def plotXY(tables, variables, startDate, endDate, lat1, lat2, lon1, lon2,exportDataFlag, extV, extVV, extV2, extVV2, marker='-', msize=15, clr='green'):
p = []
lw = 2
w = 500
h = 500
TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
tablePairs = list(itt.combinations(tables, 2))
variablePairs = list(itt.combinations(variables, 2))
extVPairs = list(itt.combinations(extV, 2))
extVVPairs = list(itt.combinations(extVV, 2))
extV2Pairs = list(itt.combinations(extV2, 2))
extVV2Pairs = list(itt.combinations(extVV2, 2))
for i in range(len(tablePairs)):
t1, y1, y_std1 = TS.timeSeries(tablePairs[i][0], variablePairs[i][0], startDate, endDate, lat1, lat2, lon1, lon2, extVPairs[i][0], extVVPairs[i][0], extV2Pairs[i][0], extVV2Pairs[i][0])
t2, y2, y_std2 = TS.timeSeries(tablePairs[i][1], variablePairs[i][1], startDate, endDate, lat1, lat2, lon1, lon2, extVPairs[i][1], extVVPairs[i][1], extV2Pairs[i][1], extVV2Pairs[i][1])
if exportDataFlag:
exportData(t1, y1, y_std1, t2, y2, y_std2, tablePairs[i][0], variablePairs[i][0], tablePairs[i][1], variablePairs[i][1], lat1, lat2, lon1, lon2, extVPairs[i][0], extVVPairs[i][0], extV2Pairs[i][0], extVV2Pairs[i][0], extVPairs[i][1], extVVPairs[i][1], extV2Pairs[i][1], extVV2Pairs[i][1])
p1 = figure(tools=TOOLS, toolbar_location="above", plot_width=w, plot_height=h)
p1.xaxis.axis_label = variablePairs[i][0] + ' [' + db.getVar(tablePairs[i][0], variablePairs[i][0]).iloc[0]['Unit'] + ']'
p1.yaxis.axis_label = variablePairs[i][1] + ' [' + db.getVar(tablePairs[i][1], variablePairs[i][1]).iloc[0]['Unit'] + ']'
leg = variablePairs[i][0] + ' / ' + variablePairs[i][1]
if extVPairs[i][0] != None:
leg = leg + ' ' + extVPairs[i][0] + ': ' + ( '%d' % float(extVVPairs[i][0]) )
if tablePairs[i][0].find('Pisces') != -1:
leg = leg + ' ' + 'm'
if extVPairs[i][1] != None:
leg = leg + ' ' + extVPairs[i][1] + ': ' + ( '%d' % float(extVVPairs[i][1]) )
if tablePairs[i][1].find('Pisces') != -1:
leg = leg + ' ' + 'm'
fill_alpha = 0.6
#if tablePairs[i][0].find('Pisces') != -1 or tablePairs[i][1].find('Pisces') != -1:
# fill_alpha = 0.3
cr = p1.circle(y1, y2, fill_color="grey", hover_fill_color="firebrick", fill_alpha=fill_alpha, hover_alpha=0.6, line_color=None, hover_line_color="white", legend=leg, size=msize)
#p1.line(y1, y2, line_color=clr, line_width=lw, legend=leg)
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
output_file(dirPath + fname + ".html", title="XY")
show(column(p))
return
def plotAlongTrack(dt, fmt, tables, variables, track, spMargin, extV, extVV, extV2, extVV2, exportDataFlag, fname, marker='-', msize=30, clr='purple'):
p = []
#fmt = '%Y-%m-%d %H:%M:%S'
dt = dt/60 # time resolution (minutes)
loadedTrack = pd.DataFrame(track)
lw = 2
w = 800
h = 400
TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
for i in range(len(tables)):
ts, ys, y_stds = [], np.array([]), np.array([])
for j in range(len(track)):
startDate = track.iloc[j]['time'].strftime(fmt)
endDate = startDate
lat1 = float(track.iloc[j]['lat']) - spMargin
lat2 = float(track.iloc[j]['lat']) + spMargin
lon1 = float(track.iloc[j]['lon']) - spMargin
lon2 = float(track.iloc[j]['lon']) + spMargin
t, y, y_std = TS.timeSeries(tables[i], variables[i], startDate, endDate, lat1, lat2, lon1, lon2, extV[i], extVV[i], extV2[i], extVV2[i], fmt=fmt, dt=dt)
ts.append(track.iloc[j]['time'])
ys = np.append(ys, y[0])
y_stds = np.append(y_stds, y_std[0])
loadedTrack = appendVar(loadedTrack, ts, ys, y_stds, variables[i], extV[i], extVV[i], extV2[i], extVV2[i])
p1 = figure(tools=TOOLS, toolbar_location="above", plot_width=w, plot_height=h)
#p1.xaxis.axis_label = 'Time'
p1.yaxis.axis_label = variables[i] + ' [' + db.getVar(tables[i], variables[i]).iloc[0]['Unit'] + ']'
leg = 'Along Tracer Track ' + variables[i]
if extV[i] != None:
leg = leg + ' ' + extV[i] + ': ' + ( '%d' % float(extVV[i]) )
if tables[i].find('Pisces') != -1:
leg = leg + ' ' + 'm'
fill_alpha = 0.07
if tables[i].find('Pisces') != -1:
fill_alpha = 0.3
cr = p1.circle(ts, ys, fill_color="grey", hover_fill_color="firebrick", fill_alpha=fill_alpha, hover_alpha=0.3, line_color=None, hover_line_color="white", legend=leg, size=msize)
p1.line(ts, ys, line_color=clr, line_width=lw, legend=leg)
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
p1.xaxis.formatter=DatetimeTickFormatter(
hours=["%d %B %Y"],
days=["%d %B %Y"],
months=["%d %B %Y"],
years=["%d %B %Y"],
)
p1.xaxis.major_label_orientation = pi/4
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
output_file(dirPath + fname + ".html", title="Lagrangian")
show(column(p))
if exportDataFlag:
exportData(loadedTrack, ts, ys, y_stds, tables[i], variables[i], spMargin, extV[i], extVV[i], extV2[i], extVV2[i])
return
import sys
sys.path.append('../../')
import timeSeries as TS
import pandas as pd
import matplotlib.pyplot as plt
def plot(t, y):
plt.plot(t, y, 'o')
plt.show()
table = 'tblPisces_NRT'
field = 'Fe'
startDate, endDate = '2016-01-19', '2016-03-19'
lat1, lat2, lon1, lon2 = 30.57, 35.21, -163.43, -156.17
arg8_name, arg8_val, extV2, extVV2 = 'depth', '0.494024991989', None, None
t, y, y_std = TS.timeSeries(table, field, startDate, endDate, lat1, lat2, lon1,
lon2, arg8_name, arg8_val, extV2, extVV2)
plot(t, y)
def plotAlongTrack(tables,
variables,
cruiseName,
resampTau,
track,
spMargin,
depth1,
depth2,
fname,
exportDataFlag,
marker='-',
msize=30,
clr='purple'):
p = []
fmt = '%Y-%m-%d %H:%M:%S'
dt = resampleToTimeStep(resampTau) # time step (minutes)
loadedTrack = pd.DataFrame(track)
lw = 2
w = 800
h = 400
TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
for i in range(len(tables)):
ts, ys, y_stds = [], np.array([]), np.array([])
for j in range(len(track)):
startDate = track.iloc[j]['time'].strftime(fmt)
endDate = startDate
lat1 = float(track.iloc[j]['lat']) - spMargin
lat2 = float(track.iloc[j]['lat']) + spMargin
lon1 = float(track.iloc[j]['lon']) - spMargin
lon2 = float(track.iloc[j]['lon']) + spMargin
t, y, y_std = TS.timeSeries(tables[i],
variables[i],
startDate,
endDate,
lat1,
lat2,
lon1,
lon2,
depth1,
depth2,
fmt=fmt,
dt=dt)
ts.append(track.iloc[j]['time'])
ys = np.append(ys, y[0])
y_stds = np.append(y_stds, y_std[0])
loadedTrack = appendVar(loadedTrack, ts, ys, y_stds, variables[i])
p1 = figure(tools=TOOLS,
toolbar_location="above",
plot_width=w,
plot_height=h)
#p1.xaxis.axis_label = 'Time'
p1.yaxis.axis_label = variables[i] + ' [' + db.getVar(
tables[i], variables[i]).iloc[0]['Unit'] + ']'
leg = 'Along Track (' + cruiseName + ') ' + variables[i]
fill_alpha = 0.3
cr = p1.circle(ts,
ys,
fill_color="grey",
hover_fill_color="firebrick",
fill_alpha=fill_alpha,
hover_alpha=0.3,
line_color=None,
hover_line_color="white",
legend=leg,
size=msize)
p1.line(ts, ys, line_color=clr, line_width=lw, legend=leg)
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
p1.xaxis.formatter = DatetimeTickFormatter(
hours=["%d %B %Y %H:%M:%S"],
days=["%d %B %Y %H:%M:%S"],
months=["%d %B %Y %H:%M:%S"],
years=["%d %B %Y %H:%M:%S"],
)
p1.xaxis.major_label_orientation = pi / 4
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
if not inline: ## if jupyter is not the caller
output_file(dirPath + fname + ".html", title="Along Track")
show(column(p))
############### export retrieved data ###############
if exportDataFlag:
exportData(loadedTrack, ts, ys, y_stds, cruiseName, spMargin)
#####################################################
return loadedTrack
import sys
sys.path.append('../../')
import timeSeries as TS
import pandas as pd
import matplotlib.pyplot as plt
def plot(y1, y2):
plt.plot(y1, y2, 'o')
plt.xlabel(field1)
plt.ylabel(field2)
plt.show()
table1 = 'tblPisces_NRT'
field1 = 'NO3'
table2 = 'tblSST_AVHRR_OI_NRT'
field2 = 'sst'
startDate, endDate = '2016-01-19', '2016-04-19'
lat1, lat2, lon1, lon2 = 30.57, 35.21, -163.43, -156.17
extV, extVV, extV2, extVV2 = 'depth', '0.494024991989', None, None
t1, y1, y_std1 = TS.timeSeries(table1, field1, startDate, endDate, lat1, lat2,
lon1, lon2, extV, extVV, extV2, extVV2)
extV, extVV, extV2, extVV2 = None, None, None, None
t2, y2, y_std2 = TS.timeSeries(table2, field2, startDate, endDate, lat1, lat2,
lon1, lon2, extV, extVV, extV2, extVV2)
plot(y1, y2)
def colocalize(tables,
variables,
eddy,
spMargin,
depth1,
depth2,
exportDataFlag,
fname,
marker='-'):
def spectrum(ind):
colList = [
'grey', 'purple', 'darkturquoise', 'black', 'red', 'blue', 'pink',
'lime', 'green', 'orange'
]
ind = ind % len(colList)
col = colList[ind]
return col
fmt = '%Y-%m-%d'
dt = 24 * 60
msize = 10
p = []
loadedEddy = pd.DataFrame(eddy)
tracks = eddy.track.unique()
lw = 2
w = 800
h = 400
TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
for i in tqdm(range(len(tables)), desc='overall'):
ts, ys, y_stds, track_ids = [], np.array([]), np.array([]), np.array(
[])
for j in tqdm(range(len(eddy)), desc=variables[i]):
startDate = eddy.iloc[j]['time']
endDate = startDate
lat1 = float(eddy.iloc[j]['lat']) - spMargin
lat2 = float(eddy.iloc[j]['lat']) + spMargin
lon1 = float(eddy.iloc[j]['lon']) - spMargin
lon2 = float(eddy.iloc[j]['lon']) + spMargin
t, y, y_std = TS.timeSeries(tables[i],
variables[i],
startDate,
endDate,
lat1,
lat2,
lon1,
lon2,
depth1,
depth2,
fmt=fmt,
dt=dt)
ts.append(datetime.strptime(str(eddy.iloc[j]['time']), fmt))
ys = np.append(ys, y[0])
y_stds = np.append(y_stds, y_std[0])
track_ids = np.append(track_ids, eddy.iloc[j]['track'])
loadedEddy = appendVar(loadedEddy, ts, ys, y_stds, variables[i])
p1 = figure(tools=TOOLS,
toolbar_location="above",
plot_width=w,
plot_height=h)
#p1.xaxis.axis_label = 'Time'
p1.yaxis.axis_label = variables[i] + ' [' + db.getVar(
tables[i], variables[i]).iloc[0]['Unit'] + ']'
leg = 'Eddy Centric ' + variables[i]
fill_alpha = 0.7
for ind in range(len(tracks)):
track = tracks[ind]
first, last = findFirstAndLast(track_ids, track)
filCol = spectrum(ind)
cr = p1.circle(ts[first:last + 1],
ys[first:last + 1],
fill_color=filCol,
hover_fill_color="firebrick",
fill_alpha=fill_alpha,
hover_alpha=0.3,
line_color=None,
hover_line_color="white",
legend=leg,
size=msize)
p1.line(ts[first:last + 1],
ys[first:last + 1],
line_color=filCol,
line_width=lw,
legend=leg)
#p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
p1.xaxis.formatter = DatetimeTickFormatter(
days=["%d %B %Y"],
months=["%d %B %Y"],
years=["%d %B %Y"],
)
p1.xaxis.major_label_orientation = pi / 4
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
if not inline: ## if jupyter is not the caller
output_file(dirPath + fname + ".html", title="Eddy")
show(column(p))
if exportDataFlag:
exportData(loadedEddy, ts, ys, y_stds, tables[i], variables[i],
spMargin)
print('')
return
def plotTS(tables,
variables,
startDate,
endDate,
lat1,
lat2,
lon1,
lon2,
depth1,
depth2,
fname,
exportDataFlag,
marker='-',
msize=20,
clr='purple'):
p = []
lw = 2
w = 800
h = 400
TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
for i in range(len(tables)):
t, y, yErr = TS.timeSeries(tables[i], variables[i], startDate, endDate,
lat1, lat2, lon1, lon2, depth1, depth2)
if exportDataFlag:
exportData(t, y, yErr, tables[i], variables[i], lat1, lat2, lon1,
lon2, depth1, depth2)
p1 = figure(tools=TOOLS,
toolbar_location="above",
plot_width=w,
plot_height=h)
#p1.xaxis.axis_label = 'Time'
p1.yaxis.axis_label = variables[i] + ' [' + db.getVar(
tables[i], variables[i]).iloc[0]['Unit'] + ']'
leg = variables[i]
fill_alpha = 0.3
cr = p1.circle(t,
y,
fill_color="grey",
hover_fill_color="firebrick",
fill_alpha=fill_alpha,
hover_alpha=0.3,
line_color=None,
hover_line_color="white",
legend=leg,
size=msize)
p1.line(t, y, line_color=clr, line_width=lw, legend=leg)
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
if not db.isClimatology(tables[i]):
p1.xaxis.formatter = DatetimeTickFormatter(
hours=["%d %B %Y"],
days=["%d %B %Y"],
months=["%d %B %Y"],
years=["%d %B %Y"],
)
p1.xaxis.major_label_orientation = pi / 4
#p1.xaxis.visible = False
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
if not inline: ## if jupyter is not the caller
output_file(dirPath + fname + ".html", title="TimeSeries")
show(column(p))
return
def colocalize(tables,
variables,
eddy,
spMargin,
extV,
extVV,
extV2,
extVV2,
exportDataFlag,
fname,
marker='-'):
def spectrum(ind):
colList = [
'grey', 'purple', 'darkturquoise', 'black', 'red', 'blue', 'pink',
'lime', 'green', 'orange'
]
ind = ind % len(colList)
col = colList[ind]
return col
fmt = '%Y-%m-%d'
dt = 24 * 60
msize = 10
p = []
loadedEddy = pd.DataFrame(eddy)
lw = 2
w = 800
h = 400
TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
for i in range(len(tables)):
ts, ys, y_stds, track_ids = [], np.array([]), np.array([]), np.array(
[])
for j in range(len(eddy)):
startDate = eddy.iloc[j]['time']
endDate = startDate
lat1 = float(eddy.iloc[j]['lat']) - spMargin
lat2 = float(eddy.iloc[j]['lat']) + spMargin
lon1 = float(eddy.iloc[j]['lon']) - spMargin
lon2 = float(eddy.iloc[j]['lon']) + spMargin
t, y, y_std = TS.timeSeries(tables[i],
variables[i],
startDate,
endDate,
lat1,
lat2,
lon1,
lon2,
extV[i],
extVV[i],
extV2[i],
extVV2[i],
fmt=fmt,
dt=dt)
ts.append(datetime.strptime(eddy.iloc[j]['time'], fmt))
ys = np.append(ys, y[0])
y_stds = np.append(y_stds, y_std[0])
loadedEddy = appendVar(loadedEddy, ts, ys, y_stds, variables[i],
extV[i], extVV[i], extV2[i], extVV2[i])
#plot_single_hist(ys, clr='m', labelx='', labely='', leg='', yscale='linear', store_path='', bincount=50)
hist, edges = np.histogram(ys, density=False, bins=50)
p1 = figure(tools=TOOLS,
toolbar_location="above",
plot_width=w,
plot_height=h)
p1.yaxis.axis_label = 'Density'
p1.xaxis.axis_label = variables[i] + ' [' + db.getVar(
tables[i], variables[i]).iloc[0]['Unit'] + ']'
leg = variables[i]
if extV[i] != None:
leg = leg + ' ' + extV[i] + ': ' + ('%d' % float(extVV[i]))
if tables[i].find('Pisces') != -1:
leg = leg + ' ' + 'm'
fill_alpha = 0.4
cr = p1.quad(top=hist,
bottom=0,
left=edges[:-1],
right=edges[1:],
fill_color="dodgerblue",
line_color=None,
hover_fill_color="firebrick",
fill_alpha=fill_alpha,
hover_alpha=0.7,
hover_line_color="white",
legend=leg)
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='mouse'))
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
output_file(dirPath + fname + ".html", title="Front")
show(column(p))
'''
p1 = figure(tools=TOOLS, toolbar_location="above", plot_width=w, plot_height=h)
#p1.xaxis.axis_label = 'Time'
p1.yaxis.axis_label = variables[i] + ' [' + db.getVar(tables[i], variables[i]).iloc[0]['Unit'] + ']'
leg = 'Front ' + variables[i]
if extV[i] != None:
leg = leg + ' ' + extV[i] + ': ' + ( '%d' % float(extVV[i]) )
if tables[i].find('Pisces') != -1:
leg = leg + ' ' + 'm'
fill_alpha = 0.7
if tables[i].find('Pisces') != -1:
fill_alpha = 0.7
cr = p1.circle(ts, ys, fill_color=filCol, hover_fill_color="firebrick", fill_alpha=fill_alpha, hover_alpha=0.3, line_color=None, hover_line_color="white", legend=leg, size=msize)
p1.line(ts, ys, line_color=filCol, line_width=lw, legend=leg)
#p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
p1.xaxis.formatter=DatetimeTickFormatter(
days=["%d %B %Y"],
months=["%d %B %Y"],
years=["%d %B %Y"],
)
p1.xaxis.major_label_orientation = pi/4
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
output_file(dirPath + fname + ".html", title="Eddy")
show(column(p))
'''
if exportDataFlag:
exportData(loadedEddy, ts, ys, y_stds, tables[i], variables[i],
spMargin, extV[i], extVV[i], extV2[i], extVV2[i])
return
def plotXY(tables,
variables,
startDate,
endDate,
lat1,
lat2,
lon1,
lon2,
depth1,
depth2,
fname,
exportDataFlag,
marker='-',
msize=15,
clr='green'):
p = []
lw = 2
w = 500
h = 500
TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
tablePairs = list(itt.combinations(tables, 2))
variablePairs = list(itt.combinations(variables, 2))
for i in tqdm(range(len(tablePairs)), desc='overall'):
t1, y1, y_std1 = TS.timeSeries(tablePairs[i][0], variablePairs[i][0],
startDate, endDate, lat1, lat2, lon1,
lon2, depth1, depth2)
t2, y2, y_std2 = TS.timeSeries(tablePairs[i][1], variablePairs[i][1],
startDate, endDate, lat1, lat2, lon1,
lon2, depth1, depth2)
if exportDataFlag:
exportData(t1, y1, y_std1, t2, y2, y_std2, tablePairs[i][0],
variablePairs[i][0], tablePairs[i][1],
variablePairs[i][1], lat1, lat2, lon1, lon2, depth1,
depth2)
if len(y1[~np.isnan(y1)]) < 1:
com.printTQDM(
'%d: No matching entry found: Table: %s, Variable: %s ' %
(i + 1, tablePairs[i][0], variablePairs[i][0]),
err=True)
# continue
com.printTQDM('%d: %s retrieved (%s).' %
(i + 1, variablePairs[i][0], tablePairs[i][0]),
err=False)
if len(y2[~np.isnan(y2)]) < 1:
com.printTQDM(
'%d: No matching entry found: Table: %s, Variable: %s ' %
(i + 1, tablePairs[i][1], variablePairs[i][1]),
err=True)
# continue
com.printTQDM('%d: %s retrieved (%s).' %
(i + 1, variablePairs[i][1], tablePairs[i][1]),
err=False)
if len(t1) < 1 or len(y1) < 1 or len(t2) < 1 or len(y2) < 1:
continue
if (len(t1) - len(t2) != 0) or (len(y1) - len(y2) != 0):
continue
p1 = figure(tools=TOOLS,
toolbar_location="above",
plot_width=w,
plot_height=h)
p1.xaxis.axis_label = variablePairs[i][0] + ' [' + db.getVar(
tablePairs[i][0], variablePairs[i][0]).iloc[0]['Unit'] + ']'
p1.yaxis.axis_label = variablePairs[i][1] + ' [' + db.getVar(
tablePairs[i][1], variablePairs[i][1]).iloc[0]['Unit'] + ']'
leg = variablePairs[i][0] + ' / ' + variablePairs[i][1]
fill_alpha = 0.3
cr = p1.circle(y1,
y2,
fill_color="grey",
hover_fill_color="firebrick",
fill_alpha=fill_alpha,
hover_alpha=0.6,
line_color=None,
hover_line_color="white",
legend=leg,
size=msize)
#p1.line(y1, y2, line_color=clr, line_width=lw, legend=leg)
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
p.append(p1)
dirPath = 'embed/'
if not os.path.exists(dirPath):
os.makedirs(dirPath)
if not inline: ## if jupyter is not the caller
output_file(dirPath + fname + ".html", title="XY")
show(column(p))
return
