def plotDist(tables,
variables,
startDate,
endDate,
lat1,
lat2,
lon1,
lon2,
depth1,
depth2,
fname,
exportDataFlag,
marker='-',
msize=30,
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 tqdm(range(len(tables)), desc='overall'):
y = subset.spaceTime(tables[i], variables[i], startDate, endDate, lat1,
lat2, lon1, lon2, depth1, depth2)
if len(y) < 1:
com.printTQDM(
'%d: No matching entry found: Table: %s, Variable: %s ' %
(i + 1, tables[i], variables[i]),
err=True)
continue
com.printTQDM('%d: %s retrieved (%s).' %
(i + 1, variables[i], tables[i]),
err=False)
y = y[variables[i]]
if exportDataFlag:
exportData(y, tables[i], variables[i], startDate, endDate, lat1,
lat2, lon1, lon2, depth1, depth2)
try:
y = y[~np.isnan(y)] # remove nans
except Exception as e:
continue
hist, edges = np.histogram(y, density=True, 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]
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)
if not inline: ## if jupyter is not the caller
output_file(dirPath + fname + ".html", title="Histogram")
show(column(p))
return
def plotMonthly(tables,
variables,
lat1,
lat2,
lon1,
lon2,
extV,
extVV,
extV2,
extVV2,
marker='-',
msize=30,
clr='purple'):
p = []
lw = 2
w = 800
h = 400
months = range(1, 13)
TOOLS = "hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,"
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)):
monthly = np.array([])
monthly_std = np.array([])
for mon in months:
mon = int(mon)
args = [
tables[i], variables[i], mon, lat1, lat2, lon1, lon2, extV[i],
extVV[i]
]
query = 'EXEC uspMonthly ?, ?, ?, ?, ?, ?, ?, ?, ?'
df = db.dbFetchStoredProc(query, args)
df = pd.DataFrame.from_records(
df, columns=['lat', 'lon', variables[i]])
############# removing outlier values ################
vals = df[variables[i]]
vals = vals[abs(vals) < 1e30] # remove outliers
######################################################
monthly = np.append(monthly, np.nanmean(vals))
monthly_std = np.append(monthly_std, np.nanstd(vals))
p1 = figure(tools=TOOLS,
toolbar_location="above",
plot_width=w,
plot_height=h)
#p1.xaxis.axis_label = 'Month'
p1.yaxis.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 = 0.07
if tables[i].find('Pisces') != -1:
fill_alpha = 0.3
cr = p1.circle(months,
monthly,
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(months, monthly, line_color=clr, line_width=lw, legend=leg)
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
#p1.xaxis.major_label_orientation = pi/4
#p1.xaxis.visible = False
p.append(p1)
output_file("embed/" + fname + ".html", title="Monthly Trend")
show(column(p))
'''
p1_script, p1_div = components(p1)
embedComponents('embed/scriptMon1.js', p1_script)
embedComponents('embed/divMon1.js', p1_div)
p2_script, p2_div = components(p2)
embedComponents('embed/scriptMon2.js', p1_script)
embedComponents('embed/divMon2.js', p1_div)
p3_script, p3_div = components(p3)
embedComponents('embed/scriptMon3.js', p1_script)
embedComponents('embed/divMon3.js', p1_div)
'''
return
def colocalize(ftleTable,
ftleField,
ftleValue,
tables,
variables,
startDate,
endDate,
lat1,
lat2,
lon1,
lon2,
depth1,
depth2,
spMargin,
exportDataFlag,
fname,
bkgComparison,
marker='-'):
fmt = '%Y-%m-%d'
dt = 24 * 60
msize = 10
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 tqdm(range(len(tables))):
df = match(ftleTable, tables[i], startDate, endDate, lat1, lat2, lon1,
lon2, depth1, depth2, ftleField, ftleValue, variables[i],
spMargin, '0')
if len(df) < 1:
continue
if i == 0:
loadedFTLE = pd.DataFrame(df)
ts, ys, y_stds = df[df.columns[0]], df[variables[i]], ''
if i > 0:
loadedFTLE = appendVar(loadedFTLE, ts, ys, y_stds, variables[i])
#plot_single_hist(ys, clr='m', labelx='', labely='', leg='', yscale='linear', store_path='', bincount=50)
ys = ys[~np.isnan(ys)] # remove nans
hist, edges = np.histogram(ys, density=False, bins=50)
if bkgComparison:
########## get backgrounf distribution (not matched with fronts)
dfBkg = match(ftleTable, tables[i], startDate, endDate, lat1, lat2,
lon1, lon2, depth1, depth2, ftleField, ftleValue,
variables[i], spMargin, '1')
ysBkg = dfBkg[variables[i]]
ysBkg = ysBkg[~np.isnan(ysBkg)] # remove nans
histBkg, edgesBkg = np.histogram(ysBkg, 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]
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 + ' on fronts')
if bkgComparison:
cr = p1.quad(top=histBkg,
bottom=0,
left=edgesBkg[:-1],
right=edgesBkg[1:],
fill_color="purple",
line_color=None,
hover_fill_color="firebrick",
fill_alpha=fill_alpha,
hover_alpha=0.7,
hover_line_color="white",
legend='background')
p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='mouse'))
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="Front")
show(column(p))
if exportDataFlag:
exportData(loadedFTLE, ts, ys, y_stds, tables[i], variables[i],
spMargin)
print('')
return df
def plotAlongTrack(dt,
fmt,
tables,
variables,
track,
spMargin,
depth1,
depth2,
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 tqdm(range(len(tables)), desc='overall'):
ts, ys, y_stds = [], np.array([]), np.array([])
for j in tqdm(range(len(track)), desc=variables[i]):
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 Tracer Track ' + 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"],
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="Lagrangian")
show(column(p))
if exportDataFlag:
exportData(loadedTrack, ts, ys, y_stds, tables[i], variables[i],
spMargin)
print('')
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 range(len(tablePairs)):
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(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
def mutualTrends(loadedTrack,
tables,
variables,
extV,
extVV,
extV2,
extVV2,
msize=20,
fname='MutualTrends'):
if len(variables) < 2:
print('Error: at least 2 variables are needed to plot mutual trends.')
return
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))
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'
for i in range(len(tablePairs)):
t1, y1, y_std1 = loadedTrack['time'], loadedTrack[
variablePairs[i][0]], loadedTrack[variablePairs[i][0] + '_std']
t2, y2, y_std2 = loadedTrack['time'], loadedTrack[
variablePairs[i][1]], loadedTrack[variablePairs[i][1] + '_std']
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="Mutual Trends")
show(column(p))
return
def plotAlongTrack(tables,
variables,
cruiseName,
track,
spMargin,
extV,
extVV,
extV2,
extVV2,
exportDataFlag,
marker='-',
msize=30,
clr='purple'):
p = []
fmt = '%Y-%m-%d %H:%M:%S'
dt = resampleToTimeResolution(resampTau) # 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 Track (' + cruiseName + ') ' + 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 %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)
output_file(dirPath + fname + ".html", title="Along Track")
show(column(p))
if exportDataFlag:
exportData(loadedTrack, ts, ys, y_stds, cruiseName, tables[i],
variables[i], spMargin, extV[i], extVV[i], extV2[i],
extVV2[i])
return loadedTrack
def getUnit(table, variable):
return ' [' + db.getVar(table, variable).iloc[0]['Unit'] + ']'