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
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)