def makeBulkCHL(itnum, nrt):
if itnum < 1998001:
print('Error: CHL data is only availabe after 1998.')
return
if nrt:
path = cfgv.nrt_chl_raw + cfgv.nrt_chl_prefix + '%7.7d.nc' % itnum
else:
path = cfgv.rep_chl_raw + cfgv.rep_chl_prefix + '%7.7d.nc' % itnum
prefix = 'chl_oi_'
df = nc.ncToDF(path)
df = ip.removeColumn(['CHL_error'], df)
#df = ip.removeMissings(['CHL'], df) # remove land
df['ID'] = None
exportBase = cfgv.opedia_proj + 'db/dbInsert/export/'
export_path = '%s%s%d.csv' % (exportBase, prefix, itnum)
df.to_csv(export_path)
## seems like the code below is only needed for the NRT product
## longitude range in NRT products: 0 - 360
if nrt:
ip.mapTo180180(export_path, 'lon') # only use if necessary
###df = downsampleCHL(export_path)
ip.sortByLatLon(df, export_path, 'lon', 'lat')
return export_path
def makeBulkSST(itnum, nrt):
if itnum < 1981244:
print('Error: SST data is only availabe after 1981224.')
return
if nrt:
path = cfgv.nrt_sst_raw + cfgv.nrt_sst_prefix + '%7.7d.nc' % itnum
else:
path = 'unknown'
#path = cfgv.rep_sst_raw + cfgv.rep_sst_prefix + '%7.7d.nc' % itnum
prefix = 'sst_'
df = nc.ncToDF(path)
df = ip.removeColumn(
['analysis_error', 'mask', 'sea_ice_fraction', 'lat_bnds', 'lon_bnds'],
df)
if 'time_bnds' in df.columns:
df = ip.removeColumn(['time_bnds'], df)
#df = ip.removeMissings(['analysed_sst'], df) # remove land
df['ID'] = None
exportBase = cfgv.opedia_proj + 'db/dbInsert/export/'
export_path = '%s%s%d.csv' % (exportBase, prefix, itnum)
df.to_csv(export_path)
#ip.mapTo180180(export_path, 'lon') # only use if necessary
ip.sortByLatLon(df, export_path, 'lon', 'lat')
## 1. only keep records with nv=1 (remove nv=0 records)
## 2. drop nv column
## 3. unit conversion; kelvin to centigrade
## 4. fix time stamp: one or some of the sst files have incorrecty timestamp.
df = pd.read_csv(export_path)
df = df[df['nv'] == 1]
df = ip.removeColumn(['nv'], df)
df['analysed_sst'] = df['analysed_sst'] - 273.15
if itnum in [
2016097
]: # this is to account for a little bug in the time variable in the SST netcdf file
df['time'] = pd.DatetimeIndex(df['time']) + pd.DateOffset(1)
df.to_csv(export_path, index=False)
####################################################
return export_path
def makeGlobal_PicoPhytoPlankton(rawFilePath, rawFileName, tableName):
path = rawFilePath + rawFileName
prefix = tableName
exportBase = cfgv.opedia_proj + 'db/dbInsert/export/'
export_path = '%s%s.csv' % (exportBase, prefix)
df = pd.read_excel(path, sep=',', sheet_name='data', usecols=usecols)
df['year'] = df['year'].astype('str')
df['month'] = ((df['month'].astype('str')).apply(lambda x: x.zfill(2)))
df['day'] = ((df['day'].astype('str')).apply(lambda x: x.zfill(2)))
print(len(df))
df = df[(df['day'] != '-9') & (df['day'] != '-1')]
df['year'] = df['year'].replace('10', '2010')
df['year'] = df['year'].replace('11', '2011')
df['year'] = df['year'].replace('6', '2006')
# df = df[(df['year'] != '10') & (df['year'] != '11')& (df['year'] != '6')]
df['time'] = pd.to_datetime(df[['year', 'month', 'day']], format='%Y%m%d')
ip.renameCol(df, 'Lat', 'lat')
ip.renameCol(df, 'Long', 'lon')
ip.renameCol(df, 'Depth', 'depth')
ip.renameCol(df, 'PromL', 'prochlorococcus_abundance')
ip.renameCol(df, 'SynmL', 'synechococcus_abundance')
ip.renameCol(df, 'PEukmL', 'picoeukaryote_abundance')
ip.renameCol(df, 'pico_abund', 'picophytoplankton_abundance')
ip.renameCol(df, 'picophyto [ug C/L]', 'picophytoplankton_biomass')
ip.removeColumn(['year', 'day', 'month'], df)
df = ip.reorderCol(df, [
'time', 'lat', 'lon', 'depth', 'prochlorococcus_abundance',
'synechococcus_abundance', 'picoeukaryote_abundance',
'picophytoplankton_abundance', 'picophytoplankton_biomass'
])
df = ip.removeMissings(['time', 'lat', 'lon', 'depth'], df)
df = ip.NaNtoNone(df)
df = ip.colDatatypes(df)
df = ip.addIDcol(df)
df = ip.removeDuplicates(df)
df.to_csv(export_path, index=False)
ip.sortByTimeLatLonDepth(df, export_path, 'time', 'lat', 'lon', 'depth')
print('export path: ', export_path)
return export_path
def makeWOA_climatology(rawFilePath, tableName):
for month in month_list: # ie 1 = jan
print('Month: ' + month)
df = merge_WOA_variables(month)
df = ip.removeColumn(['I_gp', 'C_gp'], df)
df = ip.removeMissings(['lat', 'lon', 'depth'], df)
df = ip.NaNtoNone(df)
df = ip.addIDcol(df)
df.sort_values(['lat', 'lon', 'depth'], ascending=[True, True, True], inplace=True)
df.to_csv(exportBase + tableName + '_' + month + '.csv', index=False)
print('export path: ' , exportBase + tableName + '_' + month + '.csv')
iF.toSQLbcp(exportBase + tableName + '_' + month + '.csv', tableName)
def makeBulkWind(itnum, nrt, hour):
if itnum < 2012320:
print('Error: Wind data is only availabe after 2012320.')
return
if nrt:
path = cfgv.nrt_wind_raw + cfgv.nrt_wind_prefix + '%7.7d_%2.2dh.nc' % (
itnum, hour)
else:
path = 'unknown'
#path = cfgv.rep_wind_raw + cfgv.rep_wind_prefix + '%7.7d_%2.2dh.nc' % (itnum, hour)
prefix = 'wind_'
df = nc.ncToDF(path)
df = ip.removeColumn(['land_ice_mask', 'sampling_length'], df)
#df = ip.removeMissings(['wind_stress'], df) # remove land
## arrange the columns: making sure that the columns are arranged in the correct (consistent with the undelying table) order
df = ip.arrangeColumns([
'wind_speed_rms', 'eastward_wind_rms', 'wind_stress', 'eastward_wind',
'surface_downward_eastward_stress', 'wind_speed',
'surface_downward_northward_stress', 'northward_wind',
'northward_wind_rms'
], df)
df['hour'] = hour
df['ID'] = None
exportBase = cfgv.opedia_proj + 'db/dbInsert/export/'
export_path = '%s%s%d_%2.2dh.csv' % (exportBase, prefix, itnum, hour)
df.to_csv(export_path)
#ip.mapTo180180(export_path, 'longitude') # only use if necessary
ip.sortByLatLon(df, export_path, 'longitude', 'latitude')
########### drop depth column ###############
df = pd.read_csv(export_path)
df = ip.removeColumn(['depth'], df)
df.to_csv(export_path, index=False)
##############################################
return export_path
def makeBulkSeaFlow():
path = cfgv.seaflow_raw + 'SeaFlow_colocal.csv'
prefix = 'seaflow'
df = pd.read_csv(path)
df = ip.removeColumn(['file'], df)
df = ip.removeMissings(['lat', 'lon', 'abundance'],
df) # remove rows with missing lat/lon/abundance
df['ID'] = None
exportBase = cfgv.opedia_proj + 'db/dbInsert/export/'
export_path = '%s%s.csv' % (exportBase, prefix)
df.to_csv(export_path, index=False)
#ip.mapTo180180(export_path, 'lon') # only use if necessary
ip.sortByDepthLatLon(df, export_path, 'lon', 'lat', 'cruise')
return export_path
def makeBulkALT(itnum, nrt):
if itnum < 1993001:
print('Error: Altimetry data is only available after 1993.')
return
if nrt:
path = cfgv.nrt_alt_raw + cfgv.nrt_alt_prefix + '%7.7d.nc' % itnum
else:
path = cfgv.rep_alt_raw + cfgv.rep_alt_prefix + '%7.7d.nc' % itnum
prefix = 'alt_'
df = nc.ncToDF(path)
df = ip.removeColumn(['err'], df)
#df = ip.removeMissings(['sla'], df) # remove land
df['ID'] = None
exportBase = cfgv.opedia_proj + 'db/dbInsert/export/'
export_path = '%s%s%d.csv' % (exportBase, prefix, itnum)
df.to_csv(export_path)
ip.mapTo180180(export_path, 'longitude') # only use if necessary
ip.sortByLatLon(df, export_path, 'longitude', 'latitude')
return export_path
def makeBulkALT(itnum, nrt):
if itnum < 1993001:
print('Error: Altimetry data is only available after 1993.')
return
if nrt:
path = cfgv.nrt_alt_raw + cfgv.nrt_alt_prefix + '%7.7d.nc' % itnum
else:
path = cfgv.rep_alt_raw + cfgv.rep_alt_prefix + '%7.7d.nc' % itnum
prefix = 'alt_'
df = nc.ncToDF(path)
df = ip.removeColumn(['err'], df)
#df = ip.removeMissings(['sla'], df) # remove land
## arrange the columns: making sure that the columns are arranged in the correct (consistent with the undelying table) order
df = ip.arrangeColumns(['vgosa', 'vgos', 'sla', 'adt', 'ugosa', 'ugos'], df)
# df['ID'] = None
exportBase = cfgv.opedia_proj + 'db/dbInsert/export/'
export_path = '%s%s%d.csv' % (exportBase, prefix, itnum)
df.to_csv(export_path)
ip.mapTo180180(export_path, 'longitude') # only use if necessary
ip.sortByLatLon(df, export_path, 'longitude', 'latitude')
return export_path
