#!python from SVG import Graph from itertools import chain __all__ = ( 'VerticalBar', 'HorizontalBar' ) class Bar( Graph ): "A superclass for bar-style graphs. Do not instantiate directly." # gap between bars bar_gap = True # how to stack adjacent dataset series # overlap - overlap bars with transparent colors # top - stack bars on top of one another # side - stack bars side-by-side stack = 'overlap' scale_divisions = None def __init__( self, fields, *args, **kargs ): self.fields = fields super( Bar, self ).__init__( *args, **kargs ) def data_max( self ): return max( chain( *map( lambda set: set['data'], self.data ) ) ) # above is same as # return max( map( lambda set: max( set['data'] ), self.data ) ) def data_min( self ): if not getattr(self, 'min_scale_value') is None: return self.min_scale_value min_value = min( chain( *map( lambda set: set['data'], self.data ) ) ) min_value = min( min_value, 0 ) return min_value def get_css( self ): return """\ /* default fill styles for multiple datasets (probably only use a single dataset on this graph though) */ .key1,.fill1{ fill: #ff0000; fill-opacity: 0.5; stroke: none; stroke-width: 0.5px; } .key2,.fill2{ fill: #0000ff; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key3,.fill3{ fill: #00ff00; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key4,.fill4{ fill: #ffcc00; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key5,.fill5{ fill: #00ccff; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key6,.fill6{ fill: #ff00ff; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key7,.fill7{ fill: #00ffff; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key8,.fill8{ fill: #ffff00; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key9,.fill9{ fill: #cc6666; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key10,.fill10{ fill: #663399; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key11,.fill11{ fill: #339900; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } .key12,.fill12{ fill: #9966FF; fill-opacity: 0.5; stroke: none; stroke-width: 1px; } """ def float_range( start = 0, stop = None, step = 1 ): "Much like the built-in function range, but accepts floats" while start < stop: yield float( start ) start += step class VerticalBar( Bar ): """ # === Create presentation quality SVG bar graphs easily # # = Synopsis # # require 'SVG/Graph/Bar' # # fields = %w(Jan Feb Mar); # data_sales_02 = [12, 45, 21] # # graph = SVG::Graph::Bar.new( # :height => 500, # :width => 300, # :fields => fields # ) # # graph.add_data( # :data => data_sales_02, # :title => 'Sales 2002' # ) # # print "Content-type: image/svg+xml\r\n\r\n" # print graph.burn # # = Description # # This object aims to allow you to easily create high quality # SVG[http://www.w3c.org/tr/svg bar graphs. You can either use the default # style sheet or supply your own. Either way there are many options which # can be configured to give you control over how the graph is generated - # with or without a key, data elements at each point, title, subtitle etc. # # = Notes # # The default stylesheet handles upto 12 data sets, if you # use more you must create your own stylesheet and add the # additional settings for the extra data sets. You will know # if you go over 12 data sets as they will have no style and # be in black. # # = Examples # # * http://germane-software.com/repositories/public/SVG/test/test.rb # # = See also # # * SVG::Graph::Graph # * SVG::Graph::BarHorizontal # * SVG::Graph::Line # * SVG::Graph::Pie # * SVG::Graph::Plot # * SVG::Graph::TimeSeries """ top_align = top_font = 1 def get_x_labels( self ): return self.fields # adapted from plot (very much like calling data_range('y')) def data_range( self ): min_value = self.data_min( ) max_value = self.data_max( ) range = max_value - min_value top_pad = range / 20.0 or 10 scale_range = ( max_value + top_pad ) - min_value scale_division = self.scale_divisions or ( scale_range / 10.0 ) if self.scale_integers: scale_division = round(scale_division) or 1 return min_value, max_value, scale_division # adapted from Plot def get_data_values( self ): min_value, max_value, scale_division = self.data_range() result = tuple( float_range( min_value, max_value + scale_division, scale_division ) ) if self.scale_integers: result = map(int, result) return result # adapted from Plot def get_y_labels( self ): return map( str, self.get_data_values() ) def x_label_offset( self, width ): return width / 2.0 def draw_data( self ): min_value = self.data_min() unit_size = (float(self.graph_height) - self.font_size*2*self.top_font) unit_size /= (max(self.get_data_values()) - min(self.get_data_values()) ) bar_gap = 0 if self.bar_gap: bar_gap = 10 if self.get_field_width() < 10: bar_gap = self.get_field_width() / 2 bar_width = self.get_field_width() - bar_gap if self.stack == 'side': bar_width /= len( self.data ) x_mod = ( self.graph_width - bar_gap )/2 if self.stack == 'side': x_mod -= bar_width/2 bottom = self.graph_height for field_count, field in enumerate( self.fields ): for dataset_count, dataset in enumerate( self.data ): # cases (assume 0 = +ve): # value min length # +ve +ve value - min # +ve -ve value - 0 # -ve -ve value.abs - 0 value = dataset['data'][field_count] left = self.get_field_width() * field_count length = ( abs(value) - max( min_value, 0 ) ) * unit_size # top is 0 if value is negative top = bottom - (( max(value,0) - min_value ) * unit_size ) if self.stack == 'side': left += bar_width * dataset_count rect = self._create_element( 'rect', { 'x': str(left), 'y': str(top), 'width': str(bar_width), 'height': str(length), 'class': 'fill%s' % (dataset_count+1), } ) self.graph.appendChild( rect ) self.make_datapoint_text( left + bar_width/2.0, top-6, value )