def depthProfile_iterative(table, field, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag):
if db.isClimatology(table) and db.hasField(table, 'month'):
m1 = clim.timeToMonth(dt1)
m2 = clim.timeToMonth(dt2)
if m2>m1:
timesteps = range(m1, m2+1)
else:
timesteps = range(m2, m1+1)
timesteps = ['2016-%2.2d-01' % m for m in timesteps]
else:
delta = datetime.strptime(dt2, '%Y-%m-%d') - datetime.strptime(dt1, '%Y-%m-%d')
timesteps = [(datetime.strptime(dt1, '%Y-%m-%d') + timedelta(days=x)).strftime('%Y-%m-%d') for x in range(delta.days+1)]
zs, ys, y_stds = [], [], []
for dt in timesteps:
df = subset.depthProfile(table, field, dt, dt, lat1, lat2, lon1, lon2, depth1, depth2)
if len(df[field]) < 1:
continue
zs.append(df['depth'])
ys.append(df[field])
y_stds.append(df[field + '_std'])
depth = np.mean( np.stack(zs, axis=0), axis=0 )
y = np.mean( np.stack(ys, axis=0), axis=0 )
y_std = np.mean( np.stack(y_stds, axis=0), axis=0 )
if exportDataFlag:
exportData(depth, y, y_std, table, field, dt1, dt2, lat1, lat2, lon1, lon2, fname)
return depth, y, y_std
def depthProfile_iterative(table, field, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag):
if db.isClimatology(table) and db.hasField(table, 'month'):
m1 = clim.timeToMonth(dt1)
m2 = clim.timeToMonth(dt2)
if m2>m1:
timesteps = range(m1, m2+1)
else:
timesteps = range(m2, m1+1)
timesteps = ['2016-%2.2d-01' % m for m in timesteps]
elif table.lower().find('tblPisces'.lower()) != -1: # currently (Nov 2018) only Pisces table has a calendar table. all datasets have to have a calendar table. we can then remove thw else: clause below
calTable = table+'_Calendar'
timesteps = com.timesBetween(calTable, dt1, dt2)
else:
delta = datetime.strptime(dt2, '%Y-%m-%d') - datetime.strptime(dt1, '%Y-%m-%d')
timesteps = [(datetime.strptime(dt1, '%Y-%m-%d') + timedelta(days=x)).strftime('%Y-%m-%d') for x in range(delta.days+1)]
zs, ys, y_stds = [], [], []
for dt in timesteps:
df = subset.depthProfile(table, field, dt, dt, lat1, lat2, lon1, lon2, depth1, depth2)
if len(df[field]) < 1:
continue
zs.append(df['depth'])
ys.append(df[field])
y_stds.append(df[field + '_std'])
depth = np.mean( np.stack(zs, axis=0), axis=0 )
y = np.mean( np.stack(ys, axis=0), axis=0 )
y_std = np.mean( np.stack(y_stds, axis=0), axis=0 )
if exportDataFlag:
exportData(depth, y, y_std, table, field, dt1, dt2, lat1, lat2, lon1, lon2, fname)
return depth, y, y_std
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)
return
def prepareDepthProfileQuery(table, field, date1, lat1, lat2, lon1, lon2,
depth1, depth2):
query = "SELECT AVG(lat) AS lat, AVG(lon) AS lon, AVG(%s) AS %s, STDEV(%s) AS %s_std, depth FROM %s WHERE "
if clim.isClimatology(table, field):
query = query + "[month]=%d AND "
else:
query = query + "[time]='%s' AND "
query = query + "lat BETWEEN %f AND %f AND "
query = query + "lon BETWEEN %f AND %f AND "
query = query + "depth BETWEEN %f AND %f AND "
query = query + "ABS(%s) < 1e30 " ## removing potential outliers (mostly in PISCES model)
query = query + "GROUP BY depth "
query = query + "ORDER BY depth "
if clim.isClimatology(table, field):
month = clim.timeToMonth(date1)
query = query % (field, field, field, field, table, month, lat1, lat2,
lon1, lon2, depth1, depth2, field)
else:
query = query % (field, field, field, field, table, date1, lat1, lat2,
lon1, lon2, depth1, depth2, field)
return query
def prepareSectionQuery(table, field, date1, lat1, lat2, lon1, lon2, depth1,
depth2):
if clim.isClimatology(table, field):
query = "SELECT [month], lat, lon, depth, %s FROM %s WHERE "
query = query + "[month]=%d AND "
else:
query = "SELECT [time], lat, lon, depth, %s FROM %s WHERE "
query = query + "[time]='%s' AND "
query = query + "lat BETWEEN %f AND %f AND "
query = query + "lon BETWEEN %f AND %f AND "
query = query + "depth BETWEEN %f AND %f "
query = query + "ORDER BY lat, lon, depth "
if clim.isClimatology(table, field):
month = clim.timeToMonth(date1)
query = query % (field, table, month, float(lat1), float(lat2),
float(lon1), float(lon2), float(depth1),
float(depth2))
else:
query = query % (field, table, date1, float(lat1), float(lat2),
float(lon1), float(lon2), float(depth1),
float(depth2))
return query