This file is indexed.

/usr/lib/python2.7/dist-packages/chaco/plot.py is in python-chaco 4.5.0-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

   1
   2
   3
   4
   5
   6
   7
   8
   9
  10
  11
  12
  13
  14
  15
  16
  17
  18
  19
  20
  21
  22
  23
  24
  25
  26
  27
  28
  29
  30
  31
  32
  33
  34
  35
  36
  37
  38
  39
  40
  41
  42
  43
  44
  45
  46
  47
  48
  49
  50
  51
  52
  53
  54
  55
  56
  57
  58
  59
  60
  61
  62
  63
  64
  65
  66
  67
  68
  69
  70
  71
  72
  73
  74
  75
  76
  77
  78
  79
  80
  81
  82
  83
  84
  85
  86
  87
  88
  89
  90
  91
  92
  93
  94
  95
  96
  97
  98
  99
 100
 101
 102
 103
 104
 105
 106
 107
 108
 109
 110
 111
 112
 113
 114
 115
 116
 117
 118
 119
 120
 121
 122
 123
 124
 125
 126
 127
 128
 129
 130
 131
 132
 133
 134
 135
 136
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
""" Defines the Plot class.
"""
# Major library imports
import itertools
import warnings
from numpy import arange, array, ndarray, linspace
from types import FunctionType

# Enthought library imports
from traits.api import Delegate, Dict, Instance, Int, List, Property, Str

# Local, relative imports
from abstract_colormap import AbstractColormap
from abstract_data_source import AbstractDataSource
from abstract_plot_data import AbstractPlotData
from array_data_source import ArrayDataSource
from array_plot_data import ArrayPlotData
from base_xy_plot import BaseXYPlot
from barplot import BarPlot
from candle_plot import CandlePlot
from colormapped_scatterplot import ColormappedScatterPlot
from contour_line_plot import ContourLinePlot
from contour_poly_plot import ContourPolyPlot
from cmap_image_plot import CMapImagePlot
from data_range_1d import DataRange1D
from data_view import DataView
from default_colormaps import Spectral
from grid_data_source import GridDataSource
from grid_mapper import GridMapper
from image_data import ImageData
from image_plot import ImagePlot
from legend import Legend
from lineplot import LinePlot
from linear_mapper import LinearMapper
from log_mapper import LogMapper
from plot_label import PlotLabel
from polygon_plot import PolygonPlot
from scatterplot import ScatterPlot
from filled_line_plot import FilledLinePlot
from quiverplot import QuiverPlot




#-----------------------------------------------------------------------------
# The Plot class
#-----------------------------------------------------------------------------

class Plot(DataView):
    """ Represents a correlated set of data, renderers, and axes in a single
    screen region.

    A Plot can reference an arbitrary amount of data and can have an
    unlimited number of renderers on it, but it has a single X-axis and a
    single Y-axis for all of its associated data. Therefore, there is a single
    range in X and Y, although there can be many different data series. A Plot
    also has a single set of grids and a single background layer for all of its
    renderers.  It cannot be split horizontally or vertically; to do so,
    create a VPlotContainer or HPlotContainer and put the Plots inside those.
    Plots can be overlaid as well; be sure to set the **bgcolor** of the
    overlaying plots to "none" or "transparent".

    A Plot consists of composable sub-plots.  Each of these is created
    or destroyed using the plot() or delplot() methods.  Every time that
    new data is used to drive these sub-plots, it is added to the Plot's
    list of data and data sources.  Data sources are reused whenever
    possible; in order to have the same actual array drive two de-coupled
    data sources, create those data sources before handing them to the Plot.
    """

    #------------------------------------------------------------------------
    # Data-related traits
    #------------------------------------------------------------------------

    # The PlotData instance that drives this plot.
    data = Instance(AbstractPlotData)

    # Mapping of data names from self.data to their respective datasources.
    datasources = Dict(Str, Instance(AbstractDataSource))

    #------------------------------------------------------------------------
    # General plotting traits
    #------------------------------------------------------------------------

    # Mapping of plot names to *lists* of plot renderers.
    plots = Dict(Str, List)

    # The default index to use when adding new subplots.
    default_index = Instance(AbstractDataSource)

    # Optional mapper for the color axis.  Not instantiated until first use;
    # destroyed if no color plots are on the plot.
    color_mapper = Instance(AbstractColormap)

    # List of colors to cycle through when auto-coloring is requested. Picked
    # and ordered to be red-green color-blind friendly, though should not
    # be an issue for blue-yellow.
    auto_colors = List(["green", "lightgreen", "blue", "lightblue", "red",
                        "pink", "darkgray", "silver"])

    # index into auto_colors list
    _auto_color_idx = Int(-1)
    _auto_edge_color_idx = Int(-1)
    _auto_face_color_idx = Int(-1)

    # Mapping of renderer type string to renderer class
    # This can be overriden to customize what renderer type the Plot
    # will instantiate for its various plotting methods.
    renderer_map = Dict(dict(line = LinePlot,
                             bar = BarPlot,
                             scatter = ScatterPlot,
                             polygon = PolygonPlot,
                             filled_line = FilledLinePlot,
                             cmap_scatter = ColormappedScatterPlot,
                             img_plot = ImagePlot,
                             cmap_img_plot = CMapImagePlot,
                             contour_line_plot = ContourLinePlot,
                             contour_poly_plot = ContourPolyPlot,
                             candle = CandlePlot,
                             quiver = QuiverPlot,))

    #------------------------------------------------------------------------
    # Annotations and decorations
    #------------------------------------------------------------------------

    # The title of the plot.
    title = Property()

    # The font to use for the title.
    title_font = Property()

    # Convenience attribute for title.overlay_position; can be "top",
    # "bottom", "left", or "right".
    title_position = Property()

    # Use delegates to expose the other PlotLabel attributes of the plot title
    title_text = Delegate("_title", prefix="text", modify=True)
    title_color = Delegate("_title", prefix="color", modify=True)
    title_angle = Delegate("_title", prefix="angle", modify=True)

    # The PlotLabel object that contains the title.
    _title = Instance(PlotLabel)

    # The legend on the plot.
    legend = Instance(Legend)

    # Convenience attribute for legend.align; can be "ur", "ul", "ll", "lr".
    legend_alignment = Property

    #------------------------------------------------------------------------
    # Public methods
    #------------------------------------------------------------------------

    def __init__(self, data=None, **kwtraits):
        if 'origin' in kwtraits:
            self.default_origin = kwtraits.pop('origin')
        if "title" in kwtraits:
            title = kwtraits.pop("title")
        else:
            title = None
        super(Plot, self).__init__(**kwtraits)
        if data is not None:
            if isinstance(data, AbstractPlotData):
                self.data = data
            elif type(data) in (ndarray, tuple, list):
                self.data = ArrayPlotData(data)
            else:
                raise ValueError, "Don't know how to create PlotData for data" \
                                  "of type " + str(type(data))

        if not self._title:
            self._title = PlotLabel(font="swiss 16", visible=False,
                                   overlay_position="top", component=self)
        if title is not None:
            self.title = title

        if not self.legend:
            self.legend = Legend(visible=False, align="ur", error_icon="blank",
                                 padding=10, component=self)

        # ensure that we only get displayed once by new_window()
        self._plot_ui_info = None

        return

    def add_xy_plot(self, index_name, value_name, renderer_factory, name=None,
        origin=None, **kwds):
        """ Add a BaseXYPlot renderer subclass to this Plot.

        Parameters
        ----------
        index_name : str
            The name of the index datasource.
        value_name : str
            The name of the value datasource.
        renderer_factory : callable
            The callable that creates the renderer.
        name : string (optional)
            The name of the plot.  If None, then a default one is created
            (usually "plotNNN").
        origin : string (optional)
            Which corner the origin of this plot should occupy:
                "bottom left", "top left", "bottom right", "top right"
        **kwds :
            Additional keywords to pass to the factory.
        """
        if name is None:
            name = self._make_new_plot_name()
        if origin is None:
            origin = self.default_origin
        index = self._get_or_create_datasource(index_name)
        self.index_range.add(index)
        value = self._get_or_create_datasource(value_name)
        self.value_range.add(value)

        if self.index_scale == "linear":
            imap = LinearMapper(range=self.index_range)
        else:
            imap = LogMapper(range=self.index_range)
        if self.value_scale == "linear":
            vmap = LinearMapper(range=self.value_range)
        else:
            vmap = LogMapper(range=self.value_range)

        renderer = renderer_factory(
            index = index,
            value = value,
            index_mapper = imap,
            value_mapper = vmap,
            orientation = self.orientation,
            origin = origin,
            **kwds
        )
        self.add(renderer)
        self.plots[name] = [renderer]
        self.invalidate_and_redraw()
        return self.plots[name]

    def plot(self, data, type="line", name=None, index_scale="linear",
             value_scale="linear", origin=None, **styles):
        """ Adds a new sub-plot using the given data and plot style.

        Parameters
        ----------
        data : string, tuple(string), list(string)
            The data to be plotted. The type of plot and the number of
            arguments determines how the arguments are interpreted:

            one item: (line/scatter)
                The data is treated as the value and self.default_index is
                used as the index.  If **default_index** does not exist, one is
                created from arange(len(*data*))
            two or more items: (line/scatter)
                Interpreted as (index, value1, value2, ...).  Each index,value
                pair forms a new plot of the type specified.
            two items: (cmap_scatter)
                Interpreted as (value, color_values).  Uses **default_index**.
            three or more items: (cmap_scatter)
                Interpreted as (index, val1, color_val1, val2, color_val2, ...)

        type : comma-delimited string of "line", "scatter", "cmap_scatter"
            The types of plots to add.
        name : string
            The name of the plot.  If None, then a default one is created
            (usually "plotNNN").
        index_scale : string
            The type of scale to use for the index axis. If not "linear", then
            a log scale is used.
        value_scale : string
            The type of scale to use for the value axis. If not "linear", then
            a log scale is used.
        origin : string
            Which corner the origin of this plot should occupy:
                "bottom left", "top left", "bottom right", "top right"
        styles : series of keyword arguments
            attributes and values that apply to one or more of the
            plot types requested, e.g.,'line_color' or 'line_width'.

        Examples
        --------
        ::

            plot("my_data", type="line", name="myplot", color=lightblue)

            plot(("x-data", "y-data"), type="scatter")

            plot(("x", "y1", "y2", "y3"))

        Returns
        -------
        [renderers] -> list of renderers created in response to this call to plot()
        """
        if len(data) == 0:
            return

        if isinstance(data, basestring):
            data = (data,)

        self.index_scale = index_scale
        self.value_scale = value_scale

        # TODO: support lists of plot types
        plot_type = type
        if name is None:
            name = self._make_new_plot_name()
        if origin is None:
            origin = self.default_origin

        if plot_type in ("line", "scatter", "polygon", "bar", "filled_line"):
            # Tie data to the index range
            if len(data) == 1:
                if self.default_index is None:
                    # Create the default index based on the length of the first
                    # data series
                    value = self._get_or_create_datasource(data[0])
                    self.default_index = ArrayDataSource(arange(len(value.get_data())),
                                                         sort_order="none")
                    self.index_range.add(self.default_index)
                index = self.default_index
            else:
                index = self._get_or_create_datasource(data[0])
                if self.default_index is None:
                    self.default_index = index
                self.index_range.add(index)
                data = data[1:]

            # Tie data to the value_range and create the renderer for each data
            new_plots = []
            simple_plot_types = ("line", "scatter")
            for value_name in data:
                value = self._get_or_create_datasource(value_name)
                self.value_range.add(value)
                if plot_type in simple_plot_types:
                    cls = self.renderer_map[plot_type]
                    # handle auto-coloring request
                    if styles.get("color") == "auto":
                        self._auto_color_idx = \
                            (self._auto_color_idx + 1) % len(self.auto_colors)
                        styles["color"] = self.auto_colors[self._auto_color_idx]
                elif plot_type in ("polygon", "filled_line"):
                    cls = self.renderer_map[plot_type]
                    # handle auto-coloring request
                    if styles.get("edge_color") == "auto":
                        self._auto_edge_color_idx = \
                            (self._auto_edge_color_idx + 1) % len(self.auto_colors)
                        styles["edge_color"] = self.auto_colors[self._auto_edge_color_idx]
                    if styles.get("face_color") == "auto":
                        self._auto_face_color_idx = \
                            (self._auto_face_color_idx + 1) % len(self.auto_colors)
                        styles["face_color"] = self.auto_colors[self._auto_face_color_idx]
                elif plot_type == 'bar':
                    cls = self.renderer_map[plot_type]
                    # handle auto-coloring request
                    if styles.get("color") == "auto":
                        self._auto_color_idx = \
                            (self._auto_color_idx + 1) % len(self.auto_colors)
                        styles["fill_color"] = self.auto_colors[self._auto_color_idx]
                else:
                    raise ValueError("Unhandled plot type: " + plot_type)

                if self.index_scale == "linear":
                    imap = LinearMapper(range=self.index_range,
                                stretch_data=self.index_mapper.stretch_data)
                else:
                    imap = LogMapper(range=self.index_range,
                                stretch_data=self.index_mapper.stretch_data)
                if self.value_scale == "linear":
                    vmap = LinearMapper(range=self.value_range,
                                stretch_data=self.value_mapper.stretch_data)
                else:
                    vmap = LogMapper(range=self.value_range,
                                stretch_data=self.value_mapper.stretch_data)

                plot = cls(index=index,
                           value=value,
                           index_mapper=imap,
                           value_mapper=vmap,
                           orientation=self.orientation,
                           origin = origin,
                           **styles)

                self.add(plot)
                new_plots.append(plot)

            if plot_type == 'bar':
                # For bar plots, compute the ranges from the data to make the
                # plot look clean.

                def custom_index_func(data_low, data_high, margin, tight_bounds):
                    """ Compute custom bounds of the plot along index (in
                    data space).
                    """
                    bar_width = styles.get('bar_width', cls().bar_width)
                    plot_low = data_low - bar_width
                    plot_high = data_high + bar_width
                    return plot_low, plot_high

                if self.index_range.bounds_func is None:
                    self.index_range.bounds_func = custom_index_func

                def custom_value_func(data_low, data_high, margin, tight_bounds):
                    """ Compute custom bounds of the plot along value (in
                    data space).
                    """
                    plot_low = data_low - (data_high-data_low)*0.1
                    plot_high = data_high + (data_high-data_low)*0.1
                    return plot_low, plot_high

                if self.value_range.bounds_func is None:
                    self.value_range.bounds_func = custom_value_func

                self.index_range.tight_bounds = False
                self.value_range.tight_bounds = False
                self.index_range.refresh()
                self.value_range.refresh()

            self.plots[name] = new_plots

        elif plot_type == "cmap_scatter":
            if len(data) != 3:
                raise ValueError("Colormapped scatter plots require (index, value, color) data")
            else:
                index = self._get_or_create_datasource(data[0])
                if self.default_index is None:
                    self.default_index = index
                self.index_range.add(index)
                value = self._get_or_create_datasource(data[1])
                self.value_range.add(value)
                color = self._get_or_create_datasource(data[2])
                if not styles.has_key("color_mapper"):
                    raise ValueError("Scalar 2D data requires a color_mapper.")

                colormap = styles.pop("color_mapper", None)

                if self.color_mapper is not None and self.color_mapper.range is not None:
                    color_range = self.color_mapper.range
                else:
                    color_range = DataRange1D()

                if isinstance(colormap, AbstractColormap):
                    self.color_mapper = colormap
                    if colormap.range is None:
                        color_range.add(color)
                        colormap.range = color_range

                elif callable(colormap):
                    color_range.add(color)
                    self.color_mapper = colormap(color_range)
                else:
                    raise ValueError("Unexpected colormap %r in plot()." % colormap)

                if self.index_scale == "linear":
                    imap = LinearMapper(range=self.index_range,
                                stretch_data=self.index_mapper.stretch_data)
                else:
                    imap = LogMapper(range=self.index_range,
                                stretch_data=self.index_mapper.stretch_data)
                if self.value_scale == "linear":
                    vmap = LinearMapper(range=self.value_range,
                                stretch_data=self.value_mapper.stretch_data)
                else:
                    vmap = LogMapper(range=self.value_range,
                                stretch_data=self.value_mapper.stretch_data)

                cls = self.renderer_map["cmap_scatter"]
                plot = cls(index=index,
                           index_mapper=imap,
                           value=value,
                           value_mapper=vmap,
                           color_data=color,
                           color_mapper=self.color_mapper,
                           orientation=self.orientation,
                           origin=origin,
                           **styles)
                self.add(plot)

            self.plots[name] = [plot]
        else:
            raise ValueError("Unknown plot type: " + plot_type)

        return self.plots[name]


    def img_plot(self, data, name=None, colormap=None,
                 xbounds=None, ybounds=None, origin=None, hide_grids=True, **styles):
        """ Adds image plots to this Plot object.

        If *data* has shape (N, M, 3) or (N, M, 4), then it is treated as RGB or
        RGBA (respectively) and *colormap* is ignored.

        If *data* is an array of floating-point data, then a colormap can
        be provided via the *colormap* argument, or the default of 'Spectral'
        will be used.

        *Data* should be in row-major order, so that xbounds corresponds to
        *data*'s second axis, and ybounds corresponds to the first axis.

        Parameters
        ----------
        data : string
            The name of the data array in self.plot_data
        name : string
            The name of the plot; if omitted, then a name is generated.
        xbounds, ybounds : string, tuple, or ndarray
            Bounds where this image resides. Bound may be: a) names of
            data in the plot data; b) tuples of (low, high) in data space,
            c) 1D arrays of values representing the pixel boundaries (must
            be 1 element larger than underlying data), or
            d) 2D arrays as obtained from a meshgrid operation
        origin : string
            Which corner the origin of this plot should occupy:
                "bottom left", "top left", "bottom right", "top right"
        hide_grids : bool, default True
            Whether or not to automatically hide the grid lines on the plot
        styles : series of keyword arguments
            Attributes and values that apply to one or more of the
            plot types requested, e.g.,'line_color' or 'line_width'.
        """
        if name is None:
            name = self._make_new_plot_name()
        if origin is None:
            origin = self.default_origin

        value = self._get_or_create_datasource(data)
        array_data = value.get_data()
        if len(array_data.shape) == 3:
            if array_data.shape[2] not in (3,4):
                raise ValueError("Image plots require color depth of 3 or 4.")
            cls = self.renderer_map["img_plot"]
            kwargs = dict(**styles)
        else:
            if colormap is None:
                if self.color_mapper is None:
                    colormap = Spectral(DataRange1D(value))
                else:
                    colormap = self.color_mapper
            elif isinstance(colormap, AbstractColormap):
                if colormap.range is None:
                    colormap.range = DataRange1D(value)
            else:
                colormap = colormap(DataRange1D(value))
            self.color_mapper = colormap
            cls = self.renderer_map["cmap_img_plot"]
            kwargs = dict(value_mapper=colormap, **styles)
        return self._create_2d_plot(cls, name, origin, xbounds, ybounds, value,
                                    hide_grids, **kwargs)


    def contour_plot(self, data, type="line", name=None, poly_cmap=None,
                     xbounds=None, ybounds=None, origin=None, hide_grids=True, **styles):
        """ Adds contour plots to this Plot object.

        Parameters
        ----------
        data : string
            The name of the data array in self.plot_data, which must be
            floating point data.
        type : comma-delimited string of "line", "poly"
            The type of contour plot to add. If the value is "poly"
            and no colormap is provided via the *poly_cmap* argument, then
            a default colormap of 'Spectral' is used.
        name : string
            The name of the plot; if omitted, then a name is generated.
        poly_cmap : string
            The name of the color-map function to call (in
            chaco.default_colormaps) or an AbstractColormap instance
            to use for contour poly plots (ignored for contour line plots)
        xbounds, ybounds : string, tuple, or ndarray
            Bounds where this image resides. Bound may be: a) names of
            data in the plot data; b) tuples of (low, high) in data space,
            c) 1D arrays of values representing the pixel boundaries (must
            be 1 element larger than underlying data), or
            d) 2D arrays as obtained from a meshgrid operation
        origin : string
            Which corner the origin of this plot should occupy:
                "bottom left", "top left", "bottom right", "top right"
        hide_grids : bool, default True
            Whether or not to automatically hide the grid lines on the plot
        styles : series of keyword arguments
            Attributes and values that apply to one or more of the
            plot types requested, e.g.,'line_color' or 'line_width'.
        """
        if name is None:
            name = self._make_new_plot_name()
        if origin is None:
            origin = self.default_origin

        value = self._get_or_create_datasource(data)
        if value.value_depth != 1:
            raise ValueError("Contour plots require 2D scalar field")
        if type == "line":
            cls = self.renderer_map["contour_line_plot"]
            kwargs = dict(**styles)
            # if colors is given as a factory func, use it to make a
            # concrete colormapper. Better way to do this?
            if "colors" in kwargs:
                cmap = kwargs["colors"]
                if isinstance(cmap, FunctionType):
                    kwargs["colors"] = cmap(DataRange1D(value))
                elif getattr(cmap, 'range', 'dummy') is None:
                    cmap.range = DataRange1D(value)
        elif type == "poly":
            if poly_cmap is None:
                poly_cmap = Spectral(DataRange1D(value))
            elif isinstance(poly_cmap, FunctionType):
                poly_cmap = poly_cmap(DataRange1D(value))
            elif getattr(poly_cmap, 'range', 'dummy') is None:
                poly_cmap.range = DataRange1D(value)
            cls = self.renderer_map["contour_poly_plot"]
            kwargs = dict(color_mapper=poly_cmap, **styles)
        else:
            raise ValueError("Unhandled contour plot type: " + type)

        return self._create_2d_plot(cls, name, origin, xbounds, ybounds, value,
                                    hide_grids, **kwargs)


    def _process_2d_bounds(self, bounds, array_data, axis):
        """Transform an arbitrary bounds definition into a linspace.

        Process all the ways the user could have defined the x- or y-bounds
        of a 2d plot and return a linspace between the lower and upper
        range of the bounds.

        Parameters
        ----------
        bounds : any
            User bounds definition

        array_data : 2D array
            The 2D plot data

        axis : int
            The axis along which the bounds are to be set
        """

        num_ticks = array_data.shape[axis] + 1

        if bounds is None:
            return arange(num_ticks)

        if type(bounds) is tuple:
            # create a linspace with the bounds limits
            return linspace(bounds[0], bounds[1], num_ticks)

        if type(bounds) is ndarray and len(bounds.shape) == 1:
            # bounds is 1D, but of the wrong size

            if len(bounds) != num_ticks:
                msg = ("1D bounds of an image plot needs to have 1 more "
                       "element than its corresponding data shape, because "
                       "they represent the locations of pixel boundaries.")
                raise ValueError(msg)
            else:
                return linspace(bounds[0], bounds[-1], num_ticks)

        if type(bounds) is ndarray and len(bounds.shape) == 2:
            # bounds is 2D, assumed to be a meshgrid
            # This is triggered when doing something like
            # >>> xbounds, ybounds = meshgrid(...)
            # >>> z = f(xbounds, ybounds)

            if bounds.shape != array_data.shape:
                msg = ("2D bounds of an image plot needs to have the same "
                       "shape as the underlying data, because "
                       "they are assumed to be generated from meshgrids.")
                raise ValueError(msg)
            else:
                if axis == 0: bounds = bounds[:,0]
                else: bounds = bounds[0,:]
                interval = bounds[1] - bounds[0]
                return linspace(bounds[0], bounds[-1]+interval, num_ticks)

        raise ValueError("bounds must be None, a tuple, an array, "
                         "or a PlotData name")


    def _create_2d_plot(self, cls, name, origin, xbounds, ybounds, value_ds,
                        hide_grids, **kwargs):
        if name is None:
            name = self._make_new_plot_name()
        if origin is None:
            origin = self.default_origin

        array_data = value_ds.get_data()

        # process bounds to get linspaces
        if isinstance(xbounds, basestring):
            xbounds = self._get_or_create_datasource(xbounds).get_data()

        xs = self._process_2d_bounds(xbounds, array_data, 1)

        if isinstance(ybounds, basestring):
            ybounds = self._get_or_create_datasource(ybounds).get_data()

        ys = self._process_2d_bounds(ybounds, array_data, 0)

        # Create the index and add its datasources to the appropriate ranges
        index = GridDataSource(xs, ys, sort_order=('ascending', 'ascending'))
        self.range2d.add(index)
        mapper = GridMapper(range=self.range2d,
                            stretch_data_x=self.x_mapper.stretch_data,
                            stretch_data_y=self.y_mapper.stretch_data)

        plot = cls(index=index,
                   value=value_ds,
                   index_mapper=mapper,
                   orientation=self.orientation,
                   origin=origin,
                   **kwargs)

        if hide_grids:
            self.x_grid.visible = False
            self.y_grid.visible = False

        self.add(plot)
        self.plots[name] = [plot]
        return self.plots[name]


    def candle_plot(self, data, name=None, value_scale="linear", origin=None,
                    **styles):
        """ Adds a new sub-plot using the given data and plot style.

        Parameters
        ----------
        data : list(string), tuple(string)
            The names of the data to be plotted in the ArrayDataSource.  The
            number of arguments determines how they are interpreted:

            (index, bar_min, bar_max)
                filled or outline-only bar extending from **bar_min** to
                **bar_max**

            (index, bar_min, center, bar_max)
                above, plus a center line of a different color at **center**

            (index, min, bar_min, bar_max, max)
                bar extending from **bar_min** to **bar_max**, with thin
                bars at **min** and **max** connected to the bar by a long
                stem

            (index, min, bar_min, center, bar_max, max)
                like above, plus a center line of a different color and
                configurable thickness at **center**

        name : string
            The name of the plot.  If None, then a default one is created.

        value_scale : string
            The type of scale to use for the value axis.  If not "linear",
            then a log scale is used.

        Styles
        ------
        These are all optional keyword arguments.

        bar_color : string, 3- or 4-tuple
            The fill color of the bar; defaults to "auto".
        bar_line_color : string, 3- or 4-tuple
            The color of the rectangular box forming the bar.
        stem_color : string, 3- or 4-tuple (default = bar_line_color)
            The color of the stems reaching from the bar to the min and
            max values.
        center_color : string, 3- or 4-tuple (default = bar_line_color)
            The color of the line drawn across the bar at the center values.
        line_width : int (default = 1)
            The thickness, in pixels, of the outline around the bar.
        stem_width : int (default = line_width)
            The thickness, in pixels, of the stem lines
        center_width : int (default = line_width)
            The width, in pixels, of the line drawn across the bar at the
            center values.
        end_cap : bool (default = True)
            Whether or not to draw bars at the min and max extents of the
            error bar.

        Returns
        -------
        [renderers] -> list of renderers created in response to this call.
        """
        if len(data) == 0:
            return
        self.value_scale = value_scale

        if name is None:
            name = self._make_new_plot_name()
        if origin is None:
            origin = self.default_origin

        # Create the datasources
        if len(data) == 3:
            index, bar_min, bar_max = map(self._get_or_create_datasource, data)
            self.value_range.add(bar_min, bar_max)
            center = None
            min = None
            max = None
        elif len(data) == 4:
            index, bar_min, center, bar_max = map(self._get_or_create_datasource, data)
            self.value_range.add(bar_min, center, bar_max)
            min = None
            max = None
        elif len(data) == 5:
            index, min, bar_min, bar_max, max = \
                            map(self._get_or_create_datasource, data)
            self.value_range.add(min, bar_min, bar_max, max)
            center = None
        elif len(data) == 6:
            index, min, bar_min, center, bar_max, max = \
                            map(self._get_or_create_datasource, data)
            self.value_range.add(min, bar_min, center, bar_max, max)
        self.index_range.add(index)

        if styles.get("bar_color") == "auto" or styles.get("color") == "auto":
            self._auto_color_idx = \
                (self._auto_color_idx + 1) % len(self.auto_colors)
            styles["color"] = self.auto_colors[self._auto_color_idx]

        if self.index_scale == "linear":
            imap = LinearMapper(range=self.index_range,
                        stretch_data=self.index_mapper.stretch_data)
        else:
            imap = LogMapper(range=self.index_range,
                        stretch_data=self.index_mapper.stretch_data)
        if self.value_scale == "linear":
            vmap = LinearMapper(range=self.value_range,
                        stretch_data=self.value_mapper.stretch_data)
        else:
            vmap = LogMapper(range=self.value_range,
                        stretch_data=self.value_mapper.stretch_data)

        cls = self.renderer_map["candle"]
        plot = cls(index = index,
                          min_values = min,
                          bar_min = bar_min,
                          center_values = center,
                          bar_max = bar_max,
                          max_values = max,
                          index_mapper = imap,
                          value_mapper = vmap,
                          orientation = self.orientation,
                          origin = self.origin,
                          **styles)
        self.add(plot)
        self.plots[name] = [plot]
        return [plot]

    def quiverplot(self, data, name=None, origin=None,
                    **styles):
        """ Adds a new sub-plot using the given data and plot style.

        Parameters
        ----------
        data : list(string), tuple(string)
            The names of the data to be plotted in the ArrayDataSource.  There
            is only one combination accepted by this function:

            (index, value, vectors)
                index and value together determine the start coordinates of
                each vector.  The vectors are an Nx2

        name : string
            The name of the plot.  If None, then a default one is created.

        origin : string
            Which corner the origin of this plot should occupy:
                "bottom left", "top left", "bottom right", "top right"

        Styles
        ------
        These are all optional keyword arguments.

        line_color : string (default = "black")
            The color of the arrows
        line_width : float (default = 1.0)
            The thickness, in pixels, of the arrows.
        arrow_size : int (default = 5)
            The length, in pixels, of the arrowhead

        Returns
        -------
        [renderers] -> list of renderers created in response to this call.
        """
        if name is None:
            name = self._make_new_plot_name()
        if origin is None:
            origin = self.default_origin

        index, value, vectors = map(self._get_or_create_datasource, data)

        self.index_range.add(index)
        self.value_range.add(value)

        imap = LinearMapper(range=self.index_range,
                            stretch_data=self.index_mapper.stretch_data)
        vmap = LinearMapper(range=self.value_range,
                            stretch_data=self.value_mapper.stretch_data)

        cls = self.renderer_map["quiver"]
        plot = cls(index = index,
                   value = value,
                   vectors = vectors,
                   index_mapper = imap,
                   value_mapper = vmap,
                   name = name,
                   origin = origin,
                   **styles
                   )
        self.add(plot)
        self.plots[name] = [plot]
        return [plot]

    def delplot(self, *names):
        """ Removes the named sub-plots. """

        # This process involves removing the plots, then checking the index range
        # and value range for leftover datasources, and removing those if necessary.

        # Remove all the renderers from us (container) and create a set of the
        # datasources that we might have to remove from the ranges
        deleted_sources = set()
        for renderer in itertools.chain(*[self.plots.pop(name) for name in names]):
            self.remove(renderer)
            deleted_sources.add(renderer.index)
            deleted_sources.add(renderer.value)

        # Cull the candidate list of sources to remove by checking the other plots
        sources_in_use = set()
        for p in itertools.chain(*self.plots.values()):
                sources_in_use.add(p.index)
                sources_in_use.add(p.value)

        unused_sources = deleted_sources - sources_in_use - set([None])

        # Remove the unused sources from all ranges
        for source in unused_sources:
            if source.index_dimension == "scalar":
                # Try both index and range, it doesn't hurt
                self.index_range.remove(source)
                self.value_range.remove(source)
            elif source.index_dimension == "image":
                self.range2d.remove(source)
            else:
                warnings.warn("Couldn't remove datasource from datarange.")

        return

    def hideplot(self, *names):
        """ Convenience function to sets the named plots to be invisible.  Their
        renderers are not removed, and they are still in the list of plots.
        """
        for renderer in itertools.chain(*[self.plots[name] for name in names]):
            renderer.visible = False
        return

    def showplot(self, *names):
        """ Convenience function to sets the named plots to be visible.
        """
        for renderer in itertools.chain(*[self.plots[name] for name in names]):
            renderer.visible = True
        return

    def new_window(self, configure=False):
        """Convenience function that creates a window containing the Plot

        Don't call this if the plot is already displayed in a window.
        """
        from chaco.ui.plot_window import PlotWindow
        if self._plot_ui_info is None:
            if configure:
                self._plot_ui_info = PlotWindow(plot=self).configure_traits()
            else:
                self._plot_ui_info = PlotWindow(plot=self).edit_traits()
        return self._plot_ui_info

    #------------------------------------------------------------------------
    # Private methods
    #------------------------------------------------------------------------



    def _make_new_plot_name(self):
        """ Returns a string that is not already used as a plot title.
        """
        n = len(self.plots)
        plot_template = "plot%d"
        while 1:
            name = plot_template % n
            if name not in self.plots:
                break
            else:
                n += 1
        return name

    def _get_or_create_datasource(self, name):
        """ Returns the data source associated with the given name, or creates
        it if it doesn't exist.
        """

        if name not in self.datasources:
            data = self.data.get_data(name)

            if type(data) in (list, tuple):
                data = array(data)

            if isinstance(data, ndarray):
                if len(data.shape) == 1:
                    ds = ArrayDataSource(data, sort_order="none")
                elif len(data.shape) == 2:
                    ds = ImageData(data=data, value_depth=1)
                elif len(data.shape) == 3 and data.shape[2] in (3,4):
                    ds = ImageData(data=data, value_depth=int(data.shape[2]))
                else:
                    raise ValueError("Unhandled array shape in creating new "
                                     "plot: %s" % str(data.shape))
            elif isinstance(data, AbstractDataSource):
                ds = data
            else:
                raise ValueError("Couldn't create datasource for data of "
                                 "type %s" % type(data))

            self.datasources[name] = ds

        return self.datasources[name]

    #------------------------------------------------------------------------
    # Event handlers
    #------------------------------------------------------------------------

    def _color_mapper_changed(self):
        for plist in self.plots.values():
            for plot in plist:
                plot.color_mapper = self.color_mapper
        self.invalidate_draw()

    def _data_changed(self, old, new):
        if old:
            old.on_trait_change(self._data_update_handler, "data_changed",
                                remove=True)
        if new:
            new.on_trait_change(self._data_update_handler, "data_changed")

    def _data_update_handler(self, name, event):
        # event should be a dict with keys "added", "removed", and "changed",
        # per the comments in AbstractPlotData.
        if "removed" in event:
            for name in event["removed"]:
                del self.datasources[name]

        if "added" in event:
            for name in event["added"]:
                self._get_or_create_datasource(name)

        if "changed" in event:
            for name in event["changed"]:
                if name in self.datasources:
                    source = self.datasources[name]
                    source.set_data(self.data.get_data(name))

    def _plots_items_changed(self, event):
        if self.legend:
            self.legend.plots = self.plots

    def _index_scale_changed(self, old, new):
        if old is None: return
        if new == old: return
        if not self.range2d: return
        if self.index_scale == "linear":
            imap = LinearMapper(range=self.index_range,
                                screen_bounds=self.index_mapper.screen_bounds,
                                stretch_data=self.index_mapper.stretch_data)
        else:
            imap = LogMapper(range=self.index_range,
                             screen_bounds=self.index_mapper.screen_bounds,
                             stretch_data=self.index_mapper.stretch_data)
        self.index_mapper = imap
        for key in self.plots:
            for plot in self.plots[key]:
                if not isinstance(plot, BaseXYPlot):
                    raise ValueError("log scale only supported on XY plots")
                if self.index_scale == "linear":
                    imap = LinearMapper(range=plot.index_range,
                                screen_bounds=plot.index_mapper.screen_bounds,
                                stretch_data=self.index_mapper.stretch_data)
                else:
                    imap = LogMapper(range=plot.index_range,
                                screen_bounds=plot.index_mapper.screen_bounds,
                                stretch_data=self.index_mapper.stretch_data)
                plot.index_mapper = imap

    def _value_scale_changed(self, old, new):
        if old is None: return
        if new == old: return
        if not self.range2d: return
        if self.value_scale == "linear":
            vmap = LinearMapper(range=self.value_range,
                                screen_bounds=self.value_mapper.screen_bounds,
                                stretch_data=self.value_mapper.stretch_data)
        else:
            vmap = LogMapper(range=self.value_range,
                             screen_bounds=self.value_mapper.screen_bounds,
                                stretch_data=self.value_mapper.stretch_data)
        self.value_mapper = vmap
        for key in self.plots:
            for plot in self.plots[key]:
                if not isinstance(plot, BaseXYPlot):
                    raise ValueError("log scale only supported on XY plots")
                if self.value_scale == "linear":
                    vmap = LinearMapper(range=plot.value_range,
                                screen_bounds=plot.value_mapper.screen_bounds,
                                stretch_data=self.value_mapper.stretch_data)
                else:
                    vmap = LogMapper(range=plot.value_range,
                                screen_bounds=plot.value_mapper.screen_bounds,
                                stretch_data=self.value_mapper.stretch_data)
                plot.value_mapper = vmap

    def __title_changed(self, old, new):
        self._overlay_change_helper(old, new)

    def _legend_changed(self, old, new):
        self._overlay_change_helper(old, new)
        if new:
            new.plots = self.plots

    def _handle_range_changed(self, name, old, new):
        """ Overrides the DataView default behavior.

        Primarily changes how the list of renderers is looked up.
        """
        mapper = getattr(self, name+"_mapper")
        if mapper.range == old:
            mapper.range = new
        if old is not None:
            for datasource in old.sources[:]:
                old.remove(datasource)
                if new is not None:
                    new.add(datasource)
        range_name = name + "_range"
        for renderer in itertools.chain(*self.plots.values()):
            if hasattr(renderer, range_name):
                setattr(renderer, range_name, new)

    #------------------------------------------------------------------------
    # Property getters and setters
    #------------------------------------------------------------------------

    def _set_legend_alignment(self, align):
        if self.legend:
            self.legend.align = align

    def _get_legend_alignment(self):
        if self.legend:
            return self.legend.align
        else:
            return None

    def _set_title(self, text):
        self._title.text = text
        if text.strip() != "":
            self._title.visible = True
        else:
            self._title.visible = False

    def _get_title(self):
        return self._title.text

    def _set_title_position(self, pos):
        if self._title is not None:
            self._title.overlay_position = pos

    def _get_title_position(self):
        if self._title is not None:
            return self._title.overlay_position
        else:
            return None

    def _set_title_font(self, font):
        old_font = self._title.font
        self._title.font = font
        self.trait_property_changed("title_font", old_font, font)

    def _get_title_font(self):
        return self._title.font