from unittest import TestCase

from plotly.graph_objs import graph_objs as go

from plotly.exceptions import PlotlyError

import plotly.tools as tls

from plotly.tests.test_optional.optional_utils import NumpyTestUtilsMixin

'curve_type': 'curve'}

self.assertRaisesRegexp(PlotlyError, "curve_type must be defined as "

"'kde' or 'normal'",

def test_wrong_histdata_format(self):

# will fail)

kwargs = {'hist_data': [1, 2, 3], 'group_labels': ['group']}

**kwargs)

def test_unequal_data_label_length(self):

kwargs = {'hist_data': [[1, 2]], 'group_labels': ['group', 'group2']}

**kwargs)

kwargs = {'hist_data': [[1, 2], [1, 2, 3]], 'group_labels': ['group']}

**kwargs)

def test_simple_distplot(self):

# we should be able to create a single distplot with a simple dataset

# and default kwargs

expected_dp_layout = {'barmode': 'overlay',

'hovermode': 'closest',

'legend': {'traceorder': 'reversed'},

hist_data = [hist1_x] + [hist2_x]

group_labels = ['2012', '2013']

dp['layout'].update(title='Dist Plot')

expected_dp_layout = {'barmode': 'overlay',

'u': [[-1, -5], [-1, -5]],

'v': [[1, 1], [-3, -3]],

'arrow_scale': 0}

**kwargs)

def test_wrong_density(self):

'u': [[-1, -5], [-1, -5]],

'v': [[1, 1], [-3, -3]],

'density': 0}

**kwargs)

def test_uneven_x(self):

kwargs = {'x': [0, 2, 7, 9], 'y': [0, 2, 4, 6],

'u': [[-1, -5], [-1, -5]],

'v': [[1, 1], [-3, -3]]}

**kwargs)

def test_uneven_y(self):

kwargs = {'x': [0, 2, 4, 6], 'y': [1.5, 2, 3, 3.5],

'u': [[-1, -5], [-1, -5]],

'v': [[1, 1], [-3, -3]]}

**kwargs)

def test_unequal_length_xy(self):

kwargs = {'x': [0, 2, 4, 6], 'y': [1.5, 2, 3.5],

'u': [[-1, -5], [-1, -5]],

'v': [[1, 1], [-3, -3]]}

**kwargs)

def test_unequal_length_uv(self):

kwargs = {'x': [0, 2, 4, 6], 'y': [1.5, 2, 3, 3.5],

'u': [[-1, -5], [-1, -5], [-1, -5]],

'v': [[1, 1], [-3, -3]]}

**kwargs)

def test_simple_streamline(self):

# u = u.T #transpose

# v = v.T #transpose

expected_strln_0_100 = {

'y': [-1.0, -0.9788791845863757, -0.9579399744939614, -0.9371777642073374, -0.9165881396413338, -0.8961668671832106, -0.8759098835283448, -0.8558132862403048, -0.835873324973195, -0.8160863933003534, -0.7964490210989816, -0.7769578674451656, -0.7576097139780906, -0.7384014586961288, -0.7193301101509343, -0.7003927820087748, -0.681586687951103, -0.6629091368888596, -0.64435752846723, -0.6259293488396024, -0.6076221666912738, -0.5894336294951057, -0.5713614599827976, -0.5534034528167977, -0.5355574714490806, -0.5178214451541254, -0.5001933662244311, -0.4826712873178177, -0.4652533189465894, -0.44793762709939944, -0.4307224309873414, -0.4136060009064273, -0.39658665620919065, -0.3796627633786812, -0.3628327341986042, -0.34609502401380254, -0.3294481300756896, -0.31289058996761565, -0.2964209801054992, -0.28003791430937197, -0.2637400424417804, -0.24752604910925968, -0.23139465242334434, -0.21534460281781365, -0.19937468191908325, -0.18348370146685278, -0.1676705022823033, -0.15193395328130999, -0.13627295053029143, -0.1206864163424669, -0.10517329841242584, -0.08973256898704507, -0.07436322407090357, -0.05906428266445696, -0.04383478603333624, -0.028673797007230273, -0.013580399306900914, 0.0014484211645073852, 0.01648792568956914, 0.03159429687713278, 0.04676843461935776, 0.062011259175942746, 0.07732371182540754, 0.09270675554339824, 0.10816137570939799, 0.12368858084331191, 0.1392894033734846, 0.1549649004378033, 0.1707161547196483, 0.1865442753205595, 0.20245039867161063, 0.21843568948560943, 0.23450134175238246, 0.25064857977955146, 0.26687865928136767, 0.2831928685183458, 0.29959252949062387, 0.3160789991881776, 0.33265367090123643, 0.3493179755944802, 0.366073383348855, 0.3829214048751186, 0.39986359310352526, 0.41690154485438513, 0.4340369025945845, 0.4512713562855355, 0.46860664532844054, 0.4860445606132082, 0.5035869466778524, 0.5212357039857456, 0.5389927913286829, 0.5568602283643591, 0.5748400982975623, 0.5929345507151613, 0.6111458045858065, 0.6294761514361948, 0.6479279587167714, 0.6665036733708583, 0.6852058256224467, 0.704037032999252],

'x': [-1.0, -0.9788791845863756, -0.9579399744939614, -0.9371777642073374, -0.9165881396413338, -0.8961668671832106, -0.8759098835283448, -0.8558132862403048, -0.835873324973195, -0.8160863933003534, -0.7964490210989816, -0.7769578674451656, -0.7576097139780906, -0.7384014586961289, -0.7193301101509344, -0.7003927820087748, -0.6815866879511031, -0.6629091368888596, -0.6443575284672302, -0.6259293488396025, -0.6076221666912739, -0.5894336294951058, -0.5713614599827976, -0.5534034528167978, -0.5355574714490807, -0.5178214451541254, -0.5001933662244312, -0.4826712873178177, -0.4652533189465894, -0.44793762709939944, -0.4307224309873414, -0.4136060009064273, -0.39658665620919065, -0.3796627633786812, -0.3628327341986042, -0.34609502401380254, -0.3294481300756896, -0.31289058996761565, -0.2964209801054992, -0.28003791430937197, -0.2637400424417804, -0.24752604910925968, -0.23139465242334434, -0.21534460281781365, -0.19937468191908325, -0.18348370146685278, -0.1676705022823033, -0.15193395328130999, -0.13627295053029143, -0.1206864163424669, -0.10517329841242584, -0.08973256898704507, -0.07436322407090357, -0.05906428266445696, -0.04383478603333624, -0.028673797007230273, -0.013580399306900914, 0.0014484211645073852, 0.01648792568956914, 0.03159429687713278, 0.04676843461935776, 0.062011259175942746, 0.07732371182540754, 0.09270675554339824, 0.10816137570939799, 0.12368858084331191, 0.1392894033734846, 0.1549649004378033, 0.1707161547196483, 0.1865442753205595, 0.20245039867161063, 0.21843568948560943, 0.23450134175238246, 0.25064857977955146, 0.26687865928136767, 0.2831928685183458, 0.29959252949062387, 0.3160789991881776, 0.33265367090123643, 0.3493179755944802, 0.366073383348855, 0.3829214048751186, 0.39986359310352526, 0.41690154485438513, 0.4340369025945845, 0.4512713562855355, 0.46860664532844054, 0.4860445606132082, 0.5035869466778524, 0.5212357039857456, 0.5389927913286829, 0.5568602283643591, 0.5748400982975623, 0.5929345507151613, 0.6111458045858065, 0.6294761514361948, 0.6479279587167714, 0.6665036733708583, 0.6852058256224467, 0.704037032999252],

def test_default_dendrogram(self):

X = np.array([[1, 2, 3, 4], [1, 1, 3, 4], [1, 2, 1, 4], [1, 2, 3, 1]])

expected_dendro = go.Figure(

data=go.Data([

X[2, :] = sum(X, 0)

names = ['Jack', 'Oxana', 'John', 'Chelsea', 'Mark']

expected_dendro = go.Figure(

data=go.Data([

def test_dendrogram_orientation(self):

X = np.random.rand(5, 5)

X, orientation='left')

self.assertEqual(len(dendro_left['layout']['yaxis']['ticktext']), 5)

tickvals_left = np.array(dendro_left['layout']['yaxis']['tickvals'])

self.assertTrue((tickvals_left <= 0).all())

X, orientation='right')

tickvals_right = np.array(dendro_right['layout']['yaxis']['tickvals'])

self.assertTrue((tickvals_right >= 0).all())

X, orientation='bottom')

self.assertEqual(len(dendro_bottom['layout']['xaxis']['ticktext']), 5)

tickvals_bottom = np.array(

dendro_bottom['layout']['xaxis']['tickvals']

)

self.assertTrue((tickvals_bottom >= 0).all())

tickvals_top = np.array(dendro_top['layout']['xaxis']['tickvals'])

self.assertTrue((tickvals_top <= 0).all())

'rgb(220,220,220)', # gainsboro

'rgb(245,245,245)'] # white smoke

expected_dendro = go.Figure(

data=go.Data([