#print(of)
#data = np.load(of)
#snod = data['snow']
#it = data['ice']
##get rid of nans
#m1 = np.ma.masked_invalid(it)
##it = np.ma.masked_invalid(it)
#snod = snod[m1.mask == False]
#it = it[m1.mask == False]
#load the csv data created in tt_grid.py
fname = glob(inpath_table + '*/magna+gem2-transect-' + date + '*' + loc +
'*.csv')[0]
snod = getColumn(fname, 5, delimiter=',', magnaprobe=False)
it = getColumn(fname, 6, delimiter=',', magnaprobe=False)
snod = np.array(snod, dtype=np.float)
it = np.array(it, dtype=np.float)
#print(it)
#exit()
#means and modes
mn = np.mean(snod)
print(mn)
#mni = np.mean(it)
#find mode
hist = np.histogram(it, bins=irbins)
srt = np.argsort(hist[0]) #indexes that would sort the array
#dates = ['20200123']
print(loc)
colors = plt.cm.rainbow(np.linspace(0, 1, len(dates)))
inpath = '../data/MCS/MP/'
outpath = '../plots_AGU/'
inpath_grid = '../data/grids_AGU/'
outname = 'profile_'+loc+str(stp)+'.png'
#choose one 'most perfct' MP track to compare to the others
fname = glob(inpath+'*/magnaprobe-transect-'+fixed_date+'*'+loc+'-track-icecs-xy_corr.csv')[0]
print(fname)
mxx = getColumn(fname,3, delimiter=',', magnaprobe=False)
mxx = np.array(mxx,dtype=np.float)
myy = getColumn(fname,4, delimiter=',', magnaprobe=False)
myy = np.array(myy,dtype=np.float)
#fix cooridinate shift
if fixed_date == '20200220':
mxx = mxx-10
if loc == 'Nloop':
myy = myy-3
if fixed_date == '20191222':
mxx = mxx+3
myy = myy+3
nit_ts=[]
for dd in range(0,len(dates)):
date = dates[dd]
dt = datetime.strptime(date, '%Y%m%d')
dt_list.append(dt)
print(date)
#outname = 'profile_'+date+'_'+loc+'gridded.png'
if gridded==False:
#choose one 'most perfct' MP track to compare to the others
fname = glob(inpath_table+'*/magna+gem2-transect-'+date+'*'+loc+'.csv')[0]
print(fname)
mxx = getColumn(fname,3, delimiter=',', magnaprobe=False)
myy = getColumn(fname,4, delimiter=',', magnaprobe=False)
snod = getColumn(fname,5, delimiter=',', magnaprobe=False)
it = getColumn(fname,6, delimiter=',', magnaprobe=False)
mxx = np.array(mxx,dtype=np.float)
myy = np.array(myy,dtype=np.float)
si = np.array(snod,dtype=np.float)
it = np.array(it,dtype=np.float)
else:
inf = inpath_grid+loc+'_'+stp+'m_'+method_gem2+ch_name+'_track_test.npz'
inf = inpath_grid+loc+'_'+stp+'m_'+method_gem2+ch_name+'_track.npz'
data = np.load(inf)
transect_snow = data['snow']
loc = 'Nloop'
dates = [
'20191024', '20191031', '20191107', '20191114', '20191121', '20191128',
'20191205', '20191219', '20191226', '20200102', '20200109', '20200116',
'20200130', '20200206', '20200220', '20200227', '20200305', '20200320',
'20200326', '20200403', '20200416', '20200424', '20200430', '20200507'
]
inpath_snow = '../data/MCS/MP/'
inpath_grid = '../data/grids_AGU/'
outpath = '../plots_AGU/'
#MP coordinates (for a day with good spacing!)
fn = glob(inpath_snow + '*/magnaprobe-transect-' + '20200116' + '*' + loc +
'-track-icecs-xy_corr.csv')[0]
x_track = getColumn(fn, 3, delimiter=',', magnaprobe=False)
y_track = getColumn(fn, 4, delimiter=',', magnaprobe=False)
x_track = np.array(x_track, dtype=np.float)
y_track = np.array(y_track, dtype=np.float)
#storage space
transect_snow = np.zeros((len(x_track), 2 + len(dates)))
transect_ice = np.zeros((len(x_track), 2 + len(dates)))
#print(transect_snow.shape)
transect_snow[:, 0] = x_track
transect_snow[:, 1] = y_track
dt_list = []
#print(xx)
#print(yy)
#ice thickness data
fname = glob(inpath_ice + date_gem2 +
'*/mosaic-*-*-gem2-*-channel-thickness.csv')[0]
mit1, mit2, mit3, mit4, mit5, mit6, mit7, mit8, mit9, mit10 = ridge_thick(
fname)
#print(mit1)
#MP
fname = glob(inpath_snow + '*/magnaprobe-transect-' + date + '*' + loc +
'-track-icecs-xy_corr.csv')[0]
print(fname)
dt = getColumn(fname, 0, delimiter=',', magnaprobe=False)
lon = getColumn(fname, 1, delimiter=',', magnaprobe=False)
lat = getColumn(fname, 2, delimiter=',', magnaprobe=False)
mxx = getColumn(fname, 3, delimiter=',', magnaprobe=False)
mxx = np.array(mxx, dtype=np.float)
myy = getColumn(fname, 4, delimiter=',', magnaprobe=False)
myy = np.array(myy, dtype=np.float)
#get some meta data for the MP transect:
dx = mxx[1:] - mxx[:-1]
dy = myy[1:] - myy[:-1]
d = np.sum(np.sqrt(dx**2 + dy**2))
print('transect length:')
print(d)
import numpy as np from glob import glob from tt_func import getColumn import matplotlib.pyplot as plt #GEM-2 inpath = '../../../MOSAiC/thickness_workspace/01-ice-thickness/20200220-PS122-2_25-117/' outpath = '../plots/' fname = 'mosaic-transect-20200220-gem2-556-track-icecs-xy.csv' xx = getColumn(inpath + fname, 3, delimiter=',', magnaprobe=False) xx = np.array(xx, dtype=np.float) yy = getColumn(inpath + fname, 4, delimiter=',', magnaprobe=False) yy = np.array(yy, dtype=np.float) #print(xx) #MP inpath = '../data/' outpath = '../plots/' #early date fname = '20200102_Sloop_MP_transect_track-icecs-xy.csv' mxx1 = getColumn(inpath + fname, 3, delimiter=',', magnaprobe=False) mxx1 = np.array(mxx1, dtype=np.float) myy1 = getColumn(inpath + fname, 4, delimiter=',', magnaprobe=False) myy1 = np.array(myy1, dtype=np.float) fname = '20200102_Nloop_MP_transect_track-icecs-xy.csv'
print(loc)
print(date)
print(date_gem2)
#we need to merge all GEM-2 survery for that day first
#coordinates
xx = []
yy = []
fname = glob(inpath_ice+date_gem2+'*/mosaic-transect-*-gem2-*-track-icecs-xy.csv')
for fn in fname:
print(fname)
x = getColumn(fn,3, delimiter=',', magnaprobe=False)
y = getColumn(fn,4, delimiter=',', magnaprobe=False)
xx.extend(x); yy.extend(y)
xx_full = np.array(xx,dtype=np.float)
yy_full = np.array(yy,dtype=np.float)
#ice thickness data
tt18 = []; tt5 = []; tt93 = []
fname = glob(inpath_ice+date_gem2+'*/mosaic-transect-*-gem2-*-channel-thickness.csv')
for fn in fname:
#time, record_id, longitude, latitude, xc, yc, f1525Hz_hcp_i, f1525Hz_hcp_q, f5325Hz_hcp_i, f5325Hz_hcp_q, f18325Hz_hcp_i, f18325Hz_hcp_q, f63025Hz_hcp_i, f63025Hz_hcp_q, f93075Hz_hcp_i, f93075Hz_hcp_q
t18 = getColumn(fn,10, delimiter=',', magnaprobe=False) #take 18KHz ip
t5 = getColumn(fn,8, delimiter=',', magnaprobe=False) #take 5KHz ip
#if date == '20191024': continue #Nloop has partially different track here - more resembling the planned square
#if date == '20191031': continue #Nloop has partially different track here - even more strange...
#if date == '20200123': continue #long transect
if date == '20200716': continue #missing data and bad coordinates
if date == '20200717': continue #bad GEM-2 coordinates
if date == '20200723': continue
if date == '20200724': continue
if date == '20200116':
#there is something wrong with the GEM-2 coordintes for this date - was supposed to be a regular transect day (good data)
#try to use a GEM-2 file for one week earlier
fname = flist[7]
print(fname)
xx = getColumn(fname, 3, delimiter=',', magnaprobe=False)
xx = np.array(xx, dtype=np.float)
yy = getColumn(fname, 4, delimiter=',', magnaprobe=False)
yy = np.array(yy, dtype=np.float)
#GEM-2 files contain nans
xx = np.ma.masked_invalid(xx)
yy = np.ma.masked_invalid(yy)
dx = xx[1:] - xx[:-1]
dy = yy[1:] - yy[:-1]
d = np.sum(np.sqrt(dx**2 + dy**2))
print('transect length:')
print(d)