def plot_bars(df, columns, figsize=None, ax=None, padding=0.02, **kwargs):
# FIXME allow to pass pandas objects as columns
columns = columns if isinstance(columns, List) else list(columns)
if ax is None:
fig, ax = new_fig_ts_axis(figsize)
if padding is not None:
b, t = ax.get_ylim()
if b == 0 and t == 1:
b = np.inf
t = -np.inf
ax.set_ylim(
min(df[columns].values.min(), b) * (1 - padding),
max(df[columns].values.max(), t) * (1 + padding))
width = 1 * (1 - padding) / len(columns)
for i, column in enumerate(columns):
ax.bar(
mdates.date2num(df.index) + width * i, df[column].values, width,
**kwargs)
return ax
def plot_bar(df,
fields,
figsize=None,
ax=None,
colors=None,
color_map: str = 'afmhot',
**kwargs):
data = get_pandas_object(df, fields)
if has_indexed_columns(data):
for col in data.columns:
plot_bar(data, col, figsize, ax, colors, color_map, **kwargs)
return ax
colors = get_pandas_object(df, colors)
if ax is None:
fig, ax = new_fig_ts_axis(figsize)
bars = ax.bar(df.index, height=data.values, label=str(data.name), **kwargs)
if colors is not None:
color_function = plt.get_cmap(color_map)
domain = (colors.values.min(), colors.values.max()) if isinstance(
colors, PandasObject) else (colors.min(), colors.max())
r = ReScaler(domain, (0, 1))
for i, c in enumerate(colors):
color = color_function(r(c))
# TODO if alpha is provided then color = (*color[:-1], alpha)
bars[i].set_color(color)
return ax
def plot_matrix(df, fields, figsize=None, ax=None, **kwargs):
data = fields if isinstance(fields, np.ndarray) else (get_pandas_object(
df, fields)._.values.squeeze())
if ax is None:
fig, ax = new_fig_ts_axis(figsize)
ax.matshow(data)
ax.set_yticklabels([])
ax.set_xticklabels([])
return ax
def plot_line(df, fields, figsize=None, ax=None, **kwargs):
data = get_pandas_object(df, fields)
if ax is None:
fig, ax = new_fig_ts_axis(figsize)
if has_indexed_columns(data):
for col in data.columns:
plot_line(data, col, figsize, ax, **kwargs)
else:
ax.plot(df.index, data.values, label=str(data.name), **kwargs)
return ax
def plot_candlestick(self, open="Open", high="High", low="Low", close="Close", ax=None, figsize=None, **kwargs):
df = self._parent if hasattr(self, '_parent') else self
if ax is None:
fig, ax = new_fig_ts_axis(figsize)
# Plot candlestick chart
data = pd.DataFrame({
"Date": mdates.date2num(df.index),
"open": get_pandas_object(df, open),
"high": get_pandas_object(df, high),
"low": get_pandas_object(df, low),
"close": get_pandas_object(df, close),
})
candlestick_ohlc(ax, data.values, width=0.6, colorup='g', colordown='r')
return ax
def plot_stacked_bar(df,
columns,
figsize=None,
ax=None,
padding=0.02,
colors=None,
color_map: str = 'afmhot',
**kwargs):
data = get_pandas_object(df, columns)
if not has_indexed_columns(data):
return plot_bar(df, columns, figsize, ax, colors, color_map, **kwargs)
# TODO add colors ...
if ax is None:
fig, ax = new_fig_ts_axis(figsize)
if padding is not None:
b, t = ax.get_ylim()
if b == 0 and t == 1:
b = np.inf
t = -np.inf
ax.set_ylim(
min(data.values.min(), b) * (1 - padding),
max(data.values.max(), t) * (1 + padding))
bottom = None
for column in columns:
data = get_pandas_object(df, column)
if bottom is not None:
kwargs["bottom"] = bottom
height = data - bottom
else:
height = data
bottom = height if bottom is None else bottom + height
ax.bar(mdates.date2num(df.index), height, **kwargs)
return ax
