Can someone help me determine why these images are coming out different from one another? I am trying to re-generate a plot I made with plotnine. Unfortunately, running the same code seems to generate a different output. Here's the original output I was getting: Here is the new output I am getting: But I have not changed the code at all. Here is the code: from dfply import select, X from matplotlib.font_manager import FontProperties from mizani.breaks import log_breaks, mpl_breaks from mizani.formatters import currency_format, custom_format, mpl_format,scientific_format from mizani.transforms import log10_trans, identity_trans from pandas import DataFrame, read_csv from pathlib import Path from plotnine import ( aes, element_blank, element_line, element_rect, element_text, facet_wrap, geom_blank, geom_line, geom_point, geom_vline, ggplot, labeller, scale_x_continuous, scale_y_continuous, scale_y_log10, theme) from sys import path from time import asctime, localtime, time module_path = Path(__file__).resolve() analysis_folder = module_path.parent scripts_folder = analysis_folder.parent operations_folder = str(scripts_folder) + '/operations' project_folder = scripts_folder.parent output_folder = str(project_folder) + '/output_analysis' path.insert(0, str(operations_folder)) path.insert(0, str(scripts_folder)) from fixed_precision import fixed_precision as fixed_precision from label_title import label_title as label_title from revised_phase1_y_breaks import revised_phase1_y_breaks as revised_phase1_y_breaks from revised_phase1_y_format import revised_phase1_y_format as revised_phase1_y_format font_path = str(project_folder) + '/resources/fonts/Arial-Unicode-Regular.ttf' plot_title_properties = FontProperties(fname=font_path, size=12) axis_title_properties = FontProperties(fname=font_path, size=11) legend_title_properties = FontProperties(fname=font_path, size=11) strip_text_properties = FontProperties(fname=font_path, size=11) text_properties = FontProperties(fname=font_path, size=10) phase1_dataframe = read_csv( str(output_folder) + '/phase1_dataframe.csv') def create_phase1_plot(): """ Create the Phase 1 plot. :return: 'Date, time, and year: date_time_year Module: module_path Saved image: phase1_plot_location' :rtype: str """ phase1_dataframe_combined = phase1_dataframe >> select ( X.series, X.trait, X.time, X.ancestor_seed, X.ancestor_value, X.ancestor_value_log10, X.evolved_population_average_value, X.evolved_population_average_value_log10) # Use ancestor data to plot ancestor values. fitness_ancestor_value = ( phase1_dataframe_combined.query( "trait == 'fitness'") # ['ancestor_value_log10'] ['ancestor_value'] .mean()) genome_length_ancestor_value = ( phase1_dataframe_combined.query( "trait == 'genome_length'") # ['ancestor_value_log10'] ['ancestor_value'] .mean()) trait_names = ["fitness", "genome_length",] value = list([fitness_ancestor_value, genome_length_ancestor_value]) ancestor_values = {'trait': trait_names, 'ancestor_value': value} ancestor_data = DataFrame(ancestor_values) # Generate fake data for framing. trait_names = ["fitness", "fitness", "genome_length", "genome_length"] # trait_names = ["fitness", "fitness"] value = [0.001, 10000000000, 70, 130] # value = [-0.0000000001, 10000000000] frame_nums = {'trait': trait_names, 'value': value} frame_data = DataFrame(frame_nums) phase1_plot = ( ggplot( data=phase1_dataframe_combined, mapping=aes( x='time', y='evolved_population_average_value', color= 'factor(ancestor_seed)', group= 'series')) + geom_line(size= .5) + geom_vline(xintercept=20000) + geom_vline(xintercept=100000) + geom_vline(xintercept=500000) + geom_point( data= ancestor_data, mapping=aes( x=0, y='ancestor_value' ), shape= '*', size= 4, inherit_aes= False) + geom_blank( data=frame_data, mapping=aes(y='value'), inherit_aes=False) + facet_wrap( '~ trait', nrow=2, scales='free_y', labeller= labeller(trait=label_title)) + scale_x_continuous( name='Time (updates)', labels = currency_format( prefix='', suffix='', digits=0, big_mark=',')) + scale_y_log10(name='Average value', breaks=revised_phase1_y_breaks, labels=revised_phase1_y_format ) + theme( figure_size=(5.2, 4.375), legend_title_align= 'center', text=element_text(fontproperties=text_properties), title=element_text( margin= {'b': 20}, fontproperties=plot_title_properties), legend_position= 'none', plot_background=element_rect(fill='white'), axis_text_x=element_text( margin={'t':6}, color='black', size=8, fontproperties=text_properties), axis_text_y=element_text( margin={'r':6}, color='black', size=8, fontproperties=text_properties), axis_title_x=element_text( margin= {'t': 12}, fontproperties=axis_title_properties), axis_title_y=element_text( margin= {'r': 18}, fontproperties=axis_title_properties), axis_ticks_major_x=element_blank(), axis_ticks_major_y=element_blank(), rect=element_rect( color='white', size=3, fill='white'), panel_grid_major_y=element_line( linetype='solid', color='gray', size=.5), strip_text_x = element_text( fontproperties=strip_text_properties), panel_spacing = .3)) phase1_plot_name= 'revised_phase1_2.pdf' phase1_plot.save( filename = phase1_plot_name, path = ( output_folder + '/masked/phase1_plot')) date_time_year = asctime(localtime(time())) return ( '\n\n' 'Date, time, and year: %s \n' 'Module: %s \n' 'Saved image: %s' % ( date_time_year, module_path, ( output_folder + '/masked/phase1_plot/' + phase1_plot_name))) if __name__ == '__main__': print(create_phase1_plot()) Continue reading...
