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Chaco Documentation - Enthought Tool Suite

1. 59 8 2 Data Ranges ok RED A GR RO OR Ss dede 65 5 9 XMODDGES iusso ee SAA Rente T A oo ae ud Bee 69 8 4 Containers o4 _____ 71 Annotated Examples 75 plOtip bok ee esos eA RUE GG cR USE cn 75 02 JbzgdstecDy dd _ _ CR ECCE 76 039 ctool cdemo DY secre YR TI 94 data labelS py os baw RD RO o bor o EGA oe X m s e UR S E he RO 78 05 data ssori we d RUN Sek RU Rote e e d e TR HL ABUS D RO 79 O16 edit IinepV ex Kawai RR ETE RR 80 97 financial plotpy xes emere uu Red 4 ba Xue deb ed 81 9 8 financial plot sapera REGE GC m pa 82 9 9 i330 64004 Se 654 244 4 S04 foe RS go 83 910 Using PY eps vues ee ee Aes OE Ee ee oe De Fus 84 911 moninteractive py x 622442886 ERG EEA EEE ORS X RU 85 O12 range selectriot Rom eR Rue RGN RARUS RU Ae G 86 913 scales test ipy 224 24 4484 be be ae Vs bee SEER EEE 87 9 14 Simple Lines o sex Aw edem a m 88 9 15 tornados ty ac ew ox X ROS RAT OR eoe _______ 89 O16 PY x aue Bh dh eS BARE oS E RA
2. EE Ebo __________ 109 936 dog DlOt2opY dax Goes lo ebony owe ok d ee Selah Geta eld 110 O37 lt lt PRA ER AES we Be eee Rex ER Fox us 110 9 38 polygon plot Dy ao ordered eee ae bod dG eee Res ede Baw GE RS 111 0 30 polygon MOVE SOY nuper _____ 112 9 10 regression dpy xw xo bin Ro Row de Ye eee eed E B eh v poo N 113 Oud Scatter PY aeret Pees 114 9 42 scatter inspector py c9 3 4 99 B Eee PE BASE Nx CR eR 115 9 43 scatter 5 asda bu ok FOROS ROB Pa cm m XUE 3 d X3 Wo E 116 944 ROW E eA 117 045 tabbed DlOBSUDV ie ta ide fede d 118 9046 traits Editor py ssr 2664 0 et eG eR AER Re MAS eGR 119 947 Zeomable GOLOrbars csc sons m Ro Aa ee RR UR RD 120 9 48 zoomed plot 10 Tech Notes 10 1 About the Chaco Scales package Index Chaco Documentation Release 3 0 0 Chaco is a Python toolkit for building interactive 2 D visualizations It includes renderers for many popular plot types built in implementations of common interactions with those plots and a framework for extending and customizing plots and interactions Chaco can also render graphics in a non interactive fashion to images in either raster or vect
3. 100 sin x plot x r 18 Contents Chaco Documentation Release 3 0 0 title First plot ytitle sin x show First plot The basic structure of this example is that we generate some data then we call functions to plot the data and configure the plot There is a global concept of the active plot and the functions do high level manipulations on it The generated plot is then usually saved to disk for inclusion in a journal article or presentation slides Now as it so happens this particular example uses the chaco shell script plotting package so when you run this script the plot that Chaco opens does have some basic interactivity You can pan and zoom and even move forwards and backwards through your zoom history But ultimately it s a pretty static view into the data 3 1 5 Application oriented Plotting The second approach to plotting can be thought of as application oriented for lack of a better term There is definitely a bit more code and the plot initially doesn t look much different but it sets us up to do more interesting things as you ll see later on class LinePlot HasTraits plot Instance Plot Contents 19 Chaco Documentation Release 3 0 0 traits view View Item plot editor ComponentEditor show label False width 500 height 500 resizable True title Chaco Plot def init J self x linspace 14 14 100 y sin x x 3 plotdata
4. Line Plot Scatter plot 9 35 line plot hold py Demonstrates the different hold styles of LinePlot source line_plot_hold py render_style hold render_style connectedhold Contents 109 Chaco Documentation Release 3 0 0 9 36 log plot py Draws some x y log plots No Tools source log plot py 9 37 nans plot py This plot displays chaco s ability to handle data interlaced with NaNs source nans plot py exp x x 2 110 Contents Chaco Documentation Release 3 0 0 9 38 polygon plot py Draws some different polygons source polygon plot py Contents 111 Chaco Documentation Release 3 0 0 My First Polygon Plot 9 39 polygon move py Shares same basic interactions as polygon plot py but adds a new one right click and drag to move a polygon around source polygon move py 112 Contents Chaco Documentation Release 3 0 0 Polygon Plot Polygon Plot 9 40 regression py Demonstrates the Regression Selection tool Hold down the left mouse button to use the mouse to draw a selection region around some points and a line fit is drawn through the center of the points The parameters of the line are displayed at the bottom of the plot region You can do this repeatedly to draw different regions source regression py Contents 113 Chaco Documentation Release
5. 3 1 8 Image Plot Image plots can be created in a similar fashion class ImagePlot HasTraits plot Instance Plot traits view View Item plot editor ComponentEditor show label False width 500 height 500 resizable True title Chaco Plot def init self x linspace 0 10 50 y linspace 0 5 50 xgrid ygrid meshgrid x y 24 Contents Chaco Documentation Release 3 0 0 2 exp xgrid xgridtygrid ygrid 100 plotdata ArrayPlotData imagedata z plot Plot plotdata plot img plot imagedata xbounds x ybounds y colormap jet self plot plot if name main ImagePlot configure traits There a few more steps to create the input Z data and we also call a different method on the Plot img plot instead of plot The details of the method parameters are not that important right now this is just to demonstrate how we can apply the same basic pattern from the first plot example above to do other kinds of plots Chaco Plot Contents 25 Chaco Documentation Release 3 0 0 3 1 9 A Slight Modification Earlier it was mentioned that the Plot object is both a container of renderers and a factory or generator of renderers This modification of the previous example illustrates this point We only create a single instance of Plot but we call its plot method twice Each call creates a new renderer and adds it to the P1ot s list of renderers A
6. 68 Contents Chaco Documentation Release 3 0 0 8 3 Mappers 8 3 1 AbstractMapper class Abst ractMapper Bases enthought traits has traits HasTraits Defines an abstract mapping from a region in input space to a region in output space Traits updated Event A generic update event that generally means that anything that relies on this mapper for visual output should do a redraw or repaint map data screen val map data screen val gt data val Maps values from screen space into data space map data array screen vals map data array screen vals gt data vals Maps an array of values from screen space into data space By default this method just loops over the points calling map data on each one For vectorizable mapping functions override this implmentation with a faster one map screen data array map screen data array gt screen array Maps values from data space into screen space 8 3 2 BaselDMapper class Base1DMapper Bases enthought chaco abstract mapper AbstractMapper Defines an abstract mapping from a 1 D region in input space to a 1 D region in output space Traits range Instance DataRangelD The data space bounds of the mapper low pos Float 0 0 The screen space position of the lower bound of the data space high pos Float 1 0 The screen space position of the upper bound of the data space screen bounds Property Convenience property to get low and high positions in one struct
7. ArrayPlotData x x y y plot Plot plotdata plot plot x y type line color blue plot title sin x x 3 self plot plot if name main LinePlot configure traits This produces a plot similar to the previous script oriented code snippet 20 Contents Chaco Documentation Release 3 0 0 sin x x 3 So this is our first real Chaco plot We ll walk through this code and look at what each bit does This example serves as the basis for many of the later examples 3 1 6 Understanding the First Plot Let s start with the basics First we declare a class to represent our plot called LinePlot class LinePlot HasTraits plot Instance Plot This class uses the Enthought Traits package and all of our objects subclass from HasTraits Next we declare a Traits UI View for this class Contents 21 Chaco Documentation Release 3 0 0 traits view View Item plot editor ComponentEditor show label False width 500 height 500 resizable True title Chaco Plot Inside this view we are placing a reference to the plot trait and telling Traits UI to use the ComponentEditor to display it If the trait were an Int or Str or Float Traits can automatically pick an appropriate GUI element to display it Since Traits UI doesn t natively know how to display Chaco components we explicitly tell it what kind of editor to use The other parameters in the View cons
8. O OverlayPlotContainer class 71 P PointDataSource class 62 H refresh DataRangelD method 67 refresh DataRange2D method 68 remove BaseDataRange method 66 remove mask ArrayDataSource method 61 reset DataRangelD method 67 reset DataRange2D method 68 reverse map ArrayDataSource method 61 reverse map PointDataSource method 63 S scale_tracking_amount DataRange1D method 67 set_bounds AbstractDataRange method 66 set bounds DataRangelD method 67 set bounds DataRange2D method 68 set data ArrayDataSource method 61 set data GridDataSource method 63 set data ImageData method 65 set data MultiArrayDataSource method 62 set default tracking amount DataRangelD method 67 set_mask ArrayDataSource method 61 set tracking amount DataRangelD method 67 SizePrefs GridPlotContainer method 73 V VPlotContainer class 72 126 Index
9. Press Z and then draw a box region to zoom in on There is no box based zoom out Press Ctrl Left and Ctrl Right to go back and forward in your zoom box history Drag hold down the right mouse button and drag the mouse up or down Up zooms in and down zooms out For any of the above press Escape to resets the zoom to the original view To move the legend hold down the right mouse button inside the legend and drag it around Note that you can move the legend outside of the plot area To exit the plot click the close window button on the window frame Windows Linux or choose the Quit option on the Python menu on Mac Alternatively can you press Ctrl C in the terminal You can run most of the examples in the top level examples directory the examples basic directory and the examples shell directory The examples advanced directory has some examples that may or may not work on your system Contents 5 Chaco Documentation Release 3 0 0 spectrum py requires that you have PyAudio installed and a working microphone data cube py needs to download about 7 3mb of data from the Internet the first time it is executed so you must have a working Internet connection Once the data is downloaded you can save it so you can run the example offline in the future For detailed information about each built in example see the Annotated Examples section 1 2 2 IPython While all of the Chaco examples can be launche
10. data Returns a tuple of indices for the start and end of the first run of data that falls within the range Implements AbstractDataRange clip_data data Returns a list of data values that are within the range Implements AbstractDataRange mask_data data Returns a mask array indicating whether values in the given array are inside the range Implements AbstractDataRange refresh If any of the bounds is auto this method refreshes the actual low and high values from the set of the view filters data sources reset Resets the bounds of this range based on default_state scale_tracking_amount multiplier Sets the tracking amount to a new value scaled by multiplier set bounds low high Sets all the bounds of the range simultaneously Implements AbstractDataRange set default tracking amount amount Sets the default tracking amount to a new value amount set tracking amount amount Sets the tracking amount to a new value amount Contents 67 Chaco Documentation Release 3 0 0 8 2 4 DataRange2D class DataRange2D args kwargs Bases enthought chaco base data range BaseDataRange A range on 2 D image data In a mathematically general sense a 2 D range is an arbitrary region in the plane Arbitrary regions are difficult to implement well so this class supports only rectangular regions for now Traits low Property The actual value of the lower bound of this range To set it use low setting hi
11. depends on temperature attraction totVolume def volume default self return arange 1 100 Pressure is recalculated whenever one of the elements the property depends on changes No need to use on trait change def get pressure self return self r constant self temperature self volume self totVolume self attraction self volumexself volume You now no longer have to call an inconvenient calculation function before the first call to configure_traits 3 2 9 Source Code The final version on the program vanderwaals py from enthought traits api import HasTraits Array Range Float Enum on_trait_change Property from enthought traits ui api import View Item from enthought chaco chaco plot editor import ChacoPlotItem from numpy import arange class Data HasTraits volume Array pressure Property Array depends on temperature attraction totVolume attraction Range low 50 0 high 50 0 value 0 0 totVolume Range low 01 high 100 0 value 0 01 Contents 41 Chaco Documentation Release 3 0 0 temperature Range low 50 0 high 50 0 value 50 0 r constant Float 8 314472 plot type Enum line scatter traits view View ChacoPlotItem volume pressure type trait plot type resizable True x label 2 Volume y label 2 Pressure x bounds 10 120 x auto False y bounds 2000 4000 y auto False color blue b
12. 3 0 0 9 41 scatter py Draws a simple scatterplot of a set of random points source scatter py 114 Contents Chaco Documentation Release 3 0 0 Scatter Plot 9 42 scatter inspector py Example of using tooltips on Chaco plots ource scatter inspector py Contents 115 Chaco Documentation Release 3 0 0 Scatter Inspector Demo 9 43 scatter select py Draws a simple scatterplot of random data The only interaction available is the lasso selector which allows you to circle a set of points Upon completion of the lasso operation the indices of the selected points are printed to the console source scatter select py 116 Contents Chaco Documentation Release 3 0 0 Scatter Plot With Selection console output New selection 789 799 819 830 835 836 851 867 892 901 902 909 2913 924 929 931 933 938 956 971 972 975 976 996 999 1011 1014 1016 1021 1030 1045 1049 1058 1061 1073 1086 1087 1088 9 44 scrollbar py Draws some x y line and scatter plots source scrollbar py Contents 117 Chaco Documentation Release 3 0 0 9 45 tabbed plots py Draws some x y line and scatter plots source tabbed plots py 118 Contents Chaco Documentation Release 3 0 0 9 46 traits editor py This example creates a simple 1 D function examiner illustrating the use of ChacoPlotEditors for displaying simple plot relations as well as Tr
13. 90 OLI estuduoal plokpy ss p uei Bk ce ug ey EUM c gs 91 918 data cubecpy 14 44 4464 54544200 00 e605 04054 25445 2 be Set 92 O19 data stream DY coc gue Ree eee wee Ace ee OSE Re eee oe 93 9 20 scalar_image function inspectorepy ss s seag ye ob ORA BEE Y ee 94 921 Spectrum pu caedes eee Gate poe NUR bs et s rp e 95 0 22 image Plot Py sesa cde eed oe eee EAS EE ON Ro eee eee ERAS 96 9 23 cmap image Select PY sce m map Ee RS OS ee 97 0 24 cmap scatter Py c do XL Re e oed c deck a a a UR 98 925 PY de ERU TREO BR UR IR dA TR Re 99 9026 contour plot pDV 100 027 grid container py x xe etre ate ek doin aba Rapide Spe BA SK d Rod dene i 101 9 28 grid container aspect ratio xis eee Rowe oR ee RON dox Le eue 102 929 image from file py cS e pup Bes ope BA DEE OSHS eR S 103 9 30 image Inspector PY sudo aem ok odo OU E 104 9 3 amage d Robo e 8 105 O32 INSEE PLOC 5 68s see or GUERRE EVA UBRO BORN RUE RE See ERE UE E 106 0 39 Mane wow Qo X os QU S ee Dec e T XO m RO RO e 107 034 line ploti py 3 Re o ee BRT UR Re are 108 935 line plot hola py
14. Event Event that fires when the data values change bounds changed Event Event that fires when just the bounds change metadata changed Event Event that fires when metadata structure is changed persist data Bool True Should the data that this datasource refers to be serialized when the datasource is seri alized get bounds gt tuple min max Returns a tuple min max of the bounding values for the data source In the case of 2 D data min and 59 Chaco Documentation Release 3 0 0 max are 2 D points that represent the bounding corners of a rectangle enclosing the data set Note that these values are not view dependent but represent intrinsic properties of the data source If data is the empty set then the min and max vals are 0 0 get data get data gt data array Returns a data array of the dimensions of the data source This data array must not be altered in place and the caller must assume it is read only This data is contiguous and not masked In the case of structured gridded 2 D data this method may return two 1 D ArrayDataSources as an optimization get data mask gt data array mask array Returns the full raw source data array and a corresponding binary mask array Treat both arrays as read only The mask is a superposition of the masks of all upstream data sources The length of the returned array may be much larger than what get size returns the unmasked portion however match
15. PlotAxis Legend Data handling classes that wrap input data interface with application specific data sources and transform coor dinates between data and screen space e g ArrayDataSource GridDataSource LinearMapper Tools that handle keyboard or mouse events and modify other components e g PanTool ZoomTool ScatterInspector Contents 47 48 CHAPTER FIVE Commonly Used Modules and Classes 5 1 Base Classes Plot Component All visual components in Chaco subclass from PlotComponent It defines all of the common visual attributes like background color border styles and color and whether the component is visible Actually most of these visual attributes are inherited from the Enable drawing framework More importantly it provides the base behaviors for participating in layout handling event dispatch to tools and overlays and drawing various layers in the correct order Subclasses almost never need to override or customize these base behaviors but if they do there are several easy extension points 5 2 Data Objects 5 2 1 Data Source A data source is a wrapper object for the actual data that it will be handling It provides methods for retrieving data estimating a size of the dataset indications about the dimensionality of the data a place for metadata such as selections and annotations and events that fire when the data gets changed There are two primary reasons for a data source class t provi
16. api import ColorTraits from enthought chaco api import marker trait class ScatterPlotTraits HasTraits plot Instance Plot color ColorTrait blue marker marker trait marker size Int 4 traits view View Group Item color label Color style custom Item marker label Marker Item marker_size label Size Item plot ditor ComponentEditor show_label False orientation vertical width 800 height 600 resizable True title Chaco Plot 34 Contents Chaco Documentation Release 3 0 0 def init self x linspace 14 14 100 y sin x x 3 plotdata ArrayPlotData x x y plot Plot plotdata y self renderer plot plot x y type scatter color blue 0 self plot plot def _color_changed self self renderer color self color def _marker_changed self self renderer marker self marker def _marker_size_changed self self renderer marker_size self marker_size af name main ScatterPlotTraits configure traits Let s step through the changes First we add traits for color marker type and marker size class ScatterPlotTraits HasTraits plot Instance Plot color ColorTrait blue marker marker_trait marker size Int 4 We re also going to change our Traits UI View to include references to these new traits We ll put them in a Traits UI Group so that we can control
17. be nested BasePlotContainer implements the logic to correctly render and dispatch events to sub components while its subclasses implement the different layout calculations Chaco currently has three types of containers described in the following sections Interface BasePlotContainer Subclasses OverlayPlotContainer HPlotContainer VPlotContainer GridPlotContainer 5 4 Renderers 5 5 Tools 5 6 Overlays 5 7 Miscellaneous 50 Contents Note This section is currently under active development 6 1 Ba How do I sics render data to an image file def save plot plot filename plot outer bounds width height plot do layout force True gc PlotGraphicsContext siz dpi 72 gc render component width height gc save filename render data to screen integrate a Chaco plot into my WX app integrate a Chaco plot into my Traits UI CHAPTER SIX How Do 2 width height import numpy from enthought chaco api import Plot ArrayPlotData from enthought enable enable component import EnableComponent from enthought traits api import HasTraits Instance from enthought traits ui api import Item View class MyPlot HasTraits plot Instance Plot traits view View Item plot ditor ComponentEditor def __ init self index data series super MyPlot self init kw index plot data plot data self plot self plot set data data series Plot plo
18. data Implements AbstractDataSource get data mask gt data array mask array Implements AbstractDataSource 60 Contents Chaco Documentation Release 3 0 0 get size get size gt int Implements AbstractDataSource is masked is masked gt bool Implements AbstractDataSource remove mask Removes the mask on this data source reverse map pt index 0 outside returns none True Returns the index of pt in the data source Parameters pt scalar value value to find index ignored for data series with 1 D indices outside returns none Boolean Whether the method returns None if pt is outside the range of the data source if False the method returns the value of the bound that pt is outside of set data newdata sort order None Sets the data and optionally the sort order for this data source Parameters newdata array The data to use sort order SortOrderTrait The sort order of the data set mask mask Sets the mask for this data source 8 1 3 MultiArrayDataSource class MultiArrayDataSource data array dtype float64 sort order ascending traits Bases enthought chaco abstract data source AbstractDataSource A data source representing a single continuous array of numerical data of potentially more than one dimension This class does not listen to the array for value changes To implement such behavior define a subclass that hooks up the appropriate listeners Traits index d
19. fire a trait change whenever our depends on traits are changed Meanwhile the Chaco plot is automatically listening to the pressure variable so the plot display will get the 40 Contents Chaco Documentation Release 3 0 0 new value of pressure whenever someone changes the input parameters When the value of a Property trait is requested the get trait name method is called to calculate and return its current value so we define use the get pressure method as our new calculation method It is important to note that this implementation does have a weakness Since we are calculating new pressures each time someone changes the value of the input variables this could slow down the program if your calculation is long When the user drags a slider widget each stopping point along the slider will request a recompute For the new implementation these are the necessary changes 1 Define the Y coordinate array variable as a Property instead of an Array 2 Perform the calculations in the get trait method for the Y coordinate array variable which will be _get_pressure in this example 3 Define the trait default method to set the initial value of the X coordinate array so get pressure does not have to keep recalculating it 4 Remove the previous trait change decorator and calculation method The new pieces of code to add to the Data class are class Data HasTraits pressure Property Array
20. sort_order none kwargs Bases enthought chaco abstract_data_source AbstractDataSource Implements a structured gridded 2 D data source suitable as an index for an image for example Traits index dimension Constant image The dimensionality of the indices into this data source overrides AbstractDataSource value dimension Constant scalar The dimensionality of the value at each index point overrides AbstractDataSource sort order Tuple SortOrderTrait SortOrderTrait The sort order of the data overrides AbstractDataSource There is no overall sort order on 2 D data but for gridded 2 D data each axis can have a sort order get bounds gt URx URy Implements AbstractDataSource Returns two 2 D points min and max that represent the bounding cor ners of a rectangle enclosing the data set Note that these values are not view dependent but represent intrinsic properties of the DataSource If data axis is the empty set then the min and max valuess are 0 0 get data gt Implements AbstractDataSource Because this class uses structured gridded data this method returns the pair of data axes instead of for example a full mesh grid This behavious differs from other data sources set data xdata sort order None Sets the data and optionally the sort order for this data source Parameters xdata ydata array The data to use so
21. the exam ples are located in the examples subdirectory inside the root of the Chaco source tree next to docs and the enthought directories If you installed Chaco as a binary egg from PyPI for your platform or if you happen to be on a machine with Chaco installed but you don t know the exact installation mechanism then you will need to download the examples separately using Subversion ETS 3 0 or Chaco 3 0 svn co https svn enthought com svn enthought Chaco tags 3 0 0 examples ETS 2 8 or Chaco 2 0 x svn co https svn enthought com svn enthought Chaco tags enthought chaco2_2 0 5 examples Almost all of the Chaco examples are stand alone files that can be run individually from any location All of the following instructions that involve the command line assume that you are in the same directory as the examples 1 2 1 Command line Run the simple line example python simple line py This opens a plot of several Bessel functions and a legend 4 Contents Chaco Documentation Release 3 0 0 Bessel functions Besselj 0 1 E Besselj 2 Besselj 3 E Besselj 4 Besselj 5 E Besselj 6 Bessel j 7 E Besselj 8 You can interact with the plot in several ways To pan the plot hold down the left mouse button inside the plot area but not on the legend and drag the mouse To zoom the plot Mouse wheel scroll up to zoom in and scroll down to zoom out 66 52 Zoom box
22. the layout in the dialog a little better here we re setting the layout orientation of the elements in the dialog to vertical traits view View Group Item color label Color style custom Item marker label Marker Item marker size label Size Item plot ditor ComponentEditor show label False orientation vertical width 500 height 500 resizable True title Chaco Plot Now we have to do something with those traits We re going to modify the constructor so that we grab a handle to the renderer that is created by the call to plot self renderer plot plot x y type scatter color blue 0 Recall that the Plot is a container for renderers and a factory for them When called its plot method returns a list of the renderers that the call created In previous examples we ve been just ignoring or discarding the return value since we had no use for it In this case however we re going to grab a reference to that renderer so that we can modify its attributes in later methods Contents 35 Chaco Documentation Release 3 0 0 The plot method returns a list of renderers because for some values of the t ype argument it will create multiple renderers In our case here we are just doing a scatter plot and this creates just a single renderer Next we are going to define some Traits event handlers These are specially named methods that get called whe
23. update the pressure data array and cause the plot to update showing the new values Contents 39 Chaco Documentation Release 3 0 0 3 2 7 Screenshots Here is what the program looks like 50 Volume Attraction 0 37 58 Tot volume 00T 0 77 3222 Temperature 50 077 0 40 4 R constant 8 314472 Plot type line OK 3 2 8 But it could be better It seems inconvenient to have to call a calculation function manually before we configure traits Also the pressure equation depends on the values of other variables it would be nice to make the relationship between the dependant and independent variables clearer There is another way we could define our variables that is easier for the user and provides better source documentation Since our X values remain constant in this example it is wasteful to keep recreating the volume array The Y array pressure is the single array that needs to be updated when the independent variables change So instead of defining pressure as an Array we will define it as a Property Property is a Traits type that allows you to define a variable whose value is recalculated whenever it is requested In addition when the depends on argument of a Property constructor is set to list of traits in your HasTraits class the property s trait events will fire whenever any of the dependent trait s change events fire This means that the pressure variable will
24. we created previously plot Plot plotdata Chaco s Plot object serves two roles it is both a container of renderers which are the objects that do the actual task of transformining data into lines and markers and colors on the screen and it is a factory for instantiating renderers Once you get more familiar with Chaco you can choose to not use the Plot object and instead directly create renderers and containers manually Nonetheless the Plot object does a lot of nice housekeeping that is useful in a large majority of use cases Next we call the plot method on the Plot object we just created plot plot x y type line color blue ee o This creates a blue line plot of the data items named x and y Note that we are not passing in an actual array here we are passing in the names of arrays in the ArrayPlotData we created previously This method call creates a new renderer in this case a line renderer and adds it to the Plot This may seem kind of redundant or roundabout to folks who are used to passing in a pile of numpy arrays to a plot function but consider this ArrayPlotData objects can be shared between multiply Plots If you wanted several different plots of the same data you don t have to externally keep track of which plots are holding on to identical copies of what data and then remember to shove in new data into every single one of those plots The ArrayPlotData acts almost like a symlink between con
25. Chaco Documentation Release 3 0 0 Enthought October 07 2008 Quickstart 1 1 Installation Overview ss cR RS 1 2 Running Some Examples 13 Cr ating a Plot uem aoea BRE 1 4 Further Reading Installing and Building Chaco 2 1 Installing via EPD 2 o mE RR 2 4 easyomstall eec zl RR Rx 2 3 Building from Source Tutorials 3 1 Interactive Plotting with Chaco 3 2 Modelling Van Der Waal s Equation With Chaco 33 3 4 Exploring Chaco with IPython Architecture Overview 4 Core Ideas 4 2 The Relationship Between Chaco Enable and Kiva Commonly Used Modules and Classes 9 1 ok R be c RR Bed 3 2 DataObjects oo pa ossoa m e 5 9 Containers 4x3 Owe wR ea xS SA Renderers lt 4 eh ee woh 2 9 Too 45 ety ng ummy deduc x 5 6 Overlays uen et Sif Miscellaneous Gem x3 y xS How Do I 61 oo 24406 544 66446 3 6 2 Layout and Rendering 63 Writing Components 6 4 Advanced Frequently Asked Questions 7 Where does the name Chaco come from 7 2 Why was Chaco named Chaco fora while 7 3 What are the pros and cons of Chaco vs matplotlib CONTENTS 55 55 8 Programmer s Reference 59 9 S l ean bdeiadeige
26. actDataRange sources kwargs Bases enthought traits has traits HasTraits Abstract class for ranges that represent sub regions of data space They support autoscaling by querying their associated data sources Traits sources List Instance AbstractDataSource The list of data sources to which this range responds low Float 0 0 The actual value of the lower bound of this range To set it use low setting Setting this attribute directly just calls the setter for low setting Although the default value is specified as 0 0 subclasses can redefine the default Also subclasses can redefined the type to correspond to their dimensionality high Float 1 0 The actual value of the upper bound of this range To set it use high setting Setting this attribute directly just calls the setter for high setting Although the default value is specified as 1 0 subclasses can redefine the default Also subclasses can redefined the type to correspond to their dimensionality low setting Trait auto auto Float Setting for the lower bound of this range high setting Trait auto auto Float Setting for the upper bound of this range updated Event Event that is fired when the actual bounds values change the value of the event is a tuple low bound high bound bound data data Returns a tuple of indices for the start and end of the first run of data that falls within the range Given an array of data valu
27. aits UI integration Any 1 D numpy scipy special function works in the function text box source traits editor py Contents 119 Chaco Documentation Release 3 0 0 Plot type Eq sin x 9 47 zoomable colorbar py Draws a colormapped scatterplot of some random data Interactions on the plot are the same as for simple line py and additionally pan and zoom are available on the colorbar Left click pans the colorbar s data region Right click drag selects a zoom range Mousewheel up and down zoom in and out on the data bounds of the color bar source zoomable colorbar py 120 Contents Chaco Documentation Release 3 0 0 9 48 zoomed plot The main executable file for the zoom plot demo Right click and drag on the upper plot to select a region to view in detail in the lower plot The selected region can be moved around by dragging or resized by clicking on one of its edges and dragging source zoomed plot Contents 121 Chaco Documentation Release 3 0 0 122 Contents CHAPTER TEN Tech Notes This section compiles some of the more detailed architecture level notes and discussions from the mailing list Much of the information here will eventually find its way into the User Manual or the Reference Manual 10 1 About the Chaco Scales package In the summer of 2007 I spent a few weeks working through the axis ticking and labelling problem The basic goal was that I wanted to create a fl
28. al microseconds up to days and weeks it also handles non uniform calendar units like day of the month and month of the year So you can tell Chaco to generate ticks on the 1st of every month and it will give you non uniformly spaced tick and grid lines The scale system mechanism is configurable so although all of the examples use the CalendarScaleSystem you don t have to use it In fact if you look at CalendarScaleSystem init it just initializes its list of scales with HMSScales MDYScales HMSScales TimeScale microseconds 1 TimeScale milliseconds 1 TimeScale seconds dt for dt in 1 5 15 30 TimeScale minutes dt for dt in 1 5 15 30 TimeScale hours dt for dt in 1 2 3 4 6 12 24 MDYScales TimeScale day of month range 1 31 3 123 Chaco Documentation Release 3 0 0 TimeScale day of month 1 8 15 22 r imeScale day of month 1 15 TimeScale month of year range 1 13 TimeScale month of year range 1 13 3 TimeScale month of year 1 7 TimeScale month of year 1 So if you wanted to create your own ScaleSystem with days weeks and whatnot you could do ExtendedScales HSMScales TimeScale days n for n in 1 7 14 28 yScaleSystem CalendarScaleSystem ExtendedScales To use the Scales package in your Chaco plots just import PlotAxis from chaco2 scales axis instead of chaco2 axis You will still n
29. antly Enable defines the concept of components and containers that form the foundation of Chaco s architecture In the Enable model the top most Window object is responsible for dispatching events and drawing a single component Usually this component is a container with other containers and components inside it The container can perform layout on its internal components and it controls how events are subsequently dispatched to its set of components 4 2 2 Enable Almost every graphical component in Chaco is an instance of an Enable component or container We re currently trying to push more of the layout system implemented as the various different kinds of Chaco plot containers down into Enable but as things currently stand you have to use Chaco containers if you want to get layout The general trend has been that we implement some nifty new thing in Chaco and then realize that it is a more general tool or overlay that will be useful for other non plotting visual applications We then move it into Enable and if there are plotting specific aspects of it we will create an appropriate subclass in Chaco to encapsulate that behavior The sorts of applications that can and should be done at the Enable level include things like a visual programming canvas or a vector drawing tool There is nothing at the Enable level that understands the concept of mapping between a data space to screen space and vice versa Although there has been some de
30. ata space and a simple line fit is applied to the selected points The equation of the line is then displayed in a text label The lasso selection tool and regression overlay are both built in to Chaco but they serve an additional purpose of demonstrating how one can build complex data centric interactions and displays on top of the Chaco framework Contents 17 Chaco Documentation Release 3 0 0 0 75 0 5 0 25 0 0 25 0 50 75 This is a much more complex demonstration of Chaco s capabilities The user can view the cross sections of a 2D scalar valued function The cross sections update in real time as the user moves the mouse and the bubble on each line plot represents the location of the cursor along that dimension By using drop down menus not show here the user can change plot attributes like the colormap and the number of contour levels used in the center plot as well as the actual function being plotted 3 1 4 Script oriented Plotting We distinguish between static plots and interactive visualizations because these different applications of a library affect the structure of how the library is written as well as the code you write to use the library Here is a simple example of the script oriented approach for creating a static plot This is probably familiar to anyone who has used Gnuplot MATLAB or Matplotlib from numpy import x from enthought chaco shell import y 1 2 2
31. bars Likewise plot type will be shown as a drop down menu since it is defined as an Enum Contents 37 Chaco Documentation Release 3 0 0 4 We ll also import a few things to be used later from enthought traits api import HasTraits Array Range Float Enum on trait change Property from enthought traits ui api import View Item from enthought chaco chaco plot editor import ChacoPlotItem from numpy import arange class Data HasTraits volume Array pressure Array attraction Range low 50 0 high 50 0 value 0 0 totVolume Range low 01 high 100 0 value 0 01 temperature Range low 50 0 high 50 0 value 50 0 r_constant Float 8 314472 plot_type Enum line scatter 3 2 4 Creating the View The main GUI window is created by defining a Traits View instance This View contains all of the GUI elements including the plot To link a variable with a widget element on the GUI we create a Traits Item instance with the same name as the variable and pass it as an argument of the Traits View instance declaration The Traits UI user manual discusses the View and Item objects in depth In order to embed a Chaco plot into a Traits View you need to import the ChacoPlotItem which can be passed as a parameter to View just like a the Item objects The first two arguments to ChacoPlotItem are the lists of X and Y coordinates for the graph The variables volume and pressure hold the lists of X and Y coor
32. bate about the incorporating rudimentary mapping into Enable for the time being if you want some kind of canvas like thing to model more than just pixel space on the screen implement it using the mechanisms in Chaco The way that Enable hooks up to the underlying GUI toolkit system is via an enable Window object Each toolkit has its own implementation of this object and they all subclass from enable AbstractWindow They usually contain an instance of the GUI toolkit s specific window object whether it s a wx Window Qt QWidget or pyglet window Window This instance is created upon initialization of the enable Window and stored as the control attribute on the Enable window From the perspective of the GUI toolkit an opaque widget gets created and stuck inside a parent control or dialog or frame or window This instance serves as a proxy between the GUI toolkit and the world of Enable When the user clicks inside the widget area the cont rol widget calls a method on the enable Window object which then in turn can dispatch the event down the stack of Enable containers and components When the system tells the widget to draw itself e g as the result of a PAINT or EXPOSE event from the OS the enable Window is responsible for creating an appropriate Kiva GraphicsContext GC then passing it down through the object hierarchy so that everyone gets a chance to draw After all the components have drawn onto the GC for the AGG based bitmap backen
33. class BasePlotContainer components traits Bases enthought enable container Container A container for PlotComponents that conforms to being laid out by PlotFrames Serves as the base class for other PlotContainers PlotContainers define a layout i e a spatial relationship between their contained components BasePlotCon tainer doesn t define one but its various subclasses do BasePlotContainer is a subclass of Enable Container so it is possible to insert Enable level components into it However because Enable components don t have the correct interfaces to participate in layout the visual results will probably be incorrect Traits container under layers Tuple background image underlay plot Redefine the container layers to name the main layer as plot instead of the Enable default of mainlayer draw order Instance list argssc DEFAULT DRAWING ORDER draw layer Str plot use draw order Bool True Deprecated flag to indicate that a component needed to do old style drawing Unused by any recent Chaco component plot components Property Deprecated property for accessing the components in the container 8 4 2 OverlayPlotContainer class OverlayPlotContainer components traits Bases enthought chaco base plot container BasePlotContainer A plot container that stretches all its components to fit within its space All of its components must therefore be resizable Tra
34. d Swig 1 3 30 and wxPython 2 8 If you are on OS X you also need a recent Pyrex 2 3 1 Obtaining the source You can get Chaco and its dependencies from PyPI as source tarballs or you can download the source directly from Enthought s Subversion server The URL is https svn enthought com svn enthought Chaco trunk Note This build instructions section is currently under construction Please see the ETS Install From Source wiki page for more information on building Chaco and the rest of ETS on your platform 12 Contents CHAPTER THREE Tutorials Note 8 28 08 This section is currently being updated to unify the information from several past presentations and tutorials Until it is complete here are links to some of those The HTML versions are built using S5 which uses Javascript heavily You can navigate the slide deck by using left and right arrows as well as a drop down box in the lower right hand corner e SciPy 2006 Tutorial Also available in pdf Pycon 2007 presentation slides e SciPy 2008 Tutorial slides pdf These slides are currently being converted into the Interactive Plotting with Chaco tutorial 3 1 Interactive Plotting with Chaco 3 1 1 Overview This tutorial is an introduction to Chaco We re going to build several mini applications of increasing capability and complexity Chaco was designed to be used primarily by scientific programmers and this tutorial only requires basic familiarity with Py
35. d from the command line using the standard Python interpreter if you have IPython installed you can poke around them in a more interactive fashion Chaco provides a subpackage currently named the Chaco Shell for doing command line plotting like Matlab or Matplotlib The examples in examples shel11 directory use this subpackage and they are particularly amenable to exploration with IPython The first example we ll look at is the 1ines py example First we ll run it using the standard Python interpreter python lines py This shows two overlapping line plots simple line plots 6 Contents Chaco Documentation Release 3 0 0 You can interact with the plot in the following ways To pan the plot hold down the left mouse button inside the plot area and dragging the mouse To zoom the plot Mouse wheel scroll up zooms in and scroll down zooms out Zoom box hold down the right mouse button and then draw a box region to zoom in on There is no box based zoom out Press Ctrl Left and Ctrl Right to go back and forward in your zoom box history For either of the above press Escape to reset the zoom to the original view Now exit the plot and start Python with the wthread option ipython wthread This tells Python to start a wxPython mainloop in a background thread Now run the previous example again In 1 run lines py This displays the plot window but gives you another Python pro
36. data_cube py 92 Contents Chaco Documentation Release 3 0 0 eoo Cube analyzer 0 20 40 60 100 0 50 100 150 200 250 9 19 data stream py This demo shows how Chaco and Traits can be used to easily build a data acquisition and visualization system Two frames are opened one has the plot and allows configuration of various plot properties and one which simulates controls for the hardware device from which the data is being acquired in this case it is a mockup random number generator whose mean and standard deviation TODO Sentence incomplete source data stream py Contents 93 Chaco Documentation Release 3 0 0 000 Edit properties 2 Mean 0 0 Stddev 1 0 Max num points 100 Plo 2 80 100 120 140 160 Time Plot type line 4 OK 9 20 scalar image function inspector py Renders a colormapped image of a scalar value field and a cross section chosen by a line interactor source scalar image function inspector py 94 Contents Chaco Documentation Release 3 0 0 0 750 5 0 25 0 0 25 0 50 75 9 21 spectrum py This plot displays the audio spectrum from the microphone source spectrum py Contents 95 Chaco Documentation Release 3 0 0 Spectrogram 1000 2000 3000 4000 5000 T 004 0 06 Frequency hz Time seconds 9 22 cmap image plot py Draws a colormapped image plot source image plot py 96 Cont
37. des a way for different plotting objects to reference the same data It defines the interface for embedding Chaco into an existing application In most cases the standard Array DataSource will suffice Interface AbstractDataSource Subclasses ArrayDataSource MultiArrayDataSource PointDataSource GridDataSource ImageData 5 2 2 Data Range A data range expresses bounds on data space of some dimensionality The simplest data range is just a set of two scalars representing low high bounds in 1 D One of the important aspects of DataRanges is that their bounds can be set to auto which means that they automatically scale to fit their associated datasources Each DataSource can be associated with multiple ranges and each DataRange can be associated with multiple datasources Interface AbstractDataRange 49 Chaco Documentation Release 3 0 0 Subclasses BaseDataRange DataRangelD DataRange2D 5 2 3 Mapper Mappers perform the job of mapping a data space region to screen space and vice versa Interface AbstractMapper Subclasses BaselDMapper LinearMapper LogMapper GridMapper PolarMapper 5 3 Containers 5 3 1 PlotContainer PlotContainers are Chaco s way of handling layout Because they logically partition the screen space they also serve as a Way for efficient event dispatch They are very similar to sizers or layout grids in GUI toolkits like WX Containers are subclasses of PlotComponent thus allowing them to
38. dinates and therefore are the first two arguments to the Chaco2PlotItem Other parameters have been provided to the plot for additional customization class Data HasTraits traits_view View ChacoPlotItem volume pressure type_trait plot_type resizable True x_label Volume y_label Pressure x_bounds 10 120 x_auto False y bounds 2000 4000 y auto False color blue bgcolor white border_visible True border_width 1 title Pressure vs Volume padding_bg_color lightgray Item name attraction Item name totVolume Item name temperature Item name r constant style readonly Item name plot type resizable True buttons OK title Van der waal Equation 38 Contents Chaco Documentation Release 3 0 0 width 900 height 800 3 2 5 Updating the Plot The power of Traits and Chaco enable the plot to update itself whenever the X or Y arrays are changed So we need a function to re calculate the X and Y coordinate lists whenever the input parameters are changed by the user moving the sliders in the GUI The volume variable is the independent variable and pressure is the dependent variable The relationship between pressure and volume as derived from the equation found on the wiki page is r constant Temperature attraction Pressur Volume totVolume Volumex 2 Next there are two programing tasks to complete 1 Define tra
39. ds the enable Window object is responsible for blitting the rastered off screen buffer of the GC into the actual widget s space on the screen For Kiva s OpenGL backend there is no final blit since calls to the GC render in immediate mode in the window s active OpenGL context but the idea is the same and the enable Window object does perform initialization on the GL GraphicsContext Some of the advantages to using Enable are that it makes mouse and key events from disparate windowing systems all share the same kind of signature and be accessible via the same name So if you write bare wxPython and handle a key pressed event in wx this might generate a value of wx WXK BACK Using Enable you would just get a key back and its value would be the string Backspace and this would hold true on Qt4 and Pyglet Almost all of the event handling and rendering code in Chaco is identical under all of the backends there are very few backend specific changes that need to be handled at the Chaco level The enable Window object has a reference to a single top level graphical component which includes containers since they are subclasses of component Whenever it gets user input events it recursively dispatches all the way down the potentially nested stack of components Whenever a components wants to signal that it needs to be redrawn it calls self request redraw which ultimately reaches the enable Window which can then make sure it schedule
40. e now since almost the beginning of Enthought as a company Enthought has delivered many applications using these toolkits The Kiva and Enable packages are bundled together in the Enable project 4 2 1 Kiva Kiva is a 2 D vector drawing library for Python It serves a purpose similar to Cairo It allows us to compose vector graphics for display on the screen or for saving to a variety of vector and image file formats To use Kiva a program instantiates a Kiva GraphicsContext object of an appropriate type and then makes drawing calls on it like gc draw_image gc line to and gc show_text Kiva integrates with windowing toolkits like wxWindows and Qt 45 Chaco Documentation Release 3 0 0 and it has an OpenGL backend as well For wxPython and Qt Kiva actually performs a high quality fast software rasterization using the Anti Grain Geometry AGG library For OpenGL Kiva has a python extension that makes native OpenGL calls from C Kiva provides a GraphicsContext for drawing onto the screen or saving out to disk but it provides no mechanism for user input and control For this control layer it would be convenient to have to write only one set of event callbacks or handlers for all the platforms we support and all the toolkits on each platform Enable provides this layer It insulates all the rendering and event handling code in Chaco from the minutiae of each GUI toolkit Additionally and to some extent more import
41. e upper bound is automatically set at or above the maximum of the data 66 Contents Chaco Documentation Release 3 0 0 track The upper bound tracks the lower bound by tracking amount CFloat An explicit value for the upper bound tight bounds Bool True Do auto bounds imply an exact fit to the data If False they pad a little bit of margin on either side bounds func Callable A user supplied function returning the proper bounding interval bounds func takes data low data high margin tight bounds and returns low high margin Float 0 05 The amount of margin to place on either side of the data expressed as a percentage of the full data width epsilon CFloat 1e 20 The minimum percentage difference between low and high That is high low gt epsilon low default tracking amount CFloat 20 0 When either high or low tracks the other track by this amount tracking amount default tracking amount The current tracking amount This value changes with zooming default state Enum auto high_track low track Default tracking state This value is used when self reset is called auto Both bounds reset to auto high track The high bound resets to track and the low bound resets to auto e low_track The low bound resets to track and the high bound resets to auto fit_to_subset Bool False Is this range dependent upon another range bound_data
42. eed to createa ScalesTickGenerator and pass it in The financial plot dates py demo is a good example of how to do this Search Page 124 Contents A AbstractDataRange class 65 AbstractDataSource class 59 AbstractMapper class 69 BaseDataRange method 66 ArrayDataSource class 60 B BaselDMapper class 69 BaseDataRange class 66 BasePlotContainer class 71 bound data AbstractDataRange method 65 bound data DataRangelD method 67 bound data DataRange2D method 68 C clip data AbstractDataRange method 65 clip data DataRangelD method 67 clip data DataRange2D method 68 D DataRangelD class 66 DataRange2D class 68 F fromfile ImageData attribute 64 G get array bounds ImageData method 64 get bounds AbstractDataSource method 59 get bounds ArrayDataSource method 60 get bounds GridDataSource method 63 get bounds ImageData method 64 get bounds MultiArrayDataSource method 61 get data AbstractDataSource method 60 get data ArrayDataSource method 60 get data GridDataSource method 63 get data ImageData method 64 get data MultiArrayDataSource method 62 get data PointDataSource method 63 get data mask AbstractDataSource method 60 INDEX get data mask ArrayDataSource method 60 get data mask MultiArrayDataSource method 62 get height ImageData m
43. el functions E Besselj 0 Besselj 1 E Besselj 2 Bessel j 3 E Besselj 4 Besselj 5 E Besselj 6 Besselj 7 Bessel j_8 You ve actually already seen OverlayPlotContainer the Plot class is actually a special subclass of OverlayPlotContainer All of the plots inside this container appear to share the same X and Y axis but this is not a requirement of the container For instance the following plot shows plots sharing only the X axis Contents 31 Chaco Documentation Release 3 0 0 Multi Y Plot i 25 3 1 11 Using a Container 5 7 5 Containers can have any Chaco componeny added to them The following code creates a separate Plot instance for the scatter plot and the line plot and adds them both to the HPlotContainer class ContainerExample HasTraits plot Instance HPlotContainer traits view View Item plot editor ComponentEditor width 1000 height 600 resizable True def init self x linspace 14 14 100 y sin x x 3 plotdata ArrayPlotData x x y y scatter Plot plotdata scatter plot x y type scatter color blue line Plot plotdata line plot x y type line color blue container HPlotContainer scatter line self plot container This produces the following plot show label False title Chaco Plot 32 Contents Chaco Documentation Release 3 0 0 There are many parame
44. ences Slides and demos from these are described below Currently the examples and the scipy 2006 tutorial are the best ways to get going quickly See http code enthought com projects files chaco_scipy06 chaco_talk html Some tutorial examples were recently added into the examples tutorials scipy2008 directory on the trunk These examples are numbered and introduce concepts one at a time going from a simple line plot to building a custom overlay with its own trait editor and reusing an existing tool from the built in set of tools You can browse them on our SVN server at https svn enthought com enthought browser Chaco trunk examples tutorials scipy2008 1 4 2 API Docs The API docs for Chaco 3 0 in ETS 3 0 are at http code enthought com projects files ETS3_API enthought chaco html The API docs for Chaco2 in ETS 2 7 1 are at http code enthought com projects files ets_api enthought chaco2 html Contents 9 10 CHAPTER TWO Installing and Building Chaco Note 8 28 08 This section is still incomplete For the time being the most up to date information can be found on the ETS Wiki and more specifically the Install pages Chaco is one of the packages in the Enthought Tool Suite It can be installed as part of ETS or as a separate package Even when it is installed as a standalone package it depends on a few other packages 2 1 Installing via EPD Chaco and the rest of ETS are installed as part of the Enthought Py
45. ents Chaco Documentation Release 3 0 0 eoo Basic Colormapped Image Plot My First Image Plot 9 23 cmap image select py Draws a colormapped image plot Selecting colors in the spectrum on the right highlights the corresponding colors in the color map source cmap image select py Contents 97 Chaco Documentation Release 3 0 0 Selectable Image Plot 9 24 cmap scatter py Draws a colormapped scatterplot of some random data Selection works the same as in cmap image select py source cmap scatter py 98 Contents Chaco Documentation Release 3 0 0 9 25 contour cmap plot py Renders some contoured and colormapped images of a scalar value field source countour plot py Contents 99 Chaco Documentation Release 3 0 0 Some contour plots Colormap and contours contour lines Varying 9 26 contour plot py Draws an contour polygon plot with a contour line plot on top source countour plot py 100 Contents Chaco Documentation Release 3 0 0 My First Contour Plot 9 27 grid container py Draws several overlapping line plots source grid container py Contents 101 Chaco Documentation Release 3 0 0 9 28 grid container aspect ratio Similar to grid container py but demonstrates Chaco s capability to used a fixed screen space aspect ratio for plot components source grid containe
46. er Maps values from data space to screen space 8 3 5 GridMapper class GridMapper x linear y_type linear kwargs Bases enthought chaco abstract mapper AbstractMapper Maps a 2 D data space to and from screen space by specifying a 2 tuple in data space or by specifying a pair of screen coordinates The mapper concerns itself only with metric and not with orientation So to flip a screen space orientation swap the appropriate screen space values for x low pos x high pos y low pos and y high pos Traits range Instance DataRange2D The data space bounds of the mapper low pos Float 0 0 The screen space position of the lower bound of the horizontal axis x high pos Float 1 0 The screen space position of the upper bound of the horizontal axis y low pos Float 0 0 The screen space position of the lower bound of the vertical axis y high pos Float 1 0 The screen space position of the upper bound of the vertical axis screen bounds Property Convenience property for low and high positions in one structure Must be a tuple x low pos x high pos low pos high pos 70 Contents Chaco Documentation Release 3 0 0 map data screen pts map data screen pts gt data vals Maps values from screen space into data space map screen data pts map screen data pts gt screen array Maps values from data space into screen space 8 4 Containers 8 4 1 BasePlotContainer
47. eractive visualizations You hook up pieces to build a plot that is then easy to inspect interact with add configuration UIs for using Traits UI etc The layout of plot areas the multiplicity and types of renderers within those windows the appearance and locations of axes etc are all completely configurable since these are all first class objects participating in a visual canvas They can all receive mouse and keyboard events and it s easy to subclass them or attach tools to them to achieve new kinds of behavior We ve tried to make all the plot renderers adhere to a standard interface so that tools and interactors can easily inspect data and map between screen space and data space Once these are all hooked up you can swap out or update the data independently of the plots One of the downsides we had a for a while was that this rich set of objects required the programmer to put several different classes together just to make a basic plot To solve this problem we ve assembled some higher level classes that have the most common behaviors built in by default but which can still be easily customized or extended It s clear to me that this is a good general approach to preserving flexibility while reducing verbosity At this point Chaco is definitely capable of handling a large number of different plotting tasks and a lot of them don t require too much typing or hacking skills Folks will probably require m
48. es of the same dimensionality as the range returns a tuple of indices start end corresponding to the first and last elements of the first run of data that falls within the range For monotonic data this basically returns the first and last elements that fall within the range Using this method is not advised for non monotonic data in that case it returns the first and last elements of the first chunk of data that falls within the range clip data data Returns a list of data values that are within the range Given an array of data values of the same dimensionality as the range returns a list of data values that are inside the range Contents 65 Chaco Documentation Release 3 0 0 mask data data Returns a mask array indicating whether values in the given array are inside the range Given an array of data values of the same dimensionality as the range this method returns a mask array of the same length as data filled with 1s and Os corresponding to whether the data value at that index is inside or outside the range set bounds new bounds Sets all the bounds of the range simultaneously Because each bounds change probably fires an event this method allows tools to set all range elements in a single atomic step Parameters new bounds a tuple of low high The new bounds for the range the dimensionality and cardinality depend on the specific subclass This method not only reduces the number of spurious events
49. es what get size returns get size get size gt int Returns an integer estimate or the exact size of the dataset that get data returns for this object This method is useful for down sampling is masked is masked gt bool Returns True if this data source s data uses a mask In this case to retrieve the data call get data mask instead of get data If you call get data for this data source it returns data but that data might not be the expected data 8 1 2 ArrayDataSource class ArrayDataSource data array dtypezfloat64 sort_order none kw Bases enthought chaco abstract data source AbstractDataSource A data source representing a single continuous array of numerical data This class does not listen to the array for value changes if you need that behavior create a subclass that hooks up the appropriate listeners Traits index dimension Constant scalar The dimensionality of the indices into this data source overrides AbstractDataSource value dimension Constant scalar The dimensionality of the value at each index point overrides AbstractDataSource sort order SortOrderTrait The sort order of the data This is a specialized optimization for 1 D arrays but it s an important one that s used everywhere get bounds Returns the minimum and maximum values of the data source s data Implements AbstractDataSource get data Returns the data for this data source or 0 0 if it has no
50. ethod 64 get preferred size GridPlotContainer method 73 get preferred size OverlayPlotContainer method 71 get shape MultiArrayDataSource method 62 get size AbstractDataSource method 60 get size ArrayDataSource method 61 get size ImageData method 64 get size MultiArrayDataSource method 62 get value size MultiArrayDataSource method 62 get width ImageData method 64 GridDataSource class 63 GridMapper class 70 GridPlotContainer class 72 H HPlotContainer class 72 ImageData class 63 is masked AbstractDataSource method 60 is masked ArrayDataSource method 61 is masked ImageData method 65 is masked MultiArrayDataSource method 62 L LinearMapper class 69 LogMapper class 70 M map data AbstractMapper method 69 map data GridMapper method 71 map data LinearMapper method 70 map data LogMapper method 70 map data array AbstractMapper method 69 map data array LinearMapper method 70 125 Chaco Documentation Release 3 0 0 ap screen AbstractMapper method 69 ap screen GridMapper method 71 ap screen LinearMapper method 70 m m m map screen LogMapper method 70 m m m LENS EQ GS EG LM LO ask data AbstractDataRange method 66 ask data DataRangelD method 67 ask data DataRange2D method 68 MultiArrayDataSource class 61
51. exible ticking system that would produce nicely spaced axis labels for arbitrary sets of labels and arbitrary intervals The chaco2 scales package is the result of this effort It is an entirely standalone package that does not import from any other Enthought package not even traits and the idea was that it could be used in other plotting packages as well The overall idea is that you create a ScaleSystem consisting of various Scales When the ScaleSystem is presented with a data range low high and a screen space amount it searches through its list of scales for the scale that produces the nicest set of labels It takes into account whitespace the formatted size of labels produced by each scale in the ScaleSystem etc So the basic numerical Scales defined in scales py are FixedScale Simple scale with a fixed interval places ticks at multiples of the resolution DefaultScale Scale that tries to place ticks at 1 2 5 and 10 so that ticks don t pop or suddenly jump when the resolution changes when zooming LogScale Dynamic scale that only produces ticks and labels that work well when doing logarithmic plots By comparison the default ticking logic in DefaultTickGenerator in ticks py is basically just the DefaultScale This is currently the default tick generator used by PlotAxis In time scale py I define an additional scale the TimeScale TimeScale not only handles time oriented data using units of uniform interv
52. gcolor white border_visible True border_width 1 title Pressure vs Volume padding_bg_color lightgray Item name attraction Item name totVolume Item name temperature Item name r constant style readonly Item name plot type resizable True buttons OK title Van der waal Equation width 900 height 800 def _volume_default self Default handler for volume Trait Array return arange 1 100 def get pressure self Recalculate when one a trait the property depends on changes return self r_constantxself temperature self volume self totVolume self attraction self volume xself volume if name main viewer Data viewer configure traits 3 3 WX based Tutorial 3 4 Exploring Chaco with IPython There are several tutorials for Chaco each covering slightly different aspects 1 Tutorial 1 Interactive Plotting with Chaco introduces some basic concepts of how to use Chaco and Traits UI to do basic plots customize layout and add interactivity Although Traits UI is not required to use Chaco it is the by far the most common usage of Chaco It is a good approach for those who are relatively new to developing GUI applications Using Chaco with Traits UI allows 42 Chaco Documentation Release 3 0 0 the scientist or novice programmer to easily develop plotting applications but it also provides them
53. gh Property The actual value of the upper bound of this range To set it use high setting low setting Property Property for the lower bound of this range overrides AbstractDataRange high setting Property Property for the upper bound of this range overrides AbstractDataRange x range Property Property for the range in the x dimension y range Property Property for the range in the y dimension tight bounds Tuple Bool True Bool True Do auto bounds imply an exact fit to the data One Boolean per dimension If False the bounds pad a little bit of margin on either side epsilon Tuple CFloat 0 0001 CFloat 0 0001 The minimum percentage difference between low and high for each dimension That is high low gt epsilon low bound_data data Not implemented for this class clip_data data Returns a list of data values that are within the range Implements AbstractDataRange mask_data data Returns a mask array indicating whether values in the given array are inside the range Implements AbstractDataRange refresh If any of the bounds is auto this method refreshes the actual low and high values from the set of the view filters data sources reset Resets the bounds of this range set_bounds low high Sets all the bounds of the range simultaneously Implements AbstractDataRange Parameters low x y Lower left corner of the range high x y Upper right corner of the range
54. hitectural ideas 4 2 Plots are compositions of visual components Everything you see in a plot is some sort of graphical widget with position shape and appearance attributes and with an opportunity to respond to events Separation between data and screen space Although everything in a plot eventually ends up rendering into a common visual area there are aspects of the plot which are intrinsically screen space and some which are fundamentally data space Preserving the distinction between these two domains allows us to think about visualizations in a structured way Modular design and extensible classes Chaco is meant to be used for writing tools and applications and code reuse and good class design are important We use the math behind the data and visualizations to give us architectural direction and conceptual modularity The Traits framework allows us to use events to couple disjoint components at another level of modularity Also rather than building super flexible core objects with myriad configuration attributes Chaco s classes are written with subclassing in mind While they are certainly configurable the classes themselves are written in a modular way so that subclasses can easily customize particular aspects of a visual component s appearance or a tool s behavior The Relationship Between Chaco Enable and Kiva Chaco Enable and Kiva are three packages in the Enthought Tool Suite They have been there for a long tim
55. imension Int 0 The dimensionality of the indices into this data source overrides AbstractDataSource value dimension Int 1 The dimensionality of the value at each index point overrides AbstractDataSource sort order SortOrderTrait The sort order of the data This is a specialized optimization for 1 D arrays but it s an important one that s used everywhere get bounds gt tuple min max Returns a tuple min max of the bounding values for the data source In the case of 2 D data min and max are 2 D points that represent the bounding corners of a rectangle enclosing the data set Note that these values are not view dependent but represent intrinsic properties of the data source If data is the empty set then the min and max vals are 0 0 If value and index are both None then the method returns the global minimum and maximum for the entire data set If value is an integer then the method returns the minimum and maximum along the value slice in the value dimension If index is an integer then the method returns the minimum and maximum along the index slice in the index direction Contents 61 Chaco Documentation Release 3 0 0 get data axes None remove nans False get data gt data array If called with no arguments this method returns a data array Treat this data array as read only and do not alter it in place This data is contiguous and not masked If axes is an integer or tuple this method returns the da
56. it listener methods for your input parameters These methods should be automatically called whenever the parameters are changed since it will be time to recalculate the pressure array 2 Write a calculation method that will update your lists of X and Y coordinates for your plot The following is the code for these two needs Re calculate when attraction totVolume or temperature are changed Qon trait change attraction totVolume temperature def calc self Update the data based on the numbers specified by the user self volume arange 1 100 self pressure self r constant self temperature self volume self totVolume self attraction self volumexself volume return The calc function computes the pressure array using the current values of the independent variables Meanwhile the on_trait_change decorator provided by Traits tells Python to call calc whenever any of the variables attraction totVolume or temperature change 3 2 6 Testing your Program The application is complete and can be tested by instantiating a copy of the class and then creating the view by calling the configure_traits method on the class For a simple test run these lines from an interpreter or a separate module from vanderwaals import Data viewer Data viewer calc Must calculate the initial x y lists viewer configure traits Clicking and dragging on the sliders in the GUI will dynamically
57. its draw order Instance list argss DEFAULT DRAWING ORDER use backbuffer False Do not use an off screen backbuffer get preferred size components None Returns the size width height that is preferred for this component Overrides PlotComponent Contents 71 Chaco Documentation Release 3 0 0 8 4 3 HPlotContainer class HPlotContainer components traits Bases enthought chaco plot containers StackedPlotContainer A plot container that stacks all of its components horizontally Resizable components share the free space evenly All components are stacked from according to stack order in the same order that they appear in the components list Traits draw order Instance list argss DEFAULT DRAWING ORDER stack order Enum left to right right to left The order in which components in the plot container are laid out spacing Float 0 0 The amount of space to put between components valign Enum bottom top center The vertical alignment of objects that don t span the full height 8 4 4 vPlotContainer class VPlotContainer components traits Bases enthought chaco plot_containers StackedPlotContainer A plot container that stacks plot components vertically Traits draw order Instance list args DEFAULT_DRAWING_ORDER stack_dimension v Overrides StackedPlotContainer other_dimension h Overrides StackedPlotContainer stack index 1 Overrides StackedPlotCo
58. le List Array The shape of this container i e rows columns The items in components are shuffled appropriately to match this specification If there are fewer components than cells the remaining cells are filled in with spaces If there are more components than cells the remainder wrap onto new rows as appropriate component grid Property This property exposes the underlying grid structure of the container and is the preferred way of setting and reading its contents When read this property returns a Numpy ar ray with dtype object values for setting it can be nested tuples lists or 2 D arrays The array is in row major order so that component grid 0 is the first row and compo nent grid 0 is the first column The rows are ordered from top to bottom SizePrefs Object to hold size preferences across spans in a particular dimension For instance if SizePrefs is be ing used for the row axis then each element in the arrays below express sizing information about the corresponding column get preferred size components None Returns the size width height that is preferred for this component Overrides PlotComponent Contents 73 74 CHAPTER NINE Annotated Examples This section describes each of the examples provided with Chaco Each example is designed to be a stand alone demonstration of some of Chaco s features Though they are simple many of the examples have capabilities that are difficult to find i
59. lso notice that we are reusing the x array from the ArrayPlotData class OverlappingPlot HasTraits plot Instance Plot traits view View Item plot editor ComponentEditor show label False title Chaco Plot width 500 height 500 resizable True def init self x linspace 14 14 100 y x 2 sin x y2 cos x plotdata ArrayPlotData x x y y plot Plot plotdata plot plot x y type scatter plot plot x y2 type line self plot plot if name main OverlappingPlot configure traits color blue color red 26 Contents Chaco Documentation Release 3 0 0 3 1 10 Container Overview So far we ve only seen single plots but frequently we need to plot data side by side Chaco uses various subclasses of Container to do layout Horizontal containers HPlotContainer place components horizontally Contents 27 Chaco Documentation Release 3 0 0 HEBEL Vertical containers VPlotContainer array component vertically 28 Contents Chaco Documentation Release 3 0 0 es FN Co NN aM NN Nn ALL De ae id Grid container GridPlotContainer lays plots in a grid Contents 29 Chaco Documentation Release 3 0 0 Overlay containers OverlayPlotContainer just overlay plots on top of each other 30 Contents Chaco Documentation Release 3 0 0 Bess
60. mpt You can now use various commands from the chaco shell package to interact with the plot Import the shell commands In 2 from enthought chaco shell import x Set the X axis title In 3 xtitle X data Toggle the legend In 4 legend After running these commands your plot looks like this Contents 7 Chaco Documentation Release 3 0 0 simple line plots The chaco commands function display a list of commands with brief descriptions You can explore the Chaco object hierarchy as well The chaco shell commands are just convenience functions that wrap a rich object hierarchy that comprise the actual plot See the Exploring Chaco with IPython section for information on more complex and interesting things you can do with Chaco from within IPython 1 2 3 Start Menu MS Windows If you installed the Enthought Python Distribution EPD you have shortcuts installed in your Start Menu for many of the Chaco examples You can run them by just clicking the shortcut This just invokes python exe on the example file itself 1 3 Creating a Plot TODO 8 Contents Chaco Documentation Release 3 0 0 1 4 Further Reading Once you have Chaco installed you can either visit the Tutorials to learn how to use the package or you can run the examples see the Annotated Examples section 1 4 1 Presentations There have been several presentations on Chaco at previous PyCon and SciPy confer
61. n which is a modification to the ideal gas law that takes into account the nonzero size of molecules and the attraction to each other that they experience Contents Modelling Van Der Waal s Equation With Chaco Overview Development Setup Writing the Program Creating the View Updating the Plot Testing your Program Screenshots But it could be better Source Code 3 2 2 Development Setup In review Traits is a manifest typing and reactive programming package for Python It also provides UI features that will be used to create a simple GUI The Traits and Traits UI user manuals are good resources for learning about the packages and can be found on the Traits Wiki The wiki includes features technical notes cookbooks FAQ and more You must have Chaco and its dependencies installed Traits TraitsGUI Enable 3 2 3 Writing the Program First define a Traits class and the elements necessary need to model the task The following Traits class is made for the Van Der Waal equation whose variables can be viewed on this wiki page Wikipedia link The volume and pressure variables hold lists of our X and Y coordinates respectively and are defined as arrays The variables attraction and totVolume are the input parameters specified by the user The type of the variables as will dictate their appearance in the GUI For example attraction and totVolume are defined as Ranges so they will show up as slider
62. n other plotting packages Extensibility is a core design goal of Chaco and many people have used the examples as starting points for their own applications 9 1 bar plot py An example showing Chaco s BarPlot class source bar plot py 75 Chaco Documentation Release 3 0 0 e Categories 9 2 bigdata py Demonstrates chaco performance with large datasets There are 10 plots with 100 000 points each Right click and drag to create a range selection region The region can be moved around and resized drag the edges These interactions are very fast because of the backbuffering built into chaco Zooming with the mousewheel and the zoombox as described in simple line py is also available but panning is not source bigdata py 76 Contents Chaco Documentation Release 3 0 0 5 2 5 0 25 5 7 5 10 12 5 15 9 3 cursor tool demo py A Demonstration of the CursorTool functionality Left button drag to move the cursors round Right drag to pan the plots z key to Zoom source cursor tool demo py Contents 77 Chaco Documentation Release 3 0 0 0 75 0 5 0 25 0 25 0 5 0 75 10 7 5 5 2 5 0 2 5 5 7 5 10 4 2 2 4 1 0 019569471624265589 0 0195682225802 Cursor2pos 0 95959595959595934 1 46464646464646 9 4 data labels py Draws a line plot with several points labelled Demonstrates how to annotate plots source data labels py 78 C
63. nd color 52 Contents Chaco Documentation Release 3 0 0 def make black plot index data series plot data ArrayPlotData index index plot data set data data series data series plot Plot plot data bgcolor black plot plot index data series def change bgcolor plot plot bgcolor black turn off borders def make borderless plot index data series plot data ArrayPlotData index index plot data set data data series data series plot Plot plot data border visible False plot plot index data series def change to borderless plot plot plot border visible False 6 3 Writing Components How do I compose multiple renderers write a custom renderer write a custom overlay underlay write a custom tool write a new container 6 4 Advanced How do I properly change override draw dispatch modify event dispatch customize backbuffering embed custom native WX widgets on the plot Contents 53 54 CHAPTER SEVEN Frequently Asked Questions 7 1 Where does the name Chaco come from It is named after Chaco Canyon which had astronomical markings that served as an observatory for Native Americans The original version of Chaco was built as part of a project for the Space Telescope Science Institute This is also the origin of the name Kiva for our vector graphics layer that Chaco uses for rendering 7 2 Why was Chac
64. never the value of a particular trait changes Here is the handler for color trait def color changed self self renderer color self color This event handler gets called whenever the value of self color changes whether due to user interaction with GUI or due to code elsewhere The Traits framework automatically calls this method because its name follows the name template of TRAITNAME changed Since this gets called after the new value has already been updated we can read out the new value just by accessing self color We are just going to copy the color to the scatter renderer You can see why we needed to hold on to the renderer in the constructor Now we do the same thing for the marker type and marker size traits def marker changed self self renderer marker self marker def marker size changed self self renderer marker size self marker size Running the code produces an app that looks like this Marker square Size 4 36 Contents Chaco Documentation Release 3 0 0 Depending on your platform the color editor swatch at the top may look different This is how it looks on Mac OS X All of the controls here are live You can modify them and the plot will update 3 2 Modelling Van Der Waal s Equation With Chaco 3 2 1 Overview This tutorial walks through the creation of an example program that plots a scientific equation In particular we will model Van Der Waal s Equatio
65. ntainer halign Enum left right center The horizontal alignment of objects that don t span the full width stack order Enum bottom_to_top top_to_bottom The order in which components in the plot container are laid out spacing Float 0 0 The amount of space to put between components 8 4 5 GridPlotContainer error while formatting signature for GridPlotContainer SizePrefs arg is not a Python function class GridPlotContainer components traits Bases enthought chaco base plot container BasePlotContainer A GridPlotContainer consists of rows and columns in a tabular format Each cell s width is the same as all other cells in its column and each cell s height is the same as all other cells in its row Although grid layout requires more layout information than a simple ordered list this class keeps components as a simple list and exposes a shape trait Traits draw order Instance list argss DEFAULT DRAWING ORDER spacing Either Tuple List Array 72 Contents Chaco Documentation Release 3 0 0 The amount of space to put on either side of each component expressed as a tuple h spacing v spacing valign Enum bottom top center The vertical alignment of objects that don t span the full height halign Enum left right center The horizontal alignment of objects that don t span the full width shape Trait 0 0 Either Tup
66. nto this data source overrides ArrayDataSource value dimension ReadOnly point The dimensionality of the value at each index point overrides ArrayDataSource sort order SortOrderTrait The sort order of the data Although sort order is less common with point data it can be useful in case where the value data is sorted along some axis Note that sort index is used only if sort order is not none sort index Enum 0 1 Which of the value axes the sort order refers to If sort order is none this attribute is ignored In the unlikely event that the value data is sorted along both X and Y i e monotonic in both axes then set sort index to whichever one has the best binary search performance for hit testing 62 Contents Chaco Documentation Release 3 0 0 get data Returns the data for this data source or 0 0 0 0 if it has no data Overrides ArryDataSource reverse map pt index 0 outside returns none True Returns the index of pt in the data source Overrides ArrayDataSource Parameters pt x y value to find index 0 or 1 Which of the axes of pt the sort_order refers to outside_returns_none Boolean Whether the method returns None if pt is outside the range of the data source if False the method returns the value of the bound that pt is outside of in the index dimension 8 1 5 GridDataSource class GridDataSource xdata array dtype float64 ydata array dtype float64
67. o named Chaco for a while Starting in January of 2006 we refactored and reimplemented much of the core Chaco API The effort has been named chaco2 and lives in the enthought chaco2 namespace During that time the original Chaco package Chaco Classic was in maintenance only mode but there was still code that needed features from both Chaco Classic and Chaco2 That code has finally been either shelved or refactored and the latest versions of Chaco 3 0 and up are back to residing in the enthought chaco namespace We still have compatibility modules in enthought chaco2 but they just proxy for the real code in enthought chaco The same applies to the enthought enable and enthought enable2 packages 7 3 What are the pros and cons of Chaco vs matplotlib This question comes up quite a bit The bottom line is that the two projects initially set out to do different things and although each project has grown a lot of overlapping features the different original charters are reflected in the capabilities and feature sets of the two projects Here is an excerpt from a thread about this question on the enthought dev mailing list Gael Varoquaux s response On Fri May 11 2007 at 10 03 21PM 0900 Bill Baxter wrote gt Just curious What are the pros and cons of chaco vs matplotlib To me it seem the big pro of chaco is that it is much easier to use in a programatic way I have no clue this means something in English I
68. of transposed so be sure to set it to its proper value when using the same ImageData instance interchangeably to store transposed and non transposed data transposed Bool False Is raw_value the actual underlying image data array transposed from value Le does the first axis correspond to the x direction and the second axis correspond to the y direction Rather than transposing or swapping axes on the data and destroying continuity this class exposes the data as both value and raw_value raw_value Property ImageTrait A read only attribute that exposes the underlying array fromfile Alternate constructor to create an ImageData from an image file on disk filename may be a file path or a file object get array bounds Always returns 0 width 0 height for x bounds and y bounds get bounds Returns the minimum and maximum values of the data source s data Implements AbstractDataSource get data Returns the data for this data source Implements AbstractDataSource get height Returns the shape of the y axis get size get size gt int Implements AbstractDataSource get width Returns the shape of the x axis 64 Contents Chaco Documentation Release 3 0 0 is masked is masked gt False Implements AbstractDataSource set data data Sets the data for this data source Parameters data array The data to use 8 2 Data Ranges 8 2 1 AbstractDataRange class Abstr
69. ontents Chaco Documentation Release 3 0 0 1 000000 0 765198 11 000000 0 171190 3 000000 0 260052 5 2 5 0 2 5 5 75 10 12 5 15 9 5 data_view py Example of how to use a DataView and bare renderers to create plots source data_view py Contents 79 Chaco Documentation Release 3 0 0 9 6 edit line py Allows editing of a line plot source edit line py 80 Contents Chaco Documentation Release 3 0 0 9 7 financial plot py Implementation of a standard financial plot visualization using Chaco renderers and scales Right clicking and select ing an area in the top window zooms in the corresponding area in the lower window source financial plot py Contents 81 Chaco Documentation Release 3 0 0 eoo Financial plot example o 2 Financial Plot 120000 100000 9 8 financial plot dates py Implementation of a standard financial plot visualization using Chaco renderers and scales Right clicking and se lecting an area in the top window zooms in the corresopnding area in the lower window This differs from the finan cial plot py example in that it uses a date oriented axis source financial plot dates py 82 Contents Chaco Documentation Release 3 0 0 e00 Financial plot example Financial Plot with Date Axis Oct08 Janog 09 Julog Octo9 Janio 9 9 multiaxis py Draws se
70. or formats and it has a subpackage for doing command line plotting or simple scripting Chaco is built on three other Enthought packages Traits as an event notification framework Kiva for rendering 2 D graphics to a variety of backends across platforms Enable as a framework for writing interactive visual components and for abstracting away GUI toolkit specific details of mouse and keyboard handling Currently Chaco requires either wxPython or PyQt to display interactive plots but a cross platform OpenGL backend using Pyglet is in the works and it will not require WX or Qt Contents 1 CHAPTER ONE Quickstart This section is meant to help users on well supported platforms and common Python environments get started using Chaco as quickly as possible If your platform is not listed here or your Python installation has some quirks then some of the following instructions might not work for you If you encounter any problems in the steps below please refer to the Installing and Building Chaco section for more detailed instructions 1 1 Installation Overview There are several different ways to get Chaco Install the Enthought Python Distribution Chaco and the rest of the Enthought Tool Suite are bundled with it Go to the main Enthought Python Distribution EPD web site and download the appropriate version for your platform After running the installer you will have a working version of Chaco Available platform
71. ore documentation however but I m working on that I linked to the source for all of the screenshots in the gallery to demonstrate that you can do a lot of things with Chaco in a few dozen lines of code For instance the audio spectrogram at the bottom of the gallery is just a little over 100 lines 56 Contents Chaco Documentation Release 3 0 0 Fundamentally I like the Chaco model of plots as compositions of interactive components This really helps me think about visualization apps in a modular way and it fits my head Of course the fact that I wrote much of it might have something to do with that as well The goal is to have data related operations clearly happen in one set of objects the view layout and configuration happen in another and the interaction controls fit neatly into a third IMHO a good toolkit should help me design architect my application better and we definitely aspire to make Chaco meet that criterion Finally one major perk is that since Chaco is built completely on top of traits and its event based component model you can call edit traits on any visual component from within your app or ipython and get a live GUI that lets you tweak all of its various parameters in realtime This applies to the axis grid renderers etc This seems so natural to me that I sometimes forget what an awesome feature it is Contents 57 58 CHAPTER EIGHT Programmer s Refe
72. r aspect ratio py 102 Contents Chaco Documentation Release 3 0 0 9 29 image from file py Loads and saves RGB images from disk source image from file py Contents 103 Chaco Documentation Release 3 0 0 capitol jpg 200 9 30 image inspector py Demonstrates the ImageInspectorTool and overlay on a colormapped image plot The underlying plot is similar to the one in cmap image plot py source image inspector py 104 Contents Chaco Documentation Release 3 0 0 My First Image Plot 400 174 179 255 66 0 663334944505 9 31 image plot py Draws a simple RGB image source image plot py Contents 105 Chaco Documentation Release 3 0 0 Simple image plot 9 32 inset plot py A modification of line plotl py that shows the second plot as a subwindow of the first You can pan and zoom the second plot just like the first and you can move it around my right click and dragging in the smaller plot source inset plot py 106 Contents Chaco Documentation Release 3 0 0 Inset Plot 9 33 line drawing py Demonstrates using a line segment drawing tool on top of the scatter plot from simple scatter py source line drawing py Contents 107 Chaco Documentation Release 3 0 0 9 34 line plotl py Draws some x y line and scatter plots source line plotl py Contents 108 Chaco Documentation Release 3 0 0
73. rence 8 1 Data Sources 8 1 1 AbstractDataSource class AbstractDataSource Bases enthought traits has traits HasTraits This abstract interface must be implemented by any class supplying data to Chaco Chaco does not have a notion of a data format For the most part a data source looks like an array of values with an optional mask and metadata If you implement this interface you are responsible for adapting your domain specific or application specific data to meet this interface Chaco provides some basic data source implementations In most cases the easiest strategy is to create one of these basic data source with the numeric data from a domain model In cases when this strategy is not possible domain classes or an adapter must implement AbstractDataSource Traits value dimension DimensionTrait The dimensionality of the value at each index point Subclasses re declare this trait as a read only trait with the right default value index dimension DimensionTrait The dimensionality of the indices into this data source Subclasses re declare this trait as a read only trait with the right default value metadata Dict A dictionary keyed on strings In general it maps to indices or tuples of indices depending on value dimension as in the case of selections and annotations Applica tions and renderers can add their own custom metadata but must avoid using keys that might result in name collision data changed
74. room to grow as their requirements change and increase in complexity Traits UI can also be used by a more experienced developer to build more involved applications and Chaco can be used to embed visualizations or to leverage interactive graphs as controllers for an application 2 Tutorial 2 Modelling Van Der Waal s Equation With Chaco is another example of creating a data model and then using Traits and Chaco to rapidly create interactive plot GUIs 3 WX based Tutorial Creating a stand alone wxPython application or embedding a Chaco plot within an existing Wx application This tutorial is suited for those who are familiar with programming using wxPython or Qt and prefer to write directly to those toolkits It shows how to embed Chaco components directly into an enclosing widget panel or dialog It also demonstrates more advanced usages like using a wxPython Timer to display live updating data streams 4 Usingthe Chaco Shell command line plotting interface to build plots in a Matlab or gnuplot like style Although this approach doesn t lend itself to building more reusable utilities or applications it can be a quick way to get plots on the screen and build one off visualizations See Exploring Chaco with IPython Contents 43 44 Note 4 1 CHAPTER FOUR Architecture Overview At this time this is an overview of not just Chaco but also Kiva and Enable Core Ideas The Chaco toolkit is defined by a few core arc
75. rt order SortOrderTrait The sort order of the data 8 1 6 ImageData class ImageData Bases enthought chaco abstract data source AbstractDataSource Represents a grid of data to be plotted using a Numpy 2 D grid Contents 63 Chaco Documentation Release 3 0 0 The data array has dimensions NxM but it may have more than just 2 dimensions The appropriate dimension ality of the value array depends on the context in which the ImageData instance will be used Traits dimension ReadOnly DimensionTrait image The dimensionality of the data value_depth Int 1 Depth of the values at each i j Values that are used include 3 color images without alpha channel 4 color images with alpha channel data Property ImageTrait Holds the grid data that forms the image The shape of the array is N M D where Dis 1 3 or 4 Nis the length of the y axis Mis the length of the x axis Thus data 0 must be the first row of data If D is 1 then the array must be of type float if D is 3 or 4 then the array must be of type uint8 NOTE If this ImageData was constructed with a transposed data array then internally it is still transposed i e the x axis is the first axis and the y axis is the second and the data array property might not be contiguous If contiguousness is required and calling copy is too expensive use the raw_value attribute Also note that setting this trait does not change the value
76. s Windows 32 bit Mac OS X 10 4 and 10 5 RedHat Enterprise Linux 3 32 bit and 64 bit Note Enthought Python Distribution is free for academic and personal use and fee based for commercial and government use Windows Mac Install from PyPI using easy install part of setuptools from the command line easy install Chaco Linux Install distribution specific eggs from Enthought s repository See the ETS wiki for instructions for installing pre built binary eggs for your specific distribution of Linux Linux Install via the distribution s packaging mechanism We provide debs for Debian and Ubuntu and rpms for Redhat TODO Download source as tarballs or from Subversion and build See the nstalling and Building Chaco section Chaco requires Python version 2 5 1 2 Running Some Examples Depending on how you installed Chaco you may or may not have the examples already If you installed Chaco as part of EPD the location of the examples depends on your platform Chaco Documentation Release 3 0 0 e On Windows they are in the Examples subdirectory of your installation location This is typically C Python25 Examples On Linux they are in the Examples subdirectory of your installation location On Mac OS X they are in the Applications lt EPD Version gt Examples directory If you downloaded and installed Chaco from source via the PyPI tar gz file or from an SVN checkout
77. s a PAINT 46 Contents Chaco Documentation Release 3 0 0 event with the OS The nice thing about having the enable Window object between the GUI toolkits and our apps and sitting at the very top of event dispatch is that we can easily interject new kinds of events this is precisely what we did to enable all of our tools to work with Multitouch The basic things to remember about Enable are that Any place that your GUI toolkit allows you stick a generic widget you can stick an Enable component and this extends to Chaco components as well Dave Morrill had a neat demonstration of this by embedding small Chaco plots as cells in a wx Table control If you have some new GUI toolkit and you want to provide an Enable backend for it all you have to do is implement a new Window class for that backend You also need to make sure that Kiva can actually create a GraphicsContext for that toolkit Once the kiva gl branch is committed to the trunk Kiva will be able to render into any GL context So if your newfangled unsupported GUI toolkit has a GL Window type of thing then you will be able to use Kiva Enable and Chaco inside it This is a pretty major improvement to interoperability if only because users now don t have to download and install wxPython just to play with Chaco 4 2 3 Chaco At the highest level Chaco consists of Visual components that render to screen or an output device e g LinePlot ScatterPlot PlotGrid
78. static plots and dynamic data visualizations that let you interactively explore your data Here are four basic examples of Chaco plots 14 Contents Chaco Documentation Release 3 0 0 This plot shows a static tornado plot with a categorical Y axis and continuous X axis The plot is resizable but the user cannot interact or explore the data in any way Contents 15 Chaco Documentation Release 3 0 0 Bessel functions E Besselj 0 Besselj 1 E Besselj 2 Besselj 3 E Besselj 4 Bessel j 5 E Besselj 6 Besselj 7 E Besselj 8 Besselj 9 This is an overlaid composition of line and scatter plots with a legend Unlike the previous plot the user can pan and zoom this plot exploring the relationship between data curves in areas that appear densely overlapping Furthermore the user can move the legend to an arbitrary position on the plot and as they resize the plot the legend maintains the same screen space separation relative to its closest corner 16 Contents Chaco Documentation Release 3 0 0 This example starts to demonstrate interacting with the dataset in an exploratory way Whereas interactivity in the previous example was limited to basic pan and zoom which are fairly common in most plotting libraries this is an example of a more advanced interaction that allows a level of data exploration beyond the standard view manipuations With this example the user can select a region of d
79. sumers of data and the actual data itself Next we set a title on the plot 22 Contents Chaco Documentation Release 3 0 0 plot title sin x x 3 And then we set our plot trait to the new plot self plot plot The last thing we do in this script is set up some code to run when the script is executed af name main LinePlot configure traits This one liner instantiates a LinePlot object and calls its configure traits method This brings up a dialog with a traits editor for the object built up according to the View we created earlier In our case the editor will just display our plot attribute using the Component Editor 3 1 7 Scatter Plots We can use the same pattern to build a scatter plot class ScatterPlot HasTraits plot Instance Plot traits view View Item plot editor ComponentEditor show label False width 500 height 500 resizable True title Chaco Plot def init self x linspace 14 14 100 y sin x 3 plotdata ArrayPlotData x x y y plot Plot plotdata plot plot x y type scatter color blue plot title sin x x 3 self plot plot i name main ScatterPlot configure traits Note that we have only changed the type argument to the plot plot call and the name of the object from LinePlot to ScatterPlot This produces the following Contents 23 Chaco Documentation Release 3 0 0
80. t data plot index numpy array 1 2 3 4 5 ArrayPlotData index index data series data series 51 Chaco Documentation Release 3 0 0 data series index 2 my plot MyPlot index data series my plot configure traits make an application to render many streams of data def plot several series index series list plot data ArrayPlotData index index plot Plot plot data for i data series in enumerate series list Series name series i plot data set data series name data series plot plot index series name make a plot the right size def resize plot plot width height plot outer bounds width height copy a plot the the clipboard def copy to clipboard plot WX specific though QT implementation is similar using QImage and QClipboard import wx width height plot outer bounds gc PlotGraphicsContext width height dpi 72 gc render component plot component Create a bitmap the same size as the plot and copy the plot data to it bitmap wx BitmapFromBufferRGBA width 1 height 1 gc bmp_array flatten data wx BitmapDataObject data SetBitmap bitmap if wx TheClipboard Open wx TheClipboard SetData data wx TheClipboard Close else wx MessageBox Unable to open the clipboard Error 6 2 Layout and Rendering How do I put multiple plots in a single window change the backgrou
81. t is fully traited and rely quite a lot on inversion of control sorry I love this concept so it has become my new buzz word You can make very nice object oriented interactive code Another nice aspect is that it is much faster than MPL 55 Chaco Documentation Release 3 0 0 The cons are that it is not as fully featured as MPL that it does not has an as nice interactively useable functional interface ie chaco shell vs pylab and that it is not as well documented and does not have the same huge community I would say that the codebase of chaco is nicer but than if you are not developping interactive application it is MPL is currently an option that is lickely to get you where you want to go quicker Not that I wouldn t like to see chaco building up a bit more and becoming reference Developers if you want chaco to pick up momentum give it a pylab like interface as close as you can to pylab My 2 cents Gael Peter Wang s response excerpt On May 11 2007 at 8 03 AM Bill Baxter wrote Just curious What are the pros and cons of chaco vs matplotlib You had to go and ask didn t you There are many more folks here who have used MPL more extensively than myself so I ll defer the comparisons to them Gael as always thanks for your comments and feedback I can comment however on the key goals of Chaco Chaco is a plotting toolkit targeted towards developers for building int
82. ta array sliced along the index dimension get data mask gt data array mask array Implements AbstractDataSource get shape Returns the shape of the multi dimensional data source get size get size gt int Implements AbstractDataSource Returns an integer estimate or the exact size of the dataset that get data returns This method is useful for downsampling get value size get value size size Returns the size along the value dimension is masked is masked gt bool Returns True if this data source s data uses a mask In this case retrieve the data using get data mask instead of get data If you call get data for this data source it returns data but that data may not be the expected data set data value Sets the data for this data source Parameters value array The data to use 8 1 4 PointDataSource class PointDataSource data array shapez 0 2 dtype float64 kw Bases enthought chaco array data source ArrayDataSource A data source representing a possibly unordered set of X Y points This is internally always represented by an Nx2 array so that data i refers to a single point represented as a length 2 array Most of the traits and methods of ArrayDataSeries work for the PointDataSeries as well since its data is linear This class overrides only the methods and traits that are different Traits index dimension ReadOnly scalar The dimensionality of the indices i
83. ters you can configure on a container like background color border thickness spacing and padding We re going to modify the last two lines of the previous example a little bit to make the two plots touch in the middle container HPlotContainer scatter line container spacing 0 scatter padding_right 0 line padding_left 0 line y_axis orientation right self plot container Something to note here is that all Chaco components have both bounds and padding or margin In order to make our plots touch we need to zero out the padding on the appropriate side of each plot We also move the Y axis for the line plot which is on the right hand side to the right side This produces the following Contents 33 Chaco Documentation Release 3 0 0 3 1 12 Editing Plot Traits So far the stuff you ve seen is pretty standard building up a plot of some sort and doing some layout on them Now we re going to start taking advantage of the underlying framework Chaco is written using Traits This means that all the graphical bits you see and many of the bits you don t see are all objects with various traits generating events and capable of responding to events We re going to modify our previous ScatterPlot example to demonstrate some of these capabilities Here is the full listing of the modified code including some of the new import lines from enthought traits api import HasTraits Instance Int from enthought enable
84. the ones that result from having to set both high and low but also allows listeners to differentiate between translation and resize operations 8 2 2 BaseDataRange class BaseDataRange datasources kwtraits Bases enthought chaco abstract data range AbstractDataRange Ranges represent sub regions of data space They support autoscaling by querying their associated data sources add datasources Convenience method to add a data source remove datasources Convenience method to remove a data source 8 2 3 DataRange1D class DataRange1D datasources kwtraits Bases enthought chaco base data range BaseDataRange Represents a 1 D data range Traits low Property The actual value of the lower bound of this range overrides AbstractDataRange To set it use low setting high Property The actual value of the upper bound of this range overrides AbstractDataRange To set it use high setting low setting Property Trait auto auto track CFloat Property for the lower bound of this range overrides AbstractDataRange auto The lower bound is automatically set at or below the minimum of the data track The lower bound tracks the upper bound by tracking amount CFloat An explicit value for the lower bound high setting Property Trait auto auto track CFloat Property for the upper bound of this range overrides AbstractDataRange auto Th
85. thon Knowledge of Numpy can be helpful for certain parts of the tutorial Knowledge of GUI programming concepts such as widgets windows and events are helpful for the last portion of the tutorial but it is not required This tutorial will demonstrate using Chaco with Traits UI so knowledge of the Traits framework is also helpful We don t use very many sophisticated aspects of Traits or Traits UI and it is entirely possible to pick it up as you go through the tutorial It s also worth pointing out that you don t have to use Traits UI in order to use Chaco you can integrate Chaco directly with Qt or wxPython but for this tutorial we use Traits UI to make things easier 13 Chaco Documentation Release 3 0 0 Contents Interactive Plotting with Chaco Overview Goals Introduction Script oriented Plotting Application oriented Plotting Understanding the First Plot Scatter Plots mage Plot A Slight Modification Container Overview Using a Container Editing Plot Traits 3 1 2 Goals By the end of this tutorial you will have learned how to create Chaco plots of various types arrange plots of data items in various layouts configure and interact with your plots using Traits UI create a custom plot overlay create a custom tool that interacts with the mouse 3 1 3 Introduction Chaco is a plotting application toolkit This means that it can build both
86. thon Distribution EPD If you have installed EPD then you already have Chaco Note Enthought Python Distribution is free for academic and personal use and fee based for commercial and government use 2 2 easy install Chaco and its dependencies are available as binary eggs for Windows and Mac OS X from the Python Package Index Chaco depends on Numpy and either wxPython or Qt These packages are not installed by the default installation command If you do not have these packages installed use the following command to install Chaco easy install Chaco nonets If you do have Numpy and either wxPython or Qt installed you can use a simpler command to install Chaco easy install Chaco Because eggs do not distinguish between various distributions of Linux Enthought hosts its own egg repository for Linux eggs See the ETS wiki page on our egg repo for instructions for installing pre built binary eggs for your specific distribution of Linux For systems that don t have binary eggs it is also possible to build Chaco from source since PyPI hosts the source tarballs for all dependencies 2 3 Building from Source Chaco itself is not very hard to build from source there are only a few C extensions and they build with most modern compilers Frequently the more difficult to build piece is actually the Enable package on which Chaco depends 11 Chaco Documentation Release 3 0 0 On most platforms in order to build Enable you nee
87. tructor are pretty self explanatory and the Traits UI manual documents all the various properties you can set here For our purposes this Traits View is sort of boilerplate It gets us a nice little window that we can resize We ll be using something like this View in most of the examples in the rest of the tutorial Now let s look at the constructor where the real work gets done def _ init self x linspace 14 14 100 y sin x xx 3 plotdata ArrayPlotData x x y y The first thing we do here is create some mock data just like in the script oriented approach But rather than directly calling some sort of plotting function to throw up a plot we create this ArrayPlotData object and stick the data in there The ArrayPlotData is a simple structure that associates a name with numpy array In a script oriented approach to plotting whenever you have to update the data or tweak any part of the plot you basically re run the entire script Chaco s model is based on having objects representing each of the little pieces of a plot and they all use Traits events to notify one another that some attribute has changed So the ArrayPlotData is an object that interfaces your data with the rest of the objects in the plot In a later example we ll see how we can use the ArrayPlotData to quickly swap data items in and out without affecting the rest of the plot The next line creates an actual Plot object and gives it the ArrayPlotData instance
88. ts opens a Traits editor for the plot source scales test py Contents 87 Chaco Documentation Release 3 0 0 Bessel functions E Besselj 0 Besselj 1 E Besselj 2 Besselj 3 E Besselj 4 Besselj 5 E Besselj 6 Besselj 7 E Besselj 8 9 14 simple line py Draws several overlapping line plots Double clicking on line or scatter plots opens a Traits editor for the plot source simple line py 88 Contents Chaco Documentation Release 3 0 0 Bessel functions E Besselj 0 Bessel j 1 E Besselj 2 Bessel 3 E Besselj 4 Bessel j 5 E Besselj 6 Bessel j 7 E Besselj 8 Bessel j 9 9 15 tornado py Tornado plot example from Brennan Williams source tornado py Contents 89 Chaco Documentation Release 3 0 0 9 16 two plots py Demonstrates plots sharing datasources ranges etc source two plots py 90 Contents Chaco Documentation Release 3 0 0 9 17 vertical plot py Draws a static plot of bessel functions oriented vertically side by side You can experiment with using different containers uncomment lines 32 33 or different orientations on the plots comment out line 43 and uncomment 44 source vertical plot py Contents 91 Chaco Documentation Release 3 0 0 J Allows isometric viewing of a 3 D data cube downloads the necessary data about 7 8 MB 9 18 data_cube py source
89. ure Must be a tuple low pos high pos 8 3 3 LinearMapper class LinearMapper Bases enthought chaco base 1d mapper BaselDMapper Maps a 1 D data space to and from screen space by specifying a range in data space and a corresponding fixed line in screen space This class concerns itself only with metric and not with orientation So to flip the screen space orientation simply swap the values for low pos and high pos Contents 69 Chaco Documentation Release 3 0 0 map data screen val map data screen val gt data val Overrides AbstractMapper Maps values from screen space into data space map data array screen vals map data array screen vals gt data vals Overrides AbstractMapper Maps an array of values from screen space into data space map screen data array map screen data array gt screen array Overrides AbstractMapper Maps values from data space into screen space 8 3 4 LogMapper class LogMapper Bases enthought chaco base 1d mapper BaselDMapper Defines a 1 D logarithmic scale mapping from a 1 D region in input space to a 1 D region in output space Traits fill value Float 1 0 The value to map when asked to map values lt LOG MINIMUM to screen space map data screen val map data screen val gt data val Overrides Abstract Mapper Maps values from screen space into data space map screen data array map screen data array gt screen array Overrides AbstractMapp
90. veral overlapping line plots like simple_line py but uses a separate Y range for each plot Also has a second Y axis on the right hand side Demonstrates use of the BroadcasterTool source multiaxis py Contents 83 Chaco Documentation Release 3 0 0 Bessel functions 9 10 multiaxis using Plot py Draws some x y line and scatter plots On the left hand plot Left drag pans the plot Mousewheel up and down zooms the plot in and out 66 0 Pressing 7 opens the Zoom Box and you can click drag a rectangular region to zoom If you use a sequence of zoom boxes pressing alt left arrow and alt right arrow moves you forwards and backwards through the zoom history source multiaxis using Plot py 84 Contents Chaco Documentation Release 3 0 0 9 11 noninteractive py This demonstrates how to create a plot offscreen and save it to an image file on disk The image is what is saved source noninteractive py Contents 85 Chaco Documentation Release 3 0 0 5 25 25 5 75 10 125 15 9 12 range selection demo py Demo of the RangeSelection on a line plot Left click and drag creates a horizontal range selection this selection can then be dragged around or resized by dragging its edges source range selection demo py 86 Contents Chaco Documentation Release 3 0 0 9 13 scales test py Draws several overlapping line plots Double clicking on line or scatter plo

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