def test_change_plotter_datasource_when_sorting(self):
df = self.df
model = self.analyzer_klass(source_df=df)
plot_manager = DataFramePlotManager(data_source=df)
plot_manager2 = DataFramePlotManager(data_source=df)
# Connect the 3 managers:
model.plot_manager_list.append(plot_manager)
model.plot_manager_list.append(plot_manager2)
with self.assertTraitChanges(plot_manager, "data_source"):
with self.assertTraitChanges(plot_manager2, "data_source"):
model.sort_by_col = "b"
def setUp(self):
super(TestPlotManagerExportVega, self).setUp()
if isdir(self.target_dir):
rmtree(self.target_dir)
self.converter = "to_vega"
self.model = DataFramePlotManager(data_source=TEST_DF)
self.exporter = NonInteractiveExporter(
df_plotter=self.model, target_dir=self.target_dir,
export_format=VEGA_FORMAT, export_data=EXPORT_NO
)
self.target_filename = "temp.json"
self.target_file = join(self.target_dir, self.target_filename)
def setUp(self):
self.config = HistogramPlotConfigurator(data_source=TEST_DF)
self.config.x_col_name = "a"
self.desc = PlotDescriptor(x_col_name="a", plot_config=self.config,
plot_title="Plot 0")
self.config2 = BarPlotConfigurator(data_source=TEST_DF)
self.config2.x_col_name = "a"
self.config2.y_col_name = "b"
self.desc2 = PlotDescriptor(x_col_name="a", y_col_name="b",
plot_config=self.config2,
plot_title="Plot 1")
self.config3 = ScatterPlotConfigurator(data_source=TEST_DF)
self.config3.x_col_name = "a"
self.config3.y_col_name = "b"
self.desc3 = PlotDescriptor(x_col_name="a", y_col_name="b",
plot_config=self.config3,
plot_title="Plot 2")
self.config4 = ScatterPlotConfigurator(data_source=TEST_DF)
self.config4.x_col_name = "a"
self.config4.y_col_name = "b"
self.config4.z_col_name = "d"
self.desc4 = PlotDescriptor(x_col_name="a", y_col_name="b",
z_col_name="d", plot_config=self.config4,
plot_title="Plot 3")
self.model = DataFramePlotManager(contained_plots=[self.desc],
data_source=TEST_DF)
self.target_dir = join(HERE, "test_dir")
if isdir(self.target_dir):
rmtree(self.target_dir)
def test_colored_scatter_plot_style_view(self):
""" Make sure plot style displays renderer styles labeled correctly.
"""
# Create data for a colored scatter plot where the labels aren't sorted
# alphabetically:
test_df = pd.DataFrame({"Col_1": range(4),
"Col_2": range(4),
"Col_3": ["xyz", "fpq", "abc", "xyz"]})
config = ScatterPlotConfigurator(data_source=test_df)
config.x_col_name = "Col_1"
config.y_col_name = "Col_2"
config.z_col_name = "Col_3"
plot_manager = DataFramePlotManager(contained_plots=[config],
data_source=test_df)
desc = plot_manager.contained_plots[0]
style = desc.plot_config.plot_style
# Check view building works:
all_labels = set()
col_3_values = set(test_df["Col_3"].values)
renderer_style_manager = style.renderer_style_manager
renderer_style_manager_view = renderer_style_manager.traits_view()
renderer_items = renderer_style_manager_view.content.content[0].content
for group in renderer_items:
group_label = group.label
all_labels.add(group_label)
trait_name = group.content[0].name
rend_style = renderer_style_manager.trait_get(trait_name)[
trait_name
]
self.assertEqual(rend_style.renderer_name, group_label)
self.assertEqual(all_labels, col_3_values)
def test_export_1_plot_special_title(self):
self.desc.plot_title = "blah!"
model = DataFramePlotManager(contained_plots=[self.desc],
data_source=TEST_DF)
exporter = NonInteractiveExporter(df_plotter=model,
target_dir=self.target_dir)
export_func = getattr(exporter, self.converter)
export_func()
content = os.listdir(self.target_dir)
self.assertEqual(len(content), 1)
fname = content[0]
self.assertEqual(fname, "0_blah.PNG")
def test_export_3_plots_no_data(self):
model = DataFramePlotManager(contained_plots=[self.desc, self.desc2,
self.desc3],
data_source=TEST_DF)
exporter = NonInteractiveExporter(df_plotter=model,
target_dir=self.target_dir)
export_func = getattr(exporter, self.converter)
export_func()
content = os.listdir(self.target_dir)
self.assertEqual(len(content), 3)
self.assertEqual(set(content), {"0_Plot_0.PNG", "1_Plot_1.PNG",
"2_Plot_2.PNG"})
def test_export_1_plot(self):
model = DataFramePlotManager(contained_plots=[self.desc],
data_source=TEST_DF)
exporter = NonInteractiveExporter(df_plotter=model,
target_file=self.target_file)
export_func = getattr(exporter, self.converter)
export_func()
content = os.listdir(self.target_dir)
self.assertEqual(len(content), 1)
fname = content[0]
self.assertEqual(fname, self.target_filename)
self.assert_valid_pptx(exporter)
def test_plotter_selection_connected(self):
df = self.df
model = self.analyzer_klass(source_df=df)
plot_manager = DataFramePlotManager(data_source=df)
# Connect the 2 managers:
model.plot_manager_list.append(plot_manager)
# Set analyzer selection:
model.selected_idx = [0, 2, 5]
self.assertEqual(plot_manager.index_selected, [0, 2, 5])
# Append to analyzer selection
model.selected_idx.append(6)
self.assertEqual(plot_manager.index_selected, [0, 2, 5, 6])
# Set analyzer selection:
plot_manager.index_selected = [1, 4]
self.assertEqual(model.selected_idx, [1, 4])
# Append to analyzer selection
plot_manager.index_selected.append(3)
self.assertEqual(model.selected_idx, [1, 4, 3])
def test_export_1_plot_export_source_data(self):
model = DataFramePlotManager(contained_plots=[self.desc],
data_source=TEST_DF)
for fmt in [".h5", ".csv", ".xlsx"]:
exporter = NonInteractiveExporter(
df_plotter=model, target_file=self.target_file,
export_data=EXPORT_YES, data_format=fmt
)
export_func = getattr(exporter, self.converter)
export_func()
content = os.listdir(self.target_dir)
self.assertEqual(len(content), 2)
self.assertEqual(set(content),
{EXTERNAL_DATA_FNAME + fmt, self.target_filename})
self.assert_valid_pptx(exporter)
fname = join(self.target_dir, EXTERNAL_DATA_FNAME + fmt)
self.assert_data_in_file(fname)
# clean up at every loop:
self.tearDown()
def test_export_multi_renderer_plot_export_source_and_plot_data(self):
model = DataFramePlotManager(contained_plots=[self.desc4],
data_source=TEST_DF)
for fmt in [".h5", ".csv", ".xlsx"]:
exporter = NonInteractiveExporter(
df_plotter=model, target_file=self.target_file,
export_data=EXPORT_YES, data_format=fmt,
export_each_plot_data=True
)
export_func = getattr(exporter, self.converter)
export_func()
content = os.listdir(self.target_dir)
self.assertEqual(len(content), 2)
if fmt in [".h5", ".xlsx"]:
expected = {self.target_filename, EXTERNAL_DATA_FNAME + fmt}
else:
expected = {self.target_filename, EXTERNAL_DATA_FNAME + ".zip"}
self.assertEqual(set(content), expected)
self.assert_valid_pptx(exporter)
# Check data file content:
if fmt == ".csv":
zip_path = join(self.target_dir, EXTERNAL_DATA_FNAME + ".zip")
with ZipFile(zip_path) as f:
f.extractall(join(self.target_dir, "data"))
source_fname = string2filename(DEFAULT_DATASET_NAME) + ".csv"
data_file = join(self.target_dir, "data", source_fname)
else:
data_file = join(self.target_dir, EXTERNAL_DATA_FNAME + fmt)
num_d = len(set(TEST_DF["d"]))
self.assert_data_in_file(data_file, num_dataset=1 + num_d)
# clean up at every loop:
self.tearDown()
def test_overwrite_flag(self):
model = DataFramePlotManager(contained_plots=[self.desc],
data_source=TEST_DF)
exporter = NonInteractiveExporter(
df_plotter=model, target_dir=self.target_dir,
target_file=self.target_file
)
export_func = getattr(exporter, self.converter)
export_func()
self.assertIn(self.target_filename, os.listdir(self.target_dir))
# Can't do it a second time because the file is already there...
with self.assertRaises(IOError):
export_func()
# ... unless the overwrite flag is True:
exporter = NonInteractiveExporter(
df_plotter=model, target_dir=self.target_dir,
overwrite_file_if_exists=True
)
export_func = getattr(exporter, self.converter)
export_func()
def test_export_3_plots_export_source_data(self):
model = DataFramePlotManager(contained_plots=[self.desc, self.desc2,
self.desc3],
data_source=TEST_DF)
for fmt in [".h5", ".csv", ".xlsx"]:
exporter = NonInteractiveExporter(df_plotter=model,
target_dir=self.target_dir,
export_data=EXPORT_YES,
data_format=fmt)
export_func = getattr(exporter, self.converter)
export_func()
content = os.listdir(self.target_dir)
self.assertEqual(len(content), 4)
expected = {"0_Plot_0.PNG", "1_Plot_1.PNG", "2_Plot_2.PNG",
EXTERNAL_DATA_FNAME + fmt}
self.assertEqual(set(content), expected)
# Check data file content: csv isn't zipped because there is only 1
# file:
data_file = join(self.target_dir, EXTERNAL_DATA_FNAME + fmt)
self.assert_data_in_file(data_file)
self.tearDown()
def create_plot_manager(self, df=None, n_plots=0):
if df is None:
df = self.df
plot_manager = DataFramePlotManager(data_source=df)
if n_plots > 0:
config1 = ScatterPlotConfigurator(data_source=df,
plot_title="Plot1")
config1.x_col_name = "a"
config1.y_col_name = "b"
plot_manager._add_new_plot(config1)
if n_plots > 1:
config2 = ScatterPlotConfigurator(data_source=df,
plot_title="Plot2")
config2.x_col_name = "a"
config2.y_col_name = "b"
plot_manager._add_new_plot(config2)
return plot_manager
def setUp(self):
self.model = DataFramePlotManager(data_source=TEST_DF)
self.exporter = NonInteractiveExporter(
df_plotter=self.model, target_dir=".",
)
class TestPlotManagerExportVega(BasePlotManagerExportTester, TestCase,
UnittestTools):
def setUp(self):
super(TestPlotManagerExportVega, self).setUp()
if isdir(self.target_dir):
rmtree(self.target_dir)
self.converter = "to_vega"
self.model = DataFramePlotManager(data_source=TEST_DF)
self.exporter = NonInteractiveExporter(
df_plotter=self.model, target_dir=self.target_dir,
export_format=VEGA_FORMAT, export_data=EXPORT_NO
)
self.target_filename = "temp.json"
self.target_file = join(self.target_dir, self.target_filename)
def test_export_no_plot_no_data_no_file(self):
content = self.exporter.to_vega()
self.assertEqual(content[CONTENT_KEY], [])
self.assertEqual(content[DATASETS_KEY], {})
def test_export_no_plot_w_data_no_file(self):
self.exporter.export_data = EXPORT_IN_FILE
content = self.exporter.to_vega()
self.assertEqual(content[CONTENT_KEY], [])
self.assert_rebuild_df(content)
def test_export_line_plot_w_data(self):
config = LinePlotConfigurator(data_source=TEST_DF,
plot_title="Plot")
config.x_col_name = "a"
config.y_col_name = "b"
self.model._add_new_plot(config)
self.exporter.export_data = EXPORT_IN_FILE
content = self.exporter.to_vega()
dataset = content[DATASETS_KEY][DEFAULT_DATASET_NAME]
self.assertIsInstance(dataset["data"], list)
self.assert_rebuild_df(content, cascading_data=False)
self.assertEqual(len(content[CONTENT_KEY]), 1)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {'x': {'field': 'a', 'type': 'quantitative'},
'y': {'field': 'b', 'type': 'quantitative'}},
'mark': 'line'}
self.assert_vega_export_equal(content[CONTENT_KEY][0], expected)
def test_export_1_plot_to_file(self):
config = LinePlotConfigurator(data_source=TEST_DF,
plot_title="Plot")
config.x_col_name = "a"
config.y_col_name = "b"
self.model._add_new_plot(config)
self.exporter.target_file = self.target_file
content = self.exporter.to_vega()
self.assertTrue(isfile(self.target_file))
file_content = json.load(open(self.target_file))
self.assertEqual(content, file_content)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {},
'encoding': {'x': {'field': 'a', 'type': 'quantitative'},
'y': {'field': 'b', 'type': 'quantitative'}},
'mark': 'line'}
self.assert_vega_export_equal(content[CONTENT_KEY][0], expected)
self.assertEqual(content[DATASETS_KEY], {})
def test_export_2line_plots_w_data(self):
config = LinePlotConfigurator(data_source=TEST_DF,
plot_title="Plot")
config.x_col_name = "a"
config.y_col_name = "b"
self.model._add_new_plot(config)
config = LinePlotConfigurator(data_source=TEST_DF,
plot_title="Plot2")
config.x_col_name = "b"
config.y_col_name = "a"
self.model._add_new_plot(config)
self.exporter.export_data = EXPORT_IN_FILE
content = self.exporter.to_vega()
self.assert_rebuild_df(content)
self.assertEqual(len(content[CONTENT_KEY]), 2)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {'x': {'field': 'a', 'type': 'quantitative'},
'y': {'field': 'b', 'type': 'quantitative'}},
'mark': 'line'}
self.assert_vega_export_equal(content[CONTENT_KEY][0], expected)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {'x': {'field': 'b', 'type': 'quantitative'},
'y': {'field': 'a', 'type': 'quantitative'}},
'mark': 'line'}
self.assert_vega_export_equal(content[CONTENT_KEY][1], expected)
def test_export_2line_plots_w_cascading_data(self):
config = LinePlotConfigurator(data_source=TEST_DF,
plot_title="Plot")
config.x_col_name = "a"
config.y_col_name = "b"
self.model._add_new_plot(config)
config = LinePlotConfigurator(data_source=TEST_DF,
plot_title="Plot2")
config.x_col_name = "b"
config.y_col_name = "a"
self.model._add_new_plot(config)
# In the cascade style, the data is rewritten in the description of
# every plot:
self.exporter.export_data = EXPORT_INLINE
content = self.exporter.to_vega()
self.assertEqual(content[DATASETS_KEY], {})
self.assert_rebuild_df(content, cascading_data=True)
self.assertEqual(len(content[CONTENT_KEY]), 2)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"values": df_to_vega(TEST_DF),
IDX_NAME_KEY: None},
'encoding': {'x': {'field': 'a', 'type': 'quantitative'},
'y': {'field': 'b', 'type': 'quantitative'}},
'mark': 'line'}
self.assert_vega_export_equal(content[CONTENT_KEY][0], expected)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"values": df_to_vega(TEST_DF),
IDX_NAME_KEY: None},
'encoding': {'x': {'field': 'b', 'type': 'quantitative'},
'y': {'field': 'a', 'type': 'quantitative'}},
'mark': 'line'}
self.assert_vega_export_equal(content[CONTENT_KEY][1], expected)
def test_export_2scatter_plots_w_data(self):
config = ScatterPlotConfigurator(data_source=TEST_DF,
plot_title="Plot")
config.x_col_name = "a"
config.y_col_name = "b"
self.model._add_new_plot(config)
config = ScatterPlotConfigurator(data_source=TEST_DF,
plot_title="Plot2")
config.x_col_name = "b"
config.y_col_name = "a"
self.model._add_new_plot(config)
self.exporter.export_data = EXPORT_IN_FILE
content = self.exporter.to_vega()
self.assert_rebuild_df(content)
self.assertEqual(len(content[CONTENT_KEY]), 2)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {'x': {'field': 'a', 'type': 'quantitative'},
'y': {'field': 'b', 'type': 'quantitative'}},
'mark': 'point'}
self.assert_vega_export_equal(content[CONTENT_KEY][0], expected)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {'x': {'field': 'b', 'type': 'quantitative'},
'y': {'field': 'a', 'type': 'quantitative'}},
'mark': 'point'}
self.assert_vega_export_equal(content[CONTENT_KEY][1], expected)
def test_export_2hist_plots_w_data(self):
config = HistogramPlotConfigurator(data_source=TEST_DF,
plot_title="Plot")
config.x_col_name = "a"
self.model._add_new_plot(config)
config = HistogramPlotConfigurator(data_source=TEST_DF,
plot_title="Plot2")
config.x_col_name = "b"
self.model._add_new_plot(config)
self.exporter.export_data = EXPORT_IN_FILE
content = self.exporter.to_vega()
self.assert_rebuild_df(content)
self.assertEqual(len(content[CONTENT_KEY]), 2)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {
"x": {
"bin": True,
"field": "a",
"type": "quantitative"
},
"y": {
"aggregate": "count",
"type": "quantitative"
}
},
'mark': 'bar'}
self.assert_vega_export_equal(content[CONTENT_KEY][0], expected)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {
"x": {
"bin": True,
"field": "b",
"type": "quantitative"
},
"y": {
"aggregate": "count",
"type": "quantitative"
}
},
'mark': 'bar'}
self.assert_vega_export_equal(content[CONTENT_KEY][1], expected)
def test_export_multi_hist_plots_w_data(self):
config = MultiHistogramPlotConfigurator(data_source=TEST_DF,
plot_title="Plot {i}")
config.x_col_names = ["a", "b"]
self.model._add_new_plots(config)
self.exporter.export_data = EXPORT_IN_FILE
content = self.exporter.to_vega()
self.assert_rebuild_df(content)
self.assertEqual(len(content[CONTENT_KEY]), 2)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {
"x": {
"bin": True,
"field": "a",
"type": "quantitative"
},
"y": {
"aggregate": "count",
"type": "quantitative"
}
},
'mark': 'bar'}
self.assert_vega_export_equal(content[CONTENT_KEY][0], expected)
expected = {'$schema': TARGET_VEGA_SCHEMA,
'data': {"name": DEFAULT_DATASET_NAME},
'encoding': {
"x": {
"bin": True,
"field": "b",
"type": "quantitative"
},
"y": {
"aggregate": "count",
"type": "quantitative"
}
},
'mark': 'bar'}
self.assert_vega_export_equal(content[CONTENT_KEY][1], expected)
# Assertion methods -------------------------------------------------------
def assert_vega_export_equal(self, desc1, desc2):
self.assertEqual(set(desc1.keys()), set(desc2.keys()))
for key in desc1:
self.assertEqual(desc1[key], desc2[key],
msg="{} unequal".format(key))
def assert_rebuild_df(self, content, cascading_data=False):
if cascading_data:
for desc in content[CONTENT_KEY]:
dataset = desc["data"]
idx_col = dataset[IDX_NAME_KEY]
if idx_col is None:
rebuilt_df = DataFrame(dataset["values"]).set_index(
"index")
rebuilt_df.index.name = None
else:
rebuilt_df = DataFrame(dataset["values"]).set_index(
idx_col)
assert_frame_equal(rebuilt_df, TEST_DF, check_index_type=False,
check_names=False)
else:
datasets = content[DATASETS_KEY]
for name, dataset in datasets.items():
self.assertIsInstance(dataset["data"], list)
idx_col = dataset[IDX_NAME_KEY]
if idx_col is None:
rebuilt_df = DataFrame(dataset["data"]).set_index("index")
rebuilt_df.index.name = None
else:
rebuilt_df = DataFrame(dataset["data"]).set_index(idx_col)
assert_frame_equal(rebuilt_df, TEST_DF, check_index_type=False,
check_names=False)
def base_round_trip_analysis_and_plotter_with_plot(self, style, config_klass, analysis=None, # noqa
frozen=False, change_title="", **config_kw): # noqa
""" Build an analysis with plots and check serialize/deserialize.
"""
if analysis is None:
analysis = DataFrameAnalyzer(source_df=TEST_DF)
data_len = len(analysis.filtered_df)
orig_filter = analysis.filter_exp
plot_manager = DataFramePlotManager(source_analyzer=analysis,
data_source=analysis.filtered_df)
configurator = config_klass(
data_source=analysis.filtered_df, **config_kw
)
plot_manager.add_new_plot(style, configurator)
if frozen:
# Setting the descriptor as frozen was requested: freeze it and
# decouple the analysis:
desc = plot_manager.contained_plots[0]
desc.frozen = True
# Change the analysis filter to test that the frozen plot is made
# with the original threshold
analysis.filter_exp = "Col_1 > 22"
analysis.recompute_filtered_df()
self.assertEqual(desc.data_filter, orig_filter)
if change_title:
desc = plot_manager.contained_plots[0]
desc.plot_title = change_title
new_analysis = self.assert_roundtrip_identical(analysis)
# Manual Plotter checks:
new_desc = new_analysis.plot_manager_list[0].contained_plots[0]
self.assertEqual(len(new_desc.plot_config.data_source), data_len)
if frozen:
self.assertIsNot(new_desc.plot_config.data_source,
new_analysis.filtered_df)
else:
self.assertIs(new_desc.plot_config.data_source,
new_analysis.filtered_df)
if change_title:
self.assertEqual(new_desc.plot.title.text, change_title)
self.assertEqual(new_desc.plot_title, change_title)
self.assertEqual(new_desc.plot_config.plot_title, change_title)
plot_managers = new_analysis.plot_manager_list
self.assertEqual(len(plot_managers), 1)
self.assertEqual(len(plot_managers[0].contained_plots), 1)
plot_desc = plot_managers[0].contained_plots
container_manager = plot_managers[0].canvas_manager.container_managers[0] # noqa
plot_container_map = container_manager.plot_map
plot_container = container_manager.container
self.assertEqual(len(plot_container_map), 1)
self.assertIn(plot_desc[0].plot,
[plot for plot, position in plot_container_map.values()])
self.assertEqual(len(plot_container.components), 1)
self.assertIs(plot_desc[0].plot, plot_container.components[0])
return new_analysis
idx_description = "Row"
else:
idx_description = "Column"
values = [AUTO_TARGET_CONTAINER] + list(range(self.num_containers))
view = self.view_klass(
HGroup(
Item('plot_type', label="Select plot type"),
Item('container_idx',
editor=EnumEditor(values=values),
label="{} #".format(idx_description),
visible_when="num_containers > 1",
tooltip="Can be changed later in the plot list."),
),
buttons=[OKButton, CancelButton, self.man_temp_button],
title="Select Plot Type and {}".format(idx_description),
resizable=True,
handler=ManageTemplatesHandler())
return view
if __name__ == "__main__":
plot_manager = DataFramePlotManager(
template_interactor=FakePlotTemplateInteractor())
selector = PlotTypeSelector(
num_containers=plot_manager.canvas_manager.num_container_managers,
container_idx=AUTO_TARGET_CONTAINER,
plot_types=plot_manager.plot_types,
model=plot_manager.template_manager)
ui = selector.configure_traits()
