def test_histogram_plot_parse_column():
plat = platform.system()
if plat == 'Darwin':
plt_name = '../data/test/hist1.eps'
else:
plt_name = r'..\data\test\hist1.eps'
np.random.seed(19680801)
x = np.random.normal(15.0, 3.0, 1000)
y = np.random.normal(20.0, 3.0, 1000)
data = [x, y]
labels = ['one', 'two']
one = np.repeat('one', len(x))
two = np.repeat('two', len(x))
x = np.hstack((x, y))
y = np.hstack((one, two))
dictionary = {'data': x, 'type': y}
df = pd.DataFrame(dictionary)
obj = MatPlotDataFrame(df)
obj.histogram_plot_parse_column('data',
'type',
labels,
x_label='x-axis',
y_label='y-axis',
shading=[0.9, 0.4],
save=True,
plot_name=plt_name)
assert os.path.isfile(plt_name)
if os.path.isfile(plt_name):
os.remove(plt_name)
def test_matplot_line_plot_column():
plat = platform.system()
if plat == 'Darwin':
plt_name = '../data/test/line2.eps'
else:
plt_name = r'..\data\test\line2.eps'
length = 20
x = np.linspace(0, 20, num=20)
linear = x
squared = x**2.0
# create dataframe
dictionary = {'x': x, 'linear': linear, 'squared': squared}
df = pd.DataFrame(dictionary)
# plot data
obj = MatPlotDataFrame(df)
x_headers = ['x', 'x']
y_headers = ['linear', 'squared']
obj.line_plot_columns(x_headers,
y_headers,
y_headers,
x_label='x-axis',
y_label='y-axis',
title='Test',
style_name='default',
line_colors=['red', 'green'],
fill_alpha=0.7,
label_pos='upper left',
grid=True,
save=True,
plot_name=plt_name)
assert os.path.isfile(plt_name)
if os.path.isfile(plt_name):
os.remove(plt_name)
def test_matplot_timedate_plot_parse_column():
plat = platform.system()
if plat == 'Darwin':
plt_name = '../data/test/time1.eps'
else:
plt_name = r'..\data\test\time.eps'
length = 6
dates = pd.date_range(start=pd.to_datetime('2016-09-24'),
periods=length,
freq='y')
x = np.linspace(0, length, num=length)
linear = x
squared = x**2.0
lin = np.repeat('linear', length)
sq = np.repeat('squared', length)
# Combine arrays into one
x = np.hstack((dates, dates))
y = np.hstack((linear, squared))
power = np.hstack((lin, sq))
# Create dataframe
dictionary = {'dates': x, 'y': y, 'power': power}
df = pd.DataFrame(dictionary)
# Plot data
obj = MatPlotDataFrame(df)
parsing_header = 'power'
column_values = ['linear', 'squared']
obj.timedate_plot_parse_column('dates',
'y',
parsing_header,
column_values,
x_label='x-axis',
y_label='y-axis',
title='Test',
style_name='default',
line_colors=['red', 'green'],
fill_alpha=0.7,
label_pos='upper left',
grid=True,
save=True,
plot_name=plt_name)
assert os.path.isfile(plt_name)
if os.path.isfile(plt_name):
os.remove(plt_name)
def test_matplot_scatter_plot_parse_column():
plat = platform.system()
if plat == 'Darwin':
plt_name = '../data/test/scatter1.eps'
else:
plt_name = r'..\data\test\scatter1.eps'
length = 20
x = np.linspace(0, 20, num=20)
linear = x
squared = x**2.0
lin = np.repeat('linear', 20)
sq = np.repeat('squared', 20)
# Combine arrays into one
x = np.hstack((x, x))
y = np.hstack((linear, squared))
power = np.hstack((lin, sq))
# Create dataframe
dictionary = {'x': x, 'y': y, 'power': power}
df = pd.DataFrame(dictionary)
# Plot data
obj = MatPlotDataFrame(df)
parsing_header = 'power'
column_values = ['linear', 'squared']
obj.scatter_plot_parse_column('x',
'y',
parsing_header,
column_values,
x_label='x-axis',
y_label='y-axis',
title='Test',
style_name='default',
marker_colors=['red', 'green'],
fill_alpha=0.7,
marker_style=['o', '^'],
label_pos='upper left',
grid=True,
save=True,
plot_name=plt_name)
assert os.path.isfile(plt_name)
if os.path.isfile(plt_name):
os.remove(plt_name)
def test_histogram_plot_columns():
plat = platform.system()
if plat == 'Darwin':
plt_name = '../data/test/hist2.eps'
else:
plt_name = r'..\data\test\hist2.eps'
np.random.seed(19680801)
x = np.random.normal(15.0, 3.0, 1000)
y = np.random.normal(20.0, 3.0, 1000)
data = [x, y]
labels = ['one', 'two']
dictionary = {'x': x, 'y': y}
df = pd.DataFrame(dictionary)
obj = MatPlotDataFrame(df)
obj.histogram_plot_columns(['x', 'y'],
labels,
x_label='x-axis',
y_label='y-axis',
shading=[0.9, 0.4],
save=True,
plot_name=plt_name)
assert os.path.isfile(plt_name)
if os.path.isfile(plt_name):
os.remove(plt_name)
def test_matplot_timedate_plot_parse_column():
plat = platform.system()
if plat == 'Darwin':
plt_name = '../data/test/line1.eps'
else:
plt_name = r'..\data\test\line.eps'
length = 6
dates = pd.date_range(start=pd.to_datetime('2016-09-24'),
periods=length,
freq='y')
x = np.linspace(0, length, num=length)
linear = x
squared = x**2.0
dictionary = {'dates': dates, 'squared': squared, 'linear': linear}
df = pd.DataFrame(dictionary)
# Plot data
obj = MatPlotDataFrame(df)
time_axis = ['dates', 'dates']
y_axis = ['linear', 'squared']
obj.timedate_plot_columns(time_axis,
y_axis,
y_axis,
x_label='x-axis',
y_label='y-axis',
title='Test',
style_name='default',
line_colors=['red', 'green'],
fill_alpha=0.7,
label_pos='upper left',
grid=True,
save=True,
plot_name=plt_name)
assert os.path.isfile(plt_name)
if os.path.isfile(plt_name):
os.remove(plt_name)