def exportData(t1, y1, yErr1, t2, y2, yErr2, table1, variable1, table2,
variable2, lat1, lat2, lon1, lon2, depth1, depth2):
df = pd.DataFrame()
df['time_X'] = t1
df[variable1] = y1
df[variable1 + '_std_X'] = yErr1
df['time_Y'] = t2
df[variable2] = y2
df[variable2 + '_std_Y'] = yErr2
df['lat1'] = lat1
df['lat2'] = lat2
df['lon1'] = lon1
df['lon2'] = lon2
if db.hasField(table1, 'depth') or db.hasField(table2, 'depth'):
df['depth1'] = depth1
df['depth2'] = depth2
# dirPath = 'data/'
# if not os.path.exists(dirPath):
# os.makedirs(dirPath)
# path = dirPath + 'XY_' + table1 + '_' + variable1 + '_vs_' + table2 + '_' + variable2 + '.csv'
# df.to_csv(path, index=False)
export.dump(df, table1, variable1, prefix='Mutual', fmt='.csv')
export.dump(df, table2, variable2, prefix='Mutual', fmt='.csv')
return
def exportData(cruiseTrack, t, y, yErr, table, variable, margin):
df = cruiseTrack
df['margin'] = margin
# dirPath = 'data/'
# path = dirPath + 'Front.csv'
# if not os.path.exists(dirPath):
# os.makedirs(dirPath)
# df.to_csv(path, index=False)
export.dump(df, table, variable, prefix='Front', fmt='.csv')
return
def sectionMap(tables, variables, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag):
data, lats, lons, subs, frameVars, units = [], [], [], [], [], []
xs, ys, zs = [], [], []
for i in tqdm(range(len(tables)), desc='overall'):
if not db.hasField(tables[i], 'depth'):
continue
df = subset.section(tables[i], variables[i], dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2)
if len(df) < 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)
if exportDataFlag:
export.dump(df, tables[i], variables[i], prefix='Section', fmt='.csv')
times = df[df.columns[0]].unique()
lats = df.lat.unique()
lons = df.lon.unique()
depths = df.depth.unique()
shape = (len(lats), len(lons), len(depths))
hours = [None]
if 'hour' in df.columns:
hours = df.hour.unique()
unit = com.getUnit(tables[i], variables[i])
for t in times:
for h in hours:
frame = df[df[df.columns[0]] == t]
sub = variables[i] + unit + ', ' + df.columns[0] + ': ' + str(t)
if h != None:
frame = frame[frame['hour'] == h]
sub = sub + ', hour: ' + str(h) + 'hr'
try:
shot = frame[variables[i]].values.reshape(shape)
except Exception as e:
continue
data.append(shot)
xs.append(lons)
ys.append(lats)
zs.append(depths)
frameVars.append(variables[i])
units.append(unit)
subs.append(sub)
bokehSec(data=data, subject=subs, fname=fname, ys=ys, xs=xs, zs=zs, units=units, variables=frameVars)
return
def exportData(t, y, yErr, table, variable, lat1, lat2, lon1, lon2, extV,
extVV, extV2, extVV2):
df = pd.DataFrame()
df['month'] = t
df[variable] = y
df[variable + '_std'] = yErr
df['lat1'] = lat1
df['lat2'] = lat2
df['lon1'] = lon1
df['lon2'] = lon2
df[extV] = extVV
df[extV2] = extVV2
# dirPath = 'data/'
# if not os.path.exists(dirPath):
# os.makedirs(dirPath)
# path = dirPath + 'Monthly_' + table + '_' + variable + '.csv'
# df.to_csv(path, index=False)
export.dump(df, table, variable, prefix='Monthly', fmt='.csv')
return
def exportData(z, y, yErr, table, variable, date1, date2, lat1, lat2, lon1, lon2, fname):
df = pd.DataFrame()
df['depth'] = z
df[variable] = y
df[variable+'_std'] = yErr
if db.isClimatology(table):
df['month1'] = clim.timeToMonth(date1)
df['month2'] = clim.timeToMonth(date2)
else:
df['time1'] = date1
df['time2'] = date2
df['lat1'] = lat1
df['lat2'] = lat2
df['lon1'] = lon1
df['lon2'] = lon2
# dirPath = 'data/'
# if not os.path.exists(dirPath):
# os.makedirs(dirPath)
# path = dirPath + fname + '_' + table + '_' + variable + '.csv'
# df.to_csv(path, index=False)
export.dump(df, table, variable, prefix='DepthProfile', fmt='.csv')
return
def regionalMap(tables, variables, dt1, dt2, lat1, lat2, lon1, lon2, depth1,
depth2, fname, exportDataFlag):
data, lats, lons, subs, frameTables, frameVars = [], [], [], [], [], []
for i in tqdm(range(len(tables)), desc='overall'):
df = subset.spaceTime(tables[i], variables[i], dt1, dt2, lat1, lat2,
lon1, lon2, depth1, depth2)
if len(df) < 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)
if exportDataFlag:
export.dump(df,
tables[i],
variables[i],
prefix='Regional',
fmt='.csv')
if com.isGrid(tables[i], variables[i]):
data, lats, lons, subs, frameTables, frameVars = structuredMap(
df, tables[i], variables[i], data, lats, lons, subs,
frameTables, frameVars)
else:
dashboardPanels(df, tables[i], variables[i])
# data, lats, lons, subs, frameTables, frameVars = interpolatedMap(df, tables[i], variables[i], data, lats, lons, subs, frameTables, frameVars)
bokehMap(data=data,
subject=subs,
fname=fname,
lat=lats,
lon=lons,
tables=frameTables,
variables=frameVars)
return
def exportData(y, table, variable, startDate, endDate, lat1, lat2, lon1, lon2,
depth1, depth2):
df = pd.DataFrame()
df[variable] = y
timeField = 'time'
if db.isClimatology(table) and db.hasField(table, 'month'):
timeField = 'month'
df['start_' + timeField] = startDate
df['end_' + timeField] = endDate
df['lat1'] = lat1
df['lat2'] = lat2
df['lon1'] = lon1
df['lon2'] = lon2
if db.hasField(table, 'depth'):
df['depth1'] = depth1
df['depth2'] = depth2
# dirPath = 'data/'
# if not os.path.exists(dirPath):
# os.makedirs(dirPath)
# path = dirPath + 'Hist_' + table + '_' + variable + '.csv'
# df.to_csv(path, index=False)
export.dump(df, table, variable, prefix='Hist', fmt='.csv')
return