def hist_plot(ticker, alias, hsource, df, plot=None, selected_df=None):
if selected_df is None:
selected_df = df
global_hist, global_bins = np.histogram(df[ticker + "_returns"], bins=50)
hist, bins = np.histogram(selected_df[ticker + "_returns"], bins=50)
top = hist.max()
start = global_bins.min()
end = global_bins.max()
width = 0.7 * (bins[1] - bins[0])
hdata = dict(
width = [width] * len(hist),
center = (bins[:-1] + bins[1:]) / 2,
hist2 = hist / 2.0,
hist = hist
)
hsource.data = hdata
if plot is None:
plot = Figure(
plot_width=500, plot_height=200,
tools="",
title_text_font_size="10pt",
x_range=[start, end],
y_range=[0, top],
)
plot.rect('center', 'hist2', 'width', 'hist', source=hsource)
plot.x_range.start = start
plot.x_range.end = end
plot.y_range.start = 0
plot.y_range.end = top
plot.title = "%s hist" % alias
return plot
def render_correlation_impact(itmdt: Intermediate, plot_width: int,
plot_height: int,
palette: Sequence[str]) -> Dict[str, Any]:
"""
Render correlation heatmaps in to tabs
"""
tabs: List[Panel] = []
tooltips = [("x", "@x"), ("y", "@y"),
("correlation", "@correlation{1.11}")]
axis_range = itmdt["axis_range"]
for method, df in itmdt["data"].items():
# in case of numerical column names
df = df.copy()
df["x"] = df["x"].apply(str)
df["y"] = df["y"].apply(str)
mapper, color_bar = create_color_mapper(palette)
x_range = FactorRange(*axis_range)
y_range = FactorRange(*reversed(axis_range))
fig = Figure(
x_range=x_range,
y_range=y_range,
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="below",
tools="hover",
toolbar_location=None,
tooltips=tooltips,
background_fill_color="#fafafa",
title=" ",
)
tweak_figure(fig)
fig.rect(
x="x",
y="y",
width=1,
height=1,
source=df,
fill_color={
"field": "correlation",
"transform": mapper
},
line_color=None,
)
fig.frame_width = plot_width
fig.add_layout(color_bar, "left")
tab = Panel(child=fig, title=method)
tabs.append(tab)
return {
"insights": itmdt["insights"],
"tabledata": itmdt["tabledata"],
"layout": [panel.child for panel in tabs],
"meta": [panel.title for panel in tabs],
"container_width": plot_width + 150,
}
def buildVendorsTab():
defaultGfxVendors = [
gfxVendors.index('NVIDIA Corporation'),
gfxVendors.index('Advanced Micro Devices, Inc. [AMD/ATI]'),
gfxVendors.index('Intel Corporation')
]
gfxVendorCheckbox = CheckboxGroup(labels=gfxVendors, active=defaultGfxVendors)
source_release = ColumnDataSource(data=dict(x=[], y=[], height=[]))
source_beta = ColumnDataSource(data=dict(x=[], y=[], height=[]))
fig = Figure(title="GFX Vendors",
x_range=[],
y_range=[0, 0],
plot_width=1000, plot_height=650)
hover = HoverTool(tooltips=[
('Users', '@height %')
])
fig.add_tools(hover)
fig.rect(x='x', y='y', height='height', source=source_release,
width=0.4, color='orange', legend='Release')
fig.rect(x='x', y='y', height='height', source=source_beta,
width=0.4, color='blue', legend='Beta')
fig.xaxis.major_label_orientation = np.pi / 3
def update(selected):
vendors = [gfxVendors[i] for i in range(len(gfxVendors)) if i in selected]
releaseUsers = 100 * getUsersForVendors('release', vendors) / gfxTotalReleaseUsers
betaUsers = 100 * getUsersForVendors('beta', vendors) / gfxTotalBetaUsers
fig.x_range.factors = vendors
fig.y_range.end = max([releaseUsers.max(), betaUsers.max()])
source_release.data = dict(
x=[c + ':0.3' for c in vendors],
y=releaseUsers / 2,
height=releaseUsers,
)
source_beta.data = dict(
x=[c + ':0.7' for c in vendors],
y=betaUsers / 2,
height=betaUsers,
)
gfxVendorCheckbox.on_click(update)
update(gfxVendorCheckbox.active)
vendorComparison = HBox(HBox(VBoxForm(*[gfxVendorCheckbox]), width=300), fig, width=1100)
return Panel(child=vendorComparison, title="GFX Vendor Comparison")
def render_heatmaps_tab(itmdt: Intermediate, plot_width: int, plot_height: int,
palette: Sequence[str]) -> Figure:
"""
Render missing heatmaps in to tabs
"""
tooltips = [("x", "@x"), ("y", "@y"),
("correlation", "@correlation{1.11}")]
axis_range = itmdt["axis_range"]
df = itmdt["data_heatmap"]
df = df.where(
np.triu(np.ones(df.shape)).astype(np.bool) # pylint: disable=no-member
).T
df = df.unstack().reset_index(name="correlation")
df = df.rename(columns={"level_0": "x", "level_1": "y"})
df = df[df["x"] != df["y"]]
df = df.dropna()
# in case of numerical column names
df["x"] = df["x"].apply(str)
df["y"] = df["y"].apply(str)
mapper, color_bar = create_color_mapper_heatmap(palette)
x_range = FactorRange(*axis_range)
y_range = FactorRange(*reversed(axis_range))
fig = Figure(
x_range=x_range,
y_range=y_range,
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="below",
tools="hover",
toolbar_location=None,
tooltips=tooltips,
background_fill_color="#fafafa",
)
tweak_figure(fig)
fig.grid.grid_line_color = None
fig.axis.axis_line_color = None
fig.rect(
x="x",
y="y",
width=1,
height=1,
source=df,
fill_color={
"field": "correlation",
"transform": mapper
},
line_color=None,
)
fig.add_layout(color_bar, "right")
return fig
def buildOSesTab():
osesCheckbox = CheckboxGroup(labels=oses, active=[i for i in range(len(oses))])
source_release = ColumnDataSource(data=dict(x=[], y=[], height=[]))
source_beta = ColumnDataSource(data=dict(x=[], y=[], height=[]))
fig = Figure(title='OS', x_range=[], y_range=[0, 0], plot_width=1000, plot_height=650)
hover = HoverTool(tooltips=[
('Users', '@height %')
])
fig.add_tools(hover)
fig.rect(x='x', y='y', height='height', source=source_release,
width=0.4, color='orange', legend='Release')
fig.rect(x='x', y='y', height='height', source=source_beta,
width=0.4, color='blue', legend='Beta')
fig.xaxis.major_label_orientation = np.pi / 3
def update(selected):
cur_oses = [oses[i] for i in range(len(oses)) if i in selected]
releaseUsers = 100 * getUsersForOses('release', cur_oses) / osTotalReleaseUsers
betaUsers = 100 * getUsersForOses('beta', cur_oses) / osTotalBetaUsers
fig.x_range.factors = cur_oses
fig.y_range.end = max([releaseUsers.max(), betaUsers.max()])
source_release.data = dict(
x=[c + ':0.3' for c in cur_oses],
y=releaseUsers / 2,
height=releaseUsers,
)
source_beta.data = dict(
x=[c + ':0.7' for c in cur_oses],
y=betaUsers / 2,
height=betaUsers,
)
osesCheckbox.on_click(update)
update(osesCheckbox.active)
osesComparison = HBox(HBox(VBoxForm(*[osesCheckbox]), width=300), fig, width=1100)
return Panel(child=osesComparison, title="OS Comparison")
def render_correlation_heatmaps(itmdt: Intermediate, plot_width: int, plot_height: int) -> Tabs:
"""
Render correlation heatmaps in to tabs
"""
tabs: List[Panel] = []
tooltips = [("x", "@x"), ("y", "@y"), ("correlation", "@correlation{1.11}")]
axis_range = itmdt["axis_range"]
for method, df in itmdt["data"].items():
# in case of numerical column names
df = df.copy()
df["x"] = df["x"].apply(str)
df["y"] = df["y"].apply(str)
mapper, color_bar = create_color_mapper(RDBU)
x_range = FactorRange(*axis_range)
y_range = FactorRange(*reversed(axis_range))
fig = Figure(
x_range=x_range,
y_range=y_range,
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="below",
tools="hover",
toolbar_location=None,
tooltips=tooltips,
background_fill_color="#fafafa",
)
tweak_figure(fig)
fig.rect(
x="x",
y="y",
width=1,
height=1,
source=df,
fill_color={"field": "correlation", "transform": mapper},
line_color=None,
)
fig.add_layout(color_bar, "right")
tab = Panel(child=fig, title=method)
tabs.append(tab)
tabs = Tabs(tabs=tabs)
return tabs
def empty_figure() -> Figure:
# If no data to render in the heatmap, i.e. no missing values
# we render a blank heatmap
fig = Figure(
x_range=[],
y_range=[],
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="below",
tools="hover",
toolbar_location=None,
background_fill_color="#fafafa",
)
# Add at least one renderer to fig, otherwise bokeh
# gives us error -1000 (MISSING_RENDERERS): Plot has no renderers
fig.rect(x=0, y=0, width=0, height=0)
return fig
color=selected["color"].astype(object)
))
source.data.update(src.data)
p.x_range.update(factors=list(set(source.data.get("x"))))
p.y_range.update(factors=list(set(source.data.get("y"))))
selected = select_facts()
source = ColumnDataSource(data=dict(x=[], y=[], color=[]))
source.data.update(ColumnDataSource(dict(
x=selected["x"].astype(object),
y=selected["y"].astype(object),
color=selected["color"].astype(object)
)).data)
p = Figure(plot_height=900, plot_width=900, title="", toolbar_location=None,
x_range=list(set(source.data.get("x"))), y_range=list(set(source.data.get("y"))))
p.rect(x="x", y="y", source=source, color="color", width=1, height=1)
p.xaxis.major_label_orientation = pi/4
p.yaxis.major_label_orientation = pi/4
controls = [top_n, x_axis, y_axis]
for control in controls:
control.on_change('value', update)
inputs = HBox(VBoxForm(*controls), width=300)
update(None, None, None) # initial load of the data
layout = HBox(inputs, p)
curdoc().add_root(layout)
# initialise a white block for the first plot
hex_color = rgb_to_hex((255, 255, 255))
# initialise the text color as black. This will be switched to white if the block color gets dark enough
text_color = '#000000'
# create a data source to enable refreshing of fill & text color
source = ColumnDataSource(data=dict(color=[hex_color], text_color=[text_color]))
# create first plot, as a rect() glyph and centered text label, with fill and text color taken from source
p1 = Figure(x_range=(-8, 8), y_range=(-4, 4),
plot_width=600, plot_height=300,
title='move sliders to change', tools='')
p1.rect(0, 0, width=18, height=10, fill_color='color',
line_color = 'black', source=source)
p1.text(0, 0, text='color', text_color='text_color',
alpha=0.6667, text_font_size='36pt', text_baseline='middle',
text_align='center', source=source)
# the callback function to update the color of the block and associated label text
# NOTE: the JS functions for converting RGB to hex are taken from the excellent answer
# by Tim Down at http://stackoverflow.com/questions/5623838/rgb-to-hex-and-hex-to-rgb
callback = CustomJS(args=dict(source=source), code="""
function componentToHex(c) {
var hex = c.toString(16);
return hex.length == 1 ? "0" + hex : hex;
}
function rgbToHex(r, g, b) {
return "#" + componentToHex(r) + componentToHex(g) + componentToHex(b);
def render_heatmaps(df: Optional[pd.DataFrame], plot_width: int, plot_height: int) -> Figure:
"""
Render missing heatmaps in to tabs
"""
tooltips = [("x", "@x"), ("y", "@y"), ("correlation", "@correlation{1.11}")]
mapper, color_bar = create_color_mapper_heatmap(RDBU)
def empty_figure() -> Figure:
# If no data to render in the heatmap, i.e. no missing values
# we render a blank heatmap
fig = Figure(
x_range=[],
y_range=[],
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="below",
tools="hover",
toolbar_location=None,
background_fill_color="#fafafa",
)
# Add at least one renderer to fig, otherwise bokeh
# gives us error -1000 (MISSING_RENDERERS): Plot has no renderers
fig.rect(x=0, y=0, width=0, height=0)
return fig
if df is not None:
df = df.where(np.triu(np.ones(df.shape)).astype(np.bool)).T # pylint: disable=no-member
if df.size != 0:
x_range = FactorRange(*df.columns)
y_range = FactorRange(*reversed(df.columns))
df = df.unstack().reset_index(name="correlation")
df = df.rename(columns={"level_0": "x", "level_1": "y"})
df = df[df["x"] != df["y"]]
df = drop_null(df)
# in case of numerical column names
df["x"] = df["x"].apply(str)
df["y"] = df["y"].apply(str)
fig = Figure(
x_range=x_range,
y_range=y_range,
plot_width=plot_width,
plot_height=plot_height,
x_axis_location="below",
tools="hover",
toolbar_location=None,
tooltips=tooltips,
background_fill_color="#fafafa",
title=" ",
)
fig.rect(
x="x",
y="y",
width=1,
height=1,
source=df,
fill_color={"field": "correlation", "transform": mapper},
line_color=None,
)
else:
fig = empty_figure()
else:
fig = empty_figure()
tweak_figure(fig)
fig.grid.grid_line_color = None
fig.axis.axis_line_color = None
fig.add_layout(color_bar, "left")
fig.frame_width = plot_width
return fig
('months after IP', '@counts'),
('percentage', '@percents%'),
])
# customize plot
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "10pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = np.pi / 3
p.rect('counts',
'epoch',
1,
1,
source=source,
color='colors',
alpha='percents',
line_color=None)
def get_cohorts_now():
c = counts.value
sm = start_mon.value
em = end_mon.value
r = relativedelta.relativedelta(pd.to_datetime(em), pd.to_datetime(sm))
nmons = r.years * 12 + r.months + 1
c = min(c, nmons)
pro = product.value
#cohorts_now = b_cohorts[b_cohorts['y_m'].isin(pd.date_range(sm,em,freq='MS'))]
cohorts_now = b_cohorts[b_cohorts['pname'] == pro]
def plot_amp(self,
dz,
refexp,
name="",
font_size="1.2vw",
description="",
nrg=[],
wrg=[],
status_plot=False):
''' Initializing AMP plot
'''
ztext, cbarformat = self.set_amp(dz)
dx = [0, 1, 0, 1]
dy = [1, 1, 0, 0]
zvalid = np.array([x if x > -999 else np.nan for x in dz])
data_source = dict(
x=dx,
y=dy,
z=dz,
zvalid=zvalid,
ref=["{:.2f}".format(x) for x in refexp],
zdiff=zvalid - np.array(refexp),
y_offset1=[i + 0.15 for i in dy],
y_offset2=[i - 0.10 for i in dy],
amp=['AMP %s' % i for i in range(1, 5)],
amp_number=['%s' % i for i in range(1, 5)],
ztext=ztext,
)
if status_plot:
text_val = 'status'
color_status = ['green', 'darkgrey', 'yellow', 'red']
color = {
'NaN': 'darkgrey',
'NORMAL': 'green',
'WARNING': 'yellow',
'ALARM': 'red',
}
status = [
self.val_status(x, nrg=nrg, wrg=wrg)
for x in zvalid - np.array(refexp)
]
color_status = [
color[self.val_status(x, nrg=nrg, wrg=wrg)]
for x in zvalid - np.array(refexp)
]
fill_color = "color_status"
data_source.update({
'status': status,
'color_status': color_status,
})
fill_alpha = 0.8
if not status_plot:
text_val = 'ztext'
cmap = get_palette("RdBu_r")
mapper = LinearColorMapper(palette=cmap,
low=wrg[0],
high=wrg[1],
nan_color="darkgrey")
formatter = PrintfTickFormatter(format=cbarformat)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_align='left',
major_label_text_font_size='10pt',
label_standoff=2,
location=(0, 0),
formatter=formatter,
title="(Val-Ref)",
title_standoff=15,
title_text_baseline="alphabetic")
fill_color = {'field': 'zdiff', 'transform': mapper}
fill_alpha = 0.9
cmap_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">AMP: </span>
<span style="font-size: 1vw; color: #515151;">@amp_number</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">counts: </span>
<span style="font-size: 1vw; color: #515151">@text_val</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Reference: </span>
<span style="font-size: 1vw; color: #515151;">@ref</span>
</div>
</div>
""".replace("counts:",
name.replace("_AMP", "") + ":").replace(
"text_val", text_val)
hover = HoverTool(tooltips=cmap_tooltip)
p = Figure(title=name,
tools=[hover],
x_range=list([-0.5, 1.5]),
y_range=list([-0.5, 1.5]),
plot_width=450,
plot_height=400)
source = ColumnDataSource(data=data_source)
text_props = {
"source": source,
"angle": 0,
"color": "black",
"text_color": "black",
"text_align": "center",
"text_baseline": "middle"
}
p.rect("x",
"y",
.98,
.98,
0,
source=source,
fill_color=fill_color,
fill_alpha=fill_alpha)
p.text(x="x",
y="y_offset1",
text="amp",
text_font_size="2vw",
**text_props)
p.text(x="x",
y="y_offset2",
text="ztext",
text_font_style="bold",
text_font_size="2.5vw",
**text_props)
if not status_plot:
p.add_layout(color_bar, 'right')
# Format:
p.xaxis.axis_label_text_font_size = font_size
p.legend.label_text_font_size = font_size
p.title.text_font_size = font_size
p.xaxis.axis_label = description
# Clear Axis:
p.grid.grid_line_color = None
p.outline_line_color = None
p.axis.minor_tick_line_color = None
p.axis.major_label_text_font_size = '0pt'
p.yaxis.major_label_text_font_size = '0pt'
p.xaxis.major_tick_line_color = None
p.xaxis.minor_tick_line_color = None
p.yaxis.major_tick_line_color = None
p.yaxis.minor_tick_line_color = None
p.yaxis.visible = False
p.xaxis.visible = True
p.axis.clear
return p
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
text_props = {
"source": source,
"angle": 0,
"color": "black",
"text_color": "black",
"text_align": "center",
"text_baseline": "middle"
}
p.rect("x",
"y",
.98,
.98,
0,
source=source,
fill_color={
'field': 'z',
'transform': mapper
},
fill_alpha=0.9) #, color="color")
p.axis.minor_tick_line_color = None
p.text(x="x",
y="y_offset2",
text="ztext",
text_font_style="bold",
text_font_size="20pt",
**text_props)
p.text(x="x", y="y_offset1", text="amp", text_font_size="18pt", **text_props)
formatter = PrintfTickFormatter(format='%2.1e')
def buildDevicesTab():
gfxVendorSelect = Select(title='Vendor', options=gfxVendors, value=gfxVendors[0])
gfxDeviceCheckbox = CheckboxGroup()
source_release = ColumnDataSource(data=dict(x=[], y=[], height=[]))
source_beta = ColumnDataSource(data=dict(x=[], y=[], height=[]))
fig = Figure(title="GFX Devices",
x_range=[],
y_range=[0, 0],
plot_width=1000, plot_height=650)
hover = HoverTool(tooltips=[
('Users', '@height %')
])
fig.add_tools(hover)
fig.rect(x='x', y='y', height='height', source=source_release,
width=0.4, color='orange', legend='Release')
fig.rect(x='x', y='y', height='height', source=source_beta,
width=0.4, color='blue', legend='Beta')
fig.xaxis.major_label_orientation = np.pi / 3
def update_view():
vendor = gfxVendorSelect.value
deviceNames = getDeviceNames('release', vendor, True)
gfxDeviceCheckbox.labels = deviceNames
devices = [deviceNames[i] for i in range(len(deviceNames)) if i in gfxDeviceCheckbox.active]
releaseUsers = 100 * getUsersForDevices('release', vendor, devices) / gfxTotalReleaseUsers
betaUsers = 100 * getUsersForDevices('beta', vendor, devices) / gfxTotalBetaUsers
fig.x_range.factors = devices
fig.y_range.end = max([releaseUsers.max(), betaUsers.max()])
source_release.data = dict(
x=[c + ':0.3' for c in devices],
y=releaseUsers / 2,
height=releaseUsers,
)
source_beta.data = dict(
x=[c + ':0.7' for c in devices],
y=betaUsers / 2,
height=betaUsers,
)
def update(attrname, old, new):
gfxDeviceCheckbox.active = [i for i in range(5)]
update_view()
def click(selected):
update_view()
gfxVendorSelect.on_change('value', update)
gfxDeviceCheckbox.on_click(click)
update('value', '', gfxVendorSelect.value)
deviceComparison = HBox(HBox(VBoxForm(*[gfxVendorSelect, gfxDeviceCheckbox]), width=300), fig, width=1100)
return Panel(child=deviceComparison, title="GFX Device Comparison")
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 periodic_plot(cds, title="Periodic Table", width=PLOT_WIDTH,
height=PLOT_HEIGHT, cmap="viridis",
showfblock=True, long_version=False,
color_mapper=None):
"""
Create the periodic plot
Args:
df : DataFrame
Pandas DataFrame with the data on elements
tile : str
Title to appear above the periodic table
colorby : str
Name of the column containig the colors
width : int
Width of the figure in pixels
height : int
Height of the figure in pixels
cmap : str
Colormap to use, see matplotlib colormaps
long_version : bool
Show the long version of the periodic table with the f block between
the s and d blocks
showfblock : bool
Show the elements from the f block
.. note::
`property` attribute holds the current property to be displayed
"""
fig = Figure(title=title,
x_axis_location="above",
x_range=(0.5, 18.5),
y_range=(10.0, 0.5),
plot_width=width,
plot_height=height,
tools="box_zoom,pan,resize,save,reset",
toolbar_location="above",
toolbar_sticky=False,
)
if color_mapper is None:
color_dict = "#1F77B4"
else:
color_dict = {"field": "value", "transform": color_mapper}
fig.rect("x", "y", 0.9, 0.9, source=cds, fill_alpha=0.6,
fill_color=color_dict, line_color=color_dict)
# adjust the ticks and axis bounds
fig.yaxis.bounds = (1, 7)
fig.axis[1].ticker.num_minor_ticks = 0
fig.axis[0].ticker = FixedTicker(ticks=list(range(1, 19)))
text_props = {
"source": cds,
"angle": 0,
"color": "black",
"text_align": "center",
"text_baseline": "middle"
}
fig.text(x="x", y="y_symbol", text="symbol",
text_font_style="bold", text_font_size="15pt", **text_props)
fig.text(x="x", y="y_anumber", text="atomic_number",
text_font_size="9pt", **text_props)
fig.text(x="x", y="y_name", text="name",
text_font_size="7pt", **text_props)
fig.text(x="x", y="y_prop", text="value_str",
text_font_size="8pt", **text_props)
fig.grid.grid_line_color = None
hover = HoverTool(tooltips=HOVER_TOOLTIPS)
fig.add_tools(hover)
return fig
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)
selected = select_facts()
source = ColumnDataSource(data=dict(x=[], y=[], color=[]))
source.data.update(
ColumnDataSource(
dict(x=selected["x"].astype(object),
y=selected["y"].astype(object),
color=selected["color"].astype(object))).data)
p = Figure(plot_height=900,
plot_width=900,
title="",
toolbar_location=None,
x_range=list(set(source.data.get("x"))),
y_range=list(set(source.data.get("y"))))
p.rect(x="x", y="y", source=source, color="color", width=1, height=1)
p.xaxis.major_label_orientation = pi / 4
p.yaxis.major_label_orientation = pi / 4
controls = [top_n, x_axis, y_axis]
for control in controls:
control.on_change('value', update)
inputs = HBox(VBoxForm(*controls), width=300)
update(None, None, None) # initial load of the data
layout = HBox(inputs, p)
curdoc().add_root(layout)
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 plotTracks(preds,tag,n=3,title=None,width=820,height=None,
seqdepot=None,bcell=None,exp=None):
"""Plot epitopes as parallel tracks"""
from bokeh.models import Range1d,HoverTool,FactorRange,Grid,GridPlot,ColumnDataSource
from bokeh.plotting import Figure
alls=1
if title == None:
title=tag
for m in preds:
alls += len(preds[m].data.groupby('allele'))
if height==None:
height = 130+10*alls
yrange = Range1d(start=0, end=alls+3)
plot = Figure(title=title,title_text_font_size="11pt",plot_width=width,
plot_height=height, y_range=yrange,
y_axis_label='allele',
tools="xpan, xwheel_zoom, resize, hover, reset, save",
background_fill="#FAFAFA",
toolbar_location="below")
h=3
if bcell != None:
plotBCell(plot, bcell, alls)
if seqdepot != None:
plotAnnotations(plot,seqdepot)
if exp is not None:
plotExp(plot, exp)
#plotRegions(plot)
#lists for hover data
#we plot all rects at once
x=[];y=[];allele=[];widths=[];clrs=[];peptide=[]
predictor=[];position=[];score=[];leg=[]
l=80
for m in preds:
pred = preds[m]
cmap = mpl.cm.get_cmap(colormaps[m])
df = pred.data
sckey = pred.scorekey
pb = pred.getPromiscuousBinders(data=df,n=n)
if len(pb) == 0:
continue
l = pred.getLength()
grps = df.groupby('allele')
alleles = grps.groups.keys()
if len(pb)==0:
continue
c=colors[m]
leg.append(m)
for a,g in grps:
b = pred.getBinders(data=g)
b = b[b.pos.isin(pb.pos)] #only promiscuous
b.sort('pos',inplace=True)
scores = b[sckey].values
score.extend(scores)
pos = b['pos'].values
position.extend(pos)
x.extend(pos+(l/2.0)) #offset as coords are rect centers
widths.extend([l for i in scores])
clrs.extend([c for i in scores])
y.extend([h+0.5 for i in scores])
alls = [a for i in scores]
allele.extend(alls)
peptide.extend(list(b.peptide.values))
predictor.extend([m for i in scores])
h+=1
source = ColumnDataSource(data=dict(x=x,y=y,allele=allele,peptide=peptide,
predictor=predictor,position=position,score=score))
plot.rect(x,y, width=widths, height=0.8,
#x_range=Range1d(start=1, end=seqlen+l),
color=clrs,line_color='gray',alpha=0.7,source=source)
hover = plot.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
("allele", "@allele"),
("position", "@position"),
("peptide", "@peptide"),
("score", "@score"),
("predictor", "@predictor"),
])
seqlen = pred.data.pos.max()+l
plot.set(x_range=Range1d(start=0, end=seqlen+1))
plot.xaxis.major_label_text_font_size = "8pt"
plot.xaxis.major_label_text_font_style = "bold"
plot.ygrid.grid_line_color = None
plot.yaxis.major_label_text_font_size = '0pt'
plot.xaxis.major_label_orientation = np.pi/4
#js,html = embedPlot(plot)
script, div = embedPlot(plot)
return script, div
# create a data source to enable refreshing of fill & text color
source = ColumnDataSource(
data=dict(color=[hex_color], text_color=[text_color]))
# create first plot, as a rect() glyph and centered text label, with fill and text color taken from source
p1 = Figure(x_range=(-8, 8),
y_range=(-4, 4),
plot_width=600,
plot_height=300,
title='move sliders to change',
tools='')
p1.rect(0,
0,
width=18,
height=10,
fill_color='color',
line_color='black',
source=source)
p1.text(0,
0,
text='color',
text_color='text_color',
alpha=0.6667,
text_font_size='36pt',
text_baseline='middle',
text_align='center',
source=source)
# the callback function to update the color of the block and associated label text
TOOLS="tap, reset"
hover = HoverTool(names = ["glyphs"],
tooltips=[("Cooccurring", "@x, @y"),
("Counts", "@counts"),
("wikidataID for this item (x-axis)", "@wikidataID")])
fig = Figure(plot_height=700, plot_width=700, title="",
tools=TOOLS, toolbar_location="above",
x_range=new_x_factors[:top_n.value], y_range=new_y_factors[:top_n.value])
fig.add_tools(hover)
update(None, None, None) # initial load of the data
rects = fig.rect(x="x", y="y", source=source, color="color", width=0.95, height=0.95, name="glyphs")
fig.xaxis.major_label_orientation = np.pi/4
fig.yaxis.major_label_orientation = np.pi/4
fig.xgrid.visible = False
fig.ygrid.visible = False
url = "https://www.wikidata.org/wiki/@wikidataID"
taptool = fig.select(type=TapTool)
taptool.callback = OpenURL(url=url)
renderer = fig.select(name="glyphs")[0]
renderer.selection_glyph = renderer.glyph
renderer.nonselection_glyph = renderer.glyph
renderer.hover_glyph = renderer.glyph
# table_columns = [TableColumn(field="x", title="X-axis facts"),
