def test_plot_linear(x, expected_linear_plot):
title = "My linear plot"
plt_str = plot(
x=x,
y=x,
figsize=(19, 10),
xlim=(0, 9),
ylim=(0, 9),
xlabel="x",
ylabel="y",
return_type="str",
title=title,
)
assert plt_str == expected_linear_plot
fig = figure(figsize=(19, 10),
xlim=(0, 9),
ylim=(0, 9),
xlabel="x",
ylabel="y")
fig.plot(x, x)
fig.set_title(title)
assert fig.draw() == expected_linear_plot
fig.clear()
plot(x, x, fig=fig)
fig.set_title(title)
assert fig.draw() == expected_linear_plot
def test_plot_linear_color(x, color, expected_linear_plot_color):
plt_str = plot(
x=x,
y=x,
color=color,
figsize=(19, 10),
return_type="str",
)
assert plt_str == expected_linear_plot_color
def _series_line(data, **kwargs):
x_col = kwargs.pop("x")
y_col = kwargs.pop("y")
# why do we get both x and y here?
if x_col is not None:
data = data[x_col]
if y_col is not None:
data = data[y_col]
return plt.plot(x=data.index, y=data, **kwargs)
def test_plot_interp(x, y, expected_interp_plot):
plt_str = plot(
x=x,
y=y,
figsize=(19, 10),
xlim=(0, 9),
ylim=(0, 9),
line=True,
marker=None,
return_type="str",
)
assert plt_str == expected_interp_plot
def test_plot_linear_line(x, expected_linear_line_plot):
plt_str = plot(
x=x,
y=x,
line=True,
figsize=(10, 10),
xlim=(0, 9),
ylim=(0, 9),
xlabel="x",
ylabel="y",
return_type="str",
)
assert plt_str == expected_linear_line_plot
def test_plot_linear_pd_labels(expected_linear_plot):
x = pd.Series(np.arange(0, 10, 1), name="x")
y = pd.Series(np.arange(0, 10, 1), name="y")
plt_str = plot(
x=x,
y=y,
figsize=(19, 10),
xlim=(0, 9),
ylim=(0, 9),
xlabel="x",
ylabel="y",
title="My linear plot",
return_type="str",
)
assert plt_str == expected_linear_plot
def test_plot_multi_linear(x, y, expected_linear_multi_plot):
if isinstance(x, np.ndarray):
label = ["up", "down"]
else:
label = None
plt_str = plot(
x=x,
y=y,
figsize=(19, 10),
xlim=(0, 9),
ylim=(0, 9),
label=label,
return_type="str",
)
assert plt_str == expected_linear_multi_plot
def _frame_line(data, **kwargs):
x_col = kwargs.pop("x")
y_col = kwargs.pop("y")
color = kwargs.pop("color", None)
if x_col is None and y_col is None:
x = pd.concat([data.index.to_frame()] * data.shape[1], axis=1)
y = data
else:
if x_col is None or y_col is None:
raise ValueError("Please provide both x, y column names")
x = data[x_col]
y = data[y_col]
if color in data.columns:
color = data[color]
return plt.plot(x=x, y=y, color=color, **kwargs)