def __init__(self, src_file ):
super(data_reader, self).__init__(src_file, f=ncfile)
f = ncfile(self.src,'r'):
self.__labels = f.variables()
self.__timevar = f.variables.keys()[0]
f.close()
def variables(self):
try:
f = ncfile(self.src,'r')
variables = f.variables.keys()
time_vec = f.variables[timevar][:]
f.close()
except:
msg = 'error reading header labels from {}'.format(self.src)
self.logger.info(msg)
raise Exception(msg)
return labels
def get_timestamp(self, timestep = 0, time_header = None):
try:
f = ncfile(self.src,'r')
timevar = time_header.keys()[0]
time_vec = f.variables(timevar)[:]
outtime = time_vec[timestep]
f.close()
except:
self.logger.info('cannot return time at timestep {}'.format(timestep))
raise Exception('cannot return time at timestep {}'.format(timestep))
return outtime
def gen_vec(self, vname, args = {} ):
self.logger.info(' generating {} '.format(vname) )
if vname in self.__labels:
column = vname
else:
column = self.__labels[0]
self.logger.info('calling nc reader with: {} '.format(str(arg)))
try:
f = ncfile(self.src,'r')
vec = f.variables[column].get_values()
f.close()
except:
msg = 'error reading {} from {}'.format(columnm,self.src)
self.logger.info(msg)
raise Exception(msg)
return vec
vtype = 'Amon'
gtype = '*' # this could be gn or gr, this is the grid type (gn = native, gr = regridded)
# Path for data
path_input = '/badc/cmip6/data/CMIP6/'+project+'/' # Path to read in input files
data_path = '/home/users/dmitchell/Radiosonde/Data/Models/' # Path to write out final product
# If the file already exists, should it be overwritten?
overw = True
# Specifics if you interpolate on to a grid. Note that this is set up for radio sonde data, but you will have to change to your require obs
if (inter == True):
path_obs = './Data/Obs/'
nc = ncfile(path_obs + 'raobcore15_gridded_2017.nc')
data_rao2 = nc.variables['anomalies'][:,:,::-1,:] # reverse direction of lats
data_rao = np.ma.zeros((data_rao2.shape))
data_rao[:,:,:,0:18] = data_rao2[:,:,:,18:] # Shift lon data to be consistent with model
data_rao[:,:,:,18:] = data_rao2[:,:,:,:18] # Shift lon data to be consistent with model
lat_obs = nc.variables['lat'][::-1] # Reverse lat values so they are asscending, for interp
lon_obs = nc.variables['lon'][:] + 180 # Shift lon data to be consistent with model
p_obs = nc.variables['pressure'][:]
# Specify a mask if one is used
if (mask == True):
mask_vals = np.ma.getmask(data_rao)
# ========================================= Main body of code ==========================================
# Do not alter anything below this line unless you really hve to========================================
def main():
cwd = os.getcwd()
subpath = r'NEWINPUT/WAH_MODEL/'
fname = r'NEWINPUT/WAH_MODEL/item5216_monthly_mean_h000_1985-12_1986-12.nc'
path = os.path.join(cwd, fname)
dir_path = os.path.join(cwd, subpath)
print dir_path
print path
#get lat/lon dims
dataset = ncfile(path)
lats = dataset.variables['global_latitude0'][:]
lons = dataset.variables['global_longitude0'][:]
lat_dim = lats.shape[0]
lon_dim = lats.shape[1]
#get all file names
onlyfiles = [
f for f in os.listdir(dir_path)
if os.path.isfile(os.path.join(dir_path, f))
]
#print onlyfiles
files_split = [[f, f[0:-3].split('_')] for f in onlyfiles]
for el in files_split:
fname = el.pop(0)
el[0].append(fname)
files_split = [item for sublist in files_split for item in sublist]
#files_split = [item for sublist in files_split for item in sublist]
#print files_split
keys = [
'variable_name', 'time_period', 'stat', 'ensemble_member',
'start_date', 'end_date', 'filename'
]
files_dict = [dict(zip(keys, f)) for f in files_split]
#print files_dict
for el in files_dict:
el['end_year'] = el['end_date'][0:4]
el['start_year'] = el['start_date'][0:4]
selection = [
el['filename'] for el in files_dict if el['end_date'][0:4] == '1990'
]
#get number of years
years = list(set([el['end_year'] for el in files_dict]))
print years
year_dim = len(years)
#get max number of runs
n_runs = []
for year in years:
selection = [el for el in files_dict if el['end_year'] == year]
n_runs.append(len(selection))
max_runs = max(n_runs)
print max_runs
#initialise the data arrays
precip = np.zeros((13, year_dim, lat_dim, lon_dim))
precip_ens = np.zeros((13, year_dim, max_runs, lat_dim, lon_dim))
#path = '/ouce-home/staff/sedm4922/Validation/'
# factors to convert units to mm/month
fac = 86400 # for the model
fac2 = 30.5 # for the obs
spatial = 'GHoA'
# Define months array
mon = [
'01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'
]
#precip = np.zeros((12,25,146,209))
#precip_ens = np.zeros((12,25,100,146,209))
# get all the model run data into shape
for year_ind in range(year_dim):
selection = [
el['filename'] for el in files_dict
if el['end_year'] == years[year_ind]
]
for run_ind in range(max_runs):
file_path = os.path.join(dir_path, selection[run_ind])
#print file_path
ds = ncfile(path)
ds_precip = ds.variables['item5216_monthly_mean'][:]
ds_precip = np.squeeze(ds_precip)
precip_ens[:, year_ind, run_ind, :, :] = ds_precip
print 'precip', precip_ens.shape
#average it by axis
precip_output = np.nanmean(precip_ens, axis=4) #lat
precip_output = np.nanmean(precip_output, axis=3) #lon
precip_output = np.nanmean(precip_output, axis=1) #year -> 13 mos x N runs
#get the average of all the runs
precip_mean = np.nanmean(precip_output, axis=1)
#filter GHOA extents
min_lat = min(lats.flatten())
max_lat = max(lats.flatten())
min_lon = min(lons.flatten())
max_lon = max(lons.flatten())
#print min_lat, max_lat, min_lon, max_lon
#load in the historic data
subpath = r'NEWINPUT/'
fname = r'chirps_clim.nc'
file_path = os.path.join(subpath, fname)
chirps_ds = ncfile(file_path)
#print chirps_ds.dimensions.keys()
#print chirps_ds.variables.keys()
chirps_precip = chirps_ds.variables['precip'][:]
#get the lats and lons and times
lats = chirps_ds.variables['latitude'][:]
lons = chirps_ds.variables['longitude'][:]
times = chirps_ds.variables['time'][:]
print times
#create masks using the GHOA extents
lat_mask = ((lats > min_lat) & (lats < max_lat))
lon_mask = ((lons > min_lon) & (lons < max_lon))
#tile one dimension of those badboys out
tile = np.tile(lat_mask, (len(lons), 1))
#create a mask of same global shape
chirps_mask = np.ones((12, len(lats), len(lons)), dtype=bool)
#multiply the boolean tile and lon mask, broadcast into the global mask
chirps_mask[:, :, :] = np.multiply(tile.T, lon_mask)
print chirps_mask.shape
#get the masked data, (convert to 'not ~') np.ma.where(data, mask)
#chirps_ghoa = chirps_precip[~chirps_mask]
#print chirps_ghoa.shape
chirps_ghoa = np.ma.masked_where(chirps_precip, ~chirps_mask)
chirps_mean = np.nanmean(chirps_ghoa, axis=(1, 2))
#years, months, basemap
#gen months
print chirps_mean
precip_mean = np.mean(precip_ens, axis=(3, 4))
precip_mean = np.delete(precip_mean, (0), axis=0)
print precip_mean.shape
print precip_mean
shape = precip_mean.shape
dats = np.random.rand(shape[0], shape[1], shape[2])
#gr_monthly(chirps_mean, dats)
gr_map(chirps_ghoa, precip_ens, min_lat, max_lat, min_lon, max_lon)
from netCDF4 import Dataset as ncfile
import numpy as np
from tools import calc_es, calc_rh, calc_ws, col_av
import matplotlib.pyplot as plt
#filepath = 'E:/ATM115 Data/SST310k-selected/'
filepath = '/home/lsterzinger/Documents/ATM115-Data/SST310k-selected/'
data3d = '/home/lsterzinger/Documents/ATM115-Data/SST310k-selected/sam3d.nc'
data2d = '/home/lsterzinger/Documents/ATM115-Data/SST310k-selected/sam2d.nc'
outputfilename = '310K_vars.nc'
sam3d = ncfile(data3d)
sam2d = ncfile(data2d)
x = sam3d.variables['x'][:]
z = sam3d.variables['z'][:]
t = sam3d.variables['time'][:]
qv = sam3d['QV'][:]
temp = sam3d['TABS'][:]
pressure_ref = sam3d['p'][:]
pres_pert = sam3d['PP'][:]
pressure = np.zeros((3600, 71, 1024))
# Pressure perturbarion + base state
for i in range(0, 1024):
for j in range(0, 3600):
pressure[j, :, i] = pres_pert[j, :, i] + pressure_ref[:]
#Delete unneeded variables
pressure_ref = None
from netCDF4 import Dataset as ncfile
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
path = 'E:\\project\\oilspill_rescue\\rescue\\output\\'
nc_file = ncfile(path + 'qz_ty1_100p.nc', 'r')
# print(nc_file)
lon_list = nc_file.variables['lon'] # 读取NC文件中的变量
lat_list = nc_file.variables['lat']
lon = np.array(lon_list) # 转成数组
lat = np.array(lat_list)
# lon_0 = lon_list[0, :] # 读取数组第1行的所有列
# lon_0[lon_0 > 360] = np.nan # 数组中大于360的数据都赋值NAN
# lon_mean = np.nanmean(lon_0) # 求数组中除NAN外数据的平均值
lon_mean = []
lat_mean = []
for i in range(len(lon[0, :])):
lon_temp = lon[:, i]
lat_temp = lat[:, i]
lon_temp[lon_temp > 360] = np.nan
lat_temp[lat_temp > 360] = np.nan
lon_temp_mean = np.nanmean(lon_temp)
lat_temp_mean = np.nanmean(lat_temp)
vtype = 'Amon'
gtype = '*' # this could be gn or gr, this is the grid type (gn = native, gr = regridded)
# Path for data
path_input = '/badc/cmip6/data/CMIP6/' + project + '/' # Path to read in input files
data_path = '/home/users/dmitchell/Radiosonde/Data/Models/' # Path to write out final product
# If the file already exists, should it be overwritten?
overw = False
# Specifics if you interpolate on to a grid. Note that this is set up for radio sonde data, but you will have to change to your require obs
if (inter == True):
path_obs = './Data/Obs/'
nc = ncfile(path_obs + 'raobcore15_gridded_2017.nc')
data_rao2 = nc.variables['anomalies'][:, :, ::
-1, :] # reverse direction of lats
data_rao = np.ma.zeros((data_rao2.shape))
data_rao[:, :, :, 0:
18] = data_rao2[:, :, :,
18:] # Shift lon data to be consistent with model
data_rao[:, :, :,
18:] = data_rao2[:, :, :, :
18] # Shift lon data to be consistent with model
lat_obs = nc.variables[
'lat'][::-1] # Reverse lat values so they are asscending, for interp
lon_obs = nc.variables[
'lon'][:] + 180 # Shift lon data to be consistent with model
p_obs = nc.variables['pressure'][:]
fname = el.pop(0)
el[0].append(fname)
files_split = [item for sublist in files_split for item in sublist]
#files_split = [item for sublist in files_split for item in sublist]
#print files_split
keys = ['variable_name','time_period','stat','ensemble_member','start_date','end_date','filename']
files_dict = [dict(zip(keys,f)) for f in files_split]
#print files_dict
selection = [el['filename'] for el in files_dict if el['end_date'][0:4]=='1990']
print selection
#convert filenames to a dict
#write a loop that opens each file and adds to an empty np array
os.chdir(subpath)
for s in selection:
print s
nc=ncfile(s)
temp = nc.variables['item5216_monthly_mean'][:]
print temp.shape
exit()
#sarah test
"""
dataset = ncfile(path)
print dataset.file_format
print dataset.dimensions.keys()
print dataset.variables.keys()
lats = dataset.variables['global_latitude0'][:]
lons = dataset.variables['global_longitude0'][:]
time = dataset.variables['global_longitude0'][:]
pr = dataset.variables['item5216_monthly_mean'][:]
#plt.xticks(time2)
#plt.xticks(np.arange(1,525,130))
#fig.autofmt_xdate()
#plt.show()
plt.savefig('/home/sps/Desktop/ursi_images/windmagnitude.png')
#############################################################################################################################
#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
from netCDF4 import Dataset as ncfile
from cfplot import *
import math
nc = ncfile('/home/sps/Desktop/python/gdata.nc')
lats = nc.variables['lat'][:]
p = nc.variables['p'][:]
temp = nc.variables['temp'][:, :, 0]
p0 = 1013.25
H = 8
p1 = (p / p0)
p2 = np.log(p1)
z = (-8) * p2
print z
fig = plt.figure(figsize=(11, 8))
ax1 = fig.add_subplot(111)
#ax1.axis([-90, 90, 0, 20])
#ax1.tick_params(direction='out', which='both')
@author: anonymous
"""
# Python3 Script to read and plot example of Era 5 SST
from netCDF4 import Dataset as ncfile
import numpy as np
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import cartopy
# -------------------------
# LOAD DATA:
# -------------------------
fil = ncfile('./e5.moda.an.sfc.128_034_sstk.ll025sc.2018010100_2018120100.nc','r')
lat = fil.variables['latitude'][:]
lon = fil.variables['longitude'][:]
sst = fil.variables['SSTK'][:,:,:] - 273.15 # SST in Degrees C
# -------------------------
# PLOT January 2018 Sea Surface Temperature
# -------------------------
#
map_proj = cartopy.crs.LambertCylindrical(central_longitude=200.0)
LAND_highres = cartopy.feature.NaturalEarthFeature('physical', 'land', '50m', edgecolor='face', facecolor = 'black', linewidth=.1)
fig, axs = plt.subplots(1,1, figsize=(10, 3), facecolor='w', edgecolor='k')
axs = plt.subplot(1, 1, 1, projection = map_proj)
axs.add_feature(LAND_highres)
axs.set_title('Era5 SST ($^\circ$C): January 2018', fontsize = 12)
lon_lower = 33.5
# compare with: 1 = None, 2 = TRMM, 3 = CRU, 4 = None_TRMM, 5 = None_CRU
compare = 'TRMM'
# ==============================================================================
# Path where data is stored
path = '/ouce-home/staff/sedm4922/Validation/'
# factors to convert units to mm/month
fac = 3600 * 24 * 30 # for the model
fac2 = 24 * 30.5 # for the obs
# load landsea mask
nc = ncfile(path + 'lsm_africa.nc')
lsm = nc.variables['lsm'][0, 0, :, :] * -1.
lsm[lsm == 0] = np.nan
# Define months array
mon = [
'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct',
'nov', 'dec'
]
dimo = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
if test_mode == 1:
precip = np.zeros((12, 25, 138, 200))
precip_ens = np.zeros((12, 25, 100, 138, 200))
for moncount in np.arange(len(mon)):
# IMPORTANT: THIS SCRIPT ASSUMES YOU HAVE 3X0_VARS.NC GENERATED FROM MAIN.PY
# AND LOCATED IN THE SAME FOLDER AS THIS FILE. THE VARIABLES PREC_DAILY,
# PREC_MONTHLY, RH_DAILY, AND RH_MONTHLY WILL BE APPENDED
from netCDF4 import Dataset as ncfile
import numpy as np
from tools import calc_es, calc_rh, calc_ws, run_param, col_av
import matplotlib.pyplot as plt
#filepath = 'E:/ATM115 Data/SST300k-selected/'
filepath = './SST300k-selected/'
data3d = './SST300k-selected/sam3d.nc'
data2d = './SST300k-selected/sam2d.nc'
outputfilename = '300K_vars.nc'
sam3d = ncfile(data3d)
sam2d = ncfile(data2d)
varfile = ncfile('./300K_vars.nc','r+')
prec = sam2d['Prec'][:]
rh = varfile['rh'][:]
# Daily Averages
prec_daily = np.zeros((150,1024))
rh_daily = np.zeros((150,71,1024))
tmin = 0
tmax = 23
for day in range(0,150):
for x in range(0,1024):
# -*- coding: utf-8 -*- """ Created on Fri Dec 6 11:38:22 2019 @author: 25006773 """ from netCDF4 import Dataset as ncfile import datetime as dt import pandas as pd import matplotlib.pyplot as plt import numpy as np # Give the location of the MJO file and open it infile = '/Users/seren/Documents/University/Year 3/MT37J (Boundary Layer Met)/Assignment 2/MJO_rmm1_rmm2.jan-dec_dmeans_ts.1979-2018 (1) - Copy.nc' nc_infile = ncfile(infile) # Read in variables mjo_phase = nc_infile['phase_ts'][:] mjo_amp = nc_infile['amplitude_ts'][:] rmm1 = nc_infile['rmm1_ts'][:] rmm2 = nc_infile['rmm2_ts'][:] #Read in time variable to get list of dates time = nc_infile['time'][:] #create empty MJO date lists phase0_dates = [] phase1_dates = [] phase2_dates = [] phase3_dates = [] phase4_dates = []
fac = 3600 * 24 * 30 # for the model
fac2 = 24 * 30.5 # for the obs
#mon = ['01','02','03','04','05','06','07','08','09','10','11','12']
mon = [
'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct',
'nov', 'dec'
]
dimo = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
precip = np.zeros((12, 25, 138, 200))
precip_ens = np.zeros((12, 25, 100, 138, 200))
for moncount in np.arange(len(mon)):
if var == 'prp':
nc = ncfile(path + mon[moncount] +
'_precip_Africa_1986-2010_Dec-Nov.nc')
temp = nc.variables['precip'][:] * fac
temp[temp > np.std(temp) * 10] = np.nan
temp[temp < 0] = np.nan
temp[temp == 0] = np.nan
precip[moncount, :, :, :] = np.nanmean(temp, axis=1)
precip_ens[moncount, :, :, :, :] = temp
precip_ens[:, :, 40, :, :] = np.nan
precip_ens[:, :, 11, :, :] = np.nan
lat = nc.variables['latitude'][:]
lon = nc.variables['longitude'][:]
if spatial == 'Whole':
lat1 = np.arange(len(lat))
import cartopy.crs as ccrs import matplotlib as mpl from scipy import ndimage from sys import exit #Choose variable to look at: var = 'prp' #Choose path where data stored path = '/ouce-home/staff/sedm4922/Validation/' #Choose factor to convert to mm/month fac = 3600*24*30 # for the model fac2 = 24*30.5 # for the obs #Choose mask nc=ncfile(path+'ethiopiamask_12.nc') eth = nc.variables['mask'][:,:,:] mon = ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'] dimo = [31,28,31,30,31,30,31,31,30,31,30,31] precip = np.zeros((12,25,138,200)) precip_ens = np.zeros((12,25,100,138,200)) # compare with: 1 = None, 2 = TRMM, 3 = CRU, 4 = CHIRPS compare = 'None' precip = np.zeros((12,25,138,200)) precip_ens = np.zeros((12,25,100,138,200))