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  • Generating a KML heatmap from given data set of [lat, lon, density]

    - by Will Croft
    I am looking to build a static KML (Google Earth markup) file which displays a heatmap-style rendering of a few given data sets in the form of [lat, lon, density] tuples. A very straightforward data set I have is for population density. My requirements are: must be able to feed in data for a given lat, lon must be able to specific the given density of the data at that lat, lon must export to KML The requirements are language agnostic for this project as I will be generating these files offline in order to build the KML used elsewhere. I have looked at a few projects, most notably heatmap.py, which is a port of gheat in Python with KML export. I have hit a brick wall in the sense that the projects I have found to date all rely on building the heatmap from the density of [lat, lon] points fed into the algorithm. If I am missing an obvious way to adapt my data set to feed in just the [lat, lon] tuples but adjusting how I feed them using the density values I have, I would love to know!

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  • ggplot2 heatmap : how to preserve the label order ?

    - by Tg
    I'm trying to plot heatmap in ggplot2 using csv data following casbon's solution in http://biostar.stackexchange.com/questions/921/how-to-draw-a-csv-data-file-as-a-heatmap-using-numpy-and-matplotlib the problem is x-label try to re-sort itself. For example, if I swap label COG0002 and COG0001 in that example data, the x-label still come out in sort order (cog0001, cog0002, cog0003.... cog0008). Is there anyway to prevent this ? I want to it to be ordered as in csv file thanks pp

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  • [R] Select columns for heatmap in R

    - by Philipp
    Hi stackoverflow-pros, I need your help again :) I wrote an R script, that generates a heatmap out of a given tab-seperated txt or xls file. At the moment, I delete all columns I don't want to have in the heatmap by hand in the xls file. Now I want to automatize it, but I don't know how :( The interesting columns all start the same in all xls files, followed by an individual name: xls-file 1: L1_tpm_xxxx L2_tpm_xxxx L3_tpm_xxxx xls-file 2: L1_tpm_xxxx L2_tpm_xxxx L3_tpm_xxxx L4_tpm_xxxx L5_tpm_xxxx Any ideas how to select those columns? Thanking you in anticipation, Philipp

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  • geom_tile heatmap with different high fill colours based on factor

    - by Michael
    I'm interested in building a heatmap with geom_tile in ggplot2 that uses a different gradient high color based on a factor. The plot below creates the plot where the individual tiles are colored blue or red based on the xy_type, but there is no gradient. ggplot() + geom_tile(data=mydata, aes(x=factor(myx), y=myy, fill=factor(xy_type))) + scale_fill_manual(values=c("blue", "red")) The plot below does not use the xy_type factor to choose the color, but I get a single group gradient based on the xy_avg_value. ggplot() + geom_tile(data=mydata, aes(x=factor(myx), y=myy, fill=xy_avg_value)) Is there a technique to blend these two plots? I can use a facet_grid(xy_type ~ .) to create separate plots of this data, with the gradient. As this is ultimately going to be a map (x~y coordinates), I'd like to find a way to display the different gradient together in a single geom_tile map.

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  • Towards HEATMAP representation - R -

    - by user3710390
    I am trying to plot a simple heatmap of some data distribution in R. My data = matrix (5000 x3( Time , Complexity, Localisation )). Time ( 0- 7000) Cmplx (0-4) Localisation (1-15). i.e Time Cmplx Localisation 567 3 1 54 0 2 345 3 12 567 4 12 345 2 9 989 4 7 ... ... ... The idea is to plot the Time in relation to each Cmplx and each Localisation (Something like accumarray in mathlab) Have someone an idea? Thanks in advance, Guillon_

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  • R heatmaps - non-text labels?

    - by Carl
    I am making a heatmap plot; currently the axes are labeled by index number. However, the index number corresponds to a series of 1/0s, which my group typically represents with a row of filled(1) or unfilled(0) boxes (think chessboard, though not strictly alternating colors). I'd like to use that representation instead of the index numbers. Any suggestions? I've considered simply making the labels as a plot, and positioning that adjacent to the heatmap, but I'm not finding a convenient way to do that either. I will also appreciate answers that are more generically applicable. edit - current code: map <- data.matrix(read.csv("./heatmap.out", header=F, sep=" ")) # ...some clean-up p<-heatmap(map, Rowv=NA, Colv=NA, col=grey(10:0 / 10)) this simply labels the heatmap rows/cols by index number. heatmap.out is raw numeric data.

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  • Trulia Adds Commute Time Calculator to Their Neighborhood Heat Maps

    - by Jason Fitzpatrick
    Trulia–a popular real estate site well known for their neighborhood heat maps covering crime, school locations, and property values–now shows commute times in heat map form; see instantly how far away your potential new place is from where you want to work. Accessing the commute heatmap is just like any of Trulia’s other top-down views. Search for your city, hit up the map, and select which heatmap overlay you want to view. Trulia [via Flowing Data] How to Make Your Laptop Choose a Wired Connection Instead of Wireless HTG Explains: What Is Two-Factor Authentication and Should I Be Using It? HTG Explains: What Is Windows RT and What Does It Mean To Me?

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  • Labeling values in a GNUplot heatmap

    - by andremo
    I am trying to generate a heat map using gnu plot. set title "Heat Map" plot '-' matrix with image 10 20 30 40 50 60 70 80 90 100 20 30 40 50 60 70 80 90 100 0 30 40 50 60 70 80 90 100 0 0 40 50 60 70 80 90 100 0 0 0 50 60 70 80 90 100 0 0 0 0 60 70 80 90 100 0 0 0 0 0 70 80 90 100 0 0 0 0 0 0 80 90 100 0 0 0 0 0 0 0 90 100 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 e Along the axes I get values -2 0 2 4 6 8 10, and I would like to replace those with a custom string. I cannot find out how to do this.

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  • draw csv file data as a heatmap using numpy and matplotlib

    - by Schrodinger's Cat
    Hello all, I was able to load my csv file into a numpy array: data = np.genfromtxt('csv_file', dtype=None, delimiter=',') Now I would like to generate a heatmap. I have 19 categories from 11 samples, along these lines: cat,1,2,3... a,0.0,0.2,0.3 b,1.0,0.4,0.2 . . . I wanted to use matplotlib colormesh. but I'm at loss. all the examples I could find used random number arrays. any help and insights would be greatly appreciated. many thanks

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  • Countour Mapping API

    - by Crash
    I have done some searching on Google, but haven't come up with much as of yet. I am wanting to take a set of point data, which I had previously been using to create weighted points for a heat map through the Google API, and turn them into a contour map to overlay on the Google Maps API. I haven't seen anything in Google's code that would let me do this. Does anyone know of a good API to create such an overlay? Or is there possibly something I have overlooked that Google offers?

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  • Algorithm for heat map?

    - by eshan
    I have a list of values each with latitude and longitude. I'm looking to create a translucent heatmap image to overlay on Google Maps. I know there are server side and flash based solutions already, but I want to build this in javascript using the canvas tag. However, I can't seem to find a concise description of the algorithm used to turn coordinates and values into a heatmap. Can anyone provide or link to one? Thanks.

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  • Recording user data for heatmap with javascript

    - by Hanpan
    Hi, I was wondering how sites such as crazyegg.com store user click data during a session. Obviously there is some underlying script which is storing each clicks data, but how is that data then populated into a database? It seems to me the simple solution would be to send data via AJAX but when you consider that it's almost impossible to get a cross browser page unload function setup, I'm wondering if there is perhaps some other more advanced way of getting metric data. I even saw a site which records each mouse movement and I am guessing they are definitely not sending that data to a database on each mouse move event. So, in a nutshell, what kind of technology would I need in order to monitor user activity on my site and then store this information in order to create metric data? I am not looking to recreate GA, I'm just very interested to know how this sort of thing is done. Thanks in advance

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  • How do I superimpose one bitmap image on another in GDI+?

    - by Old Man
    Using GDI+ I've made a heatmap bmp and I'd like to superimpose it on top of my bmp map. I've saved the two bmps to disk, and they look good, I just need a way to put them together. Is there any way to do this, perhaps using the Graphics object? How is transparency/alpa involved? I'm very new to GDI programming so please be as specific as possible.

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  • How can I create a heat map based on data from Google Analytics?

    - by tnorthcutt
    How would one go about creating a heat map (say, of the US) based on location data from Google Analytics? I'd like to somehow create such a map with the visitor data from several websites that use Google Analytics. I'm not really looking for a step-by-step tutorial, just some suggestions on how to start. Assume little to no programming experience, but a willingness to learn and hack together stuff to make it work.

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  • Good examples of MapServer / OpenLayers

    - by MarkJ
    I want to convince some clients to use MapServer and OpenLayers. Please can anyone suggest attractive websites to show off the possiblities! The clients will be impressed by: A density map (otherwise known as a heat map, colour-shaded grid coverage, contour plot...). The ability for the user to download the underlying data for the density map, restricted to the area being viewed, in some format such as netCDF. Standard OpenLayers stuff. Zooming, panning, scale bar, overview map... Different base layers. Could be WMS, Google, Bing... Searching for a placename, map is panned to display the place. MapServer.org seems to be down right now :( But from memory their examples didn't have the "wow" factor. The OpenLayers examples demonstrate only one or two features per example - I want something to wow the clients by showing all the capabilities in one example. PS If you have good examples that use some other open source tools, post them by all means. But just JavaScript please: customer says no rich client.

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  • How can I create a Google Visualization intensity/heat map using a KMZ document?

    - by dommer
    I'd like to create a Google Visualization API intensity/heat map based on UK counties. To do this, it appears that I need to use a custom KML/KMZ file that contains the county mapping data (which I've located). Could anyone provide me with a sample of how I can display an intensity map based on a KMZ file? I know how to display Google Visualization intensity maps in general. I just can't find any examples that use a custom KMZ file.

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  • 2D Histogram in R: Converting from Count to Frequency within a Column

    - by Jac
    Would appreciate help with generating a 2D histogram of frequencies, where frequencies are calculated within a column. My main issue: converting from counts to column based frequency. Here's my starting code: # expected packages library(ggplot2) library(plyr) # generate example data corresponding to expected data input x_data = sample(101:200,10000, replace = TRUE) y_data = sample(1:100,10000, replace = TRUE) my_set = data.frame(x_data,y_data) # define x and y interval cut points x_seq = seq(100,200,10) y_seq = seq(0,100,10) # label samples as belonging within x and y intervals my_set$x_interval = cut(my_set$x_data,x_seq) my_set$y_interval = cut(my_set$y_data,y_seq) # determine count for each x,y block xy_df = ddply(my_set, c("x_interval","y_interval"),"nrow") # still need to convert for use with dplyr # convert from count to frequency based on formula: freq = count/sum(count in given x interval) ################ TRYING TO FIGURE OUT ################# # plot results fig_count <- ggplot(xy_df, aes(x = x_interval, y = y_interval)) + geom_tile(aes(fill = nrow)) # count fig_freq <- ggplot(xy_df, aes(x = x_interval, y = y_interval)) + geom_tile(aes(fill = freq)) # frequency I would appreciate any help in how to calculate the frequency within a column. Thanks! jac EDIT: I think the solution will require the following steps 1) Calculate and store overall counts for each x-interval factor 2) Divide the individual bin count by its corresponding x-interval factor count to obtain frequency. Not sure how to carry this out though. .

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  • how to pass arguments into function within a function in r

    - by jon
    I am writing function that involve other function from base R with alot of arguments. For example (real function is much longer): myfunction <- function (dataframe, Colv = NA) { matrix <- as.matrix (dataframe) out <- heatmap(matrix, Colv = Colv) return(out) } data(mtcars) myfunction (mtcars, Colv = NA) The heatmap has many arguments that can be passed to: heatmap(x, Rowv=NULL, Colv=if(symm)"Rowv" else NULL, distfun = dist, hclustfun = hclust, reorderfun = function(d,w) reorder(d,w), add.expr, symm = FALSE, revC = identical(Colv, "Rowv"), scale=c("row", "column", "none"), na.rm = TRUE, margins = c(5, 5), ColSideColors, RowSideColors, cexRow = 0.2 + 1/log10(nr), cexCol = 0.2 + 1/log10(nc), labRow = NULL, labCol = NULL, main = NULL, xlab = NULL, ylab = NULL, keep.dendro = FALSE, verbose = getOption("verbose"), ...) I want to use these arguments without listing them inside myfun. myfunction (mtcars, Colv = NA, col = topo.colors(16)) Error in myfunction(mtcars, Colv = NA, col = topo.colors(16)) : unused argument(s) (col = topo.colors(16)) I tried the following but do not work: myfunction <- function (dataframe, Colv = NA) { matrix <- as.matrix (dataframe) out <- heatmap(matrix, Colv = Colv, ....) return(out) } data(mtcars) myfunction (mtcars, Colv = NA, col = topo.colors(16))

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  • data handling with javascript

    - by Vincent Warmerdam
    Python has a very neat package called pandas which allows for quick data transformation; tables, aggregation, that sort of thing. A lot of these types of functionality can also be found in the python itertools module. The plyR package in R is also very similar. Usually one woud use this functionality to produce a table which is later visualized with a plot. I am personally very fond of d3, and I would like to allow the user to first indicate what type of data aggregation he wants on the dataset before it is visualized. The visualisation in question involves making a heatmap where the user gets to select the size of the bins of the heatmap beforehand (I want d3 to project this through leaflet). I want to visually select the ideal size of the bins for the heatmap. The way I work now is that I take the dataset, aggregate it with python and then manually load it in d3. This is a process that takes a lot of human effort and I was wondering if the data aggregation can be done through the javascript of the browser. I couldn't find a package for javascript specifically built for data, suggesting (to me) that this is a bad idea and that one should not use javascript for the data handling. Is there a good module/package for javascript to handle data aggregation? Is it a good/bad idea to do the data aggregation in javascript (performance wise)?

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  • Converting python objects for rpy2

    - by bgbg
    The following code is supposed to created a heatmap in rpy2 import numpy as np from rpy2.robjects import r data = np.random.random((10,10)) r.heatmap(data) However, it results in the following error Traceback (most recent call last): File "z.py", line 8, in <module> labRow=rowNames, labCol=colNames) File "C:\Python25\lib\site-packages\rpy2\robjects\__init__.py", line 418, in __call__ new_args = [conversion.py2ri(a) for a in args] File "C:\Python25\lib\site-packages\rpy2\robjects\__init__.py", line 93, in default_py2ri raise(ValueError("Nothing can be done for the type %s at the moment." %(type(o)))) ValueError: Nothing can be done for the type <type 'numpy.ndarray'> at the moment. From the documentation I learn that r.heatmap expects "a numeric matrix". How do I convert np.array to the required data type?

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  • DLL Load Failed, Not a Valid Win32 App showing for both x86 & x64 DLLs

    - by mitrebox
    Trying to run the latest version of heatmap. http://jjguy.com/heatmap/ DLL load keeps crapping out on me in both 64 & 32 bit dlls. (Similar questions on this seemed irrelevant as I've tried loading both DLLs) I'm running Windows 7. I have uninstalled and re-installed 2.7.3 64 bit. Idle Top line: Python 2.7.3 (default, Apr 10 2012, 23:24:47) [MSC v.1500 64 bit (AMD64)] on win32 I've tried loading C:\Python27\DLLs\cHeatmap-x86.dll ImportError: DLL load failed: %1 is not a valid Win32 application. C:\Python27\DLLs\cHeatmap-x64.dll ImportError: DLL load failed: %1 is not a valid Win32 application. I can run heatmap 1.1 but that was before DLLs were added.

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  • How can a code editor effectively hint at code nesting level - without using indentation?

    - by pgfearo
    I've written an XML text editor that provides 2 view options for the same XML text, one indented (virtually), the other left-justified. The motivation for the left-justified view is to help users 'see' the whitespace characters they're using for indentation of plain-text or XPath code without interference from indentation that is an automated side-effect of the XML context. I want to provide visual clues (in the non-editable part of the editor) for the left-justified mode that will help the user, but without getting too elaborate. I tried just using connecting lines, but that seemed too busy. The best I've come up with so far is shown in a mocked up screenshot of the editor below, but I'm seeking better/simpler alternatives (that don't require too much code). [Edit] Taking the heatmap idea (from: @jimp) I get this and 3 alternatives - labelled a, b and c: The following section describes the accepted answer as a proposal, bringing together ideas from a number of other answers and comments. As this question is now community wiki, please feel free to update this. NestView The name for this idea which provides a visual method to improve the readability of nested code without using indentation. Contour Lines The name for the differently shaded lines within the NestView The image above shows the NestView used to help visualise an XML snippet. Though XML is used for this illustration, any other code syntax that uses nesting could have been used for this illustration. An Overview: The contour lines are shaded (as in a heatmap) to convey nesting level The contour lines are angled to show when a nesting level is being either opened or closed. A contour line links the start of a nesting level to the corresponding end. The combined width of contour lines give a visual impression of nesting level, in addition to the heatmap. The width of the NestView may be manually resizable, but should not change as the code changes. Contour lines can either be compressed or truncated to keep acheive this. Blank lines are sometimes used code to break up text into more digestable chunks. Such lines could trigger special behaviour in the NestView. For example the heatmap could be reset or a background color contour line used, or both. One or more contour lines associated with the currently selected code can be highlighted. The contour line associated with the selected code level would be emphasized the most, but other contour lines could also 'light up' in addition to help highlight the containing nested group Different behaviors (such as code folding or code selection) can be associated with clicking/double-clicking on a Contour Line. Different parts of a contour line (leading, middle or trailing edge) may have different dynamic behaviors associated. Tooltips can be shown on a mouse hover event over a contour line The NestView is updated continously as the code is edited. Where nesting is not well-balanced assumptions can be made where the nesting level should end, but the associated temporary contour lines must be highlighted in some way as a warning. Drag and drop behaviors of Contour Lines can be supported. Behaviour may vary according to the part of the contour line being dragged. Features commonly found in the left margin such as line numbering and colour highlighting for errors and change state could overlay the NestView. Additional Functionality The proposal addresses a range of additional issues - many are outside the scope of the original question, but a useful side-effect. Visually linking the start and end of a nested region The contour lines connect the start and end of each nested level Highlighting the context of the currently selected line As code is selected, the associated nest-level in the NestView can be highlighted Differentiating between code regions at the same nesting level In the case of XML different hues could be used for different namespaces. Programming languages (such as c#) support named regions that could be used in a similar way. Dividing areas within a nesting area into different visual blocks Extra lines are often inserted into code to aid readability. Such empty lines could be used to reset the saturation level of the NestView's contour lines. Multi-Column Code View Code without indentation makes the use of a multi-column view more effective because word-wrap or horizontal scrolling is less likely to be required. In this view, once code has reach the bottom of one column, it flows into the next one: Usage beyond merely providing a visual aid As proposed in the overview, the NestView could provide a range of editing and selection features which would be broadly in line with what is expected from a TreeView control. The key difference is that a typical TreeView node has 2 parts: an expander and the node icon. A NestView contour line can have as many as 3 parts: an opener (sloping), a connector (vertical) and a close (sloping). On Indentation The NestView presented alongside non-indented code complements, but is unlikely to replace, the conventional indented code view. It's likely that any solutions adopting a NestView, will provide a method to switch seamlessly between indented and non-indented code views without affecting any of the code text itself - including whitespace characters. One technique for the indented view would be 'Virtual Formatting' - where a dynamic left-margin is used in lieu of tab or space characters. The same nesting-level data used to dynamically render the NestView could also used for the more conventional-looking indented view. Printing Indentation will be important for the readability of printed code. Here, the absence of tab/space characters and a dynamic left-margin means that the text can wrap at the right-margin and still maintain the integrity of the indented view. Line numbers can be used as visual markers that indicate where code is word-wrapped and also the exact position of indentation: Screen Real-Estate: Flat Vs Indented Addressing the question of whether the NestView uses up valuable screen real-estate: Contour lines work well with a width the same as the code editor's character width. A NestView width of 12 character widths can therefore accommodate 12 levels of nesting before contour lines are truncated/compressed. If an indented view uses 3 character-widths for each nesting level then space is saved until nesting reaches 4 levels of nesting, after this nesting level the flat view has a space-saving advantage that increases with each nesting level. Note: A minimum indentation of 4 character widths is often recommended for code, however XML often manages with less. Also, Virtual Formatting permits less indentation to be used because there's no risk of alignment issues A comparison of the 2 views is shown below: Based on the above, its probably fair to conclude that view style choice will be based on factors other than screen real-estate. The one exception is where screen space is at a premium, for example on a Netbook/Tablet or when multiple code windows are open. In these cases, the resizable NestView would seem to be a clear winner. Use Cases Examples of real-world examples where NestView may be a useful option: Where screen real-estate is at a premium a. On devices such as tablets, notepads and smartphones b. When showing code on websites c. When multiple code windows need to be visible on the desktop simultaneously Where consistent whitespace indentation of text within code is a priority For reviewing deeply nested code. For example where sub-languages (e.g. Linq in C# or XPath in XSLT) might cause high levels of nesting. Accessibility Resizing and color options must be provided to aid those with visual impairments, and also to suit environmental conditions and personal preferences: Compatability of edited code with other systems A solution incorporating a NestView option should ideally be capable of stripping leading tab and space characters (identified as only having a formatting role) from imported code. Then, once stripped, the code could be rendered neatly in both the left-justified and indented views without change. For many users relying on systems such as merging and diff tools that are not whitespace-aware this will be a major concern (if not a complete show-stopper). Other Works: Visualisation of Overlapping Markup Published research by Wendell Piez, dated from 2004, addresses the issue of the visualisation of overlapping markup, specifically LMNL. This includes SVG graphics with significant similarities to the NestView proposal, as such, they are acknowledged here. The visual differences are clear in the images (below), the key functional distinction is that NestView is intended only for well-nested XML or code, whereas Wendell Piez's graphics are designed to represent overlapped nesting. The graphics above were reproduced - with kind permission - from http://www.piez.org Sources: Towards Hermenutic Markup Half-steps toward LMNL

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  • GDB not breaking on breakpoints set on object creation in C++

    - by Drew
    Hi all, I've got a c++ app, with the following main.cpp: 1: #include <stdio.h> 2: #include "HeatMap.h" 3: #include <iostream> 4: 5: int main (int argc, char * const argv[]) 6: { 7: HeatMap heatMap(); 8: printf("message"); 9: return 0; 10: } Everything compiles without errors, I'm using gdb (GNU gdb 6.3.50-20050815 (Apple version gdb-1346) (Fri Sep 18 20:40:51 UTC 2009)), and compiled the app with gcc (gcc version 4.2.1 (Apple Inc. build 5646) (dot 1)) with the commands "-c -g". When I add breakpoints to lines 7, 8, and 9, and run gdb, I get the following... (gdb) break main.cpp:7 Breakpoint 1 at 0x10000177f: file src/main.cpp, line 8. (gdb) break main.cpp:8 Note: breakpoint 1 also set at pc 0x10000177f. Breakpoint 2 at 0x10000177f: file src/main.cpp, line 8. (gdb) break main.cpp:9 Breakpoint 3 at 0x100001790: file src/main.cpp, line 9. (gdb) run Starting program: /DevProjects/DataManager/build/DataManager Reading symbols for shared libraries ++. done Breakpoint 1, main (argc=1, argv=0x7fff5fbff960) at src/main.cpp:8 8 printf("message"); (gdb) So, why of why, does anyone know, why my app does not break on the breakpoints for the object creation, but does break on the printf line? Drew J. Sonne.

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  • How to put a rgb image on gnuplot 3d plot?

    - by Stefano Borini
    I want to plot an rgb image with gnuplot, and it must be hovering inside a 3d plot box, so that it can act as a "floor" for my data. How can I do it ? THe gnuplot examples use a heatmap to map the rgb values, but this is not what I want. Apart from receiving an error GNUPLOT (plot_image): Color boxes cannot handle RGB components. It is not what I want in any case. I need the full rgb data in the box, not mapped through a heatmap.

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  • pyplot: really slow creating heatmaps

    - by cvondrick
    I have a loop that executes the body about 200 times. In each loop iteration, it does a sophisticated calculation, and then as debugging, I wish to produce a heatmap of a NxM matrix. But, generating this heatmap is unbearably slow and significantly slow downs an already slow algorithm. My code is along the lines: import numpy import matplotlib.pyplot as plt for i in range(200): matrix = complex_calculation() plt.set_cmap("gray") plt.imshow(matrix) plt.savefig("frame{0}.png".format(i)) The matrix, from numpy, is not huge --- 300 x 600 of doubles. Even if I do not save the figure and instead update an on-screen plot, it's even slower. Surely I must be abusing pyplot. (Matlab can do this, no problem.) How do I speed this up?

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