diff --git a/.gitignore b/.gitignore index b0862a3..e5eb9ba 100644 --- a/.gitignore +++ b/.gitignore @@ -59,3 +59,5 @@ g++ gcc mpicc mpicxx +examples/rect.png +examples/gr_wrapper/*.png diff --git a/.rspec b/.rspec new file mode 100644 index 0000000..34c5164 --- /dev/null +++ b/.rspec @@ -0,0 +1,3 @@ +--format documentation +--color +--require spec_helper diff --git a/.rubocop.yml b/.rubocop.yml index f600400..508f660 100644 --- a/.rubocop.yml +++ b/.rubocop.yml @@ -13,7 +13,7 @@ AllCops: - 'tmp/*' - '*.so' DisplayCopNames: true - TargetRubyVersion: 2.2 + TargetRubyVersion: 2.3+ # Preferred codebase style --------------------------------------------- Layout/ExtraSpacing: @@ -34,7 +34,7 @@ Layout/SpaceInsideBlockBraces: Layout/SpaceInsideHashLiteralBraces: EnforcedStyle: no_space -Layout/AlignParameters: +Layout/ParameterAlignment: EnforcedStyle: with_fixed_indentation Style/EmptyElse: @@ -52,6 +52,9 @@ Metrics/ClassLength: Style/ParallelAssignment: Enabled: false +Style/CommentedKeyword: + Enabled: false + Style/DoubleNegation: Enabled: false @@ -125,6 +128,3 @@ Style/MethodDefParentheses: # Bans methods like `has_missing_data?`, `is_number?` and so on - started # with unnecessary has_ or is_. - - - diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..68a3a04 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,18 @@ +language: + ruby + +rvm: + - '2.0' + - '2.1' + - '2.2' + - '2.3.0' + - '2.4.0' + +script: + - bundle exec rspec + - bundle exec rubocop + +install: + - gem install bundler + - gem install rainbow -v '2.2.1' + - bundle install diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..ca85f43 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,9 @@ +# 0.1-a1 + +Name: First alpha release. + +Major updates: + +* Integrate work from Arafat's magick-rubyplot into rubyplot after a full rewrite. +* Automated test suite. +* Integration with rubocop and Travis CI. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a7f2648..51e97b5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,20 +1,23 @@ -# Developer notes +# Rubyplot developer notes ## Co-ordinate system -Rubyplot assumes that the co-ordinate system has the origin at the top left corner -of the graph. This helps in keeping all pixel co-ordinates positive values. +Rubyplot assumes that the co-ordinate system has the origin at the bottom left corner +of the graph. This helps in keeping all pixel co-ordinates positive values. The bottom +left corner is `(Rubyplot.min_x, Rubyplot.min_x)` and the upper left corner is +`(Rubyplot::max_x, Rubyplot.max_y)`. The backend should be accomodated to work with +this system. They are also denoted as and . This system is known as the +`Rubyplot Artist Co-ordinates` system internally within the codebase. -Each Artist contains a `(abs_x, abs_y)` pair that denotes the absolute position of the -Artist on the canvas. For `Figure` and `Axes` this pair denotes the top left corner. +The only time where actual pixel valus are used is when specifying the width/height +of the `Figure` or when actually plotting things using the backend. -The absolute co-ordinates are calculated during the draw phase. Therefore there -should be no code except in the `draw` methods where actual co-ordinates are calcualted. +The co-ordinates are always specified by proportions otherwise. The proportions are +always specified w.r.t the full canvas. -Varible naming conventions: -* All values that are absolute values will be prefixed with `abs_**. -* Variables relating to positioning of the graph other than the absolute -variables are always ratios. +However font sizes and things that we don't have control over are still specified in pixels. +Here's the list of things that are still written in pixels: ++ All font sizes. ## Drawing flow @@ -42,7 +45,83 @@ The `after(:example)` block requires a `@figure` instance variable which it will for performing the plotting. A check will be performed for the `@figure` instance variable before the example is run. -## Artist defaults convention +## Units and measurements -Due to nature of a viz library, each Artist tends to have many instance variables -for storing various kinds of information about the Artist. +It is important to read up on units and measurements as used in a computer system if you +want to contribute to rubyplot. Here's some suggested reading: +* Point (unit) - https://en.wikipedia.org/wiki/Point_(typography) + +# GR extension notes + +## Typography + +The most common way of specifying font sizes is by using the 'point' unit. This is what rubyplot +uses at a top level. However, the backend can work with some different units, which makes it +important to translate points to the right unit. + +### Font sizes in GR + +The only way to tell GR to change the way a font looks is by changing the font height, +spacing etc. using the `settextfontprec`, `settextcolorind`, `setcharheight`, `setcharup`, +and other such functions. + +All these features are abstracted using `Rubyplot::Artist::Text`, which accepts inputs +in human-understadable format and uses the appropriate GR primitives to do its job. + +## Figure sizes + +GR does not allow changing the internal DPI setting as of now (which is 600). +The size of the figure can be set using the `setwsviewport` or `setwswindow` functions. +Therefore, `setwsviewport(0, 6 * 0.0254, 0, 3 * 0.0254)` would create a 6 by 3 +inch image at 600 dpi. + +## setwindow and setviewport + +The `Rubyplot::GR.setwindow` function allows setting up 'world co-ordinates' that are +basically as a point of reference for future activities like setting up axes and +plotting points. For example, if you call the `setwindow` function as like this: +``` ruby +Rubyplot::GR.setwindow(0,100,0,100) +``` +GR will set the lower left corner of the 'world' to `(0,0)` and the upper left corner +to `(100,100)`. So when you use the `GR.axes()` function to setup co-ordinate axes +you will only see the 0th quadrant of the co-ordinate space. + +In order to change the position of this 'world' on the canvas, it is necessary to use +the `setviewport` function. Imagine this function as the zoom function of a camera. You +can use to either 'zoom out' fully and see everything that is front of you, or you can +'zoom in' to a particular area and see only that. Your zoom level determines your 'world'. + +The only difference is that in case of viewports there is no actual zooming in but only +demarcation of areas within the canvas that are to treated as the 'world'. + +The implication on rubyplot would be that an `Axes` within a `Figure` would get mappped +to a `viewport` and the plotting within the viewport would take place by setting up the +world co-ordinates to between the X and Y range. + +## GR binaries + +You can download the GR binaries for your system from [here](https://gr-framework.org/c.html#installation). + +GR uses certain environment variables for working with fonts and searching the GR shared +object binary. These variables must be set before you use the GR backend of rubyplot. +They are: + +### GRDIR +Directory to which GR is installed. Should contain files in the standard structure. Example: +``` +export GRDIR="/home/sameer/gr" +``` + +### GKS\_WSTYPE +Type of output that you wish from the workspace. Set to `png` for PNG output. +Example: +``` +export GKS_WSTYPE=png +``` + +### GKS\_FILEPATH +Name of output file in case of wanting to write to file. +``` +export GKS_FILEPATH="hello.png" +``` diff --git a/Gemfile b/Gemfile index 851fabc..fe47135 100644 --- a/Gemfile +++ b/Gemfile @@ -1,2 +1,8 @@ source 'https://rubygems.org' + gemspec + +group :test do + gem 'rake' + gem 'rspec' +end diff --git a/README.md b/README.md index df27395..200c0a5 100644 --- a/README.md +++ b/README.md @@ -2,31 +2,24 @@ An advanced plotting library for Ruby. **Rubyplot aims to be the most advanced visualization solution for Rubyists.** -It aims to allow you to visualize anything, anywhere with a simple, Ruby-like API. -# Roadmap by priority -The release schedule and feature roadmap is as follows. Check issues labelled with -the version tags for knowing what we're working on currently. Listed by priority. +It aims to allow you to visualize anything, anywhere with a flexible, extensible, Ruby-like API. -## Release v0.1.rc1 -Deadline: 15 January 2019 +# Usage -* Support currently available plots fully with various customization options on Axes. -* Fully automated testing infrastructure. Travis integration with rubocop. -* Create a 'debug mode' for easy debugging. +Install the GR framework from the [website](https://gr-framework.org/c.html). -## Release v0.1.rc2 -Deadline: 26 February 2019 +Then set the `GRDIR` and `GKS_FONTPATH` ENV variables to point to your GR installation +and GR font path. Set the `RUBYPLOT_BACKEND` ENV variable to "GR". For example: +``` +export GRDIR="/home/sameer/Downloads/gr" +export GKS_FONTPATH="/home/sameer/Downloads/gr" +export RUBYPLOT_BACKEND="GR" +``` -* Support multiple Axes on the same Figure. -* Support atleast 3 new kinds of plots. +# Short term priorities -## Release v0.1.rc3 -Deadline: 15 April 2019 - -* Support atleast 3 new kinds of plots. -* Support Simple Plotting Interface. -* Move from using Ruby Arrays to a typed-array based system. +Check milestones in GitHub for more information. # Long term vision Rubyplot's long term vision, by priority: @@ -34,4 +27,15 @@ Rubyplot's long term vision, by priority: * Integrate the Rubyplot interface with the GR framework. * Generate various types of publication quality plots. * Interactive plotting using QT/GTK. -* Interactive or static plots for web backend. +* Integrate with bokehjs (or some other web framework) for plotting in the web browser. + +# Release cycle + +The library is currently in alpha stage. Version `0.1.0` is designated to be the +first 'final' release. Until then we will utilize various numbering schemes in the +third position of the version number to denote alpha, beta and RC releases. They +are as follows: + +* `a` for `alpha`. For example `0.1-a1` is the first alpha release. +* `b` for `beta`. For example `0.1-b1` is the first beta release. +* `rc` for `RC`. For example `0.1-rc1` is the first RC release. diff --git a/Rakefile b/Rakefile index 4b5fcc9..ab5e022 100644 --- a/Rakefile +++ b/Rakefile @@ -1,6 +1,32 @@ -require 'rspec/core/rake_task' +require "bundler/gem_tasks" +require "rake" require 'rake/extensiontask' +require 'rspec/core/rake_task' +RSpec::Core::RakeTask.new + +require 'rubocop/rake_task' +RuboCop::RakeTask.new + +desc 'Default: run unit specs.' +task :default => [:spec, :rubocop] + +task :test => ["test:magick", "test:gr"] + +namespace :test do + task :gr do + ENV['RUBYPLOT_BACKEND'] = "GR" + Rake::Task["spec"].reenable + Rake::Task["spec"].invoke + end + + task :magick do + ENV['RUBYPLOT_BACKEND'] = "MAGICK" + Rake::Task["spec"].reenable + Rake::Task["spec"].invoke + end +end + gemspec = eval(IO.read('rubyplot.gemspec')) ext_name = 'grruby' diff --git a/examples/gr_wrapper/axes_labels.rb b/examples/gr_wrapper/axes_labels.rb new file mode 100644 index 0000000..3163de9 --- /dev/null +++ b/examples/gr_wrapper/axes_labels.rb @@ -0,0 +1,23 @@ +# Custom labels for axes. + +require_relative '../../lib/grruby.so' + +# ndc_x - NDC X co-ordinate. +# ndc_y - NDC Y co-ordinate. +# svalue - internal string represenation of the label. +# value - floating point representation of label drawn at x,y. +x_axis_proc = Proc.new { |ndc_x, ndc_y, svalue, value| + Rubyplot::GR.text(ndc_x, ndc_y, "hello") +} + +y_axis_proc = Proc.new { |ndc_x, ndc_y, svalue, value| + Rubyplot::GR.text(ndc_x, ndc_y, "world") +} + +Rubyplot::GR.beginprint("rect.png") +Rubyplot::GR.setviewport(0.1, 0.9, 0.1, 0.9) +#Rubyplot::GR.setwindow(0,100,0,100) +Rubyplot::GR.axeslbl(0.2, 10, 0, 0, 2, 2, 0.01, x_axis_proc, y_axis_proc) +Rubyplot::GR.drawrect(0.01, 0.9, 0.3, 0.8) +Rubyplot::GR.endprint + diff --git a/examples/gr_wrapper/polygon.rb b/examples/gr_wrapper/polygon.rb new file mode 100644 index 0000000..4690d8f --- /dev/null +++ b/examples/gr_wrapper/polygon.rb @@ -0,0 +1,11 @@ +# Filled polygon GR example. + +require_relative '../../lib/grruby.so' + +Rubyplot::GR.beginprint("polygon.png") +Rubyplot::GR.setviewport(0,1,0,1) +Rubyplot::GR.setwindow(0,100,0,100) +Rubyplot::GR.setfillintstyle(1) +Rubyplot::GR.settransparency(0.5) +Rubyplot::GR.fillarea([1, 5, 25, 30, 50, 30], [1, 5, 25, 30, 25, 5]) +Rubyplot::GR.endprint diff --git a/examples/gr_wrapper/rectangle.rb b/examples/gr_wrapper/rectangle.rb new file mode 100644 index 0000000..6db68a0 --- /dev/null +++ b/examples/gr_wrapper/rectangle.rb @@ -0,0 +1,9 @@ +# Scatter plot using the bare-bones GR C interface +require_relative '../../lib/grruby.so' + +Rubyplot::GR.beginprint("rect.png") +Rubyplot::GR.setviewport(0, 1, 0, 1) +#Rubyplot::GR.setwindow(0,100,0,100) +Rubyplot::GR.axes(0.2, 10, 0, 0, 2, 2, 0.01) +Rubyplot::GR.drawrect(0.01, 0.9, 0.3, 0.8) +Rubyplot::GR.endprint diff --git a/examples/gr_wrapper/simple_scatter.rb b/examples/gr_wrapper/simple_scatter.rb new file mode 100644 index 0000000..2a88df6 --- /dev/null +++ b/examples/gr_wrapper/simple_scatter.rb @@ -0,0 +1,15 @@ +# Scatter plot using the bare-bones GR C interface +require_relative '../../lib/grruby.so' + +x1 = [-10, 0, 5, 28] +y1 = [1, 2, 3, 4] +x2 = [2, 4, 16] +y2 = [10, 20, -40] + +Rubyplot::GR.beginprint("abc.png") +Rubyplot::GR.setviewport(0.05, 0.95, 0.05, 0.95) +Rubyplot::GR.setwindow(0, 5, 0, 100) +Rubyplot::GR.polymarker(x2,y2) +Rubyplot::GR.polyline(y1, y1) +Rubyplot::GR.axes(0.2, 10, 0, 0, 2, 2, 0.01) +Rubyplot::GR.endprint diff --git a/examples/rubyplot/lines_multiple_subplots.rb b/examples/rubyplot/lines_multiple_subplots.rb new file mode 100644 index 0000000..4a95e50 --- /dev/null +++ b/examples/rubyplot/lines_multiple_subplots.rb @@ -0,0 +1,11 @@ +# Create a plot showing multiple subplots and some random lines across them. +x = [1,2,3,4,5] +y = [10,20,30,40,50] + +fig = Rubyplot::Figure.new +axes = fig.add_subplot! 0,0 +axes.scatter! do |p| + p.data x, y + p.label = "data1" + p.color = :plum_purple +end diff --git a/ext/grruby/grruby.c b/ext/grruby/grruby.c index 4c662b0..681e6f0 100644 --- a/ext/grruby/grruby.c +++ b/ext/grruby/grruby.c @@ -1,11 +1,11 @@ #include #include -double* rb_ar_2_dbl_ar(VALUE ar){ +double* rb_ar_2_dbl_ar(VALUE ar) { long ar_size=RARRAY_LEN(ar); double *arc = (double *)malloc(ar_size * sizeof(double)); int i; - for (i=0; i true + * + * Draw a polyline using the current line attributes, starting from the + * first data point and ending at the last data point. + * + * Parameters: + * + * `x` : + * A list containing the X coordinates + * `y` : + * A list containing the Y coordinates + * + * The values for `x` and `y` are in world coordinates. The attributes that + * control the appearance of a polyline are linetype, linewidth and color + * index. +*/ +static VALUE polyline(VALUE self,VALUE x, VALUE y) { int x_size = RARRAY_LEN(x); int y_size = RARRAY_LEN(y); int size = (x_size <= y_size)?x_size:y_size; double *xc = rb_ar_2_dbl_ar(x); double *yc = rb_ar_2_dbl_ar(y); gr_polyline(size,xc,yc); - return Qtrue; -} - -static VALUE polymarker(VALUE self,VALUE x, VALUE y){ + + return Qtrue; +} + +/** + * call-seq: + * Rubyplot::GR.polymarker(x, y) -> true + * + * Draw marker symbols centered at the given data points. + * + * **Parameters:** + * + * `x` : + * A list containing the X coordinates + * `y` : + * A list containing the Y coordinates + * + * The values for `x` and `y` are in world coordinates. The attributes that + * control the appearance of a polymarker are marker type, marker size + * scale factor and color index. + */ +static VALUE polymarker(VALUE self,VALUE x, VALUE y) { int x_size = RARRAY_LEN(x); int y_size = RARRAY_LEN(y); - int size = (x_size <= y_size)?x_size:y_size; + int size = (x_size <= y_size) ? x_size : y_size; double *xc = rb_ar_2_dbl_ar(x); - double *yc = rb_ar_2_dbl_ar(y); + double *yc = rb_ar_2_dbl_ar(y); + gr_polymarker(size,xc,yc); return Qtrue; - } -static VALUE text(VALUE self,VALUE x, VALUE y, VALUE string){ - double xc=NUM2DBL(x); - double yc=NUM2DBL(y); +/** + * call-seq: + * Rubyplot::GR.text(x, y, text) -> true + * + * Draw a text at position `x`, `y` using the current text attributes. + * + * **Parameters:** + * + * `x` : + * The X coordinate of starting position of the text string + * `y` : + * The Y coordinate of starting position of the text string + * `string` : + * The text to be drawn + * + * The values for `x` and `y` are in normalized device coordinates. + * The attributes that control the appearance of text are text font and precision, + * character expansion factor, character spacing, text color index, character + * height, character up vector, text path and text alignment. + */ +static VALUE text(VALUE self,VALUE x, VALUE y, VALUE string) { + double xc = NUM2DBL(x); + double yc = NUM2DBL(y); char *stringc=StringValueCStr(string); gr_text(xc,yc,stringc); + return Qtrue; } -static VALUE inqtext(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e){ +static VALUE inqtext(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e) { double ac = NUM2DBL(a); double bc = NUM2DBL(b); char *cc = StringValueCStr(c); @@ -116,17 +245,36 @@ static VALUE inqtext(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e){ return Qtrue; } +/** + * call-seq: + * Rubyplot::GR.fillarea([1,2,3], [1,2,3]) -> true + * + * Allows you to specify a polygonal shape of an area to be filled. + * + * **Parameters:** + * + * `x` : + * A list containing the X coordinates + * `y` : + * A list containing the Y coordinates + * + * The attributes that control the appearance of fill areas are fill area interior + * style, fill area style index and fill area color index. + */ static VALUE fillarea(VALUE self,VALUE x, VALUE y){ int x_size = RARRAY_LEN(x); int y_size = RARRAY_LEN(y); - int size = (x_size <= y_size)?x_size:y_size; + int size = (x_size <= y_size) ? x_size : y_size; double *xc = rb_ar_2_dbl_ar(x); double *yc = rb_ar_2_dbl_ar(y); gr_fillarea(size,xc,yc); + return Qtrue; } -static VALUE cellarray(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax,VALUE dimx,VALUE dimy,VALUE scol,VALUE srow,VALUE ncol,VALUE nrow,VALUE color){ +static VALUE cellarray(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax, + VALUE dimx,VALUE dimy,VALUE scol,VALUE srow,VALUE ncol, + VALUE nrow,VALUE color) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); @@ -163,7 +311,8 @@ static VALUE spline(VALUE self,VALUE n,VALUE px,VALUE py,VALUE m,VALUE method){ return Qtrue; } -static VALUE gridit(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e,VALUE f,VALUE g,VALUE h,VALUE i){ +static VALUE gridit(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e,VALUE f, + VALUE g,VALUE h,VALUE i) { int ac = NUM2INT(a); double *bc = rb_ar_2_dbl_ar(b); double *cc = rb_ar_2_dbl_ar(c); @@ -178,6 +327,45 @@ static VALUE gridit(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e,VALUE f,V //Can be optimised for Ruby } +/** + * call-seq: + * Rubyplot::GR.setlinetype(type) -> true + * + * Specify the line style for polylines. + * + * **Parameters:** + * + * `style` : + * The polyline line style + * + * The available line types are: + * + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_SOLID | 1|Solid line | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_DASHED | 2|Dashed line | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_DOTTED | 3|Dotted line | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_DASHED_DOTTED | 4|Dashed-dotted line | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_DASH_2_DOT | -1|Sequence of one dash followed by two dots | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_DASH_3_DOT | -2|Sequence of one dash followed by three dots | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_LONG_DASH | -3|Sequence of long dashes | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_LONG_SHORT_DASH | -4|Sequence of a long dash followed by a short dash | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_SPACED_DASH | -5|Sequence of dashes double spaced | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_SPACED_DOT | -6|Sequence of dots double spaced | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_DOUBLE_DOT | -7|Sequence of pairs of dots | + * +---------------------------+----+---------------------------------------------------+ + * |LINETYPE_TRIPLE_DOT | -8|Sequence of groups of three dots | + * +---------------------------+----+---------------------------------------------------+ + */ static VALUE setlinetype(VALUE self,VALUE type){ int typec = NUM2INT(type); gr_setlinetype(typec); @@ -190,9 +378,25 @@ static VALUE inqlinetype(VALUE self,VALUE a){ return Qtrue; } -static VALUE setlinewidth(VALUE self,VALUE width){ +/** + * call-seq: + * Rubyplot::GR.setlinewidth(1.3) -> true + * Define the line width of subsequent polyline output primitives. + * + * Parameters: + * + * `width` : + * The polyline line width scale factor + * + * The line width is calculated as the nominal line width generated + * on the workstation multiplied by the line width scale factor. + * This value is mapped by the workstation to the nearest available line width. + * The default line width is 1.0, or 1 times the line width generated on the graphics device. + */ +static VALUE setlinewidth(VALUE self,VALUE width) { double widthc = NUM2DBL(width); gr_setlinewidth(widthc); + return Qtrue; } @@ -202,9 +406,21 @@ static VALUE inqlinewidth(VALUE self,VALUE a){ return Qtrue; } -static VALUE setlinecolorind(VALUE self,VALUE color){ +/** + * call-seq: + * Rubyplot::GR.setlinecolorind(4) -> true + * + * Define the color of subsequent polyline output primitives. + * + * **Parameters:** + * + * `color` : + * The polyline color index (COLOR < 1256) + */ +static VALUE setlinecolorind(VALUE self,VALUE color) { int colorc = NUM2INT(color); gr_setlinecolorind(colorc); + return Qtrue; } @@ -214,6 +430,97 @@ static VALUE inqlinecolorind(VALUE self,VALUE a){ return Qtrue; } +/** + * call-seq: + * Rubyplot::GR.setmarkertype(style) -> true + * + * Specifiy the marker type for polymarkers. + * + * **Parameters:** + * + * `style` : + * The polymarker marker type + * + * The available marker types are: + * + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_DOT | 1|Smallest displayable dot | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_PLUS | 2|Plus sign | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_ASTERISK | 3|Asterisk | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_CIRCLE | 4|Hollow circle | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_DIAGONAL_CROSS | 5|Diagonal cross | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_CIRCLE | -1|Filled circle | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_TRIANGLE_UP | -2|Hollow triangle pointing upward | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_TRI_UP | -3|Filled triangle pointing upward | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_TRIANGLE_DOWN | -4|Hollow triangle pointing downward | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_TRI_DOWN | -5|Filled triangle pointing downward | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SQUARE | -6|Hollow square | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_SQUARE | -7|Filled square | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_BOWTIE | -8|Hollow bowtie | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_BOWTIE | -9|Filled bowtie | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_HGLASS | -10|Hollow hourglass | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_HGLASS | -11|Filled hourglass | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_DIAMOND | -12|Hollow diamond | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_DIAMOND | -13|Filled Diamond | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_STAR | -14|Hollow star | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_STAR | -15|Filled Star | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_TRI_UP_DOWN | -16|Hollow triangles pointing up and down overlaid | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_TRI_RIGHT | -17|Filled triangle point right | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID_TRI_LEFT | -18|Filled triangle pointing left | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_HOLLOW PLUS | -19|Hollow plus sign | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_SOLID PLUS | -20|Solid plus sign | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_PENTAGON | -21|Pentagon | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_HEXAGON | -22|Hexagon | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_HEPTAGON | -23|Heptagon | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_OCTAGON | -24|Octagon | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_STAR_4 | -25|4-pointed star | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_STAR_5 | -26|5-pointed star (pentagram) | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_STAR_6 | -27|6-pointed star (hexagram) | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_STAR_7 | -28|7-pointed star (heptagram) | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_STAR_8 | -29|8-pointed star (octagram) | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_VLINE | -30|verical line | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_HLINE | -31|horizontal line | + * +-----------------------------+-----+------------------------------------------------+ + * |MARKERTYPE_OMARK | -32|o-mark | + * +-----------------------------+-----+------------------------------------------------+ + * + * Polymarkers appear centered over their specified coordinates. + */ static VALUE setmarkertype(VALUE self, VALUE type){ int typec = NUM2INT(type); gr_setmarkertype(typec); @@ -230,12 +537,20 @@ static VALUE inqmarkertype(VALUE self,VALUE a){ static VALUE setmarkersize(VALUE self, VALUE size){ double sizec = NUM2DBL(size); gr_setmarkersize(sizec); + return Qtrue; } +/** + * call-seq: + * Rubyplot::GR::setmarkercolorind(color) -> true + * + * + */ static VALUE setmarkercolorind(VALUE self,VALUE color){ double colorc = NUM2INT(color); gr_setmarkercolorind(colorc); + return Qtrue; } @@ -245,10 +560,105 @@ static VALUE inqmarkercolorind(VALUE self,VALUE a){ return Qtrue; } +/** + * call-seq: + * Rubyplot::GR.settextprecision(font, precision)-> true + * + * Specify the text font and precision for subsequent text output primitives. + * + * **Parameters:** + * + * `font` : + * Text font (see tables below) + * `precision` : + * Text precision (see table below) + * + * The available text fonts are: + * + * +--------------------------------------+-----+ + * |FONT_TIMES_ROMAN | 101| + * +--------------------------------------+-----+ + * |FONT_TIMES_ITALIC | 102| + * +--------------------------------------+-----+ + * |FONT_TIMES_BOLD | 103| + * +--------------------------------------+-----+ + * |FONT_TIMES_BOLDITALIC | 104| + * +--------------------------------------+-----+ + * |FONT_HELVETICA | 105| + * +--------------------------------------+-----+ + * |FONT_HELVETICA_OBLIQUE | 106| + * +--------------------------------------+-----+ + * |FONT_HELVETICA_BOLD | 107| + * +--------------------------------------+-----+ + * |FONT_HELVETICA_BOLDOBLIQUE | 108| + * +--------------------------------------+-----+ + * |FONT_COURIER | 109| + * +--------------------------------------+-----+ + * |FONT_COURIER_OBLIQUE | 110| + * +--------------------------------------+-----+ + * |FONT_COURIER_BOLD | 111| + * +--------------------------------------+-----+ + * |FONT_COURIER_BOLDOBLIQUE | 112| + * +--------------------------------------+-----+ + * |FONT_SYMBOL | 113| + * +--------------------------------------+-----+ + * |FONT_BOOKMAN_LIGHT | 114| + * +--------------------------------------+-----+ + * |FONT_BOOKMAN_LIGHTITALIC | 115| + * +--------------------------------------+-----+ + * |FONT_BOOKMAN_DEMI | 116| + * +--------------------------------------+-----+ + * |FONT_BOOKMAN_DEMIITALIC | 117| + * +--------------------------------------+-----+ + * |FONT_NEWCENTURYSCHLBK_ROMAN | 118| + * +--------------------------------------+-----+ + * |FONT_NEWCENTURYSCHLBK_ITALIC | 119| + * +--------------------------------------+-----+ + * |FONT_NEWCENTURYSCHLBK_BOLD | 120| + * +--------------------------------------+-----+ + * |FONT_NEWCENTURYSCHLBK_BOLDITALIC | 121| + * +--------------------------------------+-----+ + * |FONT_AVANTGARDE_BOOK | 122| + * +--------------------------------------+-----+ + * |FONT_AVANTGARDE_BOOKOBLIQUE | 123| + * +--------------------------------------+-----+ + * |FONT_AVANTGARDE_DEMI | 124| + * +--------------------------------------+-----+ + * |FONT_AVANTGARDE_DEMIOBLIQUE | 125| + * +--------------------------------------+-----+ + * |FONT_PALATINO_ROMAN | 126| + * +--------------------------------------+-----+ + * |FONT_PALATINO_ITALIC | 127| + * +--------------------------------------+-----+ + * |FONT_PALATINO_BOLD | 128| + * +--------------------------------------+-----+ + * |FONT_PALATINO_BOLDITALIC | 129| + * +--------------------------------------+-----+ + * |FONT_ZAPFCHANCERY_MEDIUMITALIC | 130| + * +--------------------------------------+-----+ + * |FONT_ZAPFDINGBATS | 131| + * +--------------------------------------+-----+ + * + * The available text precisions are: + * + * +---------------------------+---+--------------------------------------+ + * |TEXT_PRECISION_STRING | 0|String precision (higher quality) | + * +---------------------------+---+--------------------------------------+ + * |TEXT_PRECISION_CHAR | 1|Character precision (medium quality) | + * +---------------------------+---+--------------------------------------+ + * |TEXT_PRECISION_STROKE | 2|Stroke precision (lower quality) | + * +---------------------------+---+--------------------------------------+ + * + * The appearance of a font depends on the text precision value specified. + * STRING, CHARACTER or STROKE precision allows for a greater or lesser + * realization of the text primitives, for efficiency. STRING is the default + * precision for GR and produces the highest quality output. + */ static VALUE settextfontprec(VALUE self, VALUE font, VALUE precision){ int fontc = NUM2INT(font); int precisionc = NUM2INT(precision); - gr_settextfontprec(fontc,precisionc); + gr_settextfontprec(fontc, precisionc); + return Qtrue; } @@ -264,53 +674,214 @@ static VALUE setcharspace(VALUE self,VALUE a){ return Qtrue; } +/** + * call-seq: + * Rubyplot::GR.settextcolorind(color) -> true + * + * Sets the current text color index. + * + * **Parameters:** + * + * `color` : + * The text color index (COLOR < 1256) + * + * `settextcolorind` defines the color of subsequent text output primitives. + * GR uses the default foreground color (black=1) for the default text color index. + */ static VALUE settextcolorind(VALUE self,VALUE color){ int colorc = NUM2INT(color); gr_settextcolorind(colorc); return Qtrue; } -static VALUE setcharheight(VALUE self, VALUE height){ +/** + * call-seq: + * Rubyplot::GR.setcharheight(0.05) -> true + * + * Set the current character height. + * + * **Parameters:** + * + * `height` : + * Text height value + * + * `setcharheight` defines the height of subsequent text output primitives. Text height + * is defined as a percentage of the default window. GR uses the default text height of + * 0.027 (2.7% of the height of the default window). + */ +static VALUE setcharheight(VALUE self, VALUE height) { double heightc= NUM2DBL(height); gr_setcharheight(heightc); - return Qtrue; -} - -static VALUE setcharup(VALUE self,VALUE ux,VALUE uy){ + + return Qtrue; +} + +/** + * call-seq: + * Rubyplot::GR.setcharup(0, 0.5) -> true + * + * Set the current character text angle up vector. + * + * **Parameters:** + * + * `ux`, `uy` : + * Text up vector + * + * `setcharup` defines the vertical rotation of subsequent text output primitives. + * The text up vector is initially set to (0, 1), horizontal to the baseline. + */ +static VALUE setcharup(VALUE self,VALUE ux,VALUE uy) { double uxc = NUM2DBL(ux); double uyc = NUM2DBL(uy); gr_setcharup(uxc,uyc); - return Qtrue; -} - + + return Qtrue; +} + +/** + * call-seq: + * Rubyplot::GR.settextpath(2) -> true + * + * Define the current direction in which subsequent text will be drawn. + * + * **Parameters:** + * + * `path` : + * Text path (see table below) + * + * +----------------------+---+---------------+ + * |TEXT_PATH_RIGHT | 0|left-to-right | + * +----------------------+---+---------------+ + * |TEXT_PATH_LEFT | 1|right-to-left | + * +----------------------+---+---------------+ + * |TEXT_PATH_UP | 2|downside-up | + * +----------------------+---+---------------+ + * |TEXT_PATH_DOWN | 3|upside-down | + * +----------------------+---+---------------+ + */ static VALUE settextpath(VALUE self,VALUE path){ int pathc = NUM2INT(path); + gr_settextpath(pathc); return Qtrue; } -static VALUE settextalign(VALUE self, VALUE horizontal, VALUE vertical){ - int horizontalc=NUM2INT(horizontal); - int verticalc=NUM2INT(vertical); +/** + * call-seq: + * Rubyplot::GR.settextalign(Rubyplot::GR::TEXT_HALIGN_NORMAL, + * Rubyplot::GR::TEXT_VALIGN_LEFT) -> true + * + * Set the current horizontal and vertical alignment for text. + * + * **Parameters:** + * + * `horizontal` : + * Horizontal text alignment (see the table below) + * `vertical` : + * Vertical text alignment (see the table below) + * + * `settextalign` specifies how the characters in a text primitive will be aligned + * in horizontal and vertical space. The default text alignment indicates horizontal left + * alignment and vertical baseline alignment. + * + * +-------------------------+---+----------------+ + * |TEXT_HALIGN_NORMAL | 0| | + * +-------------------------+---+----------------+ + * |TEXT_HALIGN_LEFT | 1|Left justify | + * +-------------------------+---+----------------+ + * |TEXT_HALIGN_CENTER | 2|Center justify | + * +-------------------------+---+----------------+ + * |TEXT_HALIGN_RIGHT | 3|Right justify | + * +-------------------------+---+----------------+ + * + * +-------------------------+---+------------------------------------------------+ + * |TEXT_VALIGN_NORMAL | 0| | + * +-------------------------+---+------------------------------------------------+ + * |TEXT_VALIGN_TOP | 1|Align with the top of the characters | + * +-------------------------+---+------------------------------------------------+ + * |TEXT_VALIGN_CAP | 2|Aligned with the cap of the characters | + * +-------------------------+---+------------------------------------------------+ + * |TEXT_VALIGN_HALF | 3|Aligned with the half line of the characters | + * +-------------------------+---+------------------------------------------------+ + * |TEXT_VALIGN_BASE | 4|Aligned with the base line of the characters | + * +-------------------------+---+------------------------------------------------+ + * |TEXT_VALIGN_BOTTOM | 5|Aligned with the bottom line of the characters | + * +-------------------------+---+------------------------------------------------+ + */ +static VALUE settextalign(VALUE self, VALUE horizontal, VALUE vertical) { + int horizontalc = NUM2INT(horizontal); + int verticalc = NUM2INT(vertical); + gr_settextalign(horizontalc,verticalc); return Qtrue; } -static VALUE setfillintstyle(VALUE self,VALUE style){ +/** + * call-seq: + * Rubyplot::GR.setfillintstyle(3) -> true + * + * Set the fill area interior style to be used for fill areas. + * + * **Parameters:** + * + * `style` : + * The style of fill to be used + * + * `setfillintstyle` defines the interior style for subsequent fill area output + * primitives. The default interior style is HOLLOW. + * + * +------------------+-+-----------------------------------------------------------------------+ + * |INTSTYLE_HOLLOW |0|No filling. Just draw the bounding polyline | + * +------------------+-+-----------------------------------------------------------------------+ + * |INTSTYLE_SOLID |1|Fill the interior of the polygon using the fill color index | + * +------------------+-+-----------------------------------------------------------------------+ + * |INTSTYLE_PATTERN |2|Fill the interior of the polygon using the style index as a pattern index| + * +-----------------+--+------------------------------------------------------------------------+ + * |INTSTYLE_HATCH |3|Fill the interior of the polygon using the style index as a cross-hatched style| + * +-----------------+-+--------------------------------------------------------------------------+ + */ +static VALUE setfillintstyle(VALUE self,VALUE style) { int stylec = NUM2INT(style); gr_setfillintstyle(stylec); - return Qtrue; -} - + + return Qtrue; +} + +/** + *Sets the fill style to be used for subsequent fill areas. + * + ***Parameters:** + * + *`index` : + *The fill style index to be used + * + *`setfillstyle` specifies an index when PATTERN fill or HATCH fill is requested by the + *`setfillintstyle` function. If the interior style is set to PATTERN, the fill style + * index points to a device-independent pattern table. If interior style is set to HATCH + * the fill style index indicates different hatch styles. If HOLLOW or SOLID is specified + * for the interior style, the fill style index is unused. + */ static VALUE setfillstyle(VALUE self,VALUE index){ int indexc = NUM2INT(index); gr_setfillstyle(indexc); return Qtrue; } -static VALUE setfillcolorind(VALUE self,VALUE color){ +/* + *Sets the current fill area color index. + * + ***Parameters:** + * + *`color` : + *The fill area color index (COLOR < 1256) + * + * `setfillcolorind` defines the color of subsequent fill area output primitives. + * GR uses the default foreground color (black=1) for the default fill area color index. + */ +static VALUE setfillcolorind(VALUE self,VALUE color) { int colorc = NUM2INT(color); gr_setfillcolorind(colorc); + return Qtrue; } @@ -323,7 +894,19 @@ static VALUE setcolorrep(VALUE self,VALUE index,VALUE red,VALUE green,VALUE blue return Qtrue; } -static VALUE setwindow(VALUE self, VALUE xmin, VALUE xmax,VALUE ymin, VALUE ymax){ +/* + * call-seq: + * Rubyplot::GR.setwindow(xmin, xmax, ymin, ymax) -> true + * + * Set a window or rectangular subspace of world co-ordinates to be plotted. + * + * xmin [Numeric] : Minimum value of X data in this window. + * xmax [Numeric] : Maximum value of X data in this window. + * ymin [Numeric] : Minimum value of Y data in this window. + * ymax [Numeric] : Maximum value of Y data in this window. + * + */ +static VALUE setwindow(VALUE self, VALUE xmin, VALUE xmax,VALUE ymin, VALUE ymax) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); @@ -332,7 +915,7 @@ static VALUE setwindow(VALUE self, VALUE xmin, VALUE xmax,VALUE ymin, VALUE ymax return Qtrue; } -static VALUE inqwindow(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d){ +static VALUE inqwindow(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d) { double *ac = rb_ar_2_dbl_ar(a); double *bc = rb_ar_2_dbl_ar(b); double *cc = rb_ar_2_dbl_ar(c); @@ -341,12 +924,31 @@ static VALUE inqwindow(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d){ return Qtrue; } -static VALUE setviewport(VALUE self, VALUE xmin, VALUE xmax,VALUE ymin, VALUE ymax){ +/* + * call-seq: + * Rubyplot::GR.setviewport(xmin, xmax, ymin, ymax) -> true + * + * `setviewport` establishes a rectangular subspace of normalized device coordinates. + * + * `setviewport` defines the rectangular portion of the Normalized Device Coordinate + * (NDC) space to be associated with the specified normalization transformation. The + * NDC viewport and World Coordinate (WC) window define the normalization transformation + * through which all output primitives pass. The WC window is mapped onto the rectangular + * NDC viewport which is, in turn, mapped onto the display surface of the open and active + * workstation, in device coordinates. + * + * xmin [Numeric] : + * xmax [Numeric] : + * ymin [Numeric] : + * ymax [Numeric] : + */ +static VALUE setviewport(VALUE self, VALUE xmin, VALUE xmax, VALUE ymin, VALUE ymax) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); double ymaxc = NUM2DBL(ymax); gr_setviewport(xminc,xmaxc,yminc,ymaxc); + return Qtrue; } @@ -371,21 +973,61 @@ static VALUE setclip(VALUE self,VALUE indicator){ return Qtrue; } -static VALUE setwswindow(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax){ +/* + * Set the area of the NDC viewport that is to be drawn in the workstation window. + * + * **Parameters:** + * + * `xmin` : + * The left horizontal coordinate of the workstation window. + * `xmax` : + * The right horizontal coordinate of the workstation window (0 <= `xmin` < `xmax` <= 1). + * `ymin` : + * The bottom vertical coordinate of the workstation window. + * `ymax` : + * The top vertical coordinate of the workstation window (0 <= `ymin` < `ymax` <= 1). + * + * `setwswindow` defines the rectangular area of the Normalized Device Coordinate space + * to be output to the device. By default, the workstation transformation will map the + * range [0,1] x [0,1] in NDC onto the largest square on the workstation’s display + * surface. The aspect ratio of the workstation window is maintained at 1 to 1. + **/ +static VALUE setwswindow(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); double ymaxc = NUM2DBL(ymax); gr_setwswindow(xminc,xmaxc,yminc,ymaxc); - return Qtrue; -} - -static VALUE setwsviewport(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax){ + + return Qtrue; +} + +/* + * Define the size of the workstation graphics window in meters. + * + * **Parameters:** + * + * `xmin` : + * The left horizontal coordinate of the workstation viewport. + * `xmax` : + * The right horizontal coordinate of the workstation viewport. + * `ymin` : + * The bottom vertical coordinate of the workstation viewport. + * `ymax` : + * The top vertical coordinate of the workstation viewport. + * + * `setwsviewport` places a workstation window on the display of the specified size in + * meters. This command allows the workstation window to be accurately sized for a + * display or hardcopy device, and is often useful for sizing graphs for desktop + * publishing applications. + **/ +static VALUE setwsviewport(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); double ymaxc = NUM2DBL(ymax); gr_setwsviewport(xminc,xmaxc,yminc,ymaxc); + return Qtrue; } @@ -462,15 +1104,109 @@ static VALUE inqscale(VALUE self,VALUE a){ return Qtrue; } -static VALUE textext(VALUE self,VALUE x, VALUE y, VALUE string){ +/** + * call-seq: + * Rubyplot::GR.textext(x, y, "string to write") -> true + * + * Draw a text at position `x`, `y` using the current text attributes. Strings can be + * defined to create basic mathematical expressions and Greek letters. + * + * **Parameters:** + * + * `x` : + * The X coordinate of starting position of the text string + * `y` : + * The Y coordinate of starting position of the text string + * `string` : + * The text to be drawn + * + * The values for X and Y are in normalized device coordinates. + * The attributes that control the appearance of text are text font and precision, + * character expansion factor, character spacing, text color index, character + * height, character up vector, text path and text alignment. + * + * The character string is interpreted to be a simple mathematical formula. + * The following notations apply: + * + * Subscripts and superscripts: These are indicated by carets ('^') and underscores + * ('_'). If the sub/superscript contains more than one character, it must be enclosed + * in curly braces ('{}'). + * + * Fractions are typeset with A '/' B, where A stands for the numerator and B for the + * denominator. + * + * To include a Greek letter you must specify the corresponding keyword after a + * backslash ('\') character. The text translator produces uppercase or lowercase + * Greek letters depending on the case of the keyword. + * + * +--------+---------+ + * |Letter |Keyword | + * +--------+---------+ + * |Α α |alpha | + * +--------+---------+ + * |Β β |beta | + * +--------+---------+ + * |Γ γ |gamma | + * +--------+---------+ + * |Δ δ |delta | + * +--------+---------+ + * |Ε ε |epsilon | + * +--------+---------+ + * |Ζ ζ |zeta | + * +--------+---------+ + * |Η η |eta | + * +--------+---------+ + * |Θ θ |theta | + * +--------+---------+ + * |Ι ι |iota | + * +--------+---------+ + * |Κ κ |kappa | + * +--------+---------+ + * |Λ λ |lambda | + * +--------+---------+ + * |Μ μ |mu | + * +--------+---------+ + * |Ν ν |Nu / v | + * +--------+---------+ + * |Ξ ξ |xi | + * +--------+---------+ + * |Ο ο |omicron | + * +--------+---------+ + * |Π π |pi | + * +--------+---------+ + * |Ρ ρ |rho | + * +--------+---------+ + * |Σ σ |sigma | + * +--------+---------+ + * |Τ τ |tau | + * +--------+---------+ + * |Υ υ |upsilon | + * +--------+---------+ + * |Φ φ |phi | + * +--------+---------+ + * |Χ χ |chi | + * +--------+---------+ + * |Ψ ψ |psi | + * +--------+---------+ + * |Ω ω |omega | + * +--------+---------+ + * + * Note: `\v` is a replacement for `\nu` which would conflict with `\n` (newline) + * + * For more sophisticated mathematical formulas, you should use the `gr.mathtex` + * function. + */ + +static VALUE textext(VALUE self,VALUE x, VALUE y, VALUE string) { double xc=NUM2DBL(x); double yc=NUM2DBL(y); char *stringc=StringValueCStr(string); gr_textext(xc,yc,stringc); + return Qtrue; } -static VALUE inqtextext(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e){ +static VALUE inqtextext(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e) { double ac = NUM2DBL(a); double bc = NUM2DBL(b); char *cc = StringValueCStr(c); @@ -480,25 +1216,96 @@ static VALUE inqtextext(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e){ return Qtrue; } -static VALUE axes(VALUE self, VALUE x_tick, VALUE y_tick, VALUE x_org, VALUE y_org, VALUE major_x, VALUE major_y, VALUE tick_size){ - double x_tickc=NUM2DBL(x_tick); - double y_tickc=NUM2DBL(y_tick); - double x_orgc=NUM2DBL(x_org); - double y_orgc=NUM2DBL(y_org); - int major_xc=NUM2INT(major_x); - int major_yc=NUM2INT(major_y); +/* + * call-seq: + * Rubyplot::GR.axes(x_tick, y_tick, x_org, y_org, major_x, major_y, tick_size) -> true + * + * Draw X and Y co-ordinate axes with linear or logarithmically spaced tick marks. + * + * `x_tick`, `y_tick` : + * The interval between minor tick marks on each axis. + * `x_org`, `y_org` : + * The world coordinates of the origin (point of intersection) of the X + * and Y axes. + * `major_x`, `major_y` : + * Unitless integer values specifying the number of minor tick intervals + * between major tick marks. Values of 0 or 1 imply no minor ticks. + * Negative values specify no labels will be drawn for the associated axis. + * `tick_size` : + * The length of minor tick marks specified in a normalized device + * coordinate unit. Major tick marks are twice as long as minor tick marks. + * A negative value reverses the tick marks on the axes from inward facing + * to outward facing (or vice versa). + * + * Tick marks are positioned along each axis so that major tick marks fall on the axes + * origin (whether visible or not). Major tick marks are labeled with the corresponding + * data values. Axes are drawn according to the scale of the window. Axes and tick marks + * are drawn using solid lines; line color and width can be modified using the + * `setlinetype` and `setlinewidth` functions. Axes are drawn according to + * the linear or logarithmic transformation established by the `setscale` function. + * + */ +static VALUE axes(VALUE self, VALUE x_tick, VALUE y_tick, VALUE x_org, + VALUE y_org, VALUE major_x, VALUE major_y, VALUE tick_size) { + double x_tickc = NUM2DBL(x_tick); + double y_tickc = NUM2DBL(y_tick); + double x_orgc = NUM2DBL(x_org); + double y_orgc = NUM2DBL(y_org); + int major_xc = NUM2INT(major_x); + int major_yc = NUM2INT(major_y); double tick_sizec=NUM2DBL(tick_size); - gr_axes(x_tickc,y_tickc,x_orgc,y_orgc,major_xc,major_yc,tick_sizec); - return Qtrue; -} - -/* figure this one -static VALUE axeslbl(VALUE self,VALUE,VALUE,VALUE,VALUE,VALUE,VALUE,VALUE,VALUE){ - return Qtrue; -} -*/ - -static VALUE grid(VALUE self,VALUE x_tick,VALUE y_tick,VALUE x_org,VALUE y_org,VALUE major_x,VALUE major_y){ + + gr_axes(x_tickc, y_tickc, x_orgc,y_orgc,major_xc,major_yc,tick_sizec); + + return Qtrue; +} + +static VALUE global_fpx_proc; /* Proc for axeslbl X axis. */ +static VALUE global_fpy_proc; /* Proc for axeslbl Y axis. */ + +static void rb_grruby_axeslbl_fpx_internal_callback(double x, double y, + const char * svalue, double value) { + rb_funcall(global_fpx_proc, rb_intern("call"), 4, + DBL2NUM(x), DBL2NUM(y), rb_str_new2(svalue), DBL2NUM(value)); +} + +static void rb_grruby_axeslbl_fpy_internal_callback(double x, double y, + const char * svalue, double value) { + rb_funcall(global_fpy_proc, rb_intern("call"), 4, + DBL2NUM(x), DBL2NUM(y), rb_str_new2(svalue), DBL2NUM(value)); +} + +/* call-seq: + * Rubyplot::GR.axeslbl(x_tick, y_tick, x_org, y_org, major_x, major_y, tick_size, + * fpx, fpy) -> true + * + * Similar to axes() but allows more fine grained control over tick labels and + * text positioning. + * + * References: + * http://clalance.blogspot.com/2011/01/writing-ruby-extensions-in-c-part-11.html + */ +static VALUE axeslbl(VALUE self, VALUE x_tick, VALUE y_tick, VALUE x_org, + VALUE y_org, VALUE major_x, VALUE major_y ,VALUE tick_size, + VALUE fpx, VALUE fpy) { + global_fpx_proc = fpx; + global_fpy_proc = fpy; + + gr_axeslbl(NUM2DBL(x_tick), + NUM2DBL(y_tick), + NUM2DBL(x_org), + NUM2DBL(y_org), + NUM2INT(major_x), + NUM2INT(major_y), + NUM2DBL(tick_size), + rb_grruby_axeslbl_fpx_internal_callback, + rb_grruby_axeslbl_fpy_internal_callback); + + return Qtrue; +} + +static VALUE grid(VALUE self,VALUE x_tick,VALUE y_tick,VALUE x_org,VALUE y_org, + VALUE major_x,VALUE major_y) { double x_tickc = NUM2DBL(x_tick); double y_tickc = NUM2DBL(y_tick); double x_orgc = NUM2DBL(x_org); @@ -509,7 +1316,8 @@ static VALUE grid(VALUE self,VALUE x_tick,VALUE y_tick,VALUE x_org,VALUE y_org,V return Qtrue; } -static VALUE grid3d(VALUE self,VALUE x_tick,VALUE y_tick,VALUE z_tick,VALUE x_org,VALUE y_org,VALUE z_org,VALUE major_x,VALUE major_y,VALUE major_z){ +static VALUE grid3d(VALUE self,VALUE x_tick,VALUE y_tick,VALUE z_tick,VALUE x_org, + VALUE y_org,VALUE z_org,VALUE major_x,VALUE major_y,VALUE major_z) { double x_tickc = NUM2DBL(x_tick); double y_tickc = NUM2DBL(y_tick); double z_tickc = NUM2DBL(z_tick); @@ -620,7 +1428,8 @@ static VALUE contour(VALUE self,VALUE px,VALUE py,VALUE ph,VALUE pz,VALUE major_ return Qtrue; } -static VALUE tricontour(VALUE self,VALUE npoints,VALUE x,VALUE y,VALUE z,VALUE nlevels,VALUE levels){ +static VALUE tricontour(VALUE self,VALUE npoints,VALUE x,VALUE y,VALUE z, + VALUE nlevels,VALUE levels) { int npointsc = NUM2INT(npoints); double *xc = rb_ar_2_dbl_ar(x); double *yc = rb_ar_2_dbl_ar(y); @@ -631,7 +1440,7 @@ static VALUE tricontour(VALUE self,VALUE npoints,VALUE x,VALUE y,VALUE z,VALUE n return Qtrue; } -static VALUE hexbin(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d){ +static VALUE hexbin(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d) { int ac = NUM2INT(a); double* bc = rb_ar_2_dbl_ar(b); double* cc = rb_ar_2_dbl_ar(c); @@ -639,7 +1448,7 @@ static VALUE hexbin(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d){ return INT2NUM(gr_hexbin(ac,bc,cc,dc)); } -static VALUE setcolormap(VALUE self,VALUE a){ +static VALUE setcolormap(VALUE self,VALUE a) { int ac = NUM2INT(a); gr_setcolormap(ac); return Qtrue; @@ -663,10 +1472,21 @@ static VALUE inqcolor(VALUE self,VALUE a,VALUE b){ return Qtrue; } -static VALUE inqcolorfromrgb(VALUE self,VALUE a,VALUE b,VALUE c){ - double ac = NUM2DBL(a); - double bc = NUM2DBL(b); - double cc = NUM2DBL(c); +/** + * call-seq: + * Rubyplot::GR.inqcolorfromrgb(r, g, b) -> color_index + * + * Get a GR-compatible color index from RGB color values. + * + * Arguments: + * * `r` - Value between 0-255 representing red. + * * `g` - Value between 0-255 representing green. + * * `b` - Value between 0-255 representing blue. + */ +static VALUE inqcolorfromrgb(VALUE self,VALUE r,VALUE g,VALUE b){ + double ac = NUM2DBL(r); + double bc = NUM2DBL(g); + double cc = NUM2DBL(b); return INT2NUM(gr_inqcolorfromrgb(ac,bc,cc)); } @@ -681,9 +1501,17 @@ static VALUE hsvtorgb(VALUE self,VALUE h,VALUE s,VALUE v ,VALUE r,VALUE g,VALUE return Qtrue; } -static VALUE tick(VALUE self,VALUE a,VALUE b){ - double ac = NUM2DBL(a); - double bc = NUM2DBL(b); +/* + * call-seq: + * Rubyplot::GR.tick(amin, amax) -> Numeric + * + * Return a tick unit that evenly divides into the difference between the maximum + * and minimum values. + */ +static VALUE tick(VALUE self,VALUE amin,VALUE amax) { + double ac = NUM2DBL(amin); + double bc = NUM2DBL(amax); + return DBL2NUM(gr_tick(ac,bc)); } @@ -693,11 +1521,22 @@ static VALUE validaterange(VALUE self,VALUE a,VALUE b){ return INT2NUM(gr_validaterange(ac,bc)); } -static VALUE adjustlimits(VALUE self,VALUE a,VALUE b){ - double *ac = rb_ar_2_dbl_ar(a); - double *bc = rb_ar_2_dbl_ar(b); - gr_adjustlimits(ac,bc); - return Qtrue; +/* + * call-seq: + * Rubyplot::GR.adjustlimits(amin, amax) -> [adjusted_amin, adjusted_amax] + * + */ +static VALUE adjustlimits(VALUE self,VALUE amin,VALUE amax) { + VALUE ret = rb_ary_new2(2); + double ac = NUM2DBL(amin); + double bc = NUM2DBL(amax); + + gr_adjustlimits(&ac,&bc); + + rb_ary_push(ret, DBL2NUM(ac)); + rb_ary_push(ret, DBL2NUM(bc)); + + return ret; } static VALUE adjustrange(VALUE self,VALUE a,VALUE b){ @@ -707,18 +1546,52 @@ static VALUE adjustrange(VALUE self,VALUE a,VALUE b){ return Qtrue; } -static VALUE beginprint(VALUE self,VALUE pathname){ +/* + * Open and activate a print device. + * + * **Parameters:** + * + * `pathname` : + * Filename for the print device. + * + * `beginprint` opens an additional graphics output device. The device type is obtained + * from the given file extension. The following file types are supported: + * + * +-------------+---------------------------------------+ + * |.ps, .eps |PostScript | + * +-------------+---------------------------------------+ + * |.pdf |Portable Document Format | + * +-------------+---------------------------------------+ + * |.bmp |Windows Bitmap (BMP) | + * +-------------+---------------------------------------+ + * |.jpeg, .jpg |JPEG image file | + * +-------------+---------------------------------------+ + * |.png |Portable Network Graphics file (PNG) | + * +-------------+---------------------------------------+ + * |.tiff, .tif |Tagged Image File Format (TIFF) | + * +-------------+---------------------------------------+ + * |.fig |Xfig vector graphics file | + * +-------------+---------------------------------------+ + * |.svg |Scalable Vector Graphics | + * +-------------+---------------------------------------+ + * |.wmf |Windows Metafile | + * +-------------+---------------------------------------+ + */ +static VALUE beginprint(VALUE self, VALUE pathname) { char *pathnamec = StringValueCStr(pathname); + gr_beginprint(pathnamec); return Qtrue; } -static VALUE beginprintext(VALUE self,VALUE pathname,VALUE mode,VALUE format,VALUE orientation){ +static VALUE beginprintext(VALUE self, VALUE pathname, VALUE mode, + VALUE format, VALUE orientation){ char *pathnamec = StringValueCStr(pathname); char *modec = StringValueCStr(mode); char *formatc = StringValueCStr(format); char *orientationc = StringValueCStr(orientation); gr_beginprintext(pathnamec,modec,formatc,orientationc); + return Qtrue; } @@ -749,25 +1622,81 @@ static VALUE wc3towc(VALUE self,VALUE a,VALUE b,VALUE c){ return Qtrue; } -static VALUE drawrect(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax){ +/* +* Draw a rectangle using the current line attributes. +* +* **Parameters:** +* +* `xmin` : +* Lower left edge of the rectangle +* `xmax` : +* Lower right edge of the rectangle +* `ymin` : +* Upper left edge of the rectangle +* `ymax` : +* Upper right edge of the rectangle +*/ +static VALUE drawrect(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); double ymaxc = NUM2DBL(ymax); gr_drawrect(xminc,xmaxc,yminc,ymaxc); - return Qtrue; -} - + + return Qtrue; +} + +/* + * Draw a filled rectangle using the current fill attributes. + * + * **Parameters:** + * + * `xmin` : + * Lower left edge of the rectangle + * `xmax` : + * Lower right edge of the rectangle + * `ymin` : + * Upper left edge of the rectangle + * `ymax` : + * Upper right edge of the rectangle + */ static VALUE fillrect(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax){ double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); double ymaxc = NUM2DBL(ymax); gr_fillrect(xminc,xmaxc,yminc,ymaxc); - return Qtrue; -} - -static VALUE drawarc(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax,VALUE a1,VALUE a2){ + + return Qtrue; +} + +/** + * call-seq: + * Rubyplot::GR.drawarc(0,1,0,1,0,360) -> true + * + * Draw a circular or elliptical arc covering the specified rectangle. + * + * **Parameters:** + * + * `xmin` : + * Lower left edge of the rectangle + * `xmax` : + * Lower right edge of the rectangle + * `ymin` : + * Upper left edge of the rectangle + * `ymax` : + * Upper right edge of the rectangle + * `a1` : + * The start angle + * `a2` : + * The end angle + * + * The resulting arc begins at `a1` and ends at `a2` degrees. Angles are interpreted + * such that 0 degrees is at the 3 o'clock position. The center of the arc is the center + * of the given rectangle. + */ +static VALUE drawarc(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax, + VALUE a1,VALUE a2) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); @@ -775,10 +1704,37 @@ static VALUE drawarc(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax,VALU int a1c = NUM2INT(a1); int a2c = NUM2INT(a2); gr_drawarc(xminc,xmaxc,yminc,ymaxc,a1c,a2c); - return Qtrue; -} - -static VALUE fillarc(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax,VALUE a1,VALUE a2){ + + return Qtrue; +} + +/** + * call-seq: + * Rubyplot::GR.drawarc(0,1,0,1,0,360) -> true + * + * Fill a circular or elliptical arc covering the specified rectangle. + * + * **Parameters:** + * + * `xmin` : + * Lower left edge of the rectangle + * `xmax` : + * Lower right edge of the rectangle + * `ymin` : + * Upper left edge of the rectangle + * `ymax` : + * Upper right edge of the rectangle + * `a1` : + * The start angle + * `a2` : + * The end angle + * + * The resulting arc begins at `a1` and ends at `a2` degrees. Angles are interpreted + * such that 0 degrees is at the 3 o'clock position. The center of the arc is the center + * of the given rectangle. + */ +static VALUE fillarc(VALUE self, VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax, + VALUE a1, VALUE a2) { double xminc = NUM2DBL(xmin); double xmaxc = NUM2DBL(xmax); double yminc = NUM2DBL(ymin); @@ -786,6 +1742,7 @@ static VALUE fillarc(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax,VALU int a1c = NUM2INT(a1); int a2c = NUM2INT(a2); gr_fillarc(xminc,xmaxc,yminc,ymaxc,a1c,a2c); + return Qtrue; } @@ -796,24 +1753,95 @@ static VALUE fillarc(VALUE self,VALUE xmin,VALUE xmax,VALUE ymin,VALUE ymax,VALU requires a struct do that */ +/*! + * Set the arrow style to be used for subsequent arrow commands. + * + * \param[in] style The arrow style to be used + * + * This function defines the arrow style for subsequent arrow primitives. + * The default arrow style is 1. + * + * \verbatim embed:rst:leading-asterisk + * + * +---+----------------------------------+ + * | 1|simple, single-ended | + * +---+----------------------------------+ + * | 2|simple, single-ended, acute head | + * +---+----------------------------------+ + * | 3|hollow, single-ended | + * +---+----------------------------------+ + * | 4|filled, single-ended | + * +---+----------------------------------+ + * | 5|triangle, single-ended | + * +---+----------------------------------+ + * | 6|filled triangle, single-ended | + * +---+----------------------------------+ + * | 7|kite, single-ended | + * +---+----------------------------------+ + * | 8|filled kite, single-ended | + * +---+----------------------------------+ + * | 9|simple, double-ended | + * +---+----------------------------------+ + * | 10|simple, double-ended, acute head | + * +---+----------------------------------+ + * | 11|hollow, double-ended | + * +---+----------------------------------+ + * | 12|filled, double-ended | + * +---+----------------------------------+ + * | 13|triangle, double-ended | + * +---+----------------------------------+ + * | 14|filled triangle, double-ended | + * +---+----------------------------------+ + * | 15|kite, double-ended | + * +---+----------------------------------+ + * | 16|filled kite, double-ended | + * +---+----------------------------------+ + * | 17|double line, single-ended | + * +---+----------------------------------+ + * | 18|double line, double-ended | + * +---+----------------------------------+ + * + * \endverbatim + */ static VALUE setarrowstyle(VALUE self,VALUE style){ int stylec = NUM2INT(style); gr_setarrowstyle(stylec); return Qtrue; } -static VALUE setarrowsize(VALUE self,VALUE size){ - int sizec = NUM2INT(size); - gr_setarrowsize(size); - return Qtrue; -} - -static VALUE drawarrow(VALUE self,VALUE x1,VALUE y1,VALUE x2,VALUE y2){ +/*! + * Set the arrow size to be used for subsequent arrow commands. + * + * \param[in] size The arrow size to be used + * + * This function defines the arrow size for subsequent arrow primitives. + * The default arrow size is 1. + */ +static VALUE setarrowsize(VALUE self,VALUE size) { + gr_setarrowsize(NUM2DBL(size)); + + return Qtrue; +} + +/*! + * Draw an arrow between two points. + * + * \param[in] x1 The X coordinate of the arrow start point (tail) + * \param[in] y1 The Y coordinate of the arrow start point (tail) + * \param[in] x2 The X coordinate of the arrow end point (head) + * \param[in] y2 The Y coordinate of the arrow end point (head) + * + * Different arrow styles (angles between arrow tail and wing, optionally filled + * heads, double headed arrows) are available and can be set with the + * gr_setarrowstyle function. + */ +static VALUE drawarrow (VALUE self,VALUE x1,VALUE y1,VALUE x2,VALUE y2) { double x1c = NUM2DBL(x1); double x2c = NUM2DBL(x2); double y1c = NUM2DBL(y1); double y2c = NUM2DBL(y2); - gr_drawarrow(x1c,x2c,y1c,y2c); + gr_drawarrow(x1c, y1c, x2c, y2c); + return Qtrue; } @@ -850,9 +1878,21 @@ static VALUE setshadow(VALUE self,VALUE offsetx,VALUE offsety,VALUE blur){ return Qtrue; } + +/** + * call-seq: + * Rubyplot::GR.settransparency(alpha) -> true + * + * Set the value of the alpha component associated with GR colors + * + * **Parameters:** + * + * `alpha` : + * An alpha value (0.0 - 1.0) + */ static VALUE settransparency(VALUE self,VALUE alpha){ - double alphac = NUM2DBL(alpha); - gr_settransparency(alphac); + gr_settransparency(NUM2DBL(alpha)); + return Qtrue; } @@ -936,7 +1976,7 @@ static VALUE inqbbox(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d){ return Qtrue; } -static VALUE precision(VALUE self){ +static VALUE precision(VALUE self) { return DBL2NUM(gr_precision()); } @@ -987,7 +2027,7 @@ static VALUE reducepoints(VALUE self,VALUE,VALUE,VALUE,VALUE,VALUE,VALUE){ return Qtrue; } */ -static VALUE trisurface(VALUE self,VALUE px,VALUE py,VALUE pz){ +static VALUE trisurface(VALUE self,VALUE px,VALUE py,VALUE pz) { int x_size = RARRAY_LEN(px); int y_size = RARRAY_LEN(py); int z_size = RARRAY_LEN(pz); @@ -1000,7 +2040,8 @@ static VALUE trisurface(VALUE self,VALUE px,VALUE py,VALUE pz){ return Qtrue; } -static VALUE gradient(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e,VALUE f,VALUE g){ +static VALUE gradient(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e, + VALUE f,VALUE g){ int ac = NUM2INT(a); int bc = NUM2INT(b); double *cc = rb_ar_2_dbl_ar(c); @@ -1012,7 +2053,8 @@ static VALUE gradient(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e,VALUE f return Qtrue; } -static VALUE quiver(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e,VALUE f,VALUE g){ +static VALUE quiver(VALUE self,VALUE a,VALUE b,VALUE c,VALUE d,VALUE e, + VALUE f,VALUE g){ int ac = NUM2INT(a); int bc = NUM2INT(b); double *cc = rb_ar_2_dbl_ar(c); @@ -1031,9 +2073,12 @@ static VALUE version(VALUE self){ void Init_grruby() { - VALUE mGRruby = rb_define_module("GR"); - VALUE mGR3ruby = rb_define_module("GR3"); + /* Module definitions. */ + VALUE mRubyplot = rb_define_module("Rubyplot"); + VALUE mGRruby = rb_define_module_under(mRubyplot, "GR"); + VALUE mGR3ruby = rb_define_module_under(mRubyplot, "GR3"); + /* Function definitions. */ rb_define_singleton_method(mGRruby,"opengks",opengks,0); rb_define_singleton_method(mGRruby,"closegks",closegks,0); rb_define_singleton_method(mGRruby,"inqdspsize",inqdspsize,4); @@ -1097,6 +2142,7 @@ void Init_grruby() rb_define_singleton_method(mGRruby,"textext",textext,3); rb_define_singleton_method(mGRruby,"inqtextext",inqtextext,5); rb_define_singleton_method(mGRruby,"axes",axes,7); + rb_define_singleton_method(mGRruby,"axeslbl",axeslbl, 9); rb_define_singleton_method(mGRruby,"grid",grid,6); rb_define_singleton_method(mGRruby,"grid3d",grid3d,9); rb_define_singleton_method(mGRruby,"verrorbars",verrorbars,4); @@ -1159,4 +2205,133 @@ void Init_grruby() rb_define_singleton_method(mGRruby,"gradient",gradient,7); rb_define_singleton_method(mGRruby,"quiver",quiver,7); rb_define_singleton_method(mGRruby,"version",version,0); + + /* Constants */ + rb_define_const(mGRruby, "TEXT_HALIGN_NORMAL", DBL2NUM(0)); + rb_define_const(mGRruby, "TEXT_HALIGN_LEFT", DBL2NUM(1)); + rb_define_const(mGRruby, "TEXT_HALIGN_CENTER", DBL2NUM(2)); + rb_define_const(mGRruby, "TEXT_HALIGN_RIGHT", DBL2NUM(3)); + + rb_define_const(mGRruby, "TEXT_VALIGN_NORMAL", DBL2NUM(0)); + rb_define_const(mGRruby, "TEXT_VALIGN_TOP", DBL2NUM(1)); + rb_define_const(mGRruby, "TEXT_VALIGN_CAP", DBL2NUM(2)); + rb_define_const(mGRruby, "TEXT_VALIGN_HALF", DBL2NUM(3)); + rb_define_const(mGRruby, "TEXT_VALIGN_BASE", DBL2NUM(4)); + rb_define_const(mGRruby, "TEXT_VALIGN_BOTTOM", DBL2NUM(5)); + + rb_define_const(mGRruby, "FONT_TIMES_ROMAN", DBL2NUM(101)); + rb_define_const(mGRruby, "FONT_TIMES_ITALIC", DBL2NUM(102)); + rb_define_const(mGRruby, "FONT_TIMES_BOLD", DBL2NUM(103)); + rb_define_const(mGRruby, "FONT_TIMES_BOLD_ITALIC", DBL2NUM(104)); + rb_define_const(mGRruby, "FONT_HELVETICA", DBL2NUM(105)); + rb_define_const(mGRruby, "FONT_HELVETICA_OBLIQUE", DBL2NUM(106)); + rb_define_const(mGRruby, "FONT_HELVETICA_BOLD", DBL2NUM(107)); + rb_define_const(mGRruby, "FONT_HELVETICA_BOLD_OBLIQUE", DBL2NUM(108)); + rb_define_const(mGRruby, "FONT_COURIER", DBL2NUM(109)); + rb_define_const(mGRruby, "FONT_COURIER_OBLIQUE", DBL2NUM(110)); + rb_define_const(mGRruby, "FONT_COURIER_BOLD", DBL2NUM(111)); + rb_define_const(mGRruby, "FONT_COURIER_BOLD_OBLIQUE", DBL2NUM(112)); + rb_define_const(mGRruby, "FONT_SYMBOL", DBL2NUM(113)); + rb_define_const(mGRruby, "FONT_BOOKMAN_LIGHT", DBL2NUM(114)); + rb_define_const(mGRruby, "FONT_BOOKMAN_LIGHT_ITALIC", DBL2NUM(115)); + rb_define_const(mGRruby, "FONT_BOOKMAN_DEMI", DBL2NUM(116)); + rb_define_const(mGRruby, "FONT_BOOKMAN_DEMI_ITALIC", DBL2NUM(117)); + rb_define_const(mGRruby, "FONT_NEWCENTURYSCHLBK_ROMAN", DBL2NUM(118)); + rb_define_const(mGRruby, "FONT_NEWCENTURYSCHLBK_ITALIC", DBL2NUM(119)); + rb_define_const(mGRruby, "FONT_NEWCENTURYSCHLBK_BOLD", DBL2NUM(120)); + rb_define_const(mGRruby, "FONT_NEWCENTURYSCHLBK_BOLD_ITALIC", DBL2NUM(121)); + rb_define_const(mGRruby, "FONT_AVANTGARDE_BOOK", DBL2NUM(122)); + rb_define_const(mGRruby, "FONT_AVANTGARDE_BOOK_OBLIQUE", DBL2NUM(123)); + rb_define_const(mGRruby, "FONT_AVANTGARDE_DEMI", DBL2NUM(124)); + rb_define_const(mGRruby, "FONT_AVANTGARDE_DEMI_OBLIQUE", DBL2NUM(125)); + rb_define_const(mGRruby, "FONT_PALATINO_ROMAN", DBL2NUM(126)); + rb_define_const(mGRruby, "FONT_PALATINO_ITALIC", DBL2NUM(127)); + rb_define_const(mGRruby, "FONT_PALATINO_BOLD", DBL2NUM(128)); + rb_define_const(mGRruby, "FONT_PALATINO_BOLD_ITALIC", DBL2NUM(129)); + rb_define_const(mGRruby, "FONT_ZAPFCHANCERY_MEDIUM_ITALIC", DBL2NUM(130)); + rb_define_const(mGRruby, "FONT_ZAPFDINGBATS", DBL2NUM(131)); + + rb_define_const(mGRruby, "TEXT_PRECISION_STRING", DBL2NUM(0)); + rb_define_const(mGRruby, "TEXT_PRECISION_CHAR", DBL2NUM(1)); + rb_define_const(mGRruby, "TEXT_PRECISION_STROKE", DBL2NUM(2)); + + rb_define_const(mGRruby, "TEXT_PATH_RIGHT", DBL2NUM(0)); + rb_define_const(mGRruby, "TEXT_PATH_LEFT", DBL2NUM(1)); + rb_define_const(mGRruby, "TEXT_PATH_UP", DBL2NUM(2)); + rb_define_const(mGRruby, "TEXT_PATH_DOWN", DBL2NUM(3)); + + rb_define_const(mGRruby, "MARKERTYPE_DOT", DBL2NUM(1)); + rb_define_const(mGRruby, "MARKERTYPE_PLUS", DBL2NUM(2)); + rb_define_const(mGRruby, "MARKERTYPE_ASTERISK", DBL2NUM(3)); + rb_define_const(mGRruby, "MARKERTYPE_CIRCLE", DBL2NUM(4)); + rb_define_const(mGRruby, "MARKERTYPE_DIAGONAL_CROSS", DBL2NUM(5)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_CIRCLE", DBL2NUM(-1)); + rb_define_const(mGRruby, "MARKERTYPE_TRIANGLE_UP", DBL2NUM(-2)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_TRI_UP", DBL2NUM(-3)); + rb_define_const(mGRruby, "MARKERTYPE_TRIANGLE_DOWN", DBL2NUM(-4)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_TRI_DOWN", DBL2NUM(-5)); + rb_define_const(mGRruby, "MARKERTYPE_SQUARE", DBL2NUM(-6)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_SQUARE", DBL2NUM(-7)); + rb_define_const(mGRruby, "MARKERTYPE_BOWTIE", DBL2NUM(-8)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_BOWTIE", DBL2NUM(-9)); + rb_define_const(mGRruby, "MARKERTYPE_HGLASS", DBL2NUM(-10)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_HGLASS", DBL2NUM(-11)); + rb_define_const(mGRruby, "MARKERTYPE_DIAMOND", DBL2NUM(-12)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_DIAMOND", DBL2NUM(-13)); + rb_define_const(mGRruby, "MARKERTYPE_STAR", DBL2NUM(-14)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_STAR", DBL2NUM(-15)); + rb_define_const(mGRruby, "MARKERTYPE_TRI_UP_DOWN", DBL2NUM(-16)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_TRI_RIGHT", DBL2NUM(-17)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_TRI_LEFT", DBL2NUM(-18)); + rb_define_const(mGRruby, "MARKERTYPE_HOLLOW_PLUS", DBL2NUM(-19)); + rb_define_const(mGRruby, "MARKERTYPE_SOLID_PLUS", DBL2NUM(-20)); + rb_define_const(mGRruby, "MARKERTYPE_PENTAGON", DBL2NUM(-21)); + rb_define_const(mGRruby, "MARKERTYPE_HEXAGON", DBL2NUM(-22)); + rb_define_const(mGRruby, "MARKERTYPE_HEPTAGON", DBL2NUM(-23)); + rb_define_const(mGRruby, "MARKERTYPE_OCTAGON", DBL2NUM(-24)); + rb_define_const(mGRruby, "MARKERTYPE_STAR_4", DBL2NUM(-25)); + rb_define_const(mGRruby, "MARKERTYPE_STAR_5", DBL2NUM(-26)); + rb_define_const(mGRruby, "MARKERTYPE_STAR_6", DBL2NUM(-27)); + rb_define_const(mGRruby, "MARKERTYPE_STAR_7", DBL2NUM(-28)); + rb_define_const(mGRruby, "MARKERTYPE_STAR_8", DBL2NUM(-29)); + rb_define_const(mGRruby, "MARKERTYPE_VLINE", DBL2NUM(-30)); + rb_define_const(mGRruby, "MARKERTYPE_HLINE", DBL2NUM(-31)); + rb_define_const(mGRruby, "MARKERTYPE_OMARK", DBL2NUM(-32)); + + rb_define_const(mGRruby, "LINETYPE_SOLID", DBL2NUM(1)); + rb_define_const(mGRruby, "LINETYPE_DASHED", DBL2NUM(2)); + rb_define_const(mGRruby, "LINETYPE_DOTTED", DBL2NUM(3)); + rb_define_const(mGRruby, "LINETYPE_DASHED_DOTTED", DBL2NUM(4)); + rb_define_const(mGRruby, "LINETYPE_DASH_2_DOT", DBL2NUM(-1)); + rb_define_const(mGRruby, "LINETYPE_DASH_3_DOT", DBL2NUM(-2)); + rb_define_const(mGRruby, "LINETYPE_LONG_DASH", DBL2NUM(-3)); + rb_define_const(mGRruby, "LINETYPE_LONG_SHORT_DASH", DBL2NUM(-4)); + rb_define_const(mGRruby, "LINETYPE_SPACED_DASH", DBL2NUM(-5)); + rb_define_const(mGRruby, "LINETYPE_SPACED_DOT", DBL2NUM(-6)); + rb_define_const(mGRruby, "LINETYPE_DOUBLE_DOT", DBL2NUM(-7)); + rb_define_const(mGRruby, "LINETYPE_TRIPLE_DOT", DBL2NUM(-8)); + + rb_define_const(mGRruby, "FILLSTYLE_HOLLOW", DBL2NUM(0)); + rb_define_const(mGRruby, "FILLSTYLE_SOLID", DBL2NUM(1)); + rb_define_const(mGRruby, "FILLSTYLE_PATTERN", DBL2NUM(2)); + rb_define_const(mGRruby, "FILLSTYLE_HATCH", DBL2NUM(3)); + + rb_define_const(mGRruby, "ARROW_STYLE_SIMPLE_SINGLE_ENDED", DBL2NUM(1)); + rb_define_const(mGRruby, "ARROW_STYLE_SIMPLE_SINGLE_ENDED_ACUTE", DBL2NUM(2)); + rb_define_const(mGRruby, "ARROW_STYLE_HOLLOW_SINGLE_ENDED", DBL2NUM(3)); + rb_define_const(mGRruby, "ARROW_STYLE_FILLED_SINGLED_ENDED", DBL2NUM(4)); + rb_define_const(mGRruby, "ARROW_STYLE_TRIANGLE_SINGLE_ENDED", DBL2NUM(5)); + rb_define_const(mGRruby, "ARROW_STYLE_FILLED_TRIANGLE_SINGLE_ENDED", DBL2NUM(6)); + rb_define_const(mGRruby, "ARROW_STYLE_KITE_SINGLE_ENDED", DBL2NUM(7)); + rb_define_const(mGRruby, "ARROW_STYLE_FILLED_KITE_SINGLE_ENDED", DBL2NUM(8)); + rb_define_const(mGRruby, "ARROW_STYLE_SIMPLE_DOUBLE_ENDED", DBL2NUM(9)); + rb_define_const(mGRruby, "ARROW_STYLE_SIMPLE_DOUBLE_ENDED_ACUTE", DBL2NUM(10)); + rb_define_const(mGRruby, "ARROW_STYLE_HOLLOW_DOUBLE_ENDED", DBL2NUM(11)); + rb_define_const(mGRruby, "ARROW_STYLE_FILLED_DOUBLE_ENDED", DBL2NUM(12)); + rb_define_const(mGRruby, "ARROW_STYLE_TRIANGLE_DOUBLE_ENDED", DBL2NUM(13)); + rb_define_const(mGRruby, "ARROW_STYLE_FILLED_TRIANGLE_DOUBLE_ENDED", DBL2NUM(14)); + rb_define_const(mGRruby, "ARROW_STYLE_KITE_DOUBLE_ENDED", DBL2NUM(15)); + rb_define_const(mGRruby, "ARROW_STYLE_FILLED_KITE_DOUBLE_ENDED", DBL2NUM(16)); + rb_define_const(mGRruby, "ARROW_STYLE_DOUBLE_LINE_SINGLE_ENDED", DBL2NUM(17)); + rb_define_const(mGRruby, "ARROW_STYLE_DOUBLE_LINE_DOUBLE_ENDED", DBL2NUM(18)); } diff --git a/lib/rubyplot.rb b/lib/rubyplot.rb index 2840aa3..770bfaf 100644 --- a/lib/rubyplot.rb +++ b/lib/rubyplot.rb @@ -1,6 +1,8 @@ require 'bigdecimal' -require 'rmagick' +unless ENV["RUBYPLOT_BACKEND"] + ENV["RUBYPLOT_BACKEND"] = "GR" +end require 'rubyplot/color' require 'rubyplot/utils' @@ -11,15 +13,168 @@ require 'rubyplot/figure' require 'rubyplot/subplot' require 'rubyplot/spi' - -require 'grruby.so' -require 'rubyplot/gr_wrapper' +require 'rubyplot/image' module Rubyplot - def self.backend - b = ENV['RUBYPLOT_BACKEND'] - return b.to_sym if %w[magick gr].include?(b) + LINE_TYPES = [ + :solid, + :dashed, + :dotted, + :dashed_dotted, + :dash_2_dot, + :dash_3_dot, + :long_dash, + :long_short_dash, + :spaced_dash, + :spaced_dot, + :double_dot, + :triple_dot, + ].freeze + + MARKER_TYPES = [ + :dot, + :plus, + :asterisk, + :circle, + :diagonal_cross, + :solid_circle, + :triangle_up, + :solid_triangle_up, + :triangle_down, + :solid_triangle_down, + :square, + :solid_square, + :bowtie, + :solid_bowtie, + :hglass, + :solid_hglass, + :diamond, + :solid_diamond, + :star, + :solid_star, + :tri_up_down, + :solid_tri_right, + :solid_tri_left, + :hollow_plus, + :solid_plus, + :pentagon, + :hexagon, + :heptagon, + :octagon, + :star_4, + :star_5, + :star_6, + :star_7, + :star_8, + :vline, + :hline, + :omark + ].freeze + + TEXT_FONTS = [ + :times_roman, + :times_italic, + :times_bold, + :times_bolditalic, + :helvetica, + :helvetica_oblique, + :helvetica_bold, + :helvetica_boldoblique, + :courier, + :courier_oblique, + :courier_bold, + :courier_boldoblique, + :symbol, + :bookman_light, + :bookman_lightitalic, + :bookman_demi, + :bookman_demiitalic, + :newcenturyschlbk_roman, + :newcenturyschlbk_italic, + :newcenturyschlbk_bold, + :newcenturyschlbk_bolditalic, + :avantgarde_book, + :avantgarde_bookoblique, + :avantgarde_demi, + :avantgarde_demioblique, + :palatino_roman, + :palatino_italic, + :palatino_bold, + :palatino_bolditalic, + :zapfchancery_mediumitalic, + :zapfdingbats + ].freeze + + TEXT_PRECISION = [ + :high, + :med, + :low + ].freeze + + TEXT_DIRECTION = [ + :left_right, + :right_left, + :down_up, + :up_down + ].freeze + + FILL_STYLES = [ + :hollow, + :solid, + :pattern, + :hatch + ].freeze + + ARROW_STYLES = [ + :simple_single_ended, + :simple_single_ended_acute, + :hollow_single_ended, + :filled_singled_ended, + :triangle_single_ended, + :filled_triangle_single_ended, + :kite_single_ended, + :filled_kite_single_ended, + :simple_double_ended, + :simple_double_ended_acute, + :hollow_double_ended, + :filled_double_ended, + :triangle_double_ended, + :filled_triangle_double_ended, + :kite_double_ended, + :filled_kite_double_ended, + :double_line_single_ended, + :double_line_double_ended + ].freeze - :magick + class << self + + attr_accessor :iruby_inline + + def backend + @backend + end + + def inline + @iruby_inline = true + end + + def stop_inline + @iruby_inline = false + end + + def QuantumRange + # Setting Backend to Magick as Image is only implemented for Magick backend + @backend = Rubyplot::Backend::MagickWrapper.new + Rubyplot.backend.QuantumRange + end + + def set_backend b + case b + when :magick + @backend = Rubyplot::Backend::MagickWrapper.new + when :gr + @backend = Rubyplot::Backend::GRWrapper.new + end + end end -end +end # module Rubyplot diff --git a/lib/rubyplot/artist.rb b/lib/rubyplot/artist.rb index a3914dc..52ec81d 100644 --- a/lib/rubyplot/artist.rb +++ b/lib/rubyplot/artist.rb @@ -11,3 +11,5 @@ require_relative 'artist/rectangle' require_relative 'artist/circle' require_relative 'artist/polygon' +require_relative 'artist/arrow' +require_relative 'artist/image' diff --git a/lib/rubyplot/artist/arrow.rb b/lib/rubyplot/artist/arrow.rb new file mode 100644 index 0000000..fd28a13 --- /dev/null +++ b/lib/rubyplot/artist/arrow.rb @@ -0,0 +1,19 @@ +module Rubyplot + module Artist + class Arrow < Artist::Base + def initialize x1:, y1:, x2:, y2:, size: 0.5, style: :simple_single_ended + @x1 = x1 + @y1 = y1 + @x2 = x2 + @y2 = y2 + @size = size + @style = style + end + + def draw + Rubyplot.backend.draw_arrow(x1: @x1, y1: @y1, x2: @x2, y2: @y2, + size: @size, style: @style) + end + end # class Arrow + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/axes.rb b/lib/rubyplot/artist/axes.rb index 8370889..3595e76 100644 --- a/lib/rubyplot/artist/axes.rb +++ b/lib/rubyplot/artist/axes.rb @@ -1,23 +1,12 @@ module Rubyplot module Artist class Axes < Base - TITLE_MARGIN = 20.0 - # Space around text elements. Mostly used for vertical spacing. - # This way the vertical text doesn't overlap. - LEGEND_MARGIN = TITLE_MARGIN = 20.0 + TITLE_MARGIN = 10.0 + LEGEND_MARGIN = 15.0 LABEL_MARGIN = 10.0 - DEFAULT_MARGIN = 20.0 + DEFAULT_MARGIN = 10.0 THOUSAND_SEPARATOR = ','.freeze - # FIXME: most of the below accessors should just be name= methods which - # will access the required Artist and set the variable in there directly. - # Range of X axis. - attr_accessor :x_range - # Range of Y axis. - - attr_accessor :y_range, :text_font, :grid, :bounding_box, :origin, :title_shift, :title_margin - - # Main title for this Axes. - attr_accessor :title + # Rubyplot::Figure object to which this Axes belongs. attr_reader :figure # Array of plots contained in this Axes. @@ -26,35 +15,56 @@ class Axes < Base :title_font_size, :scale, :font_color, :marker_color, :axes, :legend_margin, :backend, :marker_caps_height attr_reader :label_stagger_height - # FIXME: possibly disposable attrs - attr_reader :title_caps_height - # Set true if title is to be hidden. - attr_accessor :hide_title - # Margin between the X axis and the bottom of the Axes artist. - attr_accessor :x_axis_margin - # Margin between the Y axis and the left of the Axes artist. - attr_accessor :y_axis_margin - # Position of the legend box. - attr_accessor :legend_box_position # Rubyplot::Artist::XAxis object. attr_reader :x_axis # Rubyplot::Artist::YAxis object. attr_reader :y_axis - # Array of X ticks. - attr_reader :x_ticks - # Number of X ticks. - attr_accessor :num_x_ticks + # Position of the legend box. + attr_accessor :legend_box_position + # Set true if title is to be hidden. + attr_accessor :hide_title + # Top margin. + attr_accessor :top_margin + # Left margin. + attr_accessor :left_margin + # Bottom margin. + attr_accessor :bottom_margin + # Right margin. + attr_accessor :right_margin + attr_accessor :grid, :bounding_box, :title_shift + # Main title for this Axes. + attr_accessor :title + # X co-ordinate of lower left corner of this Axes. + attr_accessor :abs_x + # Y co-ordinate of lower left corner of this Axes. + attr_accessor :abs_y + # Width of this Axes object. Between Rubyplot.min_x and max_x. + attr_accessor :width + # Height of this Axes object. Between Rubyplot.min_y and MAX_Y. + attr_accessor :height + # Font size of the Axes title in pt. scale. + attr_accessor :title_font_size + # Flag for square axes + attr_accessor :square_axes # @param figure [Rubyplot::Figure] Figure object to which this Axes belongs. - def initialize(figure) + # @param abs_x [Float] Absolute X co-ordinate of the lower left corner of the Axes. + # @param abs_y [Flot] Absolute Y co-ordinate of the lower left corner of the Axes. + # @param width [Float] Width between MIN_X and MAX_Y of this Axes object. + # @param height [Float] Height between MIN_Y and MAX_Y of this Axes object. + def initialize(figure, abs_x:, abs_y:, width:, height:) @figure = figure + @abs_x = abs_x + @abs_y = abs_y + @width = width + @height = height @x_title = '' @y_title = '' - @x_axis_margin = 40.0 - @y_axis_margin = 40.0 - @x_range = [nil, nil] - @y_range = [nil, nil] + @top_margin = 5.0 + @left_margin = 10.0 + @bottom_margin = 10.0 + @right_margin = 5.0 @title = '' @title_shift = 0 @title_margin = TITLE_MARGIN @@ -64,32 +74,27 @@ def initialize(figure) @plots = [] @raw_rows = width * (height/width) @theme = Rubyplot::Themes::CLASSIC_WHITE - vera_font_path = File.expand_path('Vera.ttf', ENV['MAGICK_FONT_PATH']) - @font = File.exist?(vera_font_path) ? vera_font_path : nil - @font_color = '#000000' - @marker_font_size = 15.0 + @font = :times_roman + @font_color = :black + @font_size = 15.0 @legend_font_size = 20.0 @legend_margin = LEGEND_MARGIN @title_font_size = 25.0 - @backend = @figure.backend @plot_colors = [] @legends = [] @lines = [] @texts = [] - @origin = [nil, nil] - calculate_xy_axes_origin @x_axis = Rubyplot::Artist::XAxis.new(self) @y_axis = Rubyplot::Artist::YAxis.new(self) - @x_ticks = nil - @num_x_ticks = 5 @legend_box_position = :top + @square_axes = true end # X co-ordinate of the legend box depending on value of @legend_box_position. def legend_box_ix case @legend_box_position when :top - abs_y + width / 2 + abs_x + width / 2 end end @@ -97,107 +102,134 @@ def legend_box_ix def legend_box_iy case @legend_box_position when :top - abs_x + @x_axis_margin + @legend_margin + abs_y + height - top_margin - legend_margin end end - # Write an image to a file by communicating with the backend. - # FIXME: (refactor) Currently draw first assigns default colors and then - # performs consolidation etc of the data and then assigns the default X ticks. - # The reason for this is that default labels are needed by the consolidated - # plots when they create the required rectangles etc. However assigning - # default ticks should be a part of the 'assign defaults' step and should - # therefore should be clubbed with the assign labels step. - def draw - set_axes_ranges - normalize_plotting_data + def process_data assign_default_label_colors consolidate_plots + @plots.each(&:process_data) + set_axes_ranges + end + + # Write an image to a file by communicating with the backend. + def draw + Rubyplot.backend.active_axes = self configure_title configure_legends assign_x_ticks - actually_draw + assign_y_ticks + @x_axis.draw + @y_axis.draw + @texts.each(&:draw) + @legend_box.draw + @plots.each(&:draw) end - def scatter!(*_args) - plot = Rubyplot::Artist::Plot::Scatter.new self - yield(plot) if block_given? - @plots << plot + def scatter!(*_args, &block) + add_plot! :Scatter, &block end - def bar!(*_args) - plot = Rubyplot::Artist::Plot::Bar.new self - yield(plot) if block_given? - @plots << plot + def bar!(*_args, &block) + add_plot! :Bar, &block end - def line!(*_args) - plot = Rubyplot::Artist::Plot::Line.new self - yield(plot) if block_given? - @plots << plot + def line!(*_args, &block) + add_plot! :Line, &block end - def area!(*_args) - plot = Rubyplot::Artist::Plot::Area.new self - yield(plot) if block_given? - @plots << plot + def area!(*_args, &block) + add_plot! :Area, &block end - def bubble!(*_args) - plot = Rubyplot::Artist::Plot::Bubble.new self - yield(plot) if block_given? - @plots << plot + def bubble!(*_args, &block) + add_plot! :Bubble, &block end - def dot!(*args, &block) - add_plot 'Dot', *args, &block + def stacked_bar!(*_args, &block) + add_plot! :StackedBar, &block end - def stacked_bar!(*_args) - plot = Rubyplot::Artist::Plot::StackedBar.new self - yield(plot) if block_given? - @plots << plot + def histogram!(*_args, &block) + add_plot! :Histogram, &block end - def write(file_name) - @plots[0].write file_name + def candle_stick!(*_args, &block) + add_plot! :CandleStick, &block end - # Absolute X co-ordinate of the Axes. Top left corner. - def abs_x - @figure.top_spacing * @figure.height + @figure.abs_x + def error_bar!(*_args, &block) + add_plot! :ErrorBar, &block end - # Absolute Y co-ordinate of the Axes. Top left corner. - def abs_y - @figure.top_spacing * @figure.height + @figure.abs_y + def box_plot!(*_args, &block) + add_plot! :BoxPlot, &block end - # Absolute width of the Axes in pixels. - def width - (1 - (@figure.left_spacing + @figure.right_spacing)) * @figure.width + def plot!(*_args, &block) + add_plot! :BasicPlot, &block end - # Absolute height of the Axes in pixels. - # FIXME: expand for multiple axes on same figure. width too. - def height - (1 - (@figure.top_spacing + @figure.bottom_spacing)) * @figure.height + def write(file_name) + @plots[0].write file_name end def x_ticks= x_ticks - @x_ticks = x_ticks + @x_axis.major_ticks = x_ticks + end + + def y_ticks= y_ticks + @y_axis.major_ticks = y_ticks end def x_title= x_title @x_axis.title = x_title end + def x_title_font_size= x_font_size + @x_axis.title_font_size = x_font_size + end + def y_title= y_title @y_axis.title = y_title end + def y_title_font_size= y_font_size + @y_axis.title_font_size = y_font_size + end + + def x_range + [@x_axis.min_val, @x_axis.max_val] + end + + def y_range + [@y_axis.min_val, @y_axis.max_val] + end + + # Set the X range of the Axes. + def x_range= xr + @x_axis.min_val = xr[0] + @x_axis.max_val = xr[1] + end + + def y_range= xy + @y_axis.min_val = xy[0] + @y_axis.max_val = xy[1] + end + + def origin + [@x_axis.min_val, @y_axis.min_val] + end + private + def add_plot! klass, &block + plot = Kernel.const_get("Rubyplot::Artist::Plot::#{klass}").new self + yield(plot) if block_given? + @plots << plot + end + def assign_default_label_colors @plots.each_with_index do |p, i| if p.color == :default @@ -208,54 +240,90 @@ def assign_default_label_colors end def assign_x_ticks - unless @x_ticks - val_distance = (@x_range[1] - @x_range[0]).abs / @num_x_ticks.to_f - @x_ticks = (@x_range[0]..@x_range[1]).step(val_distance).map { |i| i } + value_distance = @x_axis.spread / (@x_axis.major_ticks_count.to_f - 1) + unless @x_axis.major_ticks # create labels if not present + @x_axis.major_ticks = @x_axis.major_ticks_count.times.map do |i| + @x_axis.min_val + i * value_distance + end end - unless @x_ticks.all? { |t| t.is_a?(Rubyplot::Artist::XTick) } - inter_ticks_distance = @x_axis.length / (@num_x_ticks - 1) - @x_ticks.map!.with_index do |tick_label, i| + + unless @x_axis.major_ticks.all? { |t| t.is_a?(Rubyplot::Artist::XTick) } + @x_axis.major_ticks.map!.with_index do |tick_label, i| Rubyplot::Artist::XTick.new( self, - abs_x: i * inter_ticks_distance + @x_axis.abs_x1, - abs_y: @origin[1], - label: Rubyplot::Utils.format_label(tick_label), - length: 6, - label_distance: 10 + coord: i * value_distance + @x_axis.min_val, + label: Rubyplot::Utils.format_label(tick_label) ) end end - end - def add_plot plot_type, *args, &block - plot = with_backend plot_type, *args - yield(plot) if block_given? - @plots << plot + @x_axis.minor_ticks = [] + @x_axis.major_ticks.each_with_index do |major_tick, i| + minor_tick_value_distance = value_distance / (@x_axis.minor_ticks_count.to_f + 1) + if i < (@x_axis.major_ticks_count-1) # Skip the last tick + for j in 1..@x_axis.minor_ticks_count do + @x_axis.minor_ticks.push(major_tick.coord + j * minor_tick_value_distance) + end + end + end + + unless @x_axis.minor_ticks.all? { |t| t.is_a?(Rubyplot::Artist::XTick) } + @x_axis.minor_ticks.map!.with_index do |coord, i| + Rubyplot::Artist::XTick.new( + self, + coord: coord, + label: nil, + tick_size: @x_axis.major_ticks[0].tick_size/2 + ) + end + end end - def with_backend(plot_type, *args) - plot = - case Rubyplot.backend - when :magick - Kernel.const_get("Rubyplot::MagickWrapper::Plot::#{plot_type}").new self, *args - when :gr - Kernel.const_get("Rubyplot::GRWrapper::Plot::#{plot_type}").new self, *args + def assign_y_ticks + value_distance = @y_axis.spread / (@y_axis.major_ticks_count.to_f-1) + unless @y_axis.major_ticks + @y_axis.major_ticks = (y_range[0]..y_range[1]).step(value_distance).map { |i| i } + end + + unless @y_axis.major_ticks.all? { |t| t.is_a?(Rubyplot::Artist::YTick) } + @y_axis.major_ticks.map!.with_index do |tick_label, i| + Rubyplot::Artist::YTick.new( + self, + coord: @y_axis.min_val + i * value_distance, + label: Rubyplot::Utils.format_label(tick_label) + ) + end + end + + @y_axis.minor_ticks = [] + @y_axis.major_ticks.each_with_index do |major_tick, i| + minor_tick_value_distance = value_distance / (@y_axis.minor_ticks_count.to_f + 1) + if i < (@y_axis.major_ticks_count-1) # Skip the last tick + for j in 1..@y_axis.minor_ticks_count do # Skip the 0th index as major tick is already present + @y_axis.minor_ticks.push(major_tick.coord + j * minor_tick_value_distance) + end end - plot + end + + unless @y_axis.minor_ticks.all? { |t| t.is_a?(Rubyplot::Artist::YTick) } + @y_axis.minor_ticks.map!.with_index do |coord, i| + Rubyplot::Artist::XTick.new( + self, + coord: coord, + label: nil, + tick_size: @y_axis.major_ticks[0].tick_size/2 + ) + end + end end # Figure out the co-ordinates of the title text w.r.t Axes. def configure_title @texts << Rubyplot::Artist::Text.new( - @title, self, abs_x: abs_x + width / 2, abs_y: abs_y + @title_margin, + @title, self, + abs_x: abs_x + width / 2, abs_y: abs_y + height, font: @font, color: @font_color, - pointsize: @title_font_size, internal_label: 'axes title.' - ) - end - - def calculate_xy_axes_origin - @origin[0] = abs_x + @x_axis_margin - @origin[1] = abs_y + height - @y_axis_margin + size: @title_font_size, internal_label: 'axes title.') end # Figure out co-ordinates of the legends @@ -265,24 +333,6 @@ def configure_legends ) end - # Make adjustments to the data that will be plotted. Maps the data - # contained in the plot to actual pixel values. - def normalize_plotting_data - @plots.each do |plot| - plot.normalize - end - end - - # Call the respective draw methods on each of the elements of this Axes. - def actually_draw - @x_axis.draw - @x_ticks.each(&:draw) - @y_axis.draw - @texts.each(&:draw) - @legend_box.draw - @plots.each(&:draw) - end - def consolidate_plots bars = @plots.grep(Rubyplot::Artist::Plot::Bar) unless bars.empty? @@ -295,34 +345,46 @@ def consolidate_plots @plots.delete_if { |p| p.is_a?(Rubyplot::Artist::Plot::StackedBar) } @plots << Rubyplot::Artist::Plot::MultiStackedBar.new(self, stacked_bars: stacked_bars) end + + candle_sticks = @plots.grep(Rubyplot::Artist::Plot::CandleStick) + unless candle_sticks.empty? + @plots.delete_if { |p| p.is_a?(Rubyplot::Artist::Plot::CandleStick) } + @plots << Rubyplot::Artist::Plot::MultiCandleStick.new(self, + candle_sticks: candle_sticks) + end + + box_plots = @plots.grep(Rubyplot::Artist::Plot::BoxPlot) + unless box_plots.empty? + @plots.delete_if { |p| p.is_a?(Rubyplot::Artist::Plot::BoxPlot) } + @plots << Rubyplot::Artist::Plot::MultiBoxPlot.new(self, + box_plots: box_plots) + end end - # FIXME: replace x_range and y_range with XAxis::max/min_value and YAxis::max/min_value. def set_axes_ranges set_xrange set_yrange end def set_xrange - if @x_range[0].nil? && @x_range[1].nil? - @x_range[0] = @plots.map { |p| p.x_min }.min - @x_range[1] = @plots.map { |p| p.x_max }.max + if @x_axis.min_val.nil? + @x_axis.min_val = @plots.map(&:x_min).min + end + + if @x_axis.max_val.nil? + @x_axis.max_val = @plots.map(&:x_max).max end - @x_axis.min_val = @x_range[0] - @x_axis.max_val = @x_range[1] end def set_yrange - if @y_range[0].nil? && @y_range[1].nil? - @y_range[0] = @plots.map { |p| p.y_min }.min - @y_range[1] = @plots.map { |p| p.y_max }.max + if @y_axis.min_val.nil? + @y_axis.min_val = @plots.map(&:y_min).min + end + + if @y_axis.max_val.nil? + @y_axis.max_val = @plots.map(&:y_max).max end - @y_axis.min_val = @y_range[0] - @y_axis.max_val = @y_range[1] - end - end - # class Axes - end - # moudle Artist -end -# module Rubyplot + end + end # class Axes + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/axis/base.rb b/lib/rubyplot/artist/axis/base.rb index 7513b9a..ff55440 100644 --- a/lib/rubyplot/artist/axis/base.rb +++ b/lib/rubyplot/artist/axis/base.rb @@ -2,37 +2,45 @@ module Rubyplot module Artist class Axis class Base - # Length in pixels of the arrow after the last major tick. - FINISH_ARROW_LENGTH = 10.0 + # Length of the arrow after the last major tick. + FINISH_ARROW_LENGTH = 2.0 - attr_reader :label, :ticks, :major_ticks_count - attr_reader :abs_x1, :abs_x2, :abs_y1, :abs_y2, :backend, :length - attr_reader :stroke_width, :major_ticks - attr_accessor :title, :min_val, :max_val + # Array of Rubyplot::XTick objects representing major ticks. + attr_accessor :major_ticks + # Number of major ticks to be plotted. + attr_accessor :major_ticks_count + attr_accessor :minor_ticks # TODO + # Number of minor ticks between each major tick. + attr_reader :minor_ticks_count + # Title (or label) given to this Axis. + attr_accessor :title + # The minimum value that this Axis contains. + attr_accessor :min_val + # The maximum value that this Axis contains. + attr_accessor :max_val + # Font size of title. + attr_accessor :title_font_size + # Font of the title. + attr_accessor :title_font def initialize axes @axes = axes @title = '' @min_val = nil @max_val = nil - @stroke_width = 1.0 - @backend = @axes.backend @major_ticks_count = 5 - @x_ticks = nil + @minor_ticks_count = 2 @texts = [] @lines = [] + @major_ticks = nil + @minor_ticks = nil + @title_font_size = 25.0 end - def draw - configure_title - @lines.each(&:draw) - @texts.each(&:draw) + def spread + (@max_val - @min_val).abs end - end - # class Base - end - # class Axis - end - # class Artist -end -# module Rubyplot + end # class Base + end # class Axis + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/axis/x_axis.rb b/lib/rubyplot/artist/axis/x_axis.rb index 88460c0..00b3b0d 100644 --- a/lib/rubyplot/artist/axis/x_axis.rb +++ b/lib/rubyplot/artist/axis/x_axis.rb @@ -5,34 +5,32 @@ module Artist class XAxis < Axis::Base def initialize axes super - @abs_x1 = @axes.origin[0] - @abs_x2 = @axes.abs_x + @axes.width - @axes.y_axis_margin - @major_ticks_distance = (@abs_x2 - @abs_x1) / @major_ticks_count - @length = (@abs_x2 - @abs_x1).abs - configure_axis_line end - - private - def configure_axis_line - @lines << Rubyplot::Artist::Line2D.new( - self, abs_x1: @abs_x1, abs_y1: @axes.origin[1], abs_x2: @abs_x2, abs_y2: @axes.origin[1], - stroke_width: @stroke_width + def draw + configure_title + Rubyplot.backend.draw_x_axis( + origin: @axes.origin[0], + major_ticks: @major_ticks, + minor_ticks: @minor_ticks, + major_ticks_count: @major_ticks_count, + minor_ticks_count: @minor_ticks_count ) + @texts.each(&:draw) end + private + def configure_title + @title = 'X axis' if @title == '' @texts << Rubyplot::Artist::Text.new( @title, self, - pointsize: @axes.marker_font_size, - abs_y: @axes.origin[1] + 20, - abs_x: @axes.origin[0] + (@abs_x2 - @abs_x1)/2 + size: @title_font_size, + abs_y: @axes.abs_y, + abs_x: @axes.abs_x + @axes.width/2 ) end - end - # class XAxis - end - # class Artist -end -# module Rubyplot + end # class XAxis + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/axis/y_axis.rb b/lib/rubyplot/artist/axis/y_axis.rb index 47b34f7..c8e32ce 100644 --- a/lib/rubyplot/artist/axis/y_axis.rb +++ b/lib/rubyplot/artist/axis/y_axis.rb @@ -3,41 +3,33 @@ module Artist class YAxis < Axis::Base def initialize(*) super - @abs_x1 = @axes.origin[0] - @abs_y1 = @axes.origin[1] - @abs_x2 = @axes.origin[0] - @abs_y2 = @axes.origin[1] - (@axes.height - @axes.x_axis_margin) - @y_ticks = [] - @length = (@abs_y1 - @abs_y2).abs - configure_axis_line end - private - - def configure_axis_line - @lines << Rubyplot::Artist::Line2D.new( - self, - abs_x1: @abs_x1, - abs_y1: @abs_y1, - abs_x2: @abs_x2, - abs_y2: @abs_y2, - stroke_width: @stroke_width + def draw + configure_title + Rubyplot.backend.draw_y_axis( + origin: @axes.origin[1], + major_ticks: @major_ticks, + minor_ticks: @minor_ticks, + major_ticks_count: @major_ticks_count, + minor_ticks_count: @minor_ticks_count ) + @texts.each(&:draw) end + private + def configure_title + @title = 'Y axis' if @title == '' @texts << Rubyplot::Artist::Text.new( @title, self, rotation: -90.0, - abs_x: @axes.origin[0] - 10, - abs_y: (@abs_y1 - @abs_y2) / 2, - pointsize: @axes.marker_font_size + abs_x: @axes.abs_x, + abs_y: @axes.abs_y + @axes.height / 2, + size: @title_font_size ) end - end - # class YAxis - end - # class Artist -end -# module Rubyplot + end # class YAxis + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/base.rb b/lib/rubyplot/artist/base.rb index 6e2cb66..f1f8ecc 100644 --- a/lib/rubyplot/artist/base.rb +++ b/lib/rubyplot/artist/base.rb @@ -1,20 +1,14 @@ module Rubyplot module Artist class Base - # Artist backend. ImageMagick or whatever. - attr_reader :backend # Absolute X co-ordinate of this Aritist on the canvas. attr_reader :abs_x # Absolute Y co-ordinate of this Aritist on the canvas. attr_reader :abs_y - def initialize(backend, abs_x, abs_y) - @backend = backend + def initialize(abs_x, abs_y) @abs_x = abs_x @abs_y = abs_y end - end - # class Base - end - # module Artist -end -# module Rubyplot + end # class Base + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/circle.rb b/lib/rubyplot/artist/circle.rb index 2710ca7..f07d8f8 100644 --- a/lib/rubyplot/artist/circle.rb +++ b/lib/rubyplot/artist/circle.rb @@ -2,30 +2,31 @@ module Rubyplot module Artist class Circle < Base # rubocop:disable Metrics/ParameterLists - def initialize(owner, abs_x:, abs_y:, radius:, stroke_opacity: 0.0, - color: :default, stroke_width:) - super(owner.backend, abs_x, abs_y) + def initialize(owner, x:, y:, radius:, fill_opacity: 1.0, + color: :default, border_width:, abs: false) + @x = x + @y = y @owner = owner @radius = radius - @stroke_width = stroke_width - @stroke_opacity = stroke_opacity + @border_width = border_width + @fill_opacity = fill_opacity @color = color + @abs = abs end # rubocop:enable Metrics/ParameterLists def draw - @backend.draw_circle( - x: @abs_x, - y: @abs_y, + Rubyplot.backend.draw_circle( + x: @x, + y: @y, radius: @radius, - stroke_opacity: @stroke_opacity, - stroke_width: @stroke_width, - color: Rubyplot::Color::COLOR_INDEX[@color] + border_type: :solid, + border_width: @border_width, + fill_color: @color, + border_color: @color, + fill_opacity: @fill_opacity ) end - end - # class Circle - end - # module Artist -end -# module Rubyplot + end # class Circle + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/figure.rb b/lib/rubyplot/artist/figure.rb index 1b5e6c2..397f021 100644 --- a/lib/rubyplot/artist/figure.rb +++ b/lib/rubyplot/artist/figure.rb @@ -1,8 +1,8 @@ module Rubyplot module Artist class Figure < Artist::Base - DEFAULT_TARGET_WIDTH = 800 - + DEFAULT_CANVAS_DIM = 40.0 + # Title on the figure. attr_reader :title # Number of Axes objects in the rows. @@ -27,50 +27,109 @@ class Figure < Artist::Base attr_reader :bottom_spacing attr_reader :theme_options attr_reader :marker_color + # Font color specified as a Symbol from Rubyplot::COLOR::COLOR_INDEX. attr_reader :font_color + # Unit of the figure size. + attr_reader :figsize_unit + # Max X co-ordinate in terms of Rubyplot Co-ordinate System. + attr_reader :max_x + # Max Y co-ordinate in terms of Rubyplot Co-ordinate System. + attr_reader :max_y + # Min X co-ordinate in terms of Rubyplot Co-ordinate System. + attr_reader :min_x + # Min Y co-ordinate in terms of Rubyplot Co-ordinate System. + attr_reader :min_y # Initialize a Rubyplot::Artist::Figure object. # @param height [Integer] nil Height in pixels of the complete Figure. # @param width [Integer] nil Width in pixels of the complete Figure. - def initialize(height: nil, width: nil) - super(Rubyplot::Backend::MagickWrapper.new, 0, 0) + def initialize(height: nil, width: nil, figsize_unit: :cm) + super(0, 0) @title = '' @nrows = 1 @ncols = 1 - @width = width || DEFAULT_TARGET_WIDTH - @height = height || @width * 0.75 - @top_spacing = 0.05 - @bottom_spacing = 0.05 - @left_spacing = 0.05 - @right_spacing = 0.05 + @width = (width || DEFAULT_CANVAS_DIM).to_f + @height = (height || DEFAULT_CANVAS_DIM).to_f + @top_spacing = 5 + @bottom_spacing = 5 + @left_spacing = 5 + @right_spacing = 5 @subplots = nil - @n = 0 + @figsize_unit = figsize_unit + set_rubyplot_artist_coords! setup_default_theme - add_subplots @nrows, @ncols + add_subplots! @nrows, @ncols end - def add_subplots(nrows, ncols) - @subplots = Array.new(nrows) { Array.new(ncols) { nil } } + # Create space for subplots to be added to the figure. + # + # @param nrows [Integer] Number of rows to add. + # @param ncols [Integer] Number of cols to add. + def add_subplots!(nrows, ncols) + @nrows = nrows + @ncols = ncols + @subplots = Array.new(@nrows) { Array.new(@ncols) { nil } } end - def add_subplot(nrow, ncol) - @subplots[nrow][ncol] = Rubyplot::Artist::Axes.new(self) + # Actually create a subplot at position (nrow, ncol) on the figure. + # You must call `add_subplots` and create space in the figure before + # calling this method. Returns the created Rubyplot::Axes object. + # + # @param nrow [Integer] X co-ordinate of the subplot. + # @param ncol [Integer] Y co-ordinate of the subplot. + def add_subplot!(nrow, ncol) + # FIXME: make this work for mutliple subplots. + plottable_width = (@max_x - (@left_spacing + @right_spacing)).to_f + plottable_length = (@max_y - (@top_spacing + @bottom_spacing)).to_f + @subplots[nrow][ncol] = Rubyplot::Artist::Axes.new( + self, + abs_x: @left_spacing + (plottable_width / @ncols) * ncol, + abs_y: @bottom_spacing + (plottable_length / @nrows) * nrow, + width: plottable_width / @ncols, + height: plottable_length / @nrows + ) + @subplots[nrow][ncol] end - def write(file_name) - @backend.set_base_image_gradient( - Rubyplot::Color::COLOR_INDEX[@theme_options[:background_colors][0]], - Rubyplot::Color::COLOR_INDEX[@theme_options[:background_colors][1]], - @width, - @height, - @theme_options[:background_direction] - ) - @subplots.each { |i| i.each(&:draw) } - @backend.write(file_name) + # Draw on a canvas and output to a file. + # + # @param file_name [String] File name to output to. + def write(file_name, device: :file) + print_on_device(file_name, device) + end + + def show + Rubyplot.backend.output_device = Rubyplot.iruby_inline ? :iruby : :window + print_on_device(nil, Rubyplot.backend.output_device) end private + def print_on_device(file_name, device) + Rubyplot.backend.canvas_height = @height + Rubyplot.backend.canvas_width = @width + Rubyplot.backend.figure = self + Rubyplot.backend.init_output_device(file_name, device: device) + @subplots.each { |i| i.each(&:process_data) } + @subplots.each { |i| i.each(&:draw) } + Rubyplot.backend.show + Rubyplot.backend.stop_output_device + end + + def set_rubyplot_artist_coords! + @max_x = 100.0 + @max_y = 100.0 + @min_x = 0.0 + @min_y = 0.0 + if @height > @width + aspect_ratio = @height / @width + @max_y = @max_y * aspect_ratio + elsif @height < @width + aspect_ratio = @width / @height + @max_x = @max_x * aspect_ratio + end + end + def setup_default_theme defaults = { marker_color: :white, @@ -81,9 +140,6 @@ def setup_default_theme @marker_color = @theme_options[:marker_color] @font_color = @theme_options[:font_color] || @marker_color end - end - # class Figure - end - # module Artist -end -# module Rubyplot + end # class Figure + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/image.rb b/lib/rubyplot/artist/image.rb new file mode 100644 index 0000000..2afc380 --- /dev/null +++ b/lib/rubyplot/artist/image.rb @@ -0,0 +1,51 @@ +module Rubyplot + module Artist + class Image + + attr_accessor :rows, :columns, :pixel_array + attr_reader :QuantumRange + + def initialize(columns,rows) + @rows = rows + @columns = columns + @pixel_array = [] + @image = Rubyplot.backend.init_image(columns,rows) + @QuantumRange = Rubyplot.backend.QuantumRange + end + + def imread(path) + @image = Rubyplot.backend.imread(path) + @rows = @image.rows + @columns = @image.columns + end + + def imshow + Rubyplot.backend.output_device = Rubyplot.iruby_inline ? :iruby : :window + Rubyplot.backend.imshow(@image, Rubyplot.backend.output_device) + end + + def imwrite(path) + Rubyplot.backend.imwrite(@image, path) + end + + def export_pixels(x, y, columns, rows, map) + @pixel_array = [] + map.size.times do + @pixel_array.push([]) + end + flat_pix_array = Rubyplot.backend.export_pixels(@image, x, y, columns, rows, map) + map.size.times do |channel| + rows.times do |row| + @pixel_array[channel].push(flat_pix_array[(channel*rows*columns)+(columns*row),columns]) + end + end + @pixel_array.flatten!(1) if map.size==1 + @pixel_array + end + + def import_pixels(x, y, columns, rows, map, pixels) + Rubyplot.backend.import_pixels(@image, x, y, columns, rows, map, pixels.to_a.flatten) + end + end # class Image + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/legend.rb b/lib/rubyplot/artist/legend.rb index 105245d..516e896 100644 --- a/lib/rubyplot/artist/legend.rb +++ b/lib/rubyplot/artist/legend.rb @@ -1,15 +1,15 @@ module Rubyplot module Artist class Legend < Artist::Base - TOP_MARGIN = 2.5 - BOTTOM_MARGIN = 2.5 + TOP_MARGIN = 1 + BOTTOM_MARGIN = 0.5 # Space between the color box and the legend text. - BOX_AND_TEXT_SPACE = 5.0 + BOX_AND_TEXT_SPACE = 0.5 attr_reader :legend_box_size, :font, :font_size, :font_color # rubocop:disable Metrics/ParameterLists def initialize(legend_box, axes, text:, color:,abs_x:,abs_y:) - super(legend_box.backend, abs_x, abs_y) + super(abs_x, abs_y) @legend_box = legend_box @axes = axes @text = text @@ -34,12 +34,14 @@ def draw def configure_legend_color_box @legend_color_box = Rubyplot::Artist::Rectangle.new( self, - abs_x: @abs_x, - abs_y: @abs_y + TOP_MARGIN, - width: @legend_box_size, - height: @legend_box_size, - border_color: @color, - fill_color: @color + x1: @abs_x, + y1: @abs_y + BOTTOM_MARGIN, + x2: @abs_x + @legend_box_size, + y2: @abs_y + @legend_box_size + BOTTOM_MARGIN, + border_color: :black, + border_width: 2, + fill_color: @color, + abs: true ) end @@ -48,15 +50,12 @@ def configure_legend_text @text, self, abs_x: @abs_x + @legend_box_size + BOX_AND_TEXT_SPACE, - abs_y: @legend_color_box.abs_y + @legend_box_size - 2.5, + abs_y: @abs_y + BOTTOM_MARGIN * 2, font: @font, color: @font_color, - pointsize: @font_size + size: @font_size ) end - end - # class Legend - end - # class Artist -end -# module Rubyplot + end # class Legend + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/legend_box.rb b/lib/rubyplot/artist/legend_box.rb index d1f4c2b..74d5e72 100644 --- a/lib/rubyplot/artist/legend_box.rb +++ b/lib/rubyplot/artist/legend_box.rb @@ -11,9 +11,13 @@ class LegendBox < Base RIGHT_SPACING_RATIO = 0.1 attr_accessor :border_color + attr_reader :width, :height + # @param axes [Rubyplot::Artist::Axes] Axes object to which this LegendBox belongs. + # @param abs_x [Float] Absolute X co-ordinate of the lower right corner of this legend box. + # @param abs_y [Float] Absolute Y co-ordinate of the lower right corner of this legend box. def initialize(axes, abs_x:, abs_y:) - super(axes.backend, abs_x, abs_y) + super(abs_x, abs_y) @axes = axes @border_color = :black @legends = [] @@ -45,9 +49,9 @@ def right_margin RIGHT_SPACING_RATIO * @legends_width end - # Calculation of the height that each legend takes. + # Height of each legend in Rubyplot Artist Co-ordinates. def per_legend_height - 25.0 + 5 end private @@ -55,11 +59,12 @@ def per_legend_height def configure_legend_box @bounding_box = Rubyplot::Artist::Rectangle.new( self, - abs_x: @abs_x, - abs_y: @abs_y, - width: @width, - height: @height, - border_color: @border_color + x1: @abs_x, + y1: @abs_y, + x2: @abs_x + @width, + y2: @abs_y + @height, + border_color: @border_color, + abs: true ) end @@ -72,21 +77,17 @@ def configure_dimensions def configure_legends @axes.plots.each_with_index do |plot, count| - next unless plot.label != '' - + next unless plot.label != '' @legends << Rubyplot::Artist::Legend.new( self, @axes, text: plot.label, color: plot.color, abs_x: @abs_x + left_margin, - abs_y: @abs_y + count * per_legend_height + top_margin + abs_y: @abs_y + count * per_legend_height + bottom_margin, ) end end - end - # class LegendBox - end - # module Artist -end -# module Rubyplot + end # class LegendBox + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/line2d.rb b/lib/rubyplot/artist/line2d.rb index 56cb423..6381734 100644 --- a/lib/rubyplot/artist/line2d.rb +++ b/lib/rubyplot/artist/line2d.rb @@ -2,27 +2,21 @@ module Rubyplot module Artist class Line2D < Artist::Base # rubocop:disable Metrics/ParameterLists - def initialize(owner,abs_x1:,abs_y1:,abs_x2:,abs_y2:,color: '#000000', - stroke_opacity: 0.0, stroke_width: 2.0) + def initialize(owner, x:,y:,color: :black, opacity: 0.0, width: 1.0, type: :solid) @owner = owner - @abs_x1 = abs_x1 - @abs_y1 = abs_y1 - @abs_x2 = abs_x2 - @abs_y2 = abs_y2 + @x = x + @y = y @color = color - @stroke_opacity = stroke_opacity - @stroke_width = stroke_width - @backend = @owner.backend + @opacity = opacity + @width = width + @type = type end # rubocop:enable Metrics/ParameterLists def draw - @backend.draw_line(x1: @abs_x1, y1: @abs_y1, x2: @abs_x2, y2: @abs_y2, - stroke_width: @stroke_width) + Rubyplot.backend.draw_lines(x: @x, y: @y, + width: @width, color: @color, opacity: @opacity, type: @type) end - end - # class Line2D - end - # class Artist -end -# module Rubyplot + end # class Line2D + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot.rb b/lib/rubyplot/artist/plot.rb index 40742e8..959c058 100644 --- a/lib/rubyplot/artist/plot.rb +++ b/lib/rubyplot/artist/plot.rb @@ -7,3 +7,10 @@ require_relative 'plot/multi_stacked_bar' require_relative 'plot/bubble' require_relative 'plot/area' +require_relative 'plot/histogram' +require_relative 'plot/candle_stick' +require_relative 'plot/multi_candle_stick' +require_relative 'plot/error_bar' +require_relative 'plot/box_plot' +require_relative 'plot/multi_box_plot' +require_relative 'plot/basic_plot' diff --git a/lib/rubyplot/artist/plot/area.rb b/lib/rubyplot/artist/plot/area.rb old mode 100644 new mode 100755 index 67aac0f..9144005 --- a/lib/rubyplot/artist/plot/area.rb +++ b/lib/rubyplot/artist/plot/area.rb @@ -2,40 +2,34 @@ module Rubyplot module Artist module Plot class Area < Artist::Plot::Base - attr_reader :sorted_data + attr_accessor :sort_data, :fill_opacity + def initialize(*) super - @sorted_data = true + @sort_data = true + @fill_opacity = 0.3 + end + + def stacked(bool) + @fill_opacity = 1 if bool end - def data x_values, y_values=[] - y_values = Array.new(x_values.size) { |i| i } if y_values.empty? - x_values.sort! if @sorted_data + def data x_values, y_values + x_values, y_values = x_values.to_a.zip(y_values.to_a).sort.transpose if @sort_data super(x_values, y_values) end def draw - poly_points = [] - @normalized_data[:y_values].each_with_index do |iy, idx_y| - ix = @normalized_data[:x_values][idx_y] - abs_x = ix * @axes.x_axis.length + @axes.abs_x + @axes.y_axis_margin - abs_y = (@axes.y_axis.length - iy * @axes.y_axis.length) + @axes.abs_y - poly_points << [abs_x, abs_y] - end - poly_points << [@axes.x_axis.abs_x2, @axes.origin[1] - @axes.x_axis.stroke_width] - poly_points << [@axes.origin[0], @axes.origin[1] - @axes.x_axis.stroke_width] + x_poly_points = @data[:x_values].concat([@axes.x_axis.max_val, @axes.x_axis.min_val]) + y_poly_points = @data[:y_values].concat([@axes.y_axis.min_val, @axes.y_axis.min_val]) Rubyplot::Artist::Polygon.new( - self, - coords: poly_points, + x: x_poly_points, + y: y_poly_points, color: @data[:color], - fill_opacity: 0.3 + fill_opacity: @fill_opacity ).draw end - end - # class Area - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class Area + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/bar.rb b/lib/rubyplot/artist/plot/bar.rb index f02f2a1..53f6d56 100644 --- a/lib/rubyplot/artist/plot/bar.rb +++ b/lib/rubyplot/artist/plot/bar.rb @@ -4,7 +4,7 @@ module Plot class Bar < Artist::Plot::Base # Space between the columns. attr_accessor :bar_spacing - # Width of each bar in pixels. + # Width of each bar. attr_accessor :bar_width # Number between 0 and 1.0 denoting spacing between the bars. # 0.0 means no spacing at all 1.0 means that each bars' width @@ -24,17 +24,17 @@ def initialize(*) @rectangles = [] end - # Set the spacing factor for this bar plot. - def spacing_factor=(s_f) - raise ValueError, '@spacing_factor must be between 0.00 and 1.00' unless - (s_f >= 0) && (s_f <= 1) + # Set the spacing ratio for this bar plot. + def spacing_ratio=(s_r) + raise ValueError, '@spacing_ratio must be between 0.00 and 1.00' unless + (s_r >= 0) && (s_r <= 1) - @spacing_factor = s_f + @spacing_ratio = s_r end # Set Bar plot data. def data y_values - super(Array.new(y_values.size) { |i| i }, y_values) + super(Array.new(y_values.to_a.size) { |i| i }, y_values.to_a) end # Number of bars in this Bar plot @@ -50,24 +50,19 @@ def draw private def setup_bar_rectangles - @normalized_data[:y_values].each_with_index do |iy, i| - height = iy * @axes.y_axis.length + @data[:y_values].each_with_index do |iy, i| @rectangles << Rubyplot::Artist::Rectangle.new( self, - abs_x: @abs_x_left[i], - abs_y: @abs_y_left[i] - height, - width: @bar_width, - height: height, + x1: @abs_x_left[i], + y1: @abs_y_left[i], + x2: @abs_x_left[i] + @bar_width, + y2: iy, border_color: @data[:color], fill_color: @data[:color] ) end end - end - # class Bar - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class Bar + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/bar_type.rb b/lib/rubyplot/artist/plot/bar_type.rb deleted file mode 100644 index 48826fc..0000000 --- a/lib/rubyplot/artist/plot/bar_type.rb +++ /dev/null @@ -1,35 +0,0 @@ -module Rubyplot - module Artist - module Plot - class BarType < Artist::Plot::Base - def initialize(*) - super - @spacing_ratio = 0.1 - @abs_x_left = [] - @abs_y_left = [] - @rectangles = [] - end - - def data y_values - super(Array(0...(y_values.size)), y_values) - end - - def num_bars - @data[:y_values].size - end - - def draw - setup_bar_rectangles - @rectangles.each(&:draw) - end - - protected - - end - # class BarType - end - # module Plot - end - # module Artist -end -# module Rubyplot diff --git a/lib/rubyplot/artist/plot/base.rb b/lib/rubyplot/artist/plot/base.rb index ed473a2..d9e2257 100644 --- a/lib/rubyplot/artist/plot/base.rb +++ b/lib/rubyplot/artist/plot/base.rb @@ -6,9 +6,8 @@ class Base < Artist::Base attr_writer :stroke_width, :stroke_opacity def initialize axes - super(axes.backend, axes.abs_x, axes.abs_y) + super(axes.abs_x, axes.abs_y) @axes = axes - @backend = @axes.backend @data = { label: '', color: :default @@ -25,6 +24,8 @@ def label @data[:label] end + # Color of the plot that will be used by LegenBox to configure the + # Legend colors. def color @data[:color] end @@ -33,40 +34,22 @@ def label=(label) @data[:label] = label end - def color= color + def color=(color) @data[:color] = color end def data(x_values, y_values) - @data[:x_values] = x_values - @data[:y_values] = y_values + @data[:x_values] = x_values.to_a + @data[:y_values] = y_values.to_a + end + + def process_data @y_min = @data[:y_values].min @y_max = @data[:y_values].max @x_min = @data[:x_values].min @x_max = @data[:x_values].max end - - # Normalize original data to values between 0-1. Used for obtaining relative - # values of the data. - def normalize - x_spread = @axes.x_range[1] - @axes.x_range[0] - y_spread = @axes.y_range[1] - @axes.y_range[0] - if @data[:x_values] - @normalized_data[:x_values] = @data[:x_values].map do |x| - (x.to_f - @axes.x_range[0]) / x_spread - end - end - if @data[:y_values] - @normalized_data[:y_values] = @data[:y_values].map do |y| - (y.to_f - @axes.y_range[0]) / y_spread - end - end - end - end - # class Base - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class Base + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/basic_plot.rb b/lib/rubyplot/artist/plot/basic_plot.rb new file mode 100644 index 0000000..abb9727 --- /dev/null +++ b/lib/rubyplot/artist/plot/basic_plot.rb @@ -0,0 +1,127 @@ +module Rubyplot + module Artist + module Plot + class BasicPlot < Artist::Plot::Base + attr_accessor :marker_type + attr_accessor :marker_size + attr_accessor :marker_border_color + attr_accessor :marker_fill_color + attr_accessor :line_color + attr_accessor :line_type + attr_accessor :line_width + + COLOR_TYPES_FMT = { + 'b' => :blue, + 'g' => :green, + 'r' => :red, + 'c' => :cyan, + 'm' => :magenta, + 'y' => :yellow, + 'k' => :black, + 'w' => :white + }.freeze + + MARKER_TYPES_FMT = { + '.' => :dot, + ',' => :omark, + 'o' => :circle, + 'v' => :traingle_down, + '^' => :traingle_up, + '<' => :solid_tri_left, + '>' => :solid_tri_right, + '1' => :solid_triangle_down, + '2' => :solid_triangle_up, + '3' => :solid_tri_left, + '4' => :solid_tri_right, + 's' => :square, + 'p' => :pentagon, + '*' => :star, + 'h' => :hexagon, + 'H' => :heptagon, + '+' => :plus, + 'x' => :diagonal_cross, + 'D' => :solid_diamond, + 'd' => :diamond, + '|' => :vline, + '_' => :hline + }.freeze + + LINE_TYPES_FMT ={ + '--' => :dashed, + '-.' => :dashed_dotted, + '-' => :solid, + ':' => :dotted + }.freeze + + def initialize(*) + super + @marker_type = nil + @marker_size = 1.0 + @marker_border_color = :default + # set fill to nil for the benefit of hollow markers so that legend + # color defaults to :black in case user does not specify. + @marker_fill_color = nil + @line_color = :default + @line_type = nil + @line_width = 1.0 + end + + def color + @line_color || @marker_fill_color || @marker_border_color || :default + end + + def fmt=(fmt) + unless fmt.is_a? String + raise TypeError, 'fmt argument takes a String input' + end + + COLOR_TYPES_FMT.each do |symbol, color| + if fmt.include? symbol + @marker_fill_color = color + @marker_border_color = color + @line_color = color + break + end + end + + MARKER_TYPES_FMT.each do |symbol, marker_type| + if fmt.include? symbol + @marker_type = marker_type + break + end + end + + LINE_TYPES_FMT.each do |symbol, line_type| + if fmt.include? symbol + @line_type = line_type + break + end + end + end + + def draw + # Default marker fill color + @marker_fill_color = :default if @marker_fill_color.nil? + # defualt type of plot is solid line + @line_type = :solid if @line_type.nil? && @marker_type.nil? + Rubyplot::Artist::Line2D.new( + self, + x: @data[:x_values], + y: @data[:y_values], + type: @line_type, + color: @line_color, + width: @line_width + ).draw if @line_type + Rubyplot.backend.draw_markers( + x: @data[:x_values], + y: @data[:y_values], + type: @marker_type, + fill_color: @marker_fill_color, + border_color: @marker_border_color, + size: [@marker_size] * @data[:x_values].size + ) if @marker_type + end + end # class BasicPlot + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/box_plot.rb b/lib/rubyplot/artist/plot/box_plot.rb new file mode 100644 index 0000000..e5873d0 --- /dev/null +++ b/lib/rubyplot/artist/plot/box_plot.rb @@ -0,0 +1,192 @@ +module Rubyplot + module Artist + module Plot + class BoxPlot < Artist::Plot::Base + # Determines the reach of the whiskers to beyond the first and third quartiles. + # Where IQR is the interquartile range (Q3-Q1), the upper whisker will extend + # to the datum less then Q3 + whiskers*IQR. Beyond the whiskers, data are considered + # outliers and plotted as individual points. + attr_accessor :whiskers + attr_accessor :box_width + # Array of co-ordinates of the lower left corners of the box. + attr_accessor :x_left_box + attr_accessor :median_color + attr_accessor :outlier_marker_type + attr_accessor :outlier_marker_color + attr_accessor :outlier_marker_size + attr_accessor :median_width + + def initialize(*) + super + @whiskers = 1.5 + @x_left_box = [] + @median_color = :black + @outlier_marker_type = :plus + @outlier_marker_color = nil + @outlier_marker_size = 1.0 + @median_width = 3.0 + end + + def process_data + @y_min = @vectors.map(&:min).min + @y_max = @vectors.map(&:max).max + @x_min = 0 + @x_max = @vectors.size + @vectors.map! { |v| v.sort! } + @outlier_marker_color ||= @data[:color] + + calculate_ranges! + end + + def data vectors + # @vectors = vectors.to_a unless vectors.is_a? Array + if vectors.is_a? Array + vectors.each_with_index { |_, idx| vectors[idx] = vectors[idx].to_a} + @vectors = vectors + else + @vectors = vectors.to_a + end + end + + def draw + @x_left_box.each_with_index do |x_left, i| + draw_box x_left, i + draw_whiskers x_left, i + draw_outliers x_left, i + draw_median x_left, i + end + end + + private + + def draw_box x_left, index + Rubyplot::Artist::Rectangle.new(self, + x1: x_left, + x2: x_left + @box_width, + y1: @q1s[index], + y2: @q3s[index], + border_color: :black, + fill_color: @data[:color] + ).draw + end + + def draw_whiskers x_left, index + x_coord = x_left + @box_width/2 + Rubyplot::Artist::Line2D.new(self, + x: [x_coord, x_coord], + y: [@q3s[index], @maxs[index]] + ).draw # top whisker + + Rubyplot::Artist::Line2D.new(self, + x: [x_coord, x_coord], + y: [@q1s[index], @mins[index]] + ).draw # bottom whisker + + Rubyplot::Artist::Line2D.new(self, + x: [x_coord - @box_width / 4.0, x_coord + @box_width / 4.0], + y: [@mins[index], @mins[index]] + ).draw # bottom whisker horizontal bar + + Rubyplot::Artist::Line2D.new(self, + x: [x_coord - @box_width / 4.0, x_coord + @box_width / 4.0], + y: [@maxs[index], @maxs[index]] + ).draw # top whisker horizontal bar + end + + def draw_outliers x_left, index + max = @maxs[index] + vector = @vectors[index] + max_index = first_max_outlier_index vector, max + if max_index + outliers_max = vector[max_index..-1] + Rubyplot.backend.draw_markers( + x: [x_left + @box_width/2]*outliers_max.size, y: outliers_max, + type: @outlier_marker_type, fill_color: @outlier_marker_color, + size: [@outlier_marker_size]*outliers_max.size) # draw max outliers + end + + min = @mins[index] + min_index = first_min_outlier_index vector, min + if min_index + outliers_min = vector[0..min_index] + Rubyplot.backend.draw_markers( + x: [x_left + @box_width/2] * outliers_min.size, y: outliers_min, + type: @outlier_marker_type, fill_color: @outlier_marker_color, + size: [@outlier_marker_size]*outliers_min.size + ) + end + end + + def first_max_outlier_index vector, max + return nil if vector.last >= max + vector.size.times do |i| + if vector[i] > max + return i + end + end + end + + def first_min_outlier_index vector, min + return nil if vector.first >= min + vector.size.times do |i| + if vector[i] > min + return i-1 + end + end + end + + def draw_median x_left, index + Rubyplot::Artist::Line2D.new(self, + x: [x_left, x_left + @box_width], + y: [@medians[index], @medians[index]], + color: @median_color, + width: @median_width + ).draw + end + + def calculate_ranges! + @q1s = [] + @q3s = [] + @medians = [] + @mins = [] + @maxs = [] + + @vectors.each do |sorted_vec| + m = get_percentile 50, sorted_vec + q1 = get_percentile 25, sorted_vec + q3 = get_percentile 75, sorted_vec + + @medians << m + @q1s << q1 + @q3s << q3 + + iqr = q3 - q1 + + if sorted_vec[0] >= q1 - @whiskers * iqr + @mins << sorted_vec[0] + else + @mins << q1 - @whiskers * iqr + end + + if sorted_vec.last <= q3 + @whiskers * iqr + @maxs << sorted_vec.last + else + @maxs << q3 + @whiskers * iqr + end + end + end + + def get_percentile percentile, values + + return values.first if values.size == 1 + + values.sort! + return values.last if p == 100 + rank = percentile / 100.0 * (values.size - 1) + lower, upper = values[rank.floor,2] + lower + (upper - lower) * (rank - rank.floor) + end + end # class BoxPlot + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/bubble.rb b/lib/rubyplot/artist/plot/bubble.rb index 86242cd..33ab389 100644 --- a/lib/rubyplot/artist/plot/bubble.rb +++ b/lib/rubyplot/artist/plot/bubble.rb @@ -3,43 +3,38 @@ module Artist module Plot class Bubble < Artist::Plot::Base # Width in pixels of the border of each bubble. - attr_reader :stroke_width + attr_accessor :border_width attr_reader :z_max, :z_min + # Opacity of the circles + attr_accessor :fill_opacity + def initialize(*) super @bubbles = [] - @stroke_width = 1.0 + @border_width = 1.0 + @fill_opacity = 0.5 end def data x_values, y_values, z_values - super(x_values, y_values) - @data[:z_values] = z_values - @z_max = @data[:z_values].max - @z_min = @data[:z_values].min + super(x_values.to_a, y_values.to_a) + @data[:z_values] = z_values.to_a end def draw - @normalized_data[:y_values].each_with_index do |iy, idx_y| - ix = @normalized_data[:x_values][idx_y] - iz = @data[:z_values][idx_y] - abs_x = ix * @axes.x_axis.length + @axes.abs_x + @axes.y_axis_margin - abs_y = (@axes.y_axis.length - iy * @axes.y_axis.length) + @axes.abs_y - @bubbles << Rubyplot::Artist::Circle.new( + @data[:x_values].each_with_index do |_, idx| + Rubyplot::Artist::Circle.new( self, - abs_x: abs_x, - abs_y: abs_y, - radius: iz, + x: @data[:x_values][idx], + y: @data[:y_values][idx], + radius: @data[:z_values][idx], + fill_opacity: @fill_opacity, color: @data[:color], - stroke_width: @stroke_width - ) + border_width: @border_width, + abs: false + ).draw end - @bubbles.each(&:draw) end - end - # class Bubble - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class Bubble + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/candle_stick.rb b/lib/rubyplot/artist/plot/candle_stick.rb new file mode 100644 index 0000000..60fa59d --- /dev/null +++ b/lib/rubyplot/artist/plot/candle_stick.rb @@ -0,0 +1,76 @@ +module Rubyplot + module Artist + module Plot + class CandleStick < Artist::Plot::Base + attr_reader :lows + attr_reader :highs + attr_reader :opens + attr_reader :closes + attr_accessor :bar_width + attr_accessor :x_left_candle + attr_accessor :x_low_stick + attr_accessor :border_color + + attr_reader :x_min, :x_max, :y_min, :y_max + + def initialize(*) + super + @lows = [] + @highs = [] + @opens = [] + @closes = [] + @x_left_candle = [] + @x_low_stick = [] + @border_color = :black + end + + def lows=(lows_arr) + @lows = lows_arr.to_a + end + + def highs=(highs_arr) + @highs = highs_arr.to_a + end + + def opens=(opens_arr) + @opens = opens_arr.to_a + end + + def closes=(closes_arr) + @closes = closes_arr.to_a + end + + def process_data + if @lows.size != @highs.size || @opens.size != @closes.size + raise Rubyplot::SizeError, "all given parameters must be of equal size." + end + @y_min = [@lows.min, @highs.min, @opens.min, @closes.min].min + @y_max = [@lows.max, @highs.max, @opens.max, @closes.max].max + @x_min = 0 + @x_max = @lows.size + end + + def draw + @x_low_stick.each_with_index do |ix_stick, i| + Rubyplot::Artist::Line2D.new( + self, + x: [ix_stick, ix_stick], + y: [@lows[i], @highs[i]] + ).draw + end + @x_left_candle.each_with_index do |ix_candle, i| + Rubyplot::Artist::Rectangle.new( + self, + x1: ix_candle, + y1: @opens[i], + x2: ix_candle + @bar_width, + y2: @closes[i], + border_color: @border_color, + fill_color: @data[:color] + ).draw + end + end + end + end + end +end diff --git a/lib/rubyplot/artist/plot/error_bar.rb b/lib/rubyplot/artist/plot/error_bar.rb new file mode 100644 index 0000000..78d11c9 --- /dev/null +++ b/lib/rubyplot/artist/plot/error_bar.rb @@ -0,0 +1,178 @@ +module Rubyplot + module Artist + module Plot + class ErrorBar < Artist::Plot::Base + attr_accessor :xuplims, :xlolims, :yuplims, :ylolims, :line_width, :xerr_width, :yerr_width, :xerr_color, :yerr_color + attr_reader :xerr, :yerr + + def initialize(*) + super + @line_width = 1.0 + @xerr_width = 1.0 + @yerr_width = 1.0 + @xerr_color = nil + @yerr_color = nil + end + + def xerr=(xerror) + if (xerror.is_a?(Float) || xerror.is_a?(Integer)) + @xerr = xerror + else + @xerr = xerror.to_a + end + end + + def yerr=(yerror) + if (yerror.is_a?(Float) || yerror.is_a?(Integer)) + @yerr = yerror + else + @yerr = yerror.to_a + end + end + + def process_data + super + preprocess_err_values! + + check_lims_sizes + check_err_sizes + + adjust_axes_ranges! + @line = Rubyplot::Artist::Line2D.new( + self, + x: @data[:x_values], + y: @data[:y_values], + color: @data[:color], + width: @line_width + ) + generate_yerr if @yerr + generate_xerr if @xerr + end + + def draw + @line.draw + @yerr_lines.each(&:draw) if @yerr_lines + @xerr_lines.each(&:draw) if @xerr_lines + end + + private + + def adjust_axes_ranges! + if @xerr + @axes.x_axis.max_val = @data[:x_values].max + @xerr.max + @axes.x_axis.min_val = @data[:x_values].min - @xerr.min + end + + if @yerr + @axes.y_axis.max_val = @data[:y_values].max + @yerr.max + @axes.y_axis.min_val = @data[:y_values].min - @yerr.min + end + end + + def preprocess_err_values! + if @yerr + if @yerr.is_a?(Float) + @yerr = [@yerr] * @data[:y_values].size + end + end + + if @xerr + if @xerr.is_a?(Float) + @xerr = [@xerr] * @data[:x_values].size + end + end + end + + def generate_xerr + @xerr_lines = @xerr.map.with_index do |xe, idx| + xcoord = @data[:x_values][idx] + ycoord = @data[:y_values][idx] + + if !@xuplims && !@xlolims + Rubyplot::Artist::Line2D.new( + self, + x: [xcoord - xe, xcoord + xe], + y: [ycoord, ycoord], + color: @xerr_color.nil? ? @data[:color] : @xerr_color, + width: @xerr_width + ) + else + arrows = [] + if @xuplims && @xuplims[idx] + arrows << Rubyplot::Artist::Arrow.new( + x1: xcoord, + y1: ycoord, + x2: xcoord + xe, + y2: ycoord + ) + end + + if @xlolims && @xlolims[idx] + arrows << Rubyplot::Artist::Arrow.new( + x1: xcoord, + y1: ycoord, + x2: xcoord - xe, + y2: ycoord + ) + end + arrows + end + end + @xerr_lines.flatten! + end + + def generate_yerr + @yerr_lines = @yerr.map.with_index do |ye, idx| + xcoord = @data[:x_values][idx] + ycoord = @data[:y_values][idx] + + if !@yuplims && !@ylolims + Rubyplot::Artist::Line2D.new( + self, + x: [xcoord, xcoord], + y: [ycoord - ye, ycoord + ye], + color: @yerr_color.nil? ? @data[:color] : @yerr_color, + width: @yerr_width + ) + else + arrows = [] + if @yuplims && @yuplims[idx] + arrows << Rubyplot::Artist::Arrow.new( + x1: xcoord, + y1: ycoord, + x2: xcoord, + y2: ycoord + ye + ) + end + + if @ylolims && @ylolims[idx] + arrows << Rubyplot::Artist::Arrow.new( + x1: xcoord, + y1: ycoord, + x2: xcoord, + y2: ycoord - ye + ) + end + arrows + end + end + @yerr_lines.flatten! + end + + def check_lims_sizes + + end + + def check_err_sizes + if @yerr && @yerr.size != @data[:y_values].size + raise Rubyplot::SizeError, "yerr.size != data(y_values).size. Must be the same." + end + + if @xerr && @xerr.size != @data[:x_values].size + raise Rubyplot::SizeError, "xerr.size != data(x_values).size. Must be the same." + end + end + end # class ErrorBar + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/histogram.rb b/lib/rubyplot/artist/plot/histogram.rb new file mode 100644 index 0000000..91b0926 --- /dev/null +++ b/lib/rubyplot/artist/plot/histogram.rb @@ -0,0 +1,84 @@ +module Rubyplot + module Artist + module Plot + class Histogram < Artist::Plot::Base + # Values that need to be shown as a histogram. + attr_reader :x + # Array of bins into which the data should be subdivided. + attr_accessor :bins + # Width of each bar. + attr_accessor :bar_width + + def initialize(*) + super + end + + def x=(xvals) + @x = xvals.to_a + end + + def data x_values + @x = x_values.to_a + end + + def process_data + groups = @x.group_by { |v| v }.map { |k,v| [k, v.size] }.sort_by { |a| a[0] } + unique_nums = groups.map { |g| g[0] } + freqs = groups.map { |g| g[1] } + @bins = unique_nums.size unless @bins + + if @bins.is_a?(Array) + unless @bins.each_cons(2).all? { |a, b| a <= b } + raise RangeError, "Histogram bins must be consecutive" + end + elsif @bins.is_a?(Integer) + subdivisions = ((unique_nums.max - unique_nums.min).to_f / @bins).ceil + subdivisions = 1 if subdivisions.zero? + @bins = Range.new(unique_nums.min, unique_nums.max).step(subdivisions).to_a + @bins << unique_nums.last + subdivisions + end + combined_freqs = [] + + @bins.each_cons(2) do |start, stop| + sum = 0 + unique_nums.each_with_index do |num, i| + if num >= start + if (stop == freqs.last && num <= stop) || (stop != freqs.last && num < stop) + sum += freqs[i] + end + elsif num > stop + break + end + end + combined_freqs << sum + end + + @step = (@bins[1] - @bins[0]) + @x_min = @bins.first - @step + @x_max = @bins.last + @step + @y_min = 0 + @y_max = combined_freqs.max + @data[:x_values] = @bins + @data[:y_values] = combined_freqs + @bar_width = @step unless @bar_width + @axes.x_axis.major_ticks_count = @bins.size - 1 + end + + def draw + @data[:y_values].each_with_index do |iy, i| + ix = @data[:x_values][i] + Rubyplot::Artist::Rectangle.new( + self, + x1: @bins.first + i*@bar_width, + y1: @y_min, + x2: @bins.first + i*@bar_width + @bar_width, + y2: iy, + border_color: :black, + fill_color: @data[:color] + ).draw + end + end + end # class Histogram + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/line.rb b/lib/rubyplot/artist/plot/line.rb index 9c77ff6..1ee19e3 100644 --- a/lib/rubyplot/artist/plot/line.rb +++ b/lib/rubyplot/artist/plot/line.rb @@ -2,64 +2,37 @@ module Rubyplot module Artist module Plot class Line < Artist::Plot::Base - # Set true if you want to see only the vertices of the line plot. - attr_writer :hide_lines + # Type of line to draw. Default solid. + attr_writer :line_type + # The number of times that you want the width to be of the graphic device. Default 1.0. + attr_writer :line_width + + def line_color=(color) + @line_color = color + @data[:color] = color + end def initialize(*) super - @hide_lines = false + @line_width = 1.0 + @line_type = :solid + @line_color = :black end - def data(x_values, y_values=[]) - y_values = Array.new(x_values.size) { |i| i } if y_values.empty? - super x_values, y_values + def data(x_values, y_values) + super x_values.to_a, y_values.to_a end def draw - if @normalized_data[:x_values].size == 1 - draw_single_point - else - draw_lines - end - end - - private - - # FIXME: make this edge case happen. - def draw_single_point - # Rubyplot::Artist::Circle.new() - end - - def draw_lines - prev_x = prev_y = nil - @normalized_data[:x_values].each_with_index do |ix, idx_ix| - iy = @normalized_data[:y_values][idx_ix] - next if ix.nil? || iy.nil? - - new_x = ix * (@axes.x_axis.abs_x2 - @axes.x_axis.abs_x1).abs + @axes.abs_x + - @axes.y_axis_margin - new_y = (@axes.y_axis.length - iy * @axes.y_axis.length) + @axes.abs_y - - unless prev_x.nil? && prev_y.nil? - Rubyplot::Artist::Line2D.new( - self, - abs_x1: prev_x, - abs_y1: prev_y, - abs_x2: new_x, - abs_y2: new_y, - stroke_opacity: @stroke_opacity, - stroke_width: @stroke_width - ).draw - end - prev_x = new_x - prev_y = new_y - end + Rubyplot.backend.draw_lines( + x: @data[:x_values], + y: @data[:y_values], + width: @line_width, + type: @line_type, + color: @line_color + ) end - end - # class Line - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class Line + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/multi_bars.rb b/lib/rubyplot/artist/plot/multi_bars.rb index 4109196..438a1e4 100644 --- a/lib/rubyplot/artist/plot/multi_bars.rb +++ b/lib/rubyplot/artist/plot/multi_bars.rb @@ -14,16 +14,16 @@ class MultiBars < Artist::Plot::Base def initialize(*args, bar_plots:) super(args[0]) @bar_plots = bar_plots + end + + def process_data + @bar_plots.each(&:process_data) @x_min = @bar_plots.map(&:x_min).min @y_min = @bar_plots.map(&:y_min).min @x_max = @bar_plots.map(&:x_max).max @y_max = @bar_plots.map(&:y_max).max - configure_plot_geometry_data - configure_x_ticks - end - - def normalize - @bar_plots.each(&:normalize) + configure_ranges! + configure_plot_geometry_data! end def draw @@ -32,10 +32,14 @@ def draw private - def configure_plot_geometry_data + def configure_ranges! + @y_min = @y_min > 0 ? 0 : @y_min + @axes.y_range = [@y_min, @y_max] + end + + def configure_plot_geometry_data! @num_max_slots = @bar_plots.map(&:num_bars).max - @max_slot_width = (@axes.x_axis.abs_x2 - @axes.x_axis.abs_x1).abs / @num_max_slots - # FIXME: figure out a way to specify inter-box space somehow. + @max_slot_width = (@x_max - @x_min) / @num_max_slots.to_f @spacing_ratio = @bar_plots[0].spacing_ratio @padding = @spacing_ratio * @max_slot_width @max_bars_width = @max_slot_width - @padding @@ -44,36 +48,16 @@ def configure_plot_geometry_data set_bar_dims bar, index end end - - def configure_x_ticks - @axes.num_x_ticks = @num_max_slots - labels = @axes.x_ticks || Array.new(@num_max_slots, &:to_s) - labels = labels[0...@axes.num_x_ticks] if labels.size != @axes.num_x_ticks - @axes.x_ticks = labels.map.with_index do |label, i| - Rubyplot::Artist::XTick.new( - @axes, - abs_x: @axes.abs_x + @axes.y_axis_margin + i * @max_slot_width + @max_slot_width / 2, - abs_y: @axes.origin[1], - label: label, - length: 6, - label_distance: 10 - ) - end - end - + def set_bar_dims bar_plot, index bar_plot.bar_width = @max_bars_width / @bars_per_slot @num_max_slots.times do |i| - bar_plot.abs_x_left[i] = @axes.abs_x + @axes.y_axis_margin + - i * @max_slot_width + @padding / 2 + index * bar_plot.bar_width - bar_plot.abs_y_left[i] = @axes.origin[1] - @axes.x_axis.stroke_width + bar_plot.abs_x_left[i] = @x_min + i*@max_slot_width + @padding/2 + + index * bar_plot.bar_width + bar_plot.abs_y_left[i] = @y_min end end - end - # class MultiBars - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class MultiBars + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/multi_box_plot.rb b/lib/rubyplot/artist/plot/multi_box_plot.rb new file mode 100644 index 0000000..b538e62 --- /dev/null +++ b/lib/rubyplot/artist/plot/multi_box_plot.rb @@ -0,0 +1,46 @@ +module Rubyplot + module Artist + module Plot + class MultiBoxPlot < Artist::Plot::Base + def initialize(*args, box_plots:) + super(args[0]) + @box_plots = box_plots + @spacing_ratio = 0.1 + end + + def process_data + @box_plots.each(&:process_data) + @x_min = @box_plots.map(&:x_min).min + @y_min = @box_plots.map(&:y_min).min - 1 + @x_max = @box_plots.map(&:x_max).max + @y_max = @box_plots.map(&:y_max).max + 1 + + configure_plot_geometry_data! + end + + def draw + @box_plots.each(&:draw) + end + + private + + def configure_plot_geometry_data! + @max_slot_width = 1.0 + @max_box_slot_width = @max_slot_width / @box_plots.size + @padding = @max_box_slot_width * @spacing_ratio + @box_plots.each_with_index do |box_plot, index| + set_bar_properties! box_plot, index + end + end + + def set_bar_properties! box_plot, index + box_plot.box_width = @max_box_slot_width - @padding * 2 + (@x_max).to_i.times do |i| + box_plot.x_left_box[i] = @max_box_slot_width * index + @max_slot_width * i + + (@padding / 2) + end + end + end + end + end +end diff --git a/lib/rubyplot/artist/plot/multi_candle_stick.rb b/lib/rubyplot/artist/plot/multi_candle_stick.rb new file mode 100644 index 0000000..4abd870 --- /dev/null +++ b/lib/rubyplot/artist/plot/multi_candle_stick.rb @@ -0,0 +1,47 @@ +module Rubyplot + module Artist + module Plot + class MultiCandleStick < Artist::Plot::Base + def initialize(*args, candle_sticks:) + super(args[0]) + @candle_sticks = candle_sticks + @spacing_ratio = 0.1 + end + + def process_data + @candle_sticks.each(&:process_data) + @y_min = @candle_sticks.map(&:y_min).min + @y_max = @candle_sticks.map(&:y_max).max + @x_min = 0 + @x_max = @candle_sticks.map(&:x_max).max + @axes.x_axis.max_val = @x_max + @max_slot_width = 1.0 + @candles_per_slot = @candle_sticks.size + @max_candle_width = @max_slot_width / @candles_per_slot + @candle_sticks.each_with_index do |candle_stick, index| + set_bar_properties candle_stick, index + end + @axes.x_axis.major_ticks_count = @x_max + end + + def draw + @candle_sticks.each(&:draw) + end + + private + + def set_bar_properties candle_stick, index + candle_stick.bar_width = (@max_slot_width - @candles_per_slot * @spacing_ratio) / + @candles_per_slot + (@x_max).to_i.times do |i| + candle_stick.x_left_candle[i] = @max_slot_width * i + + @max_candle_width * index + + (@spacing_ratio/2) * @max_candle_width + candle_stick.x_low_stick[i] = (candle_stick.x_left_candle[i] + + candle_stick.bar_width / 2) + end + end + end # class MultiCandlestick + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/multi_stacked_bar.rb b/lib/rubyplot/artist/plot/multi_stacked_bar.rb index 7939c00..47cca75 100644 --- a/lib/rubyplot/artist/plot/multi_stacked_bar.rb +++ b/lib/rubyplot/artist/plot/multi_stacked_bar.rb @@ -5,14 +5,18 @@ class MultiStackedBar < Artist::Plot::Base def initialize(*args, stacked_bars:) super(args[0]) @stacked_bars = stacked_bars + end + + def process_data + @stacked_bars.each(&:process_data) @x_min = @stacked_bars.map(&:x_min).min @y_min = @stacked_bars.map(&:y_min).min @x_max = @stacked_bars.map(&:x_max).max - @y_max = @stacked_bars.map(&:y_max).max + @y_max = @stacked_bars. + map { |s| s.y_values }.transpose. + map { |a| a.inject(:+) }.max reset_axes_ranges - renormalize_data configure_plot_geometry_data - configure_x_ticks end def draw @@ -22,21 +26,15 @@ def draw private def reset_axes_ranges - @axes.y_range[0] = 0 - @axes.x_range[0] = 0 - end - - # Normalize data in the stacked bar plot w.r.t each other and new X & Y ranges. - def renormalize_data - @stacked_bars.each do |p| - p.normalize - p.renormalize @stacked_bars.size - end + @axes.y_axis.min_val = 0 + @axes.y_axis.max_val = @y_max + @axes.x_axis.min_val = 0 + @axes.x_axis.max_val = @x_max end def configure_plot_geometry_data @num_max_slots = @stacked_bars.map(&:num_bars).max - @max_slot_width = @axes.x_axis.length / @num_max_slots + @max_slot_width = (@axes.x_axis.max_val - @axes.x_axis.min_val) / @num_max_slots.to_f @spacing_ratio = @stacked_bars[0].spacing_ratio @padding = @spacing_ratio * @max_slot_width @max_bars_width = @max_slot_width - @padding @@ -46,37 +44,16 @@ def configure_plot_geometry_data end end - def configure_x_ticks - @axes.num_x_ticks = @num_max_slots - labels = @axes.x_ticks || Array.new(@num_max_slots, &:to_s) - labels = labels[0...@axes.num_x_ticks] if labels.size != @axes.num_x_ticks - @axes.x_ticks = labels.map.with_index do |label, i| - Rubyplot::Artist::XTick.new( - @axes, - abs_x: @axes.abs_x + @axes.y_axis_margin + i * @max_slot_width + @max_slot_width / 2, - abs_y: @axes.origin[1], - label: label, - length: 6, - label_distance: 10 - ) - end - end - def set_bar_dims bar, plot_index bar.bar_width = @max_bars_width plots_below = @stacked_bars[0...plot_index] bar.num_bars.times do |i| - pedestal_height = plots_below.map { |p| p.member_height(i) }.inject(:+) || 0 - bar.abs_x_left[i] = @axes.abs_x + @axes.y_axis_margin + - i * @max_slot_width + @padding / 2 - bar.abs_y_left[i] = (@axes.abs_y + @axes.y_axis.length) - pedestal_height + pedestal_height = plots_below.map { |p| p.y_values[i] }.inject(:+) || 0 + bar.abs_x_left[i] = @x_min + i * @max_slot_width + @padding / 2 + bar.abs_y_left[i] = @axes.y_axis.min_val + pedestal_height end end - end - # class StackedBar - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class StackedBar + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/scatter.rb b/lib/rubyplot/artist/plot/scatter.rb index 763a121..c6353bb 100644 --- a/lib/rubyplot/artist/plot/scatter.rb +++ b/lib/rubyplot/artist/plot/scatter.rb @@ -2,31 +2,52 @@ module Rubyplot module Artist module Plot class Scatter < Artist::Plot::Base - attr_writer :circle_radius + attr_accessor :marker_size + # Set a symbol as the marker type for this plot. Should be one from + # Rubyplot::MARKER_TYPES. + attr_accessor :marker_type + # Set a color from Rubyplot::Color::COLOR_INDEX as the color of the + # fill of the marker. + attr_accessor :marker_fill_color + # Set a color from Rubyplot::Color::COLOR_INDEX as the color of the + # border of the marker. + attr_accessor :marker_border_color def initialize(*) super - @circle_radius = 4.0 + @marker_size = 1.0 + @marker_type = :circle + @marker_border_color = :black + # set fill to nil for the benefit of hollow markers so that legend + # color defaults to :black in case user does not specify. + @marker_fill_color = nil end - def draw - @normalized_data[:y_values].each_with_index do |iy, idx_y| - ix = @normalized_data[:x_values][idx_y] - next if iy.nil? || ix.nil? + # Color used for creating the legend. Defaults to the marker_fill_color + # but will use the marker_border_color if marker_fill_color does not + # exist (for example for hollow circles). + def color + @marker_fill_color || @marker_border_color || :default + end - abs_x = ix * @axes.x_axis.length + @axes.abs_x + @axes.y_axis_margin - abs_y = (@axes.y_axis.length - iy * @axes.y_axis.length) + @axes.abs_y - Rubyplot::Artist::Circle.new( - self, abs_x: abs_x, abs_y: abs_y, radius: @circle_radius, stroke_opacity: @stroke_opacity, - stroke_width: @stroke_width - ).draw - end + # Set both marker_fill_color and marker_border_color to the same color. + def color= color + @marker_fill_color = color + @marker_border_color = color + end + + def draw + @marker_fill_color = :default if @marker_fill_color.nil? + Rubyplot.backend.draw_markers( + x: @data[:x_values], + y: @data[:y_values], + type: @marker_type, + fill_color: @marker_fill_color, + border_color: @marker_border_color, + size: [@marker_size] * @data[:x_values].size + ) end - end - # class Scatter - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class Scatter + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/plot/stacked_bar.rb b/lib/rubyplot/artist/plot/stacked_bar.rb index c7f3314..f86c043 100644 --- a/lib/rubyplot/artist/plot/stacked_bar.rb +++ b/lib/rubyplot/artist/plot/stacked_bar.rb @@ -21,53 +21,31 @@ def initialize(*) end def data y_values - super(Array(0...(y_values.size)), y_values) + super(Array(0...(y_values.to_a.size)), y_values.to_a) end - def num_bars - @data[:y_values].size - end - - # Normalize this data w.r.t other plots in this graph. - # - # @param max_plots [Integer] Max number of stacked bars in this plot. - def renormalize max_plots - @renormalized_y_values = @normalized_data[:y_values].map do |iy| - iy / max_plots - end + def y_values + @data[:y_values] end - # Return the height in pixels of the bar at 'index'. - def member_height index - @renormalized_y_values[index] * @axes.y_axis.length + def num_bars + @data[:y_values].size end def draw - setup_bar_rectangles - @rectangles.each(&:draw) - end - - private - - def setup_bar_rectangles - @renormalized_y_values.each_with_index do |iy, i| - height = iy * @axes.y_axis.length - @rectangles << Rubyplot::Artist::Rectangle.new( + @data[:y_values].each_with_index do |iy, i| + Rubyplot::Artist::Rectangle.new( self, - abs_x: @abs_x_left[i], - abs_y: @abs_y_left[i] - height, - width: @bar_width, - height: height, + x1: @abs_x_left[i], + y1: @abs_y_left[i], + x2: @abs_x_left[i] + @bar_width, + y2: @abs_y_left[i] + iy, border_color: @data[:color], fill_color: @data[:color] - ) + ).draw end end - end - # class StackedBar - end - # module Plot - end - # module Artist -end -# module Rubyplot + end # class StackedBar + end # module Plot + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/polygon.rb b/lib/rubyplot/artist/polygon.rb index f84ad54..0234740 100644 --- a/lib/rubyplot/artist/polygon.rb +++ b/lib/rubyplot/artist/polygon.rb @@ -1,25 +1,25 @@ module Rubyplot module Artist class Polygon < Base - def initialize(owner, coords:, fill_opacity: 0.0, color: :default, stroke: 'transparent') - @backend = owner.backend - @coords = coords + def initialize(x:, y:, fill_opacity: 1.0, color: :default, stroke: 'transparent') + @x = x + @y = y @fill_opacity = fill_opacity @color = color @stroke = stroke end def draw - @backend.draw_polygon( - coords: @coords, - stroke: @stroke, - color: Rubyplot::Color::COLOR_INDEX[@color], + Rubyplot.backend.draw_polygon( + x: @x, + y: @y, + border_color: @color, + border_width: 1.0, + border_type: :solid, + fill_color: @color, fill_opacity: @fill_opacity ) end - end - # class Polygon - end - # module Artist -end -# module Rubyplot + end # class Polygon + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/rectangle.rb b/lib/rubyplot/artist/rectangle.rb index 64444dc..82194b1 100644 --- a/lib/rubyplot/artist/rectangle.rb +++ b/lib/rubyplot/artist/rectangle.rb @@ -1,42 +1,45 @@ module Rubyplot module Artist class Rectangle < Base - attr_reader :width, :height, :border_color, :fill_color + attr_reader :x1, :x2, :y1, :y2, :border_color, :border_width, :fill_color # Create a Rectangle for drawing on the canvas. # - # @param abs_x [Float] Absolute X co-ordinate of the upper left corner. - # @param abs_y [Float] Absolute Y co-ordinate of the upper left corner. - # @param width [Float] Width in pixels of the rectangle. - # @param height [Float] Height in pixels of the rectangle. + # @param x1 [Float] X co-ordinate of the lower left corner. + # @param y1 [Float] Y co-ordinate of the lower left corner. + # @param x2 [Float] X co-ordinate of upper right corner. + # @param y2 [Float] Y co-ordinate of upper right corner. # @param border_color [Symbol] Symbol from Rubyplot::Color::COLOR_INDEX # denoting border color. + # @param border_width [Float] Width of the border. # @param fill_color [Symbol] nil Symbol from Rubyplot::Color::COLOR_INDEX # denoting the fill color. - + # @param abs [FalseClass|TrueClass] false Whether the co-ordinates are absolute co-ordinates. # rubocop:disable Metrics/ParameterLists - def initialize(owner,abs_x:,abs_y:,width:,height:,border_color:,fill_color: nil) - super(owner.backend, abs_x, abs_y) - @height = height - @width = width - @border_color = Rubyplot::Color::COLOR_INDEX[border_color] - @fill_color = Rubyplot::Color::COLOR_INDEX[fill_color] if fill_color + def initialize(owner,x1:,y1:,x2:,y2:,border_color:,border_width: 1.0,fill_color: nil, abs: false) + @x1 = x1 + @x2 = x2 + @y1 = y1 + @y2 = y2 + @border_color = border_color + @border_width = border_width + @fill_color = fill_color + @abs = abs end # rubocop:enable Metrics/ParameterLists def draw - @backend.draw_rectangle( - x1: @abs_x, - y1: @abs_y, - x2: @abs_x + @width, - y2: @abs_y + @height, + Rubyplot.backend.draw_rectangle( + x1: @x1, + y1: @y1, + x2: @x2, + y2: @y2, border_color: @border_color, - fill_color: @fill_color + border_width: @border_width, + fill_color: @fill_color, + abs: @abs ) end - end - # class Rectangle - end - # class Artist -end -# moduel Rubyplot + end # class Rectangle + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/text.rb b/lib/rubyplot/artist/text.rb index 1f00e10..2818ad7 100644 --- a/lib/rubyplot/artist/text.rb +++ b/lib/rubyplot/artist/text.rb @@ -1,53 +1,88 @@ module Rubyplot module Artist class Text < Artist::Base - # (X,Y) of upper left corner of the rectangle. - attr_reader :color, :font, :pointsize, + attr_reader :color, :font, :font_size, :stroke, :weight, :gravity, :text, :backend + # X and Y co-oridinates specified in Rubyplot Artist Co-ordinates. + attr_reader :abs_x, :abs_y + # Owner Artist of the text. + attr_reader :owner + # Array containing valid horizontal alignment types. + HAlignment = [ + :normal, + :left, + :center, + :right + ].freeze + + # Array containing valid vertical alignment types. + VAlignment = [ + :normal, + :top, + :cap, + :half, + :base, + :bottom + ].freeze + + # Initialize a Text object. + # + # @param text [String] Text to be written to the output. + # @param owner [Rubyplot::Artist::*] Owner object. Usually either Axes or Figure. + # @param abs_x [Numeric] X co-ordinate of the text to plot between MIN_X and MAX_X. + # @param abs_y [Numeric] Y co-ordinate of the text to plot between MIN_Y and MAX_Y. + # @param font [Symbol] :times_roman Font to be used. # rubocop:disable Metrics/ParameterLists - def initialize(text, owner, abs_x:, abs_y:,font: nil, - color: '#000000',pointsize:,stroke: 'transparent', - internal_label: '', rotation: nil, - weight: nil, gravity: nil - ) - super(owner.backend, abs_x, abs_y) + def initialize(text, owner, abs_x:, abs_y:,font: :times_roman, + color: :black, size:, internal_label: '', rotation: nil, + weight: nil, halign: :normal, valign: :normal, direction: :left_right) + @abs_x = abs_x + @abs_y = abs_y @text = text @owner = owner @font = font @color = color - @pointsize = pointsize - @stroke = stroke + @size = size @internal_label = internal_label @rotation = rotation + if HAlignment.include? halign + @halign = halign + else + raise "Invalid horizontal alignment #{halign}." + end + + if VAlignment.include? valign + @valign = valign + else + raise "Invalid vertical alignment #{valign}." + end end # rubocop:enable Metrics/ParameterLists # Height in pixels of this text def height - @backend.text_height(@text, @font, @font_size) + Rubyplot.backend.text_height(@text, @font, @font_size) end # Width in pixels of this text def width - @backend.text_width(@text, @font, @font_size) + Rubyplot.backend.text_width(@text, @font, @font_size) end def draw - @backend.draw_text( + Rubyplot.backend.draw_text( @text, - font_color: @color, + color: @color, font: @font, - pointsize: @pointsize, - stroke: @stroke, - x: @abs_x, - y: @abs_y, - rotation: @rotation + size: @size, + abs_x: @abs_x, + abs_y: @abs_y, + rotation: @rotation, + halign: @halign, + valign: @valign ) end - end - # class Text - end - # class Artist -end -# module Rubyplot + end # class Text + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/tick/base.rb b/lib/rubyplot/artist/tick/base.rb index aee1e58..2f6d664 100644 --- a/lib/rubyplot/artist/tick/base.rb +++ b/lib/rubyplot/artist/tick/base.rb @@ -2,38 +2,33 @@ module Rubyplot module Artist class Tick class Base < Artist::Base - # Distance between tick mark and number. - attr_reader :label_distance # Label of this Tick attr_reader :label attr_reader :tick_opacity attr_reader :tick_width + attr_reader :tick_size + attr_reader :coord + attr_reader :owner + # @param owner [Rubyplot::Artist] Artist that owns this tick. - # @param abs_x [Integer] X co-ordinate of the origin of this tick. - # @param abs_y [Integer] Y co-ordinate of the origin of this tick. - # @param length [Integer] Length of the tick. + # @param coord [Float] Co-ordinate on the Axes on which this tick exists. # @param label [String] Label below the tick. # @param label_distance [Integer] Distance between the label and tick. # @param tick_opacity [Float] Number describing the opacity of the tick drawn. 0-1.0. - + # @param tick_size [Float] Size of the ticks in terms of Rubyplot Artist Co-ordinates. # rubocop:disable Metrics/ParameterLists - def initialize(owner,abs_x:,abs_y:,length:,label:,label_distance:, - tick_opacity: 0.0,tick_width: 1.0) - super(owner.backend, abs_x, abs_y) + def initialize(owner, coord:, label:,label_distance: nil, + tick_opacity: 0.0, tick_width: 1.0, tick_size: 1.0) @owner = owner - @length = length - @label_text = label # Rubyplot::Utils.format_label label - @label_distance = label_distance + @coord = coord + @label = label @tick_opacity = tick_opacity @tick_width = tick_width + @tick_size = tick_size end # rubocop:enable Metrics/ParameterLists - end - # class Base - end - # class Tick - end - # class Artist -end -# module Rubyplot + end # class Base + end # class Tick + end # class Artist +end # module Rubyplot diff --git a/lib/rubyplot/artist/tick/x_tick.rb b/lib/rubyplot/artist/tick/x_tick.rb index b647cac..0ec583e 100644 --- a/lib/rubyplot/artist/tick/x_tick.rb +++ b/lib/rubyplot/artist/tick/x_tick.rb @@ -3,26 +3,11 @@ module Artist class XTick < Tick::Base def initialize(*) super - @label = Rubyplot::Artist::Text.new( - @label_text.to_s, - @owner, - abs_x: @abs_x - 5, - abs_y: @abs_y + @length + @label_distance, - pointsize: @owner.marker_font_size - ) end + end # class XTick + end # class Artist +end # module Rubyplot + + + - def draw - @backend.draw_line( - x1: @abs_x, y1: @abs_y, x2: @abs_x, y2: @abs_y + @length, - stroke_opacity: @tick_opacity, - stroke_width: @tick_width - ) - @label.draw - end - end - # class XTick - end - # class Artist -end -# module Rubyplot diff --git a/lib/rubyplot/artist/tick/y_tick.rb b/lib/rubyplot/artist/tick/y_tick.rb index ed22860..da3fe39 100644 --- a/lib/rubyplot/artist/tick/y_tick.rb +++ b/lib/rubyplot/artist/tick/y_tick.rb @@ -1,9 +1,9 @@ module Rubyplot module Artist class YTick < Tick::Base - end - # class YTick - end - # module Artist -end -# module Rubyplot + def initialize(*) + super + end + end # class YTick + end # module Artist +end # module Rubyplot diff --git a/lib/rubyplot/backend.rb b/lib/rubyplot/backend.rb index d765144..81c4e2b 100644 --- a/lib/rubyplot/backend.rb +++ b/lib/rubyplot/backend.rb @@ -1 +1,8 @@ -require_relative 'backend/magick_wrapper' +require_relative 'backend/base' +if ENV["RUBYPLOT_BACKEND"] == "GR" + require_relative 'backend/gr_wrapper' +elsif ENV["RUBYPLOT_BACKEND"] = "MAGICK" + require_relative 'backend/magick_wrapper' +end + + diff --git a/lib/rubyplot/backend/base.rb b/lib/rubyplot/backend/base.rb new file mode 100644 index 0000000..681bc2e --- /dev/null +++ b/lib/rubyplot/backend/base.rb @@ -0,0 +1,89 @@ +module Rubyplot + module Backend + # Base class for backend. A new backend should implement all the below methods. + class Base + # Total height and width of the canvas in pixels. + attr_accessor :canvas_height, :canvas_width + + attr_accessor :active_axes, :figure, :output_device + + # Write text anywhere on the canvas. abs_x and abs_y should be specified in terms + # of Rubyplot Artist Co-ordinates. + # + # @param text [String] String of text to write. + # @param abs_x [Numeric] X co-ordinate of the text in Rubyplot Artist Co-ordinates. + # @param abs_y [Numeric] Y co-ordinate of the text in Rubyplot Aritst Co-ordinates. + # @param font_color [Symbol] Color of the font from Rubyplot::Colors. + # @param font [Symbol] Name of the font. + # @param size [Numeric] Size of the font. + # @param font_weight [Symbol] Measure of 'bigness' of the font. + # @param rotation [Numeric] Angle between 0 and 360 degrees signifying rotation of text. + # @param halign [Symbol] Horizontal alignment of the text from Artist::Text::HAlignment. + # @param valign [Symbol] Vertical alignment of the text from Artist::Text::VAlignment + def draw_text(text,color:,font: nil,size:, + font_weight: nil, halign: nil, valign: nil, + abs_x:, abs_y:,rotation: nil, stroke: nil, abs: false) + raise NotImplementedError, "not implemented for #{__method__}." + end + + # Draw a rectangle with optional fill. + # + # @param x1 [Numeric] Lower left X co-ordinate. + # @param y1 [Numeric] Lower left Y co-ordinate. + # @param x2 [Numeric] Upper right X co-ordinate. + # @param x2 [Numeric] Upper right Y co-ordinate. + def draw_rectangle(x1:,y1:,x2:,y2:, border_color: nil, fill_color: nil, + border_width: nil, border_type: nil, abs: false) + raise NotImplementedError, "not implemented for #{__method__}." + end + + # Draw multiple markers as specified by co-ordinates. + # + # @param x [[Numeric]] Array of X co-ordinates. + # @param y [[Numeric]] Array of Y co-ordinates. + # @param marker_type [Symbol] A marker type from Rubyplot::MARKERS. + # @param marker_color [Symbol] A color from Rubyplot::Color. + # @param marker_size [Numeric] Size of the marker. + def draw_markers(x:, y:, type:, fill_color:, border_color:, size:) + raise NotImplementedError, "not implemented for #{__method__}." + end + + # Draw a circle.n + def draw_circle(x:, y:, radius:, border_width:, border_color:, border_type:, + fill_color:, fill_opacity:) + raise NotImplementedError, "not implemented for #{__method__}." + end + + # Draw a polygon and fill it with color. Co-ordinates are specified in (x,y) + # pairs in the coords Array. + # + # @param x [Array] Array containing X co-ordinates. + # @param y [Array] Array containting Y co-ordinates. + # @param border_width [Numeric] Widht of the border. + def draw_polygon(x:, y:, border_width:, border_type:, border_color:, fill_color:, + fill_opacity:) + raise NotImplementedError, "not implemented for #{__method__}." + end + + def draw_lines(x:, y:, width:, type:, color:, opacity:) + raise NotImplementedError, "not implemented for #{__method__}." + end + + def draw_arrow(x1:, y1:, x2:, y2:, size:, style:) + raise NotImplementedError, "not implemented for #{__method__}." + end + + def init_output_device file_name, device: :file + raise NotImplementedError, "not implemented for #{__method__}." + end + + def draw_x_axis(minor_ticks:, origin:, major_ticks:, major_ticks_count:) + raise NotImplementedError, "not implemented for #{__method__}." + end + + def draw_y_axis(minor_ticks:, origin:, major_ticks:, major_ticks_count:) + raise NotImplementedError, "not implemented for #{__method__}." + end + end # class Base + end # module Backend +end # module Rubyplot diff --git a/lib/rubyplot/backend/gr_wrapper.rb b/lib/rubyplot/backend/gr_wrapper.rb new file mode 100644 index 0000000..bee1775 --- /dev/null +++ b/lib/rubyplot/backend/gr_wrapper.rb @@ -0,0 +1,471 @@ +require_relative '../../grruby.so' + +module Rubyplot + module Backend + # Wrapper around a GR backend. Works differently than the Image Magick wrapper + # since in GR there is no one-one mapping between Rubyplot artists and things + # that are to be drawn on the backend. + class GRWrapper < Base + DEFAULT_DPI = 600.0 + + # Mapping Rubyplot markers to GR marker constants. + MARKER_TYPE_MAP = { + dot: GR::MARKERTYPE_DOT, + plus: GR::MARKERTYPE_PLUS, + asterisk: GR::MARKERTYPE_ASTERISK, + circle: GR::MARKERTYPE_CIRCLE, + diagonal_cross: GR::MARKERTYPE_DIAGONAL_CROSS, + solid_circle: GR::MARKERTYPE_SOLID_CIRCLE, + triangle_up: GR::MARKERTYPE_TRIANGLE_UP, + solid_triangle_up: GR::MARKERTYPE_SOLID_TRI_UP, + triangle_down: GR::MARKERTYPE_TRIANGLE_DOWN, + solid_triangle_down: GR::MARKERTYPE_SOLID_TRI_DOWN, + square: GR::MARKERTYPE_SQUARE, + solid_square: GR::MARKERTYPE_SOLID_SQUARE, + bowtie: GR::MARKERTYPE_BOWTIE, + solid_bowtie: GR::MARKERTYPE_SOLID_BOWTIE, + hglass: GR::MARKERTYPE_HGLASS, + solid_hglass: GR::MARKERTYPE_SOLID_HGLASS, + diamond: GR::MARKERTYPE_DIAMOND, + solid_diamond: GR::MARKERTYPE_SOLID_DIAMOND, + star: GR::MARKERTYPE_STAR, + solid_star: GR::MARKERTYPE_SOLID_STAR, + tri_up_down: GR::MARKERTYPE_TRI_UP_DOWN, + solid_tri_right: GR::MARKERTYPE_SOLID_TRI_RIGHT, + solid_tri_left: GR::MARKERTYPE_SOLID_TRI_LEFT, + hollow_plus: GR::MARKERTYPE_HOLLOW_PLUS, + solid_plus: GR::MARKERTYPE_SOLID_PLUS, + pentagon: GR::MARKERTYPE_PENTAGON, + hexagon: GR::MARKERTYPE_HEXAGON, + heptagon: GR::MARKERTYPE_HEPTAGON, + octagon: GR::MARKERTYPE_OCTAGON, + star_4: GR::MARKERTYPE_STAR_4, + star_5: GR::MARKERTYPE_STAR_5, + star_6: GR::MARKERTYPE_STAR_6, + star_7: GR::MARKERTYPE_STAR_7, + star_8: GR::MARKERTYPE_STAR_8, + vline: GR::MARKERTYPE_VLINE, + hline: GR::MARKERTYPE_HLINE, + omark: GR::MARKERTYPE_OMARK + }.freeze + + # Mapping between rubyplot line types and GR line types. + LINE_TYPE_MAP = { + solid: GR::LINETYPE_SOLID, + dashed: GR::LINETYPE_DASHED, + dotted: GR::LINETYPE_DOTTED, + dashed_dotted: GR::LINETYPE_DASHED_DOTTED, + dash_2_dot: GR::LINETYPE_DASH_2_DOT, + dash_3_dot: GR::LINETYPE_DASH_3_DOT, + long_dash: GR::LINETYPE_LONG_DASH, + long_short_dash: GR::LINETYPE_LONG_SHORT_DASH, + spaced_dash: GR::LINETYPE_SPACED_DASH, + spaced_dot: GR::LINETYPE_SPACED_DOT, + double_dot: GR::LINETYPE_DOUBLE_DOT, + triple_dot: GR::LINETYPE_TRIPLE_DOT + }.freeze + + # Mapping between Rubyplot text types and GR text types. + TEXT_FONT_MAP = { + times_roman: GR::FONT_TIMES_ROMAN, + times_italic: GR::FONT_TIMES_ITALIC, + times_bold: GR::FONT_TIMES_BOLD, + times_bold_italic: GR::FONT_TIMES_BOLD_ITALIC, + helvetica: GR::FONT_HELVETICA, + helvetica_oblique: GR::FONT_HELVETICA_OBLIQUE, + helvetica_bold: GR::FONT_HELVETICA_BOLD, + helvetica_bold_oblique: GR::FONT_HELVETICA_BOLD_OBLIQUE, + courier: GR::FONT_COURIER, + courier_oblique: GR::FONT_COURIER_OBLIQUE, + courier_bold: GR::FONT_COURIER_BOLD, + courier_bold_oblique: GR::FONT_COURIER_BOLD_OBLIQUE, + symbol: GR::FONT_SYMBOL, + bookman_light: GR::FONT_BOOKMAN_LIGHT, + bookman_lightitalic: GR::FONT_BOOKMAN_LIGHT_ITALIC, + bookman_demi: GR::FONT_BOOKMAN_DEMI, + bookman_demi_italic: GR::FONT_BOOKMAN_DEMI_ITALIC, + newcenturyschlbk_roman: GR::FONT_NEWCENTURYSCHLBK_ROMAN, + newcenturyschlbk_italic: GR::FONT_NEWCENTURYSCHLBK_ITALIC, + newcenturyschlbk_bold: GR::FONT_NEWCENTURYSCHLBK_BOLD, + newcenturyschlbk_bold_italic: GR::FONT_NEWCENTURYSCHLBK_BOLD_ITALIC, + avantgarde_book: GR::FONT_AVANTGARDE_BOOK, + avantgarde_book_oblique: GR::FONT_AVANTGARDE_BOOK_OBLIQUE, + avantgarde_demi: GR::FONT_AVANTGARDE_DEMI, + avantgarde_demi_oblique: GR::FONT_AVANTGARDE_DEMI_OBLIQUE, + palatino_roman: GR::FONT_PALATINO_ROMAN, + palatino_italic: GR::FONT_PALATINO_ITALIC, + palatino_bold: GR::FONT_PALATINO_BOLD, + palatino_bold_italic: GR::FONT_PALATINO_BOLD_ITALIC, + zapfchancery_medium_italic: GR::FONT_ZAPFCHANCERY_MEDIUM_ITALIC, + zapfdingbats: GR::FONT_ZAPFDINGBATS + }.freeze + + TEXT_PRECISION_MAP = { + high: GR::TEXT_PRECISION_STRING, + med: GR::TEXT_PRECISION_CHAR, + low: GR::TEXT_PRECISION_STROKE + }.freeze + + TEXT_DIRECTION_MAP = { + left_right: GR::TEXT_PATH_RIGHT, + right_left: GR::TEXT_PATH_LEFT, + down_up: GR::TEXT_PATH_UP, + up_down: GR::TEXT_PATH_DOWN + }.freeze + + TEXT_HALIGNMENT_MAP = { + normal: GR::TEXT_HALIGN_NORMAL, + left: GR::TEXT_HALIGN_LEFT, + center: GR::TEXT_HALIGN_CENTER, + right: GR::TEXT_HALIGN_RIGHT + }.freeze + + TEXT_VALIGNMENT_MAP = { + normal: GR::TEXT_VALIGN_NORMAL, + top: GR::TEXT_VALIGN_TOP, + cap: GR::TEXT_VALIGN_CAP, + half: GR::TEXT_VALIGN_HALF, + base: GR::TEXT_VALIGN_BASE, + bottom: GR::TEXT_VALIGN_BOTTOM + }.freeze + + FILL_STYLE_MAP = { + hollow: GR::FILLSTYLE_HOLLOW, + solid: GR::FILLSTYLE_SOLID, + pattern: GR::FILLSTYLE_PATTERN, + hatch: GR::FILLSTYLE_HATCH + }.freeze + + AXES_CALLBACKS = { + :f2 => Proc.new { |ndc_x, ndc_y, svalue, value| + if value.is_a?(Float) + GR.text(ndc_x, ndc_y, "%0.2f" % svalue) + else + GR.text(ndc_x, ndc_y, value) + end + } + }.freeze + + # Multiplier needed to convert given unit into meters. (GR default). + METER_MULTIPLIERS = { + inch: 0.0254, + cm: 1 / 100.0, + pixel: 0.0254 / DEFAULT_DPI + }.freeze + + ARROW_STYLE_MAP = { + simple_single_ended: GR::ARROW_STYLE_SIMPLE_SINGLE_ENDED, + simple_single_ended_acute: GR::ARROW_STYLE_SIMPLE_SINGLE_ENDED_ACUTE, + hollow_single_ended: GR::ARROW_STYLE_HOLLOW_SINGLE_ENDED, + filled_singled_ended: GR::ARROW_STYLE_FILLED_SINGLED_ENDED, + triangle_single_ended: GR::ARROW_STYLE_TRIANGLE_SINGLE_ENDED, + filled_triangle_single_ended: GR::ARROW_STYLE_FILLED_TRIANGLE_SINGLE_ENDED, + kite_single_ended: GR::ARROW_STYLE_KITE_SINGLE_ENDED, + filled_kite_single_ended: GR::ARROW_STYLE_FILLED_KITE_SINGLE_ENDED, + simple_double_ended: GR::ARROW_STYLE_SIMPLE_DOUBLE_ENDED, + simple_double_ended_acute: GR::ARROW_STYLE_SIMPLE_DOUBLE_ENDED_ACUTE, + hollow_double_ended: GR::ARROW_STYLE_HOLLOW_DOUBLE_ENDED, + filled_double_ended: GR::ARROW_STYLE_FILLED_DOUBLE_ENDED, + triangle_double_ended: GR::ARROW_STYLE_TRIANGLE_DOUBLE_ENDED, + filled_triangle_double_ended: GR::ARROW_STYLE_FILLED_TRIANGLE_DOUBLE_ENDED, + kite_double_ended: GR::ARROW_STYLE_KITE_DOUBLE_ENDED, + filled_kite_double_ended: GR::ARROW_STYLE_FILLED_KITE_DOUBLE_ENDED, + double_line_single_ended: GR::ARROW_STYLE_DOUBLE_LINE_SINGLE_ENDED, + double_line_double_ended: GR::ARROW_STYLE_DOUBLE_LINE_DOUBLE_ENDED, + }.freeze + + def initialize + @axes_map = {} # Mapping between viewports and their respective Axes. + @file_name = nil + end + + def xspread + (@figure.max_x.abs + @figure.min_x.abs).to_f + end + + def yspread + (@figure.max_y.abs + @figure.min_y.abs).to_f + end + + # Draw X axis for the currently selected Axes. + # + # @param minor_ticks [[Rubyplot::Artist::XTick]] Array of XTick objects representing + # minor ticks. + # @param origin [Numeric] X co-ordinate value that is the origin of the X axis. + # @param major_ticks [[Rubyplot::Artist::XTick]] Array of XTick objects representing + # major ticks. + # @param major_ticks_count [Integer] Number of major ticks to plot. + def draw_x_axis(origin:, minor_ticks:, major_ticks:, minor_ticks_count:, major_ticks_count:) + @axes_map[@active_axes.object_id] = { + axes: @active_axes, + x_minor_ticks: minor_ticks, + x_origin: origin, + x_major_ticks: major_ticks, + x_major_ticks_count: major_ticks_count + } + end + + # Draw Y axis for currently selected Axes. + def draw_y_axis(origin:, minor_ticks:, major_ticks:, minor_ticks_count:, major_ticks_count:) + @axes_map[@active_axes.object_id] = { + axes: @active_axes, + y_minor_ticks: minor_ticks, + y_origin: origin, + y_major_ticks: major_ticks, + y_major_ticks_count: major_ticks_count + } + end + + def draw_markers(x:, y:, type:, fill_color:, border_color: nil, size:) + within_window do + GR.setmarkercolorind(to_gr_color(fill_color)) + GR.setmarkertype(MARKER_TYPE_MAP[type]) + x.size.times do |i| + GR.setmarkersize(size[i]) + GR.polymarker([x[i]], [y[i]]) + end + end + end + + def draw_lines(x:, y:, width:, type:, color:, opacity: 1.0) + within_window do + GR.setlinewidth(width) + GR.setlinetype(LINE_TYPE_MAP[type]) + GR.setlinecolorind(to_gr_color(color)) + GR.polyline(x, y) + end + end + + def draw_polygon(x:, y:, border_width:, border_type:, border_color:, fill_color:, + fill_opacity:) + within_window do + draw_lines(x: x, y: y, width: border_width, color: border_color, + type: border_type) + if fill_color + GR.settransparency(fill_opacity) + GR.setfillintstyle(1) + GR.setfillcolorind(to_gr_color(fill_color)) + GR.fillarea(x, y) + end + end + end + + # Draw text on the canvas. + # TODO: support text with special characters and latex symbols. + def draw_text(text, color:, font: nil, size:, + font_weight: nil, gravity: nil, abs_x:,abs_y:, rotation: nil, + halign: nil, valign: nil, font_precision: :high, direction: :left_right, + abs: true) + within_window(abs) do |ndc_min_x, ndc_max_x, ndc_min_y, ndc_max_y| + if abs + x = transform_x_ndc abs_x + y = transform_y_ndc abs_y + end + + GR.setcharup(*to_gr_rotation_vector(rotation)) + GR.setcharheight(to_gr_font_size(size, ndc_min_y, ndc_max_y)) + GR.settextpath(TEXT_DIRECTION_MAP[direction]) + GR.settextcolorind(to_gr_color(color)) + GR.settextfontprec(TEXT_FONT_MAP[font], TEXT_PRECISION_MAP[font_precision]) + GR.settextalign(TEXT_HALIGNMENT_MAP[halign], TEXT_VALIGNMENT_MAP[valign]) + GR.text(x, y, text) + end + end + + def draw_rectangle(x1:,y1:,x2:,y2:,border_color: :black, + fill_color: nil, border_width: 1.0, border_type: :solid, abs: false) + within_window(abs) do + if abs + x1 = transform_x_ndc x1 + x2 = transform_x_ndc x2 + y1 = transform_y_ndc y1 + y2 = transform_y_ndc y2 + end + + GR.setlinewidth(border_width) + GR.setlinetype(LINE_TYPE_MAP[border_type]) + GR.setlinecolorind(to_gr_color(border_color)) + GR.drawrect(x1, x2, y1, y2) + if fill_color + GR.setfillintstyle(1) + GR.setfillcolorind(to_gr_color(fill_color)) + GR.fillrect(x1, x2, y1, y2) + end + end + end + + def draw_line(x1:, y1:, x2:, y2:, width:, color:, opacity:, type:) + draw_lines(x: [x1, x2], y: [y1, y2], width: width, color: color, + type: type) + end + + def draw_circle(x:, y:, radius:, border_width:, border_color:, border_type:, + fill_color:, fill_opacity:) + within_window do + xmin = x - radius + xmax = x + radius + ymin = y - radius + ymax = y + radius + + GR.setlinewidth(border_width) + GR.setlinetype(LINE_TYPE_MAP[border_type]) + GR.setlinecolorind(to_gr_color(border_color)) + GR.drawarc(xmin, xmax, ymin, ymax, 0, 360) + if fill_color + GR.setfillintstyle(1) + GR.setfillcolorind(to_gr_color(fill_color)) + GR.settransparency(fill_opacity) + GR.fillarc(xmin, xmax, ymin, ymax, 0, 360) + end + end + end + + def draw_arrow(x1:, y1:, x2:, y2:, size:, style:) + within_window do + GR.setarrowstyle(ARROW_STYLE_MAP[style]) + GR.setarrowsize(size) + GR.drawarrow(x1, y1, x2, y2) + end + end + + def draw + draw_axes + end + + def init_output_device file_name, device: :file + @file_name = file_name + @output_device = device + Rubyplot::GR.clearws + + Rubyplot::GR.beginprint(@file_name) if @output_device == :file + end + + def stop_output_device + case @output_device + when :file + Rubyplot::GR.endprint + when :window + Rubyplot::GR.updatews + end + flush + end + + def write + draw + end + + def show + draw + end + + # Refresh this backend and remove all previously set data. + def flush + @axes_map = {} + @file_name = nil + end + + private + + # FIXME + def to_gr_rotation_vector rotation + if rotation == -90.0 + [-1,0] + else + [0,1] + end + end + + # Transform font size expressed in terms of Rubyplot font size (pt.) + # to GR font height that is expressed in terms of the height of the canvas. + def to_gr_font_size rubyplot_font_size, ndc_min_y, ndc_max_y + height_in_pixels = (ndc_max_y - ndc_min_y) * + 2400 * METER_MULTIPLIERS[:pixel] * 39.3701 * + DEFAULT_DPI + (rubyplot_font_size / height_in_pixels) + end + + def to_gr_color color + r,g,b = to_rgb color + GR.inqcolorfromrgb(r, g, b) + end + + def to_rgb color + match = Rubyplot::Color::COLOR_INDEX[color].match(/#(..)(..)(..)/) + r, g, b = match[1], match[2], match[3] + [r.hex.to_f/255.0, g.hex.to_f/255.0, b.hex.to_f/255.0] + end + + # Transform a X quantity to Normalized Device Co-ordinates. + def transform_x_ndc coord + coord.to_f / xspread + end + + # Transform a Y quantity to Normalized Device Co-ordinates. + def transform_y_ndc coord + coord.to_f / yspread + end + + # Transform a quanitity that represents neither X nor Y co-ordinate into NDC. + def transform_avg_ndc coord + coord / ((xspread + yspread) / 2) + end + + # Set the window on the canvas within which the plotting will take place + # and then call the passed block for actual plotting. + def within_window(abs=false, &block) + vp_min_x = 0.0 + vp_max_x = 1.0 + vp_min_y = 0.0 + vp_max_y = 1.0 + + if abs + GR.setviewport(vp_min_x, vp_max_x, vp_min_y, vp_max_y) + GR.setwindow(0,1,0,1) + else + vp_min_x = (@active_axes.abs_x + @active_axes.left_margin) / xspread + vp_min_y = (@active_axes.abs_y + @active_axes.bottom_margin) / yspread + vp_max_x = (@active_axes.abs_x + @active_axes.width - + @active_axes.right_margin) / xspread + vp_max_y = (@active_axes.abs_y + @active_axes.height - + @active_axes.top_margin) / yspread + + GR.setviewport(vp_min_x, vp_max_x, vp_min_y, vp_max_y) + GR.setwindow( + @active_axes.x_range[0], + @active_axes.x_range[1], + @active_axes.y_range[0], + @active_axes.y_range[1] + ) + end + + block.call(vp_min_x, vp_max_x, vp_min_y, vp_max_y) + end + + def draw_axes + @axes_map.each_value do |v| + axes = v[:axes] + @active_axes = axes + tick_size = transform_avg_ndc(axes.x_axis.major_ticks[0].tick_size) / 2.0 + within_window do + GR.settransparency(1) + GR.setcharheight(0.015) + GR.setlinecolorind(to_gr_color(:black)) + GR.axeslbl( + GR.tick(axes.x_range[0], axes.x_range[1]) / + axes.x_axis.major_ticks_count.to_f, + GR.tick(axes.y_range[0], axes.y_range[1]) / + axes.y_axis.major_ticks_count.to_f, + axes.x_axis.min_val, + axes.y_axis.min_val, + axes.x_axis.minor_ticks_count, + axes.y_axis.minor_ticks_count, + -tick_size, + AXES_CALLBACKS[:f2], + AXES_CALLBACKS[:f2] + ) + end + end + end + end # class GRWrapper + end # module Backend +end # module Rubyplot diff --git a/lib/rubyplot/backend/image_backend/image_magick_wrapper.rb b/lib/rubyplot/backend/image_backend/image_magick_wrapper.rb new file mode 100644 index 0000000..4ac726e --- /dev/null +++ b/lib/rubyplot/backend/image_backend/image_magick_wrapper.rb @@ -0,0 +1,43 @@ +require 'rmagick' + +module Rubyplot + module Backend + module ImageMagickWrapper + def init_image(columns,rows) + Magick::Image.new(columns,rows) + end + + def imread(path) + Magick::Image.read(path).first + end + + def imshow(img, device) + case device + when :window + img.display + nil # return nil so that image is not printed on iruby + when :iruby + img + else + img.display + end + end + + def imwrite(img, file_name) + img.write(file_name) + end + + def QuantumRange + Magick::QuantumRange + end + + def export_pixels(img, x, y, columns, rows, map) + img.export_pixels(x, y, columns, rows, map) + end + + def import_pixels(img, x, y, columns, rows, map, pixels) + img.import_pixels(x, y, columns, rows, map, pixels) + end + end # module ImageMagickWrapper + end # module Backend +end # module Rubyplot diff --git a/lib/rubyplot/backend/magick_wrapper.rb b/lib/rubyplot/backend/magick_wrapper.rb index 4a20170..0ef54f9 100644 --- a/lib/rubyplot/backend/magick_wrapper.rb +++ b/lib/rubyplot/backend/magick_wrapper.rb @@ -1,7 +1,28 @@ +require 'rmagick' +require_relative './image_backend/image_magick_wrapper.rb' + module Rubyplot module Backend - class MagickWrapper + # Wrapper around an Image Magick backend. In case of ImageMagick, the upper + # left corner of the canvas is the (0,0) co-ordinate and the lower right corner + # is (max_width, max_height). + # + # Transformation are applied accordingly before actual plotting happens since Rubyplot + # Artists treat the co-ordinate system differently. The `transform_x` and `transform_y` + # functions are used for this purpose. + class MagickWrapper < Base include ::Magick + include ImageMagickWrapper + + NOMINAL_FACTOR_MARKERS = 15 + NOMINAL_FACTOR_CIRCLE = 27.5 + TICK_FONT_SIZE = 33.5 + AXES_WIDTH_MULTIPLIER = 5 + TICK_SIZE_MULTIPLIER = 20 + TICK_LABEL_COORD_X_MULTIPLIER = 3.5 + TICK_LABEL_COORD_Y_MULTIPLIER = 5.25 + TICK_LABEL_FONT_WEIGHT = 800 + GRAVITY_MEASURE = { nil => Magick::ForgetGravity, :center => Magick::CenterGravity, @@ -10,10 +31,419 @@ class MagickWrapper :north => Magick::NorthGravity }.freeze + # Multiplier needed to convert given unit into pixels. (Magick default). + PIXEL_MULTIPLIERS = { + inch: 96, + cm: 39.7953, + pixel: 1, + point: 4/3 # Point is the unit of measurement for the size of font in ImageMagick + }.freeze + + MARKER_TYPES = { + # Default type is circle + # Stroke width is set to 1 + default: ->(draw, x, y, fill_color, border_color, size) { + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.circle(x,y, x + size,y) + }, + circle: ->(draw, x, y, fill_color, border_color, size) { + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.circle(x,y, x + size,y) + }, + plus: ->(draw, x, y, fill_color, border_color, size) { + # size is length of one line + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.line(x - size/2, y, x + size/2, y) + draw.line(x, y - size/2, x, y + size/2) + }, + dot: ->(draw, x, y, fill_color, border_color, size) { + # Dot is a circle of size 5 pixels + # size is kept 5 to make it visible, ideally it should be 1 + # which is the smallest displayable size + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.circle(x,y, x + 5,y) + }, + asterisk: ->(draw, x, y, fill_color, border_color, size) { + # Looks like a five sided star + raise NotImplementedError, 'This marker has not yet been implemented' + }, + diagonal_cross: ->(draw, x, y, fill_color, border_color, size) { + # size is length of one line + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.line(x - size/2, y + size/2, x + size/2, y - size/2) + draw.line(x - size/2, y - size/2, x + size/2, y + size/2) + }, + solid_circle: ->(draw, x, y, fill_color, border_color, size) { + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.circle(x,y, x + size,y) + }, + triangle_down: ->(draw, x, y, fill_color, border_color, size) { + # height and base are equal to size + # x,y is center of base and height + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y - size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x, y + size/2, + x, y + size/2, x - size/2, y - size/2 + ) + }, + solid_triangle_down: ->(draw, x, y, fill_color, border_color, size) { + # height and base are equal to size + # x,y is center of base and height + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y - size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x, y + size/2, + x, y + size/2, x - size/2, y - size/2 + ) + }, + triangle_up: ->(draw, x, y, fill_color, border_color, size) { + # height and base are equal to size + # x,y is center of base and height + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y + size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x, y - size/2, + x, y - size/2, x - size/2, y + size/2 + ) + }, + solid_triangle_up: ->(draw, x, y, fill_color, border_color, size) { + # height and base are equal to size + # x,y is center of base and height + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y + size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x, y - size/2, + x, y - size/2, x - size/2, y + size/2 + ) + }, + square: ->(draw, x, y, fill_color, border_color, size) { + # size is equal to side of the square + # x,y is center of base and height i.e. center of the square + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y + size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x - size/2, y - size/2, + x - size/2, y - size/2, x - size/2, y + size/2 + ) + }, + solid_square: ->(draw, x, y, fill_color, border_color, size) { + # size is equal to side of the square + # x,y is center of base and height i.e. center of the square + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y + size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x - size/2, y - size/2, + x - size/2, y - size/2, x - size/2, y + size/2 + ) + }, + bowtie: ->(draw, x, y, fill_color, border_color, size) { + # height and width are equal to size + # x,y is center of width and height + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y - size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x - size/2, y + size/2, + x - size/2, y + size/2, x - size/2, y - size/2 + ) + }, + solid_bowtie: ->(draw, x, y, fill_color, border_color, size) { + # height and width are equal to size + # x,y is center of width and height + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y - size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x - size/2, y + size/2, + x - size/2, y + size/2, x - size/2, y - size/2 + ) + }, + hglass: ->(draw, x, y, fill_color, border_color, size) { + # height and width are equal to size + # x,y is center of width and height + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y - size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x - size/2, y + size/2, + x - size/2, y + size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x - size/2, y - size/2 + ) + }, + solid_hglass: ->(draw, x, y, fill_color, border_color, size) { + # height and width are equal to size + # x,y is center of width and height + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y - size/2, x + size/2, y + size/2, + x + size/2, y + size/2, x - size/2, y + size/2, + x - size/2, y + size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x - size/2, y - size/2 + ) + }, + diamond: ->(draw, x, y, fill_color, border_color, size) { + # size is equal to side of the square + # x,y is center of base and height i.e. center of the square + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x, y - size/2, x + size/2, y, + x + size/2, y, x, y + size/2, + x, y + size/2, x - size/2, y, + x - size/2, y, x, y - size/2 + ) + }, + solid_diamond: ->(draw, x, y, fill_color, border_color, size) { + # size is equal to side of the square + # x,y is center of base and height i.e. center of the square + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x, y - size/2, x + size/2, y, + x + size/2, y, x, y + size/2, + x, y + size/2, x - size/2, y, + x - size/2, y, x, y - size/2 + ) + }, + star: ->(draw, x, y, fill_color, border_color, size) { + # 5 sided star + raise NotImplementedError, 'This marker has not yet been implemented' + }, + solid_star: ->(draw, x, y, fill_color, border_color, size) { + # 5 sided solid star + raise NotImplementedError, 'This marker has not yet been implemented' + }, + tri_up_down: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + solid_tri_right: ->(draw, x, y, fill_color, border_color, size) { + # height and base are equal to size + # x,y is center of base and height + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x + size/2, y + size/2, x + size/2, y - size/2, + x + size/2, y - size/2, x - size/2, y, + x - size/2, y, x + size/2, y + size/2 + ) + }, + solid_tri_left: ->(draw, x, y, fill_color, border_color, size) { + # height and base are equal to size + # x,y is center of base and height + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x - size/2, y - size/2, x - size/2, y + size/2, + x - size/2, y + size/2, x + size/2, y, + x + size/2, y, x - size/2, y - size/2 + ) + }, + hollow_plus: ->(draw, x, y, fill_color, border_color, size) { + # height and width are equal to size + # x,y is center of width and height + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill_opacity 0 + draw.polyline( + x + size/4, y- size/2, x + size/4, y - size/4, + x + size/4, y - size/4, x + size/2, y - size/4, + x + size/2, y - size/4, x + size/2, y + size/4, + x + size/2, y + size/4, x + size/4, y + size/4, + x + size/4, y + size/4, x + size/4, y + size/2, + x + size/4, y + size/2, x - size/4, y + size/2, + x - size/4, y + size/2, x - size/4, y + size/4, + x - size/4, y + size/4, x - size/2, y + size/4, + x - size/2, y + size/4, x - size/2, y - size/4, + x - size/2, y - size/4, x - size/4, y - size/4, + x - size/4, y - size/4, x - size/4, y - size/2, + x - size/4, y - size/2, x + size/4, y - size/2 + ) + draw.fill_opacity 1 + }, + solid_plus: ->(draw, x, y, fill_color, border_color, size) { + # height and width are equal to size + # x,y is center of width and height + draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + draw.polyline( + x + size/4, y - size/2, x + size/4, y - size/4, + x + size/4, y - size/4, x + size/2, y - size/4, + x + size/2, y - size/4, x + size/2, y + size/4, + x + size/2, y + size/4, x + size/4, y + size/4, + x + size/4, y + size/4, x + size/4, y + size/2, + x + size/4, y + size/2, x - size/4, y + size/2, + x - size/4, y + size/2, x - size/4, y + size/4, + x - size/4, y + size/4, x - size/2, y + size/4, + x - size/2, y + size/4, x - size/2, y - size/4, + x - size/2, y - size/4, x - size/4, y - size/4, + x - size/4, y - size/4, x - size/4, y - size/2, + x - size/4, y - size/2, x + size/4, y - size/2 + ) + }, + pentagon: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + hexagon: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + heptagon: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + octagon: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + star_4: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + star_5: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + star_6: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + star_7: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + star_8: ->(draw, x, y, fill_color, border_color, size) { + raise NotImplementedError, 'This marker has not yet been implemented' + }, + vline: ->(draw, x, y, fill_color, border_color, size) { + # size is length of line + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.line(x, y - size/2, x, y + size/2) + }, + hline: ->(draw, x, y, fill_color, border_color, size) { + # size is length of line + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.line(x - size/2, y, x + size/2, y) + }, + omark: ->(draw, x, y, fill_color, border_color, size) { + # Hollow Square with truncated corners with side of square as size + # x, y is the center of the side + draw.stroke Rubyplot::Color::COLOR_INDEX[fill_color] + draw.fill_opacity 0 + draw.polyline( + x - 3*size/8, y - size/2, x + 3*size/8, y - size/2, + x + 3*size/8, y - size/2, x + size/2, y - 3*size/8, + x + size/2, y - 3*size/8, x + size/2, y + 3*size/8, + x + size/2, y + 3*size/8, x + 3*size/8, y + size/2, + x + 3*size/8, y + size/2, x - 3*size/8, y + size/2, + x - 3*size/8, y + size/2, x - size/2, y + 3*size/8, + x - size/2, y + 3*size/8, x - size/2, y - 3*size/8, + x - size/2, y - 3*size/8, x - 3*size/8, y - size/2 + ) + draw.fill_opacity 1 + } + }.freeze + + LINE_TYPES = { + # Default type is solid + default: ->(draw, x1, y1, x2, y2, width, color, opacity) { + draw.stroke_width width + draw.fill Rubyplot::Color::COLOR_INDEX[color] + draw.line x1, y1, x2, y2 + }, + solid: ->(draw, x1, y1, x2, y2, width, color, opacity) { + draw.stroke_width width + draw.fill Rubyplot::Color::COLOR_INDEX[color] + draw.line x1, y1, x2, y2 + }, + dashed: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + dotted: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + dashed_dotted: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + dash_2_dot: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + dash_3_dot: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + long_dash: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + long_short_dash: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + spaced_dash: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + spaced_dot: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + double_dot: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + }, + triple_dot: ->(draw, x1, y1, x2, y2, width, color, opacity) { + raise NotImplementedError, 'This line has not yet been implemented' + } + }.freeze + attr_reader :draw def initialize - @draw = Magick::Draw.new + @axes_map = {} + @base_image = nil + end + + def draw_x_axis(origin:, minor_ticks:, major_ticks:, minor_ticks_count:, major_ticks_count:) + if @axes_map[active_axes.object_id].nil? + @axes_map[@active_axes.object_id]={ + axes: @active_axes, + x_origin: origin, + minor_ticks: minor_ticks, + major_ticks: major_ticks, + minor_ticks_count: minor_ticks_count, + major_ticks_count: major_ticks_count + } + else + @axes_map[@active_axes.object_id].merge!( + x_origin: origin, + minor_ticks: minor_ticks, + major_ticks: major_ticks, + minor_ticks_count: minor_ticks_count, + major_ticks_count: major_ticks_count + ) + end + end + + def draw_y_axis(origin:, minor_ticks:, major_ticks:, minor_ticks_count:, major_ticks_count:) + if @axes_map[@active_axes.object_id].nil? + @axes_map[@active_axes.object_id]={ + axes: @active_axes, + y_origin: origin, + minor_ticks: minor_ticks, + major_ticks: major_ticks, + minor_ticks_count: minor_ticks_count, + major_ticks_count: major_ticks_count + } + else + @axes_map[@active_axes.object_id].merge!( + y_origin: origin, + minor_ticks: minor_ticks, + major_ticks: major_ticks, + minor_ticks_count: minor_ticks_count, + major_ticks_count: major_ticks_count + ) + end end # Height in pixels of particular text. @@ -37,10 +467,6 @@ def scale(scale) @draw.scale(scale, scale) end - def set_base_image_gradient(top_color, bottom_color, width, height, direct=:top_bottom) - @base_image = render_gradient top_color, bottom_color, width, height, direct - end - # Get the width and height of the text in pixels. def get_text_width_height(text) metrics = @draw.get_type_metrics(@base_image, text) @@ -49,75 +475,194 @@ def get_text_width_height(text) # rubocop:disable Metrics/ParameterLists # Unused method argument - stroke - def draw_text(text,font_color:,font: nil,pointsize:, - font_weight: Magick::NormalWeight, gravity: nil, - x:,y:,rotation: nil, stroke: 'transparent') - @draw.fill = font_color - @draw.font = font if font - @draw.pointsize = pointsize - @draw.font_weight = font_weight - @draw.gravity = GRAVITY_MEASURE[gravity] || Magick::ForgetGravity - @draw.stroke stroke - @draw.stroke_antialias true - @draw.text_antialias = true - @draw.rotation = rotation if rotation - @draw.annotate(@base_image, 0,0,x.to_i,y.to_i, text.gsub('%', '%%')) - @draw.rotation = 90.0 if rotation + def draw_text(text,color: :default,font: nil,size:, + font_weight: Magick::NormalWeight, halign:, valign:, + abs_x:,abs_y:,rotation: nil, stroke: 'transparent', abs: true) + unless text.empty? + within_window(abs) do + x = transform_x(x: abs_x, abs: abs) + y = transform_y(y: abs_y, abs: abs) + + @text.fill = Rubyplot::Color::COLOR_INDEX[color] + @text.font = font.to_s if font + @text.pointsize = size + @text.font_weight = font_weight + # @text.gravity = GRAVITY_MEASURE[gravity] || Magick::ForgetGravity + @text.stroke stroke + @text.stroke_antialias false + @text.text_antialias = false + modify_draw(@text, x_shift: x.to_i, y_shift: y.to_i, rotation: rotation) do |draw| + draw.text(0,0, text.gsub('%', '%%')) + end + end + end + end + + def draw_markers(x:, y:, type: :default, fill_color: :default, border_color: :default, size: nil) + y.each_with_index do |iy, idx_y| + ix = transform_x(x: x[idx_y],abs: false) + iy = transform_y(y: iy, abs: false) + # in GR backend size is multiplied by + # nominal size generated on the graphics device + # so set the nominal_factor to 15 + within_window do + size[idx_y] *= NOMINAL_FACTOR_MARKERS + MARKER_TYPES[type].call(@draw, ix, iy, fill_color, border_color, size[idx_y]) + end + end end # Draw a rectangle. - def draw_rectangle(x1:,y1:,x2:,y2:,border_color: '#000000',stroke: 'transparent', - fill_color: nil, stroke_width: 1.0) - if fill_color # solid rectangle - @draw.stroke stroke - @draw.fill fill_color - @draw.stroke_width stroke_width + def draw_rectangle(x1:,y1:,x2:,y2:, border_color: :default, + fill_color: nil, border_width: 1, border_type: nil, abs: false) + within_window(abs) do + x1 = transform_x(x: x1, abs: abs) + x2 = transform_x(x: x2, abs: abs) + y1 = transform_y(y: y1, abs: abs) + y2 = transform_y(y: y2, abs: abs) + + @draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + @draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] if fill_color + @draw.stroke_width border_width.to_f + # if fill_color is not given, the rectangle fill colour is transparent + # i.e. only edges are visible + @draw.fill_opacity 0 unless fill_color @draw.rectangle x1, y1, x2, y2 - else # just edges - @draw.stroke_width stroke_width - @draw.fill border_color - @draw.line x1, y1, x1 + (x2-x1), y1 # top line - @draw.line x1 + (x2-x1), y1, x2, y2 # right line - @draw.line x2, y2, x1, y1 + (y2-y1) # bottom line - @draw.line x1, y1, x1, y1 + (y2-y1) # left line + @draw.fill_opacity 1 unless fill_color end end - def draw_line(x1:,y1:,x2:,y2:,color: '#000000', stroke: 'transparent', - stroke_opacity: 0.0, stroke_width: 2.0) - @draw.stroke_opacity stroke_opacity - @draw.stroke_width stroke_width - @draw.fill color - @draw.line x1, y1, x2, y2 + def draw_lines(x:, y:, width: 2.0, type: :default, color: :default, opacity: 1.0) + within_window do + y.each_with_index do |_, idx_y| + if idx_y < (y.length - 1) + x1 = transform_x(x: x[idx_y], abs: false) + y1 = transform_y(y: y[idx_y], abs: false) + x2 = transform_x(x: x[idx_y + 1], abs: false) + y2 = transform_y(y: y[idx_y + 1], abs: false) + LINE_TYPES[type].call(@draw, x1, y1, x2, y2, width, color, opacity) + end + end + end + end + + def draw_line(x1:,y1:,x2:,y2:, width:, type:, color:, opacity:) + draw_lines(x: [x1, x2], y: [y1, y2], width: width, type: type, color: color, opacity: opacity) end - def draw_circle(x_pos:,y_pos:,radius:,stroke_opacity:,stroke_width:,color:) - @draw.stroke_opacity stroke_opacity - @draw.stroke_width stroke_width - @draw.fill color - @draw.circle(x_pos,y_pos,x-radius,y) + def draw_circle(x:, y:, radius:, border_type: nil, border_width: 1.0, fill_color: nil, + border_color: :default, fill_opacity: 0.0) + within_window do + x = transform_x x: x + y = transform_y y: y + + @draw.stroke_width border_width + @draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + @draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] if fill_color + @draw.fill_opacity fill_opacity + @draw.circle(x,y,x - (radius * NOMINAL_FACTOR_CIRCLE),y) + end end # rubocop:enable Metrics/ParameterLists # Draw a polygon. # - # @param coords [Array[Array]] Array of Arrays where first element of each sub-array is + # @param coords (Array[Array]) Array of Arrays where first element of each sub-array is # the X co-ordinate and the second element is the Y co-ordinate. - def draw_polygon(coords:, fill_opacity: 0.0, color: '#000000', - stroke: 'transparent') - @draw.stroke stroke - @draw.fill color - @draw.fill_opacity fill_opacity - @draw.polygon *coords.flatten + def draw_polygon(x:, y:, border_width:, border_type:, border_color:, fill_color:, + fill_opacity:) + within_window do + x = x.map! { |ix| transform_x(x: ix, abs: false) } + y = y.map! { |iy| transform_y(y: iy, abs: false) } + coords = x.zip(y) + @draw.stroke_width border_width + @draw.stroke Rubyplot::Color::COLOR_INDEX[border_color] + @draw.fill Rubyplot::Color::COLOR_INDEX[fill_color] + @draw.fill_opacity fill_opacity + @draw.polygon *coords.flatten + @draw.fill_opacity 1 + end + end + + def draw_arrow(x1:, y1:, x2:, y2:, size:, style:) + within_window do + # TODO + end + end + + def write + @draw.draw(@base_image) + draw_axes + @text.draw(@base_image) end - def write file_name + def show @draw.draw(@base_image) - @base_image.write(file_name) + draw_axes + @text.draw(@base_image) + end + + # Refresh this backend and remove all previously set data. + def flush + @axes_map = {} + @file_name = nil + end + + def init_output_device file_name = nil, device: :file + @canvas_width, @canvas_height = scale_figure(@canvas_width, @canvas_height) + @draw = Magick::Draw.new + @axes = Magick::Draw.new + @text = Magick::Draw.new + + top_color = Rubyplot::Color::COLOR_INDEX[@figure.theme_options[:background_colors][0]] + bottom_color = Rubyplot::Color::COLOR_INDEX[@figure.theme_options[:background_colors][1]] + direction = @figure.theme_options[:background_direction] + + @base_image = render_gradient top_color, bottom_color, @canvas_width, @canvas_height, direction + # Initialize base_image again even if it exists as there may be a change in properties + + @output_device = device + @file_name = file_name if @output_device == :file + end + + def stop_output_device + @canvas_width, @canvas_height = unscale_figure(@canvas_width, @canvas_height) + case @output_device + when :file + @base_image.write(@file_name) + flush + when :window + flush + @base_image.display + # return nil so that image is not printed on iruby + nil + when :iruby + flush + @base_image + end end private + # Function to convert figure size to pixels + def scale_figure(width, height) + case @figure.figsize_unit + when :pixel + raise RangeError, 'Figure with a dimension greater than 11500 pixels can not be plotted' if height>11500 || width>11500 + when :cm + raise RangeError, 'Figure with a dimension greater than 290 cms can not be plotted' if height>290 || width>290 + when :inch + raise RangeError, 'Figure with a dimension greater than 115 inches can not be plotted' if height>115 || width>115 + end + + [width * PIXEL_MULTIPLIERS[@figure.figsize_unit], height * PIXEL_MULTIPLIERS[@figure.figsize_unit]] + end + + # Function to convert figure size from pixels to original unit + def unscale_figure(width, height) + [width / PIXEL_MULTIPLIERS[@figure.figsize_unit], height / PIXEL_MULTIPLIERS[@figure.figsize_unit]] + end + # Render a gradient and return an Image. def render_gradient(top_color, bottom_color, width, height, direct) gradient_fill = case direct @@ -134,11 +679,149 @@ def render_gradient(top_color, bottom_color, width, height, direct) else GradientFill.new(0, 0, 100, 0, top_color, bottom_color) end - Image.new(width, height, gradient_fill) - end - end - # class MagickWrapper - end - # module Backend -end -# module Rubyplot + Magick::Image.new(width, height, gradient_fill) + end + + # Transform X co-ordinate. + def transform_x(x: , abs: false) + if abs + (@canvas_width.to_f * x.to_f / @figure.max_x.to_f) + else + ((x.to_f - @active_axes.x_range[0].to_f) / (@active_axes.x_range[1].to_f - @active_axes.x_range[0].to_f)) * @canvas_width.to_f + end + end + + # Transform Y co-ordinate + def transform_y(y: , abs: false) + if abs + (@canvas_height.to_f * (@figure.max_y.to_f - y.to_f) / @figure.max_y.to_f) + else + ((@active_axes.y_range[1].to_f - y.to_f) / (@active_axes.y_range[1].to_f - @active_axes.y_range[0].to_f)) * @canvas_height.to_f + end + end + + # Transform quantity that depends on X and Y. + def transform_avg q + @canvas_height * q + end + + def draw_axes + @axes_map.each_value do |v| + axes = v[:axes] + @active_axes = axes + within_window do + @axes.stroke Rubyplot::Color::COLOR_INDEX[:black] + @axes.stroke_width AXES_WIDTH_MULTIPLIER + # Drawing the X and Y axes lines + if axes.square_axes + @axes.fill_opacity 0 + @axes.rectangle(transform_x(x: v[:x_origin]),transform_y(y: v[:y_origin]), transform_x(x: axes.x_range[1]),transform_y(y: axes.y_range[1])) + else + @axes.line(transform_x(x: v[:x_origin]),transform_y(y: v[:y_origin]), transform_x(x: axes.x_range[1]),transform_y(y: v[:y_origin])) + @axes.line(transform_x(x: v[:x_origin]),transform_y(y: v[:y_origin]), transform_x(x: v[:x_origin]),transform_y(y: axes.y_range[1])) + end + # Drawing ticks + # X major ticks + axes.x_axis.major_ticks.each do |x_major_tick| + # @axes.stroke_width x_major_tick.tick_width*AXES_WIDTH_MULTIPLIER + # @axes.opacity x_major_tick.tick_opacity + @axes.line(transform_x(x: x_major_tick.coord),transform_y(y: v[:y_origin]), transform_x(x: x_major_tick.coord),(transform_y(y: v[:y_origin]) + x_major_tick.tick_size*TICK_SIZE_MULTIPLIER)) + @text.pointsize TICK_FONT_SIZE + @text.font_weight TICK_LABEL_FONT_WEIGHT + # Changed X and Y coordinates of label for better appearance + @text.text((transform_x(x: x_major_tick.coord) - TICK_FONT_SIZE*PIXEL_MULTIPLIERS[:point]),(transform_y(y: v[:y_origin]) + TICK_LABEL_COORD_X_MULTIPLIER*TICK_SIZE_MULTIPLIER*x_major_tick.tick_size), x_major_tick.label) unless ( x_major_tick.label.nil? || x_major_tick.label=='') + @text.font_weight NormalWeight + # @axes.opacity 1 + end + # X minor ticks + axes.x_axis.minor_ticks.each do |x_minor_tick| + # @axes.stroke_width x_minor_tick.tick_width*AXES_WIDTH_MULTIPLIER + # @axes.opacity x_minor_tick.tick_opacity + @axes.line(transform_x(x: x_minor_tick.coord),transform_y(y: v[:y_origin]), transform_x(x: x_minor_tick.coord),(transform_y(y: v[:y_origin]) + x_minor_tick.tick_size*TICK_SIZE_MULTIPLIER)) + # @axes.opacity 1 + end + # Y major ticks + axes.y_axis.major_ticks.each do |y_major_tick| + # @axes.stroke_width y_major_tick.tick_width*AXES_WIDTH_MULTIPLIER + # @axes.opacity y_major_tick.tick_opacity + @axes.line((transform_x(x: v[:x_origin]) - y_major_tick.tick_size*TICK_SIZE_MULTIPLIER),transform_y(y: y_major_tick.coord), transform_x(x: v[:x_origin]),transform_y(y: y_major_tick.coord)) + @text.pointsize TICK_FONT_SIZE + @text.font_weight TICK_LABEL_FONT_WEIGHT + # Changed X and Y coordinates of label for better appearance + @text.text((transform_x(x: v[:x_origin]) - TICK_LABEL_COORD_Y_MULTIPLIER*TICK_SIZE_MULTIPLIER*y_major_tick.tick_size),(transform_y(y: y_major_tick.coord) + TICK_FONT_SIZE/3*PIXEL_MULTIPLIERS[:point]), y_major_tick.label) unless ( y_major_tick.label.nil? || y_major_tick.label=='') + @text.font_weight NormalWeight + # @axes.opacity 1 + end + # Y minor ticks + axes.y_axis.minor_ticks.each do |y_minor_tick| + # @axes.stroke_width y_minor_tick.tick_width*AXES_WIDTH_MULTIPLIER + # @axes.opacity y_minor_tick.tick_opacity + @axes.line((transform_x(x: v[:x_origin]) - y_minor_tick.tick_size*TICK_SIZE_MULTIPLIER),transform_y(y: y_minor_tick.coord), transform_x(x: v[:x_origin]),transform_y(y: y_minor_tick.coord)) + # @axes.opacity 1 + end + end + end + @axes.draw(@base_image) + end + + def modify_draw(draw, x_shift: nil, y_shift: nil, scale_x: nil, scale_y: nil, rotation: nil, &block) + draw = [draw] unless draw.respond_to? :each # Making draw iterable if not iterable + draw.each do |d| + d.translate(x_shift, y_shift) if x_shift && y_shift + d.rotate(rotation) if rotation + d.scale(scale_x, scale_y) if scale_x && scale_y + end + + draw.each do |d| + yield(d) + end + + draw.each do |d| + d.scale(1 / scale_x, 1 / scale_y) if scale_x && scale_y + d.rotate(90.0) if rotation + d.translate(-1 * x_shift, -1 * y_shift) if x_shift && y_shift + end + draw = draw[0] unless draw.respond_to? :each + end + + def within_window(abs=false, &block) + if abs + # Coordinates are given in rubyplot cordinates + # Transform function handles deciding the position + # in the figure + yield + else + # Coordinates are not in rubyplot coordinates + # Shifting to adjust incorporate the margin of the figure and axes + # border! method can be used for figure margin but that will disturb rubyplot coordinates + # i.e. rubyplot coordinates include the border spacing + x_shift = (@active_axes.abs_x + @active_axes.left_margin) * @canvas_width / @figure.max_x # in pixels + y_shift = ((@active_axes.height * (@figure.nrows - 1)) - (@active_axes.abs_y - @figure.bottom_spacing) + @figure.top_spacing + @active_axes.top_margin) * @canvas_height / @figure.max_y # in pixels + @draw.translate(x_shift, y_shift) + @text.translate(x_shift, y_shift) + @axes.translate(x_shift, y_shift) + + plottable_width = @active_axes.width - (@active_axes.left_margin + @active_axes.right_margin) + plottable_height = @active_axes.height - (@active_axes.bottom_margin + @active_axes.top_margin) + # Scaling + @draw.scale(plottable_width / @figure.max_x, plottable_height / @figure.max_y) + @text.scale(plottable_width / @figure.max_x, plottable_height / @figure.max_y) + @axes.scale(plottable_width / @figure.max_x, plottable_height / @figure.max_y) + + # Calling the block + yield + + # Rescaling + @draw.scale(@figure.max_x / plottable_width, @figure.max_y / plottable_height) + @text.scale(@figure.max_x / plottable_width, @figure.max_y / plottable_height) + @axes.scale(@figure.max_x / plottable_width, @figure.max_y / plottable_height) + + # Reshifting to the original coordinates + @draw.translate(-1 * x_shift, -1 * y_shift) + @text.translate(-1 * x_shift, -1 * y_shift) + @axes.translate(-1 * x_shift, -1 * y_shift) + end + end + end # class MagickWrapper + end # module Backend +end # module Rubyplot diff --git a/lib/rubyplot/color.rb b/lib/rubyplot/color.rb index 79af3fd..f0eaa36 100644 --- a/lib/rubyplot/color.rb +++ b/lib/rubyplot/color.rb @@ -16,7 +16,6 @@ module Color falu_red: '#8b2500', royal_blue: '#436eee', crimson: '#dc143c', - crimson: '#e6194b', fruit_salad: '#3cb44b', lemon: '#ffe119', bondi_blue: '#0082c8', @@ -988,5 +987,11 @@ module Color purple: '#7e1e9c', vivid_orange: '#ff7f0e' }.freeze + + class << self + def random_color + Rubyplot::Color::CONTRASTING_COLORS[Rubyplot::Color::CONTRASTING_COLORS.keys.sample] + end + end end end diff --git a/lib/rubyplot/error.rb b/lib/rubyplot/error.rb new file mode 100644 index 0000000..6c217e1 --- /dev/null +++ b/lib/rubyplot/error.rb @@ -0,0 +1,3 @@ +module Rubyplot + class SizeError < StandardError; end +end diff --git a/lib/rubyplot/gr_wrapper.rb b/lib/rubyplot/gr_wrapper.rb deleted file mode 100644 index 4fa64d9..0000000 --- a/lib/rubyplot/gr_wrapper.rb +++ /dev/null @@ -1,3 +0,0 @@ -require_relative 'gr_wrapper/tasks' - -require_relative 'gr_wrapper/plot' diff --git a/lib/rubyplot/gr_wrapper/plot.rb b/lib/rubyplot/gr_wrapper/plot.rb deleted file mode 100644 index 0a99e6a..0000000 --- a/lib/rubyplot/gr_wrapper/plot.rb +++ /dev/null @@ -1,3 +0,0 @@ -require_relative 'plot/base_plot' - -require_relative 'plot/scatter' diff --git a/lib/rubyplot/gr_wrapper/plot/base_plot.rb b/lib/rubyplot/gr_wrapper/plot/base_plot.rb deleted file mode 100644 index 0f43800..0000000 --- a/lib/rubyplot/gr_wrapper/plot/base_plot.rb +++ /dev/null @@ -1,2 +0,0 @@ -require_relative 'base_plot/robust_base' -require_relative 'base_plot/lazy_base' diff --git a/lib/rubyplot/gr_wrapper/plot/base_plot/lazy_base.rb b/lib/rubyplot/gr_wrapper/plot/base_plot/lazy_base.rb deleted file mode 100644 index a3d2141..0000000 --- a/lib/rubyplot/gr_wrapper/plot/base_plot/lazy_base.rb +++ /dev/null @@ -1,19 +0,0 @@ -module Rubyplot - module GRWrapper - module Plot - module BasePlot - class LazyBase - attr_reader :plot_type - def initialize - @tasks = [] - @plot_type = :robust - end - - def call - @tasks.each(&:call) - end - end - end - end - end -end diff --git a/lib/rubyplot/gr_wrapper/plot/base_plot/robust_base.rb b/lib/rubyplot/gr_wrapper/plot/base_plot/robust_base.rb deleted file mode 100644 index 68a8c69..0000000 --- a/lib/rubyplot/gr_wrapper/plot/base_plot/robust_base.rb +++ /dev/null @@ -1,79 +0,0 @@ -module Rubyplot - module GRWrapper - module Plot - module BasePlot - class RobustBase - include Rubyplot::GRWrapper::Tasks - - attr_reader :plot_type - - def initialize - @tasks = [] - @plot_type = :robust - end - - # FIXME: writer to canvas is also part of this class, similar to Magick backend. - # In the future both should be abstracted to another rendering class. - def write(file_name) - BeginPrint.new(file_name).call - - # FIXME: remove @identity varible. - @identity = [@axes.figure.nrows, @axes.figure.ncols, @axes.position+1] - - r = (@identity[2].to_f / @identity[1].to_f).ceil - c = (@identity[2] % @identity[1]).zero? ? @identity[1] : @identity[2] % @identity[1] - @ymax = 1 - (1.to_f / @identity[0]) * (r - 1) - 0.095 / @identity[0] - @ymin = 1 - (1.to_f / @identity[0]) * r + 0.095 / @identity[0] - @xmin = (1.to_f / @identity[1]) * (c - 1) + 0.095 / @identity[1] - @xmax = (1.to_f / @identity[1]) * c - 0.095 / @identity[1] - - @axes.x_axis_padding = Math.log((@axes.x_range[1] - @axes.x_range[0]), 10).round - @axes.y_axis_padding = Math.log((@axes.y_range[1] - @axes.y_range[0]), 10).round - - @axes.origin[0] = @axes.x_range[0] - @axes.x_axis_padding if - @axes.origin[0] == :default - @axes.origin[1] = @axes.y_range[0] - @axes.y_axis_padding if - @axes.origin[1] == :default - - SetViewPort.new(@xmin, @xmax, @ymin, @ymax).call - SetWindow.new(@axes.x_range[0] - @axes.x_axis_padding, - @axes.x_range[1] + @axes.x_axis_padding, - @axes.y_range[0] - @axes.y_axis_padding, - @axes.y_range[1] + @axes.y_axis_padding).call - # Make sure that window is set bigger than range figure out how to manage it - SetTextAlign.new(2, 0).call - @axes.text_font = :times_roman if @axes.text_font == :default - SetTextFontPrecision.new(GR_FONTS[@axes.text_font], - GR_FONT_PRECISION[:text_precision_string]).call - SetCharHeight.new(0.012).call - @axes.y_tick_count = 10 if @axes.y_tick_count == :default - @axes.x_tick_count = 10 if @axes.x_tick_count == :default # 10 ticks by default - SetLineColorIndex.new(hex_color_to_gr_color_index( - Rubyplot::Color::COLOR_INDEX[:black] - )).call - SetLineWidth.new(1).call - SetLineType.new(GR_LINE_TYPES[:solid]).call - Grid.new((@axes.x_range[1] - @axes.x_range[0]).to_f / @axes.x_tick_count, - (@axes.y_range[1] - @axes.y_range[0]).to_f / @axes.y_tick_count, - 0, 0, 1, 1).call - Tasks::Axes.new((@axes.x_range[1] - @axes.x_range[0]).to_f / @axes.x_tick_count, - (@axes.y_range[1] - @axes.y_range[0]).to_f / @axes.y_tick_count, - @axes.origin[0], @axes.origin[1], 1, 1, 0.01).call - Tasks::AxesTitles.new(@axes.x_title, @axes.y_title, '').call - - call - # @tasks.each do |task| - # task.call() - # task.call(self) if task.plot_type == :lazy - # end - - EndPrint.new.call - end - end - end - end - # module Plot - end - # module GRWrapper -end -# module Rubyplot diff --git a/lib/rubyplot/gr_wrapper/plot/scatter.rb b/lib/rubyplot/gr_wrapper/plot/scatter.rb deleted file mode 100644 index b3f953b..0000000 --- a/lib/rubyplot/gr_wrapper/plot/scatter.rb +++ /dev/null @@ -1,56 +0,0 @@ -module Rubyplot - module GRWrapper - module Plot - class Scatter < BasePlot::RobustBase - def initialize(axes, *_args) - super() - @axes = axes - @color = hex_color_to_gr_color_index(Rubyplot::Color::COLOR_INDEX[:black]) - @marker_size = 1 - @marker_type = GR_MARKER_SHAPES[:solid_circle] - end - - def data(x_values, y_values) - @axes.x_range[0] = x_values.min if @axes.x_range[0].nil? - @axes.x_range[1] = x_values.max if @axes.x_range[1].nil? - @axes.x_range[0] = x_values.min if x_values.min < @axes.x_range[0] - @axes.x_range[1] = x_values.max if x_values.max > @axes.x_range[1] - @axes.y_range[0] = y_values.min if @axes.y_range[0].nil? - @axes.y_range[1] = y_values.max if @axes.y_range[1].nil? - @axes.y_range[0] = y_values.min if y_values.min < @axes.y_range[0] - @axes.y_range[1] = y_values.max if y_values.max > @axes.y_range[1] - - @x_values = x_values - @y_values = y_values - end - - def label=(label) - # TODO : implement labels - end - - def color=(color) - @color = hex_color_to_gr_color_index(Rubyplot::Color::COLOR_INDEX[color]) - end - - attr_writer :marker_size - - def marker_type=(marker_type) - @marker_type = GR_MARKER_SHAPES[marker_type] - end - - def call - @tasks.push(SetMarkppperColorIndex.new(@color)) - @tasks.push(SetMarkerSize.new(@marker_size)) - @tasks.push(SetMarkerType.new(@marker_type)) - @tasks.push(Polymarker.new(@x_values, @y_values)) - - @tasks.each(&:call) - end - end - # class Scatter - end - # module Plot - end - # module GRWrapper -end -# module Rubyplot diff --git a/lib/rubyplot/gr_wrapper/tasks.rb b/lib/rubyplot/gr_wrapper/tasks.rb deleted file mode 100644 index c867ee1..0000000 --- a/lib/rubyplot/gr_wrapper/tasks.rb +++ /dev/null @@ -1,414 +0,0 @@ -module Rubyplot - module GRWrapper - module Tasks - def hex_color_to_gr_color_index(rgbstring) - rgb = rgbstring.match /#(..)(..)(..)/ - GR.inqcolorfromrgb((rgb[1].hex.to_f/255.0), - (rgb[2].hex.to_f/255.0), - (rgb[3].hex.to_f/255.0)) - end - - class BeginPrint - attr_reader :file_name - - def initialize(file_name) - @file_name = file_name - end - - def call - GR.beginprint(@file_name) - end - end - - class EndPrint - def call - GR.endprint - end - end - - class UpdateWorkspace - def call - GR.updatews - end - end - - class ClearWorkspace - def call - GR.clearws - end - end - - class Polymarker - def initialize(x_coordinates, y_coordinates) - @x_coordinates = x_coordinates - @y_coordinates = y_coordinates - end - - def call - GR.polymarker(@x_coordinates, @y_coordinates) - end - end - - class Polyline - def initialize(x_coordinates, y_coordinates) - @x_coordinates = x_coordinates - @y_coordinates = y_coordinates - end - - def call - GR.polyline(@x_coordinates, @y_coordinates) - end - end - - class FillRectangle - def initialize(x_min, x_max, y_min, y_max) - @x_min = x_min - @x_max = x_max - @y_min = y_min - @y_max = y_max - end - - def call - GR.fillrect(@x_min, @x_max, @y_min, @y_max) - end - end - - class SetFillColorIndex - def initialize(color_int) - @color_int = color_int - end - - def call - GR.setfillcolorind(@color_int) - end - end - - class SetFillInteriorStyle - def initialize(style) - @style = style - end - - def call - GR.setfillintstyle(@style) - end - end - - class SetViewPort - def initialize(x_min, x_max, y_min, y_max) - @x_min = x_min - @x_max = x_max - @y_min = y_min - @y_max = y_max - end - - def call - GR.setviewport(@x_min, @x_max, @y_min, @y_max) - end - end - - class SetWindow - def initialize(x_min, x_max, y_min, y_max) - @x_min = x_min - @x_max = x_max - @y_min = y_min - @y_max = y_max - end - - def call - GR.setwindow(@x_min, @x_max, @y_min, @y_max) - end - end - - class SetLineWidth - def initialize(width) - @width = width - end - - def call - GR.setlinewidth(@width) - end - end - - class SetLineType - def initialize(type) - @type = type - end - - def call - GR.setlinetype(@type) - end - end - - class SetLineColorIndex - def initialize(index) - @index = index - end - - def call - GR.setlinecolorind(@index) - end - end - - class SetMarkerSize - def initialize(size) - @size = size - end - - def call - GR.setmarkersize(@size) - end - end - - class SetMarkerType - def initialize(type) - @type = type - end - - def call - GR.setmarkertype(@type) - end - end - - class SetMarkerColorIndex - def initialize(index) - @index = index - end - - def call - GR.setmarkercolorind(@index) - end - end - - class SetTextAlign - def initialize(horizontal, vertical) - @horizontal = horizontal - @vertical = vertical - end - - def call - GR.settextalign(@horizontal, @vertical) - end - end - - class SetTextFontPrecision - def initialize(font, precision) - @font = font - @precision = precision - end - - def call - GR.settextfontprec(@font, @precision) - end - end - - class SetCharHeight - def initialize(height) - @height = height - end - - def call - GR.setcharheight(@height) - end - end - - class AxesTitles - def initialize(x_title, y_title, z_title) - @x_title = x_title.to_s - @y_title = y_title.to_s - @z_title = z_title.to_s - end - - def call - GR.titles3d(@x_title, @y_title, @z_title) - end - end - - # rubocop:disable Metrics/ParameterLists - class Axes - def initialize(x_major_tick, y_major_tick, x_origin, y_origin, major_x, - major_y, tick_size) - @x_major_tick = x_major_tick - @y_major_tick = y_major_tick - @x_origin = x_origin - @y_origin = y_origin - @major_x = major_x - @major_y = major_y - @tick_size = tick_size - end - - def call - GR.axes(@x_major_tick, @y_major_tick, @x_origin, @y_origin, @major_x, - @major_y, @tick_size) - end - end - - class Grid - def initialize(x_major_tick, y_major_tick, x_origin, y_origin, major_x, - major_y) - @x_major_tick = x_major_tick - @y_major_tick = y_major_tick - @x_origin = x_origin - @y_origin = y_origin - @major_x = major_x - @major_y = major_y - end - - def call - GR.grid(@x_major_tick, @y_major_tick, @x_origin, @y_origin, @major_x, - @major_y) - end - end - - class Text - def initialize(x_loc, y_loc, text_string) - @x_loc = x_loc - @y_loc = y_loc - @text_string = text_string - end - - def call - GR.text(@x_loc, @y_loc, @text_string) - end - end - - class DrawArc - def initialize(x_min, x_max, y_min, y_max, starting_angle, ending_angle) - @x_min = x_min - @x_max = x_max - @y_min = y_min - @y_max = y_max - @starting_angle = starting_angle - @ending_angle = ending_angle - end - - def call - GR.drawarc(@x_min, @x_max, @y_min, @y_max, @starting_angle, - @ending_angle) - end - end - - class FillArc - def initialize(x_min, x_max, y_min, y_max, starting_angle, ending_angle) - @x_min = x_min - @x_max = x_max - @y_min = y_min - @y_max = y_max - @starting_angle = starting_angle - @ending_angle = ending_angle - end - - def call - GR.fillarc(@x_min, @x_max, @y_min, @y_max, @starting_angle, - @ending_angle) - end - end - # rubocop:enable Metrics/ParameterLists - - GR_FONTS = { - times_roman: 101, - times_italic: 102, - times_bold: 103, - times_bold_italic: 104, - helvetica: 105, - helvetica_oblique: 106, - helvetica_bold: 107, - helvetica_bold_oblique: 108, - courier: 109, - courier_oblique: 110, - courier_bold: 111, - courier_bold_oblique: 112, - symbol: 113, - bookman_light: 114, - bookman_light_italic: 115, - bookman_demi: 116, - bookman_demi_italic: 117, - newcenturyschlbk_roman: 118, - newcenturyschlbk_italic: 119, - newcenturyschlbk_bold: 120, - newcenturyschlbk_bold_italic: 121, - avantgarde_book: 122, - avantgarde_book_oblique: 123, - avantgarde_demi: 124, - avantgarde_demi_oblique: 125, - palatino_roman: 126, - palatino_italic: 127, - palatino_bold: 128, - palatino_bold_italic: 129, - zapfchancery_medium_italic: 130, - zapfdingbats: 131 - }.freeze - - GR_FONT_PRECISION = { - text_precision_string: 0, - text_precision_char: 1, - text_precision_stroke: 2 - }.freeze - - # Marker types - GR_MARKER_SHAPES = { - dot: 1, - plus: 2, - asterisk: 3, - circle: 4, - diagonal_cross: 5, - solid_circle: -1, - triangle_up: -2, - solid_tri_up: -3, - triangle_down: -4, - solid_tri_down: -5, - square: -6, - solid_square: -7, - bowtie: -8, - solid_bowtie: -9, - hglass: -10, - solid_hglass: -11, - diamond: -12, - solid_diamond: -13, - star: -14, - solid_star: -15, - tri_up_down: -16, - solid_tri_right: -17, - solid_tri_left: -18, - hollow_plus: -19, - solid_plus: -20, - pentagon: -21, - hexagon: -22, - heptagon: -23, - octagon: -24, - star_4: -25, - star_5: -26, - star_6: -27, - star_7: -28, - star_8: -29, - vline: -30, - hline: -31, - omark: -32 - }.freeze - - GR_LINE_TYPES = { - solid: 1, - dashed: 2, - dotted: 3, - dashed_dotted: 4, - dash_2_dot: -1, - dash_3_dot: -2, - long_dash: -3, - long_short_dash: -4, - spaced_dash: -5, - spaced_dot: -6, - double_dot: -7, - triple_dot: -8 - }.freeze - - GR_FILL_INTERIOR_STYLES = { - hollow: 0, - solid: 1, - pattern: 2, - hatch: 3 - }.freeze - end - # module Tasks - end - # module GRWrapper -end -# module Rubyplot diff --git a/lib/rubyplot/image.rb b/lib/rubyplot/image.rb new file mode 100644 index 0000000..079db08 --- /dev/null +++ b/lib/rubyplot/image.rb @@ -0,0 +1,57 @@ +module Rubyplot + class Image + + attr_reader :rows, :columns + + def initialize(columns=0, rows=0) + Rubyplot.set_backend :magick # Setting Magick backend as Image is not yet implemented for GR backend + @rows = rows + @columns = columns + @image = Rubyplot::Artist::Image.new(columns, rows) + end + + def imread(path) + @image.imread(path) + @rows = @image.rows + @columns = @image.columns + end + + def imshow + @image.imshow + end + + def imwrite(path) + @image.imwrite(path) + end + + def export_pixels(map='RGB', x=0, y=0, columns=@columns, rows=@rows) + @image.export_pixels(x, y, columns, rows, map) + end + + def import_pixels(pixels, map='RGB', x=0, y=0, columns=@columns, rows=@rows) + @image.import_pixels(x, y, columns, rows, map, pixels) + end + + def rows=(nrows) + @image.rows = nrows + @rows = nrows + end + + def columns=(ncols) + @image.columns = ncols + @columns = ncols + end + + def QuantumRange + @image.QuantumRange + end + + def pixel_array + @image.pixel_array + end + + def imclear + @image = nil + end + end # class Image +end # module Ruyplot diff --git a/lib/rubyplot/utils.rb b/lib/rubyplot/utils.rb index 5f1288c..682ccbe 100644 --- a/lib/rubyplot/utils.rb +++ b/lib/rubyplot/utils.rb @@ -3,14 +3,12 @@ module Utils THOUSAND_SEPARATOR = ','.freeze class << self def format_label label - if label.is_a? Float + if label.is_a? Numeric '%0.2f' % label elsif label.is_a? String label end end end - end - # module Utils -end -# module Rubyplot + end # module Utils +end # module Rubyplot diff --git a/lib/rubyplot/version.rb b/lib/rubyplot/version.rb index 3598147..ffb8a78 100644 --- a/lib/rubyplot/version.rb +++ b/lib/rubyplot/version.rb @@ -1,3 +1,3 @@ module Rubyplot - VERSION = '0.0.1'.freeze + VERSION = '0.1-a1'.freeze end diff --git a/rubyplot.gemspec b/rubyplot.gemspec index c7f5b58..e8a20e6 100644 --- a/rubyplot.gemspec +++ b/rubyplot.gemspec @@ -3,9 +3,7 @@ $:.unshift File.expand_path("../lib", __FILE__) require 'rubyplot/version.rb' -Rubyplot::DESCRIPTION = <= 2.13.4' end diff --git a/spec/axes_spec.rb b/spec/axes_spec.rb index 8abe5b4..4b734af 100644 --- a/spec/axes_spec.rb +++ b/spec/axes_spec.rb @@ -1,481 +1,1011 @@ require 'spec_helper' -["magick"].each do |b| - ENV['RUBYPLOT_BACKEND'] = b - - describe "Rubyplot::Axes b: #{Rubyplot.backend}." do - before do - @planet_data = [ - ["Moon", [25, 36, 86, 39]], - ["Sun", [80, 54, 67, 54]], - ["Earth", [22, 29, 35, 38]], - ["Mars", [95, 95, 95, 90, 85, 80, 88, 100]], - ["Venus", [90, 34, 23, 12, 78, 89, 98, 88]] - ] - end - - context "#stacked_bar!" do - it "plots multiple stacked bar graphs with default colors", hell: true do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - [ - ["Charles", [20, 10, 5, 12, 11, 6, 10, 7]], - ["Adam", [5, 10, 20, 6, 9, 12, 14, 8]], - ["Daniel", [19, 9, 6, 11, 12, 7, 15, 8]] - ].each do |label, data| - axes.stacked_bar! do |p| - p.data data - p.label = label - end - end - axes.title = "net earnings in different months." - axes.x_title = "X title" - axes.y_title = "Y title" - axes.x_ticks = ['Jan', 'Feb', 'March', 'April', 'May', 'June', 'July', - 'August', 'September', 'October', 'November', 'December'] - end - - it "plots stacked bar in a small size", hell: true do - @figure = Rubyplot::Figure.new(height: 400, width: 400) - axes = @figure.add_subplot 0,0 - [ - ["Car", [25, 36, 86, 39]], - ["Bus", [80, 54, 67, 54]], - ["Train", [22, 29, 35, 38]] - ].each do |label, data| - axes.stacked_bar! do |p| - p.data data - p.label = label - end - end - axes.title = "stacked bar." + +describe "Rubyplot::Axes b: #{Rubyplot.backend}." do + before do + @planet_data = [ + ["Moon", [25, 36, 86, 39]], + ["Sun", [80, 54, 67, 54]], + ["Earth", [22, 29, 35, 38]], + ["Mars", [95, 95, 95, 90, 85, 80, 88, 100]], + ["Venus", [90, 34, 23, 12, 78, 89, 98, 88]] + ] + end + + context "#stacked_bar!" do + it "plots a stacked bar graph" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.stacked_bar! do |p| + p.data [20, 10, 5, 12, 11, 6, 10, 7] + p.label = "Charles" + end + axes.title = "Income." + axes.x_title = "X title" + axes.y_title = "Y title" + end + + it "plots a stacked bar graph with thin bars" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.stacked_bar! do |p| + p.data [20, 10, 5, 12, 11, 6, 10, 7] + p.label = "Charles" + p.spacing_ratio = 0.5 + end + axes.title = "Income." + axes.x_title = "X title" + axes.y_title = "Y title" + end + end + + context "#multi_stacked_bar!" do + it "plots multiple stacked bar graphs with default colors" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + [ + ["Charles", [20, 10, 5, 12, 11, 6, 10, 7]], + ["Adam", [5, 10, 20, 6, 9, 12, 14, 8]], + ["Daniel", [19, 9, 6, 11, 12, 7, 15, 8]] + ].each do |label, data| + axes.stacked_bar! do |p| + p.data data + p.label = label + end + end + axes.title = "Income." + axes.x_title = "X title" + axes.y_title = "Y title" + axes.x_ticks = ['Jan', 'Feb', 'March', 'April', 'May', 'June', 'July', + 'August', 'September', 'October', 'November', 'December'] + axes.y_ticks = ['5', '10', '15', '20', '25', '30'] + end + + it "plots stacked bar with thin bars" do + @figure = Rubyplot::Figure.new(height: 20, width: 20) + axes = @figure.add_subplot! 0,0 + [ + ["Car", [25, 36, 86, 39]], + ["Bus", [80, 54, 67, 54]], + ["Train", [22, 29, 35, 38]] + ].each do |label, data| + axes.stacked_bar! do |p| + p.data data + p.label = label + p.spacing_ratio = 0.6 + end + end + axes.title = "stacked bar." + end + + it "plots multiple stacked bar graphs with custom colors" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + [ + ["Charles", [20, 10, 5, 12, 11, 6, 10, 7], :silver], + ["Adam", [5, 10, 20, 6, 9, 12, 14, 8], :black], + ["Daniel", [19, 9, 6, 11, 12, 7, 15, 8], :orangeish] + ].each do |label, data, color| + axes.stacked_bar! do |p| + p.data data + p.label = label + p.color = color + end + end + axes.title = "Income." + axes.x_title = "X title" + axes.y_title = "Y title" + axes.x_ticks = ['Jan', 'Feb', 'March', 'April', 'May', 'June', 'July', + 'August', 'September', 'October', 'November', 'December'] + axes.y_ticks = ['5', '10', '15', '20', '25', '30'] + end + end + + context "#plot!" do + it "plots a simple scatter plot with circle marker" do + @figure = Rubyplot::Figure.new(height: 40, width: 40) + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + p.marker_type = :circle + p.data (0..15).to_a, (-10..5).to_a + p.marker_size = 1.5 + end + axes.title = "simple plot with dots." + end + + it "plots a simple line plot" do + @figure = Rubyplot::Figure.new(height: 40, width: 40) + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + d = (0..360).step(5).to_a + p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } + p.line_type = :solid + p.line_width = 3 + p.label = "sine" + end + axes.title = "Simple sine wave plot." + end + + it "plots a simple dashed line plot" do + @figure = Rubyplot::Figure.new(height: 40, width: 40) + x = (0..100).to_a + y = (0..100).to_a + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + p.line_type = :dashed + p.data x, y + p.label = "line" end end - context "#dot!" do - skip "plots a single dot plot" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.dot! do |p| - p.data [0,5,8,15] - p.label = "Car" - p.color = :maroon - end - axes.num_y_ticks = 4 - axes.y_ticks = ['5/6', '5/15', '5/24', '5/30'] - end - - skip "plots multiple dot plots" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - [ - [[0, 5, 8, 15], "cars", :maroon], - [[10, 3, 2, 8], "buses", :grey], - [[2, 15, 8, 11],"science", :yellow] - ].each do |data, label, color| - axes.dot! do |p| - p.data data - p.label = label - p.color = color - p.minimum_value = 0 - end - end - axes.num_y_ticks = 4 - axes.y_ticks = ['5/6', '5/15', '5/24', '5/30'] + it "plots a simple plot with diamond marker of yellow color" do + @figure = Rubyplot::Figure.new + x = (0..100).to_a + y = (0..100).to_a + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + p.marker_type = :diamond + d = (0..360).step(15).to_a + p.data d, d.map { |a| Math.cos(a) } + p.data d, d.map { |a| Math.cos(a * Math::PI / 180) } + p.marker_fill_color = :yellow + p.marker_size = 1.5 end end - context "#bubble!" do - it "plots a single bubble plot" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.bubble! do |p| - p.data [-1, 19, -4, -23], [-35, 21, 23, -4], [45, 10, 21, 9] - p.label = "apples" - p.color = :blue - end - axes.title = "simple bubble plot." + it "plots line plot with markers" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + d = (0..360).step(5).to_a + p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } + p.marker_type = :circle + p.marker_fill_color = :blue + p.marker_size = 0.5 + p.marker_border_color = :orangeish + p.line_type = :solid + p.line_color = :black + p.line_width = 2 + p.label = "sine" + end + axes.title = "A plot function example" + axes.x_title = "X-axis" + axes.y_title = "Y-axis" + end + + it "plots line plot using fmt argument" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + d = (0..360).step(5).to_a + p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } + p.fmt='-k' + p.label = "sine" + axes.x_title = "X-axis" + axes.y_title = "Y-axis" end + end - it "plots multiple bubble plots on same axes." do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.bubble! do |p| - p.data [-1, 19, -4, -23], [-35, 21, 23, -4], [45, 10, 21, 9] - p.label = "apples" - end - axes.bubble! do |p| - p.data [20, 30, -6, -3], [-1, 5, -27, -3], [13, 10, 20, 10] - p.label = "peaches" + it "plots scatter plot using fmt argument" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + d = (0..360).step(10).to_a + p.data d, d.map { |a| Math.cos(a * Math::PI / 180) } + p.fmt='sg' + p.label = "cosine" + end + axes.x_title = "X-axis" + axes.y_title = "Y-axis" + end + + it "plots line plot with markers using fmt argument" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + d = (0..360).step(5).to_a + p.data d, d.map { |a| Math.cos(a * Math::PI / 180) } + p.fmt = 's-g' + p.line_width = 2 + p.label = "cosine" + end + axes.title = "A plot function example" + axes.x_title = "X-axis" + axes.y_title = "Y-axis" + end + end + + context "#dot!" do + skip "plots a single dot plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.dot! do |p| + p.data [0,5,8,15] + p.label = "Car" + p.color = :maroon + end + axes.num_y_ticks = 4 + axes.y_ticks = ['5/6', '5/15', '5/24', '5/30'] + end + + skip "plots multiple dot plots" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + [ + [[0, 5, 8, 15], "cars", :maroon], + [[10, 3, 2, 8], "buses", :grey], + [[2, 15, 8, 11],"science", :yellow] + ].each do |data, label, color| + axes.dot! do |p| + p.data data + p.label = label + p.color = color + p.minimum_value = 0 end - axes.title = "simple bubble plot." end + axes.num_y_ticks = 4 + axes.y_ticks = ['5/6', '5/15', '5/24', '5/30'] + end + end + + context "#bubble!" do + it "plots a single bubble plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bubble! do |p| + p.data [-1, 19, -4, -23], [-35, 21, 23, -4], [4.5, 1.0, 2.1, 0.9] + p.label = "apples" + p.color = :blue + end + axes.x_range = [-40, 30] + axes.y_range = [-40, 25] + axes.title = "simple bubble plot." + end + + it "plots multiple bubble plots on same axes." do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bubble! do |p| + p.data [-1, 19, -4, -23], [-35, 21, 23, -4], [4.5, 1.0, 2.1, 0.9] + p.label = "apples" + p.fill_opacity = 1 + end + axes.bubble! do |p| + p.data [20, 30, -6, -3], [-1, 5, -27, -3], [10.3, 10.0, 20.0, 10.0] + p.label = "peaches" + p.border_width = 3 + p.fill_opacity = 0.2 + end + axes.title = "simple bubble plot." + end + end + + context "#area!" do + it "plots a single simple Area graph" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.area! do |p| + p.data (0...8).to_a, [25, 36, 86, 39, 25, 31, 79, 88] + p.label = "Jimmy" + end + axes.title = "Visual simple area graph test." + axes.x_ticks = ['0', '22', '44', '66', '88'] end - context "#area!" do - before do - @temp_dir = SPEC_ROOT + "temp/area" - @fix_dir = SPEC_ROOT + "fixtures/area" - FileUtils.mkdir_p @temp_dir + it "plots a single simple Area graph with opaicty 1" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.area! do |p| + p.data (0...8).to_a, [20, 24, 36, 39, 18, 60, 79, 45] + p.label = "Marvin" + p.fill_opacity = 1 end + axes.title = "Visual simple area graph test." + axes.x_ticks = ['0', '22', '44', '66', '88'] + end - it "plots a single simple Area graph" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 + it "plots multiple area plots on the same Axes" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + [ + ["Jimmy", (0...8).to_a, [28, 36, 86, 39, 40, 45, 79, 88], 0.3], + ["Charles", (0...8).to_a, [80, 54, 67, 54, 68, 70, 90, 95], 0.3], + ["Julie", (0...8).to_a, [22, 29, 35, 38, 36, 40, 46, 57], 1] + ].each do |n, datax, datay, op| axes.area! do |p| - p.data [25, 36, 86, 39, 25, 31, 79, 88] - p.label = "Jimmy" - end - axes.title = "Visual simple area graph test." - axes.num_x_ticks = 5 - axes.x_ticks = ['0', '22', '44', '66', '88',] - end - - it "plots multiple area plots on the same Axes" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - [ - ["Jimmy", [25, 36, 86, 39, 25, 31, 79, 88]], - ["Charles", [80, 54, 67, 54, 68, 70, 90, 95]], - ["Julie", [22, 29, 35, 38, 36, 40, 46, 57]], - ["Jane", [3, 95, 95, 90, 85, 80, 88, 100]] - ].each do |n, data| - axes.area! do |p| - p.data data - p.label = n - end - end - axes.title = "Multiple area plots on same axes." - axes.x_ticks = { - 0 => '0', - 2 => '2', - 4 => '4', - 6 => '6' - } - end - end - - context "#line!" do - it "makes a simple line plot" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.line! do |p| - p.data [2, 4, 7, 9], [1,2,3,4] - p.label = "Marco" - p.color = :blue + p.data datax, datay + p.label = n + p.fill_opacity = op end - axes.title = "A line graph." end + axes.title = "Multiple area plots on same axes." + axes.x_ticks = ['0', '2', '4', '6'] + end - it "plots 2 simple lines on the same axes", focus: true do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.line! do |p| - p.data [3, 5, 10, 15] - p.label = "Marco" - p.color = :blue - end + it "plots multiple stacked area plots on the same Axes" do + @figure = Rubyplot::Figure.new(width: 20, height: 20) + axes = @figure.add_subplot! 0,0 + axes.area! do |p| + p.data [1, 2, 3, 4, 5, 6], [3, 2, 5, 5, 7, 4] + p.color = :black + p.label = "Stock A" + p.stacked true + end + axes.area! do |p| + p.data [1, 2, 3, 4, 5, 6], [2, 1, 3, 3, 6, 1] + p.color = :yellow + p.label = "Stock B" + p.stacked true + end + axes.title = "An area plot" + axes.x_title = "Time" + axes.y_title = "Value" + end + end + + context "#line!" do + it "makes a simple line plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.line! do |p| + p.data [2, 4, 7, 9], [1,2,3,4] + p.label = "Marco" + p.line_width = 3 + p.line_color = :yellow + end + axes.title = "A line graph." + end + + it "makes a simple line plot with dashed_dotted line type" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.line! do |p| + p.data [2, 4, 7, 9], [1,2,3,4] + p.label = "Marco" + p.line_type = :dashed_dotted + p.line_width = 3 + p.line_color = :red + end + axes.title = "A line graph." + end + + it "plots 2 simple lines on the same axes" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.line! do |p| + p.data (0...4).to_a, [3, 5, 10, 15] + p.label = "Marco" + p.color = :blue + end + axes.line! do |p| + p.data (0...4).to_a, [1, 9, 13, 28] + p.label = "John" + p.color = :green + end + axes.title = "A line graph." + end + + it "tests very small plot" do + @figure = Rubyplot::Figure.new(height: 200, width: 200, figsize_unit: :pixel) + axes = @figure.add_subplot! 0,0 + axes.title = "very small line chart 200px" + @planet_data.each do |name, d| axes.line! do |p| - p.data [1, 9, 13, 28] - p.label = "John" - p.color = :green + p.data (0...d.size).to_a, d + p.label = name end - axes.title = "A line graph." end + end - skip "fails to match the reference image" do - + it "plots multiple 0 data" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "hand value graph test" + axes.line! do |p| + p.data (0...3).to_a, [0,0,100] + p.line_width = 10 + p.label = "test" end + end - it "tests very small plot" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "very small line chart 200px" - @planet_data.each do |name, d| - axes.line! do |p| - p.data d - p.label = name - end + it "plots small values" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "small values" + [ + [[0.1, 0.14356, 0.0, 0.5674839, 0.456], "small1"], + [[0.2, 0.3, 0.1, 0.05, 0.9], "small2"] + ].each do |d, label| + axes.line! do |p| + p.data (0...d.size).to_a, d + p.label = label end end + end - it "plots multiple 0 data" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "hand value graph test" + it "plots line starting with 0" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "starting with 0" + [ + [[0, 5, 10, 8, 18], "first0"], + [[1, 2, 3, 4, 5], "normal"] + ].each do |data, name| axes.line! do |p| - p.data [0,0,100] - p.label = "test" + p.data (0...data.size).to_a, data + p.label = name end end + end - it "plots small values" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "small values" - [ - [[0.1, 0.14356, 0.0, 0.5674839, 0.456], "small"], - [[0.2, 0.3, 0.1, 0.05, 0.9], "small2"] - ].each do |d, label| - axes.line! do |p| - p.data d - p.label = label - end + it "plots line with large values" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "large values" + [ + ["large", [100_005, 35_000, 28_000, 27_000]], + ["large2", [35_000, 28_000, 27_000, 100_005]], + ["large3", [28_000, 27_000, 100_005, 35_000]], + ["large4", [1_238, 39_092, 27_938, 48_876]] + ].each do |name, data| + axes.line! do |p| + p.line_width = 3 + p.data (0...data.size).to_a, data + p.label = name end end + end - it "plots line starting with 0" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "starting with 0" - [ - [[0, 5, 10, 8, 18], "first0"], - [[1, 2, 3, 4, 5], "normal"] - ].each do |data, name| - axes.line! do |p| - p.data data - p.label = name - end - end + it "accepts both X and Y data" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "accept X and Y" + axes.line! do |p| + p.data [1, 3, 4, 5, 6, 10], [1, 2, 3, 4, 4, 3] + p.label = "X" end - - it "plots line with large values" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "large values" - axes.baseline_value = 50_000 - - [ - ["large", [100_005, 35_000, 28_000, 27_000]], - ["large2", [35_000, 28_000, 27_000, 100_005]], - ["large3", [28_000, 27_000, 100_005, 35_000]], - ["large4", [1_238, 39_092, 27_938, 48_876]] - ].each do |name, data| - axes.line! do |p| - p.dot_radius = 15 - p.line_width = 3 - p.data data - p.label = name - end - end + axes.line! do |p| + p.data [1, 3, 4, 5, 7, 9], [1, 1, 2, 2, 3, 3] + p.label = "X1" end + end + end + + context "#bar!" do + it "adds a simple bar plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bar! do |p| + p.data [5,12,9,6,7] + p.label = "data" + p.color = :yellow + end + axes.x_ticks = ["five", "twelve", "nine", "six", "seven"] + axes.title = "Random bar numbers" + end - it "accepts both X and Y data" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "accept X and Y" - axes.line! do |p| - p.data [1, 3, 4, 5, 6, 10], [1, 2, 3, 4, 4, 3] - p.label = "X" - end - axes.line! do |p| - p.data [1, 3, 4, 5, 7, 9], [1, 1, 2, 2, 3, 3] - p.label = "X1" - end + it "adds a simple bar plot with thin bars" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bar! do |p| + p.data [14, 3, 7, 11, 25, 0, 20, 19] + p.label = "data" + p.color = :red + p.spacing_ratio = 0.5 + end + axes.x_ticks = ["five", "twelve", "nine", "six", "seven"] + axes.title = "Random bar numbers" + end + + it "adds bar plot with title margin" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bar! do |p| + p.data [5,12,9,6,6] + p.label = "data" + p.color = :green + end + axes.title = "Bar with title margin = 100" + axes.title_font_size = 50.0 + axes.x_title = "Green data!" + axes.y_title = "Green Y data!" + end + + it "plots large numbers" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bar! do |p| + p.data [7025, 1024, 40_257, 933_672, 1_560_496] + p.label = "data" end + axes.title = "Large numbers" end - context "#bar!" do - it "adds a simple bar plot" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.bar!(600) do |p| - p.data [5,12,9,6,7] - p.label = "data" - p.color = :yellow - end - axes.x_ticks = ["five", "twelve", "nine", "six", "seven"] - axes.title = "Random bar numbers" - end - - it "adds bar plot with title margin" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.bar!(600) do |p| - p.marker_count = 8 - p.data [5,12,9,6,6] - p.label = "data" - p.color = :green + it "adds axes with X-Y labels" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bar! do |p| + p.data [40,50,60,80] + p.label = "students" + end + axes.title = "Plot with X-Y axes." + axes.x_title = "Students" + axes.y_title = "Score (%)" + axes.x_ticks = [ '5/6', '5/15', '5/24', '5/36' ] + end + + it "adds multiple bar plots for wide graph" do + @figure = Rubyplot::Figure.new(height: 40, width: 80) + axes = @figure.add_subplot! 0,0 + @planet_data.each do |name, nums| + axes.bar! do |p| + p.data nums + p.label = name end - axes.title = "Bar with title margin = 100" - axes.title_margin = 100 end + end - it "checks if Geometry adjusts for large numbers" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.bar!(600) do |p| - p.marker_count = 8 - p.data [7025, 1024, 40_257, 933_672, 1_560_496] - p.label = "data" - end - axes.title = "Large numbers" + it "plots both positive and negative values" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.bar! do |p| + p.data [-1, 0, 4, -4] + p.label = "apples" + end + axes.bar! do |p| + p.data [10,8,6,3] + p.label = "peaches" end + axes.title = "Pos/neg bar graph test." + axes.x_ticks = ['5/6', '5/15', '5/24', '5/30'] + end - it "adds axes with X-Y labels" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.bar!(800) do |p| - p.data [40,50,60,80] - p.label = "students" - end - axes.title = "Plot with X-Y axes." - axes.x_title = "Score (%)" - axes.y_title = "Students" - axes.x_ticks = [ '5/6', '5/15', '5/24', '5/36' ] - end - - it "adds multiple bar plots for wide graph" do - @figure = Rubyplot::Figure.new(height: 400, width: 800) - axes = @figure.add_subplot 0,0 - @planet_data.each do |name, nums| - axes.bar! do |p| - p.data nums - p.label = name - end - end + it "tests negative values" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "all negative bar graph." + axes.x_ticks = ['5/6', '5/15', '5/24', '5/30'] + axes.bar! do |p| + p.data [-1,-5,-4,-4] + p.label = "apples" end + axes.bar! do |p| + p.data [-10,-8,-6,-3] + p.label = "peaches" + end + end - it "plots both positive and negative values" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 + it "sets min-max range for Y axis" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "nearly zero graph." + axes.y_range = [0,10] + [ + [[1,2,3,4], "apples"], + [[4,3,2,1], "peaches"] + ].each do |nums, name| axes.bar! do |p| - p.data [-1, 0, 4, -4] - p.label = "apples" - end - axes.bar! do |p| - p.data [10,8,6,3] - p.label = "peaches" - end - axes.title = "Pos/neg bar graph test." - axes.x_ticks = { - 0 => '5/6', - 1 => '5/15', - 2 => '5/24', - 3 => '5/30' - } - end - - it "tests negative values" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "all negative bar graph." - axes.x_ticks = { - 0 => '5/6', - 1 => '5/15', - 2 => '5/24', - 3 => '5/30' - } - axes.bar! do |p| - p.data [-1,-5,-4,-4] - p.label = "apples" + p.data nums + p.label = name end + end + axes.x_ticks = ['5/6', '5/15', '5/24','5/30'] + end + + it "adjust legends if there are too many" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "My graph." + [ + [[1, 2, 3, 4, 4, 3], 'Apples oranges Watermelon'], + [[4, 8, 7, 9, 8, 9], "Oranges"], + [[2, 3, 1, 5, 6, 8], "Watermelon"], + [[9, 9, 10, 8, 7, 9], "Peaches"] + ].each do |d, name| axes.bar! do |p| - p.data [-10,-8,-6,-3] - p.label = "peaches" + p.data d + p.label = name end end + axes.x_ticks = ['2003', '', '2004', '', '2005'] + end + end - skip "sets min-max range for Y axis" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "nearly zero graph." - axes.y_range [0,10] - [ - [[1,2,3,4], "apples"], - [[4,3,2,1], "peaches"] - ].each do |nums, name| - axes.bar! do |p| - p.data nums - p.label = name - end - end - axes.x_ticks = { - 0 => '5/6', - 1 => '5/15', - 2 => '5/24', - 3 => '5/30' - } - - file = "/#{Rubyplot.backend}_y_axis_min_max_range.png" - fig.write(@temp_dir + file) - - expect(@temp_dir + file).to eq_image(@fix_dir + file) - end - - skip "adjust legends if there are too many" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.title = "My graph." - [ - [[1, 2, 3, 4, 4, 3], 'Apples oranges Watermelon'], - [[4, 8, 7, 9, 8, 9], "Oranges"], - [[2, 3, 1, 5, 6, 8], "Watermelon"], - [[9, 9, 10, 8, 7, 9], "Peaches"] - ].each do |d, name| - axes.bar! do |p| - p.data d - p.label = name - end - end - axes.x_ticks = { 0 => '2003', 2 => '2004', 4 => '2005' } + context "#scatter!" do + before do + @x1 = [1, 2, 3, 4, 5] + @y1 = [11, 2, 33, 4, 65] + end + + it "adds a simple scatter plot." do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.scatter! do |p| + p.data @x1, @y1 + p.label = "data1" + p.marker_fill_color = :plum_purple + p.marker_type = :circle + end + axes.title = "Nice plot" + axes.x_title = "X data" + axes.y_title = "Y data" + end + + it "adds a simple scatter plot with diamond marker" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.scatter! do |p| + p.data @x1, @y1 + p.label = "data1" + p.marker_size = 3 + p.marker_fill_color = :electric_lime + p.marker_border_color = :black + p.marker_type = :diamond + end + axes.title = "Nice plot" + axes.x_title = "X data" + axes.y_title = "Y data" + end + + it "adds multiple scatter plots on same axes" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.scatter! do |p| + p.data [2,4,3], [6,2,10] + p.label = "data1" + p.marker_size = 3 + p.marker_fill_color = :blue + p.marker_border_color = :yellow + p.marker_type = :solid_plus + end + axes.scatter! do |p| + p.data [5,7,2,5], [12,4,7,9] + p.label = "data2" + p.marker_size = 2 + p.marker_fill_color = :yellow + p.marker_border_color = :black + p.marker_type = :bowtie + end + axes.scatter! do |p| + p.data [7,3], [9, 4] + p.label = "data3" + p.marker_size = 4 + p.marker_fill_color = :red + p.marker_type = :diagonal_cross + end + axes.title = "multiple scatter plots" + axes.x_title = "X data" + axes.y_title = "Y data" + end + + it "adds scatter with all negative values" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.scatter! do |p| + p.data [-1, -1, -4, -4], [-5, -1, -3, -4] + p.label = "apples" end + axes.title = "all negative scatter graph test." end - context "#scatter!" do - it "adds a simple scatter plot." do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.scatter! do |p| - p.data @x1, @y1 - p.label = "data1" - p.color = :plum_purple - end - axes.title = "Nice plot" - axes.x_title = "X data" - axes.y_title = "Y data" - end - - it "adds a green cross scatter plot." do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.scatter!(400) do |p| - p.data @x1, @y1 - p.color = :green - p.marker_size = 2 - p.marker_type = :diagonal_cross - end + it "adds scatter with positive and negative values" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.scatter! do |p| + p.data [-2,0,2,4,6], [-3,-1, 1, 4, 8] + p.label = "values" end + axes.title = "positive + negative test." + end + end - it "adds scatter with all negative values" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.scatter!(400) do |p| - p.data [-1, -1, -4, -4], [-5, -1, -3, -4] - p.label = "apples" - end - axes.title = "all negative scatter graph test." + context "#histogram!" do + it "adds a single histogram with default bins" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.histogram! do |p| + p.x = 100.times.map{ rand(10) } end + end - it "adds multiple scatter plots" do - + it "adds a single histogram with custom bins" do + # skip "GR does not currently support custom tick marks." + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.histogram! do |p| + p.x = 100.times.map{ rand(10) } + p.bins = [1, 4, 7, 10] + p.color = :red end - end # context "#scatter!" + end - context "#x_ticks=" do - it "assigns strings to X ticks" do - @figure = Rubyplot::Figure.new - axes = @figure.add_subplot 0,0 - axes.scatter! do |p| - p.data [1,2,3,4], [1,2,3,4] - p.label = "apples" - end - axes.x_ticks = { - 0 => "hello 0", - 1 => "hello 1" - } + it "adds a single histogram with number of bins" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.histogram! do |p| + p.x = 100.times.map{ rand(10) } + p.bins = 5 + p.color = :silver + end + end + end + + context "#candle_stick!" do + # FIXME: use data method to accept the data for the plot in one go. + it "adds a simple candle stick plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.candle_stick! do |p| + p.lows = [100, 110, 120, 130, 120, 110] + p.highs = [140, 150, 160, 170, 160, 150] + p.opens = [110, 120, 130, 140, 130, 120] + p.closes = [130, 140, 150, 160, 150, 140] + p.color = :yellow + p.border_color = :white + end + axes.title = "Simple candle stick plot." + end + + it "adds multiple candle stick plots" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + + lows = [100, 110, 120, 130, 120, 110] + highs = [140, 150, 160, 170, 160, 150] + opens = [110, 120, 130, 140, 130, 120] + closes = [130, 140, 150, 160, 150, 140] + axes.candle_stick! do |p| + p.lows = lows + p.highs = highs + p.opens = opens + p.closes = closes + end + axes.candle_stick! do |p| + p.lows = lows.map { |l| l + 100 } + p.highs = highs.map { |h| h + 100 } + p.opens = opens.map { |o| o + 100 } + p.closes = closes.map { |c| c + 100 } + end + axes.candle_stick! do |p| + p.lows = lows.map { |l| l + 10 } + p.highs = highs.map { |h| h + 10 } + p.opens = opens.map { |o| o + 10 } + p.closes = closes.map { |c| c + 10 } + end + axes.title = "Multiple candle stick plot." + end + end + + context "#error_bar!" do + before do + @x = [1,2,3,4,5,6] + @y = [3,4,5,6,7,8] + end + + it "adds a simple xerr to error bar plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Simple error bar plot with xerr." + axes.error_bar! do |p| + p.data @x, @y + p.xerr = 0.1 + end + end + + it "adds a collection of xerr to the error bar plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Simple error bar plot with collection xerr." + axes.error_bar! do |p| + p.data @x, @y + p.xerr = [0.1,0.3,0.5,0.1,0.2,0.4] + p.color = :red + end + end + + it "adds a simple yerr to the error bar plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Simple error bar plot with yerr." + axes.error_bar! do |p| + p.data @x, @y + p.yerr = 0.1 + end + end + + it "adds a collection of yerr to the error bar plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Simple error bar plot with collection yerr." + axes.error_bar! do |p| + p.data @x, @y + p.yerr = [0.6,0.5,0.1,0.8,0.3,0.1] + p.color = :green end - end # context "#x_ticks=" - end # Rubyplot::Axes -end # describe backends + end + + it "adds an asymmetric collection of yerr to the error bar plot" do + skip "do this later." + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Simple error bar plot with asymmetric plotting." + axes.error_bar! do |p| + p.data @x, @y + p.yerr = [ + [0.6, 0.2], + [0.1, 0.9], + [0.1, 0.4], + [0.8, 0.84], + [0.3, 0.69], + [0.1, 1.0] + ] + end + end + + it "adds both xerr and yerr to the error bar plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Simple error bar plot with collection xerr and yerr." + axes.error_bar! do |p| + p.data [1,2,3,4], [1,4,9,16] + p.xerr = [0.5,1.0,1.5,0.3] + p.yerr = [0.6,0.2,0.8,0.1] + end + end + it "adds both xerr and yerr to the error bar plot with colour and width" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Simple error bar plot with collection xerr and yerr." + axes.error_bar! do |p| + p.data [1,2,3,4], [1,4,9,16] + p.xerr = [0.5,1.0,1.5,0.3] + p.yerr = [0.6,0.2,0.8,0.1] + p.line_width = 5 + p.xerr_width = 2 + p.yerr_width = 2 + p.xerr_color = :blue + p.yerr_color = :red + p.color = :yellow + end + end + it "adds error bar with upper limit and lower limit with collection xerr & yerr" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Error bar plot with lots of options" + axes.error_bar! do |p| + p.data [1,2,3,4], [1,4,9,16] + p.xerr = [0.5,1.0,1.5,0.3] + p.yerr = [0.6,1.0,0.8,0.5] + p.xuplims = [true, false, true, false] + p.xlolims = [false, true, false, true] + p.yuplims = [true, false, true, false] + p.ylolims = [false, true, false, true] + end + end + end + + context "#box_plot!" do + it "adds a simple box plot" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "A simple box plot." + axes.box_plot! do |p| + p.data [ + [60,70,80,70,50], + [100,40,20,80,70], + [30, 10] + ] + end + axes.x_title = "foo" + axes.y_title = "bar" + end + + it "adds a simple box plot with outliers" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "A simple box plot." + axes.box_plot! do |p| + p.data [ + [60,70,80,70,50], + [100,40,20,80,70], + [30, 10] + ] + p.whiskers = 0.1 + p.outlier_marker_type = :diamond + p.outlier_marker_size = 3.0 + p.outlier_marker_color = :red + p.median_width = 4.0 + end + axes.x_title = "foo" + axes.y_title = "bar" + end + + it "adds a simple horizontal box plot" do + skip "Leave for after initial box plot setup is complete." + end + + it "groups multiple box plots on the same axes" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Multiple box plots." + axes.box_plot! do |p| + p.data [ + [-48, 2,60,70,80,70,50], + [4,100,40,20,80,70], + [1,30, 10] + ] + end + axes.box_plot! do |p| + p.data [ + (0..100).to_a, + (5..45).to_a, + (-10..10).to_a + ] + end + axes.x_title = "hogehoge" + axes.y_title = "bokeboke" + end + + it "controls multiple of whiskers for multiple box plots" do + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.title = "Multiple box plots with controlled whiskers." + axes.box_plot! do |p| + p.data [ + [-48,60,70,80,70,50], + [4,100,40,20,80,70], + [1,30, 10] + ] + p.whiskers = 0.3 + end + axes.box_plot! do |p| + p.data [ + (0..100).to_a, + (5..45).to_a, + (-10..10).to_a + ] + p.whiskers = 0.1 + end + axes.x_title = "hoge with whiskers" + axes.y_title = "boke with whiskers" + end + end + + context "#top_margin=" do + it "sets the top margin in pixels" do + skip "do this later." + end + end + + context "#left_margin=" do + it "sets the left margin in pixels" do + skip "do this later." + end + end + + context "#bottom_margin=" do + it "sets the bottom margin in pixels" do + skip "do this later." + end + end + + context "#right_margin=" do + it "sets the right margin in pixels" do + skip "do this later." + end + end + + context "#x_ticks=" do + it "assigns strings to X ticks" do + skip "do this later." + @figure = Rubyplot::Figure.new + axes = @figure.add_subplot! 0,0 + axes.scatter! do |p| + p.data [1,2,3,4], [1,2,3,4] + p.label = "apples" + end + axes.x_ticks = ["hello0", "hello1"] + end + end # context "#x_ticks=" +end # Rubyplot::Axes diff --git a/spec/figure_spec.rb b/spec/figure_spec.rb index 42bb1d6..c91bbdc 100644 --- a/spec/figure_spec.rb +++ b/spec/figure_spec.rb @@ -1,23 +1,179 @@ require 'spec_helper' describe Rubyplot::Figure do - context "#add_subplot" do - it "creates a singular subplot inside the Figure" do - fig = Rubyplot::Figure.new - axes = fig.add_subplot 1,1,1 + context ".new" do + it "accepts figsize in centimeter (default)" do + skip "do this later." + fig = Rubyplot::Figure.new(width: 30, height: 40) + + expect(fig.width).to eq(30) + expect(fig.height).to eq(40) + expect(fig.figsize_unit).to eq(:cm) + + expect(fig.max_x).to eq(100.0) + expect(fig.max_y).to be_within(0.01).of(133.33) + end + + it "accepts figsize in pixels" do + skip "do this later." + fig = Rubyplot::Figure.new(width: 200, height: 200, figsize_unit: :pixel) + + expect(fig.width).to eq(200) + expect(fig.height).to eq(200) + expect(fig.figsize_unit).to eq(:pixel) + end + + it "accepts figsize in inches" do + skip "do this later." + fig = Rubyplot::Figure.new(width: 3.0, height: 4.0, figsize_unit: :inch) + + expect(fig.width).to eq(3.0) + expect(fig.height).to eq(4.0) + expect(fig.figsize_unit).to eq(:inch) + end + + it "accepts portrait orientation" do + skip "do this later." + fig = Rubyplot::Figure.new(width: 4.0, height: 3.0, figsize_unit: :inch) + + expect(fig.width).to eq(4.0) + expect(fig.height).to eq(3.0) + expect(fig.figsize_unit).to eq(:inch) + end + + it "changes Rubyplot Artist Co-ordinates as per aspect ratio." do + skip "do this later." + fig = Rubyplot::Figure.new(width: 20, height: 20) + + expect(fig.max_x).to eq(100.0) + expect(fig.max_y).to eq(100.0) + + fig = Rubyplot::Figure.new(width: 30, height: 20) + + expect(fig.max_x).to eq(150.0) + expect(fig.max_y).to eq(100.0) + + fig = Rubyplot::Figure.new(width: 20, height: 30) - expect(axes).to be_a(Rubyplot::Axes) + expect(fig.max_x).to eq(100.0) + expect(fig.max_y).to eq(150.0) end end - context "#reset!" do - it "resets all values to defaults" do + context "#title=" do + it "allows setting the title of the whole figure" do + skip "do this later." + @figure = Rubyplot::Figure.new + @figure.title = "Full figure title." + + @figure.add_subplots! 1, 2 + axes = @figure.add_subplot! 0,0 + axes.plot! do |p| + p.data (0..100).to_a, (0..100).to_a + end + axes.title = "Linear plot." + + axes1 = @figure.add_subplot! 0, 1 + axes1.plot! do |p| + d = (0..360).step(30).to_a + p.data d, d.map { |a| Math.sin(a) } + p.marker = :solid_line + end + axes1.title = "Sine wave." + end + end + + context "#add_subplot!" do + it "creates a singular subplot inside the Figure" do + skip "do this later." fig = Rubyplot::Figure.new - axes = fig.add_subplot 0,0 - axes.title = "reset test" - fig.reset! + axes = fig.add_subplot! 0,0 + + expect(axes).to be_a(Rubyplot::Artist::Axes) + end + + it "creates 2x1 subplots within a Figure" do + skip "do this later." + @figure = Rubyplot::Figure.new + @figure.add_subplots! 2, 1 + axes0 = @figure.add_subplot! 0,0 + axes1 = @figure.add_subplot! 1,0 + + expect(axes0.abs_x).to eq(5.0) + expect(axes0.abs_y).to eq(5.0) + expect(axes0.width).to eq(90.0) + expect(axes0.height).to eq(45.0) + + expect(axes1.abs_x).to eq(5.0) + expect(axes1.abs_y).to eq(50.0) + + axes0.x_range = [0, 10] + axes0.y_range = [0, 70] + axes0.scatter! do |p| + p.data [1, 2, 3, 4, 5], [11, 2, 33, 4, 65] + p.label = "axes0" + end + + axes1.bar! do |p| + p.data [5,12,9,6,7] + p.label = "axes1" + end + end + + it "creates 2x2 subplots with a Figure" do + skip "do this later." + @figure = Rubyplot::Figure.new + @figure.add_subplots! 2, 2 + axes0 = @figure.add_subplot! 0,0 + axes1 = @figure.add_subplot! 0,1 + axes2 = @figure.add_subplot! 1,0 + axes3 = @figure.add_subplot! 1,1 + + axes0.candle_stick! do |p| + p.lows = [100, 110, 120, 130, 120, 110] + p.highs = [140, 150, 160, 170, 160, 150] + p.opens = [110, 120, 130, 140, 130, 120] + p.closes = [130, 140, 150, 160, 150, 140] + end + axes0.title = "Simple candle stick plot." + + lows = [100, 110, 120, 130, 120, 110] + highs = [140, 150, 160, 170, 160, 150] + opens = [110, 120, 130, 140, 130, 120] + closes = [130, 140, 150, 160, 150, 140] + axes1.candle_stick! do |p| + p.lows = lows + p.highs = highs + p.opens = opens + p.closes = closes + end + axes1.candle_stick! do |p| + p.lows = lows.map { |l| l + 100 } + p.highs = highs.map { |h| h + 100 } + p.opens = opens.map { |o| o + 100 } + p.closes = closes.map { |c| c + 100 } + end + axes1.candle_stick! do |p| + p.lows = lows.map { |l| l + 10 } + p.highs = highs.map { |h| h + 10 } + p.opens = opens.map { |o| o + 10 } + p.closes = closes.map { |c| c + 10 } + end + axes1.title = "Multiple candle stick plot." + + axes2.line! do |p| + p.data [2, 4, 7, 9], [1,2,3,4] + p.label = "Marco" + p.color = :blue + end + axes2.title = "A line graph." - expect(axes.title).to eq("") + axes3.bar! do |p| + p.data [5,12,9,6,6] + p.label = "data" + p.color = :green + end + axes3.title = "Bar with title margin = 100" end end end diff --git a/spec/gr_wrapper_spec/tasks_spec.rb b/spec/gr_wrapper_spec/tasks_spec.rb deleted file mode 100644 index d837d5f..0000000 --- a/spec/gr_wrapper_spec/tasks_spec.rb +++ /dev/null @@ -1,14 +0,0 @@ -require 'spec_helper' - -describe Rubyplot::GRWrapper::Tasks do - include Rubyplot::GRWrapper::Tasks - - context Rubyplot::GRWrapper::Tasks::BeginPrint do - it "creates a beginprint task" do - t = Rubyplot::GRWrapper::Tasks::BeginPrint.new "new_file.bmp" - - expect(t.file_name).to eq("new_file.bmp") - expect(t.call).to eq(true) - end - end -end diff --git a/spec/spec_helper.rb b/spec/spec_helper.rb index 59f7584..78073a7 100644 --- a/spec/spec_helper.rb +++ b/spec/spec_helper.rb @@ -1,10 +1,24 @@ require 'fileutils' require 'pry' require 'rubyplot' +require 'rspec' +require 'rmagick' -TEMP_DIR = "temp/" -FIXTURES_DIR = "fixtures/" SPEC_ROOT = File.dirname(__FILE__) + "/" +TEMP_DIR = SPEC_ROOT + "temp/" +FIXTURES_DIR = SPEC_ROOT + "fixtures/" +display_img = nil + +backend = ENV['RUBYPLOT_BACKEND'] + +if backend == "GR" + ENV['GRDIR'] = "/usr/local/gr" + ENV['GKS_FONTPATH'] = "/usr/local/gr" + Rubyplot.set_backend :gr +elsif backend == "MAGICK" + Rubyplot.set_backend :magick +end + RSpec::Matchers.define :eq_image do |expected_image, delta| compared_delta = 0 @@ -35,19 +49,31 @@ def compare_with_reference(test_image, reference_image) RSpec.configure do |config| config.before(:suite) do - FileUtils.mkdir_p SPEC_ROOT + TEMP_DIR - FileUtils.mkdir_p SPEC_ROOT + FIXTURES_DIR + FileUtils.mkdir_p TEMP_DIR + FileUtils.mkdir_p FIXTURES_DIR + $stdout.puts 'Do you want to display the image? [y/n]' + display_img = $stdin.gets.chomp.strip[0].downcase + while display_img != "y" && display_img != "n" + $stdout.puts 'Please enter y or n' + display_img = $stdin.gets.chomp.strip[0].downcase + end + if display_img == "y" + display_img = true + else + display_img = false + end end - - config.after(:example) do |example| + + config.after(:example) do |example| + @figure.show if display_img if @figure.is_a?(Rubyplot::Artist::Figure) plot_name = example.description.split.join("_") + ".png" - base_image = SPEC_ROOT + TEMP_DIR + plot_name - other_image = SPEC_ROOT + FIXTURES_DIR + plot_name + base_image = TEMP_DIR + plot_name + other_image = FIXTURES_DIR + plot_name @figure.write(other_image) @figure.write(base_image) - expect(base_image).to eq_image(other_image, 10) + expect(base_image).to eq_image(other_image, 100) end end diff --git a/spec/scripting_layer/multi_plot_graph_spec.rb b/spec/spi/multi_plot_graph_spec.rb similarity index 93% rename from spec/scripting_layer/multi_plot_graph_spec.rb rename to spec/spi/multi_plot_graph_spec.rb index 6e5e601..c2a0558 100644 --- a/spec/scripting_layer/multi_plot_graph_spec.rb +++ b/spec/spi/multi_plot_graph_spec.rb @@ -19,7 +19,7 @@ @x2 = [2, 4, 16] @y2 = [10, 20, -40] end - it 'creates a line and scatter graph' do + skip 'creates a line and scatter graph' do a = Rubyplot::SPI.new a.title = 'My cool graph' a.line! @x1, @y1 diff --git a/spec/scripting_layer/single_plot_graph_spec.rb b/spec/spi/single_plot_graph_spec.rb similarity index 86% rename from spec/scripting_layer/single_plot_graph_spec.rb rename to spec/spi/single_plot_graph_spec.rb index ebecb3d..1b2defe 100644 --- a/spec/scripting_layer/single_plot_graph_spec.rb +++ b/spec/spi/single_plot_graph_spec.rb @@ -15,7 +15,7 @@ end context '#line' do - it 'creates a simple line graph' do + skip 'creates a simple line graph' do a = Rubyplot::SPI.new a.line! @x1, @y1 a.save 'spec/reference_images/file_name.bmp' @@ -24,7 +24,7 @@ 'line_graph.bmp', 10)).to eq(true) end - it 'creates a line graph with points marked' do + skip 'creates a line graph with points marked' do a = Rubyplot::SPI.new a.line! @x1, @y1, marker_size: 1 a.save 'spec/reference_images/file_name.bmp' @@ -33,7 +33,7 @@ 'line_marker_graph.bmp', 10)).to eq(true) end - it 'creates a red dashed line graph with points marked' do + skip 'creates a red dashed line graph with points marked' do a = Rubyplot::SPI.new a.line! @x1, @y1, line_color: :red, line_type: :dashed a.save 'spec/reference_images/file_name.bmp' @@ -45,7 +45,7 @@ end context '#scatter!' do - it 'creates a simple scatter graph' do + skip 'creates a simple scatter graph' do a = Rubyplot::SPI.new a.scatter! do |p| p.data @x1, @y1 @@ -56,7 +56,7 @@ 'scatter_graph.bmp', 10)).to eq(true) end - it 'creates a green cross scatter graph' do + skip 'creates a green cross scatter graph' do a = Rubyplot::SPI.new a.scatter! @x1, @y1, marker_color: :green, marker_size: 2, marker_type: :diagonal_cross @@ -68,7 +68,7 @@ end context '#bar' do - it 'creates a simple bar graph' do + skip 'creates a simple bar graph' do a = Rubyplot::SPI.new a.bar! @values a.save 'spec/reference_images/file_name.bmp' @@ -77,7 +77,7 @@ 'bar_graph.bmp', 10)).to eq(true) end - it 'creates a bar graph with red color bars' do + skip 'creates a bar graph with red color bars' do a = Rubyplot::SPI.new a.bar! @values, bar_color: :red a.save 'spec/reference_images/file_name.bmp' @@ -86,7 +86,7 @@ 'red_bar_graph.bmp', 10)).to eq(true) end - it 'creates a bar graph with orange color bars with spaces' do + skip 'creates a bar graph with orange color bars with spaces' do a = Rubyplot::SPI.new a.bar! @values, bar_color: :orange, bar_gap: 1 a.save 'spec/reference_images/file_name.bmp' @@ -105,7 +105,7 @@ [56, 12, 84, 30]] end - it 'creates a stacked bar graph' do + skip 'creates a stacked bar graph' do a = Rubyplot::SPI.new a.stacked_bar! @bars_data a.save 'spec/reference_images/file_name.bmp' @@ -115,7 +115,7 @@ 10)).to eq(true) end - it 'creates a stacked bar graph with user defined colors' do + skip 'creates a stacked bar graph with user defined colors' do a = Rubyplot::SPI.new a.stacked_bar! @bars_data,bar_colors: [:black, :red, :green, :blue] a.save 'spec/reference_images/file_name.bmp' @@ -134,7 +134,7 @@ [56, 12, 84, 30]] end - it 'creates a stacked bar Z graph' do + skip 'creates a stacked bar Z graph' do a = Rubyplot::SPI.new a.stacked_bar_z! @bars_data a.save 'spec/reference_images/file_name.bmp' @@ -144,19 +144,19 @@ 10)).to eq(true) end - it 'creates a stacked bar Z graph with user defined colors' do + skip 'creates a stacked bar Z graph with user defined colors' do a = Rubyplot::SPI.new a.stacked_bar! @bars_data,bar_colors: [:black, :red, :green, :blue] a.save 'spec/reference_images/file_name.bmp' - expect(compare_with_reference?('file_name.bmp', 'single_plot_graph/' \ + expect(compare_wskiph_reference?('file_name.bmp', 'single_plot_graph/' \ 'user_color_stacked_bar_z_graph.bmp', 10)).to eq(true) end end context '#line_plot!' do - it 'creates a simple line plot' do + skip 'creates a simple line plot' do a = Rubyplot::SPI.new a.line_plot_z! @freqwise a.save 'spec/reference_images/file_name.bmp' @@ -165,7 +165,7 @@ 'line_plot.bmp', 10)).to eq(true) end - it 'creates a line plot with red markers' do + skip 'creates a line plot with red markers' do a = Rubyplot::SPI.new a.line_plot! @values, marker_color: :red, marker_size: 2 a.save 'spec/reference_images/file_name.bmp' @@ -174,7 +174,7 @@ 'red_line_plot.bmp', 10)).to eq(true) end - it 'creates a line plot with green solid bowtie markers' do + skip 'creates a line plot with green solid bowtie markers' do a = Rubyplot::SPI.new a.line_plot! @values, marker_color: :green, marker_type: :solid_bowtie a.save 'spec/reference_images/file_name.bmp' @@ -186,7 +186,7 @@ end context '#pie!' do - it 'creates a simple pie chart' do + skip 'creates a simple pie chart' do a = Rubyplot::SPI.new a.pie! @portfolio, @portfolio_names a.save 'spec/reference_images/file_name.bmp' @@ -204,7 +204,7 @@ @close = [15, 24, 18, 4] # incoporate date ?? end - it 'creates a simple candle plot' do + skip 'creates a simple candle plot' do a = Rubyplot::SPI.new a.candlestick! @open, @high, @low, @close a.save 'spec/reference_images/file_name.bmp' @@ -214,7 +214,7 @@ 10)).to eq(true) end - it 'creates a candle plot with blue positive and black negative color' do + skip 'creates a candle plot with blue positive and black negative color' do a = Rubyplot::SPI.new a.candlestick! @open, @high, @low, @close, up_color: :blue, down_color: :black a.save 'spec/reference_images/file_name.bmp' diff --git a/spec/scripting_layer/subplots_spec.rb b/spec/spi/subplots_spec.rb similarity index 92% rename from spec/scripting_layer/subplots_spec.rb rename to spec/spi/subplots_spec.rb index c5245c0..58492d3 100644 --- a/spec/scripting_layer/subplots_spec.rb +++ b/spec/spi/subplots_spec.rb @@ -21,7 +21,7 @@ end context '#subplot!' do - it 'creates a SPI with 2 Subplots stacked vertically' do + skip 'creates a SPI with 2 Subplots stacked vertically' do a = Rubyplot::SPI.new a.subplot!(2, 1, 1) a.line! @x1, @y1 @@ -33,7 +33,7 @@ 'two_vertical.bmp', 10)).to eq(true) end - it 'creates a 2x2 subplots with multiple plots in a figure' do + skip 'creates a 2x2 subplots with multiple plots in a figure' do a = Rubyplot::SPI.new a.subplot!(2, 2, 1) a.line! @x1, @y1, marker_size: 1 diff --git a/tutorial/magick/Image/Rubyplot_Image_Tutorial(Magick).ipynb b/tutorial/magick/Image/Rubyplot_Image_Tutorial(Magick).ipynb new file mode 100644 index 0000000..b8e2853 --- /dev/null +++ b/tutorial/magick/Image/Rubyplot_Image_Tutorial(Magick).ipynb @@ -0,0 +1,554 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rubyplot Image Tutorial\n", + "This tutorial contains a tutorial for Rubyplot Image \n", + "P.S. - Currently only for Magick backend" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "1. Copying an image\n", + "2. Copying a part of an image\n", + "3. Changing channels \n", + " 3a. Exporting specific order of maps \n", + " 3b. Importing specific order of maps \n", + "4. Adding padding \n", + "5. Manipulating Values (using Numo array) " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "require 'rubyplot' # Import rubyplot\n", + "Rubyplot.inline # Setting inline plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Copying an Image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following steps are taken:\n", + "1. The image is read and stored in a Rubyplot::Image object\n", + "2. Pixels are exported from the image\n", + "3. A new Rubyplot::Image object is created with specified rows and columns\n", + "4. Exported pixels are imported to the Rubyplot::Image object" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "mnist0.jpg=> JPEG 28x28 28x28+0+0 PseudoClass 256c 8-bit 435b" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img0 = Rubyplot::Image.new # Creating a new Image Object\n", + "img0.imread('mnist0.jpg') # Reading a MNIST Image\n", + "img0.imshow # Showing the Image" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + " PNG 28x28 PseudoClass 120c 8-bit 544b" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img0_pix = img0.export_pixels(\"I\") # Exporting pixels from img0, map is set as intensity(I) as the image is grayscale\n", + "img0_copy = Rubyplot::Image.new(img0.columns,img0.rows) # Creating an Image to copy img0 with same number of columns and rows\n", + "img0_copy.import_pixels(img0_pix,\"I\") # Importing pixels extracted from img0\n", + "img0_copy.imshow # Showing the copied image\n", + "# img0_copy.imwrite(\"mnist0_copy.jpg\") # Image can be written with any format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Copying a part of an image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following steps are taken:\n", + "1. The image is read and stored in a Rubyplot::Image object\n", + "2. Pixels are extracted from the image with an offset and specified dimensions\n", + "3. A new Rubyplot::Image object is created with specified rows and columns equal to exported dimensions\n", + "4. Extracted pixels are imported to the Rubyplot::Image object" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "cat.jpg=> JPEG 1920x840 1920x840+0+0 DirectClass 8-bit 120kb" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img1 = Rubyplot::Image.new # Creating a new Image Object\n", + "img1.imread('cat.jpg') # Reading an image of a cat\n", + "img1.imshow # Showing the Image" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns in image = 1920\n", + "Rows in image = 840\n" + ] + } + ], + "source": [ + "# Columns and Rows in Image\n", + "puts('Columns in image = '+img1.columns.to_s)\n", + "puts('Rows in image = '+img1.rows.to_s)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 800x750 DirectClass 8-bit 725kb" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img1_pix = img1.export_pixels(\"RGB\",900,50,800,750) # Exporting pixels from img1, map is set as RGB as the image is RGB\n", + "# 800x750 pixels are exported from the position (900,50) i.e. offset is (900,50)\n", + "img1_copy = Rubyplot::Image.new(800,750) # Creating an Image to copy img1 with number of columns and rows exported\n", + "img1_copy.import_pixels(img1_pix) # Importing pixels extracted from img1\n", + "img1_copy.imshow # Showing the copied image\n", + "# img1_copy.imwrite(\"cat_copy.jpg\") # Image can be written with any format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Changing channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can be done in two ways:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "rgb.png=> PNG 400x400 400x400+0+0 PseudoClass 191c 8-bit 5kb" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img2 = Rubyplot::Image.new # Creating a new Image Object\n", + "img2.imread('rgb.png') # Reading an image\n", + "img2.imshow # Showing the Image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3a. Exporting specific order of maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following steps are taken:\n", + "1. The image is read and stored in a Rubyplot::Image object\n", + "2. Pixels are exported from the image in the order BRG\n", + "3. A new Rubyplot::Image object is created with specified rows and columns\n", + "4. Exported pixels are imported to the Rubyplot::Image object in the order RGB\n", + "\n", + "Since RGB is exported in order BRG; Blue becomes Red, Red Becomes Green and Green becomes Blue\n", + "i.e. BRG -> RGB" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 400x400 PseudoClass 191c 8-bit 5kb" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img2_pix_brg = img2.export_pixels(\"BRG\") # Exporting pixels from ima2, map is set as BRG\n", + "img2_copy_brg = Rubyplot::Image.new(img2.columns,img2.rows) # Creating an Image to copy img2 with same number of columns and rows\n", + "img2_copy_brg.import_pixels(img2_pix_brg,\"RGB\") # Importing pixels extracted from img2 in the order RGB\n", + "img2_copy_brg.imshow # Showing the copied image\n", + "# img2_copy_brg.imwrite(\"brg.png\") # Image can be written with any format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3b. Importing specific order of maps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following steps are taken:\n", + "1. The image is read and stored in a Rubyplot::Image object\n", + "2. Pixels are exported from the image in the order RGB\n", + "3. A new Rubyplot::Image object is created with specified rows and columns\n", + "4. Exported pixels are imported to the Rubyplot::Image object in the order GBR\n", + "\n", + "Since RGB is imported in order GBR; Red becomes Green, Green Becomes Blue and Blue becomes Red\n", + "i.e. RGB -> GBR" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 400x400 PseudoClass 191c 8-bit 5kb" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img2_pix_gbr = img2.export_pixels # Exporting pixels from img2, map is set as RGB as the image is RGB\n", + "img2_copy_gbr = Rubyplot::Image.new(img2.columns,img2.rows) # Creating an Image to copy img2 with same number of columns and rows\n", + "img2_copy_gbr.import_pixels(img2_pix_gbr, \"GBR\") # Importing pixels extracted from img2 in the order GBR\n", + "img2_copy_gbr.imshow # Showing the copied image\n", + "# img2_copy_gbr.imwrite(\"gbr.png\") # Image can be written with any format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Adding padding" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "cat2.jpeg=> JPEG 252x200 252x200+0+0 DirectClass 8-bit 5kb" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img3 = Rubyplot::Image.new # Creating a new Image Object\n", + "img3.imread('cat2.jpeg') # Reading an image of a cat\n", + "img3.imshow # Showing the Image" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns in image = 252\n", + "Rows in image = 200\n" + ] + } + ], + "source": [ + "# Columns and Rows in Image\n", + "puts('Columns in image = '+img3.columns.to_s)\n", + "puts('Rows in image = '+img3.rows.to_s)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BKDw8e/DBv3gyzekjX3wEBwBnve0gWCU4Jz89OTk7Prz55pt5Irm31plhmdfVan5ylGnuyUEIeaaQwmI5z5A502Xl0FOAD9ZExgmZ6LOJ4R59dgFBChXI7e7tZWUhksS2bSr4aGuk0sR7H4JDRCmlUlJLCRzyPJ9Op6vVHeccACilbNtUq+Xzz7/wy5u3bt9868KVyy++/LX+cIgIDKHpGgB4FO4MaFN6GJztgDNvjEfwNgTJidBTwEfnexEDPGwrDwQAqD6mYOZRdfmjfmZPBDdumpo9SvMn5oA+eidIwTsDwZMzgkEiRFev7r33zus/+9n2qG+bWiIFDovZWdfUAqFrTLterVYri4g6DSLpbW0hAAQPjD8x2R5Fn10M9+gpPZwFBniYgZtTmyEE8q7I8r//9//B/+Nf/Iu6WmejkScEgN/7vd8zxrz99tve+9aY2WyuEvXmG78siqIos16v1+sV3tizk+PDe3clV9988cX5YvFv//Iv3n3jrX/nH/57ly5dunf7oK7auq57vd7WeFyvG0RcL5aXrl6Znh5OplMAduHCxUF/lEl8cHg83t4GAPLeOReQGGOMAWMMEILQxrgQQpqmTWsQsTccrVarruvyPBdSWWut9QHAGFNVVa83AABE5JwjIgBtZvaTRHddt/kr772UUikFrrPtOlMcUARyh/du/+gv/+L0+Oja5QvkreTMdS0EN8jLGmC5XFLwjLEkSZqqYsIziV3Xaa2TNK3M5kQufqi1OrJn68/+ST76yeNX39Wz9nP/1BtGX5gY7tFTedjP/QNlGwjAkEEI4CkMhkMuRZIkKkl7vd63X/3mdDqtqkpKuVqvQwhtZ995793RcDjaGu/v7yvJq6raXEdJOTl6kCh15cqVP/4H/+7b79z80b/583cGg8F4tJgvJ5NJmeWHSVJkufe+Xq2nxwc12aptsiyvUpULHiRPBSXo1nXjnHPOeO+JgZRSSs6E7DxwIbWUmkPtu7brGFJT14jYVsQ594C06YdAPlGCQeCcQyBnOwbIpeDAAtFqNiWERGnkzHetaesGkGy33dPrxQzJv/baaz/5qx8+f/36H/zed/7mx39dpqlg6GxH1nDAEIJgHAVvremcd4EUMEC2Nd5BxozzjzasjaJfVwz36Ck9ORBjT2zDRJzJ4Gl7e5dzztKEC5Fk6Q9/+MPjw/vjnd2XX3651x/eu3/ApfrOhYvfffXVNNUhuKausrzK8zRYlybJeDC+f/fA1NU3v/3tv/d3vv/undu//OUvf3LzbWs7xQUfDO4cHfeKUkuFRLP1PBmVILnGnfmxTzjZeonIarSrVeW9t9547wFJCKG1FFLXHWRlqXQaksSuVrbrQKJm6JyztnWITCpEdC6EEBgiOiaYohDapubIEllIJoyz3ncueALPlVToPQQIZEz1zptv+675sz/7s9Vq9Y///X+YKPH6a78YlX2GwBmQNa1z1vm2bZ11nKNH3tnARcKVZlJfuHwFubA+EJMfet5jlXv02cRwjz7dh0aSSJvNrwMQOu8zLciYtMgBERhvrVmv12mqL1y4MF+tj09Ovv7KN3qj8Xw+v3DpYut8u1pb0wKELMullNV6ba1brCpEbI07PDzc2dl5/rlriRTeNrfffc840yyn1XqNpnFaSi6oa9FwLZMEbLeczDmdGKtVeqbU6dmUMYYcQgghOADggjEhA3GdliEExsVmun9rZ5dLzbi01nKler2e0ilaG3zgnNerBeZ5CKFdrTjnGfOgVGhb17bOOaMUKNV1XdM0y+VyOTu9+87NN9/4+QsvvPDv/L0/Xi1mx/fnu1vjrm2UUggBAxljWuOMD94FZCowiTLJs1zmGZP59t7FAIIx4T/TCxRFHxXDPXoWxJ4YwTOA4JzDTBFDxlhW9pbT03VdHR7Zb7784nPfeGVV1fPFijF2+fLlpChaY9I8U0pJm9b1er6u27oi59NET+bz0aCf5fn9B4cPjo+uPHdlMB787u9+Z9AvfvGzn958683d7TF5Gazzno0HBfKQJ0yB8bbrljQ9mwmlEfhm8lommsgb21prAwSOTAhpuFqtVsA4Y8wFqCb3kQuhEu99mhe0s5OXfe99ZywRrZdVmecAsF6vOeewHkkpm6Y5PT3lnKdp6pw7Pj4+OTmZzWaLxcxb90d/+P1Lly7NJsf1ah2871oYDAaLxUIIAUwAF8gEMsGUkGlq264YyGIwRJXqPO8NhoFAqcQa+35VDxIjetQoLU5hR88mhnv0DD5cLogAnBER55xx/txzz/18NjGddW3z85///PTk+IUXX9rZ2ZnNZo3xRX+AwugkZQzqppvNV6vlzHZNqpMkSa6/8HzXtovV0touTdOz2cT6tsizb33rG/u7wyKTy+nZYnGqt7bzotAioMCEBWqrTHDyHZna2Q6QjwaDNE2VUjbYunENWUIQDJv5hCWaNWsmRaIzGwJVLiADIY0LbjWhepZlmQvQNE3bdhywShIk1nQ1ElscKQhYt9XJ0alQXHI1W0zv3TlounrQGxa98tU/+l6/Xy7PzpbLpda6P+gjcmstAHDOVaKVybwDdAGdB66KfsqESsqSSbl98UpS9AIyxiXB+2N39mgVFhIgxcaQ0bOJ4R59RpvWKkmSGGMzxbMse/XVV9/6xc9sZwRBXmRvvfXWzXdu7V68lPWGvdFob/9yXvYODo+01gQ+L3r9fp+C995qyR0iTzWzCpi3jA6OH+i5vPHcFS348y+9wBn95K/+4k6zILCDQelsx3mQ5Jum3d7a9Z4yxZ0LQrBCC86IXG3qer2aG2OUklKrZTUtWKFcg4EJcN5ZBZlKMqV4S6ZuzbJZmCThUhtjmqrp5YV3NRKS7brWzI/btm7rtq5WlQsuuLCu103VbO1svfLi1f3LV/YuX7x15y5j4sL+btM0bVPlRS/LCmttmmZpCkImTVLLJA3OJ1kuVMKUCoCo9HPPv5ikeeeDo4ctcR7Ngz3sagkAcX1T9KxiuEfP4OFyfoTHfc/TNFnNGpbINM9vPP+i0rkxLhN8Pptfu3bt+OT0rbfe2tm9OG7aqmquPnctz0vO0XS+bWsDhEiJEkmiDm6/u7u7bZv19OwkyxKC0FX2UACSW5w+cF09Hg0EPXd6fMKQ+v3S2EZwFqzJEmGMSxWvg5cC00QQUdvZpl41q5XzXvJMcL27PR71e2fTedM0wRtvLM+yXq6NtRicN40xRjEalpkoyiYVnDiBDx54IB9as5ot5/O6bYxxddMRkVDJ3oW95567fvHalcFwe7mudnf3q6aeLxd5VuZFr+nMdD6z1uXIVZoInWdFr+g6RJ4V+bJaMy5W65pJsbO/h1J1TeuC3zQ7C/hwqM4CIDDCuJIpemYx3L9kvxXlwAFBMPawKSNuJmc2yyixquokSZbrOlNy+9LV3QtXz15/DUIIDk8nsywrRoGWsymQLxLFbdsumyRJ8iTJVeKNNbYL3jQLs5XL5vS+aeo0GGgMZ0wAnd2/m2fJ2b3bElmZZ+PegLlQLVdackRGBPt7e7PpNMtLgKAEc6Y9PT3JsqyqqhCC935rPGaMIRNeJNO1mSwqzrlzfrxzQSmVFoNSiOl8IZSeTCbbW2Ot9Wq1qparYVk01apaN4wxgcDBQ7CC47SqCCXXWX975/mXv/7cCy8mab5ujbGOTIcosjzhWgXGmFICOUtgWlW8bQFYZ0xRFGXZM9Ywxu7cuT1brf79f/JPm6YG54HxxnT4uFqGIQvI2GZlAfPwgZ3EP/S2+Wj9+2euT39Kn3T/n/QIoy9eDPfoqTxs1PKBJlYEABwoBOKcW+sV+t/7g+/fvn23mp/5uk37xXq97uVFvyhDCAz9vdu3Ll+9DI4FA0S+67q2ba13glzJSYCv52eDYW86n+3ubi+XS62la5pESwnMdJ03JkkSgdC1VibSh7CYr5qureq2ahtyRIhMGGMYQCiKgjGWZVnTNE3TMcYYYzrvKaWapkEuPEHddtauQwibv51MJkKIqqogeI3BWwNk66YDYFmmnO8fTybL5fLi1esvvfLt5154qb+1C1K2xgfBEgVZkiqljLPL5bJZV8CEUipN0qxAZ8NqtSKiANSazrTt22+/3bbt3qVL/eFAp4kH7hgD+7B95ZMrZWMZZPTZxHCPfl3WWiW45MxT+Pa3v/3f/Nf/73Y1v3D58unR/SxL1uv1eDy+cuXKql6laTqfzvI87w/KJEkYY4CBGu87a8kH8oHcpk6RiFBw4CwEJxhnwDa7djDGNl0SO+uCC9Wyst4jotSKAiRZkhVl13VNZ7hK0iyXSnXGeu83mR5C2KwpzfO8aZqTkxPOuVIqhBBCWCwWm4NBlmhC9BSEkhqhaToiYox5759/4frXv/nqy994Ncn7VWe7thNcqUKRo6ppl+tKKTUcjodj1rZtXdeJ0mVZWms5AyLiUtRVu1qtjo+P87J4/sUXyrJPRI48sA90R2Af6EUcRc8shnv0DB6V5b2PGPoQALjOMzBOJ0l/NFzNJsenJxf392ezs6Io1uv166+/9t3f/71er5zP54wDADAGWkrGUyFE0ELYul0ve71eXa8Hg17TVGmaOGeSVFlrGZBKtGOsWVfEoCiK09mcCb5q2iRJGOdlbxBcyMtccrlcrZu2S1LX7/c3D9JayxluIp4xRkRd120CnXMeQqiqyhjTdd1gMPDel2XRy7Pjk4oRcCXbxcoFKov+jRs3fueb3yqHWyaEd9991wQYjrZkyleLKk/yJMsSpYAx733wXmudpunp6Zlzrm1bY9o0zdu6mpyerKp6Z2enPxxev/48clY1LTEuuXq4p98Te69udoaKos8ghnv0VNgTGyE9me+bju3eGtt1Umrn3I0bNw5u3WSM3bt378KFvfl8TuR3dnbefvOtK1cvX7hwAZFCcE1TbUbuHFFIIZkKRkiGXVdpLavWCSFCcMCYUpIjY8g4d0qLzRw6Ms6F1Ena6/e11v3R0DRGJrJZN61xwASXOi/7TdMYF85mCyXYoN8bDodd1y0WC2PMcDjc399fr9eMsbqurbVt21prGWNJkgVAAuaJTGvqtsuy7NKlS4OtrcFofLZYH51OGxsGWztZqmvTziaTKS6lVonSjLHNzlCbtjajwcA5h+R7Rdbr9ZfLeZ5qrTWTyWA0Ho7HVdcRkRACETcHm/efXHxcCxmA4hR29GxiuEdP62N3Y/LeF/3eatbNFovt4Uhq9co3vvEXf/5nBR+m2+Ojo8PRaJTnqTFmOBrcufNekijOUSdSZzrlOiB411nTchGk4quqyoq8taYoCiKfJIl1ptfrCWTr5coHX/R6jLGmM1mvz4TkOsvyvCzLJEkCVYFI54WuK2e8McZ7DwBcKgAIIcCjpmCbDUCUUkVReO8550S0Wq1CCKvVSghhvatrUxQ9KeVyuSyKrixL4Gy1Wh2dnFadAZ6MxluJ1uvVIoDY3dmxgXfGeXJSqkFeSCm7rqnrerVaEXmtdb/sBe8W89lyMWdc7F/Z3d6/QMiYUKlgQic2METzMZ+NYqlM9JnEcI8+o82aGk8hBGKCg8Wma5WSFy9fevHllx688xan8I1vf2u5XJqm7vV6nPNvfvObp6fHXGDqNGNlIgQXGJAYuLqufVcbazgIoRTnyGWCiKElrRLGkTdNCErohDNmPAmpPIFA7gmTrAAAlWTW2jzPO2fbqm3b2jhPyLIsS9NUMAgEZ9OZMeZrX/vaZlvXQJDlBQBwIbPZXCq96QFprUXOVJIKIfKSkHMimkwmddsEgjQv0zJvqvV8ueZC9UejIh2tOl/2ttJMe++bql4ul0qwQa8HwWuth8MhUnjvvffOTo69c7LQOs+39vaqzjAuADkF9N4jMiCGBCx2cY9+bTHco19LCKFuGy5E0Su7qkFEmehXXnllcudW6Kp79+7lebq7u1v2Cq2ltd3+hd2mqbq2mc/OTNfkeZpqrbL0ZD5Zr2Zlvzebza5dv163ze5gt7MmxdQFz4DpNONc2BCIQKd5XXXGOsaYCxYYB4AkU8wYItQqFVzhoyYBxhgXCAATRCJCxMFgYK2t63pzcrVtW6VUlmW9Xm8zHW+t7fcHdV0tVkvOudZpXa/bthNC6CRN87Ju18enM6HTS1eukXPvvfv2hasvGNvS2iVJMh6PpeQQgvcWCbTWSvA7dw5+9KO/Oj09vbh/oRiM0iLvD8ez+YIrZr0FRz4QZx/uB/m4s3tcnho9K/4nf/InX/Zj+Fuharpf8bdPVgd/Np96zx/9/rPdD2x2uH74BwAAhojIJWeIwXsEaNu2bTsiwuDv37mVJVop1XXtfDZbrZYheKVkcCZN9bBXZpkO3jX1umsa62yeaq209yHPC2Q8L8pA4AEuXrgIgIcPjrROyrI3n81dIKm00JlOc0AmhTg4uF+WvSRJhRTj8dh537VN13V7uzsnJyfGmLOzs+2tcZYXgGwwHAGy/mA4my+k0gf3D50PzodefzCbL0bjrdW6Knt95wMhcPZwBlwnaa9f9nrluqqarnXOJVmS5wVDAASdZvNlZa0j7+pqPT2brJZzjphn+Xq9TlL9i9de+8EPfsC5uHDhotISpPy9P/wf1S5woQA5Yxw5Z5wzJvDh8/r+0wwUAIB+Zdn4M71Pnul1f1ZPvpPzNPm87jb6DOLIPXoqH9fP/aGAmx2skQlOiMg5MOSc9/KB3tlqmma1mBvTCsEYh7ZpO1M3gqWpTrXWildVNZ8uskR5awIQcCaUTNJUZznnHLhQaVb0RtZ5ArF/8epsNnvvzv2vf+MbOs3X63XbtoVz6/U6hBCCH/T7znTL5TJLlHOOyHdNm+qHxY5a6yRJNvtsbM58bm9vT6fT+Xy++f6m5DGEsK4rIYTSSSIYI3DWGNM2TSOEYCE4IgBCCkABgoNgt4bjo5Oz9dzv7Oz0y8Lah/WXW9vjt9566+ev/wIFH41GnbP9fv/V3/seISNgAIyABWRI+KHd9R4/u4/aDzCIomcRwz367B6e+iMCIsYY5xyQIWPAkEnhnKPgELHo9bzV1naL2XQ8HNT1elUtm5qXeZZmOtFcsXy9XBERcsaYEFxpnWZpIbVq25aAjbd3jh8cHZ9MhoMBoEjSDLkcjrcAYDabrVarg4OD565dHY/HDNA5t5xPx1evmLZhSKat0zTlnFdVlSRJmqZZliGic24+n49Go03uO+eSJDHGIKIL3hEwZEwqKQWQD8ETkXNOSekDhxACIQMHZCmI4Ox6Mb24v8M5b9tWcrh04Yq19t13bp5Oz5bLZaLTvCxGo7GnsLW1tbV30SMjZPDEwRIJABgQPTxv/WgtU5x9jz6bGO7RM0MAj++vogxEDzdlEgKJiCiE0Ov1QrVumwoRlFIyT5RBY4BznmYawXVtNZ3VeaOHw2F/2K9Wa8YYE5KhIEIXGBECMAqsqqp+f1iUg5PqeLGsvKP+YOvu3YPx9o7W2hgzn89v3bo1Gg5uPHctBKcEM23Dkdp6rYVgQFJyAJhMJsPh8NKlS/1+v67r5XK5Wq0AQGvd6/VCCJvEF0I4T2VvgIgE2FqHwQcKQj483eq9R4eOCJEYBSAPwZ6dHq1Wq6IoiqKwbXP73Vt1XTedaUw36A+LslfX7appx+Pt0dZeIAiAHhkDBGDwcNj+wc3H0X3Zr3P02y2Ge/Rr2VRnUwhEBAyDJwqhtcZ7r6RElnVdW1UVgdWcSSUmk5Mi172yMFpUq0VT1UDemhaAITKGAoA5F4yxxljGRTkY1p1BLre3d/Os76w9ODiYTs7m8+mNG8/1+/2iKJIksdZOp9O2bRMllOCbcTpQyPNUSWGtaet2ua4Go7FQGhg/PZtOpjNrbdOZCxculGVZVVXbtsid1Ml8tczLwnvvrfPOUwiIKJVCJr01ABCAggdEREYMHKNw5eIuZ5pLpZSSWgNjeZFaF6aLZVO3hCwv+1yoJC/SomRCOWABAYk9PJ2B8EmLlR5uWxuXMkXPKIZ79Ol+xZ6emxNoROSBiMhTMMY0TVPXNQEho00Cms6a2hA4FrwUoKQQDJTg3lHT1MYYKXIETkTO+a41KFouVEDZG6UXL12TXLRtp1UWAqyW9eT0TErZdR0iJqnSWvfKYr1cVKtFrgUEJzmeTU7zNOEIiLRerxfrRik1HA6NMZPJ5ODgoK7rfr8/GAzG4/Gm8br3Ps9zznlrHGcSuZTSk5fBW/IGrCfwiMgYMGKCPAJjiIJxxkKqZFGWzoXZfMFVPRiOnDH37x8PRmMsZJqXPsB8ufQEQqXEOSEDYpsekIyeZj49xGn36JnEcI9+LcgZ+U21eyAi733XdVVdn02nOcc0UVIKrTVjwYK3zmZZIiQPwRF5REySRDImpfIkGUpg6D3Udds6Ms4njfUE15+7IaX0AS0Z52zZGwzHW0cP7i6Xy52tkRbSW5dl2XK5rOva2t5mzH56fCL2901bA8BqMTceL1y6PBwOJ5OJMWa1Wkkp+/1+v99P03S1Wm3mZDjnQogsy1BwyZhgEikEb51BFzrn/aarIlJAJMaAceCcCS4SxY8O7nnCwWgYCN+99U7nqNcfX7x4kesUhHz75q337tx5TkguFKEImyH54wE7MSAGAIw8xMLH6PMQwz16So9G7wi0aX+CSAAIAsBRQCIiH9A7sibY9o3X/ubCaLSzu5VIZWztTJekusxyziDVKkkUku84IkGSqiwtqsYQCu/IdW3TGN92bWdl1Rwdn/TKfq83SJIkSbJuMgmMcynvHRyORoPnn3++LEtEFIyvFov1cgW7O01VO+uXy/XOjt+UzTRNk+T98XCkpTo5OQkhEMM0zaROiQviwgRKk8yFeWe9J1RJClwBQ0RCcoyIoWTSCwzeOiIPAEjAAAUyDsg43L17WwgFjB0d37fG6TS7fPHCcLwznZx65OVwq9/vX7x8ZXt3Py17gQl6ojYmAHvcc+AjsR7Pp0afUQz3Lwh+yqTp437cj6/PP+F+nm1Q96zXZ+xjPvszCpx5IvIEnsB5DMgCMACw1gTntUoUche6ZrVcHj+QttkflWBX06OmzPMkSVLFM63yLGnqddcBAGgts2IgGEPEzpNI0qZty0FvlG5NTqfz5aJz3fJsubO3/+7t94bD8Utf/5oQIh8Ud+4fkOLXblw/Ojmtqubq5auTk7Obb789KEeT4+nzzz1vLQEIY8PJ6ZQxZnyXZiUFkghvvfGL5XwWOLfOjXd2969eHWyNSUqZFe/dvRtsUJYKqbTSjvO8LFjws7PJerEEcpzI2yCllly83xWdIRB5Y/rjojM2BD/sF4P+KM0L76Cu50oKneWBfNd1vV4vL3p1YyxzIi8ZItCm6pwBIhJDROc8fuCFQ/5whQF+7Daqz9qf/VP7sH/Ipm3Dr/++ir54Mdyjp0LgCQgAETlyxkBspoAlF23XmdoQBl8t19PJ2dHB0d33ikSmhRZCEBGQ1TLvFXlRFFIwIgIg7wkxMCWkkFKK0Xg8mU2Nsa6utdYXL150AWaz2cHBwXNKD4dweHiY53lRlls728enR52xg9EWAKxX9c7O3tWLV+8fHHStObh733vI0mI02nKelvNZXpZKCAkMCSCQMUZmWX842trZG25vAbKq66q2c4BCKELORZKVZW2M4BK5EEIRMme88SZ400tzDxCs8RQQUTAlpWScL6tVQOAorG1X64VzTqdZliaLRWs6kZTJ/u5eG0gnKTGudRIg0Ps75wUiBo+Smj5wcpXFwXv02cRwj55K8JuZGMaQCeDv7/PpDXnrTFvXi8XJg8nx4en9e5MH93tF0S+yTdVKCC7LsqIo0jQdDHt1XS8W86qqEHGz6F8ptVgsh71BlmXrpj6bzAKRUrrfH2Z5DxlzzoUQ0iQXQuV5Kbnqum587aqUuq7r3d3dl156aT6bVVW1WCyKohBC7O/vt13nnBsMBuR9nmZcCutCa2zW03t7e1tbW1pr70NVt3XVBg9cSiGUUqosS2gaRGzquqpWVbXypuVICN44C0DIhUBkjHEukHFiLElzYMSAc843XYVDCOD9arXy63bA5GCrkA7ras24LEcjEAkRcdgMgAkxfOzAPIo+sxju0acLCBQCAgIyxtimNDtQgEBtU4WuMfVqenx499Yvp8cHtmkw2CRNhBCbvZAQSSm1Odfqg930Ut+cutQbUjkfNldIVXrlUk7Im9bMlovt7Z3jk0mitESJgZqqzZJ0Z2fnvZtvSSmV1sF7wdXO/oXtnZ27d+6cTWebXTXyMm87a4NPkiwE1+/3ETEgbI4o29vbWuv1uur1Bpy3IQRrbSITpZTQCgCSRM+Xi+Ojw8nJkakrwSHRUjKxbmqlZJqmWidCCB/AWmudT3Pd2bYzHSIK4bquc4ulcTQc7djAdSKFYJ3zGAJnoKUw5BluZuEYYgDgm743n/wifDmlMnH65bdXDPfoqXjarD0FAGAE5B05R8Hber2eny3Pjo/uvXd851ZXLbTgqeLoXVVVnPM8z7WW3vv5fE5EUm3mImizGmi5XC4WCwB4/oUXm6pdzldKp0VRcKWTJLkyGEzP5lrrNM2Xy3XX2bzXH41G4/F2kmRVVTPGBeebPr17e3uH9+9zznWacM7LMj8+Pm6aBhEHg1FV1YzLJMmyoqfTTKsUAL33mwMMl8K5EBCTNBNSNW3rgWazs9PT06ZeK8aUkowhUWCCMyGQcxCSON/sgocOORfcCeONMQaQMSaQqQCs6xqhCwh+vVosVo0nJoQAhsXWFgS2OdxA2HwqAiB6ooNPFP1aYrhHT4MRIRESMiQWgvfO+s541zar+enh3Qd3350eH7TLqcSggIKx3oamaRhjUkohWNM0y+XSGAO42TtapmlalmW/3+/1ekmSrtY1Y4Jz6Yw/O5t11uV5fuHS5TzPu6mdns2l1CwVGKha14jY6w8Xy+WyWudJulytnTFZ2St7vSvPXZeSY6AkK3SaD0Zj5IIQJrM5MZ5k6cAPlFLEkHNeZAU8PHfNgKGQUmcpk6Jt29a0XddJxVM9SrRiEGzXemeKsgQIAVjbWcY8E5JLJbUickpKShLvfdN2QoT+oFf0BoFYMSh7o0FnQt10iUjyTHEOQB6Qbc6GIvKH63zfP4sePjT1/mX5TW+0Hf3mxHCPngoBI2BALFAI3ruuNV3ju3Z2cjg5uj89OqxmZ6FbK8UCsbZtXQDviYjW63XXNc65pmmapil7edu2Z2eTtm2JSEqZJIlS+qWXXwFibdsqpQejYQhw9+7Bu+/du/HiC6tVZV3Y29tHYM76xerMBzMYjA7v33lweLw1GgTnu6Z2LozH20mank1PkUAXqUr01s6eJ1zXbZLmJlDChE4znaRSKiDGGHM2NFXbdZ3czOarhHEhJLFgy7LMUp1IgUBtvbZSMA6cc++9d+QpEAERIuOMYb1eKik2nwOc90RgTNu0lXXgkVlH69qcTZdJViZaZoIzoEAekSPBw2l3ACD/5Lid4Il5+Jil0TOK4R49IyLwLngbTBdMOz05bpbzYFtyne8a4wA5OuNsQJmkmx3siPxmq1Lv/dHRERF577z3zrn1eu2cI4K/+ekvLl28muc5MpHneUBW1TVw8cYv3/7Wt78zHI5m8+XJ6dnexQvBgw8uybNAOJnOGGP9smw60zVt0R+0relaOxgM8l4vXSxSoBBCVuTOudVqZb0jwCRJEp15CnXdJpmo67prjVAyyVJUglDIjBUStJYMiDNo68raTilR5sXk7ORho2MiAEaI3gXCkCW5lkwpRf2+sbau67ptZ7Ozohw29bptzWrdTOfr0lq6sFvkCVBARCQEhg/H7BiAGKMPlroTi+dZo88mhvsX5Dddn/5w5eSjht2fev2PrWf/FaRUmx/hvQ/O5VmSK17P7eX93cXRna5aJpKJsvBdA8FLyZvK5EqlaWqMsbbbVI8wxowx/X4/TZPlcnl4eLhcLrXW/f5ASXj75q3lctlaBwDOU5qmy3W9u7v7p//9v/7jf/xPv/GtV1988cUHx0d5XjpvrAsXLl/yxhLC6enp5OR43B9wIaRSRX8wGI8o4KWLV+bLWZ7nnoKUcl3VSZoxoVSSrdfrvOxRwLZt5/OFECor8qI3yPKyrlsg7KyTXCKFrmuscUmScSRjTFn08zxP09QFv1yuF6uVcwGBkjQhb7uuQ8EZY1pr4xx1drVaqDQj6KyxRabG/UJAqJezRKrOMyIQUqdpKbhyzneuk0JzgPBkQ/fNa/ro9Xo8H/LkK/6s75PP8D58WKb56Ob4af3ln+mnRL8JcbOOL0jdds90fcTPUh3x9OH+rL9+3gcA2GwngRQkY+Rsu17cv3v75MH9ajHlFLw1tmsheAI23trpD4ZJknjvu67tuq6qqvV6rbUyxmitbty48bWvfa3f7y+XywdHJ4TcEWZFYaw/Pu0WHSHZotdDxh4czV97/fUf/83fWOfzXi/Li7Zr00R1pus646y11q3XK8llXVcEILjo9/vI0Tq7XK+FVCrVUqnWmKLobZJXqUQIHUKom3a+WKlEj7d3t3a2kcv5YtG03dUrV60zprNKqdF4tLO1lSbKmm5/fx8BTNd556QUqU4Agu2MN50zJngPRIAQiKyzPgTkLFE6ywqVSAxgneGAUqm0PyQhtJBSSobcB2+NddZJqQCANrPtD8N9Mz3z5cTlsy56evJv81R/KY852ogj9+ipPDlgdM7Ztl3Nzk7vHzy4f7iaL7z3Hsl2neuMZygBnXNt2zLGNrvWee8RUSmltPDeb7rsXrp06cKFC6PR6N337r5750ho3N7eTosZ4T2dpsiYzvL5fL57cWexWJ2eTf/L/9u/cMH/4R/+3f/sP/tfJHkWgtcyWc1n5HzbmGmYz85Oe0W5tTV2njaV+YgopRyPtufLhUozoXS1WASoA3HnKQQKwBBRySQEmC1WTEhgfDQc/fLmLQhOS0VE1fGErIFgrWkF57bt2rbe7L4NwFzwAoisYwyQMymlTCQwVInMnCeArjPBtWlS5CppW1Mv57WxcrDN0iJJpRAciLzzRMA5g49sh/14p70oeiYx3KOngsiRArngrGmqarWcHt+7e/Duzenxg7quMSAxsj44F1ByhjiZTLlabUrOnTMAoLVO0zQv0iRJGMPFYpHn+bVr1/b393/nlcWdw8npdI2IP/vZz07Pzp67fr21znmqm26xrk4Xze//7jdfeOGlv/npT//7P/vzs9nkf/mf/ifDQbkz3rLWmqrhSvsQVlUjuCICa50nr7VGLgk5ciYTnec5Aq/b1jsSXDHGAaCzXut0PN5OypxLbYNfzefL1Wq1WJ0cHy1mZ8z7RLEy1XkqJWOLswkD4oibhaOMMSGUEmLVNkIIzRkSCGRcK50kGYJ1gWjZtja4VuuCpbpZN6vlbDo5SQZeSSmlBEDBgCnBuHQfXe5PLGDgMd+jZxTDPXoqjLHgPAGEELy3tm3Xy/nZ2aRar621HAgD+ICBSRCSUAB3zrnNVLv3FhE3lSTb29tJkjhnN7M0bdvu7OzkvUE23L/a+q7rDk9O3rl9t7+1pRtzOj0bbu/osn3v8Kzz4evf/NZzL770L//lv/zpz9/M8n/1nW998+987w+klEHYsixt2yRJMhwO0zwDhq5zWmvGWNu2p6dne5cuMi6NcUolRVbkeaF1yjnXnoyzbdueLZYesbPm7VvvHB8f/+n/50+7pjV1pRUfl3mZ6e1h7/L+blkknILgqJRMpOKcA4CjUBSlVCp1qaPgyUnyKpHIubFtkiQcedua2s6VSvJMo+ZtUzGd27YzXHGuCD72xCk+rJJ5qp7AUfQBMdyjpxQAgChwZFJKrbVSSgjhnPMuEATnnXFBcA5cBoLNlnXOOcYYAGeMpWna7/c3+9gpJcuyNMbcvn0bEa88d2MnGVtki9l8/+KF4Xi0s7fnXOh8OJlM8rIMAH/1N7+YL/5P/dGwarvt3fFPfvzzrqnzPL92+UrwDpDPlivnSSZp8GCttT4QwzQruMC6bUMILnjvvUr0YDxKlfaeGGPgfduYwwenP3v9F2/evHnn8ODtmwdNB3/4vW9e2O+11bpdL4Izd+/cm0+SQZFJDIliiimypmnbzVJbFNwrzRgL3jvnrGXomCTBGRNChBB8cLZrlNCJFIKr4NAjBu+apmJMcBmQSQBPaIVM6AMpj7EKMvpsYrhHT0UwbjFQCCG4TbfbzX+e0HhH1npngrVpojyh81ZK6Zyz1jLGEGEz953n+Wx+ZozJslQI0bZtVVVKKZlkL37zD0hpIdSlK9eK3s+A8dF4bHxQWXZ0ckoA/V62d/GSVIoLtZhP2vX87bffSZNk8Me9IkkAYDqdSobOubprE59syoHSNE3TdLZerqp6ua45YlmW+/v7RDQ9nVrj2868/vrrP3nttZ/+4rXZcrm1u/NP/+m/9+qr3/2H/+AfrZfLH/z5n99572Y/TY4P3lvNT/tlP/gOPJB1JjhrDCJlSaqVgEfNAzbbsTLHvfcCQ5Ko9bq2tuOcaS0ZAySPxEIIbdsCIRGmeV9pwRiGj5SyxMbu0WcWwz16KkII562z1lnbdV3btk3TVFVFRN6T7YyzLQavlAoBjPVlzgOh9+S9J/KM+bY1XddlaWFsu1qtOOdJkkgpj4+P7xwc3Hlw+q3f/d7e5av/bv6Hb7/5CwAP0GVadPWqlyVfe/7yzs7OxQs7xphRP08kunr93t2Tn//sF3/vD/+o3ytTrTjQYDCUkjMGWmsXnPMeg9ccXdjssuGFyvpFb/fKVVd367VtW/MX/8O//fFPfnJ0drqzu/fi177GlQjB3bnz3mu/+Pn2YNzvlzdu3NjbGiUaj+/x/ctXgmnbarFuVpKxcjTulbmUkogWVZ0gEgNJgVsmgwtALgQpNRLkeckIbGcXi0Wq0rQ/qrqVNSaYhjNM01wJCUIzQsLHte0BABgRYHi4d3YUPYsY7l+QX11f/Liw7ENlxb/imh/9/kfvZLPB6Sc8Hv+h+3y8Yd7HXn86n0opBeeeMwCo62Y2W6zX1Wq+omA3DcK04MjIeuO9v394xJig4IkoTXVRFMa4t99+Z2trZGzLiLTWpq2995xzqfX9W2/lmp/evfmNb33zf/U//w//9/+H/+PO8JXdK3tg6sls/ve//900TTvrneJ12yQMB72BZifb2/tapxwwL1JjWynA+W44Gtfduj8c37t/8PyLL3Te52XPuK43KHvlaDTchqwkgzwtfvLX//b/96//Ymt356W9/STTq9Xckft//qs/5QzQ2qtXrhRptr+/kyZquD2+/+AgHW99/cUb777z9uT0pNcrer1e8NZ7n0qRjKy11jnnuk6ikgHJcd8SOse54IxZa0MAQN551y1n3oHKilzqTBAjZ13HmUYurQ8EDDd7ZpNHJKQAAIEofLA69le/Xp/6Tvts79uPvsc+l58S/SbEcP8K+cr+ShBCr9fz3puuQcSy7NOOme/uLs5OqsUiGOu8D96TQ8bBe+tcKNK8bQ3jstfr5VlC5CFQliWIWBZ9LTkA0aNhaSAP3rzz5mvOufVi8tJLX/vf/W//8//z/+W/LJ67cWF74Lq66YzmmGdF8HQyaZt67a0jgkQqjkxLVa9XiZJNux70U4DAGHPObBqWSSG1Vs63bdctwuLypWthvV7V1Y9+8vP/+7/6r5rOPHf9xmjcX6ym3nW7+1uXLog7d923Xvnacrkc9IuLF3ePj49v3ry5vbvzrVe/nefpS1lyvW02SWu7NoQgOTbrlbdd13XeeyaEVkmqM621UJIxBsSssIwJ5xwiouDdqmG+BdcGb4K15JznntAT8IDAH47TPULAh/3c+Wd//aK/lWK4f0GePrgfb8HzZT/kD9t0AQME6qy1drVazWYzzjkheu+DNwGBcSDyT35aICIfAuMohFCaN1UtZSoT2XWtM5Zx2Gw/lCTJiy++cPPmrb/6yx+ul9WN51/43/zn/+u//KsfLZer3d3drMgD4eHxyXK1bNsmUezd40NPoBilkgtO3bq5duWykCAYSo6CYVetyyLjCKnkaaqF1PPl0lovtkZQ256H6enJOzff/v7vfy9NZJln/UFGzgRvX7hx43e/06/XqyzRo35vOZ+enR7v7+587w9+n7yDQFvDkRDMO2utJW9DCCG4YE0HIXS26Vpft3XdrutGa72Z30eAsLmec0QEngEAhE0pkXfeMG+Y35yfQPb+tAwCMCCgZ38v/KZXrkZffTHcvxKe8tPuF/lIPvhdsNZKKaWU1tnNbLu1lnO+2RTbe++dY5xxwYWQAAw46/f7RN5ai0i9Xk8r4Zzd29sriixJEmNb07REgTGGjLw1p8cnL73wYmfNu7duE9HJycnlixf/7t//Bzd/+fbPX//FL15/851b79ngGRNd23z/976zXCyuXL7wwnNX2qZaWjPo54KjtUYKzhguFrOLwyu9TKd56lzXuSARq2odTo8Y6unJdHp2nEp2cnz/d77+/JVLF5p23e2MkNH3/+D3R/3BsBiWeS/RsmvXq/mMIexsjfd3t6uqCt4aT952zrkQnHPOO5OXhVKKMYGIXbfp6i6klF3Xcc4Zis3pZWttCAERhVAPO0CQh0AQiEF4VBizaQEMYdNT7Ct3lI9+O8Rw/5I9GaYfjPivVqvVzWlDa43pOkQcDAY3btwoEvmn/91/55zbLEDlnCulpOSMCclVnpVCMu89InEpAwTr3fHJZL6UZZmnqXbebQ4SkrGrFy+0TcUD3Hv39je+9vVfvPHGYrHM8/Iv/s2f7V+89M/+2X/8H/yH/t3b7y2rGgDq1frtN96eHB1X1YqDmxwdts0qdIxxKopCIiCQbZtU8kGZCSWP5xMu2WhrzADnZ5PR1v7J8X0t6Hvf/dZPf/7aaz/7ycX9nQsXd777nW8f3L+TpUJxkWh16eLem2+++dZbb2gp/9E/+ocvvXDjweFBUeTOtG3bNtV6tVo1bbV5cpRSwYMPAMgJGBEEDxQweGCIKJAxJrnw6BCBEIQQyBnHzQebzabbgSERhYAP+8lE0a8jhvsX5FOH5E/fFubL4pzruo4h5nmuGC0myntvjPHWEhHnTAghhOCcAaCxFqqq1y8Gg4H3fj6fdnWDjCDQ5kNAr9fL0wKIrVYr7+2br7/RK/Ne0X/++o3Dg/t/9Ed/9N/8t/+tcy7vlYvFYjabDcfj0XBw5erl+Xz+5utnIthqcfburZtvXNqbn02KMjPglRK9IifvuBBZIhgSA2LBmraePJgAeSBxcnighTx9cH/Uy7/z6jf293Z++cs3//qHf/7t73z71Ve/VSbJ7ngwHo/PjiY333zj/p33/t7f+f6NGzd6RXZ8/6DMs5PjB8HZtm2Xq/lsNjPGFEXR7/fTJEfOgvNCSZhD27bGmM0LqpRSSm3aNnjvQwgBSGogohBcsC44451hzgATKBgARwLCAEgBH86+P+s+qnFaJorh/uX76szJwCdPy2zOBGqtWfBk3Gw2e/fdd19/7TVrLYSwueEmv5wLIQAQ85JEIxmvvPd123jvJOPeO09+vlgxzgf9vtRJ4oOpqvFwazE7+9EP/+rV3/3ufD7/v/4X/8X3//APK9MmWqWpJrInp0fu6P54eytN09GwPJaoGWkO05MH1hohAiNIZAk+dF2jdW9re4TBV+ulUoqzkEpRrRZZWgQHi7NT8N2LL1xnTPxP/qP/4MHR0etvvHbr1s1777093tninJ8cHf3R9/7O1cv73//ed5WSq9Xq5HhZ1/V8xu7fvy8FM8bM59Ou67IsEQyttdP5QiglOW86WzXtarXaFALt7e14etioIJADDAQeAJw3BCEAeJTEExAamQqEWkgGSIBImw9vLDx+DaLoWcRw/5J9bJh+BadlOOdSSoRg6mq5Xp+enk4mk+VySUTwqBlsCMHa4JxxLhS9QVrkTIrlcu29TZJM9zRDevDgAXm7Wq1ns9n29s7O1hZDQYT94dDbbmdn7+bbt3qD/osvvnjv3r39K5e2RmNiCERtVXXOppkK3q4W065ZkzfDsuyamsgvpm2a6u3tMREZYzjnw37fOFutFi5NlZBlkU8mk1quA+Dh3YOTydmVy1db55tqcf25y5cv7Kj/8T9+8OBwa2ccnG+aplosHzx4MD2Vd+7dXi9XWZZlWVbVK601ADOmbdtWSj4YDKSUy+VSaJ+XPZGpLMtCCEIpBiCEkFIppZIk4ZxLzzdz7gRQtQ0F8gEFdkzUrE0YTxmyLCs8MEQIMc2jX08M9y/IRwvYf3Vr7F+nfvnJH/FJfdufrHN/GpvtRheLuQDabKtUFEWv15udnjJEACQKzjmBTGudpqJp2844rXVZFEV/oJQgInL+5a/9jrMdIiaJYkx42wHAaHsny5LyuRwAx7u7gcgDjTgCZwCQpenk7CzJUin49OQ4z8ujg3uma8i7fq8IzqZ5liQJMdRZKrRquvb4dMKlGI/HRNSZhkKYn00FMmcMMGHaenJyPJ/Ofucbr+RpkqQ6GQ39YiaQvf3WL1eLRdNURZqFEB7cv5cmqki2VqvVdHKitZZShuCEEL1ez3vbNI1S6sKFC3VHxLjzQWk92krzrlstllW1attW620pJSL6gIwxb13TtUpKjxBC6JrKeQCmsryfp9qZjrhH1EwwQLaZgw8hCP6bXcTEPqd+8dFXRwz3L8hm1uJjv/itsNlejjEWnBVCFEWxGSN3XYfeBG8pWEYBGTEGwMTWzm6a5FmWPew/471SQiXae6+TLE2SzXlXgOBcINuKJFWSc8CqbarlsjWNEEKlWqfJcDhUWqdpyjg0XVfX9Xq5Ws1nFBznEhAZYwGYc75u7NZeX9pMKSWT1BE4Zz2h4Fzyhw8DvLe2g+ACgWkbBL+eny3v3rl16+bdg3ve+36/LPOi67pEKqEE51wIwRgry1KlSdvWm12/+/2+EIJLJpgMyHTKkizP85JzdJ0BACjLNE2bamWtres6yzKtNREZIkTqTMOUFkopoZlSnIGzXVOtk4IDMoBAxAAgIAQEYo8maaLoqcVw/+J8bL5/hjv5Uh78pqpPCGFMtxm3bjLLGMPJIRDnXHEpFZeSM6GklJ5Ca7pM8KIo8jxPkkQIMTk95QqN9VVdEyHnaK2vq9XNmzeTROdZJoTgHJMkKXpl0cuPj06mZ7PZfCql5HyzEx1sNq5TKvEBOmu49SBCAB6Y0Fkv45hlWZLoEIKxBMCEYFJr51xwPoTgvAEI4F3drH2w9Wo9mZyt12vJhRLSGX/WzrJUZzrZdMhp27brutYaXldpmgKi4DLN8qIomEDT2tpYlaRKp1IliORc8AQueBe8TlPbdXVdJ0milbLIAMLm7AVxRuSd7ZC4UJZB4AyQPHlHyIHxzewc4Vdgn+zot1AM9y/Ub+mwHQA4Z5sOiJslOZzzTXsZrTULjIIDcps59xAcWM+ETFJRluXW1laZF8652Wy2WCwE53Vdu87Ude1cQCQidM5kWWLq1gTo98vd4dZ4PCrLXGkhGDemTVKd5/liMTs6fEDge3lR65WWyapam6ZhnQMVirKfloPW0iDvl8OBEKxaLx0wRCAmlUwaqDcriRBRJxKImabelLWE4LSWACUibiadbr5968LeTp7niJimad4rt9JUJ0nXdYwxKXWS6STJgINSQfogkxwYZ8gYY2nCQwi27WpTK8EDwMMzAWzzTHJOgQtOyHwABwG8JWeDs+B9cJYERyIgD4TEHtVF/ja9WaKvhBjuX5DHnUA++sVnuJ+P+mIOFZuZ2a7rlsvlarXadBBjwW7CnVHgAjlHYGK0vZMWuUqTxnTrdb0Z44cQvCdjDPnAhUoUJwpSKq1VXmYAICQriiLt90WSWELbWC1d1zQhBNsZ2xkkQgAtVZZlSZJ5RBvQITrgOi+Fzpa1KUZcZgUXDI3FzhP4AAyFBGKAXCqVAg2Hw856R+Gdd97e3d3d398fDofT+WK1rDbLjvb29nq93uYfy4TPiEmhkySTUqtEp0mOAonQk+MSE83mqxqYUEIoJZCAAFBwIUTbNlJKxljTNN4ZKaXk3Dln2o5rrbTSLAUUiGS62i2xLyWhQO6QxKOFqQwwwG/VUCD6Kojh/oX67R25h0CMMSLinK/X67t3787nc+/9/v4+J4cQOCPF2WZaBrn0gD6E09NT5xw52tyQMbao6hBClmS9Xr8ois2OplmRz9crqWWitVSiMbadzsgHCK6uloIBUgjeCQb9sscYzLqZ1imXqiiFyIrpYm0DoFSOGGOSqZSpFCAQl1xrBiQYeRQAIITIsizLkyzLqqarqurs7CzNs15/2OsNpMrybD2bzVbL5fb2thKi67rWmKZplutV27YB6Ppzzyd5luc5E3yz4pQCABfLqs2KctArETE4a4zhXOZlr1qtOeeOgvfeO84YYwBEJKXknDFATw6IgXcsWBZcCB6DoxDgcRkSQwwM3l+/GkVPJYb7F+TzOqH6ZY3crbVCCO8MY2y5XN66dWu5XALAx55QJeSvfOc7hExypbUWQnRdt5jOlstlg0wqyYVynqz1SSIRubF+a29fKK6k5JwFZ+r1qqqWbVMtzs6GvXLQzyngpi98s67Pzs565dD4wBUWQp7O11VtkCmPrD8eF70hl2nb1j4wJrSUXCE0y3kAVEoVRcGVDCEkq5X3fjgeOefu3r3LhCyLflEUSZIO+v22rkIIiXO7+/tl2bferddrH8Lx8cmqrs+mM+Bsg3PJOM+KflbkaV5orSA42XKnVAhuKkTTNI2zQgglsxDcpp9MnuXA0BMFZzwFgYIRMMZCCBgcBCfIE8hf/aJH0a8Qw/2L81tdMLPZ1dqawBhrmurk5LhpGkSs6zULHoNHBMmRIxeCIZc//qsfC51sOrZLKZXUeZIO+sOXXvw6Y8xav7l5URRaa08odOaBPLFUp2l/kGclB3TO9QdDLjhjwoPrWuOSMBptjbZ2T6arZjZ3LjASdeWauuPIU5bsDbYHeV8z2ToKjhhTSmrFIZDwKDiXKusnSQIQAnE+X3GRpFmmkkTrtCiKsiwR0bbdcjGbTCbL+axuG0KGjCFjSZpevHK5brpVXRFAmhW9Xi/LMqG02ZyQAAaATCgmXFdVdbVSOlmv11217vdLIQQReRsghLZtpdZCKSGlJxaQWWtd0yQ9CkAPe2YSIWz22HvWBapRFMP9i/JJfbE3m3B+FOefNEL/+N9z9oll0B9//Y9t/v4rHr/3vmka79zk+Hg6m6SZThIlNF9MOi6QA8dA3tl13Wx21+uNhoTeg2UBwZFt3GKxooCLVdeaLtHp5cuXL168rNLEGGOsV7pI8ywEb2zDLAKXKisHARbT07PT47bSgqPk7N7BgxduvCiShKeiJwenJyc3f3kTvDDL7p2f/lK+jO8ZWO2cZr2yNc28Wl26fLksh2+/+QY5kRQ7ZV70x1tJklTVOgP1tcGOtZ3xxoYA3mHXts5aa73tJBcq0WmeTefz09mccZHnuUqz3Z39rL91KUl0kiqVcM4RMSBUnUkSRYTNemXqmiOUZT/PsmP7YGtre8XFbDZdLhajfq/Me8i4aZ13TFhGPCBXQjGVJjotVqsVKC0TFMQUCERkXHwBI/ePDjI+1A/jQ/XvH1qlET9bfAXFcI8+HRIopRCxbpvFYrFYLGxnnGnrpvLBBhc6b8GHTRVNLy9RcC7EZgmSd857IiJPCACdNVIomaRn88XRZAYAw/Foe3ffVB3KpEhTrbVt1sdHp6enB8GYfp6keVnmGUdMlOKcj3f3uUql4zor0+zOzXdu//L1169dvHz14iWFXAIbpD2lNEcMyKqqefONt954463t0Xh7a7x3+fr44j607bq1TFJRZFW9luCRApMouUCO0qsQ0rZuRJL2xzwpytZY74mAIWKS5VxInWRpXmqtgXHbGWeNlJJxiQAqTTiDpqlPj08nJ8dFlpwcHzXrKksTLUVVNU3Vpnk23tkmAALuXIAQpEYpdJIkDCRJxZVGITYf7Lz3AEF92e+Bj39jxEz/CovhHj2tzRlRhpQqNRj0zHoxbaosy3zXtrV1zm32ESWJUojLVy8TInIupRRSa6251EIIQkYBiHEiqpuubVtr7Xq9rpvpcjnP8iTPE/Suqipng1bq5PRMCgZCAxJTSZplHnhn/PbFi/uXr77wwgtSquODQxf8fLnuOqvTZL5crI/Xnbf98YgL0S/6v//7vz9fLibz+fQnP756duXqlUtbe7vGGOdNytF750zbmKYLVmhR5nmapvfXh0zoPMkHUiIXROist8EfnUyKsk9cuaperNaeMFVaJrqpV4ColVBKEYJzdlNZNJ/PGWNCiKqqDGdFURRZslkVBYgEnACAiU0ndwDIsiwIiUKjkMS4RwxEEAIK/rnk6OcVx5/czTT6qojh/iX7bfl9EJxb09bVqq3qx3uodl1XtU0qxWAw6OVFkedpmgohkDHjrbW2bpqzs7O2s845FwAALl6+0nUdE3Jra2e8tZNlmVBSqQSYXK5WZ2enpyd2UBb94eDylf3RsE+bY0bwxw+OGOLO/qXxeCx0erqqjk6OB73+8y+/9Mqr3/of/r9/+uMf/zhR+j/5n/4zmei6rXSaOOfu3rzZmm7v8sUHpyd7F/Zffvnlq8/fgEQBBXP//uTsFIOXissky6Sytgsh1J13ZPYvXwsASICcAYCzoessdJ1Ky9lidXB0yhjLsyJJkhDA2o4L6A/yXlFyJAiklLp06cLu7vaDe3ezJLVd8+Dw/nw667ouUSqgq5tG6kRJpqUklADQdZ2H9TAfCGTEGOOcGEdEAkYMv7KlMnFa5isrhnv0VLy31XI1n866tg7Ok/eJUrtb21cuX2zXq+V8sVguJ5MJIiqVSMk7ZxhjTHCt9Xg8LIpempdKKWC8ruvW2BDc5OwknAZkQus0L3uDweDF518oe/l40BcSq/WyaaqmMbt72xd2L1x+7gWObDTaXi4WjXPPf/1K1XQU/HBn6z/95/88S/N//ad/OptO7x3eB86MMb3hYNXU//aHfzmZnr30ytf/Z//8nz/3wvP5eAwQwBggnw9He0pNJycAoXO+7YztOsaYUCmXGaoMCBBQKCmE8I6orj1rh1u66Nu6rtfr9XrdnE2XHogjrVfTXpnmSco4aKl6vbKXpYyx0WjkrdOS37hx46w3OT5+UDVNX/WdC0z4wAMGCOABHBjjgbVtjcIJ4MgE45IJwRgHAG8MPksDgmfdg/dZ7wee/bRN9AWL4R49FdeZ4K2WPMuyNFGbjeCEZD//+U/JefBBSln2e9vb27tbu3kvX69XIQTrnffe+RBCqOpVVTFCZq0FxoUQD+d5uFRKDAb9fr9H4E9OjibHR1JxAEKk5WJhnD87W8/nc8nl888/37am6PfuHj7IyyJYszybXd2/+Hf/wd/ngr3xs9eK/oAYOgpCyX6//8rv/E5eFN/9/vcvP3c9GwyAMTAGGAelKNCyrplMjO1a0zatCy4oJZxHG7BaVAGZ1rrUWZqWkjGmMpQVk2bTR6GqqpOTk9ls5pwXHIAMY74zDQAgkTN6YY0xJpXCWkvWEJFK9P7+Re89YdisbLIhUNcFYEyCkMQYa+saVZCoiHMppADOgTPGvmrlMh8K9BjrX0Ex3L9kX+6K06fECIBBpmTLsV6tJidHs7PT9XLOICRJkidpLy/SNA0hVE392huvN021vb0lFU/SPM/zQdlLkkTohHO+s7vfdRYYL8syzQvGGDImpeZcJklibbdeL5umWa0XdV0BZ0maqyxXaaatt9bPK3Pv3kHRKyzzuxd2M6mtDw9Oj/N+7/mXX27b9ux0sq4aY9uu64RQL734tYsXL166dg21tggK0DJGzguDTKV7F6/cee+2SLJx2UukYohd1zXr9XxV71253hrrvW8c8M4zRq1xtfVlv79er7vVSmt99fr1S96fHE+mp4c3btzgYGxnjOmUEFmWIQVERApa69aa5XIpGC/K3Fo7m81GI80YQ84JGAJDxjgSAFlrGXDkHROScYvISRB6/nm9Tz7f91vM9K+yGO7RU8AQTBe8XS8Wh/fuTk5Osyy9tL87LLN6tfbObcofEbHX622Nd4RkVy5dQkaAHACs88aYpmmcc5OzWV3XdWe8Cy4QY6zX74/H4+3tXQBAxLLM8yLNsiwgCSFGw3F/OCrLsq5a52h7d18n+WB7aMhY1ykudrd35pOzrJRXrl51zr379tvB+WbSTafToiiuX78+HA6NcaNBf911TbNSSkghrffBWgjhua99HSBQ05IPTMqSI4RAPnSBdau6Xa4a2zrgeZKiTJKc3Ts6ynSSpLn3/uR40rYtAIzH49npXSXIW7darYJzaZrmaSIYX1drY4zguLW1xQCbpnHW93o960IilNaaC+UJPbBA5KyVMt1UuJN33hpE9J4hciE+vl7mSwz9OC3zFfdbs4jmt92iqj/2+5/XHOgnfZ+xZ+sDvnk8zrlNYwDnnDFGc+bb6uzo+Mc/+tGbr/9MIqSJPL5/8N6td8CHQb+3u7s3Ho8Tnbng67rtusaaajGfzuZLIkqyXAjRdKau26vXrm9tbeW9vtZaSAUAUquy6O9u7xVFUTV1Va3STAPAfLn03mdFmSTJhf1LRX9QVw1yUWSlJ7dsllLyMs+d6dbzBfPU1dXx4QPfmbd/+da779x65ZVX+v3+Cy+8kKd659pV28u9EEjgg3XGhhAk40Kyw4P75F2SJKvFMkmSq5evcCEgSQCV9X7zbHRta7uu67qmWmmpnDFdU4cQGBIigg/BGxaqZjVbzudNU20qR5WQjLHd7fHZ2dlqsRRCpFqH4Lz3mxG6EEImiVQJEwq4dMAdcK5SrjOdFUlWyDQXUjPBCTnnEuBjXkrv/a94HZ/+/RDCp0z8fIY+7+N+8UzvvejzFUfu0Qdwzje/5yGETQN0733nLHTm6MH9zbC9WsyA/Ljf+4Pf+/1Bv7dcLk9PJ2++9VZdtwFByURLbk1FwQkh0jTtD0fD4TArSqXU2Wzetu3Bg6OmaZI0K4qi6JX9fv/+vcOyLAN4YwwXKIToD4eXL19erqv1en379m0UsmtNmheD3pAAjK+TVPnOKCG11qZqTs+m9+7dGw4GeVH2h4P5fH58fFyW5dXLF21ngtXIhZJSiBRyAoCurmfT6Wg0QsQ8SdMkI6LOuXq+sAAi66k0K7ICAEKoPVGe5/0yn5ycMgZFUXAGGKhp6lVV1eu5a2YcLGMgpfTWbXrpJEmyWCwQcbPwVQkBoOu6rqoVAHkg36EDVIQMGHC+aT8A3gZnfbDCu8AYYECGwH/NF/ZT/Iqw/u1dVv23XAz36AM2TX3hUbgDABF1XffLn/70zjs3Z6cn/Ty7sr/LKBwfHf70pz+1ppNSKp0IIfI890AIHDh861vfsqatmrrrus60t2/fXlV10zRMyOeee+6FF24opTrjmqaRWpdl+cL1lznnwEhKiYwWi0Vru6peJ0oBAAKCDxBCtVo3y7ru6v0LOw5h1VrOeZamQKS13t3dZ0i3bt06Onzwne98pyiK9Xp9cjzBNF1OTmWR9vv9Xq+ntQYAwfnOeMtayzkXeTZOUgBAKaXUJpDMimXdzeYLpYUUgnNerVazs9Mi0RR88IYgYCDftWRb8HY46HGymydt87kn0TpJknpVO+dqW1trSKNSyrgwnS+Hoz4QOecCGgoogHHGEQWQR6LgfbAuCIucs8AAPCMIH7u77W9+JuRj22bEGZivvhjuX5Df9C/D53j/m3bEm8/vUsoQQreyidZllmapfnA4OTq4S8FJhlrrQb/XdZ0PlCTJYLyVFXnw0LX1T37yE4abRZ0oVdLv96/duD4cjJiQBwcHP/3pT7Ms+/ar3/32t7+t05SIbOvX63XTtZyj0sJ73znTdZ1zTqukLPtZVmRJ6n1wLghOzXItenkI1IUQWmOtbao6y7K7t+/827/8i/nZ9OWXX97f27XWLpfLbL6ATFdTw2wQhA2um6aRUg4Gg+MHR4QghAgIWussy6SUMtFN03HO8yKTDJ3rvLVFlggc1YuF6VrXNsEZCD5457sOvZlPK2/bx9NZWuuu67quS1UqhJBSEpHkAgBCCCE4qTgR+QCByHtC78F7QC+kRCQAguAJPIPA8Vc1l/hixGH7b6MY7tEHbKZiHs/MbnqnrKvl5Pjopz/+0cHBwaBXFlm+mJ2tbaeV1Lo3Ho+TNLPWns0XB4f3XQAtuXNue2uwu3+hLEvrwmq1Ojk5OTg4eOUb3/zGN77xB3/nj7z3ddO98847rem0ToaDbaWUEGKxmM0XU0TMslRKvr29rXWK4Gdnx6tltV7X3pMSMk3TdplviilrxReLxfxsChDqdTUcjAWy+w8Om6a5evXq7m7pnJNBSwzUtWa9LstS5Zm1fnY2hUDOu9VqBY86sOdFT+pUZ1nwwduOSe6c9abt2rpZLqenR8FZ9E5wlijBJUuZdpoETxeL2dlkUtf1Zqeqfr+fJAkROuc24/lltwSAXq/34v5LJ8cPvPfOkw0eiIUQwDkgniQMkHFAAMBASMAYbCppPtZv+oTq43v70D4EceT+1RfD/QvyW1HyCACbSXalFABYa51z6/X6wYMHP/rrv75/eOCt6Zrado1S4vkb17a3t+/fv09E6/W6MZ31Li/L3mDYL4vL298/PjqYL1fe+xs3ni/L8uRsenh4+Oabb25vT8r+UEodCNfrdd02QsjgWZrmSaLatl2vasaJyDnnziaTXq/HOTed89Y755RKkjwLnX1wdlBVlVJqMOhJrXq9nlIKx+F73/ve2ekxItZ1PZ/PlRSt6fYvXx4Mh47wrDpuVuvt3R0ttTFmZ2vLOLeuK6mVB+q6brOZ9e3bt9dNnUi1t7uzvTVK+2XCoF0uzk5PuqaqVwtnWk6BIyBACH4wKsuyvHr1apIkmyZrbdvO53MEboyRnBdFIaVcLBZ37ty5dctduXSRMeIAxJACD4ghhLDpfB+CDxY8F9aQlOQl8fDl7qAaR+6/jWK4nxOf10hq86u7ObO3aTNwfHx869YtrWWepB2hlHLQK0bDfgj+Zz/7WZKo+XxprNvZ33v55ZfzsjyZnN27d+/03u0b1698+/qN+Xz+gx/84OzsbLS9c+XKlW9961vr9fr45KRpGp1miNgZRwR1E9brtRKs3y/7/f7e/vagX7Ztfff2HQYUnA3eMSDTNsvZvJovi7SYnJx11oxGI9bvbY+39vf3+/3+gwf3q6oq85xzLhg6504nZ+v1Oi8KDqCz1HtfrZbVepVmOTDkHAOCcy4vC+AshJAkCTDx/I0bBJAmCSLUy8X05LhZzNfL+YvPX1/N57NTuZxPu3ptbeet9d62qWib6uT4ARFxLouiGA6Hw+FwPNo+Ojo6PDw8mUwUV1rrIu8ho/V6jYwYlyj4ptjfAwKAtTYgsUAcEIEhF8CEABRJ+kyv++c4cv/YE6px5P7VF4/DX5BV037s95/1+f+8lpV/HAYQNm0I8yS1rptPZ8aYd9995y/+9Z/BenF6/6DMi9F4MD09mU6nFHzXdV//+ss6TTiXq6peVWshxPbu/v7u9uTw/r27t49Ojsuif+OF58fj8Wy2OHhwmGeFp9C2BhjLssI4Oz2bV02Tl8Plcrm1Nfr6Sy/v7G5JKar18ujoMEvSJEm6utssSqqqajlfKC5np4sQwpUr1158+YXhaFT0e8PxSCl1enq8Wq2stRxZUWbgQ9d11Wp9+713vffD4fDa9ecGw2HdtdZ5nqiiV3qEumu3trelTqq2GQwGQmqRpKt1hUBpmmrOrGmnJ0dHB/fmk1MgJ4C0FKkUDMk5a20XyFnbWWuJiDEBAM45a63p3Pb29ng49N7XVWutDd4baztTbSb3lUyE0oDMo/CEXCqPyLjmUuo0y7MiyXIu07Q38sgYsYCBEQuf9mp/UmnjJ5VCfuq0zIe+eJr326BIP/U60W9ODPcvyLrtNl98bDvsj3r20H+2FSWEAWCzEcRjiEDW2lRJhGCbermYHR8evvXLN+6+83YqGDh7cnJycPeOtXZvb+/yxQtFUUwmJ7PZjDF27dq1ixcvNk3z9ttv37lzJ9XJ/v7+YDBYr+vpdGq9y9JcZ6nWWkppjAtAzrmqadrWLJZrnqjrN25sb28f3r//0ksvnZyccEAGmCbJbDZDDyGEpq611saYe7fvFSp56YUXy+GICdza293d30PBXfBciM12gN654DwG8t6TNdMHRw8O7ndd99zzNyyFK9euMs7XXdM5K7RSaaK0RiGc98bZzoedCxdByEQpyQVCAO/ImuCdYvjg/qFrm8npMYZQFsViMdMSGTqtZZamnHNrbV3X7cMoB0R8WBgTgtZ61B9mvaL1tjWNMQaACaWF1IjcBUiyHJAxkXApBFdcKc45Mdnb2gMuBJPIgQEPRCEEIiL2fo0kPTF3w9mzHex/E9OGMdy/XDHcvyBfcLh/6sDqyXAPiLDpMQDQVVWWasFwNZ/cv3v3weHdarEM3tx68/WDu7fbtr18+fKlCxfbtj24f28ymexuja9fvz7sD46Oju7cfc8YUxRFkiRArOs6a71SatOMxfngvVdpQkSDwejs7Oz0bOK9t8aPtrde/uYrD46P1uv1q6+++oN/8xeb7ZAuX74MjowxHLhzrlqtvfeLxeLBvYOdwejVb35r7+KFxhqRqAuXLvW3RkxwZEwpZY1ZLBa2bhHRd6ZerqvpVElhrT04vF937Tde/faVa1ePJqeEoLK0N+hnvVJoRQjG2c663ngHhJScI4F3pqurtmlc1wrEC7t7pydHqdLVenl4cL9XZIf37w762tuWQkjTtCzLJEkwoHNuOV/xRwXswboQAgAGhuXWIDx6oQk4IiLXyJngGjlDroUQQmohBOeCuMj628QF53xzintTaUNEwN+fWX0y3J8x22O4n0P8T/7kT77sx/C3gnHPtpLw2b2f6U91nw/rLxAAaHMrAAAoskwKbrq2rlambU3XnB4f37p18+zk+OKF/b29vel0+tabby6Xy8tXLr/66qv9sqzr+v7BwdnZmQ9OKaW1TtP05PjUe59l+Wb9Ttd1QsjhcPjg6IhzfnR0zBhbV+uzs7NvffPbr3zjGzffvdUf9Juq/vGPftx15oUXXuj3evfu3fPWb5biV1VV1fW6Ws9n82q1Prt/5K3VUglkVVWtl6tgnTdWIGeBzLqeHp8uJ9NmVa3OZmdHx01dXbv23GAwPDo9fe/Onf5wmOX5sqoIkRgCY8gYMAaAjHEmJHLBOReMCyEQKDjnrA3OLebzxWwupQCiPEv3L+wTUJYl3nZSCCEkEXjrgychZZKmeVYIITZbXTPOuRRMcEQkhlyIzQazRBhCAORcCCEU45xxKaXkQgohhJDIOROaEB+3fQeAh6n7gWmWJ3usP+O75zcwh54o+bnfZ/T0Yrh/QX7T4Y7IPpTsn7LS5BPCvVqtEq1M1967897hwYGzXZakZZFvj4bv3nrn9u3bFy9e/O53vjMcDk8np3fu3OnaZrFYIMBoNOr1y02hiDEmzwvO+eO48d53xqzXa5XopmmkVL/85S/LXvlP/sk/IYKD+/fTIv/5az9fLVcA8J3vfPeNN96oqypPM2OtFNI7X9e1tTaEgABZkqzOZqvpPE2Sa9eu9YuyretqtW6qWgDWy9ViMp2dntWzRb1YtYtVVVXbO9udMT/5m7+5+c47r//yrf5gEBhIpYqyRM6c95231jkA4EIIqYBzLmSitZZKcc4YCkDO2aDXmy8XkvNVtUaEIs/raj0c9CSHfq/cVOx45zaT7wBQrSoiStN0PB6PRqM0TZ1zdV2DYPBoMUEg2IzEnfdaJcgQmWCMMb7ZxIMBY8AkIdu8po/mzREAAGO4Rx8vVsucK79+C6fxeGA60zRNURTi4sXp2fFbt15/843Xcsm/9rWvSSnffffdH/zgB0VRJKlGxKqqdnZ2siSdTqdn01MiklI654JvOefeu67rhBBKqc1kNBJ1XbdYTP74j/+4Pxy8+eabgqvO2ftHDziy4WjIOf/Bn/+b3d3dftm7e/fu1tY2ExyAcSXV4y1nuej3+7Ojk5PDB+2q2hlvKSkXi8XqdKoC1nXtOkNEwdi2bdFTlihE/Msf//UPf/jD0+lZr98/OjkWqT6dTb/RL30A5Iwz4EKEEBCRca60FlKJh9tlPHxKETHR+tq1a4d37/T7fW+7117/xaUL+8a50fYWeWetRS6USrz3mznxq9efWy6Xy9l8uVpprcuy3N7Z2b144eDoMAA456SUSmki6iwZZ733CITgGWPe+02YB/AQAnnPGNucUXj/QP6BF/rJr+N06992ceT+BfnCRu7wxOTMr6pK/oSR+3K+rKp117bVenn7vfdu3bqJBC88f8Ob7t7dO++88w4RjYZDznnbNs65l198sW3bB4eHk8nEebtZiISIp6cTrXWeF5vpZiLyITjnzqbT7e3tP/zDP1oulwf376dpeno6sc7N5nNg2DSNUqpt2izLTGd6vZ4QUgihpBKbBvCcO+fq1bpdrn1nrPec836vv729XWQ5ArjWrBbLrm4kMvDkWyMYy4riZH52+/49xvnZdPq13/k6Mdzb33fe570CEEWi8qLo9XpZkas04ULqNOVCPpzkZig4k0Jordq6rqqKMcyzPNGKMeY3Z0qVkIJLoZChDx6IpJRZlimpAAAZ28zwbAbdPgQuBeN8swOfUolSCplAhoyJjx25EwrCTbY/HL8DMkSkTwj3Z31XxZH7+RNH7ufNr7mAUGttuma1Wi0WC+ccEZ2cnNx+753Q1lrKPM/n83m9rnZ3d3d3d733R0dHy+UyOL+9vb0p326aJkmSq1evOufatkXEzeL7qm7W6/W3v/udzfCfiIwxJycnDMXZZJbmWZplVVU9OLj//PUXptNpXdfb29tZljnnkLM0z6wQKaLQyrVd6PeKojDGHJ4eZ7duBqDRaDQcDtfLlXPOdh0DDOCllIJzxpgNdOHylf1Llz3jvcFIKLm9t98fDkBwnSZZVpRlvyiKJEu5ksiYEAIIIYRAhBQAQW6mwRHbtt3f35/P55KzC5cuvf3WGwhUrWbjUW84HCvO6razzgMyxoXUKgAZZ7uua9pGOJuzPNEpC0wIsSlY3DQt4FylIg0ekTPkH0Cc2xBgUx7zwaM1ftLIPRZK/K0Xq2W+IL/pahlEDh83LfNJ9c6fVC2znE4Z0v17d1//+U/OTk6URNd2pycPUo7VagEAZVlqqZqmsc5wzsE7ROyatqoqZJRl2WZwDbQZY/Ku61arFef82nPXn3/++Vu332OMCaEWi8XJ5LRtW63S8c727bt3dJIUeY6IzgbO+eaQcPnyleVyybmUUnrvy7KEQIf3DqaHxxLZfD5fLBZayDIvLly4cOP69atXr3Z101S1aVvTds45BghKTH1bjAbWWqmVtbY/GnIphqNRUuZMCKlVkqVpmuosVUpxIZTKrPfOGGOMM50PFgMR+SJNR6PRdHKyXq9t1xJRkSXH///2/rXJcSRJ0wX1YmYASLrHJS/V3TO9s3NWZH7n/MKV/bK7cmRPzdTkdFXlNcLdeQFgZqq6HxSAMy7ZXdldXZlNt0dCKAwGHQQZ9NcUaqqvfvvnb/7H/+/LL954IxUiTtOlTLOI5JwP+91+vyei6TKeTqd5nsW0G3ogQsRaa6mGiKk/dENvSkBIoQ8hcEgxRq+WuRTEmGKMfm1ERAYEAD9XCgn2y2Y3tWqZ26OJ+9+Ip8v42cf/epfDv9BbikxEpBoiArOqlmme57mM4+V8/J+//79+/O6PXQim+d33P5yOD1QzaHXnGVADACQgooBwuVxqLsysVnPORDQMwzwVAJjnoqpffvnl3/3d3825fPvtt7HvDodDKfLjjz9Wlbu7OzB6fHy8zFPquz51FNgXhtVvC/q+H4Z9CMGjViYKxO+//1FKmcdpHMfpMuacTcREv/zyy9d3913XSalMdHd3dzgcMDDf73jXD8MQuxRC6Pp+d3cY9jsRoRAMwRB2u91w2IvI6Xz+4ovfqSqoERGATvPl+PB4PD4mZiJKgfxxEVEpVmudp+/+/Mec8+9+97svvvhCpOR5RrT9fl/nrCZ93++HHgCenp6Ox2Pqh1wXZ+AQezO7THWcp1f3b70UMsYYYreJe9y9VmLPySzJGQqIqFcbqvZBWubXmdB0fZz7XfdX+m43/jW0tMyLZsnLXz1SSjmdTiIyDENAHC8TALim+NPc/MTvqeplvNRaYe2n8R6lGKMKnE6nw+H+H//xH83sm2++mXP54osvsiwe8a4CpRRTBIAQAiOZV40AKICrWPUBrKqiurW/K8Lw+k5KpaHjsUuHnZQKqqD28P6hgt3Bndebv3r79u3bt91+N0qJu36328WUQgicYjf0sesQsUgFwm439H1PzMR8z7HrOjNDc7VSQJVdMalVChqsb8GIKMaIKc3T9Hf/8J9LKfN4/uGHHw6HA4c0T5ecMxIycCnlfZ4IMITw5ou3FNLlcjmfz+M4clH/0EKKZgZmYP4xq6oiquGSkPmo79/Mfl3PmcZvmSbujQ+SOR4gHw6HL14fJOenR4QqgeH07kdYfQFddAAXoUkpgTeCmvluaikFEf/rf/2vXTe8f//+8fGRiA6HQymFYyil5FyJCBWnaTJFM+tCZGLXTTP14zAzIsUYOYQQAgAYYQjB1bnWyqVLQ+/NqJ5Wun/7hpndvDf1XYl0tmqo+zdv0tAfDoeu70MIbgBJgVNKT6djKUWqzVNRyETU932t1UTJABGJISDt+iEQ5jLXWmt2pwEhsEgYiN6+fSsi8zwDQM756XyKMXaB3z28H4Zh1/fMLCIqAmop9F3X+fP9czMzDkyB8ywu7qqKqiICgAYoIgYfunc1g5fGP0sT978Rv7Xfw7UK/uPUvwv069evdx3PlwuhoGjJ44VIFTffV48sASAwd12HBvM8A2rXdYiYc/77v/97nwT99PQ0TRMzp64fhqGoF8JXP4F5nk3RMzB+ZBBVQDNDAEQchj52qUtdCMEIiSilFFIc5xklUGAi0lJN1ZNFb968yVJjjIf7u9R1GDjsdul+/+XXX8eUuq6jGMysSD2PlyL1yy+/RGYyU7AiVUSMUFUJ5lqr+kylgDGyj3zq+lRrLXOcpimXSURyLrPZ7774chovpZR+t0+pd7eZLFqyEM0EkFIiRghspjlPQMHMdrtd3/dqpKpVLecMwPaJuKsBi6gtDsz4FwzL+K193xp/e5q4v2jWorrnApvT6UREXUyESyRORF708hFem1FriTEGYh8autvtiCjnfDqdSinTlM3M7QcAYBxHj7jdMh4RVdUUmZnRt3ZNVUm39WO5ShARRSAjRARCT+vXWoWZkYTKJu7dbsCcOcW0Gw73dxxjvxsOh0MFAxWUmiITU8Cgqgr2448/pr5PKRkCIsa+MzPJJYRIBlPO0zSBSdfF/X4/DB0HCiEQ9RxDymGe55wnLfXh4eHVq1ep69+/f8cR71/fzfP4/fff9/sdgOZa5pJ9CN/Qp5jSOI5e/k9EVSDnXGZR1cARCAE/Wket1mq8/Jd5qXuzZmz88zRx/xvxW/Nz/2zk7pmQoT+kwFrGlFIt4bplZmmPZN7qrClwCCEQAwDxkowWkffvHodhYOacMwB0Xcch5pxh7e7ZXs677s3W8hpmAGNAH1mXcwklcyzu0JJSElNVvbu7C0SClJA1JtDlJO/u7nItgJj6jmOMQweRZ63RVEoWUzGNMQKhmFbVH378sd8NfrXBKb558+b+/r7b7YZuV1P2tS3Po59MKVQFvPS97/suhb7vS+m11KeHY86167rXr99ILaqKyK9evcrzGEKMzG4eOc8zoRHRWtZoquqJmRhjwOjVMl7Sfh2ge1oGVjfmpZnrl3/ffo5WWHF7NHF/0VxvqLoc7Pf7LgVQURQYhuPTu3meU0rj+hwiQoY11IZI6NWWXi1zuVx88uqXX3757bffitiXX35JRA8PD6nr/9N/+k9/+u5bf2lXk5QSAocQai5EFIgJljPaxJ2lclVmpsAKRoGZOU8zqoEqA3YhMS8tQojIHQsYIiJT3/cUg4ikvitZximfLxMAFBUfgwcAl2mutc6lMPM05lL11d19hByI7w93Q9dP86XW7O89RE+MGCJSDBxDNyQ0SKE7Pj6WUt68eYXYv3v3TkTu7++PT4pogMgpEkGtdS55LrkLOy96AYBabSmbSTHPAoSGi8n7JvEu7su1zifV7o3GpzRx/9thCGgf34J9/vF/zS2Ah+L/4i0AoBGaISh4hbsBrPVzvmHoZX7Hp/Gn94+aqyEpkCETqQIQIBIg2ni+1Fojh91uB0BVTBRijKfT6YsvvihFnp6e+r7/h3/4BwP87rvvRAQA3NaQgDAERmKCAgJIiIGZDb1CpqqAl+ITKAGiIZoSWAAbj09eypI4QIyBQ/Jt1xiNsEidpaJUkKpq0zj+8TiWOddaKbCX9KAqo5VSFKBL6f6wV9U6je++/65eLvXu7m5/OBwOh37XD2GeZ9UKaLvdTlVrzSJiql6zz0h44Fr1dHp69/B0d79/8+bN6XQ6nh6//t3fPz4+Pjy+qzX3qQupq7XO8wxWkJljdEOBakpkAICMiGQIRATPGTAyEzPesjSwZmwA9KoK1n55a2rjZmni/rcC2XwkGxh5qZ+ZAjCSP/7xLeovEndQVQQy+OiWAT99PhiBKCsikFQRLWXtmJ/XTMD5PJ6PT0hxf3jzw/dTNSJOgdWETQqoAVpAmA36LnJIU54Ph8N0PDLz3es34+k85VxrjV3iGMZ5IqJ+6M4/nYgoBUZEMBQwq1K0dl00BIHq1SkcGADApO8iooHmGLsuxWk8/nR8fxmGx5/eDcOw2+26rovEzBySZ/aJAocupZSmWufjSQHM7Hw+B05d13FKJpp5doksl0tI0UQu44iIfd/fxbRPLGUSDblgqcjMu30nEqbpMo5nj7i9x0pV56n48Otu6A1VVauYmcS+fxXD49MZMO4Pr3POjJRSQCw5Q7+7E5HzOLuPJhNO03R8OhMRUGBOgRPHhMjMTBxT6pX8ogHMJOcJqCBiNwwAq/MEIQJvyfpf5Wv+i6Z5NP5daeL+N8JH5ygC2NX9X+mWQAMmAFMwAqwIiChgZvbmzSsAmMbgg4RSrv2w3w17kGo261yKzlqk1pnUZlDiLb5E9TwCB70Slo8qOzZ3FN8VZAOh6155NfNSv0UdIiMAqKrWnE3G83mapjqNZRrZFKTmMSy7iyEy82F/H9yMJiXmSESKYGZ5LhIKSEUVBGVlT4ncH3YhBPQ2rlLqND7Vcjk97u/viI0ZQwiqVbUyc9/30zRt7wvWPJVv+SJz7LtSipRaqxEBEff7fa3VCA25zvl0ngE19QMgI6GZiEJVCRT6Yej6/ul8wSth9KYCQ0UTNSMIz/sfz/a/CrCm4PHqfuNl08T9hUKEa54GGBmYYDEqIKvi5RlubYiIHENKqdYqlkVEahURUzOTvouwdjl5mXyIcavl2PZgXUk9s7zpu0e+Xq/z6Rn6ETbp9Hrwp6en8/l8iamPacalt8cHefvS8vYL8bA6xuhyvx4OQwhFqoIpWNd1HIOXtKeUwjoQfJ5n94HBC/vWwm63AwCfrbrf76/P8Mp9F7RKCIEDAsCUS62VeanqmeeZmWKM5yrn85kD7vf7pWzfRFVzziLS9X3XdTzNCHz9+QCogqCIAQDq80fK/JuNjn+zJ/aiaOJ+I/zSXycRBQ8Mwfx6wtXkeDyKyHg8evNkKcUPHmOU6hV46C5XbGAmRLCV06iqV6y7EAMAEbmab/6UMcZN9P0RVfXSv03hr73PXNbdwsy7fuZ5NrMhddcHMTM1BQCv2iylhBBc3JeFB0hEYLXkCiFwDEuRDPMQoxdxLhueNeecfVqem1G6Pzsz++7xdduXsw5IWkqAVNVMSymECLDUpw/DICJq1UuDmBkx5JyneZ7nOZfSdR0RIVxP5DBvGENVQ0Sy69f9uYGof4Pv1b84y7fp+69OE/cXSpHqheGGIKDgCREAl1EACCEMw5BCDEiEUsbzkVTmORcCtzZRMzMEFKkQFxliCiGkeV6MdFyAtjlzWwvrthi4zKEaM9uH4u53cs4u60uxSggppZTS9cGNkGJAXGolERGYlrlGYXHWFVEfgQQAQIhMLvHncTTE1Pe73a7fDe56lqWM4zjOF89N+fKQc354eOi6brsa2C5HACDG6BcQAODm9fM8llLyPMcYmQkAhmHo+/4ynk6n0/l87rouRn5ew1Rrrd1u/5f89/3Gacr+W6CJ+43wSyMsEdnKdYDArkohEXHXp2EY3LDlfDztd/3l6R1CnU6ny4hqJqJSa6156JbBFCJWcvWUyzzPCXkLabfhcF7NDVsDvYftaz7Hxf2j0O9a1r2D390Fai5+cGamGACAOVLgw/4efF+BKIQUYwwxElEp1Q+yud/EGEMINM8+PSp2yQtp/MTuX7/iM/nS4hcuftGwrVLbZ7vUr9DqygDgC1itXErxMn9VAoDY9V3XGcg8z8fHp1JK1/kgvYCIenUdQ+QR/5r5IUIi+2DG3lIj/+8dvP+rI/fGr04T9xcKES3FM4i2irsiBIiRA6QQOUipUkrNZYq+P8keRKsIgIJ4NaAnxE1EoNa0Jq9TWiyItwy7v66HvVu99nWy/lrcP9IIVY0x+gn4xmYJ2Ye1evIaACjEEMKr+zcCa4dUWDxymbmUCgDIz7E/x8gh3N/fq6qYTtO0ORz4q+z3+5TSJu5+SrXWzUV5C+HNzEvml5OR5a2llFQEALwAdJqmWisSvHr1ah4nd7RX1Zg4pWQAqlpElosLMto+PWZkViLC51y8+bIXfqO/wk3lf3V+o9+Mxr83z9E0esU6GiEBzOMZAEBFfCh0zuM4nk4nzXmeyzznPFdS4MBEYGw+3NlWLxRbcjtq0QCfU9Lbr/oWaXoIDGv6+NO0jN+6XHo233PiLvRD13dd1/e9K74hMMcQQr/fLQXghIHTouPMIksWyeN3vw4IIex2uynPS04fNh0HEfHQ3lP8RNR1HQCcz+dtTYIrCctzTimlFFW11sX2MqWEAO4OBgDjnFX1cLd7/fp1nuZpmuZ5rLVywG2JUlVCui4oJI/ZPX6Hq8hd9TcroL/ZE3tRNHH/G/HX+rr/0uN89vkIgJ4GBlAAAxMwT8EPh73kIlK7rqtTphBevXn9x2/+13Q8zvO87BZKLaWSiJrMon2/U7G+Gzh1IvLw/ulwOKCqgS32v6qeS9nCdj+NzfoxgHn47LUrtS62Yjnnu7s7r9txLe77fp7naZoO+4N75G4zRWPsOMbD4cDM4zzt9/s81/vXr8ys67p5zj6wdLfbiZm/l2EYDOHN/s3xeBzzfCACgMs4EnuVzuIpr6o+CdatHP3kr42LAWBJyqh6rY6qTlOutQ59T0TzPHmGx8w84f7l2y9ijG5IAKin0wmJvvzyy7leVNWsqmAARGAi29a/GDpflhDRkMyMQ4B1VItfh/kAll86ienn0iwt/fIflybuLxdPLhgu9e9OCKGUIiJ9SrvD/vj4dD6fp5Kfnp6Ox/PlMtUqgUKIAavWmvM8MbN6WToHlzzPqYfIsAbdAOBmYdfR+pZwdxNEW9PXWzLH1d+DfU+v+3G88SfGGLvk/0pEIfUxxq7rgHDHFEIgjv5TMUZE8jPpuq6qLl4uKS4j7lJMYGvFi9UqvjH7kYRdF6jYFQBQciaiEAkAVMTXM7/U2LaRlbjWWiV72Q8i+oQmAyGiKvL4+Ighbq91fWfJvF+luRSwKWzjn6GJ+43zc5H7p4N6PCuiqsMwnOfZ5yu5M/v9/f3pxx+W3UsiE8m56FxKmU2ViM3zPCIqoAoooKAx9p6R7/veq7lTStfiDldTAL2W8dpQLC6t+UsCZBN3v9/vhi3lYoTM3KUhpRS73oVVVLtuAAB3IQ6pc5ENIdCSrV7GbQOT+1aGGN0YodTimwefsgyE8tVovQMA/kEhGTOjmu8iIOJ4uWyJIOOQc7YsfikQY+z7HhGn+UJEWOvT09P9m7ewqvb2KW3bqhu4unI2i5nGz9HE/SWiuHQx2ifKX0rZdb1vMHYxdkM/z7Oqxr7by17zLFLG43EcR82zao0hqHpHKpm6pQIgoulSFe5S6zuxHvZ+EH6q+pP96n+L3D0f4mru9Y6ecwcAnyvtyt51XQgBmEIIXRpilwDIG4KmafIcjp/GNmwaPK8d1vsIAODLz9YW5OH2tYvLdm7+XlzQr+94EqlKTin1MYUQvEFpdWD3qUnoSRu/ghnHse+TP8Fz+ksd5wdbFc+NYOaubdsjPrjqr/SVaBcBt0cT9xvnL/yl3VSeQ5hrUdXXr1/vh+GH775/fHx89+4dIXddV4fhdAreyUkqIUSRKiKA7FaOH+2gbiWP8Ekc6qLm1o8AQES2znTeah+9+9Sj+OsCeVgVHwNj4KWUPjAzu3miLx5GqAgCVlVAxLPzIlKk+nGy1CVRzkw+KUkVmQw9ifRcjnl92p++TU/yTNM0zVOtNR58urdXDaW1DauCKAB4Ac90GcdxrDX3fe9n1fX9mzdv5iqfFfdf+3vU+I9HE/eXjuIH8XviMNey2+0Ow/5yPv2P//E/Tk/Hr776qpzP0xkvj8dSyjTOl8uF1FxdiQhQVZVCNTNRQ7TA5FH5lmdfylquUuqI6Hun4FUx+rw/uWW3vcTQg+4thvVg3LcWr+ss/Sf9hbyKkVfJ9hJ1ACilVJUQgiKYyLa6+D9tx6y1AuhWjL8d3leI7ST9TIjIUFf/3ur5dB+fnWL0I4iIp2u2hW0roFQTAAgifd9nGT8r7qoKa4n9cla+Fv5KG/U/R/Mi/u3QxL2xsOTcAQDAy/7++Mc/Pjw8fPHm7Rev7r/95pvx9Hg8Hh8fH71eW+YsUnZDZ2ZIIYRgyABQ1UTCcOhqrZ5r9qoST6f4a20Cve1PhhBM4Fqp8dlc5Vnrt3S8gHGMnuIwALyqsPT9267riuDNiGwAAEOkSURBVFpKyVtM5yl3XYdM7onjJyAiVSSX4rH5XDIQdrELIdRaNyvda3EPV3Xl12F18T1YMjctmOfZd3y9ktKXGcTnsUo+gnyex2ma3JFmHEdV3Tx7P9pQVVXwatN1F1oBAQD/pakdjRdLE/cb57MRGSnZ1VbctSHw6XwCgGrqAvSf/2//+Pb1m6cff6x5Pj48/vDjd4/vH8S073cZUMc6zQWQiUREgLwqxkAFeKimEQiYVAwCBApZCxgqAhgaISoZIRiCITOLPWv9c3yq6sH+s5UxETBFohBj4ASEXtWOiAbEzJw6LTX2Qx3HmFItsvVMoYHfMnMkJoOLqkpVEQCTWiwGJgzEU82GZK6rYgA+EZxgc7xEIEBD8Kklk0gIIQU6ix7PFzND3Pd9fzmfaTW0QTUR8WoZEdnvBzOZpwthJKJ5mkrOh7tXblwPAAiMSArAiEWNBLwPFhGBEIDELDL7IrRcftm/u9tM4z8KTdz/1nwUkf38ZeznIzL8pdMYfua3fUl/AyCAt3SaGdhi7KV5zjkfDoda68PDw+V0Oh6Px6cH38l8fHycx4uI1Jz7vp+LdF0wXNy1RGSa69svXhcpT4/nr//uq0DxfDoddnfny9inAQ2qgYkiEBKHru85nM+jD7h49cXr2KXL5dL3u9PpFLuBY2fIBgREIYaQutilUoohATG6v2XgEGLo+pSSATKFORcknkrhmMyk67rpcqHd7vR0jEyHu/3lfAIANpvPp5PB/f3hvu9VpF4uHeLT8TK8emUqatJ3g2jVol2fpvOkqoAKRF2MFAIzIGIKRARajQBjIBEpOZ+ORz87VTUTrwiqqmAWI8/TBUEPu76UUnPWWkVRup27AUMERMTAIUVKPSsYsRiAGhAszViIAGhAto5bETQAIAOmNmbvpdPE/eVCaxKGzFuZAAC61BnY+XI+nU4o6uXkAPDHf/rmhx9+uJyOvnmo2lPJZgbECuhp68UHBhQRx3FEpm7oa9VikyrMJRO5xwApGG1hNK7dNx+ufNczoD0sNVxsLM1MDcFIzRhAkchQl+oR9P5UMxADMxAwAqq1gqqIpBBynqfzJcaYcy7TCAAMhmqlTKUU7PsuDX3qQDGXCQAgdgFZwLRUMPNlEFVrrQGgGhKRlIohuNuau8eo6jRNXZfM3NudtrcGaMgAyARajUENDZaNi1INDQkVAmCBEJASQrbwXJp5bdluz2OYQNdPb6uGarxkmrg3PoAATuN4Pp9jjIe7ARGfHh7fv3+/3+/3Qz8Ng++C1lpNCBEJDFSKSiklMoYQgBkDn8/nbuiHYZimyduOvLIb1gpCT3NvJlxeheK3Sx7mKtPtbHXl277iWq74fHetTVS46jMCgHmeGbGUEmM8Hp8eHx+//vprbyby7dOc8ziNnqAPnFKfqhTf8NxWuFqrgGyHFczC7CWbS5EiGTF4yl5VfACIW69vm6hIBoZEDGjmYw4BNz+yKqJgSIhYkSqUQlQQMITFCPM6z4br/NtG41OauL9cNu287lCtpiVnZt4Pu0D0+P7hfDwh2Vdvv7g8vHt6erpcLqWUUgqsFiueQVZVIXBH8gCYc0YmETmdTm7hcj6fYW0C2oR7E/fNrgsAfMNzK0C8zl+tTf66tRHp6gd/rfgAZGb6XFhipRQjKqXsdjs/fxd3L1nxcys1uxxPOCEu1u1ekK6qzKSqYsuaVGsVKcyLf41XNNZa3PjXPWrAF5U1Lb5YBIPhEr+DT1MypK2USKfqE2q3d/1sI/OJiTx8uKfSKiYb1zRxb3zA+XQahuEw9Ofj6af3709PR++S/1//8w8PDw/H43Ecx5pnNIW1gsXMTIqKCLOIgIkZgWGt9XK5TNN0f3/vNTOllK2s+3q/1MwiJzAKnMAIjAhDLQpGSAhA/sebpVTB3SivI/dnob/y493E3VM8XjK/BMi1bvM3LpeLLyQhki8ql8slpV5EfbEZx0utZb/fM7NVAQCzWvPsJY82dGiSwjIxClTd72Y5SSkGahgMwEy2EwMDXFMqXgIES708GrIBg7ubheCukLA+s0Xujb+QJu4vEX2OApexrhs55/1+j7XmaSLAt6/ffJ/LH//nn0+np2malvpx6XPOtdZaswFtngEfBZuweijGGLdaclg93K+NB2CNaj9Ky5gZXPsgmn0UuS8Nrp9Lyywa6tcBAETknjku8Yjo01A9y+TpmoG6ZapfFURWNS98nKbR3cd8U5SIqtSc8ziOAICmS86dKYUYUgxMuZTxcpnneTk5UUUDRQUz96ckAAD2/c8rv4EYowIBBuOwjSSEzwXp/0Lk3jZIXzxN3BsAsE7tAAghTNNkOccY98NOq9ScH96977quT7GLAVSy73ZK1SrMQKBue8IEbjWpqlV02O+maXLLdc97uGHLlgff2ELXj9IyH/VnXmdm7Nq66xNx97TMVb+PxRiny8Utbrw6fhzHYRiu15gtneLJ8ZxnAHN5ZWaRMk1l6HsDNdGaS55GM4uEKaVqhkSQhBiREhMxAoIiIBggqAkqgFg1UVUVb90iQETGK1MBYrMlHr9+fy390viltKrYl4vi0p563aHa932ds9uHaZXv/vzny+n893//9yqScz6dTo+Pj09PT5fLycereqrB40uPNAFgy4EAQErJlRQRNzeuj1IoAJ+vlrk+22uDl+vlwT7k00dsnRroL+dtqF3X5Zw9bPd/3bZVzWzLz3jqxt3GzOxyuXjwbmYqRWqtpfj0EiIy1Xmex/NlnmdQCyH0qUNEcmMGADMx0W0q7HbrOxYfnfP11cm2An2alvm1v0SN3y4tcv+V+aW/n/+WsWqrgAIAyJU4glcZAqCtrl6AKSU29FZVnyyBiIGQwAKhGDCi+14BwKJfthzczIZhOJ1OXm74/v173118fHxExK+++srbON2PRVW//vrr0/G02x1i7KZpSiEi8jievvrqK0U4Ho87oL7vT6fTMAxDvz+fz8jgu6PTNBW1L774wt/OpuCHw+H94+Pd3Z2IMCIQHg4HTzoNQ+9Ov+/fv++62Pf9Ys5OlnPOOe92u9/9/RfHy3kqU5b8d199/fDwUHJ+++bV6em43++Z4fjwiIhlmmwY8jhi143jmFJCtXk873a73W4Hqg/vflyNz4iIAlGIgBiO5xEADL0/6zlyRzJDxrVT1zd4VTTulIgXG+TV4ExV8Rd+H/5a68GntUx/3eM3/u00cX+hIKL6ODpY7yAaGCN1XSclT5exTrNWQbWc8+Pj4+l08mZ6j3MjY9/3pRTP2yMiXpWld/uDd436arSNtri7u3Px9fpI3+HMOfu4iWsf3aU6kPDa9fcqwLfr6N7T9J7PMTNvLPL7nnwngM005rqaBdaZIbCmg9Zaz7zb7U6nJz+ZaZoIl3lMxEAGMUYAI0QwI0RT9WA/50wMnsdHtFWFq8g6VHaxtxy3M9fVShMA4jr729b/Jjf7/bW/L43/eDRxf6EYAi79Ps85EL/mDzGi1NN0zpfRI9nz8VjzrLUAADNHRpFFK3dDB1cx+7O4D4P3HG2v6P/kE6h9Wt4mpjnnSF5KWGHdcXXdR+LNxNxdvWBdh7YZTAAmIp54iTGKCFHYTGbA54QwuUGjy+4wDGuMqT5fqZSiiwMMuu3l4X5/Ph9VVbQcT4+H3R4AOKDkMudiKmYmuYynMzMDKBOWkkvWlFIGRNPdbhc5GIiqqYiiASNjCus8WITr8k0AgHj1cW0e7rA6/X74YX6cufot0CL33w5N3F8uLuewyvqWsBZ3wQVU1el8eXp4fHp68ljbA95Ai/6mFNz1cCvpW46MYGiAz+7tm536VhWzTSkiIqbF0fd6cN0ynHpNiLsTumsiERl67AxERPa8B7tawAevQPcnbIn1bc6UHw0ASpn9sCJiil6GP+Wcc/7y669CCDlnETmfz11MUrNVGcfxfDpO0wSi0zR57p7u9rHvPGsPS6geu67rh+TLiYiUIrh6snchChh6Bl8+lnjYitnXAdkfPPihg8WvQhPx3z5N3F8oa3IcjNaMhAfdavM0WcnMTIDn8/nh4eF8PJ1Op2maQGvX996pxAQpJa3iGRIAcOMBAFCgSqiw1Du6uLuGbrPuPJsMAH3fD8PAGABQ1WIMCIRAPmzaFAJH5kDEKfnagESETL5Vi4jeNeoC7ckcF+6u6/wlxDM2Pq919YV39a81+0FCCKjLQXyZYeaUQq3ZW5Zyni6XSxdireVyuVgVk8oIteQ8jXMKRGRVrEqdJ8moseah60IkA6tlaVhVA5WcQ4id2xCoKiBdl+dvg8VpXRXtE+vH32zk3vjt0MT9JWJLvA5GSwUkIoKbJgLUWus8k8I8z5fLZRpHLwO3rrNAwzD4tDwEd2lfwnO4rtcGCl2qunjb2pXfQIzRhTjG6K35wzDsdjtQFFEvZfH2qGEYxnE0gM1j3cNqV/AQY9/3HtErVo/EtwlK27Qmz91LKaZiAOuc7mefYVizQDHGAEupzxrUl67r3CUmhJDn+fHx8e+++irFiLpsPR8Oh2kcCXG6jLBekXiNjYGcTqfD4XBdDFNrVROgsuOoCGifL4OBq3lVLu6f7Ur9FZuYfi5ybxH9b4cm7i8URJD1jl3pOxEx0ljKeDyfHh6X0aCI94d9JvVd0MCYcy5FVLWLzwOd3bHS71yX8W2hsQ/P21x8fZ71breLMRJwzsUt4F2FfUiewJKXd3HfNmZ9npHruMAyvs73e5fpGWYpRl8tCnOpxR8fhoFoKTXxsXZeORNjBGR/v13XEcE4nn2Mn5l0XZwvYynlyzdvYJ3DR2BD31upIlJqBYDD3S5FPp8nEWGCy+lsUrd8ixs1ogGYTdOk+HETE8DSKHBV9u4jpcie/+Oaejb+Ipq4v1zcLlYR+GpDNUKoMZro8fHx9PBepaQUIkMZq2pFwxjI23yWOXm1wmbiuFawGNh8GYuaqgJhIPZh0C6mz+3+IQzD4NWWHANK9ZYeA4SAnCIEtKLIrn32PIqP1pEdYhgC62I9hqv9FiIaosfRzIw+xFU1EIWUkKCUQmttj5+M/7incUIIQDjm+W6354Cg1sV0rkfNOV9GM8nTqKpM5A1buRZEzCQEAzGZVFUB42kqKhmZAjEFDsjIQMBKMM8jEHqb1tbEZEAcfMeCgNCIgFjdn+2j/7vPpWUWv/t/bTDv3cB/xdvGr0sT91+Zf0vd+jV/eUDn3t9Vqq3jJtzjxGsZz+fz+x9/0FL/yz/+53ddfPfdn8fT+9ev9j8ef+xSJCBQrTkTYgoBEesHVuCLsCBAYA6BmZliSBw4RWZGg7vDgQCP55P3SQ3DQCEQ0XE6IeLhzf3ubrff3RHDNGaOdHd/jwxE4XI5UeDDYSemANB13ePpDIZfffVVDCn13TTnvh/MLHQJGBAAGSigoaYuPB3rYbfLOfe74Xw+3b96lWLMOR9e3c/znHNWhF3Xicg0TSGEgtYRznVOXTcfzymGjvnhdPrT//7mMPRDDMfTY0hpPB+5wyHFeZ5Uyvt331IIKYSYOJCFwH2KVdWkzGWuiBQpcYLAXSR9biE0cB83M+OAFIxD6IfD3WvudgpYxULqDBEQxQxFzPxvSB9YDiA+T3L6xV+gbWDL9S2oPY9J+QtuEZ7vN35dmri/UFIMbqe+dUKCGqpdLpc3r19Pp+Of//jN+enp69+9Rcnf/OH3fZ9oje79CNf5349uDYEpEREF9ow5IvrO7fF49NxL13X7/X6/33OMhlBEOYRh6Lu+D4kBICTuoI9d8OxEjDFG9mZRAFDALvVLa1VaymaY2QePPFuG+S4xQIwRmBSh2vp+16zH9YDWraSHyJAI2Wv1ISAFBCZiMDBTKaAGoIhGPmSpIiIQAYGaCRmoeotSjIwY4nA1ZEq1Uug2X05TNDMFULDx4WG4e3W3jzF1VaGWShwpRPvkc/4s/7oc/PZDrsh/rdvGr0sT9xeKp3YVwRCX1HhdJjh7bnq3282Xy+lpPJ1OtVYVUfigqdXxFM2WHaa1IjvFAfm5Ml1Vi4qqhhRT34UQ+r5Pfe/VKaXWYRi6vj8cDn3fbzWRWz3MsuHpRolr6chut3NXA++l8koY08WSdxu+6lLohe24GpZtxZGybtJu4r4UXDJsvl2u+L5hUGsttRpCSNGbsJCImThGwMWYVxHM1D/MasrMMSWv0vHCG1MF3hw1Da66sS6XE4ck+9nfyIrP2vtA3z/dg/1b0lL/v32auL9Q7OoW1toMBNvv92W8eMXINE3ffPPN44/vQoyIA4GhgV7dLhNECQmQAgdiZFoewaWNyPc2N0OVV29er+34AYg2E5X9Yb/f7+/v7xcBLcv+57aje71y+Dn7MuBrgNfkhBBUwKtTtuS7P3+32+WctzJ5F/etfH4zrfSCHGSGgIE5EBmzF012XdcPg9S81vinrutgLRZKKQEumZXnD9l95Nfkvn8aXdepGcSoq+OZCqyRO3Up1Vqfnp6M011Iu34AQlHjgIafoRVDNn6OJu4vlFwLAMCabWdmJCaDWvIwDD89Pf7+97//4zffAMDd3d00noAJfbMOgQxwzasehp0RBvSh1QxMAQkR8/zcsKMIGDiAeZSNiOopCVUiSn03MA+7/eFw2O123je0hdjbyA64cv0FAJfp66J1L3phYFmBq8i96zpPB7mce13NdbmhLyfL0AxEW3+WiTiE2Hf9fldKQRhMdByLiFAtASyl2A09zRnwA/MvWJ0mvf01hODrQdd1KYQCQECLITAvOXcFMsOqJDWP51OIKXDq9ncpxSXF/psR95+L3Fvp/W+HJu4vFA8/vf9o1QlggMs558v5hx9+OJ1OXdcddmk6nsfLsZaCCF6TaJ6HJiTE+zevAdEfZyJA9HwD0LyVbfiwZkQEwlevXplZrtUd4c0shpBS2u/3nuXYOku9NXTzk7mO3L1S3o26AMDL8D1Fs2WBNnsDPwdPH0XP3Mfow5Wu8/K+Hiyts6pmAGpEoIgxRjLQw15Vd106n06X6ZxzVqsxxpBCSgnUNnGHKw9Lf4/bcBJfSABRgYEMVqc2gCVPIxIDMVBUgPF8YY6x64dhmKvAtb/Yc1qmiWnj8zRxf6Gsviu0hcNo4K02P/300+l0+vLLL/Ht24d33x/fPYQQABIiBCSKIRJj4IBkhCFGRWBAIwQDI9Qri10XUEZKKcUuLcXpiEE1hDCX7LG8x7PbmCT/2ZyzT25yNf8o5x5COJ1Ou93OzOZ5vr+/dxnlwLjuIsAn27+eN49rpggAwNQ/ilprrbQ5zoOYa7UvGKEjNDHRGDnnHLukqohWRaZpGscxxvjZChW8mouNiKVWuVyAkLudETIge9qeiMyAKKWEFABDBURCVS3zNMVIsdtm7/2L26qNBjRxf7FEYgBQAFHw9lETRTWvY+n7Hupc5xk82IzhcsmIoCEEBAgQFIWRALNUAKifXI8HC55k3yyFPS/xdDqllGKMqe9il0II+/1+GIYqdp0odwWvtW6jMzzodn33kNwH+AHA9fw8ukrgXJ+PH8TXhrg2ppqZmxV/5C/vLVgmCoEY0Du7WGLqu3keFSENfUrJTE5Px8vlEgJ9/fXX8DO1Q9sFh+dn5nkuort7NsJIrKrAy68hAe66LosWEeaU+r5LUWs+Pj6++vLr7Y18ePvrRO4tLfPbp4n734i/Vj37mk4B+CQs/UXM1Udt4OoLueyonk8Xbzi6PI1eAH65XH744QfGUkpGxP1+71LoVepuG+mdn5749mCclWOMHq17KF1KOY/jq1evmNmVvO/7/d1BVR+PT3eHV5vNgOusd4f60x4fH81st9u598But6uqv/vd73yd4Bimafryyy8BYJ5nIjIA32X1HLpPXLq/v08pebb97du3iJhzFhVmPhwOpZTHx/eq+sUXX+x2u59++ilPE0Hqug6RZKmQSQIyIALZNI55vHRDryZzyX/605/6Pr19+/b169eq+vj4OE0TEXkaapsI6O+xQ0p9r4CMy//pqonIIXn1esnTXOo8z3f3r4a7Xc0FAjybYpqJVDMLIT3/p15PXfklRZHrwJTPf99+le95499OE/eXzrYph4hgllK6qKaUdl9++Uj03Z//99PTE6hRpBgjiM7zHJD6vu9CFJE6ZwNjQACIxHGIiUOMMaUBDcT0KnhfmlqrylJAQuiVLVs+fSueAYBlM3ZdzLZ/8ry8rCUoH+4ufjzX6Tp83o65vRwzm9p1hTuscub1M54xCYGJOfUdpKQgiBitqiqAhlJFK4CaLDNjAcDdclT16elp20jw5I9fzVQ1VVVY9zzs+Zwv5yNxRO4CEyEFAlORMmPXg4VPLZQbjZ+jifsLZZUG3Ar1YM2H3N3dSYr5ciyleOFKjJFsZmJRkyoI0McUmKdp9vsxBDRAgC6mGIKaLUa4qx35VjNeVaQKAMQueewMAEuJ+hrh+hluheGbuHs9zOLcK/U647GxDOhYDW0+WgA+Mh82M9PlX/2wnjxRVRNVkDJnAABInGJMXSDKdSYiIEPEwFhKARXVOs8zMuZa5HQEwv1+H7sEhOM8xRh77kNgIkIhIFRR8U5hA1pmZQMBKMD5fI6pjx1zTIyGpqC11kyqgLqNH2nK3vgXaeL+QtnEHQDMABEBAQGI6O3bt+fHhz//7z98//33Mca3b98en96PpxHByCCkNKSOEOucp3F8dbhLXReQslTJRWMUtalkwJBS6vu+73teMzNi6ia33t/k9ea4GHXR1iuLqzeva5lrusvu5upOpNdh+DataZG/tbrmWg2v9X07uOkSqruLmb8KGqQQYZ1yhwiJKYRF0RkgQDQzRmNmrUXVreSXhcQT60T0+vXrcRx983YrxEwpMWBWb14CRGQzAGBEBhhVVYqUWVUpREQE7dBA1R2UtQXvjb+QJu4vlGtxXx8CBOi6TqUej8fHx0cAuL+/x6rvfvq+zBlU+tTd7fZD19c552kWFa1iVRTNRBRtuV/lcH/PMYSUaK1FKVJ9XhKFJXb2anc3893O6qMQ21s6l9mh6+PXmv6RuG8/vj3nozkY2xOWfwWqtXpjqo9k8rl8fd+rqlhVnw+Vs6EGFTUzXCoaMQQTRVMAPRwOpcxbVY9vBR8OB1gTSl7ZmVJKKcUQLYshERgAsFfcgwLA0CVDUtVaZyjVAFMvAb22Z3Grp08GMzUan9LE/YXyc+IeAn777Q/ff//94XDY9/1PP/z54eHhdDzanBEMu56Raq3jOHoomnPepqpyYgJk5p77GKOn1H1f0dU5xliket0LEakP7dsGIZl5euRarAHAe4vcisBVdUvTX+NPdjUHezYKvq6E+VTldfUeiDFa38/z7P8UOIBqURPVpVwdtdYKYIAITGwBVFjVTBl0GIbTaQnP3dbYX3EYhq3hFgD8bfrYkE3cl6JUWDppq1qtWrRqkRqClLnmwkkNn9/Fr/31afwHoIn7C2Vx/fPRGuuAaTAbx/z09EREX/7ud6fHx//z//vjn/70p5oz1rrrukA8ni+elU4pRQ5dTGaGBl1MKSY0QIMupSLVdN26ZHoeli3QdZ2P0fCRSdem6ltCHJe50ouWbQ8uW7K1IoZPc+6wpWWuNmn9Qan12mJ+00e/MvAKn83GANTsqu3I0AxAVQUsRmZAIAAkBiMANKtkY545xdAlMxvzMt8DKqWUmNAI/cgCNpVMasjJwGy1OUP0QSngDVPMpsQli6+OGU53u9f2obK3yL3xz9PE/eWCV+VyruyIcHp8IIK3b1+/2g/vvv3zt3/648PjT68O+6rqsfP5fJ6mqR/SMAwh0t393kshuz4yUynFqlBgikCRAycvRixrS+p+v/fY1ksS17p1rDJ/WrWyNVgtA4nW1HlRiTG4yTnS0tyDiIvbOxghApoboaOiIdb1UB84BKB6QjyEgGjIjMyoamhlHBHRCJAJaZsnrpESoQEEZBGM5pqP+sMPP3z11RfDsD8eHx8fj2ay39/F6Ml/CCExiyqo1lJEc+13AQAEgNEEANXcSTEwhUDECMgAc6ma5wkU762iBlIhUAKgq/p2e7b5bRF945k2hvFvxFl+2ef8S5tEfu75PxveIWQRj5dTiIxgBlpLnUYGuTw9/f7//P/8v/9f/88//P7/msdjYmYDAru/vzezp6en12/uv3j71WU83d/fhxB4macBi91jN1QjDkFEFWEYBgCQahyDb1oC8kfpcsVlS9OXEO/3qbX2fe8toF607pl3M3v9+nVVkWrEEDgRgwpUyaYIqGAEDJFi6EIXOma+XC5w5QqwKTszz/MYY/S6nb5PtZSu607v3pcyA2rXdQZQ6ozuDgZQyszMfd+Z6ul0UtV9333//fcppRg7kTLPpdbMHEMgAGJG5ugXHqq1VlVVhOCbvikFv3bx2iSplZhDSJ5oqqa1aDW0uN/fffHq7Ztht8cQkUKIHacwZ1FcpteuBaGKiGa/4PtjCGD0y3x68V+uf98T/wUHavx70SL3FwoBJObVXAUMQErO00igpeaffvjum//1P7///lsm3e0GlMqAJuoZdkAVEdHSdV0IVGue5+KyHgKZyTidOQ6oZKYEz+WMW2T5YS6FANCXh82GZetT/TjWXlEwVTVQMzIQMzJ3cQd3JhYyMhA0NvhgXd0Osr53u3KtseUSAIEjqeHqTg9oYFpNKIRQDVSqafQdglpzlsocAUhEzHwCVL8V17v1zuKyDIRoRDxPOaXer1qmKfsy0/c7d5HcRs4CACMgh6pqmq1kqYkBLIAag5A7BisCIjAoAAKqAbRf7Ub7BrxcECCF4Fq36EEIJuW777///e9//6c//cnzLQgIpoG4avH9Rt//nOf57u6u1lprFTchqNWTJwpoWInWphsRcJ/FLZQzATPvj0VUAGROS7mkiC8Gm7hf7yJuJd6qVdU7iUgVALyvtV6PhSLypz33Ny23IO7GCM8Vk0AE1zU4KSUwUa3XNZpe7gJrL5L3x6pWdy7zB5e2ACLYHA6uNofXPV5ARNVayvObYu6IIKVQay2lLmNalxYB0FK9whJjCgaEBLWiISCjmjHA83L1a3+xGr8Nmri/UC5zDiFEJkB337UQAsfww5/f/9M//dN3332XUnrz5s3jT9+Pl0siJFt8E2OMfd+5Xddut5vncTPzmqbJEyldN9Q5M8etHAUAiBgRQU3Rp6Eabp5liAyoq5vjVhXjVefORyXeWmWxxFmtHzeb3+fnrHjK3R80s03Zr+vfPyLGaFpFFm/h6+z/tggBQIyxlLnWGkP0vVnfM4gxbpaQ1/u9S1usWdd1pRR3svRFwj/SbWvBFxV/F1aqapCa53mkwIbAjIaEhiEyfGIij9DyIY0m7i8V7wgtokQUEJSw5Dzm+Q9/+MM8jq9fv7bD8Pj9dz/VOk0TBFYkrUvJoHuj11rP5/OSyoBn8XKRLUW4Ru+5V1lcTxChSva0sBJ5IxIRAaFo2Ax4zciM1ISITIpJAa1ohuZDo0ERRERFVATIFAnIVMTP0MVR1RRQkRQJvRkUAEDBbPkDCmZoAGrgKWrzO0YGFIJUBvig6gZWb58tnN/McCjQVuGzGVJuP/hR/SUimqqXYMKVs4KZlVK2ESLb5UKVSkRWpcw5hEwhUYiGKpY5+q/wcnpmBkgG1hxeGk3cXyiJSQHGaWbmGAMAnI+nh3c/fv/9929fHd7e3337x2+Ox2PJmQFFBEC1iu92bq68l8vl9et7TyMsW6kpAcA8z6Vq1wng8zYdx8CAuRTFJRliCMpLLeMMiAyMARnQQtVKwIZqAiLFzQBAA4CiMSFsCRyP3D1Z78q75VuuIndcIlq4Tt8vwbhaVcUtTPZH3IbX7DlRY0bujMbMgEtgvpVawprh2R7fajc37cYrW5vZss/uqLXmnKdp8u1iHxflnVC0TuJDMCRSFa251hy1EpiZgNIyxnq9ItnWtl/7+9X49Wni/kJBAAaIHAAgV53n+XK5zPP8u6+/HiIf37/76fsffvzxx5xzYEZQAhRbEutyKYfDoeu64/H4mSMjmqnUbFrBLXVNBSyEQGi1ZEA0RCMyEo+MAcC0cmQMxsQAoioipWpNIZmKaQU0MEFg9C1TrWpVVIhIDcFITdQE1xhWzfdbUQ3JaFNzMzMTWKNp0eJ6LlIRUdUvPIgDbVcSuFgQqw9s6rqOOJZS3A5hC+S9UcuLXmqtXvmz7Qlvn8/W0rX47TAYSM65iogWAFCzKmYgSwjPzBBK8ZVHTCsBuG+BLKH6JugEgD4eHJq8v3ja1dsL5TLlqtBFDoGnaTo/Hc1sv9//t//235j5T3/607fffuvjihAgcUhh8VXPOY/jCABd1wGApxH6vveee69e77rOtze3NAiqkXmLZvF0ig9VNXcvUCklS6mq4v7qoFZrqbmYKahd3wIYoP2LwAdRuiySt/K8s7p0Nm139HoBcK4dCzwq3yzG1u0E2sp7nosar+b8XW+oOn3fe8WnW+u428H5fN6M7Od59sqZEMKuS4Q+lE9QFU0RbRnM97n31WhAi9z/Zvy1rpT/WsfZ9UkBihgA7PuhC3GeLnUa//znP//hD3/48fsfCLBL6eH8aETD/d35eEopeXv90A+XyyWE4NUyzHw6nUTEq+CnaUopHfYDmCBoFb1cLofDoe/i5fhUptFoyVfEGAl9pDaN49inAFrPx5GIhmGIjDXXy+kJEVPwLVYErVrVkMfp8urN65xzjLGUcjqe9vv9POc3b95M0zRNEyJy34dEDGhVhn53uVxKKYfD4eHh9OrVq1Lm02m0KrvdgIhZplLnQJBizDlLAVrdCwAghFCrutr6gz65exgGRBOR+TK7WHtB0W63SynlnLeRsG6O5vE+M5s95+49IzQMQ9/3/uQlL2/m46gQptjt+9hzTEBhni5Vrd/fp90+ECkxMG1GC1UURDl9/lf7898fhLYo3B5N3F8oBiCylHOQQS2lTHOe52+//fZ8Pi+xq4jPQXWV8Qo/j0wBwDvsRZ43A6/vAID7BEitkcPiHqNiqggQQiJmIkI01YqGuLbhbHXu7sjoW7KficTRvO3Iw2dc3YM/yoFsUbzH0czs9Z2qnoRRVfGz9dDbFoNGAvPq+OcCyuvnbArp2R7fZ76e3O2VM7BOiVotE57tE+Bn8ibbOI4Pzt9AxzHnGmJKu7uu36Wh48iixawHALIP9myVWlKm0cT9pYIGILokt1W9hnqepjrnLsTdbnd5Cp7Y9RI9t77CqxlAHsX7nW3AxebNC6YMUOe51joMuxS5lllURUrAQGiJiQnB1KoUU0QEFRMAFTMt86SqNc+BEND/AID5HyQOxHmefDaTqTBhnqeu66QWU/EfAlNTMUUEklLRIAUex5EApFRmNhVTrqUQAYIulTMqiFirIC4DUD/un1LFNcGyiXvXdfO8dLF6wbs753htqK89Wz7HI3r/2eUdIRChGRCF7RWvCvwRASTncZx6UQ5p2B1SiLaUaYoZ+mrxURao8ZJp4v5CIYTNdcuuSsL/4R/+4eldKtP43os9kLSWUznhOsUCEYmXLPMWO2/RsT9BRcgMIriFwN1dCEg556piohCAiJiIiZbFRWQYBvdA9/3Vy+XisfZ16aGLqZmBSQhhymWbp0pE0zT5fuY27kPXckMi06JEwSN3z350/TILW8SdH5e3IIIhBP/Bxen+St893t/E3cdkI2IM0a8JfNN1miY3s/RLFlij/uvyx+tjbtbEW+Dvz1y3AWDoOik5zzkXUQgYu9f90O92U6kAwXDppn0+GjanmZdOE/eXS2A0ZQQQADfwUg6vv/qqjGfJJU+TV4BU1TxNh8Nh8+/dpd6zJdM0DUO3bSq6qm7lgCrF0+4IZqYiVVSIgdAIDEDNPLiuopV4uJxOROQzr8/jWUT2+z16AYwqIoMaIoOqGacuzmXZ27Qr297z+Xyds96qFX3ikuqSQy+lIMHS/Kl1fb6UUt2QwK9CzGCrR/QPbVlmrsQdYFl+NuvKZYVbG7K2iP46fr++GthE2cV9SxM9zy9VsyoMyAGzlNPpibsuDjvuBpNlexnBLwAQiUB+2eDTxk3SxP2F4l6/q/kJqmoMwWIspRyPx4eHh2mattJsl/4t27DshZKPH+LNrddWQ3YAq5Jr9aw61lq5FAAA0RjisvVXxXjJPBAsFSYe5/qPXA9m2iJ3l0vDxfw95+yl997q6fuc28C8ZdtgKT8Hn5ixpZKqlL7viWCzkvcfBwB/s54P8Y/rn4ncXdz9hbz83z8Ev44BgPUiYIncYe0gg2fDALyuT/+0tAYJzucjc2TkFIMizvN8Ph6Vwv5wb6p+RbOlr5B8Zl/jRdPE/eWi+rz96Ht9RHR6eLycziLSdR2W/lwmZu72e89++GANWJMMXqyNiMzkuryovIqUKjzvhgMA5DwhWgjJzGIMiGggVRSFtyag8XwCFSScxwsRRSZFkJIpRk+fA9iWdkfgnLNqPZ2eXr9+rVrP52MIIedJpHhHEaI3kYIqqjLYsnnQdZ1oFa0557u7O0RfYARxqYHxXPmq+Mvit31oS57drud3m5fB+HvfbAn8wW0fwq8hnForfViEvOXJzXxR0bUZwP+dqs/VRoYAMSQiFCnzeNnt7573A3C1/8UrR+DGS6XVub9QRKFWEZEq4gIvCEacTZSpO+zuX7/tD3cUIlPohp3IIu4hBFOv93AJCwDgNuxbvw8iVZUihoEpBtc4ly9fQlAVRNxXgNCYYBovTOj7oiXPMYUuJY+jN65rZmqeTOt0GVWKVpnGs4mWPJmot1whKKiBCaihKYKBaS05xYCmJrXMU2QKxITrdqSZLq4GBVXQCpqgCYGiCYGhKYEZqBsvPu8BABQVo+fUeeQQOaBBzSUQB2IpVUplRmYuZXZnHf8FRHQrGELEjx5HRKJADDGw1PlyehrPRyk5MkYOjAQ+X9vPZ+1iWtaJv/gWAH6Z3++KF1P93G3j14X/+3//77/2ObwICij4mKK/8M8vbDFE/Pyfn4PAEMHQBLWoVlNFpMQ//fQTh/D17373X/7v/+XV/atpnB4fn86Xi4qI6TTn/WEHiG/evM2lAsLDw0PqekTq+i51vahyCKqy2+8JOediBrv9kLqkpoDWpbiE4aZmxYdB1zz1fY9gKtVUwNRMI3Hq0jheiDCEuFVJhhC6Po3TyIRMVPNsqlJyLeX+bv/0+MgEKUYpOc8zIxDi6fgUCQPB6elhPJ9M6q7vTarWqrX0KeRpNJEuBjTrYtSaIymjzuO55okRTeo4XWotZhqYmbCWAgp96gKHIqIGSMQcwEyrJI6MOJ9HVEFVQohMRFgll1rNLIToOuz/ewDgVwm1CnNgDqqWswf+3KVYpguz+eDZXKqqMceQUilV3F9BVWsxUyYmRDNTBAT4C2+9Newv/0MG7oT8z9xGarHjr0lLy7xI0MDAUHG1LwdCBDLRf/w//g+2ikUef/rh4YefYtftDq9CCOPxARYTlesNxuuJG4EIlh4fNDOgGDZnx610ZO38NABDk9WrC0wqmM+zAwAiAAOhNfuxnfhSSVJqJFYzQiAkqRnMXHAJTUWkZnTBExHMhKY1K4FJMVBgD8craEVDMkA1IkS15b6Bj71GUDNDU7Olw9ZWhxpcz2fZsEX3IVsT5QZgEJC0FkM0YWQmNEUCVVFYE/rPxfVXmRTfng0hLCX/pZSuizXrPM95KoopxF6losHQ96HvU+oosHhFvNbAwSPxv/z2X/ENur7zc7eNX5Em7i8dz/auA6b5cH8fUct5fPzpB8+lbJuZ62YpbBuDAOB2V76XiGSwdPEAA8a4jIqWMl+36cNzb86VVcBVJxFcFXpvW6PbMz0jxExaCjMzWM4zqjDzfDkHhDpPlSmlhCq1FtTIRKXMiFbKrMqey641iwSfLuKZbrMl5e1zPxA/bp4C1+Ot3enqX2m1RNiqORERA5c8GgIyswVm4rU0XlW3ynTYUvlXW6z+wfomttQaP7Q7dhMIOj69/eIr9hRQYFud1IpJy7g2mri/XFzWEZBo9exFUNXLfJmOJx9GkVLa6tm91hDW1lOXMC9NcXH3+vcQAgAlDkTLKKJtW1BELDzbo3/oECPX3TdbC49b2l7bPXpyZgjDbMJKSlJzATQCHKdLpFhqLnPgQFplrjMapCHNc/am1hCZiEJkERGtZkmfBZ3UBA0NDPC5Quaj22etv3oCIqmowTruVRfTgqVMs1aWEAIFJCWCpZDm49qY7Y1vNUK+Z1tzFZ0BLITQdYQFTeX89DjV+uVXfx/NgClgh37p8Gt/rxq/EZq4v1A8c4CI5EPgTImWSZwu3K9evRpfv44xeo12kezi7nGrmyMCQIzRK0NijC7uMUYAHbq+1uJC7CvEUkkZnzswYS3lNjP3d7xOwngFIcelSH4TOz+UgaKpqUgWlcoBEazmmSNqLZVyzVhyEclKwYRKzVSsSkFS0SBCaqJa1cRM1ATUw2dRQzMxJIBfIO5EqKqK6hVEXjvPMVAI7qNAgUMgpoCIhKhVPMlum6wDAICqVjUTZWbQxW7eRNSWiyciN+iUkqcK+NOP3/f7w0Gs2wvHBEyIjMhqrRTypdPE/eWCuBRWINKys4fgyfHU92wiIsfj8XK5bGkZT7P4AvCRuHvkvi4AwszuUuCFkq5fm28MeDBuevVXg6swdnvOarC+ltn4QAyTMk8IWmuukhEhMKkImNSafTxennWeC7rTe81aiwQQKcxmJleWirL+AbPFc8ZMDAxWX52r8Bztk7QSAKAaMaqqka1W9YjruHA3b5jnmRkTIS15LfWE+9X/BQDANrzJ3++WpQkczCwXqblKFmMkSEzw8O7HnVaMEQJ3xEhohC0j04Am7i8cRCRAAyBPETDnupi2Pz09PT4+TtOUc87TZYjkOXfvsdwsB3yb9NOc+9YyCgBuS0tEXdeZ6XMgfJXLFllct+CqxNBWBy61Z99dfzSXqQtcaynz2HVDYpprYTCtmdEItExzmUvXRUYrZTZTAzITAwRQH+rn0m8gZmIAV7O2ZR3N9EnwftVZClebBABgosiLuBcEAqDAw35XVcZ5qrOEQJ7CYsCiS2U9XCXcF6s1Ee8O86ZWf9dVM4FXmiIzKpjXOpV5qnmWWszngXhip6VmGm3XpQFrRR4CIKC3Kc3z/PT0VErZ7Xa73W7rg7/uaN0e+SzuiOtHc4F2d61rrYTP5To+MkRcJ2nI5m3geie54DJjuhD5Vq0QkUhBRACttVavmUFP4wgAAKrfIiKSeTuVmW23/q+fnt71OV+f3naHccnjLJmT1eMldsu8jlLK5nuDBvThu/5oMcs5u9nvdkCfT+uXAsMwdCGCaJ7GQEhoAQmXDI6ZWcvJNKBF7v/h+Gv5/XlaAZbhoVvdmtVawazv+1evXr0bBhEppcQYmSml6J2rtdYY4zzP3m3vYeY4jjHxMAyllGm67LretxMR0U3BxnGc57lPcauPrHnyiD6EoCDbnOics5n5sNYiUko53N275PnrDrt+OtXz+RxjfPv2La4uxG7alXN283Q/PVVNKSmYG+N46t+N3X0hcSuxWqsL6PZ2vF4lhOCblL4yZVlqOv18PH9SazWbUoi11KeHx343hBSny8XMOCAF7oZ+HqdpmgjxcDj0w96QVBY1t9W2zACG1CUOElMpJY/TeDqrKqDd7e/VqlQRMUKKiSNF47Df9aZ6Oj8JwQ6w5wBmZLjZD3y6Yftrf4UbfyOauDc+ABE5BEiwGal7fqDk0ZcAZka0GKOPAHWPLddBANiqJ+HZcFHR8LpG8KOX8zvXW6nbYKPr51xrk5nZEsG7gQEBCACZVURGVERGXDo//XZJVRgBLuOjTRHo+b7ZOlZ6HS59lWpf/vpRCH+dTgE14A/fGyGIuZOB+wBbLW6+pgLIwewzVwbbYNjrKwADzTmrVVM0Q0RkAiI0xj5FSH1I0f8j5rlEiiEZXRWVfnSn8UJo4t74GGbmRFsZn0/Rm+oMz6a+i4P5tWuKG4ettrcIAJ6LEBEItNlpmYpdOWThOr4urMUwiMi4nIYbFWyWZJvhopkRBmAyZCMyRUUSU0VCjgaCHIHMRIxYgRhRVUwRwKs+GYzMEBSACAwBCYwACIwACQzNKnwuIbMNPPK6zS2VZGogerUeIAAAofejutXa0vFfpegcegLz2kVAW2Z125UTJAEisSEpqoHmPJkJmCEwERp615QyWowh9AlTqKo5Z4wppAifk/UWtr8omrg3PkBEYC2v3tLEkWkYBiRY09/VMyEhBMTBU8MpJRJY50R3tVZVXFxl7Nl2pkrdQmC40ndE3sQdkbbCxy2ivxZ3FaMQkZAowNIQKgCkhiEkBWGOQEaESIzIBiDLRAt/CQagzcXFzOuF/BEEQDMEo+eSR5/HBGZmuLrH+MLjcsxIKqIEIAqBAUABjNAMfA6tmjIzILABAQKRlAr4fLGy7S5sFe5b3aeqGmiti6OyavUhUaYKlE6n4yGlIbwJqSsQNkMyA1raa1vY/lJp4t74ABGpqCbmyetl5qfL7dZbf4XrkWexAdUtFVW1lsq8jAyN/ByVu/r7a11H7hTi9iCF+JyTUWVmNdsy9b4/ScjIhG4Pr7aYCAByiIhMHIABiwExIAGhSjZDBEIkBEJkUzAAUwAic+sAQzfdAaBtAfpI3Ld+XU8yrUVBoWqBtZxme2uAqLjsrIKBmS6l/QpKgPScjNo2jZdlYx3It+XiU9+ZcC0FAKxq0QJQAetcBWO8e/1lCIFDX5B9dbF1RNSnBTm/9les8TeiiXvjA9z+ZYvZ16YhLXnmsMyjiJE94e6bqx7J5pwN3D3Gaq15mkLgLcnwHIR+GLZv1TWby+5SMk9k6wiOEMKcyzbAz5vyyZv5mRDQSEFQwcCQYyAzCgwAyIRAbpujupSQkKfpDQ3Mi+tNAQncE9gU4MrsYM26wPa3bX1ytrJ0VSVAVaU1Ze866oMA65xVSxWppZqoGXLX41rfeV0n45ruba64Tm5StRijEMASyBuamKIg5DKez+fL5RLu5hiHGKOFgEBe+dMi95dME/fGB3Rdp3msIu7rklIKIZhk+8DA3bxz1cxqFS9On+dZjXe7nWfhx3HsuvTRfKKtOn7JU1/pu+/Hbo7wHp6bGaIxM0D5IC2jGqgzIiMGRDVQJEUSwBiiqWKIZmZUjdiIdbHS9YJPWtMyph6wPydkaDHNXZwRYBN5gOf0+rbh6VaMS9eSqAJ+tHQBADE/75SKbHG9iNDV87e0jHeE+ScA67xvVUXfJPCdZzYz8tiejU31cj6H46mnIYaEQIigV/vATd9fJk3cXyiGurW6XO+yJSQhziYMGCKllELkaha61KXoY49Uq2fkvWAxBASgWmdEBCMEVq25FI4ckRDRkA1UDEEEkBS8iVIN2UAUyJCRCZTBAIiBCCiYiYIaKCAZAhAbgAICsQLalp93lV3ZKkxgy424mIIpGK8/qAhoz7aIDB/c//CTIgMzJABSNAQ2MDUEZIDPb7oCAK/nJEUlFykqImDEzBSJmS9TBlOf/ud2uz5WBBgwIDAsbab+uCoU9Y8d1soiIjaKSMkIp+kSTmfs9tz1MXYMoAYGxoCKgGpGSAa6PkIG/5bbxn8Imrj/jfjFQdO/s2Wq4LWmL36HCPDw8L5j6mIIkUSKSDasigpSu+5wf39fa314eMg57/f7/X6otZYizJRST0TzXFQhhJD6IcSYYo9o4zQjQYpd7JKIiEmt4j1HzFE5VKJiBIGIgwHMIlYW10khmlW73b5Wy7UgcVbb3b8CUEWQdRMSEFPXxZRiCCGEp6enYRhe393nnCPx8XLe7fr3jw/AQeucUpjPl10/KGi1SmaApCACoiCIhEhMJLUYRWQWAw4pdamIYewQSupTmbMqdF1/OZ3e53J/uDsej6WUHe7KnAHcFzPOde6HA9bzdLkgkhLlWm0uuz4RYK15ngWZuq4b9q8U4XQ6CQjo4k+pVVQrQM0lqyqsFfFM0bcPqqnVSnPO8ziMk6URkJQjEQIhmBEhrQM8cO2f+szt9oS/4A8AAD6vZ9d3qHm4/2Zo4v4S8S3CrYtxiREByLTve8kXUEW0Wss4nX0Ps+86MzudTm7e2/d9SgmWfAKupeVohqpQa0VggCVIN1QANAQ1ENNlj3CNx5EJmXRNZgCAmE/UIzMzIANSQwU1ZEA0ZPUadp+6hM+7hcu7+1wTrOdP1K9YmKBAVWFmL09UT7x8OJwIANDIcLkFCwDi5wNARgzLjsJim+PpFFW1Kst2aBUQVRARYQr+ieVymuYpIjAjEYVIAKAgZkTMHMl3WKspqikooQFBle3/7YNBLvM837+9v3v1Coin8cwpdTGFEGXZ2V3mvuBn7/9rbxv/IWjLbOMDhn4AAK1SczmdTq7mkdg3Bp+ens7nMxHtdjv3evSM8GYPsOym5oyI3hmkqmua+7km5LpPZ8svwyc9/f7gR8+/bm66TrxcF/DQVcHiJu7baGxX3s21eOP6pT/K9nx0/9O0z/UMVe+wBQCvBN02it1kzR8fx9F/xB0aFg8DZt/kAAB3mNm2OpbxXFeh83o2KlIAdRj6vu/NbJ5nt2huvHCauDc+BhFzzg8PD+/evXOZ3gxetunPvu/nf93EF9au1JwzIvv962ZLVVWx7cnwvKEa7GfYjmlXXonwicJeC71XmGwavf1126LcClG2NQB+JnV+LfGf5ve3xwHAlwr/THLOvobV+pwl9+f407zKqC7z9oKvOr5XnFLaiiCvlrSfvcLuuu54PP70008A8Pr1633f+272r/0lavz6NHF/udD65wNMCfB0Ov3TP/3T+3fvPGxUVW/GSSn1fb/VO7puwqqJ1+q2yJOAyiaRSxvOVeLj+RT+GXHf4n0Pja/F/VNgy8Cobnf+xY/iI33/KHL/NIq/XlpgrX6x1RPYDXm8ltSXEP8ML5fLOI5EdH9/7zG+T5W6XmN45aP5gv7KH583Lpcml9Pxxx++Oz09xhh3+34p+m+8bFrO/SWyzP/85BEAyDnXWn/44YdvvvnmfD4HYkYyolprpMVIC9ZZd7Ca9ALAVpoNACJeMri4F8AauqrCp5mQtZr++RG7ah0CWK4b/OCruCviOj0KFje1TZG3RqctcgcERJS6jPpzIfbnuBzjlVH7pzpuV/c/+BgRrz3Z/TrGX9Fd1baOJ/+U3KfBDdF0txvH8zzPsfZeV7pYsFHwTzLGqIBuK7YNNbxC/bRE5O7uLhf97rvvZrH/R9e9fvul9zE1XjhN3F8un5bvoIFf1H/3pz9//+13qhqIpMrQdQiGah6wuzJujZRbyOm6D+tUPHD/dDMERmAD8dh/nZG9Trt2Tby6hPggQ4LL067dCGzpioJN3K9F2RPcfp6LvAZGJNdW74HyIk5fMwA+Y7rylyTc7cPnyzouysXdzIZh8OsbP3/fUAUA96Sc53Ge566UrWnAm7NgcWdDBSylqFQRIfMeWbg+UzRgsEBgKXCVy+X8/fffIvNuf/9rf7kavz4tLdNYoLXbfpqm9+/f+8apq3lK6TDs3Nt2nmcP213mrgxhPnBEgQ/nSj+/yuIS9rGnyvaET3Pf20E+ynp/VnPhkzqZLWW02Qb4y3nkfn1uH+3l/py+f/av287EZqbma4ln3peKo7533+BpmoZh8G3YrcfVVhNNWC+Drpu/PvufBQBd143jWGv94osv7u7uHh7eff/tt+N0+bW/TY1fH/w0Zmk0Go3Gf3Ra5N5oNBo3SBP3RqPRuEGauDcajcYN0sS90Wg0bpAm7o1Go3GDNHFvNBqNG6SJe6PRaNwgTdwbjUbjBmni3mg0GjdIE/dGo9G4QZq4NxqNxg3SxL3RaDRukCbujUajcYM0cW80Go0bpIl7o9Fo3CBN3BuNRuMGaeLeaDQaN0gT90aj0bhBmrg3Go3GDdLEvdFoNG6QJu6NRqNxgzRxbzQajRukiXuj0WjcIE3cG41G4wZp4t5oNBo3SBP3RqPRuEGauDcajcYN0sS90Wg0bpAm7o1Go3GDNHFvNBqNG6SJe6PRaNwgTdwbjUbjBmni3mg0GjdIE/dGo9G4QZq4NxqNxg3SxL3RaDRukCbujUajcYM0cW80Go0bpIl7o9Fo3CBN3BuNRuMGaeLeaDQaN0gT90aj0bhBmrg3Go3GDdLEvdFoNG6QJu6NRqNxgzRxbzQajRukiXuj0WjcIE3cG41G4wZp4t5oNBo3SBP3RqPRuEGauDcajcYN0sS90Wg0bpAm7o1Go3GDNHFvNBqNG6SJe6PRaNwgTdwbjUbjBmni3mg0GjdIE/dGo9G4QZq4NxqNxg3SxL3RaDRukCbujUajcYM0cW80Go0bpIl7o9Fo3CBN3BuNRuMGaeLeaDQaN0gT90aj0bhBmrg3Go3GDdLEvdFoNG6QJu6NRqNxgzRxbzQajRukiXuj0WjcIE3cG41G4wZp4t5oNBo3SBP3RqPRuEGauDcajcYN0sS90Wg0bpAm7o1Go3GDNHFvNBqNG6SJe6PRaNwgTdwbjUbjBmni3mg0GjdIE/dGo9G4QZq4NxqNxg3SxL3RaDRukCbujUajcYM0cW80Go0bpIl7o9Fo3CBN3BuNRuMGaeLeaDQaN0gT90aj0bhBmrg3Go3GDdLEvdFoNG6QJu6NRqNxgzRxbzQajRukiXuj0WjcIE3cG41G4wZp4t5oNBo3SBP3RqPRuEGauDcajcYN0sS90Wg0bpAm7o1Go3GDNHFvNBqNG6SJe6PRaNwgTdwbjUbjBmni3mg0GjdIE/dGo9G4QZq4NxqNxg3SxL3RaDRukCbujUajcYM0cW80Go0bpIl7o9Fo3CBN3BuNRuMGaeLeaDQaN0gT90aj0bhBmrg3Go3GDdLEvdFoNG6QJu6NRqNxgzRxbzQajRukiXuj0WjcIE3cG41G4wZp4t5oNBo3SBP3RqPRuEGauDcajcYN0sS90Wg0bpAm7o1Go3GDNHFvNBqNG6SJe6PRaNwgTdwbjUbjBmni3mg0GjdIE/dGo9G4QZq4NxqNxg3SxL3RaDRukCbujUajcYM0cW80Go0bpIl7o9Fo3CBN3BuNRuMGaeLeaDQaN0gT90aj0bhBmrg3Go3GDdLEvdFoNG6QJu6NRqNxgzRxbzQajRukiXuj0WjcIE3cG41G4wZp4t5oNBo3SBP3RqPRuEGauDcajcYN0sS90Wg0bpAm7o1Go3GD/P8B6cuEg3u63/EAAAAASUVORK5CYII=", + "text/plain": [ + " PNG 500x500 DirectClass 8-bit 49kb" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img3_pix = img3.export_pixels # Exporting pixels from img3, map is set as default(=RGB) as the image is RGB\n", + "img3_copy = Rubyplot::Image.new(500,500) # Creating an Image to copy img1 with more number of columns and rows than exported\n", + "img3_copy.import_pixels(img3_pix,\"RGB\",120,150,252,200) # Importing pixels extracted from img3 to offset (120,150)\n", + "img3_copy.imshow # Showing the copied image\n", + "# img3_copy.imwrite(\"cat2_copy.jpg\") # Image can be written with any format" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns in image = 500\n", + "Rows in image = 500\n" + ] + } + ], + "source": [ + "# Columns and Rows in Image\n", + "puts('Columns in image = '+img3_copy.columns.to_s)\n", + "puts('Rows in image = '+img3_copy.rows.to_s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Manipulating Values (using Numo array)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "paris.jpeg=> JPEG 500x747 500x747+0+0 DirectClass 8-bit 87kb" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img4 = Rubyplot::Image.new # Creating a new Image Object\n", + "img4.imread('paris.jpeg') # Reading an image\n", + "img4.imshow # Showing the Image" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns in image = 500\n", + "Rows in image = 747\n" + ] + } + ], + "source": [ + "# Columns and Rows in Image\n", + "puts('Columns in image = '+img4.columns.to_s)\n", + "puts('Rows in image = '+img4.rows.to_s)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted Ruby Array to Numo array of shape [3, 747, 500]\n" + ] + } + ], + "source": [ + "require 'numo/narray'\n", + "img4_pix = img4.export_pixels # Exporting pixels from img4, map is set as RGB as the image is RGB\n", + "img4_pix_narray = Numo::DFloat.cast(img4_pix) # Casting exported pixel array as a Numo::DFloat array\n", + "puts('Converted Ruby Array to Numo array of shape '+img4_pix_narray.shape.to_s)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Choosing pixels which have more intensity than Rubyplot.QuantumRange/3 for each channel\n" + ] + } + ], + "source": [ + "# Manipulating Numo array\n", + "img4_pix_narray_new = Numo::DFloat.cast(img4_pix_narray>(Rubyplot.QuantumRange/3)) * Rubyplot.QuantumRange\n", + "puts 'Choosing pixels which have more intensity than Rubyplot.QuantumRange/3 for each channel'\n", + "# Intensity of a pixel ranges from 0 to Rubyplot.QuantumRange" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Rubyplot Tutorial\n", + "\n", + "This iruby notebook contains a tutorial and template code for Rubyplot for Magick backend. \n", + "P.S. - Tutorial for GR coming soon! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Blog Links\n", + "Blog links with explanation for different topics - \n", + "[1. Installation](https://alishdipani.github.io/gsoc2019/2019/06/09/Rubyplot-installation-guide/) \n", + "[2. A detailed scatter plot example with the entire working of Rubyplot explained with codebase](https://alishdipani.github.io/gsoc2019/2019/06/10/The-Scatter-plot-example/) \n", + "[3. Simple plots in Rubyplot](https://alishdipani.github.io/gsoc2019/2019/06/28/Simple-Plots-in-Rubyplot/) \n", + "[4. Multi plots in Rubyplot](https://alishdipani.github.io/gsoc2019/2019/07/13/Multi-plots-in-Rubyplot/) \n", + "[5. Show and plot functions explained](https://alishdipani.github.io/gsoc2019/2019/07/26/The-show-and-the-plot-functions/) \n", + "[6. IRuby integration and ticks](https://alishdipani.github.io/gsoc2019/2019/08/22/IRuby-integration-and-ticks/) \n", + "[7. Wrapping up GSoC 2019](https://alishdipani.github.io/gsoc2019/2019/08/22/Wrapping-up-GSoC-2019/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "1. [Step by Step tutorial](#Step-by-Step-tutorial) \n", + "2. [Examples](#Examples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step by Step tutorial\n", + "\n", + "Any graph in Rubyplot can be created in just 4 easy steps - \n", + "1. [Importing Rubyplot and set up important properties for Rubyplot i.e. backend, inline show, etc.](#Step-1---Setting-up-Rubyplot) \n", + "2. [Create a Figure(i.e. Canvas) on which the graphs will be plotted](#Step-2---Creating-the-Figure) \n", + "3. [Choose the types of graphs and set their properties like data, title, colour, etc.](#Step-3---Declaring-the-plots) \n", + "4. [Display the graph or save the Figure ](#Step-4---Save-or-display-the-Figure) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1 - Setting up Rubyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "true" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "require 'rubyplot' # Importing Rubyplot\n", + "# Set the backend environment variable to desired backend by the command below in the terminal\n", + "# export RUBYPLOT_BACKEND=\"GR\"\n", + "# export RUBYPLOT_BACKEND=\"MAGICK\"\n", + "Rubyplot.set_backend :magick # Choose the backend from magick or gr\n", + "\n", + "# To show the figure in iruby notebook, the function below is called\n", + "# Otherwise the figure will be shown in a pop-up window\n", + "Rubyplot.inline # showing the figure in iruby notebook\n", + "# To stop showing the figure in iruby notebook and instead show in a pop-up window, uncomment and run the next line\n", + "# Rubyplot.stop_inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2 - Creating the Figure" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[nil]]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Declare a new Figure object and specify the height, width and the unit of measurement for the Figure from cm, inch or pixels\n", + "figure = Rubyplot::Figure.new(height: 20, width: 20, figsize_unit: :cm) # Declaring a new figure\n", + "# Default dimensions are 40 cms\n", + "\n", + "# Adding subplots\n", + "figure.add_subplots! 1,1 # Declaring subplots by specifying the number of rows and columns of subplots in the Figure\n", + "# By default a Figure object contains only one subplot, so in case only one subplot is needed, the above code is not needed\n", + "\n", + "# For example, if we want to create a figure with 6 subplots having 3 rows and 2 coulmns, the code is\n", + "# figure.add_subplots! 3,2\n", + "# The oreintation of the Figure will be - \n", + "# |------------|------------|\n", + "# | | |\n", + "# | (2,0) | (2,1) |\n", + "# | | |\n", + "# |------------|------------|\n", + "# | | |\n", + "# | (1,0) | (1,1) |\n", + "# | | |\n", + "# |------------|------------|\n", + "# | | |\n", + "# | (0,0) | (0,1) |\n", + "# | | |\n", + "# |------------|------------|" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3 - Declaring the plots\n", + "\n", + "To declare plots - \n", + "1. Choose the position of the subplot by specifying the position as (row,column)\n", + "P.S. - The row and column numbers start from 0\n", + "2. Choose the type of plot\n", + "3. Set the properties of the plot i.e. title, colour, data, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "true" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "axes00 = figure.add_subplot! 0,0 # Choose the position of the subplot by specifying the row number and column number\n", + "\n", + "# Choose the plot type, here scatter plot is chosen\n", + "axes00.scatter! do |p|\n", + " # Set the properties of the plot\n", + " p.data [1, 2, 3, 4, 5],[12, 55, 4, 10, 24] # Data as arrays of x coordinates and y coordinates\n", + " # i.e. the points are (1,12), (2,55), (3,4), (4,10), (5,24)\n", + " p.marker_type = :diamond # Type of marker\n", + " p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + " p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Diamonds\" # Label for this data\n", + "end\n", + "\n", + "# Set the properties of the subplot\n", + "axes00.title = \"A scatter plot\" # Title of the plot\n", + "axes00.x_title = \"X-axis\" # Title of the X axis\n", + "axes00.y_title = \"Y-axis\" # Title of the Y-axis\n", + "axes00.square_axes = true # Setting square axes\n", + "\n", + "# If there are multiple subplots, repeat this step for each subplot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4 - Save or display the Figure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 PseudoClass 138c 16-bit 11kb" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the Figure by calling the show function\n", + "figure.show # Since inline was already called, the figure is displayed in the iruby notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Displaying in a pop-up window\n", + "Rubyplot.stop_inline # Stopping inline display\n", + "figure.show # Calling show function for displaying the Figure\n", + "\n", + "Rubyplot.inline # Reverting back to inline for convenience in the tutorial" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Saving in a file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calling the write function to save the Figure\n", + "figure.write('./1.png') # Input is a relative or absolute path with image format specified" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examples\n", + "Simple plots - \n", + "[1. Scatter plot](#1.-Scatter-plot) \n", + "[2. Bar plot](#2.-Bar-plot) \n", + "[3. Area plot](#3.-Area-plot) \n", + "[4. Bubble plot](#4.-Bubble-plot) \n", + "[5. Histogram](#5.-Histogram) \n", + "[6. Candle-stick plot](#6.-Candle-stick-plot) \n", + "[7. Error-bar plot](#7.-Error-bar-plot) \n", + "[8. Box plot](#8.-Box-plot) \n", + "[9. Line plot](#9.-Line-plot) \n", + "[10. Stacked-bar plot](#10.-Stacked-bar-plot) \n", + " \n", + "Multi plots - \n", + "[11. Multi Stacked-bar plot](#11.-Multi-Stacked-bar-plot) \n", + "[12. Multi Bar plot](#12.-Multi-Bar-plot) \n", + "[13. Multi Candle-stick plot](#13.-Multi-Candle-stick-plot) \n", + "[14. Multi Box plot](#14.-Multi-Box-plot) \n", + " \n", + "Multiple Subplots - \n", + "[15. Multiple Subplots Example 1](#15.-Multiple-Subplots-Example-1) \n", + "[16. Multiple Subplots Example 2](#16.-Multiple-Subplots-Example-2) \n", + "[17. Multiple Subplots Example 3](#17.-Multiple-Subplots-Example-3) \n", + " \n", + "Plot function- \n", + "[18. Plot function Examples](#18.-Plot-function-Examples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Scatter plot" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 PseudoClass 132c 16-bit 12kb" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.scatter! do |p|\n", + " p.data [1, 2, 3, 4, 5],[12, 55, 4, 10, 24] # Data as arrays of x coordinates and y coordinates\n", + " # i.e. the points are (1,12), (2,55), (3,4), (4,10), (5,24)\n", + " p.marker_type = :diamond # Type of marker\n", + " p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + " p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Diamonds\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A scatter plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Bar plot" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 65c 16-bit 9kb" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.bar! do |p|\n", + " p.data [23, 13, 45, 67, 5] # Data as given as heights of bars\n", + " p.color = :neon_red # Colour of the bars\n", + " p.spacing_ratio = 0.3 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Points\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A bar plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Area plot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BqaQCKMTy2AMAAADk5bG3vXv50+8KofP+/3j7914Ye5ri/MTR7/xf33xDCOGBI37m2A/FngYaar9NjVNuPHvfpsZrY49G9cxWUm2f+N7mm0Jo7VwVNsYeCiBhNjYAAID6+Pyj6394eOwhSvBQ+/HH3xx7CGg2mxoMYtFKqttiTwWQLsEGAAAAQL+cU4NhLFZJdUHsoQDSJdgAAAAA6JNNDUYxW0l16sTGzTfFngYgXYINAAAAgF5sapADlVQA+RBsAAAAAPRgU4Nc7F9JdZdKKoDBCTYAAAAAlmJTgwIsqKTaHnsagPQINgAAAACWYFODIsyrpPrHO34QVFIBDEiwAQAAANDNpgZFmqmkuve5Hzv9SpVUAIMSbAAAADRElmVZlvW+bLqqPx6+b+WwqUEZWltUUgEMQ7ABAABQU8Me+HbAnEazqUGJsq1j9x89tqCS6g9UUgH0JtgAAAComaUCiU6n0+l0el8OejsCkGrwfciHTQ1KtX8l1Z0qqQB6E2wAAADU3KCBRffnO2BeDd3fh0G/r/RgU4OIVFIBDEawAQAAADSeTQ1iUkkFMJjlsQeAJjvzE2d+4kxvlAEAKNjMX/qPurnRfX29/n+v2+t3E2TYzYRe15/XxkPZmxTd1z/z393VYDY6+rTfpsYpN569b1PjtbFHo0HmKqlOOf0jIbRPm3ze1j8PoRVWhY2fiT0cQPUINiCiT5z5iTM/cWbsKQAAqJvuA90z8joAv9T193t9w57MPK+gZNDr7ff2igoSVIEVa3ZT49yDvvyUz4WQnbfiSTY1iGW2kmr7xP/efFMIrZ2rwsbYQwFUkCoqAACAmup1cvDuv/CPdQB91JOYj3q9wwYi3bdDYpxTgwpSSQXQH8EGAABAwwwaeORl0ECg35OYj3q9Rc2ft7weL/ZxTg0qaa6S6mOnXxlC+7TJO7f+eeyhAKpHFRVEdOQLjnzBkS/o//MfvO3B2x70lxoAABSkV8BRt82EUauzin5celWJMSTn1CABKqkADkywARE9sOOBHQ/s6P/z/SIDAEA/Ym8W1NVS5xYhLc6pQQpmK6n27P3OgkqqMw9+/6ED/IEkQF2pogIAAKCR+q3c6g6GYlU+LVUhlvc5RGrLOTVIyf6VVHdtvTD2UADVIdgAAAComVHPmVHUgfBBA4F+N09Gvd6qGXXjxobO4pxTgxTNVlKdOvHqzTfFngagOgQbAAAADdMddPQKPoo+UN7vHEVd77D3z8m6E2FTg4TNVlLdtvcf7/h+mKukAmg4wQYAAEBN9aooyvvrhq126neevOfOK7DJK+DI+9wogpfpx8GmBilTSQWwKMEGAABAQwx7joZhr6/sefKar+xAKK+vL/t6K8+mBjWikgpgIcEGAAAAUDs2NagTlVQACwk2AAAAgPqwqUEdqaQCWECwAQAAANSGTQ3qrLVl5WUqqQAEGwAAAEAd2NSgAbKtYw+opAIIYXnsAQAAAPJy0Pvfc9a/Pj2E8KEn1j7+U7GnKVD7KYeMPS/2EFAts5sa5x705ad8LoTsvBVPsqlB7cxVUp1y+lUhtE+bfP7WC0NohVVh442xhwMoj2ADAACoj19+0q88dea3nO/EHgYoxX6bGqfcePa+TY3Xxh4NitLasvKyNWMhtLdP/Pvmm0Jo7VwVNsYeCqBEqqgAAACAZDmnBk2kkgpoOsEGAAAAkB7n1KDJ5iqprj79QyG0T5u8a+uFsYcCKI9gAwAAAEiOTQ0IobVl5QfXjIXQPnXi1Zu3x54GoDyCDQAAACAdNjVg1oJKqn8PKqmAxnDycAAAoHAPf+3hrz38tdhTAHWw/Kx//j/bQwhP2tr66JOeEsKPfvKgjx/+/BDCqx95/w+eH3u6Epwdzo49AhUyV0m1+vSrQmifNrlq64UhtMKqsPHG2MMBFEewAQAA5O8nw0+Gn5z7z6ed8LQTnnZC7KGApL0wPD1kIXzr2b/1qYN+KYTNE1//0ROvCOG+X7tqza+uiT1cie4Kd4WdsYegamYrqbZPfH/z9hBaO1eFjbGHAihQ1pkWexCgtyzLsixb+uOezQAAQF11zrnnsK8cEcLj597w5bd+LoTlL3nlky4SmMI+P+h88Ym/C+GxPX/1nVedGsJPXHN+eOimEMKZB7//0BfEHg4gf86xAQAAAFSXc2pAb3OVVFef/qEQ2qdN3rX1wthDARRHsAEAAABUVued9zz/K68MIaw+6IanPD2E7NYVr33WJ2JPBdU0W0l16sTGzdtjTwNQHMEGAAAAUD02NWBg2daxB44eC6F9295/uOPfQwgf//EffP+22FMB5E+wAQAAAFSOTQ0YgkoqoCEEGwAAAEB12NSAkamkAupOsAEAAABUhk0NGJ1KKqDuBBsAAABAfDY1ID+LVVJdFHsogPwINgAAAIDobGpA/hZUUt0UexqA/Ag2AAAAgHhsakBhVFIBdSXYAAAAAKKxqQEFUkkF1JRgAwAAACifTQ0oTWvLyg+uOUwlFVAfgg0AAACgdDY1oDzZ1rEHjl6hkgqoD8EGAAAAUB6bGlA+lVRAzQg2AAAAgNLY1IB4FlRSbY89DcDwBBsAAABA8WxqQHQLKqn+d9hXSfX12FMBDE6wAQAAABTOpgZUwPxKqiumK6kujD0UwOAEGwAAAEBxbGpA5bS2rPzgmjGVVEC6BBsAAABAYWxqQPWopAJSJ9gAAAAA8mdTA6pLJRWQOMFGQbIsy7Isv8uqz9fvvFV9PAAAAMiXTQ2oPpVUQKoEGwAAAEB+bGpAMlRSAakSbFAKmxYAAADNYFMDErJ/JdVOlVRACgQbJel0Op1OZ/jLVOYZdN6qPB4AAACMyKYGJGteJdWrVVIBKRBsMJClNi8EDgAAAM1mUwPSpZIKSI1gI2ejnjy7qoqeL9XHBQAAoPFsakD6VFIBiRFslKTfA/SpHdAfdVNj0McFAACAarGpAfUxr5Jqo0oqoMoEGxUX+4B+7Nuv+jxXv+fq91z9nt4bJ3ldAgAAVIZNDaideZVUUyqpgCoTbJSsLifD7nfuXgfkh308HOgHAACIy6YG1NBilVQXxR4KYH+CjZwtdaC+1wH7Xh8v+0B+XicJL+rxqIrbXnrbS297aewpAAAASmRTA2pPJRVQdYINGMELvvSCL73gS7GnAAAAKI9NDai/BZVU3wsqqYDKyTqdVP42vhn6rW6q6+1XfZ5uf/cXf/cXf/cXIdz82M2P3fxY8bf3J+/4k3f8yTuq+3gAAAA1Nr2p8dhfX7Bm5QMhLPvCKa87+zOCDaizJz61Y/VH/zKE1tiq4zd+PoTWkau+uPH62FMBCDZyN2qFU14VULHmL+vxKPtxqQqPBwAAEEvnnHsO+8oRITx+7g1ffuvnQlj+klc+6aITYk8FFKlzwve+cd/1IbS3f/0Hn3xzCMt3vv23brk/9lQAqqhK0+uA9Kjn0Jj5+u7Lqir68QAAACAnzqkBjaWSCqgqwUbJBg0gUv8L/H5Pij5oIJP64wIAAJAK59SABjske9myl4TQuve5f3X6FSG0T5vcufWi2EMBCDZy54D74vJ6XDy+AAAAJbGpAUxrbVl5+ZqxENqnTmzcvD32NACCjcLMHIAf9ED8sF83qrKqn1J5PAAAAJrOpgYwQyUVUDWCjZJ0H6Bf6jLv689rvtQeDwAAAIZkUwPoNr+S6nKVVEB8gg0AAABglk0NYCmtLSsvX7NCJRUQn2ADAAAAsKkB9KSSCqgKwQYAAABgUwPoTSUVUBGCDQAAAGgymxrAgFRSAbEJNgAAAKDBbGoAg1JJBcQm2AAAAIAmsqkBDEslFRCZYAMAAAAayKYGMKoFlVQ3x54GaBLBBgAAADSJTQ0gJwsqqR4OKqmA0gg2AAAAoEFsagC5UUkFRCLYAAAAgCawqQEURCUVUDbBBgAAADSATQ2gKCqpgLIJNgAAAKDObGoARVNJBZRMsAEAAAA1ZlMDKEtry8rL14yppAKKJ9gAAACAOrKpAZQs2zr2wNGHq6QCiifYAAAAgBqyqQGUbrFKqvfGHgqoI8EGAAAA1IlNDSCyBZVU22NPA9SRYAMAAABqxKYGEJtKKqBogg0AAACoA5saQFWopAIKJtgAAACAGrCpAVTNvEqqTSqpgDwJNgAAACBlNjWAippXSTWpkgrIk2ADAAAAEmZTA6gslVRAQQQbAAAAkCKbGkAiWltWfnC2kurm2NMAdSDYAAAAgATZ1ABSkW0de3C2kuq7IYSP//gPVVIBoxBsAAAAQEpsagCpmV9J9UGVVMDoBBsAAACQEJsaQKpaW1Z+cM2KENqnTmxUSQWMQrABAAAAKbCpASRu0Uqq22NPBaRIsAEAAAAJsKkBJG+xSqqLYg8FpEiwAQAAAFVmUwOoGZVUwKgEGwAAAFBhNjWAulFJBYxKsAEAAABVZFMDqCuVVMCIBBsAAABQQTY1gLpTSQUMS7ABAAAAVWJTA2gIlVTAsAQbAAAAUCE2NYDGUEkFDEmwAQAAAFVgUwNoKJVUwKAEGwAAAFABNjWAplJJBQxKsAEAAAAx2dQAmm6xSqr3xh4KqDLBBgAAAERkUwNgH5VUQL8EGwAAABCDTQ2ABRZUUv1bUEkFLEmwAQAAABHY1ADoMldJ9VGVVMCBCDYgoizLsizr/xIAAKgBmxoAB9TasvLy6UqqTSqpgMUINgAAAKBENjUADmxeJdUelVTAYgQbAAAAUAabGgD9UUkF9CDYAAAAgBLY1AAYjEoqYCmCDYio0+l0Op3+LwEAgATZ1AAYyqKVVHfEngqoAsEGAAAAFMimBsCQ5ldSXaaSCpgj2AAAAIAi2NQAyEVry8rL1xw+XUm1PfY0QBUINgAAAKAANjUA8qGSCugm2AAAAIA82dQAyJdKKqCLYAMAAAByZFMDoBgqqYAZgg0AAADIg00NgEKppAJmCDYAAAAgBzY1AAqmkgqYJtgAAACAUdjUAChVa8vKy9esUEkFTSbYAAAAgBHY1AAoV7Z17MGjn6aSCppMsAEAAADDsKkBEIdKKmg8wQYAAAAMwaYGQFwLKqlujj0NUCbBBgAAAAzCpgZAJSyopHooqKSCBhFsAAAAwABsagBUhEoqaCzBBgAAAPTDpgZAJamkguYRbAAAAEAfbGoAVFO2dezBow9XSQVNItgAAACAA7GpAVBth2QvW7ZWJRU0iWADAAAADsCmBkAaVFJBcwg2AAAAYDE2NQCSopIKmkOwAQAAAIuwqQGQmP0rqe5WSQX1JNgAAACA+WxqACRtXiXVRpVUUE+CDQAAAJjHpgZA2lRSQf0JNgAAACAEmxoAdbFYJdX7Yg8F5EmwAQAAAMGmBkDdqKSC+hJsAAAA0Gw2NQBqaUEl1YNBJRXUiGADAACARrOpAVBT8yup/kIlFdSJYAMAAIBmsqkB0AitLSsvX3N4CO1TJzappIJ6EGwAAADQSDY1AJphXiXVbpVUUA+CDQAAAJrFpgZAs6ikgtoRbAAAANAoNjUAmkklFdSHYAMAAIBmsKkB0GgqqaA+BBsAAAA0gk0NgIZTSQW1IdgAAACg3mxqADCPSipIn2ADAACAWrOpAcB8KqkgfYINAAAA6smmBgCLmauk+ohKKkiTYAMAAIBasqkBwIG0tqy8QiUVpEmwAQAAQL3Y1ACgD4tWUt0ZeyqgH8tjDwAAAFX2xB/feO/bvhtC+Nz/+bkf7o49DTCQLx/0tac8PYTsvBVPsqkBwH7mKqlOOv2KENqnTf7i1veG0AqrwsZrYg8HHIhgAwAAFnPdoyc88twQnnj3zSdd+Y8hLP/8+nde8M7YQwGDWLb9Z648ZVUIIYS3xZ4FgOqaraTaPvGjzbeE0Nq5KmyMPRRwQIINAABYRHvD1De3vSqE1m3H/fK6N4SQhSNPP+6Y2FMBA/mT2AMAkILZSqo9e79zxwNhrpLqzIMvPvT42NMBi3GODQAAWER7/cTu8U+F0PrQsSevfVHsaQAAKMxcJdVHTv+LENqnTd699b2xhwIORLABAADzXffoiY88N4T2X3/70h3/FEI2seKSZ54QeygAAIo2W0l16sSmzbfEngY4EMEGAADM094w9Y1trwqhdcdxR6z7hxDCWPZnyz4beyoAAIo2W0l1297dCyqpgMoRbAAAwDwqqAAAGkolFSRDsAEAACGooAIAIISgkgpSsDz2ANBknxn/zPhnxmNPAQCEMK+C6rbj/ue6s0IIY9lvLPuZ2FMBAFC22UqqPXu/s6CS6syDLz70+NjTASGEkHWmxR4EmijLsizL8rs+z2YAGN7jL9/ynRc/O4TWUcd/e9P3Q8h2Hj52zEOxpwIAIJYnPrXjpI9eHkJrbNUvbpwIoXXkqq0bPxV7KiAEVVQAADTd/Aqq+1RQAQCwT2vLystnK6lujj0NMJ9gAwCARputoLrjuCPWTYUQxrI/W/bZ2FMBABBbtnXsoaOfFkL7tr27F1RSAdEJNgAAaLT2+ond49eE0PrQsSettakBAMCMQ7KXLVsbQuve537k9C0htE+bvHvr+2IPBYQg2ICoZs6J0e8lAJCj/SuoNqugAgCgm0oqqB7BBgAAjaSCCgCAfqikguoRbAAA0EgqqAAA6ItKKqgcwQYAAM2iggoAgCHMq6R6jUoqiEuwAQBAo6igAgBgGPMqqXappIK4BBsAADSKCioAAIaikgoqQ7ABAEAzqKACACAHCyqpbok9DTSTYAMAgEZQQQUAQB4WVFLdH1RSQQSCDQAAGmFBBdWLYk8DAECyVFJBdIINAADqbbEKqhNjDwUAQOpUUkE8gg0AAGqtvWHqG9terYIKAIB8qaSCeAQbAADUmgoqAAAKsX8l1TdUUkE5BBsAANTTdY+e+MjPT1dQ3auCCgCAYsyrpNqkkgrKIdgAAKCW2humvrntVSqoAAAolkoqKJ9gAwCAWmqvn9ilggoAgMKppILSCTYAAKgXFVQAAESgkgrKI9gAAKBWFlRQTQYVVAAAlGKRSqpzVFJBMQQbAADUSnv9xO7ZCqoTYk8DAEBj7F9JdffWi2MPBfXU2GAjy7Isy/a/7P44AACJmKugGldBBQBALCqpoHiNCzYGDSyWCj4AAKgWFVQAAFTBgkqqfw0qqaAAjQk2uoOJTqfT6XSW/vylPi7gAACoJhVUAABUwvxKqktVUkERGhNsDKtXAAIAQGQqqAAAqKDWlpWXr3maSioogmADAICkqaACAKCKVFJBcQQbAAAkTQUVAACVtH8l1TdUUkE+GhNsDFsp5ZwaAAAVpYIKAIAEzKuk2qiSCvLRmGCj21KBxcz/X+rjzrkBAFAN7Q1Tu1RQAQBQdSqpIH+NCzZmgol+A4pBPx8AgHK010/sUkEFAEDlzVVSXaWSCvLRuGCjW3dwIcgAAKg4FVQAACSotWXlFdOVVJtUUsFoGh9sAACQlnkVVE9TQQUAQCrmVVJ9UyUVjKYxwUb3uTNGvQQAII72+old458OofWhY09WQQUAQDJUUkFuGhNsAACQuPkVVPeEkE2suEQFFQAAqVFJBaNrTLDR61wa/Z5rw7k3AADiaG+Y2rXt1QsqqM5XQQUAQGqyrWMPqqSC0TQm2BjVTKChigoAII72+old49eooAIAIHGHZKfuV0n1/thDQVoEGwAAVJsKKgAAakglFQxPsAEAQKWpoAIAoI4WVFL9S9hXSXVX7KkgDYKNPqmgAgCIQwUVAAC1tFgl1cWxh4I0NCbYmAkmhr0EAKBkKqgAAGiA1paVV6w5XCUVDKIxwUZeZk4inheBCgDA4tobpnZt2xhC63YVVAAA1Fe2dezBo49QSQWDyDqdvA/V049+g4uivzt5ByiDzjvo7Tf9p7XX49X0xweAenn85Vv2vvi4EFpHHf9Pm74XQrbz8BXH/FvsqQAAoBhPfGrHiR/9YAitsVUnbPxqCK0jV31h4/8deyqoJhsbRDFsoGKTBQAaQAUVAAANpJIK+pd8sDHquTPKPudG0w/M53X/m/44AkCdLaig2hNUUAEA0AgqqaB/yQcbqUjlQPxMldGwl3nfPgDQPO31E7vGrwmhdeWxJ6+1qQEAQFMckp26bG0IrXufe9Xp4yG0T5v8xtaLYw8F1ZR8sDHqgXgH8Msx7Lkhej0+qQRGAEAf5ldQfUsFFQAAzdTasvKKNU9TSQUHknywUbZBD6RX9cB7r7mKquICAFiKCioAAFBJBf1obLBR9rk1ZlR9Q6Pf+ynoAADy1l4/sVsFFQAAjaeSCnpaHnuAso16IL7fYKJpB/xn7m/Vg5u8XX/F9Vdcf0UI1/z0NT99zU/HngYAEjVXQXXijm+FsOxn1957zvoQwlg4P9jYAACgoWYrqbZPPLr5lhBaO1eFjbGHgopoTLDRHTR0H4Af9OODSu2A/1LzNi2w6eWHD/3woR8+FMINb77hzTe8OfY0AJCm2QqqHcf90rrXhRDGst9YdmzsqQAAIK7ZSqo9e/cuqKQ68+D3Hboq9nQQV2OrqLr1Ch5mPt7vuSn6vd7Y93fQk6M7mfdC26/efvX2q2NPAQBpW1BBdULsaQAAoCIWq6R6f+yhoBoEGwUb9dwdzmVRbSedddJZJ50VewoASNRcBdXmHd8KIZtYcckzT4o9FAAAVMtsJdWpE5s23xx7GqiGxlRRQRFaT7SeaD0RwjG3HHPLMbcUf3v3nnDvCff6S1YAakIFFQAA9KaSCvaXdTpVLUvK+Y4usfHQ77k2+q2YKnqzIq/vVr+PR15f3+txGbXiqhk/xR4HAOrl8Zdv+ZcXPyeE1lHH37fpuyFkOw9fccx3Y08FAADV9MSndpz40ctCaI2tOmHj9hBaR676wsZPxp4K4mhMFdWwB9wHDSp6nbui33NZLHV9veYctboq78ej1+Pf63odyAeAGlJBBQAAA1tQSVVCcwhUWWOCjRmjnhw77wPpVT0wP2hQkvcmiXOKAEB9zVZQ3X7c09btCSGMZecv+2zsqQAAoNpmK6lu2/vNO/aGuUoqaKDGBRszem1CDLthkYpY9yev263b9wMAmqS9fmL3+DUhtK489uS1zh0FAAD9OSQ7ddnaEFr3Pveq08dDaJ82+Y2t7489FMSRfLCRVwVTUw0b3Iwa+BRVyQUAVJgKKgAAGNm8SqrXqKSiqZIPNnqdOyOVwGPUDZG8NkzyOjdI3rcLAKRPBRUAAIxuXiXVN1RS0VTJBxsz+j0gvlTgUfXgAwAgde31E7vHP62CCgAARqKSCuoTbCxl0A0AgQcAQM7mV1D9owoqAADIg0oqmqz2wcZSRg08AADojwoqAADIn0oqmqyxwcZSnNsBACBfKqgAAKAAKqloMMHGtH4rqAQeAAB9UkEFAACFU0lFEzUu2Bg0wLDBAQAwnPaGqV3bNk1XUO0OKqgAAKAAKqlootoGG4OeBFyAAQCQr/b6id3j10xXUJ0YexoAAKip/SupvqmSirpLPtgYNcAQZAAA5EwFFQAAlG5eJdUmlVTUXfLBxlIEGAAAcbQ3TO1WQQUAAOVSSUWTJB9sCDAAAKqlvX5ilwoqAAAo2Vwl1V+evlklFfWWfLABAEBFXPfoiY88TwUVAADE1Nqy8oo1R0xXUn0t9jRQDMEGAAC5aG+Y2r1tYwit2487XAUVAADEkW0de2i2kuo7QSUVtSTYAAAgF+31E7tVUAEAQGQqqWgAwQYAAKOZq6C6ZLqCavMzBRsAABCVSirqTLABAMBIFq2g+uvYUwEAQLOppKLOBBsAAIxEBRUAAFTQYpVUH4g9FORDsAEAwHBUUAEAQOUtqKS6JfY0kA/BBgAAQ1FBBQAA1aeSijoSbAAAMJT2+ond459WQQUAAJWmkooaEmwAADCY+RVU/6CCCgAAUtDasvKKNU9TSUU9CDYAABiICioAAEhPtnXsoaN/SiUV9SDYAABgICqoAAAgQSqpqBHBBgAA/VFBBQAAyVtQSfW12NPAcAQbAAD0RQUVAACkb0El1beDSiqSJNgAAKAvKqgAAKAGVFJRA4INAAAO7LpHT1JBBQAA9aKSipQJNgAAOKD2hqldKqgAAKBeVFKRMsEGAAAHpIIKAABqSCUVCRNsAACwOBVUAABQe/MqqV6jkopUCDYAAFhUe8PUrm2bpiuodgUVVAAAUEPzKqnunq2k2hl7KjgwwQYAAItSQQUAAA0wv5LqkulKqvfHHgoOTLABAMBC8yuopqYrqE6KPRQAAFCk1paVV6w5QiUVaRBsAACwgAoqAABoHpVUpESwAQDAAiqoAACggVRSkRDBBgAA+6igAgCAxlNJRQoEGwAAhBBUUAEAACqpSINgAwCAEIIKKgAAICxeSfWB2EPBQoINAICmU0EFAAB0UUlFlQk2AAAaTgUVAADQbUEl1T8HlVRUimADIsqyLMuy/i8BoAjt9RN7VFABAAALqKSiwgQbAABNNVdB9QEVVAAAwGJUUlFFgg0AgIZSQQUAAPSikooqEmwAADSUCioAAKAnlVRU0PLYA0CTXfzji3988Y/7//w/PPgPD/7Dg2NPDUDy5iqoTtgxFcKyn1177znrQghj4fxgYwMAAFjEbCXV9onHN38thNbOVWFj7KForKwzLfYgQG+9TiLu2QxAP9pP3H3YDReE0N7x9Xd+clMIy978ov/5W/819lQAAECl/aCz7Ym/DeGxPX+191WnhfAT15zfemhHCOHMg9936PNjD0fTqKICAGiY9vqJPePXqqACAAAGoJKKChFsAAA0xVwF1Qd2TIaQTazY/MyTYg8FAACkZLaS6tSJ12z+WuxpaCrBBgBAQ7Q3TO3atimE1u3HHb5uVwhhLDt/mXNqAAAAA8i2jj109E+F0L5t7913/HMI4eM/Puf7O2NPRdMINgAAGkIFFQAAMLK5SqoPT1dS7VJJRdkEGwAAdaeCCgAAyFlry8oPTVdSbVJJRdkEGwAANdfeMLV722tCaN1+3Ip13wwqqAAAgJEtUkn1TpVUlEWwAQBQc+31E7vHPx1C68pjX7zWpgYAAJCHmUqqPzjmoZM3hNA55KFvT10beyiaQrABAFBXKqgAAACoIcEGAEBNqaACAACgjpbHHgAAgGLMVlAddfy3No3HngYAAADyYWMDAKBuVFABAABQY4INAICaUUEFAABAnamiAgCoGRVUAAAA1JmNDQCAulBBBQAAQAMINgAAakIFFQAAAE2gigoAoCZUUAEAANAENjYAAFJ33aMnPfJ8FVQAAAA0g2ADACBx7Q1Tu7dtUkEFAABAM6iiAgBIXHv9xO7xa1VQAQAA0Aw2NgAAUjW/gmqPCioAAACaQbABAJAoFVQAAAA0kSoqAIBEqaACAACgiWxsAACkRgUVAAAADSbYAABIjAoqAAAAmkwVFQBAYtrrJ/ZMV1D946bNsacBAACActnYAABIxVwF1ftnK6hOjj0UAAAAlEuwAQCQiPaGqd3bXjNdQfWNoIIKAACARlJFBQCQiPb6iT3jn56uoLok9jQAAAAQh40NAICqU0EFAAAAswQbAAAVp4IKAAAA5qiiAgCoOBVUAAAAMMfGBgBAVamgAgAAgP0INgAAKkoFFQAAAOxPFRUAQEW110/sGb9WBRUAAADMZ2MDAKBq5ldQ7VZBBQAAAPMJNgAAKqa9YWr3tteqoAIAAIDFqKICAKiY9vqJPeOfDqF11PH/sOkDsacBAACAarGxAQBQFftXUI2roAIAAICFBBsAABWhggoAAAB6U0UFAFARKqgAAACgNxsbAACxqaACAACAvgk2AAAiU0EFAAAA/VNFBQAQmQoqAAAA6J+NDQCAWFRQAQAAwMAEGxBRlmVZlvV/CUC9zKugGlNBBQAAAP1RRQUAEEl7/cTk+LUhtI46fkoFFQAAAPTHxgYAQNnmKqgu3rFLBRUAAAAMQrABAFCy9oapPSqoAAAAYDiqqCCiTqfT6XT6/3zn2gCoh/b6iT0qqAAAAGA4NjYAAMqiggoAAABGJtgAACjJggqqu4MKKgAAABiCKioAgJKooAIAAIDR2dgAACiaCioAAADIjWADAKBgKqgAAAAgP6qoAAAKpoIKAAAA8mNjAwCgKCqoAAAAIHeCDQCAgqigAgAAgPypogIAKIgKKgAAAMifjQ0AgLypoAIAAIDCCDYAAHLW3jC1Z9uvqqACAACAIqiiAgDIWXv9xJ7xT09XUF0cexoAAACoFxsbAAB5WayC6sWxhwIAAIB6EWwAAOREBRUAAAAUTxUVAEBO2usn9oxfq4IKAAAAimRjAwBgVPMrqL6pggoAAACKJNgAABiRCioAAAAojyoqAIARqaACAACA8tjYAAAY1nWPnvTIKhVUAAAAUCbBBgDAkNobpvZse60KKgAAACiTKioAgCGpoAIAAIDy2dgAABiUCioAAACIRrABADCgBRVUO4MKKgAAACiRKioAgAGpoAIAAIB4bGwAAPTrukdPVkEFAAAAcQk2AAD61N4wtVsFFQAAAMSligoAoE/t9ROT0xVUkyqoAAAAIA4bGwAAvcxVUL1PBRUAAADEJdgAAOihvWFqjwoqAAAAqAZVVAAAPbTXT+wZv04FFQAAAFSBjQ0AgKXMr6D6hgoqAAAAqALBBgDAEtpnTO3Z9qsqqAAAAKBKVFEBACyhvW5iz/i10xVU74s9DQAAABCCjQ0AgP2poAIAAIDKEmwAAHRRQQUAAADVpYoKAKCLCioAAACoLhsbAAAzVFABAABA5Qk2AACmzaugOkwFFQAAAFSTKqqCZVmWZdngX9fpdDqdTvXnHXbOYR+XvG4fABajggoAAACqz8ZGQUY9cJ/Xgf9+b2fY2xv068q6XwAwEBVUAAAAkAzBRs7yPnCfShCQypwAsBgVVAAAAJAOVVQl6VWZVHYw0Ov2lpq3qDlVSgEQkwoqAAAASIeNDQaSd0Aj0AAgKhVUAAAAkBzBRs5mDtR3X3Jg3ef6WOoSAPKkggoAAADSo4oqsmEroUZVlcBl2JOPV2X+u95117vuelcIkwdNHjR5UOxpABhUe93EnvHrVFABAABASgQbJev3QH5VDtwPOn9Z5+aoSsBx15PvevJdTw7hDee94bw3nBdvDgAGNFdB9aIde0JY9rNr7z1nXQhhLJwfbGwAAABApamiqqiqVS8VPc+wFV6xH6ddT9n1lF1PiXf7AAxnQQXVXUEFFQAAACTExkbJug/Y9zowH3szIa8Nk2Hnn/m62AHGUp7zo+f86Dk/ij0FAINSQQUAAADpsrERWb8bCmUf2O+3cip2FRQADGSugup9O+4OIZtYMf7MF8ceCgAAABiEYIMQwlyQEetk5qk6691nvfusdy9dpZX3JQCjaZ8xNamCCgAAANKmiionqQYCZZ3MfKnbqerjAkA9tddN7J6toHpv7GkAAACAYdjYyMmoVVKjBiPdGxd5VVcVHTwU/bgAQAhh8Qqq1bGHAgAAAIYh2ChZUQHEsHMMO2+vy1QfFwDqSQUVAAAA1IdgI2d5bxDUZSPB4wJATLMVVFceu3rtybGnAQAAAEYh2CjIsCd9rvvJoj0uAJRqsQqqF8ceCgAAABiFk4eXpOgD8sMGBbFVZQ4A6ql9xtTktteF0Lr1uJeve2XYV0H13NhTAQAAAKMQbAAAtdVeN7Fn/NoQWkcdv3vTRbGnAQAAAPKgigoAqJ+5Cqr3TldQXaqCCgAAAOpBsAEA1M5sBdXtxx227q6wr4Lqb2JPBQAAAORBFRUAUDvtdRN7xq9TQQUAAAB1ZGMDAKiP+RVUO1VQAQAAQB0JNgCA2lBBBQAAAPWnigoAqA0VVAAAAFB/NjYAgPSpoAIAAIDGEGwAAMlTQQUAAADNoYoKAEieCioAAABoDhsbAEC6VFABAABA4wg2AIBkLaigujOooAIAAIAGUEUFACRLBRUAAAA0j40NACA9KqgAAACgsQQbAEBy2mdMTW57vQoqAAAAaCJVVABActrrJvaMXztdQXVh7GkAAACAMtnYAADScd2jJz/y31RQAQAAQJMJNgCAZLTPmJrc9qsqqAAAAKDJVFEBAMlor5vYM36dCioAAABoMhsbAED1za+guksFFQAAADSZYAMAqLx5FVSHqqACAACAZlNFBQBUngoqAAAAYIaNDaiR9odvPfwjF8WeAiBHKqgAAACALoINqJEn7vzcBf/XewUcQH2ooAIAAAC6CTagRpa974zLttwn4ADqY7aC6spjV6+1qQEAAAAEwQbUSrb6oGv/y+ECDqAGVFABAAAASxBsQA0JOIDUqaACAAAAliLYgBoTcACpaq+bmFRBBQAAACxGsAENIOAAkjFXQXWRCioAAABgMYINaBABB1B17TOmJre9TgUVAAAAsDTBBjSQgAOoqva6icnxa1VQAQAAAEsTbECDCTiAylBBBQAAAPRJsAEIOIDoVFABAAAA/RJsALMEHEAsCyqoTo49DQAAAFBlgg1gPwIOoDSLVVCtjj0UAAAAUGWCDWBJAg6gaO0zpqZmK6juCCqoAAAAgJ4EG0BPAg6gKO11E3vGr5uuoHKycAAAAKAPgg2gbwIOIDfzK6juVEEFAAAA9G957AGgydb90bo/WvdHsacY3GzA8fdnbNzycAhPnHPDBW97Vgjhw+HwEEJo/fYLH37ju2JPCVTZbAXVrcedtu7lYV8F1fNiTwUAAACkQLABEX3hwi9c+IULY08xPAEHMKzZCqqjjt+16YLY0wAAAAApUUUFjExFFdA3FVQAAADAiAQbQG4EHEAvsxVUtx936Lo7wr4Kqr+JPRUAAACQElVUENHznvq8pz7vqf1//t2P3P3I3Y/Enro3FVXAUlRQAQAAAKMSbEBEO3+w8wc7f9D/52dZlmVZ7KkHmFfAAcyYq6B60Y5dISz72bX3nvOKEMJYOD/Y2AAAAAAGoIoKKJyKKkAFFQAAAJAXwQZQGgEHNNdsBdWVx65e++LY0wAAAAApE2wApRNwQIPMVVBdtOPOELKJFZc+c3XsoQAAAICUCTaAaAQcUH8qqAAAAIC8CTaA6AQcUF/tdROT49eroAIAAADyI9gAKkPAATVy3aMvnq6gunDHHSqoAAAAgPwINoDKEXBA+tpnTE2qoAIAAACKINgAKkvAAelSQQUAAAAURbABVJ6AAxKiggoAAAAomGADSIaAA6qvfcbU5LbXq6ACAAAAiiPYAJIj4IDqaq+bmBy/TgUVAAAAUBzBBpAsAQdUiAoqAAAAoCSCDSB5Ag6Ib0EF1e1BBRUAAABQGMEGUBsCDohnQQWVTQ0AAACgQIINoHYEHFAiFVQAAABAyQQbQG0JOKB48yqoDlFBBQAAAJRBsAHUnoADijOvguoUFVQAAABAGQQbQGMIOCBHKqgAAACASAQbQOMIOGB0KqgAAACAWAQbQGMJOGB47XUTk+PXq6ACAAAAyifYABpPwAEDuO7R1bMVVLeHkE2sGFdBBQAAAJRJsAEwTcABvbXPmNrTVUF1gQoqAAAAoEyCDYAuAg5Y2rwKqtUqqAAAAIAYBBsASxBwwDz7V1BdqoIKAAAAiEGwAdCDgANUUAEAAADVIdgA6JOAgyZTQQUAAABUhWADYEACDhrlukdXP/ILKqgAAACA6hBsAAxJwEETtM+Y2rPtdSqoAAAAgOoQbACMSMBBnS2ooHpx7GkAAAAABBsAuRFwUCuLVVCdEnsoAAAAAMEGQO4EHNRB+4ypyW2vV0EFAAAAVI9gA6AgAg5S1l43sWf8OhVUAAAAQPUINgAKJuAgKSqoAAAAgIoTbACURMBBChZUUH09qKACAAAAKkewAVAyAQdV1l43MTl+/XQF1erY0wAAAADsT7ABEImAg0qZq6D68x1fV0EFAAAAVJdgAyAyAQdVoIIKAAAASIVgA6AiBBzEpIIKAAAASIVgA6BiBByUSgUVAAAAkBjBBkBFCTgogwoqAAAAIDWCDYCKE3BQJBVUAAAAQGoEGwCJEHCQKxVUAAAAQKIEGwCJEXCQBxVUAAAAQKoEGwCJEnAwChVUAAAAQKoEGxBRlmVZlvV/CYsRcDAQFVQAAABA4gQbADUh4KAfKqgAAACA1Ak2AGpGwMGBtNeroAIAAADSJtgAqCkBBwvMVFB9VgUVAAAAkLblsQeAJrvsqMuOuuyo/j//LQ+85YG3PBB7alIzG3D8/RkbtzwcwhPn3HDB254VQvhwODyEEFq//cKH3/iu2FNStNkKqluPe+m6l4Z9FVSrYk8FAAAAMDjBBkT0u/f/7v2/e3//n/+W7C3ZW5xEnCEJOJqtvX5icvyGEFpHHn/3pj+NPQ0AAADA8FRRATSMiqqGmV9BdZsKKgAAACB9gg2AhhJwNMNsBdXtxx2y7uthXwXV38SeCgAAAGB4gg2AhhNw1NtsBdWVx65euzr2NAAAAACjE2wAEEIQcNSOCioAAACgpgQbACwg4KiH9hlTk9t+LYTW7cc9VQUVAAAAUCeCDQAWJeBIW3v9xOT49SG0rjz2FBVUAAAAQJ0INgA4IAFHYlRQAQAAADUn2ACgLwKONCyooLotqKACAAAAakewAcBABBzVtqCCyqYGAAAAUEOCDQCGIuCoGBVUAAAAQEMINgAYiYCjGlRQAQAAAE0h2AAgFwKOuFRQAQAAAE0h2AAgVwKOkqmgAgAAABpGsAFAIQQc5VBBBQAAADSNYAOAQgk4itVePzE1fsN0BdXq2NMAAAAAFE+wAUApBBw5m6ugumDHjukKqv8ReygAAACA4gk2ACiVgCMf7TOmJredqYIKAAAAaB7BBgBRCDhG014/MTV+vQoqAAAAoHkEGwBEJeAYkAoqAAAAoOEEGwBUgoCjPyqoAAAAgKYTbABQKQKOA1NBBQAAADSdYAOAShJwdFFBBQAAABBCEGwAUHECjn3aZ0xNqaACAAAAEGwAkIimBxzt9ROTKqgAAAAABBsAJKZxAYcKKgAAAIAFBBsAJKkpAYcKKgAAAICFBBsAJK3uAYcKKgAAAICFBBsA1ELtAo7rHl39yH9XQQUAAADQTbABQK3UJeBonzE1te3XpiuodgQVVAAAAADTBBsA1FLqAUd7/cTk+A3TFVSnxJ4GAAAAoDoEGwDUWnIBx/wKqltVUAEAAAB0E2wA0AipBBwqqAAAAAAOTLABQKNUPeBQQQUAAABwYIINABqpcgGHCioAAACAvgg2AGi0qgQc7TOmpradqYIKAAAAoBfBBgCE+AFHe/3E1Pj1KqgAAAAAehFsAMA8pQcccxVU56ugAgAAAOhteewBmiLLsizL+v/8TqfT6XTqP2cqjwvQPLMBx9+fsXHLwyE8cc4NF7ztWSGED4fDQwih9dsvfPiN7xr9dmYrqG49bu26U8K+Cqr/FvveAwAAAFSXjY2CzBywH/TAfffXlz1vXb8OYFhFb3CooAIAAAAYjGCj4oo+kJ/X9fd7PWXfHkBecg84FqugEmwAAAAA9CTYyFmvA+4zVUrdl+Zc/HYBqiavgGO2gur24566bkfYV0H1/8W+dwAAAADVJ9ioiF4H8quyoTBs4NBvkJLq4wI0z6gBx7wKqtUqqAAAAAD6J9ggV4IGoGkGDjj2r6DaooIKAAAAoH+CjZwtVeGkWgmg3voNOOZVUP2kCioAAACAwWWdjkPuVTBsVVOs2xv068u+vbJc/Z6r33P1e0J4w3lvOO8N55V/+wAAAABQBe3P/PNrbv2jELJfPvpTv3hB7GmoOxsbkaluAgAAAACA/gk2Iuk30LBPU20PXvXgVQ9eFXsKAAAAAIir9SvPvOaFf+4PuSmHYKNk/VYqCTTScORvHvmbR/5m7CkAAAAAAJpDsFGwmSCjqueIyFtd7ke/znr3We8+6929Txqf12UvZc3R9MuzHzr7obMf8v2oymUvb/+Nt//G238j/pxNufS8qMbllz72pY996WO+H1W5/J2v/s5Xf+erS38fnvyOJ7/jye+IP2dTLns59+3nvv3ct8efsymXXqeqcfn5n//8z3/+530/qnL568/59ef8+nOW/j4c8fojXn/E6+PP2ZTLXv70F/70F/70F+LP2ZRLr1PVuPzM733m9z7ze72/H1AkwUZBVE0BAAAAAED+BBuR5B1odG+GjLohstTXD9uRV9TtCYYAAAAAAJpleewB6qbfA/9FBQSx7k+qtwcAAAAAQFpsbDRcXkHJoF2HVZkbAAAAAIC02NgghDAXFJR1bpDur1c5BQAAAABAPwQbOYt1AD71TQjBBSm55IhLjrjkiBAu6VzSucTPbXReP2B/a89ce+baM0PonNk5s3Nm7Gm44uQrTr7i5BCu6FzRucLrFSzg3/FqeMXdr7j7FXeH0Amd4NsBC3mdqhbfD2CGKioAAAAAACAZgg0AAAAAACAZgg0AAAAAACAZgg0AAAAAACAZgg0AAAAAACAZgg0AAAAAACAZy2MPAPTvi8/+4rO/+OwQ9l6y95K9l4TwjN9/xu8/4/djTwUwZ+Z1asbek/aetPekEJ6x/Rnbn7E99nQA+79O3Z3dnd2dhfC8zvM6z+uEsOJvV/ztir+NPSXQZG/6pzf905v+KYTXP/v1z379s0O486A7D7rzoBCOf+z4x45/LISwPqwP62NPCTTZ8X98/B8f/8chfHHrF7d+cWsI9/7cvT9378+FcMyeY/Ycsyf2dDRF1pkWexAAAAAAAIBeVFEBAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJWB57AEhBlmVZls39d6fT6XQ6sacCAAAgb92//3Xz+yAAxCfYgCHMvNEt+w1trzfY3Uadr+zbA3pL5XlZ1pyD3k7etw/sb9jnZVnPw7xeN4adO5XXcWiivF8fqnb7S72eeD8F1VX191XDzul4FXlQRQUHEPuN7ahzpPJ1wNJmnldVf16WPafXG6ieUZ+XdX9eV/11HACojlTeV6Vy3Mn7qXoSbEDY/4BcVV7w8pqj3+sp+/aA8qTyvExlTmBO3s/bur0OeH8F1dfU51dT7zdUWSrvqxyvogpUUdFoqb+gda/SVW11OVZlF9TBsN3OZb+uVW1OrzdQHb2ej1V5Hxb7daPs93PAnKo934qqZhn0emO/LgL7q8rzsmq//y11u1V7facYNjaggob9h6KoAwhV+QcU6K0uBxJ1pgKpq9r7OaC3medfau8r8go0gHjq/u97UedU9H6q2Wxs0Ghe6ADqpd/Xbb/oQ35SeT71en0Y9hdnID11+T2v6PvhdRGqp67PS40fDEOwAdTmjT3USSpv6Koy56CvY944QzxV+4V82G5nrx9QH3V7PhdVabXU59Xt8YMq8n4F9qeKCgBqpGoHDPOeM5WT6QFzZp5nqbw+DXq/gHTU5XlbtftRtXmAOZ6f1JmNDQCogVTesJZ90rh+b9dfHEJ8sZ+HsU52CcRTt3/3+70/Rf2BSezXcWgC71dgjmADABKWyjkl8ppz2Psx83Xe8EP5up+3sQ6MFfX64UAeVF9dqlnyOkm491OQnlRft6BIgg0ASFAqlS6pzAmUp9+gQ2AAFGXUA/N1CUqA+hA80kSCDcA/gJCApm1mAACwj9/TgLrz+yHDEGwAQIWlEhSUNWdeFQzA6FLbyCrq9aNq9xNojmFff7yfgvoRgNJErdgDQBPM/AOz1GW3Xm8oe1U2LCXvN77+4YT48v4FtN/XqdhzLjX3sB/3izwMbtj3K/1+vN/rH/X1atQ58358vF5B/maeN/1eDnp9SynqfVVRino9BHob9X3VsAZ9nSrq/U3Zt+f9VD3Y2ICElf3GctDb8w8FDK/f51tRbxBTndMv3FA9qTwvY73PSeXxgSape1Vvr/sX+/0lsLRUjstU/XgV9WBjAyosr3+ABv3Lo6rMDdAt79cXr1cwulSel7Ge795fAVXl9QmqJ5XnpeNVVIGNDUhA9wtv0St1g95eXrcL0C+vU1A9qTwvY81Z9vs5IH1l/QXyoBsqXp+geKm9r/J+ihiyTse3GAAAAAAASIMqKgAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBmCDQAAAAAAIBnLYw8AAADUT5ZlWZYVd/2dTqfT6ex/OzP/HwAAqC8bGwAAAAAAQDJsbAAAALnrd3PCxgUAADAowQYAAJAsQQgAADSPKioAAAAAACAZNjYAAIBk9aqyWurjvU5u3ut6en3+sPPndb0AAFBnNjYAAIDGmAkSZgKDpYKDmc8b9PP7vf1BPx8AAJgj2AAAABpjqWAir/8/6By9LmcIOAAAYI4qKgAAgIJ1BxOCCgAAGJ6NDQAAAAAAIBk2NgAAAErmpOAAADA8GxsAAAAVNejJxgEAoAkEGwAAAAUb9GTg3R+34QEAAHNUUQEAAERiEwMAAAZnYwMAAKAkg25e2NQAAID9ZZ2Ot8oAAAAAAEAabGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJEGwAAAAAAADJ+P8BRLrGgNNkfP0AAAAASUVORK5CYII=", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 19kb" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.area! do |p|\n", + " p.data [1, 2, 3, 4, 5, 6], [3, 2, 5, 5, 7,4] # Data as x coordinate values, height of consecutive points i.e. y coordinates\n", + " p.color = :orange # Color of the area\n", + " p.label = \"Stock A\"# Label for this data\n", + " p.stacked false # stacked option makes area plot opaque i.e. opacity = 1\n", + " # Opacity of the area plot is set to 0.3 for visibility if not stacked\n", + "end\n", + "\n", + "axes00.title = \"An area plot\"\n", + "axes00.x_title = \"Time\"\n", + "axes00.y_title = \"Value\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 29kb" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Data input format may be changed in the near future (18th Aug 2019)\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.area! do |p|\n", + " p.data [1, 2, 3, 4, 5, 6], [3, 2, 5, 5, 7, 4] # Data as height of consecutive points i.e. y coordinates\n", + " p.color = :black # Color of the area\n", + " p.label = \"Stock A\"# Label for this data\n", + " p.stacked true # stacked option makes area plot opaque i.e. opacity = 1\n", + " # Opacity of the area plot is set to 0.3 for visibility if not stacked\n", + "end\n", + "axes00.area! do |p|\n", + " p.data [1, 2, 3, 4, 5, 6], [2, 1, 3, 4, 5, 1] # Data as height of consecutive points i.e. y coordinates\n", + " p.color = :yellow # Color of the area\n", + " p.label = \"Stock B\"# Label for this data\n", + " p.stacked true # stacked option makes area plot opaque i.e. opacity = 1\n", + " # Opacity of the area plot is set to 0.3 for visibility if not stacked\n", + "end\n", + "\n", + "axes00.title = \"An area plot\"\n", + "axes00.x_title = \"Time\"\n", + "axes00.y_title = \"Value\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Bubble plot" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 18kb" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.bubble! do |p|\n", + " p.data [12, 4, 25, 7, 19], [50, 30, 75, 12, 25], [0.5, 0.7, 0.4, 0.5, 1] # Data as arrays of x coordinates, y coordinates and sizes\n", + " # Size units are 27.5*pixel\n", + " p.color = :blue # Colour of the bubbles\n", + " p.label = \"Bubbles\"# Label for this data\n", + " p.fill_opacity = 0.7 # Opacity of the bubbles, default = 0.5\n", + "end\n", + "\n", + "axes00.title = \"A bubble plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 30kb" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.bubble! do |p|\n", + " p.data [12, 4, 25, 7, 19], [50, 30, 75, 12, 25], [0.5, 0.7, 0.4, 0.5, 1] # Data as arrays of x coordinates, y coordinates and sizes\n", + " # Size units are 27.5*pixel\n", + " p.color = :blue # Colour of the bubbles\n", + " p.label = \"Bubbles 1\"# Label for this data\n", + " # Opacity of the bubbles is set to 0.5 for visibility\n", + "end\n", + "axes00.bubble! do |p|\n", + " p.data [1, 7, 20, 27, 17], [41, 30, 48, 22, 5], [0.5, 1, 0.8, 0.9, 1] # Data as arrays of x coordinates, y coordinates and sizes\n", + " # Size units are 27.5*pixel\n", + " p.color = :red # Colour of the bubbles\n", + " p.label = \"Bubbles 2\"# Label for this data\n", + " # Opacity of the bubbles is set to 0.5 for visibility\n", + "end\n", + "\n", + "\n", + "axes00.title = \"A bubble plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 150c 16-bit 12kb" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.histogram! do |p|\n", + " p.data 100.times.map{ rand(10) } # Data as an array of values\n", + " p.color = :electric_lime # Colour of the bars\n", + " p.label = \"Counts\"# Label for this data\n", + " # bins are not given so they are decided by Rubyplot\n", + "end\n", + "\n", + "axes00.title = \"A histogram\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To be corrected\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.histogram! do |p|\n", + " p.x = 150.times.map{ rand(10) } # Data as an array of values\n", + " p.color = :electric_lime # Colour of the bars\n", + " p.label = \"Counts\"# Label for this data\n", + " p.bins = 2 # An integer specifying the number of bins is given\n", + "end\n", + "\n", + "axes00.title = \"A histogram\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = true\n", + "\n", + "# Step 4\n", + "# figure.show" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 48c 16-bit 9kb" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.histogram! do |p|\n", + " p.x = 100.times.map{ rand(10) } # Data as an array of values\n", + " p.color = :electric_lime # Colour of the bars\n", + " p.label = \"Counts\"# Label for this data\n", + " p.bins = [1, 4, 7, 10] # bins given directly i.e. bins are [1,4), [4,7), [7,10]\n", + "end\n", + "\n", + "axes00.title = \"A histogram\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = true\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Candle-stick plot" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 162c 16-bit 12kb" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.candle_stick! do |p|\n", + " p.lows = [100, 110, 120, 130, 120, 110] # Array for minimum values for sticks\n", + " p.highs = [140, 150, 160, 170, 160, 150] # Array for maximum value for sticks\n", + " p.opens = [110, 120, 130, 140, 130, 120] # Array for minimum value for bars\n", + " p.closes = [130, 140, 150, 160, 150, 140] # Array for maximum value for bars\n", + " p.border_color = :black # Colour of the border of the bars\n", + " p.color = :yellow # Colour of the bars\n", + " p.label = \"Data\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A candle-stick plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7. Error-bar plot" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 29kb" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.error_bar! do |p|\n", + " p.data [1,2,3,4], [1,4,9,16] # Arrays for x coordinates and y coordinates\n", + " p.xerr = [0.5,1.0,1.5,0.3] # X error for each point\n", + " p.yerr = [0.6,0.2,0.8,0.1] # Y error for each point\n", + " p.color = :red # Colour of the line\n", + " p.label = \"Values\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"An error-bar plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8. Box plot" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 PseudoClass 202c 16-bit 12kb" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.box_plot! do |p|\n", + " p.data [\n", + " [60,70,80,70,50],\n", + " [100,40,20,80,70],\n", + " [30, 10]\n", + " ] # Array of arrays for data for each box\n", + " p.color = :blue # Colours of the boxes\n", + " p.whiskers = 0.3 # whiskers for determining outliers\n", + " p.outlier_marker_type = :hglass # Type of the outlier marker\n", + " p.outlier_marker_color = :yellow # Fill colour of the outlier marker\n", + " # Border colour of the outlier marker is set to black\n", + " p.outlier_marker_size = 1 # Size of the outlier marker\n", + " p.label = \"Data\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A box plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9. Line plot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 27kb" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.line! do |p|\n", + " p.data [1, 2, 3, 4, 5],[12, 55, 4, 10, 24] # Data as arrays for values of x coordinates, y coordinates\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :yellow # Colour of the line\n", + " p.line_width = 2 # Width of the line\n", + " p.label = \"Values\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A line plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 33kb" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Colour of multiple lines will be corrected\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.line! do |p|\n", + " p.data [1, 2, 3, 4, 5], [10, 20, 30, 40, 50] # Data as arrays for values of x coordinates, y coordinates\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :blue # Colour of the line\n", + " p.line_width = 2 # Width of the line\n", + " p.label = \"Line 1\"# Label for this data\n", + "end\n", + "axes00.line! do |p|\n", + " p.data [2, 3, 4, 5, 6], [5, 55, 23, 10, 49] # Data as arrays for values of y coordinates, x coordinates\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :yellow # Colour of the line\n", + " p.line_width = 2 # Width of the line\n", + " p.label = \"Line 2\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A line plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10. Stacked-bar plot" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 66c 16-bit 9kb" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# A stacked bar plot with just one set of values looks same as a bar plot\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.stacked_bar! do |p|\n", + " p.data [1, 2, 3, 4, 5] # Data as height of bars\n", + " p.color = :lemon # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Diamonds\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A stacked-bar plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 11. Multi Stacked-bar plot" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 119c 16-bit 10kb" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.stacked_bar! do |p|\n", + " p.data [1, 2, 3, 4, 5] # Data as height of bars\n", + " p.color = :lemon # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 1\"# Label for this data\n", + "end\n", + "# Spacing ratio declared first is considered\n", + "axes00.stacked_bar! do |p|\n", + " p.data [5, 4, 3, 2, 1] # Data as height of bars\n", + " p.color = :blue # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 2\"# Label for this data\n", + "end\n", + "axes00.stacked_bar! do |p|\n", + " p.data [3, 5, 7, 5, 3] # Data as height of bars\n", + " p.color = :red # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 3\"# Label for this data\n", + "end\n", + "\n", + "\n", + "axes00.title = \"A multi stacked-bar plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 12. Multi Bar plot" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 115c 16-bit 10kb" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.bar! do |p|\n", + " p.data [1, 2, 3, 4, 5] # Data as height of bars\n", + " p.color = :lemon # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 1\"# Label for this data\n", + "end\n", + "# Spacing ratio declared first is considered\n", + "axes00.bar! do |p|\n", + " p.data [5, 4, 3, 2, 1] # Data as height of bars\n", + " p.color = :blue # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 2\"# Label for this data\n", + "end\n", + "axes00.bar! do |p|\n", + " p.data [3, 5, 7, 5, 3] # Data as height of bars\n", + " p.color = :red # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 3\"# Label for this data\n", + "end\n", + "\n", + "\n", + "axes00.title = \"A multi bar plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 13. Multi Candle-stick plot" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 PseudoClass 251c 16-bit 13kb" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.candle_stick! do |p|\n", + " p.lows = [100, 110, 120, 130, 120, 110] # Array for minimum values for sticks\n", + " p.highs = [140, 150, 160, 170, 160, 150] # Array for maximum value for sticks\n", + " p.opens = [110, 120, 130, 140, 130, 120] # Array for minimum value for bars\n", + " p.closes = [130, 140, 150, 160, 150, 140] # Array for maximum value for bars\n", + " p.border_color = :black # Colour of the border of the bars\n", + " p.color = :yellow # Colour of the bars\n", + " p.label = \"Data 1\"# Label for this data\n", + "end\n", + "axes00.candle_stick! do |p|\n", + " p.lows = [105, 105, 125, 130, 110, 110] # Array for minimum values for sticks\n", + " p.highs = [150, 140, 165, 170, 180, 160] # Array for maximum value for sticks\n", + " p.opens = [110, 120, 130, 140, 130, 120] # Array for minimum value for bars\n", + " p.closes = [135, 135, 145, 160, 175, 150] # Array for maximum value for bars\n", + " p.border_color = :red # Colour of the border of the bars\n", + " p.color = :blue # Colour of the bars\n", + " p.label = \"Data 2\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A multi candle-stick plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 14. Multi Box plot" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 16kb" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.box_plot! do |p|\n", + " p.data [\n", + " [60,70,80,70,50],\n", + " [100,40,20,80,70],\n", + " [30, 10]\n", + " ] # Array of arrays for data for each box\n", + " p.color = :lemon # Colours of the boxes\n", + " p.whiskers = 0.3 # whiskers for determining outliers\n", + " p.outlier_marker_type = :hglass # Type of the outlier marker\n", + " p.outlier_marker_color = :yellow # Fill colour of the outlier marker\n", + " # Border colour of the outlier marker is set to black\n", + " p.outlier_marker_size = 1 # Size of the outlier marker\n", + " p.label = \"Data\"# Label for this data\n", + "end\n", + "axes00.box_plot! do |p|\n", + " p.data [\n", + " [10, 30, 90, 30, 20],\n", + " [120, 140, 150, 120, 75],\n", + " [70, 90]\n", + " ] # Array of arrays for data for each box\n", + " p.color = :red # Colours of the boxes\n", + " p.whiskers = 0.1 # whiskers for determining outliers\n", + " p.outlier_marker_type = :plus # Type of the outlier marker\n", + " p.outlier_marker_color = :blue # Fill colour of the outlier marker\n", + " # Border colour of the outlier marker is set to black\n", + " p.outlier_marker_size = 1 # Size of the outlier marker\n", + " p.label = \"Data\"# Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A multi box plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 15. Multiple Subplots Example 1" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 32kb" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Overlap of X axis title and the title will be corrected\n", + "\n", + "# 2x2 Subplots\n", + "# |------------|------------|\n", + "# | | |\n", + "# | (1,0) | (1,1) |\n", + "# | | |\n", + "# |------------|------------|\n", + "# | | |\n", + "# | (0,0) | (0,1) |\n", + "# | | |\n", + "# |------------|------------|\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "figure.add_subplots! 2,2\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.scatter! do |p|\n", + " p.data 5.times.map{ rand(100) },5.times.map{ rand(100) } # Data as x_values, y_values\n", + " p.marker_type = :triangle_up # Type of marker\n", + " p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + " p.marker_size = 4 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Traingle\"# Label for this data\n", + "end\n", + "axes00.title = \"A traingle scatter plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "axes01 = figure.add_subplot! 0,1\n", + "axes01.scatter! do |p|\n", + " p.data 5.times.map{ rand(100) },5.times.map{ rand(100) } # Data as x_values, y_values\n", + " p.marker_type = :hglass # Type of marker\n", + " p.marker_fill_color = :silver # Colour to be filled inside the marker\n", + " p.marker_size = 4 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Hourglass\"# Label for this data\n", + "end\n", + "axes01.title = \"A hourglass scatter plot\"\n", + "axes01.x_title = \"X-axis\"\n", + "axes01.y_title = \"Y-axis\"\n", + "axes01.square_axes = false\n", + "\n", + "axes10 = figure.add_subplot! 1,0\n", + "axes10.scatter! do |p|\n", + " p.data 5.times.map{ rand(100) },5.times.map{ rand(100) } # Data as x_values, y_values\n", + " p.marker_type = :diagonal_cross # Type of marker\n", + " p.marker_fill_color = :red # Colour to be filled inside the marker\n", + " p.marker_size = 4 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Cross\"# Label for this data\n", + "end\n", + "axes10.title = \"A cross scatter plot\"\n", + "axes10.x_title = \"X-axis\"\n", + "axes10.y_title = \"Y-axis\"\n", + "axes10.square_axes = false\n", + "\n", + "axes11 = figure.add_subplot! 1,1\n", + "axes11.scatter! do |p|\n", + " p.data 5.times.map{ rand(100) },5.times.map{ rand(100) } # Data as x_values, y_values\n", + " p.marker_type = :solid_plus # Type of marker\n", + " p.marker_fill_color = :yellow # Colour to be filled inside the marker\n", + " p.marker_size = 4 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Plus\"# Label for this data\n", + "end\n", + "axes11.title = \"A plus scatter plot\"\n", + "axes11.x_title = \"X-axis\"\n", + "axes11.y_title = \"Y-axis\"\n", + "axes11.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 16. Multiple Subplots Example 2" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x1193 DirectClass 16-bit 66kb" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Overlap of X axis title and the title will be corrected\n", + "\n", + "# 3x2 Subplots \n", + "# |------------|------------|\n", + "# | | |\n", + "# | (2,0) | (2,1) |\n", + "# | | |\n", + "# |------------|------------|\n", + "# | | |\n", + "# | (1,0) | (1,1) |\n", + "# | | |\n", + "# |------------|------------|\n", + "# | | |\n", + "# | (0,0) | (0,1) |\n", + "# | | |\n", + "# |------------|------------|\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 30)\n", + "figure.add_subplots! 3,2\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.scatter! do |p|\n", + " p.data 5.times.map{ rand(100) },5.times.map{ rand(100) } # Data as x_values, y_values\n", + " p.marker_type = :diamond # Type of marker\n", + " p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + " p.marker_size = 5 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Diamonds\" # Label for this data\n", + "end\n", + "axes00.title = \"A scatter plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "axes01 = figure.add_subplot! 0,1\n", + "axes01.histogram! do |p|\n", + " p.data 100.times.map{ rand(10) } # Data as an array of values\n", + " p.color = :electric_lime # Colour of the bars\n", + " p.label = \"Counts\" # Label for this data\n", + " # bins are not given so they are decided by Rubyplot\n", + "end\n", + "axes01.title = \"A histogram\"\n", + "axes01.x_title = \"X-axis\"\n", + "axes01.y_title = \"Y-axis\"\n", + "axes01.square_axes = true\n", + "\n", + "axes10 = figure.add_subplot! 1,0\n", + "axes10.error_bar! do |p|\n", + " p.data [1,2,3,4], [1,4,9,16] # Arrays for x coordinates and y coordinates\n", + " p.xerr = [0.5,1.0,1.5,0.3] # X error for each point\n", + " p.yerr = [0.6,0.2,0.8,0.1] # Y error for each point\n", + " p.color = :red # Colour of the line\n", + " p.label = \"Values\" # Label for this data\n", + "end\n", + "axes10.title = \"A error-bar plot\"\n", + "axes10.x_title = \"X-axis\"\n", + "axes10.y_title = \"Y-axis\"\n", + "axes10.square_axes = false\n", + "\n", + "axes11 = figure.add_subplot! 1,1\n", + "axes11.candle_stick! do |p|\n", + " p.lows = [100, 110, 120, 130, 120, 110] # Array for minimum values for sticks\n", + " p.highs = [140, 150, 160, 170, 160, 150] # Array for maximum value for sticks\n", + " p.opens = [110, 120, 130, 140, 130, 120] # Array for minimum value for bars\n", + " p.closes = [130, 140, 150, 160, 150, 140] # Array for maximum value for bars\n", + " p.border_color = :black # Colour of the border of the bars\n", + " p.color = :yellow # Colour of the bars\n", + " p.label = \"Data 1\" # Label for this data\n", + "end\n", + "axes11.candle_stick! do |p|\n", + " p.lows = [105, 105, 125, 130, 110, 110] # Array for minimum values for sticks\n", + " p.highs = [150, 140, 165, 170, 180, 160] # Array for maximum value for sticks\n", + " p.opens = [110, 120, 130, 140, 130, 120] # Array for minimum value for bars\n", + " p.closes = [135, 135, 145, 160, 175, 150] # Array for maximum value for bars\n", + " p.border_color = :red # Colour of the border of the bars\n", + " p.color = :blue # Colour of the bars\n", + " p.label = \"Data 2\" # Label for this data\n", + "end\n", + "axes11.title = \"A multi candle-stick plot\"\n", + "axes11.x_title = \"X-axis\"\n", + "axes11.y_title = \"Y-axis\"\n", + "axes11.square_axes = true\n", + "\n", + "axes20 = figure.add_subplot! 2,0\n", + "axes20.line! do |p|\n", + " p.data [1, 2, 3, 4, 5],[12, 55, 4, 10, 24] # Data as arrays for values of x coordinates, y coordinates\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :blue # Colour of the line\n", + " p.line_width = 2 # Width of the line\n", + " p.label = \"Values\" # Label for this data\n", + "end\n", + "axes20.title = \"A line plot\"\n", + "axes20.x_title = \"X-axis\"\n", + "axes20.y_title = \"Y-axis\"\n", + "axes20.square_axes = false\n", + "\n", + "axes21 = figure.add_subplot! 2,1\n", + "axes21.scatter! do |p|\n", + " p.data 5.times.map{ rand(100) },5.times.map{ rand(100) } # Data as x_values, y_values\n", + " p.marker_type = :solid_bowtie # Type of marker\n", + " p.marker_fill_color = :black # Colour to be filled inside the marker\n", + " p.marker_size = 3 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.label = \"Bowtie\" # Label for this data\n", + "end\n", + "axes21.title = \"A scatter plot\"\n", + "axes21.x_title = \"X-axis\"\n", + "axes21.y_title = \"Y-axis\"\n", + "axes21.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 17. Multiple Subplots Example 3" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 1193x795 DirectClass 16-bit 60kb" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Overlap of X axis title and the title will be corrected\n", + "\n", + "# 2x3 Subplots\n", + "# |------------|------------|------------|\n", + "# | | | |\n", + "# | (1,0) | (1,1) | (1,2) |\n", + "# | | | |\n", + "# |------------|------------|------------|\n", + "# | | | |\n", + "# | (0,0) | (0,1) | (0,2) |\n", + "# | | | |\n", + "# |------------|------------|------------|\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 30, height: 20)\n", + "figure.add_subplots! 2,3\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.stacked_bar! do |p|\n", + " p.data [1, 2, 3, 4, 5] # Data as height of bars\n", + " p.color = :lemon # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 1\" # Label for this data\n", + "end\n", + "# Spacing ratio declared first is considered\n", + "axes00.stacked_bar! do |p|\n", + " p.data [5, 4, 3, 2, 1] # Data as height of bars\n", + " p.color = :blue # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 2\" # Label for this data\n", + "end\n", + "axes00.stacked_bar! do |p|\n", + " p.data [3, 5, 7, 5, 3] # Data as height of bars\n", + " p.color = :red # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 3\" # Label for this data\n", + "end\n", + "axes00.title = \"A multi stacked-bar plot\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "axes01 = figure.add_subplot! 0,1\n", + "axes01.line! do |p|\n", + " p.data [1, 2, 3, 4, 5],[15, 25, 3, 10, 1] # Data as arrays for values of x coordinates, y coordinates\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :green # Colour of the line\n", + " p.line_width = 20 # Width of the line\n", + " p.label = \"Values\" # Label for this data\n", + "end\n", + "axes01.title = \"A line plot\"\n", + "axes01.x_title = \"X-axis\"\n", + "axes01.y_title = \"Y-axis\"\n", + "axes01.square_axes = false\n", + "\n", + "axes02 = figure.add_subplot! 0,2\n", + "axes02.area! do |p|\n", + " p.data [1, 2, 3, 4, 5, 6], [10, 8, 4, 2, 3, 1] # Data as x coordinate values, height of consecutive points i.e. y coordinates\n", + " p.color = :orange # Color of the area\n", + " p.label = \"Stock A\" # Label for this data\n", + " p.stacked true # stacked option makes area plot opaque i.e. opacity = 1\n", + " # Opacity of the area plot is set to 0.3 for visibility if not stacked\n", + "end\n", + "axes02.title = \"A area plot\"\n", + "axes02.x_title = \"X-axis\"\n", + "axes02.y_title = \"Y-axis\"\n", + "axes02.square_axes = false\n", + "\n", + "axes10 = figure.add_subplot! 1,0\n", + "axes10.bar! do |p|\n", + " p.data [1, 2, 3, 4, 5] # Data as height of bars\n", + " p.color = :lemon # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 1\" # Label for this data\n", + "end\n", + "# Spacing ratio declared first is considered\n", + "axes10.bar! do |p|\n", + " p.data [5, 4, 3, 2, 1] # Data as height of bars\n", + " p.color = :blue # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 2\" # Label for this data\n", + "end\n", + "axes10.bar! do |p|\n", + " p.data [3, 5, 7, 5, 3] # Data as height of bars\n", + " p.color = :red # Colour of the bars\n", + " p.spacing_ratio = 0.2 # Ratio of space the bars don't occupy out of the maximum space allotted to each bar\n", + " # Each bar is allotted equal space, so maximum space for each bar is total space divided by the number of bars\n", + " p.label = \"Stock 3\" # Label for this data\n", + "end\n", + "axes10.title = \"A multi bar plot\"\n", + "axes10.x_title = \"X-axis\"\n", + "axes10.y_title = \"Y-axis\"\n", + "axes10.square_axes = false\n", + "\n", + "axes11 = figure.add_subplot! 1,1\n", + "axes11.bubble! do |p|\n", + " p.data [12, 4, 25, 7, 19], [50, 30, 75, 12, 25], [0.5, 0.7, 0.4, 0.5, 1] # Data as arrays of x coordinates, y coordinates and sizes\n", + " # Size units are 27.5*pixel\n", + " p.color = :blue # Colour of the bubbles\n", + " p.label = \"Bubbles 1\" # Label for this data\n", + " # Opacity of the bubbles is set to 0.5 for visibility\n", + "end\n", + "axes11.bubble! do |p|\n", + " p.data [1, 7, 20, 27, 17], [41, 30, 48, 22, 5], [0.5, 1, 0.8, 0.9, 1] # Data as arrays of x coordinates, y coordinates and sizes\n", + " # Size units are 27.5*pixel\n", + " p.color = :red # Colour of the bubbles\n", + " p.label = \"Bubbles 2\" # Label for this data\n", + " # Opacity of the bubbles is set to 0.5 for visibility\n", + "end\n", + "axes11.title = \"A bubble plot\"\n", + "axes11.x_title = \"X-axis\"\n", + "axes11.y_title = \"Y-axis\"\n", + "axes11.square_axes = false\n", + "\n", + "axes12 = figure.add_subplot! 1,2\n", + "axes12.box_plot! do |p|\n", + " p.data [\n", + " [60,70,80,70,50],\n", + " [100,40,20,80,70],\n", + " [30, 10]\n", + " ] # Array of arrays for data for each box\n", + " p.color = :blue # Colours of the boxes\n", + " p.whiskers = 0.3 # whiskers for determining outliers\n", + " p.outlier_marker_type = :hglass # Type of the outlier marker\n", + " p.outlier_marker_color = :yellow # Fill colour of the outlier marker\n", + " # Border colour of the outlier marker is set to black\n", + " p.outlier_marker_size = 1 # Size of the outlier marker\n", + " p.label = \"Data\" # Label for this data\n", + "end\n", + "axes12.title = \"A box plot\"\n", + "axes12.x_title = \"X-axis\"\n", + "axes12.y_title = \"Y-axis\"\n", + "axes12.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 18. Plot function Examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Only line" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 24kb" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + "# p.marker_type = :diamond # Type of marker\n", + "# p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + "# p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + "# p.marker_border_color = :black # Colour of the border of the marker\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :black # Colour of the line\n", + " p.line_width = 2 # Width of the line\n", + "# p.fmt = 'b.-' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Only markers" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 23kb" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Colour of the legend will be corrected\n", + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + " p.marker_type = :plus # Type of marker\n", + " p.marker_fill_color = :blue # Colour to be filled inside the marker\n", + " p.marker_size = 1 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :blue # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + "# p.fmt = 'b.-' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Both line and markers" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 37kb" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + " p.marker_type = :circle # Type of marker\n", + " p.marker_fill_color = :white # Colour to be filled inside the marker\n", + " p.marker_size = 0.5 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :black # Colour of the line\n", + " p.line_width = 2 # Width of the line\n", + "# p.fmt = 'b.-' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument only line" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 23kb" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(1).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + "# p.marker_type = :diamond # Type of marker\n", + "# p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + "# p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + "# p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + " p.fmt = '-' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument only colour" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 43kb" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(1).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + "# p.marker_type = :diamond # Type of marker\n", + "# p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + "# p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + "# p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + " p.fmt = 'r' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument only markers" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 11kb" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + "# p.marker_type = :diamond # Type of marker\n", + "# p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + "# p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + "# p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + " p.fmt = 's' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument line and markers" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 32kb" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + "# p.marker_type = :diamond # Type of marker\n", + "# p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + "# p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + "# p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + " p.fmt = 'x-' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument line, markers and colour" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 38kb" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + "# p.marker_type = :diamond # Type of marker\n", + "# p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + "# p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + "# p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + " p.fmt = 'b-1' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument overwriting marker type, line type and colours" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lCBq0IoEDAAAA6k/YAKDUBA3agcABAAAA9SNsAFBKggbtSOAAAACAwRM2ACgVQYNOIHAAAADAwAkbAJSCoEEnEjgAAACg/4QNAAolaIDAAQAAAP0hbABQiFYJGlXfu/17tycRNGiotW5b67aiZ+gLgQMAAIAiCRsANNVhBx52YNI6QaNqpZ+v9PMkue2Ht/2w6FloPy+98tIrSTLHLnPsUvQs/VEbOAAAAKAZhA0AmuqBXR5oqS9ua72w0gsrFT0D7efNh958qOgZBuPuo+8+uugZAAAA6BzCBgBNdfT6R6+fJNvsu82+Rc/SH2dtcNYGSbLGRmtsVPQstJ85lp1j2SS569S7Ti16lv5YYNgCw5Lkrn/c9Y+iZwEAAKBzVLo+UPQgQO8qlUqlUun5fp9mWspqWS1Jtrp0q0uT5MQpT5yy6JEmpBo0vnnuN88tehY6x92n3X1akiz13aW+W/QsEzLPufOcmyT/Xfi/CyfJ1EtOvWTRMwEAANA5rNgAoBBvr/f2ekny393/u3vRs0yIoEGRPrPZZzZLyruC47ENH9swSV49/9Xzi54FAACAziNsANBUb//h7T8kyZcm/9LkSXLTETcdUfRM3QkalEnZA8cc+8yxT5I8fcDTBxQ9CwAAAJ1D2ACgKT4RNHa4aYeiZ+pO0KDMBA4AAAD4iLABQEMJGlA/AgcAAAAkkxY9AHSyi/590b8v+nfRU0BjCBrQOB8GjtyVpHybjFcDx1N5Kkky+96z7130TAAAALSPStcHih4EOlGlUqlUKvV7PZ9mykDQgOa7+7S7T0vKFziqnvrlU79MBA4AAADqw6WoAKgLQQOK4xJVAAAAdBJhA4BBETSgPAQOAAAAOoGwAcCAlD1oTDbHZHMkyWpHr3Z00bNAsy1+0+I3JcmI6UdMX/QsEyJwAAAAMBjCBhSouidGX/+EMih70Kh656l3nkqSOe+Y844keeGZF54peiZotHd3fnfnJFn38HUPT5LrX7n+laJnmhiBAwAAgIGweTi0kN42G/dpphlWPGbFY5LyBo2eTHHZFJclyYuvvPhKkky1yVSbFD0T1NvaXWt3Jcnlk1zekr+88tK+L+2bJDP8YoZfFD0LAAAA5dWS/08vAMW5/9/3/7voGQZi3Nrj1k6StytvVwb7WlBWD6zywCpFzzAYb+371r5FzwAAAED5CRsA9MsjOzyyQ5LMsM0M2xQ9S388fMHDFyTJDN+c4ZtFzwKNctdJd52UJAt8eYEvFz1Lf9xz7z33JslsldmERwAAAHolbADQLzMsM8MySesEjmrQWGC9BdYrehZotKnnm3q+pHUCRzVoLL7Y4osVPQsAAACtwx4b0ELssUEZvXz7y7cnybwLzLtAkrw6w6szFD1TImhAkrw+5vUxSbLUFkttkSSj/zH6H0XPlAgaAAAADI4VGwAMyn1n3ndm8lHQGLLokEWLnEfQgI+UdQXH9Utcv0TRMwAAANC6hA0ABmTUXqP2SpLhhww/pPvP373/3fuT5gcOQQN6VrbAsd17272XJH8c8schRZ8bAAAAWo+wAUC/jPrpqJ8myfCDhh80scc1K3AIGtB3AgcAAADtQNgAoE9G7T1q7yQZ/uvhv+7P86qBI8fkmHrOI2jAwAkcAAAAtDKbh0MLsXk4RRi1z6h9kmT4AcMPqMfrVZarLJckXbd03TKQ5wsaUH9l22T8uEmOmyRJtn1323eLPjcAAACUj7ABLUTYoJlG7Ttq3yQZvv/w/Rvx+tVLVH24oqMXggY0nsABAABAK3ApKgA+ZtQvRv0iaVzQqOrrHhyCBjSPS1QBAADQCoQNAJIko/YbtV+SDN9v+H7NfN+eAoegAcX5ROAYscCIIucROAAAAOhO2ADocKN+OeqXSTL8F8N/UeQc1cCx1MZLbZwkc54w5wlFnxvodFNPPvXkSfK1Y752TNGzJAIHAAAA7xM2ADrUqANGHZAkw38+/OdFz9LdXX+56y9JMvyV4a8kybj1x61f9EzQcZ7KU0nyoz/86A9J8ofP/OEzRY/UncABAADQ2YQNgA4z6lejfpUkw/cZvk/Rs0zM7X+//e+JwAFNVRM0fnfI7w4peqSJETgAAAA6k7AB0CEeGvHQiCQZ/rPhPyt6lv74MHCsMXyNomeBdrf3eXufl5Q/aNSqBo6zRp01quhZAAAAaDxhA6BDTHPZNJcVPcNgLPuzZVsqyEArWuS5RZ4reobBmOPCOS4segYAAAAar9L1gaIHAXpXqVQqlUrP9/s00xeP7vbobkky32HzHVb0LH2xzbBthiXJ8Z8+/tNJkutzfdEzQbs7+dyTz02SLb6xxTeKnqUvrn3j2jeSZJWhqwwtehY+8uICLy7w4gLJTI/M9MhMjxQ9DUD7GfLGkDeGvJGMHzp+6Hj/CwhAh5m06AEAaK55fz/v75NkTNeYriSZ7w/z/aHomSZkm5m2mSlJjl/s+MWSCBrQRCM3HLlhkuS8nJckW2ywxQZFzzQh17557ZtJssqUq0xZ9Cx80vi1x689fu0kR+foHJ3c+OaNb974ZtFTAbSPlYauNHSloUm60hW/4AZAhxE2ADrUvIfNe1iSjMmYJOUJHNvMvM3MSXL8IscvkkTQgAKNXH/k+kmS83N+kmyx/hbrFz1Tklz71rVvJckqU6wyRdGz0HfDpxw+5XAJCmDQHrz3wXsfvLfoKQCgWPbYAOhwHwaOXcbsUuQc28y6zayJoAFlNPLrI7+eJCddcNIFRc5x7bhrxyWCBgAAQKcTNgA63MvTvzx9khz6+0N/nyR3HnTnQc18/21m22a2JDl+oeMXSiJoQImNXG/kekly0oUnNXWT7qvHXD0mSa4YecXIJPnPG/95o+hzAQAAQHGEDYAOVQ0a82beJMlRQ44akiQjvjviu0njA8c2c2wzR5Icv+DxCyYRNKCFjPzayK8lyUkXn3RxI9/n6kevfjRJDr/78LuT5OAzDz4zST4z9WemTgQOAACATiVsAHSY2qDx6quvvtr9/rFzjp0zSUZsPmLzpP6BY5s5t5kzSY5f4PgFkgga0MJGfnXkV5PkpEtOuqSer3v5/JfPnySH33X4XUly4dcu/NqEHidwAAAAdCZhA6BD9BY0ao2dY+wcSbLS7ivtXo/332bubeZOkuPnP37+JMl1ua7ocwLUx8h1Rq6TJCddetKl9Xi9n632s9WSnoNGLYEDAACgswgbAG2uv0Gj1utDXh/S/fYFs18we3+ev80828yTJMfPe/z7Awga0LZGrj1y7SQ56bKTLuvP8y697dLbut++7U+3/Wkg7y9wAAAAdAZhA6BNDTZo9GSzOza7I0nu/PWdv57Y47aZb5v5kuT4eY6fJ4mgAR1k5Foj10qSk6446YqJPe7qJ69+MkmOferYp+r5/gIHAABAexM2ANpMo4JG1dhZx86aJCO2HrF18snAsc3828yfJMfPdfxcSQQN6GAj1xi5RpKcdOVJV3b/+dVPXf1Ukhz+78P/nfT9klP9JXAAAAC0J2EDoE00OmjUGjvL2FmSZMQ2I7ZJkpeGvDQkSY4fffzoJIIG8KGRq49cPUne/cy7n0mSw287/LakcUGjlsABAADQXoQNgBbX7KBRa+zMY2dOknmenefZJHl50pcnLfqcAOXy3lLvLZUkG/xqg18lzQsatQSOzvCpL33qS0lSqVQq7fznA7944BdFn2sAACiKsAHQoooOGrXGfmrsp5JknufmeS4ROIDyBI1aAkd7e+3m124ueoZmGL/R+I2KngEAAIoibAC0mLIFjVpjZxo7U5LM8/w8zyfJy5O9PFnRMwHNVdagUUvgAAAAaE3CBkCLKHvQqDV22NhhicABnaRVgkYtgQMAAKC1CBsALeKny/502aT8QaPW2BnHzpgkP53hpzMUPQvQWFdeeeWVSesEjVrDDxl+SNEzAAAA0DthA6BFHLDPAfskyVTzTzV/0bP0x1RDphqSJAeceMCJRc8CNNbqU68+dZKs+/i6jxc9y0D867B/HVb0DAAAAPRO2ABoEcNWGbZKkjz+58f/nJQ/cEw1yVSTJMnj5z9+fpIMW3fYukXPBDTWkGmHTJsk509//vRJ6wSOu6e/e/okWfLlJV8uehYAAAB6J2wAtJiyB46pKlNVkuTxCx6/IBE0oBO1SuAQNAAAAFqTsAHQosoWOKbKVEkEDeAjZQ0cggYAAEBrEzYAWlzRgWNo19CuJHn8wscvTJJhXxvWkpsGA41TlsAhaMDAVSqVSqXy0Z8AAFAkYQOgTTQ7cAx9b+h7SfLERU9clAgaQO+KChyCBgAAQHsRNgBa3DOrP7N6kvz9y3//ctL4wDH03aHvJoIGMHDNChy1QePuxe5eLEnu3O3O3Yo+BwAAAAycsAHQop76wlNfSJLZr5796iRZ5Z+r/DNJ/rTMn5ZJPgocj33xsS8mSWbNrIN5v6Hjh45PkicufuLiJBm23rD1ij4HQGtrVOD4++N/fzz5KGiMOnnUyUmy1P1L3Z8knz3ss4clyQ1X3XBV0ecAWkVXV1dXV9dHfwIAQJGEDYAWUw0ac944540Tuv97d3zvjuSjwPHATx/4aZLk2TybJJPNNtls/Xm/oe8MfSdJnrjkiUsSQQOov3oHjn/s9I+dko+CxvAthm8xoceNWGPEGonAAQAA0GomLXoA6GSbfHWTr27y1aKnoFX0FjRqVQPHfO/O9273n//nzf+8mSTLzL/M/EnyxiNvPDKh5w99e+jbSfLEpU9cmggaQON9GDhyfpJk/cfXfzxJLp774rkn9rw7b77z5iT57POffT5J9v3qvl9Nkn0v2PeCvrxvNXBcf+X1VybJF1b/wupFnwuot942/e5tJUbt82sf39P9fd1svL8rQQZ7PAAAtDZhAwp09qVnX3r2pUVPQdn1N2jUGrPEmCWSZKnfLPWbJFnkR4v8KEke/9vjf0uSubeee+vko8AxdNzQcUm3oPH1YV8v+hwAnaWvgePDPTSWW3K5JNnnin2uSJL9s/+A3lfgoJ30NSjUPr5eQaC316udr7+Pb/bxAABQLi5FBVBSgw0ate768V0/Tj65B0d1k/Ftv7jtF5PkicueuCwRNIDi1V6iavcpd58y+eSm4NVLTu2/1v5r1eN9XaKKdla7V0ZvKy8G+z4Dvb+/8zf6eAAAKBcrNgBKpt5Bo1b1ElVZJsskyTa3b3N7khy3ynGrFH3sABNSDRyHvHnIm91/3tseGoNlBQftpFGhodF6WtkBAEBnEzagQPMtMt8i8y3S98ePeWDMA2MeKHpqGqXRQaNWT4EDoOwaHTRqCRy0oto9LnrbIwMAAFqJsAEFeuT+R+5/5P6+P95vqLWnZgeNWgIH0CqaHTRqCRy0osFuCl42ZZ8PAIDmsMcGQEGKDhq1qoGjugcHQFkUHTRq2YODdtKue1L0tFIFAID2IGwANFnZgkYtgQMoi7IFjVoCB2XW1y/2y/7Ff3/DS6utQAEAYGCEDYAmKXvQqCVwAEUpe9CoJXBQRj0FgXZZydBuxwMAQP8IGwBNMtf0c01f9AwDUQ0ct//w9h8WPQvQGVolaNSqBo5Xp3x1yqJngaq+rlioPq6sKxz6O1dZjwMAgPoQNgCa5M+T/Lkl/5u78GoLr5Yki2242IZFzwJ0hu9/6vufKnqGgdhmzm3mTJLpHpjugaJngVq14aK/IaO3xw80jAx2joEeDwAAra0lv2QDaEVbXrzlxUlywldP+GrRs/RFNWjcsekdmybJ0BFDRxQ9E9AZjv7x0T9OWidwVIPG8fMfP3+SZJ7MU/RMAAAA7UzYAGiysgeO2qAx1ZZTbVn0TECH+Ul+kpQ/cHwiaFyX64qeCQAAoBMIGwAFKVvgEDSA0ilp4BA0AAAAiiVsABSs6MAhaAClV5LAIWgAAACUw6RFDwDA+6qBI+tm3STZ6pKtLmnk+wkaQMupBo4c/f7t3+Q3SXLM/475XyPfVtBoLc+f9vxpSfLOJO+09S9xDVty2JJFzwAAAEURNgBKptGBQ9AAWl6TAoeg0Zqm3WjajYqeAQAAaKy2/i0mgFb24SWqFj5h4Xq8nqABtJ0GXaLqu/d+995E0AAAACgrYQOgIF1ju8Ymybabb7t5kvzutt/dNqHHrbjOiusM5n0EDaDt1TlwjJh7xNxJPhE0rr332nuTZKVJVpokSV7b9LVNiz50AACATiRsADRZNWhstfRWSyfJ8acef2qS/OjzP/p8kvzuwt9d2P3xBz1z0DMfu33LQbf05X0EDaDjDDBwbPn4lo93v73969u/niRdD3Q9kHwUNFZbfLXFk+RfXf/qSpJPz/npOROBAwAAoNmEDYAmqQ0aJz1y0iMTetyPvv6jryfJr+//9f1JcspZp5yVJPOcPc/ZSfKTz//k80lywldP+OqEni9oAB2vj4GjuofGCd8+4dtJ8rPDf3Z4knTN2jVrkhy/0fEbJR8FjVpPHPrEoYnAAQAA0Gw2DwdosL4GjVo/Xeyni3W/fcTrR7ze/XbtJuOHH3n4kUlyw99u+FsiaAB8YpPxO3Nnkryz1jtrJcnxxx9/fJIPLzm1/U3b35QkB+bAJMl2d293d1/e5sPAsfund0+Seze9d9Mkmeb0aU4v+hSUyQujXhj1wqiipwBoA9Nn+kxf9BAAUKxK1weKHgToXaVSqVQqPd/v01wuAw0aPTl44YMXTpI9HtjjgaKPDaAd1V5yaqDm2n2u9wPHk/c+mXRu4HjuJ8/95LmfJLMeMushsx5S9DQA7cv/HwhAJxI2oIUIG62h3kGj1m8v+O0FSfLD9X64XtHHCtAO6hU0agkcAAAAjWGPDYA6aXTQqKruwVG7yTgA/dOooFFlDw4AAIDGEDYABqlZQaOWwAEwMI0OGrUEDgAAgPoSNgAGqKigUUvgAOibZgeNWgIHAABAfQgbAP1UlqBRS+AAmLCig0YtgQMAAGBwhA2APipr0KglcAC8r2xBo5bAAQAAMDDCBkBvdspOSfmDRi2BA+hUZQ8atT4ROI547YiiZwIAACizStcHih4E6F2lUqlUKj3f79PcGE/t89Q+STLnAXMeUPQsg+FfB9Ap5lp1rlWT5Mm/Pfm3omcZiEvXu3S9JFn7grUvKHoWAACAMrJiA6AXc/xyjl8mydl/PvvPRc8yEDededOZRc8A0Ey37XHbHkXPMBA/Xu3HqyXJ2ievfXLRswAAAJSZsAHQRxtvtfFWSesEjmrQWH6T5TcpehaAZpp1rVnXSpJnLnvmsqJn6Ytq0Dj0r4f+NUkyQ2YoeiYAAIAyEzYA+qnsgUPQAHhf2QOHoAEAADAwwgbAAJUtcAgaABNWtsAhaAAAAAyOsAEwSEUHDkEDoG+KDhyCBgAAQH0IGwB10uzAIWgADEyzA4egAQAAUF/CBkCdNTpwCBoA9dHowCFoAAAANEal6wNFDwL0rlKpVCqVnu/3aS6nk8eePDZJtphui+kG8zqCBkBjPXv5s5cnyWxrz7b2YF5n04s2vShJThtx2ogkggYAAECdWbEB0GCTLTbZYoN5vqAB0Bz1WsEx9uSxJycRNAAAABpE2AAYoOdWfG7FJFlw0wU3TZIb/n7D3z/2gHfyTpLsNv1u03f/8Wl3nHZHX15f0AAoRn8DR/WSU58+9NOHJsmFf73wr0kydpux23R/3BMHPXFQksx17lznJskt37/l+0UfKwAAQCsSNgD6qRo0Zr1p1puSZPT/jf6/JBmxyohVko8CxyO7PrJrkjx373P3JslW2SpJsunSmy6d9LwHh6ABUA69BY7aPTQOGXLIkO73X/KZSz6TfBQ05t5r7r2S5MlvPPmNJFn+2OWPTQQOAACA/pq06AEAWkVt0OhJNXBseuymx3b/+Q+H/3B499vVTcavGnrV0CSZ6YszfTFJlplrmbmKPlYAPlINHM9O+eyUSfK3U/92apJs8pVNvpLkw0tOrTbpah/7v61/8MQPnkiS537z3G8m9vrVwHFzbk6SLHfMcscUfcwAAABlZvPwJult0+dazfpb6e9c9Z67rOelrGweXoy+Bo2eVEZURiTJe/e8d0+S5MW8WPQxAdA4G39t468lyV8v/uvFA3n+zdvfvH0icAAAAPTEpagabKDhoN7BoWycF1rBYINGVdf1XdcnyQ3n3nBu0ccEQONULzk10KBR5RJVAAAAEydsNEi9voBvty/ynRdaQb2CRq3aPTgAaA+1e2jUi8ABAAAwYS5FVWcDvVRQUZcYqn3fRv9r6O9xuvTSxzkfjdWooNGT6/92/d+S5Asrf2Hloo8dgP5rVNDoiUtUAQAAvM+KjZJo9y+kB/qFfG/nxcoN6qHZQaPKCg6A1tTsoFFlBQcAAMD7hI0WUe8v8Ht7ver9Pf0J7aCooFFL4ABoDUUFjVoCBwAA0OmEDT6mr+FC6KCVlSVo1BI4AMqpLEGjlsABAAB0KmGDuujUwHHyZidvdvJmva9wqdefDE5Zg0YtgQOgHMoaNGoJHAAAQKcRNpig6t4WtX9CK3pu4+c2TsofNGoJHADFeOL3T/w+KX/QqCVwAAAAnULY6FA9hYveAobNvD/utpNvO/m2k4uegt5cPtvlsxU9w2DsOeuesxY9A0AnuWC/C/YreobBOLDrQL+OAgAAtDVhAwZh2ZHLjlx2ZNFT0JvNZ9l8liTZ/v+2/7+iZ+mPOc6Z45wkuXTySycvehaATrLDHTvckSQjPz/y80XP0h+ffuPTbyTJGd844xtFzwIAANBIla4uFxmqp95WLAx0xUPZ/pb6e5yNOi9Fn5/rjrzuyOuOTG456ZaTbjmp8e/3o9t+dNuPbivfeWgZv8wvk+T7C31/oSQ59jvHfqfokSakGjTuW/q+pZNk2gWnXbDomQA6UdeYrjFJsuXGW26cJCffevKtRc80IdWgcdv1t12fJENXH7p60TMBAAA0krBRZ60SNgb7Pv19fruGjWZzHuqkpIFD0AAop7IGDkEDAADoVC5FVWcD/YJ+sHtTVJ9f++dAX2eg9zf7vPginwHZJ/skyTEPHfNQUvwlqgQNgHKrzFeZL0lO/MuJf0mKv0SVoAEAAHQ6YaMggw0QzZ6vWYGh7OeFNlNw4BA0AFpL0YFD0AAAAHifsNEg9fqiv1ErEopa6VD280KHanLgEDQAWluzA4egAQAA8HHCRoP19wv46uOb9cX9QN9vsHP29/nNPi90qAYHDkEDoL00OnAIGgAAABNm83BoIfYcabIPNhn/ztbf2TpJzpjzjDkH8jKCBkBnqNcm43OcNcdZSfLQjA/NmAgaAAAAtazYAOjJBys45hk9z+iBPF3QAOgs9VrB8d6z7z2bCBoAAAA9ETYAevDu0+8+nSQHf/HgL3b/+faf3f6zE3ueoAHQ2fobOKqXnNpkuU2WS5JndnlmlyR54tgnji36WAAAAMpI2AA61h073LFDkqx3+XqXJ8nrb7z+xsfuv/aOa7vfPniug+dKkmM2PGbD5JN7cAgaAHTXW+Co3UNj9yt2v6L7/adPffrU3W935f3rTe507U7XJslpt512W9HHCAAAUAR7bEALscdGfVSDxjLHLHNM958vcNwCxyXJXZvdtVmSfO/d772bJGdMd8Z0SfL4vx//d5LMtcxcyyT5cA+O8zY4b4Mk+co8X5knSaadbtrpij5GAMqnugfHhTtfuHOSrLHbGrslydBVh66aJO++9e5bSTLp0EmHdn/eu/e8e0+SbP3m1m8myUmfP+ljgeTUC0+9MEk2+9pmXyv6GAEAAJpB2IAWImwMTk9Bo9b8P5r/R0nyyG8f+W2SzDH5HJMnyZPjnhxX9DEA0P722nevfZPkoP0P2j9J1n9s/ceS5Px5zp9nYs8TOAAAgE4hbEALETYGpq9Boyd/muFPMyTJ1i9t/VLRxwJA+3tozYfWTJKFr1z4yoE8X+AAAADanT02gLY12KBRdcByByyXfHIPDgCop+oeGgd+78DvDeZ1vrved9dLktMuOu2ioo8JAACgEYQNoO3UK2hUjblqzFVJstRpS52WCBwA1Fc1aGx121a3JclJG5+0cT1eV+AAAADalbABtI16B41ao7cbvV0icABQH58IGjWbgteLwAEAALQbYQNoeY0OGrUEDgAGo1lBo5bAAQAAtAthA2hZzQ4atQQOAPqjqKBRS+AAAABanbABtJyig0YtgQOAiSlL0KglcAAAAK1K2ABaRtmCRi2BA4Duyho0agkcAABAqxE2gNIre9CoJXAAdLhVs2pS/qBRS+AAAABaRaXrA0UPAvSuUqlUKpWe72/XT/Oi5y16XpI8sOEDGxY9y0CcsfIZKyfJt/72rb8VPQsAjTd6/dHrJ8mCFyx4QdGzDEZ7/l8VAABAO7BiAyi9v//u778reoaB2PgHG/8gSb55yTcvKXoWAJpngfMXOD9Jjhpz1JiiZxmIm35906+LngEAAGBihA2g9Ga/bvbrkuSpEU+NKHqWvqgGjTN/deavkmSSqSaZquiZAGi+HebdYd6kdQJHNWgsv+fyexY9CwAAwMQIG0DLKHvgEDQAmJCyBw5BAwAAaDXCBtByyhY4BA0A+qJsgUPQAAAAWpWwAbSsogOHoAHAQBQdOAQNAACg1QkbQMtrduAQNACoh2YHDkEDAABoF8IG0DYaHTgEDQAaodGBQ9AAAADajbABtJ16Bw5BA4BmqHfgEDQAAIB2Ven6QNGDAL2rVCqVSqXn+32aJ2yVJVdZMkn+fs/f7+nP8wQNAIp09KNHP5okO86343wDef67U747ZZJM8uYkbxZ9LAAAAPVkxQbQtl6/7vXrko+CxkJPLPREX54naABQBv1dwbHDAjss0P32XY/c9UjRxwAAANAIwgbQtv556j9P7X77V0/86omk50tUCRoAlFFvgaN6yak9fr3Hr7v//OiVjl6p6NkBAAAawaWooIW4FNXHnb7L6bskyQsXv3BxkuwyepfR3e9faf+V9k+Sf+37r32T5OVVX141Saa/ZvprkuTpLz79xSQ5e6qzp0qSnc/b+bxE0ACg3P5yzV+uSZIFDlvgsCRZ9qJlL0qSPJAHkmTaRaddNEley2tJknfyTpJk0q5Ju5Lkir9f8fckuXbua+dOkoO/c/B3kiQ35aaijw0AAKAvhA1oIcLG+045+ZSTk2TkFiO36P7zg9456J0k+f6w7w9LkunHTj82SZa/cfkbk+Sm4TcNL3p2AGi0Ix484sEk2WWRXRZJklG/GvWrJHl51pdnTZK1tl5r6+6P3/qWrW9Jkj/t+KcdkwgcAABA6bkUFdAyegoaVXtOtudkSbLjtjtu2/3nP7/55zcXPTsANMtG62y0TvfbPznpJyclnwwaVX9e7s/LJck2R21zVJJkhaxQ9DEAAABMjBUb0EI6dcVGb0GjN6/N+NqMSTL1i1O/WPSxAECzzDT3THMnyYtPvPhEf55nBQcAAFB2VmwApTXYoFF15HNHPlf0sQBAs1xxwhUnJP0PGlVWcAAAAGUnbAClU6+gUVW9RNXB4w8eX/SxAUCjVINGT5ec6i+BAwAAKCthAyiNegeNWgIHAO2o3kGjlsABAACUjbABFK7RQaOWwAFAO2h00KglcAAAAGUhbACFaXbQqCVwANCKmh00agkcAABA0YQNoOmKDhq1BA4AWkHRQaOWwAEAABRF2ACapmxBo5bAAUAZlS1o1BI4AACAZhM2gIYre9CoJXAAUAZlDxq1PhE4vpqvFj0TAADQnipdHyh6EKB3lUqlUqn0fH9ZP80Tn7r8nl372bWTZJZLZ7m06FkA6Byt/r+f/5n2P9MmyRKvLvFq0bMAAADtxYoNKFA1VPT1z1Z17s/O/VnRMwzEMX895q+JoAFAMf5137/uK3qGgdh76N5DE0EDAABoHGEDaLgNDtjggKR1Akc1aGz/je2/UfQsAHSuFRddcdGkdQJHNWj88o1fvlH0LAAAQHsTNoCmKXvgEDQAKKOyBw5BAwAAaDZhA2i6sgUOQQOAVlC2wCFoAAAARbF5OLSQVt08vDfn7X3e3kmy4YEbHtjM9xU0AGhlo+4fdX+SDF9s+GLNfF9BAwAAKJoVG0Dhmr2CQ9AAoB00ewWHoAEAAJSFsAGURqMDh6ABQDtqdOAQNAAAgLIRNoDSqXfgEDQA6AT1DhyCBgAAUFbCBlBaG0yxwRRJMsmwSYYN5PmCBgCdqF6BY7ff7/b7oo8FAABgQoQNoLQeWfWRVZPkvRffezFJ1j597dP78jxBAwD6Hzi+c8l3Lul++8rTr+zT/+4CAAA0m7ABlNaZh595ePfbBz190NNJz5eoEjQA4JN6CxzVS04d/9TxT3X/+X5d+3UVPTsAAMCEVLo+UPQgQO8qlUqlUun5/rb5NP8sP0uSIW8PeTtJ3vvNe79JkvEvj385SYZMP2T6JLno8osuT5KXd3555yT57oPffbDo0QGg7G668aYbk+TW/W/dP0l2vHzHy7vfv8bBaxycJFftedWeSfLC1i9snSTD/jTsT0XPDgAAkFixARTgtdteuy1JlnpsqceS5Mz7zvzYb5A+MuSRIclHQWOnv+z0l+SjoFH1tbW+tlYiaABAf6yw0gorJZ8MGlV7zLzHzN1vX7H5FZt3v/3v8/59XpLMuOyMyybJQ5M/NHnRxwQAAHSWSYseAOgc1aCxxIZLbJgkjz322GNJ8u18+/0H3Jt7k2T06qNXT5L8Mr9Mku8t973lip4dADrFSguvtHD32/vdv9/9SbLoC4u+kCTLbrjsht3vXzjvP/zByR+cPEkWenuht4s+BgAAoL25FBW0kFa9FFVPQaPH41ypslKSdN3YdWOSvDv5u5MnySTjJhlX9LEAQKdYc6k1l0qSK+++8u7+PO/ByR6cLBE4AACAxnEpKqBh+hs0qqpBY80z1zwzETQAoAgbPLZBn/53u9bC7yz8TuISVQAAQONYsQEtpFVWbAw0aPTkjHvPuDdJvrXYtxYr+tgAoN1V99CoveTUQFnBAQAA1JsVG0Dd1DtoVH3709/+dPLJTcYBgPqpd9CosoIDAACoN2EDGLRGBY1aAgcA1F+jgkYtgQMAAKgXYQMYsGYFjVoCBwAMXrOCRi2BAwAAGCxhA+i3ooJGLYEDAPqvqKBRS+AAAAAGStgA+qwsQaOWwAEAvStL0KglcAAAAP0lbAC9KmvQqCVwAMAnlTVo1BI4AACAvhI2gB69ft7r5yXlDxq1BA4AaJ2gUevDwDHFQ1MUPQsAAFBOwgbQo7vevuvtpHWCRq1tbtnmlqJnAICiHD310VMXPcNg/OXnf/l50TMAAADlJGwAPRq+yfBNkuSwew+7t+hZBuKhdR5ap+gZAKAox3Qd05UkX5ryS1MWPUt/fHvEt0ckyZ7/2fM/Rc8CAACUk7AB9OoHi/1gsaR1AsfT/3v6f0ky20yzzVT0LABQlMnWnGzNJLn6/KvPT8ofOKpB4/S5Tp8rSSpnVM4oeiYAAKCchA2gz8oeOAQNAPiksgcOQQMAAOgvYQPot7IFDkEDAHpXtsAhaAAAAAMlbAADVnTgEDQAoP+KDhyCBgAAMFjCBjBozQ4cggYADF6zA4egAQAA1IuwAdRNowOHoAEA9dfowCFoAAAA9SZsAHVX78AhaABA49U7cGw85cZTJoIGAABQf8IG0DDfuv5b1w/m+YIGADRfvQLHVvtttV8iaAAAAPUnbAANc8lWl2zV/fYux+5ybF+eJ2gAQPH6Gzi+sdA3Fup++zcb/Wajoo8BAABoT8IG0DD7b7X/x8LGIbsfsnvS8yWqBA0AKJ/eAkd1D42/fP4vn0+SRbsW7UqSaxa8ZsEkeXPuN+cu+hgAAID2ImwAdfe/c/53TpI8evKjJyfJumese0aSTPHqFK8mH+3BceXFV16cJM+u+uyqiaABAGVWGzguXeHSFZJP7qGxz+P7PN79eaOWGrVU0bMDAADtpdL1gaIHAXpXqVQqlUrP9zfr0zz+f+P/lyTXXn7t5Umy+pWrX5kklVMqpyTJ6S+e/mKSbDbTZjMlybWrXLtKkqxy7SrXFnn+AIDG+9+Z/zszSWb+9szfTpJ1L1330iS5aO2L1u7+uGuuueaaJFl65qVnTpJPLfUpAQQAAOgTKzaAPqsGjXVOXOfEJFnzu2t+N0k2nW7T6ZKka/OuzZPkgHkPmLf781a8d8V7+/dOAECr+tS3PvWtJJn36/N+PUkuXufidZJk3LBxw5LkD2v+Yc0k+cpXvvKVJJnlzFnOTJLn//T8n4qeHQAAaA3CBtCr2qBx1R5X7dH9/jOOOuOoJNl47MZjk+S+1+57LUlWu2O1O5Jk6NNDny76GACA5vr5ST8/qfvtPebZY54k2fXKXa/s/vOuX3f9OklmHT3r6ETgAAAAeudSVNBCmn0pqt6CRm8uO+CyA5JkrZ+t9bPmny0AoEjPHv3s0Uky246z7dif51X2quyVJM8u8OwCSTLzNjNvU/SxAAAA5WLFBvAJgw0aVSecd8J5yUeXqAIAOseZF5x5wUCeZwUHAADQG2ED+FC9gkbVX277y23JJ/fgAADaV3UPjdpLTvWXwAEAAPRE2ADqHjRqVffgEDgAoH3VK2jUEjgAAIBawgZ0sEYHjVoCBwC0n0YFjVoCBwAAUCVsQAdqdtCoJXAAQOtrVtCoJXAAAADCBnSQooNGLYEDAFpPUUGjlsABAACdS9iADlC2oFFL4ACA8itL0KglcAAAQOcRNqCNlT1o1BI4AKB8yho0agkcAADQOSpdHyh6EOhEpww9ZegpQ/v++JFvjXxr5Fs931/7ad7hjh3uSJJjljlmmaKPdSB+OfsvZ0+SvZ/a+6miZwGATnPDQjcslCQjHh7xcNGzDMSwU4edmiQvbPbCZkXPAgAA1JewAQWqVCqVSqV+r1f7af73Wv9eK0mWvWLZK4o+1oG4d+17106SxS5d7NKiZwGATjP2obEPJcnCP1/450ny7BnPnlH0TP1x5vVnXp8km3xhky8UPQsAAFBfwgYUqNFho6rVAoegAQDl8erzrz6fJIv8YJEfJOUPHIIGAAC0P3tsQAf43OWfuzxJblvztjWLnmViBA0AKJ/pZp5u5iR54A8P/CFJZv32rN8ueqYJETQAAKBzCBvQQcoaOAQNACi/sgYOQQMAADqPS1FBC+nt0lX9/TQXfYkqQQMAWlfRl6gSNAAAoHNZsQEdrKgVHIIGALS+olZwCBoAAICwATQtcAgaANB+mhU4BA0AAKBK2AA+1KjAIWgAQPtrVOAQNAAAgFrCBvAJ1cDxl6P/cvRgXkfQAIDOU6/AsfU7W7+TCBoAAMAnCRtAj27b7rbtBvI8QQMAGGzguPT/Lv2/oo8BAAAoJ2ED+ISul7peSpKDhhw0pPvPb3nvlvcm9jxBAwCo1dfAUb3k1AZf3+DrSfL0yKdHJsnz+z6/b9HHAAAAlIuwAXzC6NdGv9b99o/m+tFcSfL5yucryUd7cEw15VRTJoIGANC72sCx6G2L3pZ8cg+NXU7f5fTuz7tiySuWLHp2AACgXCpdHyh6EKB3lUqlUqn0fH+9Ps2/PuvXZyXJT7/1028lyV3P3vVsknxmls/MUvQ5AADa25sbvblRkkx1zlTnJMliX17sy0ly79/v/XvRswEAAOVgxQZ0oFumvWXaJDliuSOWS5K8l/cvMXVjbkyS/b633/e6P37x6xe/vuiZAYDOMPSvQ/+aJKtutupmSXLfP+77R5K8/M2Xv9n9cee+cO4LSXLKmFPGFD0zAADQXJMWPQDQPDd//eavJ8kKr63w/qWmbs2tSfLU4U8dniQ7Hr/j8Ukybuy4sUmyzVXbXJUkQ74y5CtFzw4AdJYfL/njJZPk2lybJPnbnn/bM0neWfmdlZNkk39s8o/uj3/zqTefSpLtZt9u9qJnBwAAGsuKDegAN69/8/pJssKFK1w4ofsP2u2g3ZJkq6232rr7z7fbdrtti54dAOhMI6YcMWX323v83x7/l3wyaFRtP8f2cyTJcU8f93TRswMAAI1ljw1oIf3dY+PDoHHBChcM5P3e3vjtjZNksrMnO7voYwcAOtMC4xcYnySPTPbIZP153rFPHWsFBwAAtKmOXbHR2xfE1ftr/4RWMNigUfXzlX6+UpKP9uAAAGiSs1c+e+Wk/0GjygoOAABoXx0XNvoaNKAV1StoVFUvUbXX4XsdnkTgAAAarho0errkVH8JHAAA0H465lJUtcGip6OuPq56f0+hozPOGmXTl/DWyH+Xe/5+z98nya93+fUuSZJJOi+OAgCNUe+g0ROXqAIAgNbnS8katcFCwICPWMEBANRbs4JGlRUcAADQ+oSND7gEFfSdwAEADFazg0YtgQMAAFqXsAEMmMABAPRX0UGjlsABAACtp2PCRu0lpaorNPq7mbhLU1GEmze4eYOiZ5gYgQMA6E3ZgkYtgQMAAFpHx4SN3vQWLAQNilANGiucv8L5Rc/SF58IHF3xqQGADlf2oFFL4AAAgPKrdHX5yh7KakLriXrbC6ZMn+Y7j7nzmCRZavulti96FgCgGK2+k93z+z2/X5J86uef+nnRswAAAO+zYgNK7Mqzrjyr6BkGYovTtzg9SZbac6k9i54FACjWSc+e9GzRMwzE76/5/TWJoAEAAGXU8is2etojo6c9Neqltc8areaqs646K0nW+NYa3yrzio1q0DhhkxM2SZLKkMqQ4qYBAMrk5OdOfi5Jtph1i1mLnmViqkFj11V3XbXoWQAAgAkTNgaotc8araov/46L+HcpaAAAfVXWwCFoAABA62j5sAGdpGxhQ9AAAAaqLIFD0AAAgNZjjw2g3wQNAGCwRs4ycpakuD04BA0AAGhdHRM26nUpqnpf0gpaiaABANRbswOHoAEAAK2vY8JGVX/DRPXxggat4MozrzyzEa8raAAAjdbowCFoAABA++iYsNHTZuI9BYu+bkoOZbL6JqtvkiRXnnHlGfV4PUEDAGi2egcOQQMAANpPx28e3ttKjM4+O5RNf/+9brLDJjskydnHnH1Mf95H0AAAymKgm4xP89A0DyXJ2AXHLlj0MQAAAPUlbAgbtJC+/nvturPrziSZ5LOTfLY/ry9oAABl1dfAsdTjSz2eJHfNfdfcSfLsi8++mCSzzDjLjEUfAwAAUB8dcymqWrVfEH/4hXAPl6yCVjL6ntH3dL/94yV/vGTS8yWqBA0AoOx6u0RV9ZJTh99z+Mf+76Arl7lymaJnBwAA6qtjVmwMdM8Me21QJn1dsfHr9379XpL8dMhPhyTJ3ZPePWmSLPnOku8kyVVXXXVVklz1jau+kSQHv3TwS4mgAQC0jtP/ePofk2TsVWOvSpLt/7L9X5LkzR3e3CFJpjpmqmOSZLE9F9szSe799b2/LnpmAACgPjoubAz0aHta4QHN1GvYuKvrrq6uZOj2Q7dPkrdufOvGJBl/1PijkmTIDkN2KPoYAACaYdUDVj0gSf62z9/2SZKXrn/p+iSZ4QszfKHo2QAAgMHpmEtRDTZE9HSpKiiTJzd6cqPko6Cx1WNbPZYIGgBA5/nxvT++t/vtf1zyj0u63x699+i9k2SfO/a5I0ne/fm7Py96ZgCAVvLume+emSR7nLvHuUny38v+e1nRM9E5OiZs1Is9NyizC7e+cOvut7e7fLvLi54JAKAII/YcsWf324e8dMhLyUdBY8EDFzwwSQ5Y5oBlkuRr73ztnUTgAADoTTVorDfZepMlyaHfOPQbSbLEOkuskwgcNEfHXIrqEwc+yEDRmWeNovxuv9/tlyQ/+sWPfjGxf7fD3h32bldX8uKQF4ckybizxp2VJJN/c/JvFn0MAABF+Ox+n90vSe78xZ2/6Mvj195z7T2T5KLJLposSYbsP2T/oo8BAKAMaoPGpRtdutHEHn/PpfdcmiSLr7342kXPTvvpuBUbRQWN6vv29c+iz0+z5myV81KU7kGjL4+vBo0vzvLFWRJBAwBgq/W3Wr8/j7/soMsOSqzgAACo6m/QqLKCg0bqmLDR0+bfPYWKeu2pMdAv5Jv9RX6z52yV81KU/gaNWtc9d91zSfKPW/9xa9HHAgBQhOolp37w2R98diDPFzgAgE430KBRS+CgETombPSmt4BRvb+vX6zXe0VDozR7zlY5L0UZbNCotfJyKy+XCBwAQOeo3UNjsAQOAKDT1Cto1BI4qCdho856+8K9diVIUXt1lHXOos9LUeodNGoJHABAu6t30KglcAAA7a5RQaOWwEE9CBsl0Spf5A92j5H+vm5v79fqKzcaHTRqCRwAQLtpdNCoJXAAAO2mWUGjlsDBYHRs2OjtC/Ha+4v+Ar3o92+3OYvW7KBRS+AAAFpds4NGLYEDAGh1RQWNWgIHA9ExYaOn3/zvb+CAwSg6aNQSOACAVlN00KglcAAAraYsQaOWwEF/VLq6WuUiSHU+8JpgUXsWegoag700UqOfP9Djb/ScrXJe+uuM3c7Y7YzdkqP/dfS/jv5Xz497ctiTw5LkkcseGfR/mK+/aeL3D+Y8/P2Wv9+SJF/+/Jc/34jzBQAwUGULGj1Ze8+190ySiya7aLIkGbL/kP2LngkAIClv0OjJPZfec2mSLL724msXPQvl07Fho1Fa5Qt8YaM+Tt7s5M1O3izZ4vQtTt/i9Oa//4TPxeBfQ+AAAMqiVYJGLYEDACiLVgsatQQOJqRjLkUFjXD9bNfPdv1sRU9Rf9VLVAEAFG2VG1a5oegZBqJ6iarz3j7v7aJnAQA62+VTXz510npBo+qL235x26JnoHw6PmxUVwLYS4OBGPHMiGdGPFP0FPV39rlnn1v0DAAASXLZ7pftXvQMA7HioSsemiTrrbnemkXPAgB0trUeW+uxJFlngXUWKHqWgbjuW9d9q+gZKJ9Jix6AvmmVC4a1ypz1Mvl0k083+XTJiNlGzDZiQis3xmZskoyecvSUSfLUC0+9MNj3vL6BIaUaNDbeYOMNGvcuAAB9t/g6i6+TJPdccs8lSbLEV5f4atEzTUw1aPxj2X8smySTrzL5KkXPBAB0tiE7DtkxSS7MhUmS9X633u+S5NLRl44ueraJuefH9/w4SRY/dPFDi56F8un4PTZ620R8sK9Xa6B7SdT7b6nZc7brHht9dmtuTZJd19x1zST5w4t/eHGgL9XbyqKBnAdBAwBoFf+99L+XJuULHIIGANAq3j3q3aOS8gYOQYO+6PhLUdXbQL+gH+ylsGovqdWokDDQORv1fqUPGlWfz+eT5LArDrsiSX4w7AfDih4pETQAgNZTu4KjaIIGANBqPlzB8cMLf5iU5xJVggb9IWwUpFX29mj2nK1yXgasJIFD0AAAWl3RgUPQAABaXVkCh6DBQLR82BjsF+DV3/iv92/+1+v1Gr0iodlztsp5abiCAoegAQC0m2YHDkEDAGg3RQUOQYPBaPk9Npq1J0Wj5qzX3PU6D42ec6DvW7a/z7rrxx4cA9ljQ9AAADpFo/bgEDQAgE7R6D04BA3qoeXDxocH0ulfjNMe+hA4+hM2BA0AoFPVK3AIGgBAp6p34BA0qKe2CRufODChg1Y2kcDRl7AhaAAAvG+wgeOZfZ/ZN0lm/cWsvyj6WAAAivD2PW/fkyRTLDnFkgN5vqBBI7T8Hhs96W3vjLbdnJr2MMA9OAQNAICP6+8eHIv9bbG/db99w3E3HFf0MQAAFOmh3zz0m+63F75v4fv68jxBg0Zq27BRq7fA0d8/oSlqAkdvBA0AgAnrLXBULzl1w8437Nz95wcdedCRRc8OAFCkU9c9dd3uty+8+sKrk543GRc0aIa2vRRVjwdcpzDRWWeNsnCJNQCA+qheouoPM/5hxiQ54q0j3ko+2kNjueHLDU+SW0fdOipJxu47dt8kmeYX0/yi6NkBAJqh+i3TJJNM8rFfjn/ve+99L0neW/q9pZNkz+333D5Jttxzyz0TQYPm6Jiw0dMXwp1x9LQLYQMAoDnOmeqcqZJkozc3ejNJrtj5ip2TZI3D1zi86NkAAJph9Nmjz06SBTdZcJMk2e2d3d5Jkt9N+rtJi54N2vZSVL1dOsoXwAAAQE9W+c8q/+l++3dr/W6t7rff/tfb/0qSvcfvPT5Jnj7v6fOKnhkAoD+eWe6Z5ZJk1xt2vSFJ3pztzdm63//Xrf+6dffbW4zZYkzRM0NV26zY8JvsdAL/zgEAmmvBLRfcMklGnzT6pCR55R+v/CNJ1nx9zdeTZNQ6o9bp/vinzn3q3CSZfYPZ7X0GAJRSNWjMfuvst3b/+aLDFx2eJP++7t/XJcnMW8+8dZK8cfIbJyfJ+FXGr5IkQ64dcm3RxwAtv2KjtxUZvugFAAAGau+H9n6o++2VrlnpmuSTQaNqjg3n2DCxggMAKJ+egkbV/f+6/19JstRcS82VfBQ0tvjTFn9KBA3KpeXDRi0hAwAAqJevPPCVB7rfvmf/e/bvy/MEDgCgLHoLGrUefubhZ7rf3uK1LV4r+higVttcigo6gUtRAQA0R3UPjS+//OWXk55XaPSVS1QBAM3W36DRk0WWXmTpJLnjmTueSZKhzwx9ZjCvB/XQdis2AAAABqreQaPKCg4AoFnqFTSqHrjzgTuTZJkFllkg+eQm41AEYQMAAOh4jQoatQQOAKBR6h00alX34BA4KANhAwAA6FjNChq1BA4AoF4aHTRqCRyUgbABAAB0nKKCRi2BAwAYqGYHjVoCB0USNgAAgI5RlqBRS+AAAPqq6KBRS+CgCMIGAADQ9soaNGoJHABAT8oWNGoJHDSTsAEAALStVgkatQQOAKCq7EGjlsBBMwgbAABA2/rTmD+NSVonaNTa6Hsbfa/oGQCAYm2x/hbrFz3DQFQDx5FzHTlX0bPQfoQNAACgbY1cfOTiSbLA5AtMXvQsA3HqJqduUvQMAECx/vy9P7fkLzrM/5X5v5IkO+y0w05Fz0L7ETYAAIC2NfXSUy+dJHfdfNfNSesEjod3eHiHJFngqAWOKnoWAKBYc84y5yxJ8sSzTzxb9Cx9UQ0ad29696ZJMvUWU29R9Ey0H2EDAABoe60SOAQNAKAnZQ8cggbNJGwAAAAdo6yBQ9AAAPqqbIFD0KAIwgYAANBxyhI4BA0AYKCKDhyCBkUSNgAAgI5VVOAQNACAeml24BA0KANhAwAA6HjNChyCBgDQKI0OHIIGZSJsAAAAfKBRgUPQAACapd6BQ9CgjIQNAACAGvUKHMcueOyCiaABADRfNXD8fb+/7zeQ5wsalJmwAQAA0IPBBo6TDj7p4KKPAQDobFdNdtVk/Xm8oEErqHR9oOhBgN5VKpVKpdLz/T7NAACN9fqdr9+ZJEstv9TySTL67dFvd7+/esmpjXbbaLckuX3h2xdOkrFvjH0jSaYZOs3Qoo8BAOgQC2SBJKk8Unmk+48f/+LjX0ySua+b+7ruPxc0aCVWbAAAAPRR7QqO72793a2TT+6hsdc8e83T/Xk3TnrjpEXPDgB0lkfGPjK2++1dD9z1wCSZ659z/TP5aA+O7Y7c7shE0KC1WLEBLcSKDQCA1vDiRS9elCQzrTfTekmy5v+t+X9Jcvm3L/920bMBAJ3hkNkOmS1JfvLsT55NkjsPvvPgJFlqj6X2KHo2GCxhA1qIsAEA0FoWmnahaZPk4dcefi1J3tzvzf2SZIpzpjgnSUZ+YeQXkuRzn/3cZ5Nk12133bbomQGA1nD4iMNHJMmVF195cZKc/8r5ryTJpO9M+k6STHX5VJcnyZs7v7lzkoz/6vivJsmQi4dcXPTsMFguRQUAANAge++1917db9+8xM1LJMm3t/j2Fkly6jGnHpMku22323ZJctgfD/tj0TMDAOVWDRo/uOEHNyTJJTNeMmOSrLPZOpslyaNXPHpF8lHQ2Gr6raZPBA3ai7ABAADQIGvPvfbc3W9v9uJmLybJWT8864cTerzAAQD0pDZo1Lrq+quuT5K13l3r3e4/3+6I7Y4oenaoN5eighbiUlQAAK2la+mupZNk6ANDH0iScW+Ne6s/z//9cb8/LnGJKgDoZL0Fjd68ftfrdyXJVJ+Z6jNFHwvUixUbAAAAdVYNGtVLTvU3aFRZwQEAnWuwQaNq/R3W3yFJxj86/tGijwnqRdgAAACok9qg0dMlp/pL4ACAzlGvoFFVvURVdQ8OgYN2IGwAAAAMUqOCRi2BAwDaV72DRi2Bg3YibAAAAAxQs4JGLYEDANpHo4NGLYGDdmDzcChQb5uB95dPMwBAcxQVNHpik3EAaD3NDho9WX3E6iOS5NLTLj0tSSadd9J5iz430BsrNgAAAPqobEGjygoOAGgdZQkaVVZw0IqEDQAAgF6UNWjUEjgAoLzKFjRqCRy0EpeiggK5FBUAQLl13dl1Z5J8+9pvX5uUN2j0xCWqAKB4ZQ8aPaleouqyky47KUmGLDhkwaJngiphAwp08tiTx548tu+P32K6LabbYrqe7/dpBgCor8cff/zxJJlnnnnmKXqWwfB/JQJAcer7a63N9/CDDz+YJAsstMBCRc8CVcIGtJDeVnj4NAMANMbZ3z/7+0myybGbHFv0LP3x7xP+fUKSLLPlMlsWPQsAdKr7p7h/iiRZ7O3F3i56lv44c5czd0mSTf6wyR+KngVq2WMDAACgF9885pvHJMlZ25+1fdGz9IWgAQDlsei4RcclyX2T3zd50bP0haBBKxA2AAAA+qjsgUPQAIDyKnvgEDRoJcIGAABAP5UtcAgaANA6yhY4BA1akbABAAAwQEUHDkEDAFpX0YFD0KCVCRsAAACD1OzAIWgAQPtoduAQNGgHwgYAAECdNDpwCBoA0L4aHTgEDdqJsAEAAFBn9Q4cggYAdI56Bw5Bg3ZU6fpA0YMAvatUKpVKpef7fZoBAMrp7O+f/f0k2eTYTY7tz/Nmnn7m6ZPkuZefe7noYwAAirH+cusvlyQX3HrBrf15nqBBO7NiAwAAoMH6u4Jj1S+t+qUkef6V519JkufufO7Ooo8BAGiu8auPXz35KGhMs+s0u/bleYIGnUDYAAAAaJLeAkf1klP7rrzvyt1/ftljlz1W9OwAQHPddftdt3e//ct5fjlP0vMlqgQNOolLUUELcSkqAID2cs2l11yaJLOeMuspSbLkmUuemSRvbfTWRkky9Jyh5yTJAhcvcHGSPPzVh79a9MwAQHNs95PtfpIkfzzkj4ckyaNvPvpmkswz5TxTJslD7z30XpI8+Y0nv5EkXz7vy+cVPTM0i7ABLUTYAADoLOusvM7KSXLZPy77R5L879b/3ZokMy0707JJ8uJ6L66XJI8999hzSfLZUZ8dVfTMAEDf3HrhrRcmyVy7zLVLksw2ZrYxSfLu0e8enSST7jjpjkky+eGTH54k4xYbt1iSZPWsXvTsUDSXogIAACipHz3zo2e6377qqquuSpLnF31+0SSZ6b2Z3kuSZW5a5qYkOe+Q8w4pemYAYOLOf+P8N5Jkua8v9/Ukmf3R2R9NkidWeGKFJLlnunum6/74/dbYb40kggZ0I2wAAACU1PD5h8/f/fYvlvnFMkkyy8KzLJwkuSSXdL9/w59s+JNE4ACAMqoGjQ2m3mDqCd0/981z35wkf/jPH/7T/eeb7LLJLkXPDmXjUlTQQlyKCgCgM33hqS88lSQ3znnjnP153rkHn3twkmywxwZ7FH0MANCpegsavelaq2utJMlluazoY4GysGIDAACgpKqXnLpx/RvXH8jzreAAgOIMNmhUPfHiEy8WfSxQNsIGAABAyVSDxoeXnLoltwzm9QQOAGieegWNquolqqp7cADCBgAAQGl8ImjU7KExWAIHADROvYNGLYEDPiJsAAAAFKzRQaOWwAEA9dPooFFL4ABhAwAAoDDNDhq1BA4AGLhmB41aAgedTNgAAABosqKDRi2BAwD6ruigUUvgoBMJGwAAAE1StqBRS+AAgJ6VLWjUEjjoJMIGAABAg5U9aNQSOADgI2UPGrUEDjpBpesDRQ8C9K5SqVQqlZ7v92kGACinKZaZYpkkefuOt+8oepaBuOe8e85LksXXX3z9omcBgGZ56HMPfS5JFr594duLnmUwfFtEO7JiAwAAoMF++bVffq3oGQZitktmuyRJ5n1n3neKngUAmm3uG+a+IUmW2HOJPYueZSAOWuEgKzZoW8IGAABAg+2x/x77J8nB+xy8T9Gz9EU1aDxw3APHJcnUG0+9cdEzAUCzTTF0iqFJcutxtx6XtE7gqAaNn4z6yaiiZ4FGETYAAACapOyBozZoTHvBtBcUPRMAFG3KF6d8MSl/4BA06CTCBgAAQJOVLXAIGgDQu7IGDkGDTiRsAAAAFKTowCFoAED/lSVwCBp0MmEDAACgYM0OHIIGAAxeUYFD0ABhAwAAoDQaHTgEDQCov2YFDkEDPiJsAAAAlEy9A4egAQCN16jAIWjAJ1W6PlD0IEDvKpVKpVLp+X6fZgCA9nTIzw/5eZL85Jc/+eVAnv/M0888nSSzzjbrbEUfCwB0ijcvePOCJJlq7qnmTpIsm2X783xBA3pmxQYAAEDJ9XcFxwyZ4WO3/3Pgfw4s+hgAoNM8O+bZMUk+DBrDpho2VV+eJ2hA74QNAACAFtFb4KhecuqOr93xte4/P2T7Q7YvenYA6DRn7XrWrt1vX7bmZWsmPV+iStCAvnMpKmghLkUFAEB31UtUHTXmqDFJ8p9X/vNK8tEeGou+vujrSfLANA9MkySvP/f6c0ky1cxTzVz07ADQto7IEUky2RGTHZEk4x8c/2CSjL9g/AVJ8s4W72yRJMP3Hb5vknzrjG+dkQga0B/CBrQQYQMAgP44Y7UzVkuS71z7nWuT5OrJrp4sSVZ7e7W3k+T1+16/L0mO3+v4vZJk++233z5JplxzyjWLnh0AymrcSuNWSpLfbvfb7ZJkh0l3mDRJZth0hk2TZMxXx3w1Sea/dP5Lk2SHR3Z4JEmOmu+o+YqeHdrFpEUPAAAAQGOs+b81/9f99iFLHLJEkqx434r3JclSay61ZpKMfmz0Y0ly4oInLpgkN+WmJAIHAHRXDRor/nHFPybJHZ+54zNJcsijhzyaJGNOH3N6kpy50JkLdX/ettNuO23Rs0O7ETYAAADa1LA7h92ZJIses+gxSXLlDlfukCRLPrfkc0ky5rExj3V//F2/veu3SbJCVkgicABA0nPQqHpl3lfmTZL5HpjvgSR57cLXLux+/5IPLvlgkmSmzFT0sUC7EDYAAADa3J5P7PlEkmyZLZMkY7485ssTe7zAAQC9B41aryzyyiLdb29z4jYnJsmQFYesWPSxQLuZpOgBAAAAaIzqHhq/+PQvPj2Q538YOK5a4aokeeuKt64o+pgAoNH6GzR6cuZmZ26WJC+f/vLpRR8TtBubh0MLsXk4AAB9UQ0atXtoDNZSP1rqR0ly0+o3rZ5YwQFAe6lX0Kg1/aPTv78Hx3Vjrks+2mQcGDgrNgAAANpEo4JGlRUcALSjRgWNqg/34PjifF9MrOCAehA2AAAAWlyjg0YtgQOAdtDooFFL4ID6cSkqKFBvl5bqL59mAIDO0uyg0ROXqAKglTQ7aPTEJapg4KzYAAAAaDFlCRpVVnAA0ArKEjSqrOCAgRM2AAAAWkTZgkYtgQOAMipb0KglcED/CRsAAAAlV/agUUvgAKAMyh40agkc0HfCBhSouidGX/8EAKAz/eCCH1yQlD9o1KoGjn1O2OeEomcBoPMcvMzByyTlDxq1qoFjmwW2WaDoWaCshA0AAICS+/nKP1+56BkG44cb/XCjomcAoPPsNMlOkyTJ0GWHLlv0LAPxuy/97ktFzwBlJWwAAACU3DwrzLNCkjw66tFRRc/SH0+d/dTZSTL7xrNvXPQsAHSeYUcMOyJJnhj+xPCkdQLHo5M+OmmSzPPOPO8UPQuUlbABAADQIlolcAgaAJRJqwQOQQP6TtgAAABoMWUNHIIGAGVW1sAhaED/CRsAAAAtqiyBQ9AAoJWUJXAIGjBwwgYAAECLKypwCBoAtLKiAoegAYMnbAAAALSJZgUOQQOAdtKswCFoQP0IGwAAAG2mUYFD0ACgnX0icEwxdIp6vK6gAfUnbAAAALSpegWO33zhN19IBA0AOkM1cFxw5QVXDuZ1BA1onErXB4oeBOhdpVKpVCo93+/TDADAxDx202M3Jcm8K867Yn+eN/M7M7+TJM9N+tykRR8DADTLd2b9zqxJcsZzZzyXJFN+ecovJ8lb/3jrHxN7nqABjWfFBgAAQIfo6wqO6iWnvjPqO6OS5PnJnp8sSZ58+smniz4GAGi0cf8c98/ko6Ax68mznpwkT37myc8kPe/BIWhA8wgbAAAAHaY2cKwy5SpTJp/cQ2PXX+/66+7P++uSf11yQq933tbnbZ0kj3/u8c8VfWwA0JvHL3r8oiQ59ZlTn0mSvJE3ut9/0/U3Xd/99q+2+dU2ySf34FhvvfXWSwQNKIJLUUELcSkqAACaafxB4w9Kksn2mmyvJJly/JTjk+TNf775zyQ5aKuDtkqSvcbsNab78x6d/9H5k2Se0fOMLvoYAKDq8XUfXzdJ5rlknku6/3z33+/++yQ5ZNtDtk2Sdbded+skueTMS85MkucOf+7wJJl555l3LvoYgPcJG9BChA0AAIrw/XW+v06SHHvZsZclye4P7/5wkhy64KELTux5AgcAZdBT0Ki163273pckhy122GJJsvCnFv5Ukjzw/APPF30MwMcJG9BChA0AAIpw1x/u+kOSLL3r0rsO5PkCBwBF6GvQ6MkZ/zrjX0nyrRW/tWLRxwJ8nD02AAAAmKiLl794+cE8f95H5n0kSR5b4LEFij4WANrfYING1Y2VG9//9dKaPTiA4gkbAAAATNBB8x80f5L8bKWfrVSP1xM4AGikegWNqiNWPGLFJNnjj3v8MYnAASUibAAAAPAx1aBRuyl4vQgcANRTvYNGrUN3O3S3ROCAMhE2AAAASNL4oFFL4ABgMBodNGoJHFAewgYAAECHa3bQqCVwANAfzQ4atQQOKJ6wAQAA0KGKDhq1BA4AJqbooFFL4IDiCBsAAAAdpmxBo5bAAUB3ZQsatQQOaD5hAwAAoEOUPWjUEjgAOlvZg0YtgQOap9L1gaIHAXpXqVQqlUrP9/s0AwAwIaP2GbVPkgw/YPgBRc8yEJUnKk8kyXtzvjdn0bMA0DzTLT3d0kky9q6xdxU9y0Bc869r/pUkq6646opFzwLtxooNAACANrfM1MtMnSSfnfaz0xY9y0Ccd9h5hxU9AwDNd/6Xzv9S0TMMxGcu/szFSbLSuJXGFT0LtCsrNqCFWLEBAMBgjJtx3IxJsuK7K76bJHeMvWNs0TNNzPk/Pv/HSfL1Q79+aNGzAFCca3e+duckWe3I1Y4sepaJqQaNmx+8+cEkmXLXKXcteiZoV1ZsAAAAdIgpXpripSQZNWTUkKS8KzgEDQC6W/WIVY9Ikmt2umanomeZEEEDmk/YAAAA6DBlDRyCBgATU7bAIWhAcYQNAACADlWWwCFoANAfRQcOQQOKJ2wAAAB0uKICh6ABwGA0O3AIGlAewgYAAABJmhc4BA0A6qnRgUPQgPIRNgAAAPiYRgUOQQOARqp34BA0oLwqXR8oehCgd5VKpVKp9Hy/TzMAAI0wbsZxMybJiu+u+G6S3DH2jrH9eoF/5p9J0rVr165JkttyW9HHBED7+9zkn5s8SW5/5/Z3+vM8QQPKz4oNAAAAJmqgKzi+fuvXb02SfClfSpL757x/zqKPBYD29+pvXv1N8lHQWOgPC/2hL88TNKB1CBsAAAD0SV8DR/WSU4fMcMgM3X9+7FrHrjWhxz+y1iNrJck9M94zY9HHCED53b3x3Rsnyb3fuvdbE7r/iguuuKD77cPGHDYm6fkSVYIGtB6XooIW4lJUAACUSfUSVV9956vvJMnO39/5+0m3PTS2z/ZJMtnVk12dJOMfHv9wkrxz0zs3JckDyzywTJIsMfkSk3d/3esOv+7wJBmx84idiz5GAMrjxpE3jkySL5zyhVO6//zW9W5dL0mWvWDZC5JkoaUXWjpJHr7r4buS5PUNXt8gSaY6d6pzk+Tana/dOUkOnfvQuZPkvMnPmzwRNKCVCBvQQoQNAABa0WHjDhuXJLtNuduUSXLG3mfsnSTfPuDbB0zseQIHAEnPQaPW5WdffnaSrPXNtb6ZJOuftP5JSXLeyPNGFn0MQH0JG9BChA0AAFrR03c9fVeSzLH0HEsP5PkCB0Bn6mvQ6Mm/fvWvXyXJinutuFfRxwLUlz02WkT1C+2+/ln2+eo1d9nPCwAAkLz06Zc+PZjnf3GXL+6SJNcfcf0RRR8LAI032KBRVflC5QtFHwvQGMJGyQ32C/+yztfu5wUAAEj++85/30k+uYfGQAkcAO2tXkGjasUvr/jlJLnt67d9vehjA+pL2Cipen0B325f5DsvAABQfvUOGrUEDoD2Uu+gUevzF37+wkTggHYibLSo6l4KZd1ToXa+vv7ZqPcFAAAar9FBo5bAAdDaGh00agkc0D6EjZIZ6ObQvX2B36wVCkWFhIG+r5UbAAAweM0OGrUEDoDW0uygUUvggNYnbNAQ9d7MW4AAAIDyKTpo1BI4AMqt6KBRS+CA1jVp0QPQXgYaLFr1klHXnXTdSdedlNxyzi3n3HJO0dMAAEBzlC1o1KoGjutyXZJkxM4jdi56JoBOVragUasaOG79+q1fT5JlL1j2gqJnAiau0tXVql8pt6eBXoqqXs+v15wD1dN8ZT0vJ2928mYnb5ZscfoWp29xev3PR3/5NAMA0EhlDxo9ue7w6w5PBA6AZit70OjJrevdul4icECZCRslU9Yv8Ov1Pn0NIrXPL+t5+cEkP5jkB5Mkh3cd3nV4CT5FPs0AADRSq18g9vkznj8jST71rU99q+hZANrZy6+//HqSzDjNjNMUPctg+JYFysseG3XW170lWn3PiOoX6D392dvz2sXnvvO573zuO0VPAQAAzbHj6juuXvQMA7H25GtPniQzHTjTgUXPAtAJZjhrhrOSZLPtNtuu6FkGYvtztnfBcSg5YQMAAIA+OXLzIzdPWidwVIPGRdtdtF2SVO6u3F30TAAdYatslSSnjDhlRNI6gaMaNI6555h7ip4FmDiXoqqz/q7EaJVLLjX7PHXqeelNuxwHAAAt7rScliQ7nbLTKUly1FVHXVX0SN3VBo0hhw85vOiZADpZ12ldpyXJ5tdvfn2SnHbcaccVPVN3nwga+2SfomcCJm7SogdoN53yxXJPX7A36virr9vql/ACAIC2sFk2S5Ijc+THflx04BA0AMqpslllsyQ5JR/fQ7zowCFoQOsSNkqmty/wqz8v64qGnuar9/s067wAAAATUZLAIWgAtIayBA5BA1qfsNGiyr5yoaj5yn5eAACgLRUUOAQNgNZUVOAQNKB9CBslVa9LL7XKpaH6OmfZzwsAAHS0JgUOQQOgPTQrcAga0H6EjZIr+6WV+jtfveYs6n0BAIA+aFDgWGuVtVZJkouWvGjJRNAAaBeNChyCBrSvSleXr3qhVZQtbAEAQJ+cltOSZKdTdjolGXjg+Ne4f41LkhUnX3Hyog8JgMZ55oRnTkiS2beefeuBPF/QgPY3SdEDAAAA0OaqKzg2P3LzJNlx9R1X78vTVvntKr/tfvugykET/DWfsXuP3TtJLhlzyZgkyat5tehDBmAC3st7SXL+78//fZK8cNsLt03oYWfuduZu3W+v+bs1f9eXlxc0oHMIGwAAADRHHwNHdQ+Nq8ZcNSZJFn150ZeT5ILJL5g8Scb+d+x/k2TsymNXTpJFJl9k8iRZd/5150+S3U/d/dQkAgdAWXwQNPb66l5fTZINfrjBD5NkjqPmOCpJXtjlhV2SpOv8rvOTZLdXd/vYf78vWu+i9ZJks+02225CLy9oQOexxwYAAADN1cMeHKP3Hb1vklx01kVnJR/toXHorofumiTrZb0kyTlPnPNEkuy16l6rJskz+z6zb/fX+c1Ov9mp++1Dv3vod5Mk02W6og8doKPUBI2DLj/o8u53v33i2ycmyRxbzrFlklz1qas+1f3+n130s4uSZLIFJ1swSU4ZccqIJMmBOTBJprltmtuS5JibjrkpiaABHcQeG9BC7LEBAEAnevNzb34uSaa6farbk2SSwyc5PEne2+W9Xfry/B8f+eMjE4EDoGl6CRo9meQbk3wjSd47571zkmTMXmP2SpJ5fzXvr4o+JKBcrNgAAACg1MZPN366JJnyhSlfSJK3Znprpv483woOgCYZYND48OkfBI38JX9Jkmn+Oc0/iz4koJzssQEAAEApfbiHxqqLrJr0P2jUqgYOe3AA1Nkgg8YnbJyNk2SO1+Z4LfloDw6AKmEDAACAUqkNGrV7aAyWwAFQJ/UOGjU+3IND4ABqCBsAAACUQqODRi2BA2CAGhw0agkcQC1hAwAAgEI1O2jUEjgA+qjJQaOWwAFUCRsAAAAUouigUUvgAOhBwUGjlsABCBsAAAA0VdmCRi2BA+ADJQsatQQO6FzCBgAAAE1R9qBRS+AAOlbJg0YtgQM6T6XrA0UPAvSuUqlUKpWe7/dpBgCgzJZff/n1k+SWC265oOhZBuLsP5/95yTZeKuNtyp6FoBGuuSCSy5IknXXX3f9omcZiAWuWuCqJHn4Kw9/pehZgMawYgMAAICmOOhnB/2s6BkG5Lf5bZKs/OrKVmwAHWHEEiOWSJJhVw67suhZBuK4h497uOgZgMYSNgAAAGiKVZdbdbkkuebma24uepY++SBoPHfRcxclycy7zrxr0SMBNMP0C02/UJKMvn307UnrBI6rjr3q2CT5ynZf2a7oWYDGEjYAAABoqtIHjtqg8beZ/1b0SABFmH6P6fdIyh84BA3oPMIGAAAAhShd4BA0ACaorIFD0IDOJWwAAABQqMIDh6AB0CdlCRyCBiBsAAAAUApNDxyCBsCAFBU4BA2gStgAAACgVBoeOAQNgLpoVuAQNIBala4PFD0I0LtKpVKpVHq+36cZAIB2dO0t196SJKstv9ryg3qhkRmZJM89+tyjiaABUG+vHPLKIUky75vzvpkkr/zilV8M5vUEDaAnVmwAAABQavVawTHzKzO/kiQzXzTzRd1//kJeSJJ89sTPnpgkN71101tFHzNAGd261617JcmCuyy4S5I8vc7T63S/v7qC4ysbfGWDwbyPoAH0xooNaCFWbAAAQD9WcHxwyanvHvXdo5Lk1NGnjk6S+++8/84kmWmpmZZKkjnfnfPdJBk36bhJuz991Juj3kySFaZcYcqijxmgSNWgsdxByx00ofufWvuptZNkmleneTVJprthuhuSZP4/zv/HJHllvlfmS5IX13hxjYm9j6AB9JUVGwAAALSUXldw1Oyhsc/ofUZ3v/uAaQ6YJuk5aFStOHTFoYkVHEDn6i1oVM1x2RyXJckJ3znhO91/fuQxRx6T9L4Hh6AB9JcVG9BCrNgAAIBPqq7g2HK2LWdLkls3v3XzpNseGq/n9SSZ5a1Z3kqS5z/1/KcG8j5WcACdoq9BozdvPfDWA0kyxcJTLJx8tAfHl/79pX8nyW9X+e0qiaAB9J+wAS1E2AAAgP6r7qEx+1azb5Uk75z4zomDeT2BA2hX9QoaVdVLVM1+6eyXFn1sQHsRNqCFCBsAANB31aDR2yWnBkrgANpFvYNGLYEDqDd7bAAAANBWGh00quzBAbS6RgeNquoeHE+v8/Q6RR8z0B6EDQAAANpCs4JGLYEDaDXNChq1BA6gXoQNAAAAWlpRQaOWwAGUXVFBo5bAAQyWsAEAAEBLKkvQqCVwAGVTlqBRS+AABkrYAAAAoKWUNWjUEjiAopU1aNQSOID+EjYAAABoCS9O9eJUSfmDRi2BA2i2VgkatT4MHF9/+utFzwKUm7ABAABAS7j42YufTVonaNT6wVE/OKroGYDOsO+X9/1y0TMMxvnTnT9d0TMA5Vbp+kDRgwC9q1QqlUql5/t9mgEAaGuzZbYk2Xrs1mOT5IQ3Tnij6JH6YoaLZrgoSR75/COfT5IZZpthtqJnAtrbG8u+sWySLPWdpb6TJA//+OEfFz1TX3z3ye8+mSQnP3LyI0lS+ULlC0XPBJSTFRsAAAC0hmfyTJL8edo/T5skW0211VRFjzQxggZQlKlum+q2JLnr/+76vyRZ8DcL/qbomSZG0AD6S9gAAACgtZQ8cAgaQFmUPXAIGsBACRsAAAC0ppIFDkEDKKuyBQ5BAxisltxwDdrF/OvMv8786xQ9BQAAtLhq4Jjtzx8LCc3ag0PQAFrFh4Fj2bve34PjN0v9JmneHhyCBlAvNg+HAvW2GXh/+TQDAECatsm4oAG0umZtMi5oAPXmUlQAAAC0lwZfoqqnoHHqN0/9ZpJcPezqYUWfAoAJueb6a65PkqMrR1eSxl+iStAAGsWlqAAAAGhPDbpE1a1fvPWLSTLD9DNMnyRHXXzUxUmy0192+kv3x1127mXnJslaG661YdGnAuhsV89x9RxJsvrTqz/d/ecvfe6lzyXJz/79s38nyb8X+fciSTJ9ph/U+wkaQKO5FBUUaJPPbPKZTT7T98ef/Z+z/3P2f3q+36cZAAAmYoCXqJrm79P8PUleW/m1lZPkVzP9aqYkme6k6U5Kkp2+ttPXJvb8y8657JxE4ACar6egUeuAZQ5YJkmWm2+5+ZJkzfPWPC9JPnXKp05Jkv9t/r/N+/J+ggbQLMIGtJDe9uTwaQYAgD7oY+CoXnLq4dUfXj1JZppypikH87YCB9AsfQ0atWbYdoZtk+TlP778xyR56uanbk6SL/7zi/9Met6DQ9AAms0eGwAAAHSWXvbgqN1DY9gUw6ZIkk3f3PTNwbzt2t9Y+xtJcvm5l59b9CkA2tNAg0ZVNWgsu+yyyybJ7MvNvlzS8x4cggZQFCs2oIVYsQEAAA3wwQqOM2Y7Y7YkWfsfa/8jmcAeGr1ccqq/rOAA6mWwQaMn1UtUVffgeGP5N5ZPksu+d9n3kmTDxTdcPBE0gOYTNqCFCBsAANA8jQoatQQOYKAaFTRq1QYOgKK5FBUAAAB006ygUeUSVUB/NStoVO19+963J8mBnzvwc0UfO0AibAAAAECS5geNWgIH0JtmB41aAgdQFsIGAAAAHa3ooFFL4ABqFR00agkcQNGEDQAAADpS2YJGLYEDKFvQqCVwAEURNgAAAOgoZQ8atQQO6DxlDxq1BA6g2SpdHyh6EKB3lUqlUqn0fL9PMwAA9Oyp+Z+aP0nmHDPnmKJnGQz/Vz+0v4n/f//lN3qu0XMlyfyPz/940bMA7cmKDQAAADrCHI/M8UiS7HPkPkcWPctAnDbutHFFzwA0x0WTXTRZ0TMMxJ7v7PlOImgAjSdsAAAA0FH233H/HZPWCRzVoLHp5JtOXvQsQHOs+/a6byetEziqQePXQ349pOhZgM4gbAAAANCRyh44BA2g7IHjE0Gjkpa+hBbQOoQNAAAAOlrZAoegAdQqW+AQNICiCRsAAACQ4gOHoAH0pujAIWgAZSFsAAAAQDfNDhynPX/a80ly+1K3L5UklUqlkiRP3fPUPUWfC6Acnl3i2SWSj/77cG7XuV1JcuHGF27cjPcXNICyETYAAABgAhodOD4MGiNuH5Ekv73/t/d3v3/OJedcMhE4oJNVg8Zs/53tv91/fuL4E8cnyXnnnXde0rjAIWgAZVXp+kDRgwC9q1Sqv5sxYT7NAADQOD8/6udHJckvd/rlToN5nS/P8eU5kuTz035+2uSTQaMnT/7nyf8kyRxLzLFE0ecCaKyegkZPtpx0y0mTZJp5p5k3SY54+IiHB/P+ggZQdsIGtBBhAwAAijfQwDH7xbNfnCRPr/v0uoN5f4ED2ld/g0a9CRpAq3ApKgAAAOiH/l6iqnrJqZU3X3nzery/S1RB+6l30Bg+bvi4pO+XqBI0gFYjbAAAAMAA9BY4avfQOOPFM16s5/sLHND6GrVC419T/GuKpPc9OAQNoFW5FBW0EJeiAgCA8jpo7YPWTpJ5V5h3hSS57czbzkz6vofGYLlEFbSOZl9yqroHx4bf3vDbSfKfdf+zbpLsufGe7wcPQQNoMcIGtBBhAwAASuy5PJckP/7Sj7+UNC9o1BI4oLyK3kOjGjhOyAnv/+CdvFP0OQEYCJeiAgAAgMEoSdCocokqKJ+ig0bVieNPHJ8kW2Wr938wWSYr+twADISwAQAAAANRsqBRS+CA4pUlaNQSOIBWJ2wAAABAf5Q8aNQSOKD5yho0agkcQKsSNgAAAKAfWiVo1KoGjv8t879lip4F2tVLV750ZVL+oFHrE4EDoOSEDQAAAOiHix6/6PGiZxiM/43434iiZ4B29eofX/1j0TMMxjVXXXNV0TMA9IWwAQAAAP1w+8jbRybJQtsvtH3Rs/THtddee22SLHbEYkcUPQu0q3n/Ou9fk+SmK266ouhZ+mOeQ+c5NEnuWfyexYueBaAvhA0AAADoh6mOnuroJLmzcmclKX/gqAaNVVZZZZWiZ4FOsfway6+RlD9wfBg0Nr9n8ySZZpZpZil6JoC+EDYAAABgAMoeOAQNKF5ZA4egAbQ6YQMAAAAGoayB4/4d79+x6BmA99097O5hRc+QCBpA+xA2AAAAoA7KFji+f+/3702SYxc/1jXzoSB/vvXPtybJNstts1yRcwgaQLsRNgAAAKCOBA5A0ABoLGEDAAAAGkDggM4jaAA0h7ABAAAADSRwQPsTNACaS9gAAACAJhA4oP0IGgDFEDYAAACgiQQOaH2CBkCxhA0AAAAogMABrUfQACgHYQMAAAAKVA0cP1j0B4sWPUsicMCElCVoVG17xbZXJIIG0LkqXR8oehCgd5VKpVKp9Hy/TzMAALSe46Y9btok2f617V8repYJOebTx3w6Sbb/7/b/LXoWaLayBY1aB+120G5J8pPf/eR3Rc8C0ExWbAAAAEAByh40qqzgoBOVPWhU7fn7PX+fJAf/8OAfFj0LQDMJGwAAANBEJx160qFJ+YNGrWrgOPHgEw8uehZolDPnOXOepPxBo1Y1cBx2+mGnFz0LQDMIGwAAANBEU0475bRFzzCo+eebcr6iZ4BGmfZn0/6s6BkGY8YLZ7yw6BkAmsEeG9BC7LEBAADt49SrTr0qSTZfY/M1ip6lL05+6uSnkmTz2TefvehZoNHO/+v5f02SDTbeYOOiZ+mLP+/7532TZKtfbPWLomcBaAZhAwrUW6joL59mAABoPWUPHCfcccIdSbLl0lsuXfQs0GznLnjugknyjdHfGF30LBMiaACdyqWoAAAAoEDfXf27qyfJKVeecmXRs0zIKSedclKSjJ9v/HxFzwLN8u56766XJMdPfvzkRc8yIYIG0OmEDQAA/r+9uw+Wq6zvAP7dJKUJ8lLLZBB0uBlpo9FLHcooEkiLoFNpKdXq0IKRAFJmrAWplheZzuBMHUDaYBqxvtAwiYAwU18YmaGdsVanhaTSMlIk5cVSIZPapBZaM1iCdXr7h6yJ19y7u3dfnvPsfj7/nMnN7jm/s2efZ8+e7z7PAaABmhpwfHXDVzckyZve+qa3JgIOxls70Djzm2d+M0n+6tG/erR0TfsTaAD8kGADAAAAGkTAAaMn0ACoi3tsQEF3b797+93bu3/8WdNnTZ81Pff/a80AADB+mnoPjlMvO/WyJPnSF770hSRZ8uSSJ0vXBL0SaADUSbABFel0s3GtGQAAxpeAAwZHoAFQN1NRAQAAQAVMUQX9E2gAjAfBBgAAAFREwAG9E2gAjBfBBgAAAFRIwAGdCTQAxpNgAwAAACp23K7jdpWu4UAEHJTU9ECjbdXHVn2sdA0ANRJsAAAAQIUevPXBW5Pk+POOP690LfMRcDBKtQQabav/c/V/Jsm25duWl64FoCaCDQAAAKjIt/7nW/+TND/QmG12wAHDcOZvnflbSfMDjdnaAccjb3rkTaVrAaiBYAMAAAAqctC5B51buoZ+LD9veVWBDHWZumfqntI19GPZTy37qdI1ANRAsAEAAAAVeeldL70rSR4/7PHDStfSizM/eeYnk+TO4+88vnQtjK9PLP/E8iR558Z3bixdSy/++cZ/vjFJVtyzoupgBmBUWjMvKF0I0Fmr1Wq1WnP/v9YMAACT55uHf/PwJFm5Z+We0rV0Y+e1O69Nkpd+4KUfKF0L42fXfbvuS5KjTjnqlNK1dKMdaKz6/VW/X7oWgJoYsQEAAAAV+/nv/vx3k3pGcLzs6pddnST/dt2/XVe6FsaHQANgsgg2AAAAYAwIOJhEAg2AySTYAAAAgDEi4GASCDQAJptgAwAAAMaQgINxJNAAIBFsAAAAwFgTcDAOBBoA7E+wAQAAABNAwEGNBBoAHIhgAwAAACaIgIMaCDQAmI9gAwAAACbQ0XuP3pskR19w9AWla+mGgGMy1BZoHHLqIacmyVFfOOoLpWsBmCStmReULgTorNVqtVqtuf9fawYAALo19fzU80myY+mOpaVrWYg9a/esTZJDbz301tK10L/nXvLcS5Lk4N0H7y5dy0IcdsthtyTJdy/4bhVBIUDtjNgAAACACbRs+7LtpWvoR+vs1tmla2BwFh216Kgk+elf/+lfL13LQrz4v178X6VrAJgkgg0AAACYQF+/5OuXJMkrHnjFA6VrWYi//vxff750DQzOl2/68k1J8vzdz99dupZeTK2fWp8k2zdv31y6FoBJItgAAACACbTsvmX3JfUGHG/d/NbNSXLXBXeZ+qdi99x3z31J8mun/FoV99Romx1ovOihFz1UuiaASSLYAAAAgAkm4KAEgQYA/RBsAAAAAAIORkKgAcAgCDYAAACAHxmXgOOGh254KEmef8/z7yld0yT731X/uypJ1l+y/pJEoAHAYLRmXlC6EKCzVqvVarXm/n+tGQAAGLTnTn7u5CQ5/qPHfzRJHjvhsRNK19SLFW9Y8YYkeeRTj3wqSZb+3NKfK13TJPj+f3z/P5LkNTtfszNJHj3h0areNwINgGYzYgMAAACYU+0jOJ78ypNfSZJVF6+6OEn2/svefyld0zgTaAAwCkZsQEWM2AAAAEr70QiOPzr+j5LksdMfO710Tb0wgmM4qg803jn1ziTZ/uD2BxOBBkDTGbEBAAAAdK09guP0daevK13LQrRHcLx60asXJUZw9KsdaJxwzQnXJPUFGm2rv77664lAA6AWgg0AAACgZ1OtqVb/aynnX4/912OT5PWrX786EXD0qh1onHrjqTcmycOfePgTpWvqxyvOeMUZpWsAoHuCDQAAAKBnV+y4YkeSXL/l+i2la+nHP33nn76T7BdwPLT3oST5w1/6w19Kko/93cf+rnSNJd1yxi1nJMllj172aJJ8f+331yb7Ao1tH9724dI19uODl33wsiS55oZrbihdCwDdc48NqIh7bAAAAE314U9/+NNJctW6q6qcoqpt+ceXfzxJvvPu77x7/79/dMtHtyTJ7533e+eVrnEUNs1smkmSixZd9GM/ij3y1Ue+Okl2b9+9vXSN/fhRoPGRaz5SuhYAeifYgIoINgAAgKYbl4BjLuMecMwVaIwLgQbAeBjLDykAAACgjCvPu/K8pP4pquZyybpL1iXJTZ++6dPzPe57v/i9X0yS7fduv7dkvY+sf2R9kuw5es/R8z1OoAFATcbywwoAAAAoa1IDjj2P7nk0SY7992P/PUmm10yvSZLPf+7zn1vIdnZdsOuChTzvL6f/cjpJXvUHr/qDJHnJfS+5L0meufOZO/d/nEADgBqN5YcWAAAA0AyTEnD8yVV/clWSrHzDyjckye5du3ft/7i3vf1tb0+6Dzhuat3USpKjNh+1OUmuP//687t5XjvQ+NXtv/pj98B47uXPvTxJXnbiy05Mko1f2fiVRKABQJ3cYwMq4h4bAABA7cb9Hhzd+txnP/fZJPnNt/3m2/b/ezvQuCSXHPB51627bl2SXLX5qs37/32uQGPSCDQAJoNgY0zMvuDtqI4nwQYAADAurj7i6iOS5LpnrnumdC0ltQOOb7/9229P5g40ZmsHHK/5x9f8YyLQeO/a965Nkg23bri1dC0ADJ9go3JzXege9VHtdMF9WPV1u91xeZcLNgAAgHHzgd/9wO8myfUfv/7jpWuhPu9tvbeVJBv+b8P/la4FgNEZy3kUJ0GvQULT6uj3eb0+f6HPAwAAYLiu+7Pr/ixJrnr3Ve8uXQv1EGgATLYlpQtgfk29ED+outrrMdIAAABgsrUDjjYjODgQgQYAiamoGqvf4GDYR3WhUyLV8rymGrf9AQAAmMuOP9/x50ky9TtTv1O6Fsp74pef+OUkeflXX/7V0rUAUJ6pqBgoF9YBAAAYhGMuOuaiJHnq5qduLl0L5Qg0ADgQwUZDtQOCuZZ0x+sFAABQNwHHZBJoADAfwQY9ado9PwQXAAAAk0HAMRkEGgB0wz02KlXqXgv9bnfQ98rodj/7ff5ctqzdsnbL2uT828+//fzbF74eAAAAAKiZq8yMkhEb0Ientjy15aktpasAAAAAgLLuuP+O+++4f98ShmlJ6QLGTa9TNUky6za1bmrd1LrSVQAAAABAWeeeeO6J556479/nzJwzc47rngyJERsAAAAAAEA1BBv0pLYRJsO+2fm629bdtu62fa/LsJedjKqOSV9euuHSDZdumPs4LDp90emLTi9f56QsO7nyoisvuvKi8nVOylI/1YzlFx//4uNffNzxaMry7A+d/aGzPzT3cZjaObVzamf5Oidl2cn6xesXr19cvs5JWeqnmrHcvHfz3s17J+d4fOZFn3lR+/3X1OUpV8x/LE5+rBl1Hmi56aRNJzXhOA9q2cnmbZu3bd5Wvs5JWU5KP9X05frV61evX935eMAwCTYGbNAdMvMbdnABAADAeDnn2XOeTX4YcJSuZZxsOmnTSUly4dYLt5auBYDxJ9iYcO1gYK7lQtfX7d8BAACgBAHHYAg0AChBsMFQ9BqQdBrB0un/ezWV5p4AAAuFSURBVA1mjJgBAAAg6T3guPiOi+8oXfMwXfjfF/53N48TaABQkmCDBRlUMNDrevrdrkADAACAA+kUcHz5HV9+R5J88rc/+dtJcsO2G7aVrnmQrnnkmkeSZNPhmw5Pkq0rtq440OMEGgA0wZLSBVC32UHBoEZodPv8UW0P9nfQWQedddBZyfSa6TXTa5In733y3ifvTVacsuKUFackOTSH5tDSVU6O6QemH5h+YN+/d5yw44QdJyTHPHDMA8c8kBz02oNee9Brk9ycm3Nz6WphNBZvXLxx8cZ97ePpdz39rqfflRyx6YhNR2wqXd3kWbZ12dZlW/cdjyeufeLaJ65Njr362KuPvTo5+LSDTzv4tCSP5bE8VrpaGK3Zn+PPPvzsw88+nBwyfcj0IdOlq5scS9685M1L3rzveOx+ePfDux9Ojpw+cvrICTwO7YDj0Pcc+p4kOfysw89KkjW/suZX9n/c5a+//PVJkm3ZliRXnHTFSb1sp3Vx6+IkmfnUzKcGUfeyv1n2N0ny3GnPndbL89qBxgdf+cFX7v/3k7510reS5GsPfu3BJPn25d++PEne8qW3fGkQ9dZidj+1+/7d9+++PznydUe+7sjXJUveuOSNS96Y5Jk8k2dKVzv+2sfj2W88+41nv5Ecctwhxx1yXOmqgBJaMzMu+UItTLEFNJ1+Cmi6Tv3U+sXrF69fnLzvB+/7wft+ULpaoCZ//Pd//PdJ54Bj0dJFS5Nk19d2fS1Jtm7auilJ3rLxLRvbj1lz5Q+X995w4HWc/EIQfu/K5DO3fea2JDnjnjPuSZKpW6ZuSZI9S/csna+OuQINgE5uPPnGk288OXn/1vdvff88Y7d8/2OYTEUFAAAA0Kf2CI65pqiaHWgs/4Xlv5Akv/Gnv/GnSXLXpXdd2sv22oHGOe845x1J8jO3/8ztSfLUhU9dmCSH7T1s74GeJ9AAYBwINgAAAAAGZHbAsXTN0jXJTwYas7UDjlV/seov5lv/yu+t/F6yL9CYbXbA8bN3/uydiUADgPHiHhsAAAAAA9YOOC7/28v/tpfnHfEPR/zDfP+/fOfynUmSlVk53+PaAcfTebr0SwEAA2fEBgAAAAAAUA3BBgAAAAAAUA3BBgAAAAAAUI3WzAt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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 35kb" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + " p.marker_type = :diamond # Type of marker\n", + " p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + " p.marker_size = 2 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + " p.line_type = :solid # Type of the line\n", + " p.line_color = :black # Colour of the line\n", + " p.line_width = 2 # Width of the line\n", + " p.fmt = 'b-d' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument (markers) is overwritten by marker type and marker_fill_color and marker_border_color" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + " PNG 795x795 DirectClass 16-bit 29kb" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + " p.fmt = 'k-' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + " p.marker_type = :diamond # Type of marker\n", + " p.marker_fill_color = :white # Colour to be filled inside the marker\n", + " p.marker_size = 1 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument in combination with marker_width and overwritten marker colours" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 37kb" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + " p.fmt = 'k-s' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + "# p.marker_type = :diamond # Type of marker\n", + " p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + " p.marker_size = 1 # Size of the marker, unit is 15*pixels\n", + " p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + "# p.line_color = :black # Colour of the line\n", + "# p.line_width = 2 # Width of the line\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### fmt argument with line_width and overwritten line color" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + " PNG 795x795 DirectClass 16-bit 58kb" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Default for plot function is solid black line\n", + "\n", + "# Step 1\n", + "require 'rubyplot'\n", + "Rubyplot.set_backend :magick\n", + "Rubyplot.inline\n", + "\n", + "# Step 2\n", + "figure = Rubyplot::Figure.new(width: 20, height: 20)\n", + "\n", + "# Step 3\n", + "axes00 = figure.add_subplot! 0,0\n", + "axes00.plot! do |p|\n", + " d = (0..360).step(5).to_a\n", + " p.data d, d.map { |a| Math.sin(a * Math::PI / 180) } # Data as arrays of x coordinates and y coordinates\n", + " p.fmt = 'k-x' # fmt argument to specify line type, marker type and colour in short\n", + " # fmt argument overwrites line type, marker type and all the colours i.e. marker_fill_color, marker_border_color, line_color\n", + " # line type, marker type and colour can be in any order\n", + "# p.marker_type = :diamond # Type of marker\n", + "# p.marker_fill_color = :lemon # Colour to be filled inside the marker\n", + "# p.marker_size = 1 # Size of the marker, unit is 15*pixels\n", + "# p.marker_border_color = :black # Colour of the border of the marker\n", + "# p.line_type = :solid # Type of the line\n", + " p.line_color = :blue # Colour of the line\n", + " p.line_width = 5 # Width of the line\n", + " p.label = \"sine\" # Label for this data\n", + "end\n", + "\n", + "axes00.title = \"A plot function example\"\n", + "axes00.x_title = \"X-axis\"\n", + "axes00.y_title = \"Y-axis\"\n", + "axes00.square_axes = false\n", + "\n", + "# Step 4\n", + "figure.show" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ruby 2.6.3", + "language": "ruby", + "name": "ruby" + }, + "language_info": { + "file_extension": ".rb", + "mimetype": "application/x-ruby", + "name": "ruby", + "version": "2.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}