def hold_dbplots(fig=None, draw_every=None):
"""
Use this in a "with" statement to prevent plotting until the end.
:param fig:
:return:
"""
if is_server_plotting_on():
# For now, this does nothing. Eventually, it should be made to send a "draw" command through the pipe
yield
return
global _hold_plots
_old_hold_state = _hold_plots
_hold_plots = True
yield
_hold_plots = _old_hold_state
if _old_hold_state:
plot_now = False
elif draw_every is not None:
global _hold_plot_counter
plot_now = _hold_plot_counter % draw_every == 0
_hold_plot_counter += 1
else:
plot_now = True
if plot_now and fig in _DBPLOT_FIGURES:
redraw_figure(_DBPLOT_FIGURES[fig].figure)
def hold_dbplots(fig=None, hang=False, draw_every=None):
"""
Use this in a "with" statement to prevent plotting until the end.
:param fig:
:return:
"""
if is_server_plotting_on():
# For now, this does nothing. Eventually, it should be made to send a "draw" command through the pipe
yield
return
global _hold_plots
_old_hold_state = _hold_plots
_hold_plots = True
yield
_hold_plots = _old_hold_state
if _old_hold_state:
plot_now = False
elif draw_every is not None:
global _hold_plot_counter
if _hold_plot_counter is None:
_hold_plot_counter = Checkpoints(draw_every)
plot_now = _hold_plot_counter()
else:
plot_now = True
if plot_now and fig in _DBPLOT_FIGURES:
dbplot_redraw_all(fig, hang=hang)
def reset_dbplot():
if is_server_plotting_on():
deconstruct_plotting_server()
else:
for fig_name, plot_window in list(_DBPLOT_FIGURES.items()):
plt.close(plot_window.figure)
del _DBPLOT_FIGURES[fig_name]
def hold_dbplots(fig = None, draw_every = None):
"""
Use this in a "with" statement to prevent plotting until the end.
:param fig:
:return:
"""
if is_server_plotting_on():
# For now, this does nothing. Eventually, it should be made to send a "draw" command through the pipe
yield
return
global _hold_plots
_old_hold_state = _hold_plots
_hold_plots = True
yield
_hold_plots = _old_hold_state
if _old_hold_state:
plot_now = False
elif draw_every is not None:
global _hold_plot_counter
if _hold_plot_counter is None:
_hold_plot_counter = Checkpoints(draw_every)
plot_now = _hold_plot_counter()
else:
plot_now = True
if plot_now and fig in _DBPLOT_FIGURES:
replot_and_redraw_figure(fig)
def reset_dbplot():
if is_server_plotting_on():
deconstruct_plotting_server()
else:
for fig_name, plot_window in list(_DBPLOT_FIGURES.items()):
plt.close(plot_window.figure)
del _DBPLOT_FIGURES[fig_name]
def dbplot(data,
name=None,
plot_type=None,
axis=None,
plot_mode='live',
draw_now=True,
hang=False,
title=None,
fig=None,
xlabel=None,
ylabel=None,
draw_every=None,
layout=None,
legend=None,
grid=False,
wait_for_display_sec=0,
cornertext=None):
"""
Plot arbitrary data and continue execution. This program tries to figure out what type of plot to use.
:param data: Any data. Hopefully, we at dbplot will be able to figure out a plot for it.
:param name: A name uniquely identifying this plot.
:param plot_type: A specialized constructor to be used the first time when plotting. You can also pass
certain string to give hints as to what kind of plot you want (can resolve cases where the given data could be
plotted in multiple ways):
'line': Plots a line plot
'img': An image plot
'colour': A colour image plot
'pic': A picture (no scale bars, axis labels, etc).
:param axis: A string identifying which axis to plot on. By default, it is the same as "name". Only use this
argument if you indend to make multiple dbplots share the same axis.
:param plot_mode: Influences how the data should be used to choose the plot type:
'live': Best for 'live' plots that you intend to update as new data arrives
'static': Best for 'static' plots, that you do not intend to update
'image': Try to represent the plot as an image
:param draw_now: Draw the plot now (you may choose false if you're going to add another plot immediately after and
don't want have to draw this one again.
:param hang: Hang on the plot (wait for it to be closed before continuing)
:param title: Title of the plot (will default to name if not included)
:param fig: Name of the figure - use this when you want to create multiple figures.
:param grid: Turn the grid on
:param wait_for_display_sec: In server mode, you can choose to wait maximally wait_for_display_sec seconds before this
call returns. In case plotting is finished earlier, the call returns earlier. Setting wait_for_display_sec to a negative number will cause the call to block until the plot has been displayed.
"""
if is_server_plotting_on():
# Redirect the function call to the plotting server. The flag gets turned on in a configuration file. It is
# turned off when this file is run ON the plotting server, from the first line in plotting_server.py
arg_locals = locals().copy()
from artemis.remote.plotting.plotting_client import dbplot_remotely
dbplot_remotely(arg_locals=arg_locals)
return
if isinstance(fig, plt.Figure):
assert None not in _DBPLOT_FIGURES, "If you pass a figure, you can only do it on the first call to dbplot (for now)"
_DBPLOT_FIGURES[None] = _PlotWindow(figure=fig,
subplots=OrderedDict(),
axes={})
fig = None
elif fig not in _DBPLOT_FIGURES or not plt.fignum_exists(
_DBPLOT_FIGURES[fig].figure.number
): # Second condition handles closed figures.
_DBPLOT_FIGURES[fig] = _PlotWindow(figure=_make_dbplot_figure(),
subplots=OrderedDict(),
axes={})
if fig is not None:
_DBPLOT_FIGURES[fig].figure.canvas.set_window_title(fig)
suplot_dict = _DBPLOT_FIGURES[fig].subplots
if axis is None:
axis = name
if name not in suplot_dict:
if isinstance(plot_type, str):
plot = PLOT_CONSTRUCTORS[plot_type]()
elif plot_type is None:
plot = get_plot_from_data(data, mode=plot_mode)
else:
assert hasattr(plot_type, "__call__")
plot = plot_type()
if isinstance(axis, SubplotSpec):
axis = plt.subplot(axis)
if isinstance(axis, Axes):
ax = axis
ax_name = str(axis)
elif isinstance(axis, string_types) or axis is None:
ax = select_subplot(
axis,
fig=_DBPLOT_FIGURES[fig].figure,
layout=_default_layout if layout is None else layout)
ax_name = axis
# ax.set_title(axis)
else:
raise Exception(
"Axis specifier must be a string, an Axis object, or a SubplotSpec object. Not {}"
.format(axis))
if ax_name not in _DBPLOT_FIGURES[fig].axes:
ax.set_title(name)
_DBPLOT_FIGURES[fig].subplots[name] = _Subplot(axis=ax,
plot_object=plot)
_DBPLOT_FIGURES[fig].axes[ax_name] = ax
_DBPLOT_FIGURES[fig].subplots[name] = _Subplot(
axis=_DBPLOT_FIGURES[fig].axes[ax_name], plot_object=plot)
plt.sca(_DBPLOT_FIGURES[fig].axes[ax_name])
if xlabel is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.set_xlabel(xlabel)
if ylabel is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.set_ylabel(ylabel)
if draw_every is not None:
_draw_counters[fig, name] = -1
if grid:
plt.grid()
# Update the relevant data and plot it. TODO: Add option for plotting update interval
plot = _DBPLOT_FIGURES[fig].subplots[name].plot_object
plot.update(data)
plot.plot()
if cornertext is not None:
if not hasattr(_DBPLOT_FIGURES[fig].figure, '__cornertext'):
_DBPLOT_FIGURES[fig].figure.__cornertext = next(
iter(_DBPLOT_FIGURES[fig].subplots.values())).axis.annotate(
cornertext,
xy=(0, 0),
xytext=(0.01, 0.98),
textcoords='figure fraction')
else:
_DBPLOT_FIGURES[fig].figure.__cornertext.set_text(cornertext)
if title is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.set_title(title)
if legend is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.legend(legend,
loc='best',
framealpha=0.5)
if draw_now and not _hold_plots:
if draw_every is not None:
_draw_counters[fig, name] += 1
if _draw_counters[fig, name] % draw_every != 0:
return _DBPLOT_FIGURES[fig].subplots[name].axis
if hang:
plt.figure(_DBPLOT_FIGURES[fig].figure.number)
plt.show()
else:
redraw_figure(_DBPLOT_FIGURES[fig].figure)
return _DBPLOT_FIGURES[fig].subplots[name].axis
from six import string_types
from artemis.config import get_artemis_config_value
from artemis.plotting.matplotlib_backend import BarPlot, BoundingBoxPlot
from matplotlib.axes import Axes
from matplotlib.gridspec import SubplotSpec
from contextlib import contextmanager
import numpy as np
from matplotlib import pyplot as plt
from artemis.plotting.drawing_plots import redraw_figure
from artemis.plotting.expanding_subplots import select_subplot
from artemis.plotting.matplotlib_backend import get_plot_from_data, TextPlot, MovingPointPlot, Moving2DPointPlot, \
MovingImagePlot, HistogramPlot, CumulativeLineHistogram
from artemis.plotting.matplotlib_backend import LinePlot, ImagePlot, is_server_plotting_on
if is_server_plotting_on():
from artemis.remote.plotting.plotting_client import deconstruct_plotting_server
__author__ = 'peter'
"""
dbplot just takes your data, and plots it. No fuss, no muss. No more thinking about what kind plot to use, or how to
make updating plots of changing variables. Just dbplot it.
dbplot(data, 'my-data')
dbplot will look at your data, and figure out which type of plot is appropriate. If you don't like it, you can
customize it, using the plot_type argument.
dbplot makes online plotting easy. You want to plot updates to your variable? Just dbplot it.
dbplot(var, 'my-var')
def dbplot(data,
name=None,
plot_type=None,
axis=None,
plot_mode='live',
draw_now=True,
hang=False,
title=None,
fig=None,
xlabel=None,
ylabel=None,
draw_every=None,
layout=None,
legend=None,
grid=False,
wait_for_display_sec=0,
cornertext=None,
reset_color_cycle=False):
"""
Plot arbitrary data and continue execution. This program tries to figure out what type of plot to use.
:param data: Any data. Hopefully, we at dbplot will be able to figure out a plot for it.
:param name: A name uniquely identifying this plot.
:param Union[Callable[[],LinePlot],str,Tuple[Callable, Dict]] plot_type : A specialized constructor to be used the
first time when plotting. Several predefined constructors are defined in the DBPlotTypes class - you can pass
those. For back-compatibility you can also pass a string matching the name of one of the fields in the DBPlotTypes
class.
DBPlotTypes.LINE: Plots a line plot
DBPlotTypes.IMG: An image plot
DBPlotTypes.COLOUR: A colour image plot
DBPlotTypes.PIC: A picture (no scale bars, axis labels, etc)
You can also, pass a tuple of (constructor, keyword_args) where keyword args is a dict of arcuments to the plot
constructor.
:param axis: A string identifying which axis to plot on. By default, it is the same as "name". Only use this
argument if you indend to make multiple dbplots share the same axis.
:param plot_mode: Influences how the data should be used to choose the plot type:
'live': Best for 'live' plots that you intend to update as new data arrives
'static': Best for 'static' plots, that you do not intend to update
'image': Try to represent the plot as an image
:param draw_now: Draw the plot now (you may choose false if you're going to add another plot immediately after and
don't want have to draw this one again.
:param hang: Hang on the plot (wait for it to be closed before continuing)
:param title: Title of the plot (will default to name if not included)
:param fig: Name of the figure - use this when you want to create multiple figures.
:param grid: Turn the grid on
:param wait_for_display_sec: In server mode, you can choose to wait maximally wait_for_display_sec seconds before this
call returns. In case plotting is finished earlier, the call returns earlier. Setting wait_for_display_sec to a negative number will cause the call to block until the plot has been displayed.
"""
if is_server_plotting_on():
# Redirect the function call to the plotting server. The flag gets turned on in a configuration file. It is
# turned off when this file is run ON the plotting server, from the first line in plotting_server.py
arg_locals = locals().copy()
from artemis.remote.plotting.plotting_client import dbplot_remotely
dbplot_remotely(arg_locals=arg_locals)
return
if data.__class__.__module__ == 'torch' and data.__class__.__name__ == 'Tensor':
data = data.detach().cpu().numpy()
plot_object = _get_dbplot_plot_object(fig) # type: _PlotWindow
suplot_dict = plot_object.subplots
if axis is None:
axis = name
if name not in suplot_dict: # Initialize new axis
if isinstance(plot_type, str):
plot = DBPlotTypes.from_string(plot_type)()
elif isinstance(plot_type, tuple):
assert len(plot_type) == 2 and isinstance(
plot_type[0], str
) and isinstance(
plot_type[1], dict
), 'If you specify a tuple for plot_type, we expect (name, arg_dict). Got: {}'.format(
plot_type)
plot_type_name, plot_type_args = plot_type
if isinstance(plot_type_name, str):
plot = DBPlotTypes.from_string(plot_type_name)(
**plot_type_args)
elif callable(plot_type_name):
plot = plot_type_name(**plot_type_args)
else:
raise Exception(
'The first argument of the plot type tuple must be a plot type name or a callable plot type constructor.'
)
elif plot_type is None:
plot = get_plot_from_data(data, mode=plot_mode)
else:
assert hasattr(plot_type, "__call__")
plot = plot_type()
if isinstance(axis, SubplotSpec):
axis = plt.subplot(axis)
if isinstance(axis, Axes):
ax = axis
ax_name = str(axis)
elif isinstance(axis, string_types) or axis is None:
ax = select_subplot(
axis,
fig=plot_object.figure,
layout=_default_layout if layout is None else layout)
ax_name = axis
# ax.set_title(axis)
else:
raise Exception(
"Axis specifier must be a string, an Axis object, or a SubplotSpec object. Not {}"
.format(axis))
if ax_name not in plot_object.axes:
ax.set_title(name)
plot_object.subplots[name] = _Subplot(axis=ax, plot_object=plot)
plot_object.axes[ax_name] = ax
plot_object.subplots[name] = _Subplot(axis=plot_object.axes[ax_name],
plot_object=plot)
plt.sca(plot_object.axes[ax_name])
if xlabel is not None:
plot_object.subplots[name].axis.set_xlabel(xlabel)
if ylabel is not None:
plot_object.subplots[name].axis.set_ylabel(ylabel)
if draw_every is not None:
_draw_counters[fig, name] = Checkpoints(draw_every)
if grid:
plt.grid()
plot = plot_object.subplots[name].plot_object
if reset_color_cycle:
use_dbplot_axis(axis, fig=fig, clear=False).set_color_cycle(None)
plot.update(data)
# Update Labels...
if cornertext is not None:
if not hasattr(plot_object.figure, '__cornertext'):
plot_object.figure.__cornertext = next(
iter(plot_object.subplots.values())).axis.annotate(
cornertext,
xy=(0, 0),
xytext=(0.01, 0.98),
textcoords='figure fraction')
else:
plot_object.figure.__cornertext.set_text(cornertext)
if title is not None:
plot_object.subplots[name].axis.set_title(title)
if legend is not None:
plot_object.subplots[name].axis.legend(legend,
loc='best',
framealpha=0.5)
if draw_now and not _hold_plots and (draw_every is None or (
(fig, name) not in _draw_counters) or _draw_counters[fig, name]()):
plot.plot()
display_figure(plot_object.figure, hang=hang)
return plot_object.subplots[name].axis
dbplot(data, 'histogram', plot_type='histogram')
dbplot((x, 1./np.sqrt(2*np.pi*np.var(data)) * np.exp(-(x-np.mean(data))**2/(2*np.var(data)))), 'density', axis='histogram', plot_type='line')
def test_two_plots_in_the_same_axis_version_2():
reset_dbplot()
# Option 2: Give both plots the same 'axis' argument
for i in xrange(5):
data = np.random.randn(200)
x = np.linspace(-5, 5, 100)
with hold_dbplots():
dbplot(data, 'histogram', plot_type='histogram', axis='hist')
dbplot((x, 1./np.sqrt(2*np.pi*np.var(data)) * np.exp(-(x-np.mean(data))**2/(2*np.var(data)))), 'density', axis='hist', plot_type='line')
@pytest.mark.skipif(is_server_plotting_on(), reason = "This fails in server mode because we currently do not have an interpretation of freeze_all_dbplots")
def test_freeze_dbplot():
reset_dbplot()
def random_walk():
data = 0
for i in xrange(10):
data += np.random.randn()
dbplot(data, 'walk')#, plot_type=lambda: MovingPointPlot(axes_update_mode='expand'))
random_walk()
freeze_all_dbplots()
random_walk()
def test_trajectory_plot():
def dbplot(data, name = None, plot_type = None, axis=None, plot_mode = 'live', draw_now = True, hang = False, title=None,
fig = None, xlabel = None, ylabel = None, draw_every = None, layout=None, legend=None, grid=False,
wait_for_display_sec=0, cornertext = None, reset_color_cycle = False):
"""
Plot arbitrary data and continue execution. This program tries to figure out what type of plot to use.
:param data: Any data. Hopefully, we at dbplot will be able to figure out a plot for it.
:param name: A name uniquely identifying this plot.
:param plot_type: A specialized constructor to be used the first time when plotting. You can also pass
certain string to give hints as to what kind of plot you want (can resolve cases where the given data could be
plotted in multiple ways):
'line': Plots a line plot
'img': An image plot
'colour': A colour image plot
'pic': A picture (no scale bars, axis labels, etc).
:param axis: A string identifying which axis to plot on. By default, it is the same as "name". Only use this
argument if you indend to make multiple dbplots share the same axis.
:param plot_mode: Influences how the data should be used to choose the plot type:
'live': Best for 'live' plots that you intend to update as new data arrives
'static': Best for 'static' plots, that you do not intend to update
'image': Try to represent the plot as an image
:param draw_now: Draw the plot now (you may choose false if you're going to add another plot immediately after and
don't want have to draw this one again.
:param hang: Hang on the plot (wait for it to be closed before continuing)
:param title: Title of the plot (will default to name if not included)
:param fig: Name of the figure - use this when you want to create multiple figures.
:param grid: Turn the grid on
:param wait_for_display_sec: In server mode, you can choose to wait maximally wait_for_display_sec seconds before this
call returns. In case plotting is finished earlier, the call returns earlier. Setting wait_for_display_sec to a negative number will cause the call to block until the plot has been displayed.
"""
if is_server_plotting_on():
# Redirect the function call to the plotting server. The flag gets turned on in a configuration file. It is
# turned off when this file is run ON the plotting server, from the first line in plotting_server.py
arg_locals = locals().copy()
from artemis.remote.plotting.plotting_client import dbplot_remotely
dbplot_remotely(arg_locals=arg_locals)
return
if isinstance(fig, plt.Figure):
assert None not in _DBPLOT_FIGURES, "If you pass a figure, you can only do it on the first call to dbplot (for now)"
_DBPLOT_FIGURES[None] = _PlotWindow(figure=fig, subplots=OrderedDict(), axes={})
fig = None
elif fig not in _DBPLOT_FIGURES or not plt.fignum_exists(_DBPLOT_FIGURES[fig].figure.number): # Second condition handles closed figures.
_DBPLOT_FIGURES[fig] = _PlotWindow(figure = _make_dbplot_figure(), subplots=OrderedDict(), axes = {})
if fig is not None:
_DBPLOT_FIGURES[fig].figure.canvas.set_window_title(fig)
suplot_dict = _DBPLOT_FIGURES[fig].subplots
if axis is None:
axis=name
if name not in suplot_dict: # Initialize new axis
if isinstance(plot_type, str):
plot = PLOT_CONSTRUCTORS[plot_type]()
elif isinstance(plot_type, tuple):
assert len(plot_type)==2 and isinstance(plot_type[0], str) and isinstance(plot_type[1], dict), 'If you specify a tuple for plot_type, we expect (name, arg_dict). Got: {}'.format(plot_type)
plot_type_name, plot_type_args = plot_type
plot = PLOT_CONSTRUCTORS[plot_type_name](**plot_type_args)
elif plot_type is None:
plot = get_plot_from_data(data, mode=plot_mode)
else:
assert hasattr(plot_type, "__call__")
plot = plot_type()
if isinstance(axis, SubplotSpec):
axis = plt.subplot(axis)
if isinstance(axis, Axes):
ax = axis
ax_name = str(axis)
elif isinstance(axis, string_types) or axis is None:
ax = select_subplot(axis, fig=_DBPLOT_FIGURES[fig].figure, layout=_default_layout if layout is None else layout)
ax_name = axis
# ax.set_title(axis)
else:
raise Exception("Axis specifier must be a string, an Axis object, or a SubplotSpec object. Not {}".format(axis))
if ax_name not in _DBPLOT_FIGURES[fig].axes:
ax.set_title(name)
_DBPLOT_FIGURES[fig].subplots[name] = _Subplot(axis=ax, plot_object=plot)
_DBPLOT_FIGURES[fig].axes[ax_name] = ax
_DBPLOT_FIGURES[fig].subplots[name] = _Subplot(axis=_DBPLOT_FIGURES[fig].axes[ax_name], plot_object=plot)
plt.sca(_DBPLOT_FIGURES[fig].axes[ax_name])
if xlabel is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.set_xlabel(xlabel)
if ylabel is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.set_ylabel(ylabel)
if draw_every is not None:
_draw_counters[fig, name] = Checkpoints(draw_every)
if grid:
plt.grid()
plot = _DBPLOT_FIGURES[fig].subplots[name].plot_object
if reset_color_cycle:
get_dbplot_axis(axis_name=axis, fig=fig).set_color_cycle(None)
plot.update(data)
# Update Labels...
if cornertext is not None:
if not hasattr(_DBPLOT_FIGURES[fig].figure, '__cornertext'):
_DBPLOT_FIGURES[fig].figure.__cornertext = next(iter(_DBPLOT_FIGURES[fig].subplots.values())).axis.annotate(cornertext, xy=(0, 0), xytext=(0.01, 0.98), textcoords='figure fraction')
else:
_DBPLOT_FIGURES[fig].figure.__cornertext.set_text(cornertext)
if title is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.set_title(title)
if legend is not None:
_DBPLOT_FIGURES[fig].subplots[name].axis.legend(legend, loc='best', framealpha=0.5)
if draw_now and not _hold_plots and (draw_every is None or ((fig, name) not in _draw_counters) or _draw_counters[fig, name]()):
plot.plot()
if hang:
plt.figure(_DBPLOT_FIGURES[fig].figure.number)
plt.show()
else:
redraw_figure(_DBPLOT_FIGURES[fig].figure)
return _DBPLOT_FIGURES[fig].subplots[name].axis
from artemis.config import get_artemis_config_value
from artemis.general.checkpoint_counter import Checkpoints
from artemis.plotting.matplotlib_backend import BarPlot, BoundingBoxPlot
from matplotlib.axes import Axes
from matplotlib.gridspec import SubplotSpec
from contextlib import contextmanager
import numpy as np
from matplotlib import pyplot as plt
from artemis.plotting.drawing_plots import redraw_figure
from artemis.plotting.expanding_subplots import select_subplot
from artemis.plotting.matplotlib_backend import get_plot_from_data, TextPlot, MovingPointPlot, Moving2DPointPlot, \
MovingImagePlot, HistogramPlot, CumulativeLineHistogram
from artemis.plotting.matplotlib_backend import LinePlot, ImagePlot, is_server_plotting_on
if is_server_plotting_on():
from artemis.remote.plotting.plotting_client import deconstruct_plotting_server
__author__ = 'peter'
"""
dbplot just takes your data, and plots it. No fuss, no muss. No more thinking about what kind plot to use, or how to
make updating plots of changing variables. Just dbplot it.
dbplot(data, 'my-data')
dbplot will look at your data, and figure out which type of plot is appropriate. If you don't like it, you can
customize it, using the plot_type argument.
dbplot makes online plotting easy. You want to plot updates to your variable? Just dbplot it.
