plot_data.LineSegment2D([2, 2], [2, 1]),
plot_data.LineSegment2D([2, 1], [1, 1])],
surface_style=plot_data.SurfaceStyle(colors.LIGHTORANGE))
circle1 = plot_data.Circle2D(cx=0, cy=0, r=10)
circle2 = plot_data.Circle2D(cx=1, cy=1, r=5, surface_style=plot_data.SurfaceStyle(colors.RED))
circle3 = plot_data.Circle2D(cx=1, cy=1, r=5, surface_style=plot_data.SurfaceStyle(colors.LIGHTBROWN))
primitive_group1 = [circle1]
primitive_group2 = [contour]
primitive_group3 = [circle2]
primitive_group4 = [circle3]
primitive_groups = [primitive_group1, primitive_group2, primitive_group3, primitive_group4]
primitive_group_container = plot_data.PrimitiveGroupsContainer(primitive_groups=primitive_groups,
associated_elements=[1, 2, 3, 4],
x_variable='x', y_variable='y')
histogram = plot_data.Histogram(x_variable='x')
"""Creating the multiplot"""
plots = [parallelplot1, parallelplot2, scatterplot1,
scatterplot2, scatterplot3, graph2d, primitive_group_container,
histogram]
multiplot = plot_data.MultiplePlots(plots=plots, elements=elements,
initial_view_on=True)
# Display
plot_data.plot_canvas(plot_data_object=multiplot, debug_mode=True)
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Mar 14 15:32:37 2018 @author: Steven Masfaraud [email protected] """ import plot_data from test_objects.primitive_group_test import primitive_group # The primitive_group's definition has been moved to test_objects.primitive_group_test.py # to make MultiplePlots' more convenient # if debug mode is True, set it to False plot_data.plot_canvas(plot_data_object=primitive_group, debug_mode=True)
import networkx as nx
import plot_data.graph
import plot_data
graph = nx.Graph()
graph.add_node(1, color='rgb(0, 255, 0)', shape='s', name='node1')
graph.add_node('a', color='rgb(255, 0, 0)', shape='o', name='node2')
graph.add_node(3, color='rgb(0, 0, 255)', shape='s', name='node3')
graph.add_edge(1, 'a')
graph.add_edge('a', 3)
graph.add_edge(3, 1)
plotdata_nx_graph = plot_data.graph.NetworkxGraph(graph)
plot_data.plot_canvas(plotdata_nx_graph)
import plot_data
import plot_data.colors as colors
import random
elements = []
SHAPES = ['round', 'square', 'triangle', 'ellipse']
COLORS = [
colors.RED, colors.BLUE, colors.GREEN, colors.YELLOW, colors.ORANGE,
colors.VIOLET
]
for i in range(50):
random_shape = SHAPES[random.randint(0, len(SHAPES) - 1)]
random_color = COLORS[random.randint(0, len(SHAPES) - 1)]
elements.append({
'mass': random.uniform(0, 50),
'length': random.uniform(0, 100),
'shape': random_shape,
'color': random_color
})
histogram = plot_data.Histogram(x_variable='mass', elements=elements)
plot_data.plot_canvas(plot_data_object=histogram, debug_mode=True)
from tutorials import pattern_generator
import plot_data
list_diameters = [0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.275, 0.3]
excentricity_min_max = (0.6, 0.9)
diameter_percetage_clearence_min_max = (0.1, 0.6)
MINOR_AXIS_SIZE_IN_MM = 1
pattern_generator = pattern_generator.PatternGenerator(MINOR_AXIS_SIZE_IN_MM,
list_diameters,
excentricity_min_max,
diameter_percetage_clearence_min_max)
list_patterns = pattern_generator.generate()
primitive_group_container_plots = []
for pattern in list_patterns[:50]:
primitive_group_container_plots.append(pattern.plot_data())
primitive_group_container = plot_data.PrimitiveGroupsContainer(
primitive_group_container_plots)
plot_data.plot_canvas(primitive_group_container)
# pattern_generator.get_elipse_interpolation()
shape='square') # 'circle', 'square' or 'crux'
# Finally, axis can be personalized too
graduation_style = plot_data.TextStyle(text_color=colors.BLUE,
font_size=10,
font_style='Arial')
axis_style = plot_data.EdgeStyle(line_width=0.5,
color_stroke=colors.ROSE,
dashline=[])
axis = plot_data.Axis(nb_points_x=7,
nb_points_y=5,
graduation_style=graduation_style,
axis_style=axis_style)
# a tooltip is drawn when clicking on a point. Users can choose what information
# they want to be displayed.
tooltip = plot_data.Tooltip(attributes=['mass', 'length', 'shape', 'color'])
# Now, here is the new scatterplot
customized_scatterplot = plot_data.Scatter(x_variable='mass',
y_variable='shape',
point_style=point_style,
elements=elements,
axis=axis,
tooltip=tooltip)
# if debug_mode is True, set it to False
plot_data.plot_canvas(plot_data_object=scatterplot, debug_mode=True)
enabled=True)
# objective_settings0 = ObjectiveSettings(n_near_values=4, enabled=True)
# objective0 = Objective(coefficients=coefficients,
# settings=objective_settings0,
# name='ObjectiveName')
dirname = os.path.dirname(__file__)
relative_filepath = './data/cars.csv'
filename = os.path.join(dirname, relative_filepath)
array, variables = from_csv(filename=filename,
end=None,
remove_duplicates=True)
catalog = Catalog(array=array,
variables=variables,
choice_variables=choice_args,
objectives=[],
pareto_settings=pareto_settings,
name='Cars')
from plot_data import plot_canvas
plot_canvas(plot_data_object=catalog.plot_data()[0], canvas_id='canvas')
# cost = catalog.build_costs(pareto_settings)
# print('cost', cost)
# p_f = pareto_frontier(cost)
# print('p_f', p_f)
parallelplot = plot_data.ParallelPlot(elements=elements,
axes=['mass', 'length', 'shape', 'color'])
# The line above shows the minimum requirements for creating a
# parallel plot. However, many options are available for further customization.
# 'edge_style' option allows customization of the lines
edge_style = plot_data.EdgeStyle(line_width=1, color_stroke=VIOLET, dashline=[])
# 'disposition' = 'vertical' or 'horizontal' whether you want the axis to be
# vertical of hozizontal. This can be changed by pressing the 'disp' button on
# canvas as well.
disposition = 'horizontal'
# Next, ParallelPlots "sort" an axis when clicking on it by displaying a color
# interpolation on the lines. When the attribute 'rgbs' is not given to the ParallePlot
# it is set to [red, blue, green]. However, users are free to set as many
# colors as they want, as long as they are in rgb. A wide panel of rgb colors
# are available in plot_data/colors.py
rgbs = [BLUE, YELLOW, ORANGE]
customized_parallelplot = plot_data.ParallelPlot(elements=elements,
axes=['mass', 'length', 'shape', 'color'],
edge_style=edge_style,
disposition=disposition,
rgbs=rgbs)
# if debug_mode == True, set it to False
plot_data.plot_canvas(plot_data_object=parallelplot, debug_mode=True)
"""
Gearbox
"""
speed_ranges = [[0, 30], [20, 40], [30, 50], [45, 70]] # in km/h
speed_ranges = [[
speed_range[0] * (1000 * 2 * np.pi) / (3600 * np.pi * tire_radius),
speed_range[1] * (1000 * 2 * np.pi) / (3600 * np.pi * tire_radius)
] for speed_range in speed_ranges] #in rad/s
gearbox = objects.GearBox(engine=engine, speed_ranges=speed_ranges)
"""
GearBox Optimizer
"""
optimizer = objects.GearBoxOptimizer(gearbox=gearbox,
wltp_cycle=wltp_cycle,
firstgear_ratio_min_max=[.5, 4.5])
"""
Results
"""
results = optimizer.optimize(1)
for result in results:
print('Ratios: ', result[0].gearbox.ratios)
plot_data.plot_canvas(plot_data_object=result.plot_data()[0],
canvas_id='canvas')
plot_data.plot_canvas(plot_data_object=result.plot_data()[1],
canvas_id='canvas')
# c = Client(api_url = 'https://api.demo.dessia.tech')
# r = c.create_object_from_python_object(results[0])
import plot_data
from test_objects.graph_test import graph2d
# The graph's definition has been moved to test_objects.graph_test.py to
# make MultiplePlots.py' imports more convenient
plot_data.plot_canvas(plot_data_object=graph2d,
canvas_id='canvas',
debug_mode=True)
speed_ranges = [[0, 30], [20, 40], [30, 50]] # in km/h
speed_ranges = [[
speed_range[0] * (1000 * 2 * np.pi) / (3600 * np.pi * tire_radius),
speed_range[1] * (1000 * 2 * np.pi) / (3600 * np.pi * tire_radius)
] for speed_range in speed_ranges] #in rad/s
gearbox = objects.GearBox(engine=engine, speed_ranges=speed_ranges)
generator = objects.GearBoxGenerator(gearbox,
number_inputs=2,
max_number_shaft_assemblies=5,
max_number_gears=5)
generate_connections = generator.generate_connections()
generate_paths = generator.generate_paths(generate_connections)
# generator.draw_graph(generate_paths, 10)
# list_paths = generate_paths[1]
# list_paths_edges = generate_paths[2]
# list_dict_connections = generate_paths[-1]
clutch_analisys = generator.clutch_analisys(generate_paths)
# generator.draw_graph(clutch_analisys[0], 10)
clutch_generate = generator.generate()
# generator.draw_graph(clutch_generate[1], 25)
# for i,gearbox in enumerate(clutch_generate[0]):
# plot_data.plot_canvas(gearbox.plot_data()[0])
# if i>10:
# break
clustering = objects.Clustering(clutch_generate, 'k means')
plot_data.plot_canvas(clustering.plot_clusters())