def __init__(self,
tvar_name,
auto_color,
show_xaxis=False,
interactive=False):
self.tvar_name = tvar_name
self.auto_color = auto_color
self.show_xaxis = show_xaxis
if 'show_all_axes' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['show_all_axes']:
self.show_xaxis = True
self.interactive = interactive
# Variables needed across functions
self.colors = [
'black', 'red', 'green', 'navy', 'orange', 'firebrick', 'pink',
'blue', 'olive'
]
self.lineglyphs = []
self.linenum = 0
self.interactive_plot = None
self.fig = Figure(x_axis_type='datetime',
tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
def __init__(self,
tvar_name,
auto_color=False,
show_xaxis=False,
interactive=False,
y_axis_type='log'):
self.tvar_name = tvar_name
self.show_xaxis = show_xaxis
self.interactive = interactive
#Variables needed across functions
self.fig = None
self.colors = []
self.lineglyphs = []
self.linenum = 0
self.zscale = 'log'
self.zmin = 0
self.zmax = 1
self.callback = None
self.interactive_plot = None
self.fig = Figure(x_axis_type='datetime',
tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
def __init__(self, tvar, last_plot=False, interactive=False):
self.tvar = tvar
self.last_plot = last_plot
self.interactive = interactive
#Variables needed across functions
self.fig = None
self.colors = []
self.lineglyphs = []
self.linenum = 0
self.zscale = 'linear'
self.zmin = 0
self.zmax = 1
self.callback = None
self.interactive_plot = None
self.fig = Figure(tools="pan,crosshair,reset,box_zoom",
y_axis_type=self._getyaxistype())
self._format()
def __init__(self, tvar, auto_color, last_plot=False, interactive=False):
self.tvar = tvar
self.auto_color = auto_color
self.last_plot = last_plot
self.interactive = interactive
#Variables needed across functions
self.colors = [
'black', 'red', 'green', 'navy', 'orange', 'firebrick', 'pink',
'blue', 'olive'
]
self.lineglyphs = []
self.linenum = 0
self.interactive_plot = None
self.fig = Figure(tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
def _add_lines(
plot: BokePlot, ot_data: pd.DataFrame, date_column: str, feature: str
) -> BokePlot:
for author, colour in zip(ot_data.index.unique(), Spectral11):
tmp = ot_data.loc[[author], [date_column, feature, "title"]].copy()
source = ColumnDataSource(tmp)
plot.circle(
date_column,
feature,
source=source,
legend_label=author,
fill_color=colour,
)
plot.line(
date_column,
feature,
source=source,
legend_label=author,
line_color=colour,
)
return plot
def __init__(self,
tvar_name,
auto_color=False,
show_xaxis=False,
slice=False):
self.tvar_name = tvar_name
self.show_xaxis = show_xaxis
self.slice = slice
# Variables needed across functions
self.fig = None
self.colors = []
self.lineglyphs = []
self.linenum = 0
self.zscale = 'linear'
self.zmin = 0
self.zmax = 1
self.callback = None
self.interactive_plot = None
self.fig = Figure(tools="pan,crosshair,reset,box_zoom",
y_axis_type=self._getyaxistype())
self._format()
class TVarFigureSpec(object):
def __init__(self,
tvar_name,
auto_color=False,
show_xaxis=False,
interactive=False,
y_axis_type='log'):
self.tvar_name = tvar_name
self.show_xaxis = show_xaxis
self.interactive = interactive
#Variables needed across functions
self.fig = None
self.colors = []
self.lineglyphs = []
self.linenum = 0
self.zscale = 'log'
self.zmin = 0
self.zmax = 1
self.callback = None
self.interactive_plot = None
self.fig = Figure(x_axis_type='datetime',
tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
def getaxistype(self):
axis_type = 'time'
link_y_axis = False
return axis_type, link_y_axis
def getfig(self):
if self.interactive:
return [self.fig, self.interactive_plot]
else:
return [self.fig]
def setsize(self, width, height):
self.fig.plot_width = width
if self.show_xaxis:
self.fig.plot_height = height + 22
else:
self.fig.plot_height = height
def add_title(self):
if 'title_text' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['title_text'] != '':
title1 = Title(
text=pytplot.tplot_opt_glob['title_text'],
align=pytplot.tplot_opt_glob['title_align'],
text_font_size=pytplot.tplot_opt_glob['title_size'])
self.fig.title = title1
self.fig.plot_height += 22
def buildfigure(self):
self._setminborder()
self._setxrange()
self._setxaxis()
self._setyrange()
self._setzaxistype()
self._setzrange()
self._addtimebars()
self._visdata()
self._setyaxislabel()
self._setzaxislabel()
self._addhoverlines()
self._addlegend()
def _format(self):
#Formatting stuff
self.fig.grid.grid_line_color = None
self.fig.axis.major_tick_line_color = None
self.fig.axis.major_label_standoff = 0
self.fig.xaxis.formatter = dttf
self.fig.title = None
self.fig.toolbar.active_drag = 'auto'
if not self.show_xaxis:
self.fig.xaxis.major_label_text_font_size = '0pt'
self.fig.xaxis.visible = False
self.fig.lod_factor = 100
self.fig.lod_interval = 30
self.fig.lod_threshold = 100
self.fig.yaxis.axis_label_text_font_size = "10pt"
def _setxrange(self):
#Check if x range is not set, if not, set good ones
if 'x_range' not in pytplot.tplot_opt_glob:
pytplot.tplot_opt_glob['x_range'] = [
np.nanmin(
pytplot.data_quants[self.tvar_name].data.index.tolist()),
np.nanmax(
pytplot.data_quants[self.tvar_name].data.index.tolist())
]
tplot_x_range = Range1d(
np.nanmin(
pytplot.data_quants[self.tvar_name].data.index.tolist()),
np.nanmax(
pytplot.data_quants[self.tvar_name].data.index.tolist()))
if self.show_xaxis:
pytplot.lim_info['xfull'] = tplot_x_range
pytplot.lim_info['xlast'] = tplot_x_range
#Bokeh uses milliseconds since epoch for some reason
x_range = Range1d(pytplot.tplot_opt_glob['x_range'][0] * 1000,
pytplot.tplot_opt_glob['x_range'][1] * 1000)
self.fig.x_range = x_range
def _setyrange(self):
if self._getyaxistype() == 'log':
if pytplot.data_quants[self.tvar_name].yaxis_opt['y_range'][
0] < 0 or pytplot.data_quants[
self.tvar_name].yaxis_opt['y_range'][1] < 0:
return
y_range = Range1d(
pytplot.data_quants[self.tvar_name].yaxis_opt['y_range'][0],
pytplot.data_quants[self.tvar_name].yaxis_opt['y_range'][1])
self.fig.y_range = y_range
def _setzrange(self):
#Get Z Range
if 'z_range' in pytplot.data_quants[self.tvar_name].zaxis_opt:
self.zmin = pytplot.data_quants[
self.tvar_name].zaxis_opt['z_range'][0]
self.zmax = pytplot.data_quants[
self.tvar_name].zaxis_opt['z_range'][1]
else:
dataset_temp = pytplot.data_quants[self.tvar_name].data.replace(
[np.inf, -np.inf], np.nan)
self.zmax = dataset_temp.max().max()
self.zmin = dataset_temp.min().min()
#Cannot have a 0 minimum in a log scale
if self.zscale == 'log':
zmin_list = []
for column in pytplot.data_quants[self.tvar_name].data.columns:
series = pytplot.data_quants[self.tvar_name].data[column]
zmin_list.append(series.iloc[series.nonzero()[0]].min())
self.zmin = min(zmin_list)
def _setminborder(self):
self.fig.min_border_bottom = pytplot.tplot_opt_glob[
'min_border_bottom']
self.fig.min_border_top = pytplot.tplot_opt_glob['min_border_top']
def _addtimebars(self):
for time_bar in pytplot.data_quants[self.tvar_name].time_bar:
time_bar_line = Span(location=time_bar['location'],
dimension=time_bar['dimension'],
line_color=time_bar['line_color'],
line_width=time_bar['line_width'])
self.fig.renderers.extend([time_bar_line])
def _setxaxis(self):
xaxis1 = DatetimeAxis(major_label_text_font_size='0pt', formatter=dttf)
xaxis1.visible = False
self.fig.add_layout(xaxis1, 'above')
def _getyaxistype(self):
if 'y_axis_type' in pytplot.data_quants[self.tvar_name].yaxis_opt:
return pytplot.data_quants[self.tvar_name].yaxis_opt['y_axis_type']
else:
return 'log'
def _setzaxistype(self):
if 'z_axis_type' in pytplot.data_quants[self.tvar_name].zaxis_opt:
self.zscale = pytplot.data_quants[
self.tvar_name].zaxis_opt['z_axis_type']
def _setcolors(self):
if 'colormap' in pytplot.data_quants[self.tvar_name].extras:
for cm in pytplot.data_quants[self.tvar_name].extras['colormap']:
self.colors.append(tplot_utilities.return_bokeh_colormap(cm))
else:
self.colors.append(tplot_utilities.return_bokeh_colormap('magma'))
def _setyaxislabel(self):
self.fig.yaxis.axis_label = pytplot.data_quants[
self.tvar_name].yaxis_opt['axis_label']
def _setzaxislabel(self):
self.fig.yaxis.axis_label = pytplot.data_quants[
self.tvar_name].yaxis_opt['axis_label']
def _visdata(self):
self._setcolors()
x = pytplot.data_quants[self.tvar_name].data.index.tolist()
temp = [
a for a in x if (a <= (pytplot.tplot_opt_glob['x_range'][1])
and a >= (pytplot.tplot_opt_glob['x_range'][0]))
]
x = temp
#Sometimes X will be huge, we'll need to cut down so that each x will stay about 1 pixel in size
step_size = 1
num_rect_displayed = len(x)
if (self.fig.plot_width) < num_rect_displayed:
step_size = int(
math.floor(num_rect_displayed / (self.fig.plot_width)))
x[:] = x[0::step_size]
#Determine bin sizes
if pytplot.data_quants[self.tvar_name].spec_bins is not None:
bins = pytplot.data_quants[self.tvar_name].spec_bins
bins_vary = pytplot.data_quants[
self.tvar_name].spec_bins_time_varying
bins_increasing = pytplot.data_quants[
self.tvar_name].spec_bins_ascending
else:
bins = pd.DataFrame(
np.arange(len(pytplot.data_quants[
self.tvar_name].data.columns))).transpose()
bins_vary = False
bins_increasing = True
#Get length of arrays
size_x = len(x)
size_y = len(bins.columns)
#These arrays will be populated with data for the rectangle glyphs
color = []
bottom = []
top = []
left = []
right = []
value = []
corrected_time = []
#left, right, and time do not depend on the values in spec_bins
for j in range(size_x - 1):
left.append(x[j] * 1000)
right.append(x[j + 1] * 1000)
corrected_time.append(tplot_utilities.int_to_str(x[j]))
left = left * (size_y - 1)
right = right * (size_y - 1)
corrected_time = corrected_time * (size_y - 1)
#Handle the case of time-varying bin sizes
if bins_vary:
temp_bins = bins.loc[x[0:size_x - 1]]
else:
temp_bins = bins.loc[0]
if bins_increasing:
bin_index_range = range(0, size_y - 1, 1)
else:
bin_index_range = range(size_y - 1, 0, -1)
for i in bin_index_range:
temp = pytplot.data_quants[self.tvar_name].data[i][x[0:size_x -
1]].tolist()
value.extend(temp)
color.extend(
tplot_utilities.get_heatmap_color(color_map=self.colors[0],
min_val=self.zmin,
max_val=self.zmax,
values=temp,
zscale=self.zscale))
#Handle the case of time-varying bin sizes
if bins_vary:
bottom.extend(temp_bins[i].tolist())
if bins_increasing:
top.extend(temp_bins[i + 1].tolist())
else:
top.extend(temp_bins[i - 1].tolist())
else:
bottom.extend([temp_bins[i]] * (size_x - 1))
if bins_increasing:
top.extend([temp_bins[i + 1]] * (size_x - 1))
else:
top.extend([temp_bins[i - 1]] * (size_x - 1))
#Here is where we add all of the rectangles to the plot
cds = ColumnDataSource(data=dict(x=left,
y=bottom,
right=right,
top=top,
z=color,
value=value,
corrected_time=corrected_time))
self.fig.quad(bottom='y',
left='x',
right='right',
top='top',
color='z',
source=cds)
if self.interactive:
if 'y_axis_type' in pytplot.data_quants[self.tvar_name].yaxis_opt:
y_interactive_log = 'log'
else:
y_interactive_log = 'linear'
self.interactive_plot = Figure(plot_height=self.fig.plot_height,
plot_width=self.fig.plot_width,
y_range=(self.zmin, self.zmax),
y_axis_type=y_interactive_log)
self.interactive_plot.min_border_left = 100
spec_bins = bins
flux = [0] * len(spec_bins)
interactive_line_source = ColumnDataSource(
data=dict(x=spec_bins, y=flux))
interactive_line = Line(x='x', y='y')
self.interactive_plot.add_glyph(interactive_line_source,
interactive_line)
self.callback = CustomJS(args=dict(
cds=cds, interactive_line_source=interactive_line_source),
code="""
var geometry = cb_data['geometry'];
var x_data = geometry.x; // current mouse x position in plot coordinates
var y_data = geometry.y; // current mouse y position in plot coordinates
var d2 = interactive_line_source.get('data');
var asdf = cds.get('data');
var j = 0;
x=d2['x']
y=d2['y']
time=asdf['x']
energies=asdf['y']
flux=asdf['value']
for (i = 0; i < time.length-1; i++) {
if(x_data >= time[i] && x_data <= time[i+1] ) {
x[j] = energies[i]
y[j] = flux[i]
j=j+1
}
}
j=0
interactive_line_source.trigger('change');
""")
def _addhoverlines(self):
#Add tools
hover = HoverTool(callback=self.callback)
hover.tooltips = [("Time", "@corrected_time"), ("Energy", "@y"),
("Value", "@value")]
self.fig.add_tools(hover)
def _addlegend(self):
#Add the color bar
if 'z_axis_type' in pytplot.data_quants[self.tvar_name].zaxis_opt:
if pytplot.data_quants[
self.tvar_name].zaxis_opt['z_axis_type'] == 'log':
color_mapper = LogColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
ticker=LogTicker(),
border_line_color=None,
location=(0, 0))
color_bar.formatter = BasicTickFormatter(precision=2)
else:
color_mapper = LinearColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
ticker=BasicTicker(),
border_line_color=None,
location=(0, 0))
color_bar.formatter = BasicTickFormatter(precision=4)
else:
color_mapper = LogColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
ticker=LogTicker(),
border_line_color=None,
location=(0, 0))
color_bar.formatter = BasicTickFormatter(precision=2)
color_bar.width = 10
color_bar.major_label_text_align = 'left'
color_bar.label_standoff = 5
color_bar.major_label_text_baseline = 'middle'
if 'axis_label' in pytplot.data_quants[self.tvar_name].zaxis_opt:
color_bar.title = pytplot.data_quants[
self.tvar_name].zaxis_opt['axis_label']
color_bar.title_text_font_size = '8pt'
color_bar.title_text_font_style = 'bold'
color_bar.title_standoff = 20
self.fig.add_layout(color_bar, 'right')
def _visdata(self):
self._setcolors()
x = pytplot.data_quants[self.tvar_name].data.index.tolist()
temp = [
a for a in x if (a <= (pytplot.tplot_opt_glob['x_range'][1])
and a >= (pytplot.tplot_opt_glob['x_range'][0]))
]
x = temp
#Sometimes X will be huge, we'll need to cut down so that each x will stay about 1 pixel in size
step_size = 1
num_rect_displayed = len(x)
if (self.fig.plot_width) < num_rect_displayed:
step_size = int(
math.floor(num_rect_displayed / (self.fig.plot_width)))
x[:] = x[0::step_size]
#Determine bin sizes
if pytplot.data_quants[self.tvar_name].spec_bins is not None:
bins = pytplot.data_quants[self.tvar_name].spec_bins
bins_vary = pytplot.data_quants[
self.tvar_name].spec_bins_time_varying
bins_increasing = pytplot.data_quants[
self.tvar_name].spec_bins_ascending
else:
bins = pd.DataFrame(
np.arange(len(pytplot.data_quants[
self.tvar_name].data.columns))).transpose()
bins_vary = False
bins_increasing = True
#Get length of arrays
size_x = len(x)
size_y = len(bins.columns)
#These arrays will be populated with data for the rectangle glyphs
color = []
bottom = []
top = []
left = []
right = []
value = []
corrected_time = []
#left, right, and time do not depend on the values in spec_bins
for j in range(size_x - 1):
left.append(x[j] * 1000)
right.append(x[j + 1] * 1000)
corrected_time.append(tplot_utilities.int_to_str(x[j]))
left = left * (size_y - 1)
right = right * (size_y - 1)
corrected_time = corrected_time * (size_y - 1)
#Handle the case of time-varying bin sizes
if bins_vary:
temp_bins = bins.loc[x[0:size_x - 1]]
else:
temp_bins = bins.loc[0]
if bins_increasing:
bin_index_range = range(0, size_y - 1, 1)
else:
bin_index_range = range(size_y - 1, 0, -1)
for i in bin_index_range:
temp = pytplot.data_quants[self.tvar_name].data[i][x[0:size_x -
1]].tolist()
value.extend(temp)
color.extend(
tplot_utilities.get_heatmap_color(color_map=self.colors[0],
min_val=self.zmin,
max_val=self.zmax,
values=temp,
zscale=self.zscale))
#Handle the case of time-varying bin sizes
if bins_vary:
bottom.extend(temp_bins[i].tolist())
if bins_increasing:
top.extend(temp_bins[i + 1].tolist())
else:
top.extend(temp_bins[i - 1].tolist())
else:
bottom.extend([temp_bins[i]] * (size_x - 1))
if bins_increasing:
top.extend([temp_bins[i + 1]] * (size_x - 1))
else:
top.extend([temp_bins[i - 1]] * (size_x - 1))
#Here is where we add all of the rectangles to the plot
cds = ColumnDataSource(data=dict(x=left,
y=bottom,
right=right,
top=top,
z=color,
value=value,
corrected_time=corrected_time))
self.fig.quad(bottom='y',
left='x',
right='right',
top='top',
color='z',
source=cds)
if self.interactive:
if 'y_axis_type' in pytplot.data_quants[self.tvar_name].yaxis_opt:
y_interactive_log = 'log'
else:
y_interactive_log = 'linear'
self.interactive_plot = Figure(plot_height=self.fig.plot_height,
plot_width=self.fig.plot_width,
y_range=(self.zmin, self.zmax),
y_axis_type=y_interactive_log)
self.interactive_plot.min_border_left = 100
spec_bins = bins
flux = [0] * len(spec_bins)
interactive_line_source = ColumnDataSource(
data=dict(x=spec_bins, y=flux))
interactive_line = Line(x='x', y='y')
self.interactive_plot.add_glyph(interactive_line_source,
interactive_line)
self.callback = CustomJS(args=dict(
cds=cds, interactive_line_source=interactive_line_source),
code="""
var geometry = cb_data['geometry'];
var x_data = geometry.x; // current mouse x position in plot coordinates
var y_data = geometry.y; // current mouse y position in plot coordinates
var d2 = interactive_line_source.get('data');
var asdf = cds.get('data');
var j = 0;
x=d2['x']
y=d2['y']
time=asdf['x']
energies=asdf['y']
flux=asdf['value']
for (i = 0; i < time.length-1; i++) {
if(x_data >= time[i] && x_data <= time[i+1] ) {
x[j] = energies[i]
y[j] = flux[i]
j=j+1
}
}
j=0
interactive_line_source.trigger('change');
""")
class TVarFigureSpec(object):
def __init__(self,
tvar_name,
auto_color=False,
show_xaxis=False,
slice=False,
y_axis_type='log'):
self.tvar_name = tvar_name
self.show_xaxis = show_xaxis
if 'show_all_axes' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['show_all_axes']:
self.show_xaxis = True
self.slice = slice
# Variables needed across functions
self.fig = None
self.colors = []
self.lineglyphs = []
self.linenum = 0
self.zscale = 'log'
self.zmin = 0
self.zmax = 1
self.callback = None
self.slice_plot = None
self.fig = Figure(x_axis_type='datetime',
tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
@staticmethod
def get_axis_label_color():
if pytplot.tplot_opt_glob['black_background']:
text_color = '#000000'
else:
text_color = '#FFFFFF'
return text_color
@staticmethod
def getaxistype():
axis_type = 'time'
link_y_axis = False
return axis_type, link_y_axis
def getfig(self):
if self.slice:
return [self.fig, self.slice_plot]
else:
return [self.fig]
def setsize(self, width, height):
self.fig.plot_width = width
if self.show_xaxis:
self.fig.plot_height = height + 22
else:
self.fig.plot_height = height
def add_title(self):
if 'title_text' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['title_text'] != '':
title1 = Title(
text=pytplot.tplot_opt_glob['title_text'],
align=pytplot.tplot_opt_glob['title_align'],
text_font_size=pytplot.tplot_opt_glob['title_size'],
text_color=self.get_axis_label_color())
self.fig.title = title1
self.fig.plot_height += 22
def buildfigure(self):
self._setminborder()
self._setxrange()
self._setxaxis()
self._setyrange()
self._setzaxistype()
self._setzrange()
self._addtimebars()
self._visdata()
self._setxaxislabel()
self._setyaxislabel()
self._addhoverlines()
self._addlegend()
def _format(self):
# Formatting stuff
self.fig.grid.grid_line_color = None
self.fig.axis.major_tick_line_color = None
self.fig.axis.major_label_standoff = 0
self.fig.xaxis.formatter = dttf
self.fig.title = None
self.fig.toolbar.active_drag = 'auto'
if not self.show_xaxis:
self.fig.xaxis.major_label_text_font_size = '0pt'
self.fig.xaxis.visible = False
self.fig.lod_factor = 100
self.fig.lod_interval = 30
self.fig.lod_threshold = 100
def _setxrange(self):
# Check if x range is not set, if not, set good ones
if 'x_range' not in pytplot.tplot_opt_glob:
pytplot.tplot_opt_glob['x_range'] = [
np.nanmin(
pytplot.data_quants[self.tvar_name].coords['time'].values),
np.nanmax(
pytplot.data_quants[self.tvar_name].coords['time'].values)
]
# Bokeh uses milliseconds since epoch for some reason
x_range = Range1d(
int(pytplot.tplot_opt_glob['x_range'][0]) * 1000.0,
int(pytplot.tplot_opt_glob['x_range'][1]) * 1000.0)
if self.show_xaxis:
pytplot.lim_info['xfull'] = x_range
pytplot.lim_info['xlast'] = x_range
self.fig.x_range = x_range
def _setyrange(self):
if self._getyaxistype() == 'log':
if pytplot.data_quants[self.tvar_name].attrs['plot_options']['yaxis_opt']['y_range'][0] <= 0 \
or pytplot.data_quants[self.tvar_name].attrs['plot_options']['yaxis_opt']['y_range'][1] <= 0:
return
y_range = Range1d(
pytplot.data_quants[self.tvar_name].attrs['plot_options']
['yaxis_opt']['y_range'][0],
pytplot.data_quants[self.tvar_name].attrs['plot_options']
['yaxis_opt']['y_range'][1])
self.fig.y_range = y_range
def _setzrange(self):
# Get Z Range
if 'z_range' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']:
self.zmin = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['zaxis_opt']['z_range'][0]
self.zmax = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['zaxis_opt']['z_range'][1]
else:
dataset_temp = pytplot.data_quants[self.tvar_name].where(
pytplot.data_quants[self.tvar_name] != np.inf)
dataset_temp = dataset_temp.where(
pytplot.data_quants[self.tvar_name] != -np.inf)
self.zmax = np.float(dataset_temp.max(skipna=True).values)
self.zmin = np.float(dataset_temp.min(skipna=True).values)
# Cannot have a 0 minimum in a log scale
if self.zscale == 'log':
df = pytplot.tplot_utilities.convert_tplotxarray_to_pandas_dataframe(
self.tvar_name, no_spec_bins=True)
zmin_list = []
for column in df.columns:
series = df[column]
zmin_list.append(
series.iloc[series.to_numpy().nonzero()[0]].min())
self.zmin = min(zmin_list)
def _setminborder(self):
self.fig.min_border_bottom = pytplot.tplot_opt_glob[
'min_border_bottom']
self.fig.min_border_top = pytplot.tplot_opt_glob['min_border_top']
def _addtimebars(self):
for time_bar in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['time_bar']:
time_bar_line = Span(location=time_bar['location'] * 1000.0,
dimension=time_bar['dimension'],
line_color=time_bar['line_color'],
line_width=time_bar['line_width'])
self.fig.renderers.extend([time_bar_line])
def _set_roi_lines(self, time):
# Locating the two times between which there's a roi
roi_1 = pytplot.tplot_utilities.str_to_int(
pytplot.tplot_opt_glob['roi_lines'][0])
roi_2 = pytplot.tplot_utilities.str_to_int(
pytplot.tplot_opt_glob['roi_lines'][1])
# find closest time to user-requested time
x = np.asarray(time)
x_sub_1 = abs(x - roi_1 * np.ones(len(x)))
x_sub_2 = abs(x - roi_2 * np.ones(len(x)))
x_argmin_1 = np.nanargmin(x_sub_1)
x_argmin_2 = np.nanargmin(x_sub_2)
x_closest_1 = x[x_argmin_1]
x_closest_2 = x[x_argmin_2]
# Create roi box
roi_box = BoxAnnotation(left=x_closest_1 * 1000.0,
right=x_closest_2 * 1000.0,
fill_alpha=0.6,
fill_color='grey',
line_color='red',
line_width=2.5)
self.fig.renderers.extend([roi_box])
def _setxaxis(self):
xaxis1 = DatetimeAxis(major_label_text_font_size='0pt', formatter=dttf)
xaxis1.visible = False
self.fig.add_layout(xaxis1, 'above')
def _getyaxistype(self):
if 'y_axis_type' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['yaxis_opt']:
return pytplot.data_quants[self.tvar_name].attrs['plot_options'][
'yaxis_opt']['y_axis_type']
else:
return 'log'
def _setzaxistype(self):
if 'z_axis_type' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']:
self.zscale = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['zaxis_opt']['z_axis_type']
def _setcolors(self):
if 'colormap' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']:
for cm in pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['extras']['colormap']:
self.colors.append(tplot_utilities.return_bokeh_colormap(cm))
else:
self.colors.append(tplot_utilities.return_bokeh_colormap('magma'))
def _setxaxislabel(self):
self.fig.xaxis.axis_label = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['xaxis_opt']['axis_label']
self.fig.xaxis.axis_label_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
self.fig.xaxis.axis_label_text_color = self.get_axis_label_color()
def _setyaxislabel(self):
self.fig.yaxis.axis_label = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['yaxis_opt']['axis_label']
self.fig.yaxis.axis_label_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
self.fig.yaxis.axis_label_text_color = self.get_axis_label_color()
def _visdata(self):
self._setcolors()
x = pytplot.data_quants[self.tvar_name].coords['time'].values.tolist()
# Add region of interest (roi) lines if applicable
if 'roi_lines' in pytplot.tplot_opt_glob.keys():
self._set_roi_lines(x)
temp = [
a for a in x if (a <= (pytplot.tplot_opt_glob['x_range'][1])
and a >= (pytplot.tplot_opt_glob['x_range'][0]))
]
x = temp
# Sometimes X will be huge, we'll need to cut down so that each x will stay about 1 pixel in size
step_size = 1
num_rect_displayed = len(x)
if self.fig.plot_width < num_rect_displayed:
step_size = int(
math.floor(num_rect_displayed / self.fig.plot_width))
x[:] = x[0::step_size]
# Determine bin sizes
if 'spec_bins' in pytplot.data_quants[self.tvar_name].coords:
df, bins = pytplot.tplot_utilities.convert_tplotxarray_to_pandas_dataframe(
self.tvar_name)
bins_vary = len(pytplot.data_quants[
self.tvar_name].coords['spec_bins'].shape) > 1
bins_increasing = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['spec_bins_ascending']
else:
df = pytplot.tplot_utilities.convert_tplotxarray_to_pandas_dataframe(
self.tvar_name, no_spec_bins=True)
bins = pd.DataFrame(
np.arange(len(
pytplot.data_quants[self.tvar_name][0]))).transpose()
bins_vary = False
bins_increasing = True
# Get length of arrays
size_x = len(x)
size_y = len(bins.columns)
# These arrays will be populated with data for the rectangle glyphs
color = []
bottom = []
top = []
left = []
right = []
value = []
corrected_time = []
# left, right, and time do not depend on the values in spec_bins
for j in range(size_x - 1):
left.append(x[j] * 1000.0)
right.append(x[j + 1] * 1000.0)
corrected_time.append(tplot_utilities.int_to_str(x[j]))
left = left * (size_y - 1)
right = right * (size_y - 1)
corrected_time = corrected_time * (size_y - 1)
# Handle the case of time-varying bin sizes
if bins_vary:
temp_bins = bins.loc[x[0:size_x - 1]]
else:
temp_bins = bins.loc[0]
if bins_increasing:
bin_index_range = range(0, size_y - 1, 1)
else:
bin_index_range = range(size_y - 1, 0, -1)
for i in bin_index_range:
temp = df[i][x[0:size_x - 1]].tolist()
value.extend(temp)
color.extend(
tplot_utilities.get_heatmap_color(color_map=self.colors[0],
min_val=self.zmin,
max_val=self.zmax,
values=temp,
zscale=self.zscale))
# Handle the case of time-varying bin sizes
if bins_vary:
bottom.extend(temp_bins[i].tolist())
if bins_increasing:
top.extend(temp_bins[i + 1].tolist())
else:
top.extend(temp_bins[i - 1].tolist())
else:
bottom.extend([temp_bins[i]] * (size_x - 1))
if bins_increasing:
top.extend([temp_bins[i + 1]] * (size_x - 1))
else:
top.extend([temp_bins[i - 1]] * (size_x - 1))
# Here is where we add all of the rectangles to the plot
cds = ColumnDataSource(data=dict(x=left,
y=bottom,
right=right,
top=top,
z=color,
value=value,
corrected_time=corrected_time))
self.fig.quad(bottom='y',
left='x',
right='right',
top='top',
color='z',
source=cds)
if self.slice:
if 'y_axis_type' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['yaxis_opt']:
y_slice_log = 'log'
else:
y_slice_log = 'linear'
self.slice_plot = Figure(
plot_height=self.fig.plot_height,
plot_width=self.fig.plot_width,
y_range=(self.zmin, self.zmax),
x_range=(pytplot.data_quants[self.tvar_name].
attrs['plot_options']['yaxis_opt']['y_range'][0],
pytplot.data_quants[self.tvar_name].
attrs['plot_options']['yaxis_opt']['y_range'][1]),
y_axis_type=y_slice_log)
self.slice_plot.min_border_left = 100
spec_bins = bins
flux = [0] * len(spec_bins)
slice_line_source = ColumnDataSource(
data=dict(x=spec_bins, y=flux))
self.slice_plot.line('x', 'y', source=slice_line_source)
self.callback = CustomJS(args=dict(cds=cds,
source=slice_line_source),
code="""
var geometry = cb_data['geometry'];
var x_data = geometry.x; // current mouse x position in plot coordinates
var y_data = geometry.y; // current mouse y position in plot coordinates
var d2 = source.data;
var asdf = cds.data;
var j = 0;
x=d2['x']
y=d2['y']
time=asdf['x']
energies=asdf['y']
flux=asdf['value']
for (i = 0; i < time.length-1; i++) {
if(x_data >= time[i] && x_data <= time[i+1] ) {
x[j] = energies[i]
y[j] = flux[i]
j=j+1
}
}
j=0
source.change.emit();
""")
def _addhoverlines(self):
# Add tools
hover = HoverTool(callback=self.callback)
hover.tooltips = [("Time", "@corrected_time"), ("Energy", "@y"),
("Value", "@value")]
self.fig.add_tools(hover)
def _addlegend(self):
# Add the color bar
if 'z_axis_type' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']:
if pytplot.data_quants[self.tvar_name].attrs['plot_options'][
'zaxis_opt']['z_axis_type'] == 'log':
color_mapper = LogColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
ticker=LogTicker(),
border_line_color=None,
location=(0, 0))
color_bar.formatter = BasicTickFormatter(precision=2)
else:
color_mapper = LinearColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
ticker=BasicTicker(),
border_line_color=None,
location=(0, 0))
color_bar.formatter = BasicTickFormatter(precision=4)
else:
color_mapper = LogColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
ticker=LogTicker(),
border_line_color=None,
location=(0, 0))
color_bar.formatter = BasicTickFormatter(precision=2)
color_bar.width = 10
color_bar.major_label_text_align = 'left'
color_bar.label_standoff = 5
color_bar.major_label_text_baseline = 'middle'
color_bar.title = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']['axis_label']
color_bar.title_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
color_bar.title_text_font_style = 'bold'
color_bar.title_standoff = 20
self.fig.add_layout(color_bar, 'right')
class TVarFigureMap(object):
def __init__(self,
tvar_name,
auto_color=False,
show_xaxis=False,
interactive=False):
self.tvar_name = tvar_name
self.show_xaxis = show_xaxis
self.interactive = interactive
#Variables needed across functions
self.fig = None
self.colors = []
self.lineglyphs = []
self.linenum = 0
self.zscale = 'linear'
self.zmin = 0
self.zmax = 1
self.callback = None
self.interactive_plot = None
self.fig = Figure(tools="pan,crosshair,reset,box_zoom",
y_axis_type=self._getyaxistype())
self._format()
def getaxistype(self):
axis_type = 'map'
link_y_axis = True
return axis_type, link_y_axis
def getfig(self):
if self.interactive:
return [self.fig, self.interactive_plot]
else:
return [self.fig]
def setsize(self, width, height):
self.fig.plot_width = width
if self.show_xaxis:
self.fig.plot_height = height + 22
else:
self.fig.plot_height = height
def add_title(self):
if 'title_text' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['title_text'] != '':
title1 = Title(
text=pytplot.tplot_opt_glob['title_text'],
align=pytplot.tplot_opt_glob['title_align'],
text_font_size=pytplot.tplot_opt_glob['title_size'])
self.fig.title = title1
self.fig.plot_height += 22
def buildfigure(self):
self._setminborder()
self._setxrange()
self._setxaxis()
self._setyrange()
self._setzrange()
self._setzaxistype()
self._addtimebars()
self._setbackground()
self._visdata()
self._setyaxislabel()
self._setzaxislabel()
self._addhoverlines()
self._addlegend()
def _format(self):
#Formatting stuff
self.fig.grid.grid_line_color = None
self.fig.axis.major_tick_line_color = None
self.fig.axis.major_label_standoff = 0
self.fig.title = None
self.fig.toolbar.active_drag = 'auto'
if not self.show_xaxis:
self.fig.xaxis.major_label_text_font_size = '0pt'
self.fig.xaxis.visible = False
self.fig.lod_factor = 100
self.fig.lod_interval = 30
self.fig.lod_threshold = 100
self.fig.yaxis.axis_label_text_font_size = "10pt"
def _setxrange(self):
#Check if x range is not set, if not, set good ones
if 'map_range' not in pytplot.tplot_opt_glob:
pytplot.tplot_opt_glob['map_range'] = [0, 360]
tplot_x_range = Range1d(0, 360)
if self.show_xaxis:
pytplot.lim_info['xfull'] = tplot_x_range
pytplot.lim_info['xlast'] = tplot_x_range
#Bokeh uses milliseconds since epoch for some reason
x_range = Range1d(pytplot.tplot_opt_glob['map_range'][0],
pytplot.tplot_opt_glob['map_range'][1],
bounds=(0, 360))
self.fig.x_range = x_range
def _setyrange(self):
y_range = Range1d(-90, 90, bounds=(-90, 90))
self.fig.y_range = y_range
def _setzrange(self):
#Get Z Range
if 'z_range' in pytplot.data_quants[self.tvar_name].zaxis_opt:
self.zmin = pytplot.data_quants[
self.tvar_name].zaxis_opt['z_range'][0]
self.zmax = pytplot.data_quants[
self.tvar_name].zaxis_opt['z_range'][1]
else:
if isinstance(pytplot.data_quants[self.tvar_name].data, list):
dataset_temp = pytplot.data_quants[pytplot.data_quants[
self.tvar_name].data[0]].data.replace([np.inf, -np.inf],
np.nan)
else:
dataset_temp = pytplot.data_quants[
self.tvar_name].data.replace([np.inf, -np.inf], np.nan)
self.zmax = dataset_temp.max().max()
self.zmin = dataset_temp.min().min()
#Cannot have a 0 minimum in a log scale
if self.zscale == 'log':
zmin_list = []
for column in dataset_temp.columns:
series = dataset_temp[column]
zmin_list.append(series.iloc[series.nonzero()[0]].min())
self.zmin = min(zmin_list)
def _setminborder(self):
self.fig.min_border_bottom = pytplot.tplot_opt_glob[
'min_border_bottom']
self.fig.min_border_top = pytplot.tplot_opt_glob['min_border_top']
def _addtimebars(self):
for time_bar in pytplot.data_quants[self.tvar_name].time_bar:
time_bar_line = Span(location=time_bar['location'],
dimension=time_bar['dimension'],
line_color=time_bar['line_color'],
line_width=time_bar['line_width'])
self.fig.renderers.extend([time_bar_line])
def _setxaxis(self):
#Nothing to set for now
return
def _getyaxistype(self):
#Not going to have a log planet map
return 'linear'
def _setzaxistype(self):
if 'z_axis_type' in pytplot.data_quants[self.tvar_name].zaxis_opt:
self.zscale = pytplot.data_quants[
self.tvar_name].zaxis_opt['z_axis_type']
def _setcolors(self):
if 'colormap' in pytplot.data_quants[self.tvar_name].extras:
for cm in pytplot.data_quants[self.tvar_name].extras['colormap']:
self.colors.append(
pytplot.tplot_utilities.return_bokeh_colormap(cm))
else:
self.colors.append(
pytplot.tplot_utilities.return_bokeh_colormap('magma'))
def _setyaxislabel(self):
self.fig.yaxis.axis_label = pytplot.data_quants[
self.tvar_name].yaxis_opt['axis_label']
def _setzaxislabel(self):
self.fig.yaxis.axis_label = pytplot.data_quants[
self.tvar_name].yaxis_opt['axis_label']
def _visdata(self):
self._setcolors()
datasets = []
if isinstance(pytplot.data_quants[self.tvar_name].data, list):
for oplot_name in pytplot.data_quants[self.tvar_name].data:
datasets.append(pytplot.data_quants[oplot_name])
else:
datasets.append(pytplot.data_quants[self.tvar_name])
cm_index = 0
for dataset in datasets:
#TODO: Add a check that lon and lat are only 1D
_, x = pytplot.get_data(dataset.links['lon'])
_, y = pytplot.get_data(dataset.links['lat'])
for column_name in dataset.data.columns:
values = dataset.data[column_name].tolist()
colors = []
colors.extend(
pytplot.tplot_utilities.get_heatmap_color(
color_map=self.colors[cm_index],
min_val=self.zmin,
max_val=self.zmax,
values=values,
zscale=self.zscale))
circle_source = ColumnDataSource(
data=dict(x=x, y=y, value=values, colors=colors))
self.fig.scatter(x='x',
y='y',
radius=1.0,
fill_color='colors',
fill_alpha=1,
line_color=None,
source=circle_source)
cm_index += 1
def _addhoverlines(self):
#Add tools
hover = HoverTool()
hover.tooltips = [("Longitude", "@x"), ("Latitude", "@y"),
("Value", "@value")]
self.fig.add_tools(hover)
def _addlegend(self):
#Add the color bar
if 'z_axis_type' in pytplot.data_quants[self.tvar_name].zaxis_opt:
if pytplot.data_quants[
self.tvar_name].zaxis_opt['z_axis_type'] == 'log':
color_mapper = LogColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
border_line_color=None,
location=(0, 0),
ticker=LogTicker())
else:
color_mapper = LinearColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
border_line_color=None,
location=(0, 0),
ticker=BasicTicker())
else:
color_mapper = LinearColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
border_line_color=None,
location=(0, 0),
ticker=BasicTicker())
color_bar.width = 10
color_bar.formatter = BasicTickFormatter(precision=1)
color_bar.major_label_text_align = 'left'
color_bar.label_standoff = 5
color_bar.major_label_text_baseline = 'middle'
if 'axis_label' in pytplot.data_quants[self.tvar_name].zaxis_opt:
color_bar.title = pytplot.data_quants[
self.tvar_name].zaxis_opt['axis_label']
color_bar.title_text_font_size = '8pt'
color_bar.title_text_font_style = 'bold'
color_bar.title_standoff = 20
self.fig.add_layout(color_bar, 'right')
def _setbackground(self):
if 'alpha' in pytplot.data_quants[self.tvar_name].extras:
alpha = pytplot.data_quants[self.tvar_name].extras['alpha']
else:
alpha = 1
if 'basemap' in pytplot.data_quants[self.tvar_name].extras:
if os.path.isfile(
pytplot.data_quants[self.tvar_name].extras['basemap']):
from scipy import misc
img = misc.imread(
pytplot.data_quants[self.tvar_name].extras['basemap'],
mode='RGBA')
#Need to flip the image upside down...This will probably be fixed in
#a future release, so this will need to be deleted at some point
img = img[::-1]
self.fig.image_rgba(image=[img],
x=0,
y=-90,
dw=360,
dh=180,
alpha=alpha)
class TVarFigure1D(object):
def __init__(self, tvar, auto_color, last_plot=False, interactive=False):
self.tvar = tvar
self.auto_color = auto_color
self.last_plot = last_plot
self.interactive = interactive
#Variables needed across functions
self.colors = [
'black', 'red', 'green', 'navy', 'orange', 'firebrick', 'pink',
'blue', 'olive'
]
self.lineglyphs = []
self.linenum = 0
self.interactive_plot = None
self.fig = Figure(x_axis_type='datetime',
tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
def getaxistype(self):
axis_type = 'time'
link_y_axis = False
return axis_type, link_y_axis
def getfig(self):
if self.interactive:
return [self.fig, self.interactive_plot]
else:
return [self.fig]
def setsize(self, width, height):
self.fig.plot_width = width
if self.last_plot:
self.fig.plot_height = height + 22
else:
self.fig.plot_height = height
def add_title(self):
if 'title_text' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['title_text'] != '':
title1 = Title(
text=pytplot.tplot_opt_glob['title_text'],
align=pytplot.tplot_opt_glob['title_align'],
text_font_size=pytplot.tplot_opt_glob['title_size'])
self.fig.title = title1
self.fig.plot_height += 22
def buildfigure(self):
self._setminborder()
self._setxrange()
self._setxaxis()
self._setyrange()
self._addtimebars()
self._visdata()
self._setyaxislabel()
self._addhoverlines()
self._addlegend()
self._addextras()
def _format(self):
#Formatting stuff
self.fig.grid.grid_line_color = None
self.fig.axis.major_tick_line_color = None
self.fig.axis.major_label_standoff = 0
self.fig.xaxis.formatter = dttf
self.fig.title = None
self.fig.toolbar.active_drag = 'auto'
if not self.last_plot:
self.fig.xaxis.major_label_text_font_size = '0pt'
self.fig.xaxis.visible = False
def _setxrange(self):
#Check if x range is not set, if not, set good ones
if 'x_range' not in pytplot.tplot_opt_glob:
datasets = []
x_min_list = []
x_max_list = []
if isinstance(self.tvar.data, list):
for oplot_name in self.tvar.data:
datasets.append(pytplot.data_quants[oplot_name].data)
else:
datasets.append(self.tvar.data)
for dataset in datasets:
x_min_list.append(np.nanmin(dataset.index.tolist()))
x_max_list.append(np.nanmax(dataset.index.tolist()))
pytplot.tplot_opt_glob['x_range'] = [
np.nanmin(x_min_list),
np.nanmax(x_max_list)
]
tplot_x_range = [np.nanmin(x_min_list), np.nanmax(x_max_list)]
if self.last_plot:
pytplot.lim_info['xfull'] = tplot_x_range
pytplot.lim_info['xlast'] = tplot_x_range
#Bokeh uses milliseconds since epoch for some reason
x_range = Range1d(pytplot.tplot_opt_glob['x_range'][0] * 1000,
pytplot.tplot_opt_glob['x_range'][1] * 1000)
self.fig.x_range = x_range
def _setyrange(self):
if self._getyaxistype() == 'log':
if self.tvar.yaxis_opt['y_range'][0] < 0 or self.tvar.yaxis_opt[
'y_range'][1] < 0:
return
y_range = Range1d(self.tvar.yaxis_opt['y_range'][0],
self.tvar.yaxis_opt['y_range'][1])
self.fig.y_range = y_range
def _setminborder(self):
self.fig.min_border_bottom = pytplot.tplot_opt_glob[
'min_border_bottom']
self.fig.min_border_top = pytplot.tplot_opt_glob['min_border_top']
def _addtimebars(self):
for time_bar in self.tvar.time_bar:
time_bar_line = Span(location=time_bar['location'],
dimension=time_bar['dimension'],
line_color=time_bar['line_color'],
line_width=time_bar['line_width'])
self.fig.renderers.extend([time_bar_line])
def _setxaxis(self):
xaxis1 = DatetimeAxis(major_label_text_font_size='0pt', formatter=dttf)
xaxis1.visible = False
self.fig.add_layout(xaxis1, 'above')
def _getyaxistype(self):
if 'y_axis_type' in self.tvar.yaxis_opt:
return self.tvar.yaxis_opt['y_axis_type']
else:
return 'linear'
def _setcolors(self):
if 'line_color' in self.tvar.extras:
self.colors = self.tvar.extras['line_color']
def _setyaxislabel(self):
self.fig.yaxis.axis_label = self.tvar.yaxis_opt['axis_label']
def _visdata(self):
self._setcolors()
datasets = []
if isinstance(self.tvar.data, list):
for oplot_name in self.tvar.data:
datasets.append(pytplot.data_quants[oplot_name].data)
else:
datasets.append(self.tvar.data)
for dataset in datasets:
#Get Linestyle
line_style = None
if 'linestyle' in self.tvar.extras:
line_style = self.tvar.extras['linestyle']
#Get a list of formatted times
corrected_time = []
for x in dataset.index:
corrected_time.append(tplot_utilities.int_to_str(x))
#Bokeh uses milliseconds since epoch for some reason
x = dataset.index * 1000
#Create lines from each column in the dataframe
for column_name in dataset.columns:
y = dataset[column_name]
if self._getyaxistype() == 'log':
y.loc[y <= 0] = np.NaN
line_source = ColumnDataSource(
data=dict(x=x, y=y, corrected_time=corrected_time))
if self.auto_color:
line = Line(x='x',
y='y',
line_color=self.colors[self.linenum %
len(self.colors)],
**self.tvar.line_opt)
else:
line = Line(x='x', y='y', **self.tvar.line_opt)
if 'line_style' not in self.tvar.line_opt:
if line_style is not None:
line.line_dash = line_style[self.linenum %
len(line_style)]
else:
line.line_dash = self.tvar.line_opt['line_style']
self.lineglyphs.append(self.fig.add_glyph(line_source, line))
self.linenum += 1
def _addhoverlines(self):
#Add tools
hover = HoverTool()
hover.tooltips = [("Time", "@corrected_time"), ("Value", "@y")]
self.fig.add_tools(hover)
def _addlegend(self):
#Add the Legend if applicable
if 'legend_names' in self.tvar.yaxis_opt:
legend_names = self.tvar.yaxis_opt['legend_names']
if len(legend_names) != self.linenum:
print("Number of lines do not match length of legend names")
legend = Legend()
legend.location = (0, 0)
legend_items = []
j = 0
for legend_name in legend_names:
legend_items.append((legend_name, [self.lineglyphs[j]]))
j = j + 1
if j >= len(self.lineglyphs):
break
legend.items = legend_items
legend.label_text_font_size = "6pt"
legend.border_line_color = None
legend.glyph_height = int(self.fig.plot_height /
(len(legend_items) + 1))
self.fig.add_layout(legend, 'right')
def _addextras(self):
self.fig.renderers.extend(pytplot.extra_renderers)
class TVarFigureAlt(object):
def __init__(self, tvar_name, auto_color, show_xaxis=False, interactive=False):
self.tvar_name = tvar_name
self.auto_color = auto_color
self.show_xaxis = show_xaxis
self.interactive = interactive
# Variables needed across functions
self.colors = ['black', 'red', 'green', 'navy', 'orange', 'firebrick', 'pink', 'blue', 'olive']
self.lineglyphs = []
self.linenum = 0
self.interactive_plot = None
self.fig = Figure(tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
def getaxistype(self):
axis_type = 'altitude'
link_y_axis = False
return axis_type, link_y_axis
def getfig(self):
if self.interactive:
return [self.fig, self.interactive_plot]
else:
return [self.fig]
def setsize(self, width, height):
self.fig.plot_width = width
if self.show_xaxis:
self.fig.plot_height = height + 22
else:
self.fig.plot_height = height
def add_title(self):
if 'title_text' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['title_text'] != '':
title1 = Title(text=pytplot.tplot_opt_glob['title_text'],
align=pytplot.tplot_opt_glob['title_align'],
text_font_size=pytplot.tplot_opt_glob['title_size'])
self.fig.title = title1
self.fig.plot_height += 22
def buildfigure(self):
self._setminborder()
self._setxrange()
self._setxaxis()
self._setyrange()
self._visdata()
self._setxaxislabel()
self._setyaxislabel()
self._addhoverlines()
self._addlegend()
self._addtimebars()
def _format(self):
# Formatting stuff
self.fig.grid.grid_line_color = None
self.fig.axis.major_tick_line_color = None
self.fig.axis.major_label_standoff = 0
self.fig.title = None
self.fig.toolbar.active_drag = 'auto'
if not self.show_xaxis:
self.fig.xaxis.major_label_text_font_size = '0pt'
self.fig.xaxis.visible = False
def _setxrange(self):
# Check if x range is not set, if not, set good ones
if 'alt_range' not in pytplot.tplot_opt_glob:
datasets = []
x_min_list = []
x_max_list = []
if isinstance(pytplot.data_quants[self.tvar_name].data, list):
for oplot_name in pytplot.data_quants[self.tvar_name].data:
datasets.append(pytplot.data_quants[oplot_name])
else:
datasets.append(pytplot.data_quants[self.tvar_name])
for dataset in datasets:
_, alt = pytplot.get_data(dataset.links['alt'])
x_min_list.append(np.nanmin(alt.tolist()))
x_max_list.append(np.nanmax(alt.tolist()))
pytplot.tplot_opt_glob['alt_range'] = [np.nanmin(x_min_list), np.nanmax(x_max_list)]
tplot_x_range = [np.nanmin(x_min_list), np.nanmax(x_max_list)]
if self.show_xaxis:
pytplot.lim_info['xfull'] = tplot_x_range
pytplot.lim_info['xlast'] = tplot_x_range
x_range = Range1d(pytplot.tplot_opt_glob['alt_range'][0], pytplot.tplot_opt_glob['alt_range'][1])
self.fig.x_range = x_range
def _setyrange(self):
if self._getyaxistype() == 'log':
if pytplot.data_quants[self.tvar_name].yaxis_opt['y_range'][0] < 0 \
or pytplot.data_quants[self.tvar_name].yaxis_opt['y_range'][1] < 0:
return
y_range = Range1d(pytplot.data_quants[self.tvar_name].yaxis_opt['y_range'][0],
pytplot.data_quants[self.tvar_name].yaxis_opt['y_range'][1])
self.fig.y_range = y_range
def _setminborder(self):
self.fig.min_border_bottom = pytplot.tplot_opt_glob['min_border_bottom']
self.fig.min_border_top = pytplot.tplot_opt_glob['min_border_top']
def _addtimebars(self):
for time_bar in pytplot.data_quants[self.tvar_name].time_bar:
time_bar_line = Span(location=time_bar['location'],
dimension=time_bar['dimension'],
line_color=time_bar['line_color'],
line_width=time_bar['line_width'],
text_font_size=str(pytplot.data_quants[self.tvar_name].extras['char_size'])+'pt')
self.fig.renderers.extend([time_bar_line])
# initialize dataset variable
datasets = []
# grab tbardict
tbardict = pytplot.data_quants[self.tvar_name].time_bar
ltbar = len(tbardict)
# make sure data is in list format
if isinstance(pytplot.data_quants[self.tvar_name].data, list):
for oplot_name in pytplot.data_quants[self.tvar_name].data:
datasets.append(pytplot.data_quants[oplot_name])
else:
datasets.append(pytplot.data_quants[self.tvar_name])
for dataset in datasets:
# for location in tbar dict
for i in range(ltbar):
# get times, color, point size
test_time = pytplot.data_quants[self.tvar_name].time_bar[i]["location"]
# print(test_time)
color = pytplot.data_quants[self.tvar_name].time_bar[i]["line_color"]
pointsize = pytplot.data_quants[self.tvar_name].time_bar[i]["line_width"]
# correlate given time with corresponding data/alt points
time, altitude = pytplot.get_data(dataset.links['alt'])
altitude = altitude.transpose()[0]
nearest_time_index = np.abs(time - test_time).argmin()
data_point = dataset.data.iloc[nearest_time_index][0]
alt_point = altitude[nearest_time_index]
# color = pytplot.tplot_utilities.rgb_color(color)
self.fig.circle([alt_point], [data_point], size=pointsize, color=color)
return
def _setxaxis(self):
# Nothing to set for now
return
def _getyaxistype(self):
if 'y_axis_type' in pytplot.data_quants[self.tvar_name].yaxis_opt:
return pytplot.data_quants[self.tvar_name].yaxis_opt['y_axis_type']
else:
return 'linear'
def _setcolors(self):
if 'line_color' in pytplot.data_quants[self.tvar_name].extras:
self.colors = pytplot.data_quants[self.tvar_name].extras['line_color']
def _setxaxislabel(self):
self.fig.xaxis.axis_label = 'Altitude'
self.fig.xaxis.axis_label_text_font_size = str(pytplot.data_quants[self.tvar_name].extras['char_size'])+'pt'
def _setyaxislabel(self):
self.fig.yaxis.axis_label = pytplot.data_quants[self.tvar_name].yaxis_opt['axis_label']
self.fig.yaxis.axis_label_text_font_size = str(pytplot.data_quants[self.tvar_name].extras['char_size'])+'pt'
def _visdata(self):
self._setcolors()
datasets = []
if isinstance(pytplot.data_quants[self.tvar_name].data, list):
for oplot_name in pytplot.data_quants[self.tvar_name].data:
datasets.append(pytplot.data_quants[oplot_name])
else:
datasets.append(pytplot.data_quants[self.tvar_name])
for dataset in datasets:
# Get Linestyle
line_style = None
if 'linestyle' in pytplot.data_quants[self.tvar_name].extras:
line_style = pytplot.data_quants[self.tvar_name].extras['linestyle']
t_link, x = pytplot.get_data(dataset.links['alt'])
# Create lines from each column in the dataframe
for column_name in dataset.data.columns:
y = dataset.data[column_name]
t_tvar = dataset.data.index.values
y = dataset.data[column_name].values
while t_tvar[-1] > t_link[-1]:
t_tvar = np.delete(t_tvar, -1)
y = np.delete(y, -1)
while t_tvar[0] < t_link[0]:
t_tvar = np.delete(t_tvar, 0)
y = np.delete(y, 0)
if self._getyaxistype() == 'log':
y.loc[y <= 0] = np.NaN
line_source = ColumnDataSource(data=dict(x=x, y=y))
if self.auto_color:
line = Line(x='x', y='y', line_color=self.colors[self.linenum % len(self.colors)],
**pytplot.data_quants[self.tvar_name].line_opt)
else:
line = Line(x='x', y='y', **pytplot.data_quants[self.tvar_name].line_opt)
if 'line_style' not in pytplot.data_quants[self.tvar_name].line_opt:
if line_style is not None:
line.line_dash = line_style[self.linenum % len(line_style)]
else:
line.line_dash = pytplot.data_quants[self.tvar_name].line_opt['line_style']
self.lineglyphs.append(self.fig.add_glyph(line_source, line))
self.linenum += 1
def _addhoverlines(self):
# Add tools
hover = HoverTool()
hover.tooltips = [("Value", "@y")]
self.fig.add_tools(hover)
def _addlegend(self):
# Add the Legend if applicable
if 'legend_names' in pytplot.data_quants[self.tvar_name].yaxis_opt:
legend_names = pytplot.data_quants[self.tvar_name].yaxis_opt['legend_names']
if len(legend_names) != self.linenum:
print("Number of lines do not match length of legend names")
legend = Legend()
legend.location = (0, 0)
legend_items = []
j = 0
for legend_name in legend_names:
legend_items.append((legend_name, [self.lineglyphs[j]]))
j = j+1
if j >= len(self.lineglyphs):
break
legend.items = legend_items
legend.label_text_font_size = str(pytplot.data_quants[self.tvar_name].extras['char_size'])+'pt'
legend.border_line_color = None
legend.glyph_height = int(self.fig.plot_height / (len(legend_items) + 1))
self.fig.add_layout(legend, 'right')
class TVarFigureMap(object):
def __init__(self,
tvar_name,
auto_color=False,
show_xaxis=False,
slice=False):
self.tvar_name = tvar_name
self.show_xaxis = show_xaxis
self.slice = slice
# Variables needed across functions
self.fig = None
self.colors = []
self.lineglyphs = []
self.linenum = 0
self.zscale = 'linear'
self.zmin = 0
self.zmax = 1
self.callback = None
self.interactive_plot = None
self.fig = Figure(tools="pan,crosshair,reset,box_zoom",
y_axis_type=self._getyaxistype())
self._format()
@staticmethod
def get_axis_label_color():
if pytplot.tplot_opt_glob['black_background']:
text_color = '#000000'
else:
text_color = '#FFFFFF'
return text_color
@staticmethod
def getaxistype():
axis_type = 'map'
link_y_axis = True
return axis_type, link_y_axis
def getfig(self):
if self.slice:
return [self.fig, self.interactive_plot]
else:
return [self.fig]
def setsize(self, width, height):
self.fig.plot_width = width
if self.show_xaxis:
self.fig.plot_height = height + 22
else:
self.fig.plot_height = height
def add_title(self):
if 'title_text' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['title_text'] != '':
title1 = Title(
text=pytplot.tplot_opt_glob['title_text'],
align=pytplot.tplot_opt_glob['title_align'],
text_font_size=pytplot.tplot_opt_glob['title_size'],
text_color=self.get_axis_label_color())
self.fig.title = title1
self.fig.plot_height += 22
def buildfigure(self):
self._setminborder()
self._setxrange()
self._setxaxis()
self._setyrange()
self._setzrange()
self._setzaxistype()
self._setbackground()
self._visdata()
self._setxaxislabel()
self._setyaxislabel()
self._addhoverlines()
self._addlegend()
self._addtimebars()
def _format(self):
# Formatting stuff
self.fig.grid.grid_line_color = None
self.fig.axis.major_tick_line_color = None
self.fig.axis.major_label_standoff = 0
self.fig.title = None
self.fig.toolbar.active_drag = 'auto'
if not self.show_xaxis:
self.fig.xaxis.major_label_text_font_size = '0pt'
self.fig.xaxis.visible = False
self.fig.lod_factor = 100
self.fig.lod_interval = 30
self.fig.lod_threshold = 100
def _setxrange(self):
# Check if x range is not set, if not, set good ones
if 'map_range' not in pytplot.tplot_opt_glob:
pytplot.tplot_opt_glob['map_range'] = [0, 360]
tplot_x_range = Range1d(0, 360)
if self.show_xaxis:
pytplot.lim_info['xfull'] = tplot_x_range
pytplot.lim_info['xlast'] = tplot_x_range
# Bokeh uses milliseconds since epoch for some reason
x_range = Range1d(pytplot.tplot_opt_glob['map_range'][0],
pytplot.tplot_opt_glob['map_range'][1],
bounds=(0, 360))
self.fig.x_range = x_range
def _setyrange(self):
y_range = Range1d(-90, 90, bounds=(-90, 90))
self.fig.y_range = y_range
def _setzrange(self):
# Get Z Range
if 'z_range' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']:
self.zmin = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['zaxis_opt']['z_range'][0]
self.zmax = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['zaxis_opt']['z_range'][1]
else:
dataset_temp = pytplot.data_quants[self.tvar_name].where(
pytplot.data_quants[self.tvar_name] != np.inf)
dataset_temp = dataset_temp.where(
pytplot.data_quants[self.tvar_name] != -np.inf)
self.zmax = np.float(dataset_temp.max(skipna=True).values)
self.zmin = np.float(dataset_temp.min(skipna=True).values)
# Cannot have a 0 minimum in a log scale
if self.zscale == 'log':
df = pytplot.tplot_utilities.convert_tplotxarray_to_pandas_dataframe(
self.tvar_name, no_spec_bins=True)
zmin_list = []
for column in df.columns:
series = df[column]
zmin_list.append(
series.iloc[series.to_numpy().nonzero()[0]].min())
self.zmin = min(zmin_list)
def _setminborder(self):
self.fig.min_border_bottom = pytplot.tplot_opt_glob[
'min_border_bottom']
self.fig.min_border_top = pytplot.tplot_opt_glob['min_border_top']
def _addtimebars(self):
for time_bar in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['time_bar']:
time_bar_line = Span(location=time_bar['location'],
dimension=time_bar['dimension'],
line_color=time_bar['line_color'],
line_width=time_bar['line_width'])
self.fig.renderers.extend([time_bar_line])
# grab tbardict
tbardict = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['time_bar']
ltbar = len(tbardict)
# make sure data is in list format
datasets = [pytplot.data_quants[self.tvar_name]]
for oplot_name in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['overplots']:
datasets.append(pytplot.data_quants[oplot_name])
for dataset in datasets:
# for location in tbar dict
for i in range(ltbar):
# get times, color, point size
test_time = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['time_bar'][i]["location"]
color = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['time_bar'][i]["line_color"]
pointsize = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['time_bar'][i]["line_width"]
# correlate given time with corresponding lat/lon points
time = pytplot.data_quants[dataset.attrs['plot_options']
['links']['lat']].coords['time']
latitude = pytplot.data_quants[dataset.attrs['plot_options']
['links']['lat']].values
longitude = pytplot.data_quants[dataset.attrs['plot_options']
['links']['lon']].values
nearest_time_index = np.abs(time - test_time).argmin()
lat_point = latitude[nearest_time_index]
lon_point = longitude[nearest_time_index]
# color = pytplot.tplot_utilities.rgb_color(color)
self.fig.circle([lon_point], [lat_point],
size=pointsize,
color=color)
return
def _setxaxis(self):
# Nothing to set for now
return
def _getyaxistype(self):
# Not going to have a log planet map
return 'linear'
def _setzaxistype(self):
if 'z_axis_type' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']:
self.zscale = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['zaxis_opt']['z_axis_type']
def _setcolors(self):
if 'colormap' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']:
for cm in pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['extras']['colormap']:
self.colors.append(
pytplot.tplot_utilities.return_bokeh_colormap(cm))
else:
self.colors.append(
pytplot.tplot_utilities.return_bokeh_colormap('magma'))
def _setxaxislabel(self):
self.fig.xaxis.axis_label = 'Longitude'
self.fig.xaxis.axis_label_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
self.fig.xaxis.axis_label_text_color = self.get_axis_label_color()
def _setyaxislabel(self):
self.fig.yaxis.axis_label = 'Latitude'
self.fig.yaxis.axis_label_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
self.fig.yaxis.axis_label_text_color = self.get_axis_label_color()
def _visdata(self):
self._setcolors()
datasets = [pytplot.data_quants[self.tvar_name]]
for oplot_name in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['overplots']:
datasets.append(pytplot.data_quants[oplot_name])
cm_index = 0
for dataset in datasets:
# TODO: Add a check that lon and lat are only 1D
coords = pytplot.tplot_utilities.return_interpolated_link_dict(
dataset, ['lon', 'lat'])
t_link_lon = coords['lon'].coords['time'].values
x = coords['lon'].values
t_link_lat = coords['lat'].coords['time'].values
y = coords['lon'].values
df = pytplot.tplot_utilities.convert_tplotxarray_to_pandas_dataframe(
dataset.name, no_spec_bins=True)
for column_name in df.columns:
data = df[column_name].values
# Need to trim down the data points to fit within the link
t_tvar = df.index.values
while t_tvar[-1] > t_link_lon[-1]:
t_tvar = np.delete(t_tvar, -1)
data = np.delete(data, -1)
while t_tvar[0] < t_link_lon[0]:
t_tvar = np.delete(t_tvar, 0)
data = np.delete(data, 0)
while t_tvar[-1] > t_link_lat[-1]:
t_tvar = np.delete(t_tvar, -1)
data = np.delete(data, -1)
while t_tvar[0] < t_link_lat[0]:
t_tvar = np.delete(t_tvar, 0)
data = np.delete(data, 0)
colors = []
colors.extend(
pytplot.tplot_utilities.get_heatmap_color(
color_map=self.colors[cm_index % len(self.colors)],
min_val=self.zmin,
max_val=self.zmax,
values=data.tolist(),
zscale=self.zscale))
circle_source = ColumnDataSource(
data=dict(x=x, y=y, value=data.tolist(), colors=colors))
self.fig.scatter(x='x',
y='y',
radius=1.0,
fill_color='colors',
fill_alpha=1,
line_color=None,
source=circle_source)
cm_index += 1
def _addhoverlines(self):
# Add tools
hover = HoverTool()
hover.tooltips = [("Longitude", "@x"), ("Latitude", "@y"),
("Value", "@value")]
self.fig.add_tools(hover)
def _addlegend(self):
# Add the color bar
if 'z_axis_type' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']:
if pytplot.data_quants[self.tvar_name].attrs['plot_options'][
'zaxis_opt']['z_axis_type'] == 'log':
color_mapper = LogColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
border_line_color=None,
location=(0, 0),
ticker=LogTicker())
else:
color_mapper = LinearColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
border_line_color=None,
location=(0, 0),
ticker=BasicTicker())
else:
color_mapper = LinearColorMapper(palette=self.colors[0],
low=self.zmin,
high=self.zmax)
color_bar = ColorBarSideTitle(color_mapper=color_mapper,
border_line_color=None,
location=(0, 0),
ticker=BasicTicker())
color_bar.width = 10
color_bar.formatter = BasicTickFormatter(precision=1)
color_bar.major_label_text_align = 'left'
color_bar.label_standoff = 5
color_bar.major_label_text_baseline = 'middle'
color_bar.title = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['zaxis_opt']['axis_label']
color_bar.title_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
color_bar.title_text_font_style = 'bold'
color_bar.title_standoff = 20
self.fig.add_layout(color_bar, 'right')
def _setbackground(self):
if 'alpha' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']:
alpha = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']['alpha']
else:
alpha = 1
if 'basemap' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']:
if os.path.isfile(
pytplot.data_quants[self.tvar_name].attrs['plot_options']
['extras']['basemap']):
from scipy import misc
img = misc.imread(pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']['basemap'],
mode='RGBA')
# Need to flip the image upside down...This will probably be fixed in
# a future release, so this will need to be deleted at some point
img = img[::-1]
self.fig.image_rgba(image=[img],
x=0,
y=-90,
dw=360,
dh=180,
alpha=alpha)
conf_source.data = conf_matrix()
source = ColumnDataSource(data=random_roc_data(size=200))
text = TextInput(title="title", name='title', value='ROC Curve')
sample_size = Slider(title="Sample Size (splits 50/50)", value=400, start=50, end=800, step=2)
threshold = Slider(title="Threshold", value=50.0, start=0.0, end=100.0, step=0.1)
auc = Slider(title="Area Under Curve (AUC)", value=70.0, start=0.0, end=100.0, step=0.1)
dataurl = TextInput(title="Data Url", name='data', value='DEMO')
# Generate a figure container
plot = Figure(title_text_font_size="12pt",
plot_height=400,
plot_width=400,
tools="crosshair,pan,reset,resize,save,wheel_zoom",
title=text.value,
x_range=[0, 1],
y_range=[0, 1])
# Plot the line by the x,y values in the source property
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Plot the line by the x,y values in the source property
plot.line('xr', 'yr', source=ColumnDataSource(data=dict(xr=[0, 1], yr=[0, 1])),
line_width=3, line_alpha=0.6, color="red")
x, y = get_collide()
point_source = ColumnDataSource(data=dict(x=[x], y=[y]))
plot.circle_cross('x', 'y', source=point_source, color="blue")
class TVarFigure1D(object):
def __init__(self,
tvar_name,
auto_color,
show_xaxis=False,
interactive=False):
self.tvar_name = tvar_name
self.auto_color = auto_color
self.show_xaxis = show_xaxis
if 'show_all_axes' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['show_all_axes']:
self.show_xaxis = True
self.interactive = interactive
# Variables needed across functions
self.colors = [
'black', 'red', 'green', 'navy', 'orange', 'firebrick', 'pink',
'blue', 'olive'
]
self.lineglyphs = []
self.linenum = 0
self.interactive_plot = None
self.fig = Figure(x_axis_type='datetime',
tools=pytplot.tplot_opt_glob['tools'],
y_axis_type=self._getyaxistype())
self.fig.add_tools(BoxZoomTool(dimensions='width'))
self._format()
@staticmethod
def get_axis_label_color():
if pytplot.tplot_opt_glob['black_background']:
text_color = '#000000'
else:
text_color = '#FFFFFF'
return text_color
@staticmethod
def getaxistype():
axis_type = 'time'
link_y_axis = False
return axis_type, link_y_axis
def getfig(self):
if self.interactive:
return [self.fig, self.interactive_plot]
else:
return [self.fig]
def setsize(self, width, height):
self.fig.plot_width = width
if self.show_xaxis:
self.fig.plot_height = height + 22
else:
self.fig.plot_height = height
def add_title(self):
if 'title_text' in pytplot.tplot_opt_glob:
if pytplot.tplot_opt_glob['title_text'] != '':
title1 = Title(
text=pytplot.tplot_opt_glob['title_text'],
align=pytplot.tplot_opt_glob['title_align'],
text_font_size=pytplot.tplot_opt_glob['title_size'],
text_color=self.get_axis_label_color())
self.fig.title = title1
self.fig.plot_height += 22
def buildfigure(self):
self._setminborder()
self._setxrange()
self._setxaxis()
self._setyrange()
self._addtimebars()
self._visdata()
self._setxaxislabel()
self._setyaxislabel()
self._addhoverlines()
self._addlegend()
def _format(self):
# Formatting stuff
self.fig.grid.grid_line_color = None
self.fig.axis.major_tick_line_color = None
self.fig.axis.major_label_standoff = 0
self.fig.xaxis.formatter = dttf
self.fig.title = None
self.fig.toolbar.active_drag = 'auto'
if not self.show_xaxis:
self.fig.xaxis.major_label_text_font_size = '0pt'
self.fig.xaxis.visible = False
def _setxrange(self):
# Check if x range is not set, if not, set good ones
if 'x_range' not in pytplot.tplot_opt_glob:
datasets = [pytplot.data_quants[self.tvar_name]]
x_min_list = []
x_max_list = []
for oplot_name in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['overplots']:
datasets.append(pytplot.data_quants[oplot_name])
for dataset in datasets:
x_min_list.append(np.nanmin(dataset.coords['time']))
x_max_list.append(np.nanmax(dataset.coords['time']))
pytplot.tplot_opt_glob['x_range'] = [
np.nanmin(x_min_list),
np.nanmax(x_max_list)
]
tplot_x_range = [np.nanmin(x_min_list), np.nanmax(x_max_list)]
if self.show_xaxis:
pytplot.lim_info['xfull'] = tplot_x_range
pytplot.lim_info['xlast'] = tplot_x_range
# Bokeh uses milliseconds since epoch for some reason
x_range = Range1d(
int(pytplot.tplot_opt_glob['x_range'][0]) * 1000.0,
int(pytplot.tplot_opt_glob['x_range'][1]) * 1000.0)
self.fig.x_range = x_range
def _setyrange(self):
if self._getyaxistype() == 'log':
if pytplot.data_quants[self.tvar_name].attrs['plot_options']['yaxis_opt']['y_range'][0] < 0 or \
pytplot.data_quants[self.tvar_name].attrs['plot_options']['yaxis_opt']['y_range'][1] < 0:
return
y_range = Range1d(
pytplot.data_quants[self.tvar_name].attrs['plot_options']
['yaxis_opt']['y_range'][0],
pytplot.data_quants[self.tvar_name].attrs['plot_options']
['yaxis_opt']['y_range'][1])
self.fig.y_range = y_range
def _setminborder(self):
self.fig.min_border_bottom = pytplot.tplot_opt_glob[
'min_border_bottom']
self.fig.min_border_top = pytplot.tplot_opt_glob['min_border_top']
if 'vertical_spacing' in pytplot.tplot_opt_glob:
self.fig.min_border_bottom = int(
pytplot.tplot_opt_glob['vertical_spacing'] / 2.0)
self.fig.min_border_top = int(
pytplot.tplot_opt_glob['vertical_spacing'] / 2.0)
def _addtimebars(self):
for time_bar in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['time_bar']:
time_bar_line = Span(location=time_bar['location'] * 1000.0,
dimension=time_bar['dimension'],
line_color=time_bar['line_color'],
line_width=time_bar['line_width'])
self.fig.renderers.extend([time_bar_line])
def _set_roi_lines(self, dataset):
# Locating the two times between which there's a roi
time = dataset.coords['time'].values
roi_1 = pytplot.tplot_utilities.str_to_int(
pytplot.tplot_opt_glob['roi_lines'][0])
roi_2 = pytplot.tplot_utilities.str_to_int(
pytplot.tplot_opt_glob['roi_lines'][1])
# find closest time to user-requested time
x = np.asarray(time)
x_sub_1 = abs(x - roi_1 * np.ones(len(x)))
x_sub_2 = abs(x - roi_2 * np.ones(len(x)))
x_argmin_1 = np.nanargmin(x_sub_1)
x_argmin_2 = np.nanargmin(x_sub_2)
x_closest_1 = x[x_argmin_1]
x_closest_2 = x[x_argmin_2]
# Create roi box
roi_box = BoxAnnotation(left=x_closest_1 * 1000.0,
right=x_closest_2 * 1000.0,
fill_alpha=0.2,
fill_color='grey',
line_color='red',
line_width=2.5)
self.fig.renderers.extend([roi_box])
def _setxaxis(self):
xaxis1 = DatetimeAxis(major_label_text_font_size='0pt', formatter=dttf)
xaxis1.visible = False
self.fig.add_layout(xaxis1, 'above')
def _getyaxistype(self):
if 'y_axis_type' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['yaxis_opt']:
return pytplot.data_quants[self.tvar_name].attrs['plot_options'][
'yaxis_opt']['y_axis_type']
else:
return 'linear'
def _setcolors(self):
if 'line_color' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']:
self.colors = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']['line_color']
def _setxaxislabel(self):
self.fig.xaxis.axis_label = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['xaxis_opt']['axis_label']
self.fig.xaxis.axis_label_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
self.fig.xaxis.axis_label_text_color = self.get_axis_label_color()
def _setyaxislabel(self):
self.fig.yaxis.axis_label = pytplot.data_quants[
self.tvar_name].attrs['plot_options']['yaxis_opt']['axis_label']
self.fig.yaxis.axis_label_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']['extras']
['char_size']) + 'pt'
self.fig.yaxis.axis_label_text_color = self.get_axis_label_color()
def _visdata(self):
self._setcolors()
datasets = [pytplot.data_quants[self.tvar_name]]
for oplot_name in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['overplots']:
datasets.append(pytplot.data_quants[oplot_name])
for dataset in datasets:
# Get Linestyle
line_style = None
if 'linestyle' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['extras']:
line_style = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['extras']['linestyle']
# Get a list of formatted times
corrected_time = []
for x in dataset.coords['time'].values:
corrected_time.append(tplot_utilities.int_to_str(x))
# Bokeh uses milliseconds since epoch for some reason
x = dataset.coords['time'].values * 1000.0
# Add region of interest (roi) lines if applicable
if 'roi_lines' in pytplot.tplot_opt_glob.keys():
self._set_roi_lines(dataset)
plot_options = dataset.attrs['plot_options']
df = pytplot.tplot_utilities.convert_tplotxarray_to_pandas_dataframe(
dataset.name)
# Create lines from each column in the dataframe
for column_name in df.columns:
y = df[column_name]
# Account for log plotting
if self._getyaxistype() == 'log':
y.loc[y <= 0] = np.NaN
if 'line_style' in plot_options['line_opt']:
if plot_options['line_opt']['line_style'] == 'scatter':
Glyph = X
else:
Glyph = Line
else:
Glyph = Line
# Until what size of a data gap are we removing nan values from the dataset? Set by the user
# (default is to plot as bokeh would normally plot w/o worrying about data gap handling).
limit = pytplot.tplot_opt_glob['data_gap']
if limit != 0:
# Grabbing the times associated with nan values (nan_values), and the associated "position" of those
# keys in the dataset list (nan_keys)
nan_values = y[y.isnull().values].index.tolist()
nan_keys = [y.index.tolist().index(j) for j in nan_values]
nans = dict(zip(nan_keys, nan_values))
count = 0 # Keeping a count of how big of a time gap we have
consec_list = list(
) # List of consecutive nan values (composed of indices for gaps not bigger than
# the user-specified data gap)
for val in range(len(nan_keys)):
# Avoiding some weird issues with going to the last data point in the nan dictionary keys
if val != (len(nan_keys) - 1):
# Difference between one index and another - if consecutive indices, the diff will be 1
diff = abs(nan_keys[val] - nan_keys[val + 1])
# calculate time accumulated from one index to the next
t_now = nan_values[val]
t_next = nan_values[val + 1]
time_accum = abs(t_now - t_next)
# If we haven't reached the allowed data gap, just keep track of how big of a gap we're at,
# and the indices in the gap
if diff == 1 and count < limit:
count += time_accum
consec_list.append(nan_keys[val])
# This triggers when we initially exceed the allowed data gap
elif diff == 1 and count >= limit:
pass
# When we find that the previous index and the current one are not consecutive, stop adding to
# the consec_list/overall_list (if applicable), and start over the count of time accumulated
# in a gap, as well as the consecutive list of time values with nans
elif diff != 1:
# Restart the count and add the current val to the list of nan values to remove
count = 0
consec_list.append(nan_keys[val])
times = x.tolist()
for elem in consec_list:
# Unless the data gap was big enough, we need to remove nan values from the data,
# otherwise bokeh will automatically NOT interpolate (the exact opposite of behavior in
# pyqtgraph, which ALWAYS interpolates...).
times.remove(nans[elem] * 1000.0)
del y[nans[elem]]
del corrected_time[corrected_time.index(
tplot_utilities.int_to_str(nans[elem]))]
# Data to be plotted
line_source = ColumnDataSource(
data=dict(x=times, y=y, corrected_time=corrected_time))
else:
# Data to be plotted
line_source = ColumnDataSource(
data=dict(x=x, y=y, corrected_time=corrected_time))
if self.auto_color:
line = Glyph(x='x',
y='y',
line_color=self.colors[self.linenum %
len(self.colors)])
else:
line = Glyph(x='x', y='y')
if Glyph == Line:
if 'line_style' not in plot_options['line_opt']:
if line_style is not None:
line.line_dash = line_style[self.linenum %
len(line_style)]
else:
line.line_dash = plot_options['line_style']
self.lineglyphs.append(self.fig.add_glyph(line_source, line))
self.linenum += 1
def _addhoverlines(self):
# Add tools
hover = HoverTool()
hover.tooltips = [("Time", "@corrected_time"), ("Value", "@y")]
self.fig.add_tools(hover)
def _addlegend(self):
# Add the Legend if applicable
if 'legend_names' in pytplot.data_quants[
self.tvar_name].attrs['plot_options']['yaxis_opt']:
legend_names = pytplot.data_quants[self.tvar_name].attrs[
'plot_options']['yaxis_opt']['legend_names']
if len(legend_names) != self.linenum:
print("Number of lines do not match length of legend names")
legend = Legend()
legend.location = (0, 0)
legend_items = []
j = 0
for legend_name in legend_names:
legend_items.append((legend_name, [self.lineglyphs[j]]))
j = j + 1
if j >= len(self.lineglyphs):
break
legend.items = legend_items
legend.label_text_font_size = str(
pytplot.data_quants[self.tvar_name].attrs['plot_options']
['extras']['char_size']) + 'pt'
legend.border_line_color = None
legend.glyph_height = int(self.fig.plot_height /
(len(legend_items) + 1))
self.fig.add_layout(legend, 'right')