluminationSpectraDataset/analysis/data/B_CIE3x/w_CIE3C.csv |
Sources of data
In
IlluminationSpectraDataset/analysis/data/
, we provide the following basis functions and data sets:
B_cie
- CIE basis functions from Wyszecki & Stiles (1982), page 762.B_Granada
- Basis functions from the Granada model, obtained from the authors' website (Hernández-Andrés et al., 2001).spd_DiCarloWandell
- DiCarlo & Wandell (2000) data set, obtained from the authors.spd_Granada
- Granada spectral data set, obtained from the authors' website (Hernández-Andrés et al., 2001).spd_LightPollution
- Light pollution spectrum, digitized from Cronin et al. (2014), Figure 2.12, page 22.spd_Zabriskie
- Light pollution-free night spectrum, digitized from Cronin et al. (2014), Figure 2.14, page 24.
Full references
Matlab 2015a 8.5.0.197613 Download
- Cronin, T.W., Johnsen, S., Marshall, J., and Warrant, E.J. (2014). Visual ecology, (Princeton: Princeton University Press).
- DiCarlo, J.M., and Wandell, B.A. (2000). Illuminant estimation: beyond the bases. In IS&T/SID Eighth Color Imaging Conference. (Scottsdale, AZ), pp. 91-96.
- Hernández-Andrés, J., Romero, J., Nieves, J.L., and Lee, R.L. (2001). Color and spectral analysis of daylight in southern Europe. Journal of the Optical Society of America A 18, 1325. doi:10.1364/josaa.18.001325.
- Wyszecki, G., and Stiles, W.S. (1982). Color science: concepts and methods, quantitative data and formulae, 2nd Edition, (New York: Wiley).
Matlab 2015a 8.5.0.197613 Date
Contact
Matlab 2015a Download
For any questions, bug reports, and comments please contact Manuel Spitschan ([email protected]).