def histogram_plot(states,
height,
width,
data_top,
data_source,
n_ticks,
n_minor_ticks=0,
fill_color=None):
'''Create a bokeh histogram using quad shapes'''
from bokeh.plotting import Figure
# Create histogram object
hist_obj = Figure(plot_height=height,
plot_width=width,
title="",
toolbar_location=None)
# Data top is string of var name from data_source
fill_color = '#f0f5f5' if fill_color == None else fill_color
hist_obj.quad(top=data_top,
bottom=0,
source=data_source,
left="left",
right="right",
fill_color=fill_color,
line_color='black')
# set number of major and minor ticks
hist_obj.xaxis[0].ticker.desired_num_ticks = n_ticks
hist_obj.xaxis[0].ticker.num_minor_ticks = n_minor_ticks
hist_obj.y_range = Range1d(0, 1)
hist_obj.x_range = Range1d(states[0] - 0.5, states[-1] + 0.5)
return hist_obj
def plt(self):
source_normal = ColumnDataSource(data=get_normal(0, 1, 10000))
plot_normal = Figure(plot_width=450,
plot_height=450,
x_range=(-6, 6),
title='Normal')
plot_normal.quad(top='hist',
bottom=0,
left='left_edges',
right='right_edges',
source=source_normal,
fill_color="navy")
def update_plot_normal(attrname, old, new):
source_normal.data = get_normal(mu_slider.value,
sigma_slider.value, 10000)
mu_slider = Slider(start=-5, end=5, value=0, step=0.2, title='Mean')
mu_slider.on_change('value', update_plot_normal)
sigma_slider = Slider(start=0.1,
end=5,
value=1,
step=0.2,
title='Std_Dev')
sigma_slider.on_change('value', update_plot_normal)
layout = row(plot_normal, column(mu_slider, sigma_slider))
def landscape_plot(landscape, states, timesteps, height, width, patch_colors):
'''Plot fitness landscape'''
from bokeh.plotting import Figure
# Append extra state and timestep for index bounds of quad objects
states = numpy.append(states, states[-1]+1)
timesteps = numpy.append(timesteps, timesteps[-1]+1)
# Create points for each cell of grid
tops = list()
bottoms = list()
lefts = list()
rights = list()
for top, bottom in zip(states[1:], states[:-1]):
for left, right in zip(timesteps[:-1], timesteps[1:]):
tops.append(top)
bottoms.append(bottom)
lefts.append(left)
rights.append(right)
# Create array of color values corresponding to each cell's assoc. patch
grid_colors = list()
for i in range(len(tops)):
idx = (landscape['state']==bottoms[i]) & (landscape['t']==lefts[i])
patch = int(landscape[idx]['patch'])
grid_colors.append(patch_colors[patch])
# Create landscape object
grid_obj = Figure(plot_height=height, plot_width=width, title="", toolbar_location=None)
grid_obj.quad(top=tops, bottom=bottoms, left=lefts, right=rights, line_color='black',
fill_color=grid_colors)
return grid_obj
def make_precipitation_plot(source):
plot = Figure(x_axis_type="datetime",
plot_width=1000,
plot_height=125,
min_border_left=50,
min_border_right=50,
min_border_top=0,
min_border_bottom=0,
toolbar_location=None)
plot.title = None
plot.quad(top='actual_precipitation',
bottom=0,
left='left',
right='right',
color=Greens4[1],
source=source)
# fixed attributes
plot.border_fill_color = "whitesmoke"
plot.yaxis.axis_label = "Precipitation (in)"
plot.axis.major_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.y_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
def __plot_histogram(self):
plot = Figure(plot_height=500, plot_width=300)
plot.title.text = 'Histogram'
hist_data = self.calc_histogram_data(self.__values)
plot.toolbar.logo = None
plot.quad(top='hist', bottom=0, left='edges_left', right='edges_right',
source=hist_data,
fill_color="#036564", line_color="#033649")
return plot
def Main():
parser = argparse.ArgumentParser()
parser.add_argument("-st", dest="stationId", nargs='*')
parser.add_argument("-ct", dest="cityName", nargs='*')
parser.add_argument("-pr", dest="percentage", nargs='*')
parser.add_argument("-tc1", dest="taskColor1", nargs='*')
args = parser.parse_args()
for i in range(len(args.stationId)):
Data, Date, Max, Min = prepareCSVData(args.cityName[i])
plotDate = Data['right']
source = ColumnDataSource(data=Data)
AverageTemp = []
for index in range(len(Min)):
Average = (Min[index] + Max[index]) / 2
AverageTemp.append(Average)
plot_graph = Figure(x_axis_type="datetime",
plot_width=1000,
title="2016 Daily Growing Degree Days- " +
args.cityName[i],
toolbar_location=None)
color = "#ADD8E6"
for j in range(len(args.percentage)):
minPer, MaxPer = getComputePercentage(Min, Max, args.percentage[j])
plot_graph.quad(top=MaxPer,
bottom=minPer,
left='left',
right='right',
source=source,
color=color,
legend="percentile " + str(args.percentage[j]) +
"-" + str(100 - int(args.percentage[j])))
color = "#D2B48C"
plot_graph.circle(Max,
Min,
alpha=0.9,
color="#0000FF",
fill_alpha=0.2,
size=10,
source=source,
legend='2016')
plot_graph.line(plotDate,
AverageTemp,
source=source,
line_color='Red',
line_width=0.5,
legend='Average')
plot_graph.xaxis.axis_label = "MONTHS"
plot_graph.yaxis.axis_label = "Daily Accumulation Celcius"
plot_graph.grid[0].ticker.desired_num_ticks = 12
output_file("./plots/secTask-1" + args.cityName[i] + ".html",
title="2016 Daily Growing Degree Days-(" +
args.cityName[i] + ")")
save(plot_graph)
def make_plot(source, AverageTemp, Parcentile_5_Min, Parcentile_5_Max,
Parcentile_25_Min, Parcentile_25_Max, MinTemp, MaxTemp, plotDate,
cityName):
plot = Figure(title='Optional Task # 1 : Growing Degree-day for ' +
cityName,
x_axis_type="datetime",
plot_width=1000,
title_text_font_size='12pt',
tools="",
toolbar_location=None)
colors = Blues4[0:3]
plot.circle(MaxTemp,
MinTemp,
alpha=0.9,
color="#66ff33",
fill_alpha=0.2,
size=10,
source=source,
legend='2015')
plot.quad(top=Parcentile_5_Max,
bottom=Parcentile_5_Min,
left='left',
right='right',
source=source,
color="#e67300",
legend="Percentile 5-95")
plot.quad(top=Parcentile_25_Max,
bottom=Parcentile_25_Min,
left='left',
right='right',
source=source,
color="#66ccff",
legend="percentile 25-75")
plot.line(plotDate,
AverageTemp,
source=source,
line_color='Red',
line_width=0.75,
legend='AverageTemp')
plot.border_fill_color = "whitesmoke"
plot.xaxis.axis_label = "Months"
plot.yaxis.axis_label = "Temperature (C)"
plot.axis.major_label_text_font_size = "10pt"
plot.axis.axis_label_text_font_size = "12pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
def hist_viz(
df: pd.DataFrame,
miss_pct: float,
col: str,
yscale: str,
plot_width: int,
plot_height: int,
show_yaxis: bool,
) -> Figure:
"""
Render a histogram
"""
# pylint: disable=too-many-arguments
title = f"{col} ({miss_pct}% missing)" if miss_pct > 0 else f"{col}"
tooltips = [
("Bin", "@intervals"),
("Frequency", "@freq"),
("Percent", "@pct{0.2f}%"),
]
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
toolbar_location=None,
title=title,
tools=[],
y_axis_type=yscale,
)
bottom = 0 if yscale == "linear" or df.empty else df["freq"].min() / 2
fig.quad(
source=df,
left="left",
right="right",
bottom=bottom,
alpha=0.5,
top="freq",
fill_color="#6baed6",
)
hover = HoverTool(
tooltips=tooltips,
mode="vline",
)
fig.add_tools(hover)
tweak_figure(fig, "hist", show_yaxis)
fig.yaxis.axis_label = "Frequency"
if not df.empty:
_format_axis(fig, df.iloc[0]["left"], df.iloc[-1]["right"], "x")
if show_yaxis and yscale == "linear":
_format_axis(fig, 0, df["freq"].max(), "y")
return fig
def createPlot(df, boundaryDF):
p = Figure(plot_height=900, plot_width=PLOT_WIDTH, title="", y_range=[], title_text_font_size=TITLE_FONT_SIZE)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
quad = p.quad(
top="top",
bottom="bottom",
left="left",
right="right",
source=blockSource,
fill_color="grey",
hover_fill_color="firebrick",
fill_alpha=0.05,
hover_alpha=0.3,
line_color=None,
hover_line_color="white",
)
p.multi_line(xs="xs", ys="ys", source=blockSource, color="black", line_width=2, line_alpha=0.4, line_dash="dotted")
p.multi_line(xs="xs", ys="ys", source=source, color="color", line_width="width", line_alpha="line_alpha")
p.add_tools(
HoverTool(
tooltips=[("chromosome", "@chromosome"), ("exon", "@exon"), ("start", "@start"), ("end", "@end")],
renderers=[quad],
)
)
return p
class ColorBar(object):
def __init__(self):
self.map_colorbar = Figure(tools="", toolbar_location=None,
min_border=0, min_border_right=0,
plot_width=80, plot_height=650,
x_range=(0., 1.), y_range=conso_bounds)
self.map_colorbar.xaxis.visible = None
self.map_colorbar.grid.grid_line_color = None
colorbar_ys = np.linspace(conso_bounds[0], conso_bounds[1], 12)
colorbar_colors = scales_to_hex(np.linspace(0., 1., 11))
self.map_colorbar.quad(bottom=colorbar_ys[:-1], top=colorbar_ys[1:],
left=0., right=1.,
fill_color=colorbar_colors, line_color=None,
alpha=0.9)
def get_plot(self):
return self.map_colorbar
def make_precipitation_plot(source):
plot = Figure(x_axis_type="datetime", plot_width=1000, plot_height=125, min_border_left=50, min_border_right=50, min_border_top=0, min_border_bottom=0, toolbar_location=None)
plot.title = None
plot.quad(top='actual_precipitation', bottom=0, left='left', right='right', color=Greens4[1], source=source)
# fixed attributes
plot.border_fill_color = "whitesmoke"
plot.yaxis.axis_label = "Precipitation (in)"
plot.axis.major_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.y_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
def scoredistplots(preds):
"""Score distribution plots"""
from bokeh.models import Range1d,GridPlot
from bokeh.plotting import Figure
plots=[]
for p in preds:
pred=preds[p]
key=pred.scorekey
data = pred.data[key]
hist, edges = np.histogram(data, density=True, bins=30)
p = Figure(title=p,plot_height=250,tools='')
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color="#036564", line_color="#033649")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
plots.append(p)
plot = GridPlot(children=[plots],title='test')
js,html = embedPlot(plot)
return html
def hist_kde_viz(
df: pd.DataFrame,
pts_rng: np.ndarray,
pdf: np.ndarray,
col: str,
yscale: str,
plot_width: int,
plot_height: int,
) -> Panel:
"""
Render histogram with overlayed kde
"""
# pylint: disable=too-many-arguments
fig = Figure(
plot_width=plot_width,
plot_height=plot_height,
title=f"{col}",
tools=[],
toolbar_location=None,
y_axis_type=yscale,
)
bottom = 0 if yscale == "linear" or df.empty else df["freq"].min() / 2
hist = fig.quad(
source=df,
left="left",
right="right",
bottom=bottom,
alpha=0.5,
top="freq",
fill_color="#6baed6",
)
hover_hist = HoverTool(
renderers=[hist],
tooltips=[("Bin", "@intervals"), ("Density", "@freq")],
mode="vline",
)
line = fig.line( # pylint: disable=too-many-function-args
pts_rng,
pdf,
line_color="#9467bd",
line_width=2,
alpha=0.5)
hover_dist = HoverTool(renderers=[line],
tooltips=[("x", "@x"), ("y", "@y")])
fig.add_tools(hover_hist)
fig.add_tools(hover_dist)
tweak_figure(fig, "kde")
fig.yaxis.axis_label = "Density"
fig.xaxis.axis_label = col
_format_axis(fig, df.iloc[0]["left"], df.iloc[-1]["right"], "x")
if yscale == "linear":
_format_axis(fig, 0, df["freq"].max(), "y")
return Panel(child=fig, title="KDE plot")
def make_plot(source,AverageTemp,Parcentile_5_Min,Parcentile_5_Max,Parcentile_25_Min,Parcentile_25_Max,MinTemp,MaxTemp,plotDate,cityName):
plot = Figure(title='Optional Task # 1 : Growing Degree-day for '+cityName, x_axis_type="datetime", plot_width=1000, title_text_font_size='12pt', tools="", toolbar_location=None)
colors = Blues4[0:3]
plot.circle(MaxTemp,MinTemp, alpha=0.9, color="#66ff33", fill_alpha=0.2, size=10,source=source,legend ='2015')
plot.quad(top=Parcentile_5_Max, bottom=Parcentile_5_Min, left='left',right='right',
source=source,color="#e67300", legend="Percentile 5-95")
plot.quad(top=Parcentile_25_Max, bottom=Parcentile_25_Min,left='left',right='right',
source=source,color="#66ccff",legend="percentile 25-75")
plot.line(plotDate,AverageTemp,source=source,line_color='Red', line_width=0.75, legend='AverageTemp')
plot.border_fill_color = "whitesmoke"
plot.xaxis.axis_label = "Months"
plot.yaxis.axis_label = "Temperature (C)"
plot.axis.major_label_text_font_size = "10pt"
plot.axis.axis_label_text_font_size = "12pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
def landscape_plot(landscape, states, timesteps, height, width, patch_colors):
'''Plot fitness landscape'''
from bokeh.plotting import Figure
# Append extra state and timestep for index bounds of quad objects
states = numpy.append(states, states[-1] + 1)
timesteps = numpy.append(timesteps, timesteps[-1] + 1)
# Create points for each cell of grid
tops = list()
bottoms = list()
lefts = list()
rights = list()
for top, bottom in zip(states[1:], states[:-1]):
for left, right in zip(timesteps[:-1], timesteps[1:]):
tops.append(top)
bottoms.append(bottom)
lefts.append(left)
rights.append(right)
# Create array of color values corresponding to each cell's assoc. patch
grid_colors = list()
for i in range(len(tops)):
idx = (landscape['state'] == bottoms[i]) & (landscape['t'] == lefts[i])
patch = int(landscape[idx]['patch'])
grid_colors.append(patch_colors[patch])
# Create landscape object
grid_obj = Figure(plot_height=height,
plot_width=width,
title="",
toolbar_location=None)
grid_obj.quad(top=tops,
bottom=bottoms,
left=lefts,
right=rights,
line_color='black',
fill_color=grid_colors)
return grid_obj
def histogram_plot(states, height, width, data_top, data_source, n_ticks,
n_minor_ticks=0, fill_color=None):
'''Create a bokeh histogram using quad shapes'''
from bokeh.plotting import Figure
# Create histogram object
hist_obj = Figure(plot_height=height, plot_width=width, title="",
toolbar_location=None)
# Data top is string of var name from data_source
fill_color = '#f0f5f5' if fill_color==None else fill_color
hist_obj.quad(top=data_top, bottom=0, source=data_source, left="left",
right="right", fill_color=fill_color, line_color='black')
# set number of major and minor ticks
hist_obj.xaxis[0].ticker.desired_num_ticks = n_ticks
hist_obj.xaxis[0].ticker.num_minor_ticks = n_minor_ticks
hist_obj.y_range = Range1d(0, 1)
hist_obj.x_range = Range1d(states[0]-0.5, states[-1]+0.5)
return hist_obj
def make_plot(source, title):
print("make plot")
plot = Figure(x_axis_type="datetime",
plot_width=1000,
tools="",
toolbar_location=None)
plot.title = title
colors = Blues4[0:3]
plot.quad(top='record_max_temp',
bottom='record_min_temp',
left='left',
right='right',
color=colors[2],
source=source,
legend="Record")
plot.quad(top='average_max_temp',
bottom='average_min_temp',
left='left',
right='right',
color=colors[1],
source=source,
legend="Average")
plot.quad(top='actual_max_temp',
bottom='actual_min_temp',
left='left',
right='right',
color=colors[0],
alpha=0.5,
line_color="black",
source=source,
legend="Actual")
# fixed attributes
plot.border_fill_color = "whitesmoke"
plot.xaxis.axis_label = None
plot.yaxis.axis_label = "Temperature (F)"
plot.axis.major_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
def make_plot(source, title):
plot = Figure(x_axis_type="datetime", plot_width=1000, tools="", toolbar_location=None)
plot.title = title
colors = Blues4[0:3]
plot.quad(top='record_max_temp', bottom='record_min_temp', left='left', right='right', color=colors[2], source=source, legend="Record")
plot.quad(top='average_max_temp', bottom='average_min_temp', left='left', right='right', color=colors[1], source=source, legend="Average")
plot.quad(top='actual_max_temp', bottom='actual_min_temp', left='left', right='right', color=colors[0], alpha=0.5, line_color="black", source=source, legend="Actual")
# fixed attributes
plot.border_fill_color = "whitesmoke"
plot.xaxis.axis_label = None
plot.yaxis.axis_label = "Temperature (F)"
plot.axis.major_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_size = "8pt"
plot.axis.axis_label_text_font_style = "bold"
plot.x_range = DataRange1d(range_padding=0.0, bounds=None)
plot.grid.grid_line_alpha = 0.3
plot.grid[0].ticker.desired_num_ticks = 12
return plot
class Plot2d:
def __init__(self,
x_range=None,
y_range=None,
x_label="",
y_label="",
tooltip=None,
title="",
width=600,
height=400,
yscale="auto",
font_size="1vw",
hover_mode="mouse"):
hover = HoverTool(tooltips=tooltip, mode=hover_mode)
if y_range == None:
self.plot = Figure(
tools=[hover, "pan,wheel_zoom,box_zoom,reset,crosshair,tap"],
plot_width=width,
active_drag="box_zoom",
plot_height=height,
background_fill_color="white",
x_axis_type="auto",
y_axis_type=yscale)
else:
self.plot = Figure(
tools=[hover, "pan,wheel_zoom,box_zoom,reset,crosshair,tap"],
plot_width=width,
active_drag="box_zoom",
plot_height=height,
background_fill_color="white",
x_axis_type="auto",
y_axis_type=yscale,
y_range=y_range,
x_range=x_range)
self.plot.xaxis.axis_label = x_label
self.plot.yaxis.axis_label = y_label
self.plot.xaxis.major_label_text_font_size = font_size
self.plot.yaxis.major_label_text_font_size = font_size
self.plot.xaxis.axis_label_text_font_size = font_size
self.plot.yaxis.axis_label_text_font_size = font_size
self.plot.legend.label_text_font_size = font_size
self.plot.title.text_font_size = font_size
self.plot.title.text = title
def vbar(self, source, y="y", x="x", line_width=0.01):
self.plot.vbar(top=y,
x=x,
width=0.8,
source=source,
fill_color="dodgerblue",
line_color="black",
line_width=line_width,
alpha=0.8,
hover_fill_color='red',
hover_line_color='red',
hover_alpha=0.8)
return self.plot
def quad(self,
source,
top,
bottom='bottom',
left='left',
right='right',
name='data',
line_width=0.1):
self.plot.quad(top=top,
bottom=bottom,
left=left,
right=right,
name=name,
source=source,
fill_color="dodgerblue",
line_color="black",
line_width=line_width,
alpha=0.8,
hover_fill_color='red',
hover_line_color='red',
hover_alpha=0.8)
return self.plot
def line(self,
source,
x='x',
y='y',
color="black",
line_width=1.5,
line_alpha=0.9):
self.plot.line(x=x,
y=y,
source=source,
color=color,
line_width=line_width,
line_alpha=line_alpha)
return self
def circle(self,
source,
x='x',
y='y',
color="blue",
size=8,
line_color='black',
alpha=0.7,
hover_color="blue",
hover_alpha=1,
hover_line_color='red',
radius=0.0165,
fill_color='lightgray',
line_width=0.3,
hover_fill_color=None):
self.plot.circle(
x=x,
y=y,
source=source,
color=color,
size=size,
line_color=line_color,
alpha=alpha,
hover_color=hover_color,
hover_alpha=hover_alpha,
hover_line_color=hover_line_color,
fill_color=fill_color,
hover_fill_color=hover_fill_color,
)
return self
def set_colorbar(self, z_value):
''' Setup for colorbar
'''
dz = z_value
# removing NaN in ranges
dz_valid = [x if x > -999 else np.nan for x in dz]
dzmax, dzmin = np.nanmax(dz_valid), np.nanmin(dz_valid)
if np.log10(dzmax) > 4 or np.log10(dzmin) < -3:
ztext = ['{:4.2e}'.format(i) for i in dz_valid]
cbarformat = "%2.1e"
elif np.log10(dzmin) > 0:
ztext = ['{:5.2f}'.format(i) for i in dz_valid]
cbarformat = "%4.2f"
else:
ztext = ['{:6.2f}'.format(i) for i in dz_valid]
cbarformat = "%5.2f"
return ztext, cbarformat
def colorbar(self, mapper, title='(Val - Ref)'):
formatter = PrintfTickFormatter(format="%4.2f")
color_bar = ColorBar(color_mapper=mapper,
label_standoff=16,
title=title,
major_label_text_font_style='bold',
padding=26,
major_label_text_align='left',
major_label_text_font_size="10pt",
formatter=formatter,
title_text_baseline='alphabetic',
location=(0, 0))
self.plot.add_layout(color_bar, 'right')
return self
def wedge(self,
source,
field=None,
x="x",
y="y",
mapper=None,
radius=0.0165,
colorbar_title='counts'):
if mapper:
self.plot = self.circle(
source,
y=y,
x=x,
radius=radius,
fill_color={
'field': field,
'transform': mapper
},
line_color='black',
line_width=0.3,
).circle(source,
y=y,
x=x,
radius=radius + 0.005,
fill_color=None,
line_color=None,
line_width=3,
hover_fill_color={
'field': field,
'transform': mapper
},
hover_line_color='red').colorbar(
mapper, title=colorbar_title).plot
elif field:
self.plot = self.circle(
source,
y=y,
x=x,
radius=radius,
hover_fill_color='lightgrey',
fill_color={
'field': 'color'
},
line_color='black',
line_width=0.3,
).plot
else:
self.plot = self.circle(
source,
y=y,
x=x,
radius=radius,
hover_fill_color='lightgrey',
fill_color='darkgrey',
line_color='black',
line_width=0.3,
).plot
return self
def segment(self,
source,
x0='segx0',
x1='segx1',
y0='segy0',
y1='segy1',
line_width=2,
line_color='#1e90ff'):
seg = Segment(x0=x0,
x1=x1,
y0=y0,
y1=y1,
line_width=line_width,
line_color=line_color)
self.plot.add_glyph(source, seg)
self.plot.yaxis.ticker = FixedTicker(ticks=[0, 1])
self.plot.yaxis.major_label_overrides = {'0': 'bad', '1': 'good'}
return self
DYNAMIC_EVENTS = {"Acceleration Score": "Accel Best Time",
"Skid Pad Score": "Skid Pad Best Time",
"Autocross Score": "AutoX Best Time",
"Efficiency Score": "Endurance Adjusted Time",
"Endurance Score": "Endurance Adjusted Time"}
# Read in the FSAEM data
compdata = pd.read_excel('FSAEM_summarized_results.xlsx')
source = ColumnDataSource(data=dict())
# Initialize the plot
plot = Figure(plot_width=800, plot_height=500, toolbar_location='right',
tools="pan,wheel_zoom,box_zoom,reset,resize")
freq_bars = plot.quad(top='hist', bottom=0, left='left_edge', right='right_edge',
source=source, fill_color=YlOrBr3[0], line_color='#000000')
plot.xaxis.axis_label = "Total Score"
plot.xaxis.axis_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.yaxis.axis_label = "Frequency"
plot.yaxis.axis_line_color = None
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
plot.outline_line_color = None
'left': edges_low[:-1],
'right': edges_low[1:]
})
phi = Figure(title='NBINSHI',
tools=[hover, "pan,wheel_zoom,box_zoom,reset"],
y_axis_label='Frequency',
x_axis_label='COUNTBINS',
background_fill_color="white")
phi.quad(top='hist',
bottom='bottom',
left='left',
right='right',
source=source_hi,
fill_color="dodgerblue",
line_color="black",
alpha=0.8,
hover_fill_color='blue',
hover_line_color='black',
hover_alpha=0.8)
pmed = Figure(title='NBINSMED',
tools=[hover2, "pan,wheel_zoom,box_zoom,reset"],
y_axis_label='Frequency',
x_axis_label='COUNTBINS',
background_fill_color="white")
pmed.quad(top='hist',
bottom='bottom',
left='left',
selected_year = Slider(title="Year",
start=1900,
end=current_year,
value=2008,
step=1)
station_IDs_str = TextInput(title="Station ID", value='6551')
# Create Figure and Plot
hover = HoverTool(tooltips=[("Date", "@date_str"), ("Station ID",
"@station_id")])
p = Figure(x_axis_type="datetime", plot_width=800, tools=[hover])
p.quad(top='max_t',
bottom='min_t',
left='timel',
right='timer',
source=source,
alpha=0.5,
color='RoyalBlue',
line_color="black",
line_alpha=0.5,
legend='Min/Max')
p.line(x='time',
y='mean_t',
source=source,
color='red',
line_width=1.5,
alpha=0.8,
legend='Average')
p.x_range = DataRange1d(range_padding=0, bounds=None)
p.yaxis.axis_label = "Temperature (C)"
p.xaxis.axis_label = 'Month'
def plot(width, height):
######################################################################################################################
source_normal = ColumnDataSource(data = get_normal(0,1,10000))
plot_normal = Figure(plot_width = width, plot_height = height, x_range = (-6,6), title = 'Normal')
plot_normal.quad(top='hist', bottom=0, left='left_edges', right='right_edges',
source= source_normal, fill_color="navy")
def update_plot_normal(attrname, old , new):
source_normal.data = get_normal(mu_slider.value, sigma_slider.value, 100000)
mu_slider = Slider(start = -5, end = 5, value = 0, step = 0.2, title = 'Mean')
mu_slider.on_change('value', update_plot_normal)
sigma_slider = Slider(start = 0.1, end = 5, value = 1, step = 0.2, title = 'Std_Dev')
sigma_slider.on_change('value', update_plot_normal)
######################################################################################################################
source_weibull = ColumnDataSource(data = get_weibull(1.5, 1, 10000))
plot_weibull = Figure(plot_width = width, plot_height = height, x_range = (0,6), title = 'Weibull')
plot_weibull.quad(top='hist', bottom=0, left='left_edges', right='right_edges',
source= source_weibull, fill_color="navy")
def update_plot_weibull(attrname, old , new):
source_weibull.data = get_weibull(shape_slider.value,lambda_slider.value,100000)
shape_slider = Slider(start = 0.4, end = 5, value = 1.5, step = 0.1, title = 'Shape a')
shape_slider.on_change('value', update_plot_weibull)
lambda_slider = Slider(start = 0.5, end = 3, value = 1, step = 0.5, title = 'Lambda')
lambda_slider.on_change('value', update_plot_weibull)
######################################################################################################################
source_lognormal = ColumnDataSource(data = get_lognormal(0.9, 0.16, 10000))
plot_lognormal = Figure(plot_width = width, plot_height = height, title = 'Lognormal',
y_range=(0,2), x_range = (0,10))
plot_lognormal.quad(top='hist', bottom=0, left='left_edges', right='right_edges',
source= source_lognormal, fill_color="navy")
def update_plot_lognormal(attrname, old , new):
source_lognormal.data = get_lognormal(mean_slider_lognormal.value, sigma_slider_lognormal.value,100000)
mean_slider_lognormal = Slider(start = 0, end = 5, value =0.9, step = 0.1, title = 'Mean')
mean_slider_lognormal.on_change('value', update_plot_lognormal)
sigma_slider_lognormal = Slider(start = 0.01, end = 1, value = 0.16, step = 0.01, title = 'Std_dev')
sigma_slider_lognormal.on_change('value', update_plot_lognormal)
######################################################################################################################
source_exponential = ColumnDataSource(data = get_exponential(3, 1000))
plot_exponential = Figure(plot_width = width, plot_height = height, title = 'Exponential',
x_range=(0,50), y_range =(0,1) )
plot_exponential.quad(top='hist', bottom=0, left='left_edges', right='right_edges',
source= source_exponential, fill_color="green")
def update_plot_exponential(attrname, old , new):
source_exponential.data = get_exponential(scale_slider_exponential.value, 10000)
scale_slider_exponential = Slider(start = 0.1, end = 10, value = 3, step = 0.1, title = 'Scale')
scale_slider_exponential.on_change('value', update_plot_exponential)
layout1 = column(plot_normal, column(mu_slider, sigma_slider))
layout2 = column(plot_weibull, column(shape_slider, lambda_slider))
layout3 = column(plot_lognormal, column(mean_slider_lognormal, sigma_slider_lognormal))
layout4 = column(plot_exponential, scale_slider_exponential)
top = row(layout1, layout2)
bottom = row(layout3, layout4)
return (row(top, bottom))
def plot_dispersion_bokeh(filename, period_array, curve_data_array, boundary_data, style_parameter):
'''
Plot dispersion maps and curves using bokeh
Input:
filename is the filename of the resulting html file
period_array is a list of period
curve_data_array is a list of dispersion curves
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
# ==============================
# prepare data
map_data_all_slices_velocity = []
map_data_all_slices_period = []
map_data_all_slices_color = []
colorbar_data_all_left = []
colorbar_data_all_right = []
nperiod = len(period_array)
ncurve = len(curve_data_array)
ncolor = len(palette)
palette_r = palette[::-1]
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
for iperiod in range(nperiod):
one_slice_lat_list = []
one_slice_lon_list = []
one_slice_vel_list = []
map_period = period_array[iperiod]
for icurve in range(ncurve):
acurve = curve_data_array[icurve]
curve_lat = acurve['latitude']
curve_lon = acurve['longitude']
curve_vel = acurve['velocity']
curve_period = acurve['period']
one_slice_lat_list.append(curve_lat)
one_slice_lon_list.append(curve_lon)
if map_period in curve_period:
curve_period_index = curve_period.index(map_period)
one_slice_vel_list.append(curve_vel[curve_period_index])
else:
one_slice_vel_list.append(style_parameter['nan_value'])
# get color for dispersion values
one_slice_vel_mean = np.nanmean(one_slice_vel_list)
one_slice_vel_std = np.nanstd(one_slice_vel_list)
color_min = one_slice_vel_mean - one_slice_vel_std * style_parameter['spread_factor']
color_max = one_slice_vel_mean + one_slice_vel_std * style_parameter['spread_factor']
color_step = (color_max - color_min)*1./ncolor
one_slice_color_list = get_color_list(one_slice_vel_list,color_min,color_max,palette_r,\
style_parameter['nan_value'],style_parameter['nan_color'])
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
if one_slice_lat_list:
map_data_all_slices_velocity.append(one_slice_vel_list)
map_data_all_slices_period.append('Period: {0:6.1f} s'.format(map_period))
map_data_all_slices_color.append(one_slice_color_list)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
# get location for all points
map_lat_list, map_lon_list = [], []
map_lat_label_list, map_lon_label_list = [], []
for i in range(ncurve):
acurve = curve_data_array[i]
map_lat_list.append(acurve['latitude'])
map_lon_list.append(acurve['longitude'])
map_lat_label_list.append('Lat: {0:12.3f}'.format(acurve['latitude']))
map_lon_label_list.append('Lon: {0:12.3f}'.format(acurve['longitude']))
# data for the map view plot
map_view_label_lon = style_parameter['map_view_period_label_lon']
map_view_label_lat = style_parameter['map_view_period_label_lat']
map_data_one_slice = map_data_all_slices_color[style_parameter['map_view_default_index']]
map_data_one_slice_period = map_data_all_slices_period[style_parameter['map_view_default_index']]
map_data_one_slice_bokeh = ColumnDataSource(data=dict(map_lat_list=map_lat_list,\
map_lon_list=map_lon_list,\
map_data_one_slice=map_data_one_slice))
map_data_one_slice_period_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_period=[map_data_one_slice_period]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices_color=map_data_all_slices_color,\
map_data_all_slices_period=map_data_all_slices_period))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
# data for dispersion curves
curve_default_index = style_parameter['curve_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[map_lat_list[curve_default_index]],\
lon=[map_lon_list[curve_default_index]],\
color=[map_data_one_slice[curve_default_index]],\
index=[curve_default_index]))
selected_curve_data = curve_data_array[curve_default_index]
selected_curve_data_bokeh = ColumnDataSource(data=dict(curve_period=selected_curve_data['period'],\
curve_velocity=selected_curve_data['velocity']))
period_all = []
velocity_all = []
for acurve in curve_data_array:
period_all.append(acurve['period'])
velocity_all.append(acurve['velocity'])
curve_data_all_bokeh = ColumnDataSource(data=dict(period_all=period_all, velocity_all=velocity_all))
selected_curve_lat_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lat_label_x']], \
y=[style_parameter['curve_lat_label_y']],\
lat_label=[map_lat_label_list[curve_default_index]]))
selected_curve_lon_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lon_label_x']], \
y=[style_parameter['curve_lon_label_y']],\
lon_label=[map_lon_label_list[curve_default_index]]))
all_curve_lat_label_bokeh = ColumnDataSource(data=dict(map_lat_label_list=map_lat_label_list))
all_curve_lon_label_bokeh = ColumnDataSource(data=dict(map_lon_label_list=map_lon_label_list))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['map_view_plot_height'], \
y_range=[style_parameter['map_view_lat_min'],\
style_parameter['map_view_lat_max']], x_range=[style_parameter['map_view_lon_min'],\
style_parameter['map_view_lon_max']], tools=style_parameter['map_view_tools'],\
title=style_parameter['map_view_title'])
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add period label
map_view.rect(style_parameter['map_view_period_box_lon'], style_parameter['map_view_period_box_lat'], \
width=style_parameter['map_view_period_box_width'], height=style_parameter['map_view_period_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_period', source=map_data_one_slice_period_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
# plot dots
map_view.circle('map_lon_list', 'map_lat_list', color='map_data_one_slice', \
source=map_data_one_slice_bokeh, size=style_parameter['marker_size'],\
line_width=0.2, line_color='black', alpha=1.0,\
selection_color='map_data_one_slice', selection_line_color='black',\
selection_fill_alpha=1.0,\
nonselection_fill_alpha=1.0, nonselection_fill_color='map_data_one_slice',\
nonselection_line_color='black', nonselection_line_alpha=1.0)
map_view.circle('lon', 'lat', color='color', source=selected_dot_on_map_bokeh, \
line_color='#00ff00', line_width=4.0, alpha=1.0, \
size=style_parameter['selected_marker_size'])
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location=None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
fill_color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
curve_fig = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(style_parameter['curve_y_min'],style_parameter['curve_y_max']), \
x_range=(style_parameter['curve_x_min'],style_parameter['curve_x_max']),x_axis_type='log',\
tools=['save','box_zoom','wheel_zoom','reset','crosshair','pan'],
title=style_parameter['curve_title'])
# ------------------------------
curve_fig.rect([style_parameter['curve_label_box_x']], [style_parameter['curve_label_box_y']], \
width=style_parameter['curve_label_box_width'], height=style_parameter['curve_label_box_height'], \
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
curve_fig.text('x', 'y', \
'lat_label', source=selected_curve_lat_label_bokeh)
curve_fig.text('x', 'y', \
'lon_label', source=selected_curve_lon_label_bokeh)
# ------------------------------
curve_fig.line('curve_period', 'curve_velocity', source=selected_curve_data_bokeh, color='black')
curve_fig.circle('curve_period', 'curve_velocity', source=selected_curve_data_bokeh, size=5, color='black')
# ------------------------------
curve_fig.title.text_font_size = style_parameter['title_font_size']
curve_fig.title.align = 'center'
curve_fig.title.text_font_style = 'normal'
curve_fig.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig.xaxis.axis_label_text_font_style = 'normal'
curve_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig.yaxis.axis_label_text_font_style = 'normal'
curve_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.xgrid.grid_line_dash = [4, 2]
curve_fig.ygrid.grid_line_dash = [4, 2]
curve_fig.xaxis[0].formatter = PrintfTickFormatter(format="%4.0f")
curve_fig.toolbar.logo = None
curve_fig.toolbar_location = 'above'
curve_fig.toolbar_sticky = False
# ==============================
map_data_one_slice_bokeh.callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh), code="""
var inds = Math.round(cb_obj.selected['1d'].indices)
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_slice['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_slice['map_lon_list'][inds]]
selected_dot_on_map_bokeh.data['color'] = [new_slice['map_data_one_slice'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][inds]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][inds]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[inds]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[inds]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
# ==============================
period_slider_callback = CustomJS(args=dict(map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh, \
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_period_bokeh=map_data_one_slice_period_bokeh),\
code="""
var p_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
var map_data_new_slice = map_data_all_slices['map_data_all_slices_color'][p_index]
map_data_one_slice_bokeh.data['map_data_one_slice'] = map_data_new_slice
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][p_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][p_index]
colorbar_data_one_slice_bokeh.change.emit()
var selected_index = selected_dot_on_map_bokeh.data['index']
selected_dot_on_map_bokeh.data['color'] = [map_data_new_slice[selected_index]]
selected_dot_on_map_bokeh.change.emit()
map_data_one_slice_period_bokeh.data['map_period'] = [map_data_all_slices['map_data_all_slices_period'][p_index]]
map_data_one_slice_period_bokeh.change.emit()
""")
period_slider = Slider(start=0, end=nperiod-1, value=style_parameter['map_view_default_index'], \
step=1, title=style_parameter['period_slider_title'], \
width=style_parameter['period_slider_plot_width'],\
height=50, callback=period_slider_callback)
# ==============================
curve_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh),\
code="""
var c_index = Math.round(cb_obj.value)
var one_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['index'] = [c_index]
selected_dot_on_map_bokeh.data['lat'] = [one_slice['map_lat_list'][c_index]]
selected_dot_on_map_bokeh.data['lon'] = [one_slice['map_lon_list'][c_index]]
selected_dot_on_map_bokeh.data['color'] = [one_slice['map_data_one_slice'][c_index]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][c_index]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][c_index]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[c_index]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[c_index]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
curve_slider = Slider(start=0, end=ncurve-1, value=style_parameter['curve_default_index'], \
step=1, title=style_parameter['curve_slider_title'], width=style_parameter['curve_plot_width'],\
height=50, callback=curve_slider_callback)
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,title=style_parameter['html_title'],mode=style_parameter['library_source'])
left_fig = Column(period_slider, map_view, colorbar_fig, annotating_fig01,\
width=style_parameter['left_column_width'] )
right_fig = Column(curve_slider, curve_fig, annotating_fig02, \
width=style_parameter['right_column_width'] )
layout = Row(left_fig, right_fig)
save(layout)
def createPlot(height=600, width=1200):
"""
Create and return a plot for visualizing transcripts.
"""
TOOLS = "pan, wheel_zoom, save, reset, tap"
p = Figure(title="",
y_range=[],
webgl=True,
tools=TOOLS,
toolbar_location="above",
plot_height=height,
plot_width=width)
# This causes title to overlap plot substantially:
#p.title.text_font_size = TITLE_FONT_SIZE
p.xgrid.grid_line_color = None # get rid of the grid in bokeh
p.ygrid.grid_line_color = None
# the block of exons, there's mouse hover effect on that
quad = p.quad(top="top",
bottom="bottom",
left="left",
right="right",
source=blockSource,
fill_alpha=0,
line_dash="dotted",
line_alpha=0.4,
line_color='black',
hover_fill_color="red",
hover_alpha=0.3,
hover_line_color="white",
nonselection_fill_alpha=0,
nonselection_line_alpha=0.4,
nonselection_line_color='black')
# the block of each vertical transcript, each one can be selected
p.quad(top="top",
bottom="bottom",
right="right",
left="left",
source=tranSource,
fill_alpha=0,
line_alpha=0,
nonselection_fill_alpha=0,
nonselection_line_alpha=0)
# what exons really is
# Cannot use line_width="height" because it is broken.
p.multi_line(xs="xs",
ys="ys",
line_width=opt.height,
color="color",
line_alpha="line_alpha",
source=source)
# the start/stop codon
p.inverted_triangle(x="x",
y="y",
color="color",
source=codonSource,
size='size',
alpha=0.5)
# mouse hover on the block
p.add_tools(
HoverTool(tooltips=[("chromosome", "@chromosome"), ("exon", "@exon"),
("start", "@start"), ("end", "@end")],
renderers=[quad]))
return p
plot.scatter('x', 'y', source=source, color='black')
# create the horizontal histogram
x1 = np.random.normal(loc=5.0, size=400) * 100
hhist, hedges = np.histogram(np.array(data['x']).astype(float), bins=20)
hzeros = np.zeros(len(hedges)-1)
hmax = max(hhist)*1.1
LINE_ARGS = dict(color="#3A5785", line_color=None)
ph = Figure(toolbar_location=None, plot_width=plot.plot_width, plot_height=200, x_range=plot.x_range,
y_range=(-hmax, hmax), title=None, min_border=10, min_border_left=50)
ph.xgrid.grid_line_color = None
ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hhist, color="white", line_color="#3A5785")
hh1 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.5, **LINE_ARGS)
hh2 = ph.quad(bottom=0, left=hedges[:-1], right=hedges[1:], top=hzeros, alpha=0.1, **LINE_ARGS)
axis_map = {
"[Fe/H]": "feh_cannon",
"[a/Fe]": "alpha_fe_cannon",
"Ca": "ca_abund_sme",
"Mg": "mg_abund_sme",
}
# Set up widgets
text = TextInput(title="title", value='my abundance plot')
def plot_3DModel_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, profile_data_all, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and velocity profiles using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all constains the velocity model parameters saved for profile plots
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
ncolor = len(palette)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min) * 1. / ncolor
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append(
'Depth: {0:8.1f} km'.format(map_depth))
#
palette_r = palette[::-1]
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[
style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[
style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[
style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth],
left=[style_parameter['profile_plot_xmin']], \
right=[style_parameter['profile_plot_xmax']]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
#
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_2d = map_color.view('uint32').reshape(map_color.shape[:2])
map_color_all_slices.append(map_color_2d)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']*style_parameter['dlon']],\
dh=[style_parameter['nlat']*style_parameter['dlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
# ------------------------------
nprofile = len(profile_data_all)
grid_lat_list = []
grid_lon_list = []
width_list = []
height_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
grid_lat_list.append(aprofile['lat'])
grid_lon_list.append(aprofile['lon'])
width_list.append(style_parameter['map_view_grid_width'])
height_list.append(style_parameter['map_view_grid_height'])
grid_data_bokeh = ColumnDataSource(data=dict(lon=grid_lon_list,lat=grid_lat_list,\
width=width_list, height=height_list))
profile_default_index = style_parameter['profile_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[grid_lat_list[profile_default_index]], \
lon=[grid_lon_list[profile_default_index]], \
width=[style_parameter['map_view_grid_width']],\
height=[style_parameter['map_view_grid_height']],\
index=[profile_default_index]))
# ------------------------------
profile_vs_all = []
profile_depth_all = []
profile_ndepth = style_parameter['profile_ndepth']
profile_lat_label_list = []
profile_lon_label_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
vs_raw = aprofile['vs']
top_raw = aprofile['top']
profile_lat_label_list.append('Lat: {0:12.1f}'.format(aprofile['lat']))
profile_lon_label_list.append('Lon: {0:12.1f}'.format(aprofile['lon']))
vs_plot = []
depth_plot = []
for idepth in range(profile_ndepth):
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth])
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth + 1])
profile_vs_all.append(vs_plot)
profile_depth_all.append(depth_plot)
profile_data_all_bokeh = ColumnDataSource(data=dict(profile_vs_all=profile_vs_all, \
profile_depth_all=profile_depth_all))
selected_profile_data_bokeh = ColumnDataSource(data=dict(vs=profile_vs_all[profile_default_index],\
depth=profile_depth_all[profile_default_index]))
selected_profile_lat_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lat_label_x']], y=[style_parameter['profile_lat_label_y']],\
lat_label=[profile_lat_label_list[profile_default_index]]))
selected_profile_lon_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lon_label_x']], y=[style_parameter['profile_lon_label_y']],\
lon_label=[profile_lon_label_list[profile_default_index]]))
all_profile_lat_label_bokeh = ColumnDataSource(data=dict(
profile_lat_label_list=profile_lat_label_list))
all_profile_lon_label_bokeh = ColumnDataSource(data=dict(
profile_lon_label_list=profile_lon_label_list))
#
button_ndepth = style_parameter['button_ndepth']
button_data_all_vs = []
button_data_all_vp = []
button_data_all_rho = []
button_data_all_top = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
button_data_all_vs.append(aprofile['vs'][:button_ndepth])
button_data_all_vp.append(aprofile['vp'][:button_ndepth])
button_data_all_rho.append(aprofile['rho'][:button_ndepth])
button_data_all_top.append(aprofile['top'][:button_ndepth])
button_data_all_bokeh = ColumnDataSource(data=dict(button_data_all_vs=button_data_all_vs,\
button_data_all_vp=button_data_all_vp,\
button_data_all_rho=button_data_all_rho,\
button_data_all_top=button_data_all_top))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50)
depth_slider.js_on_change('value', depth_slider_callback)
depth_slider_callback.args["depth_index"] = depth_slider
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.rect('lon', 'lat', width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='gray', line_alpha=0.5, \
selection_line_color='gray', selection_line_alpha=0.5, selection_fill_color=None,\
nonselection_line_color='gray',nonselection_line_alpha=0.5, nonselection_fill_color=None,\
source=grid_data_bokeh, color=None, line_width=1, level='glyph')
map_view.rect('lon', 'lat',width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='#00ff00', line_alpha=1.0, \
source=selected_dot_on_map_bokeh, fill_color=None, line_width=3.,level='glyph')
# ------------------------------
grid_data_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh, \
grid_data_bokeh=grid_data_bokeh,\
profile_data_all_bokeh=profile_data_all_bokeh,\
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh, \
), code="""
var inds = cb_obj.indices
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][inds]]
selected_dot_on_map_bokeh.data['index'] = [inds]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][inds]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][inds]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[inds]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[inds]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
grid_data_bokeh.selected.js_on_change('indices', grid_data_js)
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location = None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
profile_xrange = Range1d(start=style_parameter['profile_plot_xmin'],
end=style_parameter['profile_plot_xmax'])
profile_yrange = Range1d(start=style_parameter['profile_plot_ymax'],
end=style_parameter['profile_plot_ymin'])
profile_fig = Figure(plot_width=style_parameter['profile_plot_width'], plot_height=style_parameter['profile_plot_height'],\
x_range=profile_xrange, y_range=profile_yrange, tools=style_parameter['profile_tools'],\
title=style_parameter['profile_title'])
profile_fig.line('vs',
'depth',
source=selected_profile_data_bokeh,
line_width=2,
line_color='black')
# ------------------------------
# add lat, lon
profile_fig.rect([style_parameter['profile_label_box_x']], [style_parameter['profile_label_box_y']],\
width=style_parameter['profile_label_box_width'], height=style_parameter['profile_label_box_height'],\
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black',\
level='underlay')
profile_fig.text('x',
'y',
'lat_label',
source=selected_profile_lat_label_bokeh)
profile_fig.text('x',
'y',
'lon_label',
source=selected_profile_lon_label_bokeh)
# ------------------------------
# change style
profile_fig.xaxis.axis_label = style_parameter['profile_xlabel']
profile_fig.xaxis.axis_label_text_font_style = 'normal'
profile_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.xaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.axis_label = style_parameter['profile_ylabel']
profile_fig.yaxis.axis_label_text_font_style = 'normal'
profile_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.xgrid.grid_line_dash = [4, 2]
profile_fig.ygrid.grid_line_dash = [4, 2]
profile_fig.title.text_font_size = style_parameter['title_font_size']
profile_fig.title.align = 'center'
profile_fig.title.text_font_style = 'normal'
profile_fig.toolbar_location = 'above'
profile_fig.toolbar_sticky = False
profile_fig.toolbar.logo = None
# ==============================
profile_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
grid_data_bokeh=grid_data_bokeh, \
profile_data_all_bokeh=profile_data_all_bokeh, \
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh), code="""
var p_index = Math.round(cb_obj.value)
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][p_index]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][p_index]]
selected_dot_on_map_bokeh.data['index'] = [p_index]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][p_index]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][p_index]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[p_index]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[p_index]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
profile_slider = Slider(start=0, end=nprofile-1, value=style_parameter['profile_default_index'], \
step=1, title=style_parameter['profile_slider_title'], \
width=style_parameter['profile_plot_width'], height=50)
profile_slider_callback.args['profile_index'] = profile_slider
profile_slider.js_on_change('value', profile_slider_callback)
# ==============================
simple_text_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "# Layer Top (km) Vs(km/s) Vp(km/s) Rho(g/cm^3) \\n"
while(button_data_vp[i]) {
temp+=button_data_top[i].toPrecision(6) + " " + button_data_vs[i].toPrecision(4) + " " + \
button_data_vp[i].toPrecision(4) + " " + button_data_rho[i].toPrecision(4) + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
simple_text_button = Button(
label=style_parameter['simple_text_button_label'],
button_type='default',
width=style_parameter['button_width'])
simple_text_button.js_on_click(simple_text_button_callback)
# ------------------------------
model96_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var lat = selected_dot_on_map_bokeh.data['lat']
var lon = selected_dot_on_map_bokeh.data['lon']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "MODEL." + index + " \\n"
temp += "ShearVelocityModel Lat: "+ lat +" Lon: " + lon + "\\n"
temp += "ISOTROPIC \\n"
temp += "KGS \\n"
temp += "SPHERICAL EARTH \\n"
temp += "1-D \\n"
temp += "CONSTANT VELOCITY \\n"
temp += "LINE08 \\n"
temp += "LINE09 \\n"
temp += "LINE10 \\n"
temp += "LINE11 \\n"
temp += " H(KM) VP(KM/S) VS(KM/S) RHO(GM/CC) QP QS ETAP ETAS FREFP FREFS \\n"
while(button_data_vp[i+1]) {
var thickness = button_data_top[i+1] - button_data_top[i]
temp+=" " +thickness.toPrecision(6) + " " + button_data_vp[i].toPrecision(4) + " " + button_data_vs[i].toPrecision(4) \
+ " " + button_data_rho[i].toPrecision(4) + " 0.00 0.00 0.00 0.00 1.00 1.00" + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model96.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
model96_button = Button(label=style_parameter['model96_button_label'],
button_type='default',
width=style_parameter['button_width'])
model96_button.js_on_click(model96_button_callback)
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
left_column = Column(depth_slider,
map_view,
colorbar_fig,
annotating_fig01,
width=style_parameter['left_column_width'])
button_pannel = Row(simple_text_button, model96_button)
right_column = Column(profile_slider,
profile_fig,
button_pannel,
annotating_fig02,
width=style_parameter['right_column_width'])
layout = Row(left_column, right_column)
save(layout)
def hist_viz(
hist: List[Tuple[np.ndarray, np.ndarray]],
nrows: List[int],
col: str,
yscale: str,
plot_width: int,
plot_height: int,
show_yticks: bool,
orig: List[str],
df_labels: List[str],
) -> Figure:
"""
Render a histogram
"""
# pylint: disable=too-many-arguments,too-many-locals
tooltips = [
("Bin", "@intvl"),
("Frequency", "@freq"),
("Percent", "@pct{0.2f}%"),
("Source", "@orig"),
]
fig = Figure(
plot_height=plot_height,
plot_width=plot_width,
title=col,
toolbar_location=None,
y_axis_type=yscale,
)
for i, hst in enumerate(hist):
counts, bins = hst
if sum(counts) == 0:
fig.rect(x=0, y=0, width=0, height=0)
continue
intvls = _format_bin_intervals(bins)
df = pd.DataFrame({
"intvl": intvls,
"left": bins[:-1],
"right": bins[1:],
"freq": counts,
"pct": counts / nrows[i] * 100,
"orig": orig[i],
})
bottom = 0 if yscale == "linear" or df.empty else counts.min() / 2
fig.quad(
source=df,
left="left",
right="right",
bottom=bottom,
alpha=0.5,
top="freq",
fill_color=CATEGORY10[i],
line_color=CATEGORY10[i],
)
hover = HoverTool(tooltips=tooltips, attachment="vertical", mode="vline")
fig.add_tools(hover)
tweak_figure(fig, "hist", show_yticks)
fig.yaxis.axis_label = "Frequency"
_format_axis(fig, df.iloc[0]["left"], df.iloc[-1]["right"], "x")
x_axis_label = ""
if show_yticks:
x_axis_label += col
if yscale == "linear":
_format_axis(fig, 0, df["freq"].max(), "y")
if orig != df_labels:
if x_axis_label:
x_axis_label += f", this vairable is only in {','.join(orig)}"
else:
x_axis_label += f"This vairable is only in {','.join(orig)}"
fig.xaxis.axis_label = x_axis_label
fig.xaxis.axis_label_standoff = 0
return fig
d5 = {'kind': 'cash',
'value': 15,
'max_cash': 30}
projection = Portfolio(Asset(**d1),
Asset(**d2),
Asset(**d3),
Asset(**d4),
Asset(**d5), prd=200)
source = ColumnDataSource(projection.gen_quads())
TOOLS = "crosshair, pan, reset, resize, wheel_zoom"
plot = Figure(tools=TOOLS, x_axis_type='datetime', plot_height=600,
plot_width=600, title="FI calculator")
p = plot.quad(left='left', right='right', bottom='bottom', top='top',
color='color', source=source)
def update_graphic():
"""Update the data of the Asset dictionaries and recreate the
projection.
"""
projection = Portfolio(Asset(**d1),
Asset(**d2),
Asset(**d3),
Asset(**d4),
Asset(**d5), prd=200)
bdf_quad = projection.gen_quads()
# print(bdf_quad.head())
source.data['bottom'] = bdf_quad['bottom']
source.data['top'] = bdf_quad['top']
ph = Figure(tools='pan,wheel_zoom, reset',
toolbar_location='left',
plot_width=plot.plot_width,
plot_height=200,
x_range=plot.x_range,
y_range=(-0.1 * hmax, hmax),
min_border=10,
min_border_left=50,
y_axis_location="right")
ph.xgrid.grid_line_color = None
ph.yaxis.major_label_orientation = 0
qh = ph.quad(bottom=0,
left=hedges[:-1],
right=hedges[1:],
top=hhist,
color="#a7bae1",
line_color="black")
# create the vertical histogram
vhist, vedges = np.histogram(source.data['y'], bins='sqrt')
vzeros = np.zeros(len(vedges) - 1)
vmax = max(vhist) * 1.1
pv = Figure(tools='pan,wheel_zoom, reset',
toolbar_location='above',
plot_width=200,
plot_height=plot.plot_height,
x_range=(-0.1 * vmax, vmax),
y_range=plot.y_range,
min_border=10,
v4 = hst.Potencia[(hst.oscuridad > 80) & (hst.oscuridad < 85)].sum()
v5 = hst.Potencia[(hst.oscuridad > 75) & (hst.oscuridad < 80)].sum()
v6 = hst.Potencia[(hst.oscuridad > 70) & (hst.oscuridad < 75)].sum()
v7 = hst.Potencia[(hst.oscuridad > 65) & (hst.oscuridad < 70)].sum()
v8 = hst.Potencia[(hst.oscuridad > 60) & (hst.oscuridad < 65)].sum()
v9 = hst.Potencia[(hst.oscuridad > 55) & (hst.oscuridad < 60)].sum()
v10 = hst.Potencia[(hst.oscuridad > 50) & (hst.oscuridad < 55)].sum()
ac = np.array([v10, v9, v8, v7, v6, v5, v4, v3, v2, v1])
edges = np.arange(50, 101, 5)
p3 = Figure(tools=TOOLS)
p3.quad(top=ac,
bottom=0,
left=edges[:-1],
right=edges[1:],
fill_color="orangered",
line_color="white",
alpha=1.0)
p3.xaxis.axis_label = "Porcentaje oscuridad (%)"
p3.yaxis.axis_label = "Potencia (MW)"
########################################################################################
# est_minEner = pd.read_csv('../datos/estaciones_MinEner/infoEstaciones.csv', sep=';', encoding="ISO-8859-1")
# est_minEner['Mandante'] = 'Min Energia'
# #est_minEner["weblink"] = "http://localhost:6010/est_minEner"
# wl = []
# for pl in est_minEner.index:
# try:
# wl_tmp = "http://localhost:6010/est_minEner" + "?idEst=" + str(int(pl))
'''
# Read in the FSAEM data
compdata = pd.read_excel('FSAEM_summarized_results.xlsx')
source = ColumnDataSource(data=dict())
# Initialize the plot
plot = Figure(plot_width=800,
plot_height=500,
toolbar_location='right',
tools="pan,wheel_zoom,box_zoom,reset,resize")
freq_bars = plot.quad(top='hist',
bottom=0,
left='left_edge',
right='right_edge',
source=source,
fill_color='OliveDrab',
line_color='#000000')
plot.xaxis.axis_label = "Weight [kg]"
plot.xaxis.axis_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.yaxis.axis_label = "Frequency"
plot.yaxis.axis_line_color = None
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
}
# Read in the FSAEM data
compdata = pd.read_excel('FSAEM_summarized_results.xlsx')
source = ColumnDataSource(data=dict())
# Initialize the plot
plot = Figure(plot_width=800,
plot_height=500,
toolbar_location='right',
tools="pan,wheel_zoom,box_zoom,reset,resize")
freq_bars = plot.quad(top='hist',
bottom=0,
left='left_edge',
right='right_edge',
source=source,
fill_color=YlOrBr3[0],
line_color='#000000')
plot.xaxis.axis_label = "Total Score"
plot.xaxis.axis_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.yaxis.axis_label = "Frequency"
plot.yaxis.axis_line_color = None
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
def plot_cross_section_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, cross_data, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and cross-sections using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all is a list of velocity profiles
cross_lat_data_all is a list of cross-sections along latitude
lat_value_all is a list of corresponding latitudes for these cross-sections
cross_lon_data_all is a list of cross-sections along longitude
lon_value_all is a list of corresponding longitudes for these cross-sections
boundary_data is a list of boundaries
style_parameter contains parameters to customize the plots
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
palette_r = palette[::-1]
ncolor = len(palette_r)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min)*1./ncolor
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append('Depth: {0:8.0f} km'.format(map_depth))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_all_slices.append(map_color)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']],\
dh=[style_parameter['nlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
#
plot_depth = np.shape(cross_data)[0] * style_parameter['cross_ddepth']
plot_lon = great_arc_distance(style_parameter['cross_default_lat0'], style_parameter['cross_default_lon0'],\
style_parameter['cross_default_lat1'], style_parameter['cross_default_lon1'])
vs_min = style_parameter['cross_view_vs_min']
vs_max = style_parameter['cross_view_vs_max']
cross_color = val_to_rgb(cross_data, palette_r, vmin, vmax)
cross_data_bokeh = ColumnDataSource(data=dict(x=[0],\
y=[plot_depth],dw=[plot_lon],\
dh=[plot_depth],cross_data=[cross_color]))
map_line_bokeh = ColumnDataSource(data=dict(lat=[style_parameter['cross_default_lat0'], style_parameter['cross_default_lat1']],\
lon=[style_parameter['cross_default_lon0'], style_parameter['cross_default_lon1']]))
#
ncolor_cross = len(my_palette)
colorbar_top_cross = [0.1 for i in range(ncolor_cross)]
colorbar_bottom_cross = [0 for i in range(ncolor_cross)]
color_min_cross = style_parameter['cross_view_vs_min']
color_max_cross = style_parameter['cross_view_vs_max']
color_step_cross = (color_max_cross - color_min_cross)*1./ncolor_cross
colorbar_left_cross = np.linspace(color_min_cross, color_max_cross-color_step_cross, ncolor_cross)
colorbar_right_cross = np.linspace(color_min_cross+color_step_cross, color_max_cross, ncolor_cross)
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',\
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50, \
callback=depth_slider_callback)
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.line('lon', 'lat', source=map_line_bokeh, line_dash=[8,2,8,2], line_color='#00ff00',\
nonselection_line_alpha=1.0, line_width=5.,\
nonselection_line_color='black')
map_view.text([style_parameter['cross_default_lon0']],[style_parameter['cross_default_lat0']], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
map_view.text([style_parameter['cross_default_lon1']],[style_parameter['cross_default_lat1']], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location=None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text="""<p style="font-size:16px">""", \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
# plot cross-section along latitude
cross_section_plot_width = int(style_parameter['cross_plot_height']*1.0/plot_depth*plot_lon/10.)
cross_view = Figure(plot_width=cross_section_plot_width, plot_height=style_parameter['cross_plot_height'], \
tools=style_parameter['cross_view_tools'], title=style_parameter['cross_view_title'], \
y_range=[plot_depth, -30],\
x_range=[0, plot_lon])
cross_view.image_rgba('cross_data',x='x',\
y='y',dw='dw',dh='dh',\
source=cross_data_bokeh, level='image')
cross_view.text([plot_lon*0.1], [-10], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
cross_view.text([plot_lon*0.9], [-10], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
# ------------------------------
# change style
cross_view.title.text_font_size = style_parameter['title_font_size']
cross_view.title.align = 'center'
cross_view.title.text_font_style = 'normal'
cross_view.xaxis.axis_label = style_parameter['cross_view_xlabel']
cross_view.xaxis.axis_label_text_font_style = 'normal'
cross_view.xaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.xaxis.major_label_text_font_size = xlabel_fontsize
cross_view.yaxis.axis_label = style_parameter['cross_view_ylabel']
cross_view.yaxis.axis_label_text_font_style = 'normal'
cross_view.yaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.yaxis.major_label_text_font_size = xlabel_fontsize
cross_view.xgrid.grid_line_color = None
cross_view.ygrid.grid_line_color = None
cross_view.toolbar.logo = None
cross_view.toolbar_location = 'right'
cross_view.toolbar_sticky = False
# ==============================
colorbar_fig_right = Figure(tools=[], y_range=(0,0.1),plot_width=cross_section_plot_width, \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig_right.toolbar_location=None
colorbar_fig_right.quad(top=colorbar_top_cross,bottom=colorbar_bottom_cross,\
left=colorbar_left_cross,right=colorbar_right_cross,\
color=my_palette)
colorbar_fig_right.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig_right.xgrid.grid_line_color = None
colorbar_fig_right.ygrid.grid_line_color = None
colorbar_fig_right.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig_right.title.text_font_size = xlabel_fontsize
colorbar_fig_right.title.align = 'center'
colorbar_fig_right.title.text_font_style = 'normal'
# ==============================
output_file(filename,title=style_parameter['html_title'], mode=style_parameter['library_source'])
left_column = Column(depth_slider, map_view, colorbar_fig, annotating_fig01, width=style_parameter['left_column_width'])
right_column = Column(annotating_fig02, cross_view, colorbar_fig_right, width=cross_section_plot_width)
layout = Row(left_column, right_column, height=800)
save(layout)
pay_debt_faster=True,
prd=50,
)
test = C1(d=d)
source = ColumnDataSource(test.gen_quads())
TOOLS = "crosshair, pan, reset, resize, wheel_zoom"
plot = Figure(tools=TOOLS,
x_axis_type='datetime',
plot_height=600,
plot_width=600,
title="FI calculator")
p = plot.quad(left='left',
right='right',
bottom='bottom',
top='top',
color='color',
source=source)
def update_graphic():
test = C1(d=d)
bdf_quad = test.gen_quads()
source.data['bottom'] = bdf_quad['bottom']
source.data['top'] = bdf_quad['top']
source.data['left'] = bdf_quad['left']
source.data['right'] = bdf_quad['right']
source.trigger('data', source.data, source.data)
# Set up widgets
plot2.border_fill_color = "white"
plot2.min_border_left = 0
plot2.circle('x', 'y', source=junk_points, \
fill_color='purple', radius=0.1, line_alpha=0.5, fill_alpha=1.0)
plot2.line('x', 'y', source=junk_points, line_color='purple', line_alpha=0.5)
rect_points = ColumnDataSource(data=dict(top=[totyield / 2. - 50., 9000, 9000],
bottom=[-49.8, 8800, 8800],
left=[-49.8, 8800, 9000],
strbag=' ',
right=[-45, 8800, 9200]))
plot1.quad(top="top",
bottom="bottom",
left="left",
right="right",
source=rect_points,
color="lightgreen",
fill_alpha=0.5,
line_alpha=0.)
plot1.quad(top=49.9,
bottom=-49.9,
left=-49.8,
right=-45,
line_color="lightgreen",
line_width=3,
fill_alpha=0.0) # open box
plot1.circle([-47.4],
'top',
source=rect_points,
radius=1.8,
fill_alpha=0.5,
import pandas as pd
import numpy as np
#Pandas version 0.22.0
#Bokeh version 0.12.10
#Numpy version 1.12.1
from bokeh.io import output_file, show,curdoc
from bokeh.models import Quad
from bokeh.layouts import row, layout,widgetbox
from bokeh.models.widgets import Select,MultiSelect
from bokeh.plotting import ColumnDataSource,Figure,reset_output,gridplot
d= {'A': [1,1,1,2,2,3,4,4,4,4,4], 'B': [1,2,2,2,3,3,4,5,6,6,6], 'C' : [2,2,2,2,2,3,4,5,6,6,6]}
df = pd.DataFrame(data=d)
names = ["A","B", "C"]
#Since bokeh.charts are deprecated so using the new method using numpy histogram
hist,edge = np.histogram(df['A'],bins=4)
#This is the method you need to pass the histogram objects to source data here it takes edge values for each bin start and end and hist gives count.
source = ColumnDataSource(data={'hist': hist, 'edges_rt': edge[1:], 'edges_lt':edge[:-1]})
plot = Figure(plot_height = 300,plot_width = 400)
#The quad is used to display the histogram using bokeh.
plot.quad(top='hist', bottom=0, left='edges_lt', right='edges_rt',fill_color="#036564",
line_color="#033649",source = source)
#When you change the selection it will this function and changes the source data so that values are updated.
def callback_menu(attr, old, new):
hist,edge = np.histogram(df[menu.value],bins=4)
source.data={'hist': hist,'edges_rt': edge[1:], 'edges_lt': edge[:-1]}
#These are interacting tools in the final graph
menu = MultiSelect(options=names,value= ['A','B'], title='Sensor Data')
menu.on_change('value', callback_menu)
layout = gridplot([[widgetbox(menu),plot]])
curdoc().add_root(layout)
q1_GDD=[], q3_GDD=[],
time=[], timer=[], timel=[],
date_str=[]))
# Create Input controls
current_year = datetime.today().year
min_year = Slider(title="Year Start", start=1900, end=current_year, value=1900, step=1)
max_year = Slider(title="Year End", start=1900, end=current_year, value=current_year, step=1)
station_IDs_str = TextInput(title="Station ID", value='6551')
base_temp = Slider(title="Base Temperature (C)", start=0, end=25, value=10, step=1)
# Create Figure and Plot
hover = HoverTool(tooltips=[
("Date","@date_str")])
p = Figure(x_axis_type="datetime", plot_width=800, tools=[hover])
p.quad(top='max_GDD', bottom='min_GDD', left='timel', right='timer', source=source, alpha=0.5, color='mediumslateblue', line_color="black", line_alpha=0.5, legend='Min/Max')
p.quad(top='q3_GDD', bottom='q1_GDD', left='timel', right='timer', source=source, alpha=0.5, color='black', line_color="black", line_alpha=0.5, legend='25-75 Percentile')
p.line(x='time', y='mean_GDD', source = source, color='red', line_width = 2, alpha=0.8, legend='Average' )
p.x_range = DataRange1d(range_padding=0, bounds=None)
p.yaxis.axis_label = "GDD"
p.xaxis.axis_label = 'Month'
# p.xaxis[0].formatter = DatetimeTickFormatter(formats=dict(days=["%B %d"], months=["%B"], years=["%Y"]))
# Select Data based on input info
def select_data():
# p.title = p.title + str(' (Wait..)')
global station_IDs
# Make Stations ID's as a list
station_IDs=[]
[station_IDs.append(int(n)) for n in station_IDs_str.value.split()]
src = ColumnDataSource(data=dict(arr_hist=arr_hist, left=left, right=right))
p = Figure(plot_height=400,
plot_width=500,
x_range=(xMin, xMax),
x_axis_label='parameter',
y_axis_label='probability',
tools="xpan, xwheel_zoom",
active_scroll='xwheel_zoom',
active_drag="xpan")
p.quad(source=src,
bottom=0,
top='arr_hist',
left='left',
right='right',
fill_color='cornflowerblue',
line_color='black')
p.yaxis.major_tick_line_color = None # turn off y-axis major ticks
p.yaxis.minor_tick_line_color = None # turn off y-axis minor ticks
p.yaxis.major_label_text_font_size = '0pt' # preferred method for removing tick labels
#defines the callback to be used:
callback_plot = CustomJS(args=dict(src=src,
p=p,
axis=p.xaxis[0],
x_range=p.x_range),
code="""
Use
bokeh serve historic_histogram_interactive.py
to run the plot.
'''
# Read in the FSAEM data
compdata = pd.read_excel('FSAEM_summarized_results.xlsx')
source = ColumnDataSource(data=dict())
# Initialize the plot
plot = Figure(plot_width=800, plot_height=500, toolbar_location='right',
tools="pan,wheel_zoom,box_zoom,reset,resize")
freq_bars = plot.quad(top='hist', bottom=0, left='left_edge', right='right_edge',
source=source, fill_color='OliveDrab', line_color='#000000')
plot.xaxis.axis_label = "Weight [kg]"
plot.xaxis.axis_line_color = None
plot.xaxis.minor_tick_line_color = None
plot.yaxis.axis_label = "Frequency"
plot.yaxis.axis_line_color = None
plot.yaxis.major_tick_line_color = None
plot.yaxis.minor_tick_line_color = None
plot.xgrid.grid_line_color = None
plot.ygrid.grid_line_color = None
plot.outline_line_color = None
str(int(np.sum(yields['complete2'][:]))),
str(int(np.sum(yields['complete3'][:]))),
str(int(np.sum(yields['complete4'][:]))),
str(int(np.sum(yields['complete5'][:]))),
str(int(np.sum(yields['complete6'][:]))),
str(int(np.sum(yields['complete7'][:]))),
str(int(np.sum(yields['complete8'][:]))),
str(int(np.sum(yields['complete9'][:]))),
str(int(np.sum(yields['complete10'][:]))),
str(int(np.sum(yields['complete11'][:]))),
str(int(np.sum(yields['complete12'][:]))),
str(int(np.sum(yields['complete13'][:]))),
str(int(np.sum(yields['complete14'][:])))
]
hist_plot.quad(top='yields', bottom=0., left='left', right='right', source=total_yield_label, \
color='color', fill_alpha=0.9, line_alpha=1., name='total_yield_label_hover')
hist_plot.text('xlabels',
'yields',
'labels',
0.,
20,
-3,
text_align='center',
source=total_yield_label,
text_color='black')
yield_hover = HoverTool(names=["total_yield_label_hover"],
mode='mouse',
tooltips="""
<div>
