def __reader(self):
## read pickle if pickle file exisits and 'reset=False' or process raw data
if (self.pkl_nam in listdir(self.path))&(~self.reset):
print(f"\n\t{dtm.now().strftime('%m/%d %X')} : Reading pickle of {self.nam} lidar")
with open(pth(self.path,self.pkl_nam),'rb') as f:
fout = pkl.load(f)
return fout
else:
print(f"\n\t{dtm.now().strftime('%m/%d %X')} : Reading file of {self.nam} lidar and process raw data")
##=================================================================================================================
## metadata parameter
ext_nam, dt_freq, height, col_fun, col_nam, out_nam, oth_col = self.meta.values()
## read raw data
_df_con = None
for file in listdir(self.path):
if ext_nam not in file.lower(): continue
print(f"\r\t\treading {file}",end='')
_df = self.__raw_reader(file)
if _df is not None:
_df_con = concat([_df_con,_df]) if _df_con is not None else _df
## concat the concated list
df = self.__raw_process(_df_con,dt_freq)
print()
##=================================================================================================================
## classify data
## use dictionary to store data
fout = {}
for col, nam in zip(col_nam,out_nam):
_df = df[[ eval(col_fun)(h,col) for h in height ]].copy()
_df.columns = array(height).astype(int)
_df[0] = 0
fout[nam] = _df[array([0]+height).astype(int)]
## process other parameter
if oth_col is not None:
df.rename(columns=oth_col,inplace=True)
fout['other'] = df[list(oth_col.values())].copy()
##=================================================================================================================
## dump pickle file
with open(pth(self.path,self.pkl_nam),'wb') as f:
pkl.dump(fout,f,protocol=pkl.HIGHEST_PROTOCOL)
return fout
def __reader(self):
## read pickle if pickle file exisits and 'reset=False' or process raw data
if (self.pkl_nam in listdir(self.path)) & (~self.reset):
print(
f"\n\t{dtm.now().strftime('%m/%d %X')} : Reading pickle of {self.nam}"
)
with open(pth(self.path, self.pkl_nam), 'rb') as f:
fout = pkl.load(f)
return fout
else:
print(
f"\n\t{dtm.now().strftime('%m/%d %X')} : Reading file of {self.nam} and process raw data"
)
##=================================================================================================================
## read raw data
f_list = []
for file in listdir(self.path):
if '.txt' not in file.lower(): continue
print(f"\r\t\treading {file}", end='')
f_list = self.__raw_reader(f_list, file)
print()
# fout = self.__raw_process(f_list,dt_freq)
fout = self.__raw_process(f_list)
##=================================================================================================================
## dump pickle file
with open(pth(self.path, self.pkl_nam), 'wb') as f:
pkl.dump(fout, f, protocol=pkl.HIGHEST_PROTOCOL)
return fout
def __raw_reader(self, _flist, _file):
## customize each instrument
## read one file
with open(pth(self.path, _file), 'r', errors='ignore') as f:
_temp = read_table(f, skiprows=1)
_tm_list = []
for _ in _temp['Date/time']:
try:
if '\u4e0a\u5348' in _:
_tm_list.append(
dtm.strptime(_.replace('\u4e0a\u5348', 'AM'),
'%Y/%m/%d %p %X'))
else:
_tm_list.append(
dtm.strptime(_.replace('\u4e0b\u5348', 'PM'),
'%Y/%m/%d %p %X') + dtmdt(hours=12))
except:
_tm_list.append(dtm.strptime(_, '%Y/%m/%d'))
_temp['Date/time'] = _tm_list
_temp = _temp.set_index('Date/time')
_temp.columns = array([
_key.split('-')[0].strip('> um') for _key in list(_temp.keys())
]).astype(float)
_flist.append(_temp.resample('6T').mean())
return _flist
def __raw_reader(self, _flist, _file):
## customize each instrument
## read one file
with open(pth(self.path, _file),
'r',
encoding='utf-8',
errors='ignore') as f:
_rename = {
'airtemp_Avg': 'T',
'relhumidity_Avg': 'RH',
'airpressure_Avg': 'P',
'Wsavg_Avg': 'ws',
'Wdavg_Avg': 'wd',
'WindDir_SD1_WVT': 'wd_std'
}
_temp = read_csv(f, skiprows=(0, 2, 3), parse_dates=[
'TIMESTAMP'
]).rename(columns=_rename).set_index('TIMESTAMP')
_temp['Td'] = mcalc.dewpoint_from_relative_humidity(
_temp['T'].to_numpy(dtype=float) * units.degC,
_temp['RH'].to_numpy(dtype=float) / 100.).m
_temp['u'] = -sin(_temp['wd'] / 180. * pi) * _temp['ws']
_temp['v'] = -cos(_temp['wd'] / 180. * pi) * _temp['ws']
_flist.append(
_temp[list(_rename.values()) +
['Td', 'u', 'v']].astype(float).resample('1T').mean())
return _flist
def run():
## GRIMM
'''
start_dtm = dtm(2021,4,1)
final_dtm = dtm(2021,4,4)
path = pth('..','data','GRIMM')
grimm = GRIMM.reader(path,start_dtm,final_dtm,reset=False)
# dt = grimm.get_data()
# dt = grimm.plot(pth('..','picture'),dtm(2021,4,2,6),dtm(2021,4,2,18),tick_freq='2h',mean_freq='6T')
dt = grimm.plot(pth('..','picture'),start_dtm,final_dtm,tick_freq='12h',mean_freq='30T')
# '''
## WXT
# '''
start_dtm = dtm(2021, 4, 1)
final_dtm = dtm(2021, 4, 5)
path = pth('..', 'data', 'WXT')
wxt = WXT.reader(path, start_dtm, final_dtm, reset=False)
dt = wxt.get_data()
# wxt.plot(pth('..','picture'),dtm(2021,4,3,12),dtm(2021,4,4,16),tick_freq='6h',mean_freq='30T')
# '''
return dt
def get_data_list(self):
anndir = pth(self.main_dir, self.data_name, 'annotations')
if self.split == 'rval': #use restval data as train data
''' debugging print '''
tmp = pth(anndir, 'rval_{}.json'.format(self.mode))
print('datalist path: {}'.format(tmp))
''''''
datalist = json.load(
open(pth(anndir, 'rval_{}.json'.format(self.mode))))
elif self.split == '2017':
datalist = json.load(
open(pth(anndir, '{}2017.json'.format(self.mode))))
''' debugging print '''
tmp = pth(anndir, '{}2017.json'.format(self.mode))
print('datalist path: {}'.format(tmp))
''''''
return datalist
def get_coco(self):
year = '2014' if self.coco_split is 'rval' else '2017'
caps = {}
for mode in ['train', 'val']:
caps[mode] = COCO(
pth(self.main_dir, self.data_name, 'annotations',
'captions_{}{}.json'.format(mode, year)))
# caps = COCO(pth(self.main_dir, self.data_name, 'annotations','captions_{}{}.json'.format('train', year)))
# caps_val = COCO(pth(self.main_dir, self.data_name, 'annotations','captions_{}{}.json'.format('val', year)))
return caps
def _plot_pcolor(_nam, _cmap):
print(f'\tplot {dt_nam} : {_nam}')
## parameter
fs = 13.
setting = meta[_nam]
## plot ws, wd pcolormesh
## parameter
dt = dt_dic[_nam]
dt.replace(0., n.nan, inplace=True)
x_tick = dt.asfreq(tick_freq).index
x_tick_lab = x_tick.strftime('%Y-%m-%d%n%X')
## plot
fig, ax = subplots(figsize=(12, 6), dpi=150.)
pm = ax.pcolormesh(dt.index,
dt.keys(),
dt[:-1].T[:-1],
cmap=_cmap,
vmin=setting['vmin'],
vmax=setting['vmax'])
box = ax.get_position()
ax.set_position([box.x0, box.y0 + 0.02, box.width, box.height])
cax = fig.add_axes([.92, box.y0 + 0.02, .015, box.height])
cb = fig.colorbar(pm, cax=cax)
ax.tick_params(which='major', length=6., labelsize=fs - 2.)
ax.tick_params(which='minor', length=3.5)
cb.ax.tick_params(which='major', length=5., labelsize=fs - 2.)
cb.ax.tick_params(which='minor', length=2.5)
ax.set(xticks=x_tick, ylim=(setting['ylim_bot'], setting['ylim_top']))
ax.set_xticklabels(x_tick_lab)
ax.set_xlabel('Time', fontsize=fs)
ax.set_ylabel('Height (m)', fontsize=fs)
cb.ax.set_title(setting['cb_label'], fontsize=fs - 2.)
fig.suptitle(f'{dt_nam.upper()} data : {_nam}',
fontsize=fs + 2.,
style='italic')
# show()
fig.savefig(
pth(
save_path,
f'{dt_nam}_{_nam}_{dt.index[0].strftime("%Y%m%d%H%M")}-{dt.index[-1].strftime("%Y%m%d%H%M")}.png'
))
close()
def __init__(self, config, transform, mode):
self.main_dir = config.main_dir
self.data_name = config.data_name
self.mode = mode
self.max_token_len = config.max_token_len
self.split = self.get_split_type(config)
self.data = self.get_data_list()
self.vocab = get_vocab(self.main_dir, self.data_name)
self.transform = transform
''' debugging '''
self.coco_split = config.coco_split
tmp = pth(
self.main_dir, self.data_name, 'annotations',
'captions_{}{}.json'.format(
self.mode, '2014' if self.coco_split is 'rval' else '2017'))
print('caption path: {}'.format(tmp))
''''''
self.caps = self.get_coco()
def _plot(meta_nam):
## plot quiver and plot z_ws
meta = meta_dt[meta_nam]
## parameter
fs = 15.
setting = meta['quiver']
## plot
fig, ax = subplots(figsize=(12, 8), dpi=150.)
box = ax.get_position()
ax.set_position([box.x0, box.y0 + 0.05, box.width, box.height])
## z_ws
if 'z_ws' in dt_dic.keys():
_plot_z_ws(fig, ax, fs, ax.get_position(), meta)
## quiver
x_tick, _st, _fn = _plot_quiver(fig, ax, fs, ax.get_position(), meta)
## other figure setting
ax.tick_params(which='major', length=6., labelsize=fs - 2.)
ax.tick_params(which='minor', length=3.5)
ax.set(xticks=x_tick, ylim=(setting['ylim_bot'], setting['ylim_top']))
ax.set_xticklabels(x_tick.strftime('%Y-%m-%d%n%HLST'))
# ax.set_xlabel('Time',fontsize=fs)
ax.set_ylabel('Height (m)', fontsize=fs)
fig.suptitle(
f'{dt_nam.upper()} lidar wind profile (every {setting["dt_freq"].replace("T"," min")}) ',
fontsize=fs + 2.,
style='italic')
fig.savefig(
pth(
save_path[meta_nam],
f'{dt_nam}_wswd_{_st.strftime("%Y%m%d%H%M")}-{_fn.strftime("%Y%m%d%H%M")}.png'
))
def __getitem__(self, index):
info = self.data[index]
img = Image.open(
pth(self.main_dir, self.data_name, info['filepath'],
info['filename'])).convert('RGB')
if info['filepath'] in ['train2017', 'train2014']:
caps = self.caps['train']
else:
caps = self.caps['val']
anns = caps.loadAnns(info['sentid'])
img_id = info['imgid']
# random pick one of 5 text captions
idx = random.randint(0, len(anns) - 1) if self.mode is 'train' else 0
cap = anns[idx]['caption']
vec = self.encode_token(cap)
# tokens = nltk.tokenize.word_tokenize(str(cap).lower().decode('utf-8'))
# cap = []
# # cap.append(self.vocab('<start>'))
# cap.extend([self.vocab(token) for token in tokens])
# cap.append(self.vocab('<end>'))
return self.transform(img), vec, img_id
def plot_all(dt_dic, fig_path='.', tick_freq='6h', input_tick=None):
## parameter
dt_nam = dt_dic['nam'].split('_')[0]
_ind = dt_dic['ws'].index
print(f'\nPlot {dt_nam} data')
## make picture dir
dir_path = pth(fig_path, dt_nam)
mkdir(dir_path) if not exists(dir_path) else None
save_path = {}
save_path[dt_nam] = pth(
dir_path,
f'{_ind[0].strftime("%Y-%m-%d %H")}_{_ind[-1].strftime("%Y-%m-%d %H")}'
)
mkdir(save_path[dt_nam]) if not exists(save_path[dt_nam]) else None
mkdir(
pth(fig_path,
'lidar_comp')) if not exists(pth(fig_path, 'lidar_comp')) else None
save_path['comp'] = pth(
fig_path, 'lidar_comp',
f'{_ind[0].strftime("%Y-%m-%d %H")}_{_ind[-1].strftime("%Y-%m-%d %H")}'
)
mkdir(save_path['comp']) if not exists(save_path['comp']) else None
## function
meta = meta_dt[dt_nam]
def _plot_pcolor(_nam, _cmap):
print(f'\tplot {dt_nam} : {_nam}')
## parameter
fs = 13.
setting = meta[_nam]
## plot ws, wd pcolormesh
## parameter
dt = dt_dic[_nam]
dt.replace(0., n.nan, inplace=True)
x_tick = dt.asfreq(tick_freq).index
x_tick_lab = x_tick.strftime('%Y-%m-%d%n%X')
## plot
fig, ax = subplots(figsize=(12, 6), dpi=150.)
pm = ax.pcolormesh(dt.index,
dt.keys(),
dt[:-1].T[:-1],
cmap=_cmap,
vmin=setting['vmin'],
vmax=setting['vmax'])
box = ax.get_position()
ax.set_position([box.x0, box.y0 + 0.02, box.width, box.height])
cax = fig.add_axes([.92, box.y0 + 0.02, .015, box.height])
cb = fig.colorbar(pm, cax=cax)
ax.tick_params(which='major', length=6., labelsize=fs - 2.)
ax.tick_params(which='minor', length=3.5)
cb.ax.tick_params(which='major', length=5., labelsize=fs - 2.)
cb.ax.tick_params(which='minor', length=2.5)
ax.set(xticks=x_tick, ylim=(setting['ylim_bot'], setting['ylim_top']))
ax.set_xticklabels(x_tick_lab)
ax.set_xlabel('Time', fontsize=fs)
ax.set_ylabel('Height (m)', fontsize=fs)
cb.ax.set_title(setting['cb_label'], fontsize=fs - 2.)
fig.suptitle(f'{dt_nam.upper()} data : {_nam}',
fontsize=fs + 2.,
style='italic')
# show()
fig.savefig(
pth(
save_path,
f'{dt_nam}_{_nam}_{dt.index[0].strftime("%Y%m%d%H%M")}-{dt.index[-1].strftime("%Y%m%d%H%M")}.png'
))
close()
## plot quiver and vertical wind
def _plot_quiver(fig, ax, fs, box, meta):
## parameter
def wswd2uv(_ws, _wd):
return -n.sin(_wd / 180. * n.pi) * _ws, -n.cos(
_wd / 180. * n.pi) * _ws
cmap_bot = cm.get_cmap('RdGy', 512)(n.linspace(.15, .5, 512))
cmap_top = cm.get_cmap('RdGy', 512)(n.linspace(.5, 1., 512))
cmap = mc.ListedColormap(n.vstack((cmap_bot, cmap_top)))
cmap = 'jet'
setting = meta['quiver']
# dt_ws, dt_wd = dt_dic['ws'].asfreq(dt_freq)[::setting['sep']], dt_dic['wd'].asfreq(dt_freq)[::setting['sep']]
dt_ws, dt_wd = dt_dic['ws'].asfreq(
setting['dt_freq']), dt_dic['wd'].asfreq(setting['dt_freq'])
# dt_ws[dt_ws.keys()[0]].replace(0.,n.nan,inplace=True)
# dt_wd[dt_ws.keys()[0]].replace(0.,n.nan,inplace=True)
## (1) replace 0 as nan, process the by-product after interpolate
## (2) get the mask below 2.5
## (3) set the value as nan under mask
## (4) get the mask out value, apply scatter plot
dt_ws.replace(0., n.nan, inplace=True)
_mask = dt_ws.copy() < 2.5
dt_ws.mask(_mask, n.nan, inplace=True)
_index, _height = n.meshgrid(dt_ws.index, dt_ws.keys())
## change ws, wd to u, v
## get height as y axis and get x ticks
_u, _v = wswd2uv(1.5, dt_wd)
height = n.array(list(dt_ws.keys())).astype(float)
x_tick = input_tick if input_tick is not None else dt_ws.asfreq(
tick_freq).index
## plot
sc = ax.scatter(_index[_mask.T],
_height[_mask.T],
s=15,
fc='None',
ec='#666666',
label='< 2.5 m/s')
qv = ax.quiver(_u.index,
height[::setting['sep']],
_u.T[::setting['sep']],
_v.T[::setting['sep']],
dt_ws.T[::setting['sep']].values,
cmap=cmap,
scale=50,
clim=(setting['vmin'], setting['vmax']))
cax = fig.add_axes([.92, box.y0, .015, box.height])
cb = fig.colorbar(qv, cax=cax)
cb.ax.tick_params(which='major', length=5., labelsize=fs - 2.)
cb.ax.tick_params(which='minor', length=2.5)
cb.ax.set_title(setting['cb_label'], fontsize=fs - 2.)
ax.legend(handles=[sc],
framealpha=0,
fontsize=fs - 1.,
loc=10,
bbox_to_anchor=(0.1, 1.025),
handlelength=1.)
return x_tick, dt_ws.index[0], dt_ws.index[-1]
## plot z_ws pcolormesh
def _plot_z_ws(fig, ax, fs, box, meta):
## parameter
setting = meta['z_ws']
# cmap_bot = cm.get_cmap('Blues_r',512)(n.linspace(.5,1.,512))
cmap_top = cm.get_cmap('Greys', 512)(n.linspace(.45, .6, 512))
cmap_bot = cm.get_cmap('Greys', 512)(n.linspace(.0, .3, 512))
cmap = mc.ListedColormap(n.vstack((cmap_bot, cmap_top)))
## data
dt = dt_dic['z_ws']
# dt.replace(0.,n.nan,inplace=True)
## plot
## set z_ws as background and the colorbar is horizontal
pm = ax.pcolormesh(dt.index,
dt.keys(),
dt[1:].T[1:],
cmap=cmap,
vmin=setting['vmin'],
vmax=setting['vmax'])
box = ax.get_position()
cax = fig.add_axes([box.x0, .045, box.width, .015])
cb = fig.colorbar(pm, cax=cax, extend='both', orientation='horizontal')
cb.ax.tick_params(which='major', length=5., labelsize=fs - 2.)
cb.ax.tick_params(which='minor', length=2.5)
cb.ax.set_title(setting['cb_label'], fontsize=fs - 2.)
def _plot(meta_nam):
## plot quiver and plot z_ws
meta = meta_dt[meta_nam]
## parameter
fs = 15.
setting = meta['quiver']
## plot
fig, ax = subplots(figsize=(12, 8), dpi=150.)
box = ax.get_position()
ax.set_position([box.x0, box.y0 + 0.05, box.width, box.height])
## z_ws
if 'z_ws' in dt_dic.keys():
_plot_z_ws(fig, ax, fs, ax.get_position(), meta)
## quiver
x_tick, _st, _fn = _plot_quiver(fig, ax, fs, ax.get_position(), meta)
## other figure setting
ax.tick_params(which='major', length=6., labelsize=fs - 2.)
ax.tick_params(which='minor', length=3.5)
ax.set(xticks=x_tick, ylim=(setting['ylim_bot'], setting['ylim_top']))
ax.set_xticklabels(x_tick.strftime('%Y-%m-%d%n%HLST'))
# ax.set_xlabel('Time',fontsize=fs)
ax.set_ylabel('Height (m)', fontsize=fs)
fig.suptitle(
f'{dt_nam.upper()} lidar wind profile (every {setting["dt_freq"].replace("T"," min")}) ',
fontsize=fs + 2.,
style='italic')
fig.savefig(
pth(
save_path[meta_nam],
f'{dt_nam}_wswd_{_st.strftime("%Y%m%d%H%M")}-{_fn.strftime("%Y%m%d%H%M")}.png'
))
## plot
# _plot_pcolor('ws','jet')
# _plot_pcolor('z_ws',cmap)
# _plot('comp')
_plot(dt_nam)
# from pandas import date_range, concat
import json as jsn
## bugs box
"""
# """
__all__ = [
'plot_all',
]
# parameter
cur_file_path = dirname(realpath(__file__))
with open(pth(cur_file_path, 'metadata.json'), 'r') as f:
meta_dt = jsn.load(f)
## plot all variable
def plot_all(dt_dic, fig_path='.', tick_freq='6h', input_tick=None):
## parameter
dt_nam = dt_dic['nam'].split('_')[0]
_ind = dt_dic['ws'].index
print(f'\nPlot {dt_nam} data')
## make picture dir
dir_path = pth(fig_path, dt_nam)
mkdir(dir_path) if not exists(dir_path) else None
def plot(self,
fig_path='.',
start=None,
final=None,
mean_freq=None,
tick_freq='6h'):
## plot time series
## make picture dir
save_path = pth(fig_path, 'WXT')
mkdir(save_path) if not exists(save_path) else None
## get data
df = self.get_data(start, final, mean_freq)
## plot (T, Td), pressure, uv wind
## parameter
fs = 13.
x_tick = df.asfreq(tick_freq).index
x_tick_lab = x_tick.strftime('%Y-%m-%d%n%X')
## plot
fig, axes = subplots(3,
1,
figsize=(10, 7),
dpi=150.,
gridspec_kw=dict(wspace=.325, hspace=.45),
sharex=False,
sharey=False)
## plot T and P
axes[0].plot(df['Td'], color='#4cffff', label='$T_d$')
for ax, _nam in zip(axes, ['T', 'P']):
setting = self.meta[_nam]
ax.plot(df[_nam], color=setting['color'], label=_nam)
ax.set(ylim=(setting['ylim_bot'], setting['ylim_top']),
xticks=x_tick)
ax.set_title(setting['title'], fontsize=fs)
ax.set_ylabel(setting['ylabel'], fontsize=fs)
ax.set_xticklabels('')
axes[0].legend(framealpha=0, fontsize=fs - 2.5)
## plot uv
ax = axes[2]
setting = self.meta['wind']
itv = setting['itv']
ax.quiver(df.index[::itv],
0.,
df['u'][::itv],
df['v'][::itv],
scale=5,
scale_units='inches',
color='#cc99ff',
headwidth=2.)
# ax.quiver(df.index[::itv],0.,df['u'][::itv],df['v'][::itv],angles='xy', scale_units='xy', scale=1.,color='#cc99ff')
ax.set(xticks=x_tick, ylim=(-2, 2), xlim=(df.index[0], df.index[-1]))
ax.set_title(setting['title'], fontsize=fs)
ax.set_ylabel(setting['ylabel'], fontsize=fs)
ax.set_xticklabels(x_tick_lab)
fig.text(.5, .03, 'Time', ha='center', fontsize=fs)
fig.suptitle('WXT data', fontsize=fs + 2., style='italic')
fig.savefig(
pth(
save_path,
f'WXT_{df.index[0].strftime("%Y%m%d%H%M")}-{df.index[-1].strftime("%Y%m%d%H%M")}.png'
))
# show()
close()
## plot diurnal cycle
df['diurnal'] = df.index.map(lambda _: _.strftime('%H:00'))
df_diu = df.groupby('diurnal').mean()
x_tick = list(df['diurnal'])[::6]
## parameter
fs = 13.
#'''
## plot
fig, axes = subplots(3,
1,
figsize=(8, 6),
dpi=150.,
gridspec_kw=dict(wspace=.325, hspace=.325),
sharex=False,
sharey=False)
for _nam, ax in zip(['T', 'Td', 'P'], axes.flatten()):
setting = self.meta['diu'][_nam]
ax.plot(df_diu[_nam], c=setting['color'])
ax.tick_params(which='major',
direction='in',
length=7,
labelsize=fs - 2.5)
[ax.spines[axis].set_visible(False) for axis in ['right', 'top']]
ax.set(ylim=(setting['ylim_bot'], setting['ylim_top']),
xticks=x_tick)
ax.set_xticklabels('')
ax.set_ylabel(setting['ylabel'], fontsize=fs)
ax.set_title(setting['title'], fontsize=fs)
ax.set_xticklabels(x_tick)
fig.text(.5, .03, 'Time', ha='center', fontsize=fs)
fig.suptitle(
f'WXT diurnal cycle : from {df.index[0].strftime("%Y/%m/%d %H%M")} to {df.index[-1].strftime("%Y/%m/%d %H%M")}',
fontsize=fs + 2.,
style='italic')
fig.savefig(
pth(
save_path,
f'WXT_diuunal_{df.index[0].strftime("%Y%m%d%H%M")}-{df.index[-1].strftime("%Y%m%d%H%M")}.png'
))
close()
return df
}
# Internationalization
# https://docs.djangoproject.com/en/1.6/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.6/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = pth(BASE_DIR, "static_root")
MEDIA_ROOT = pth(BASE_DIR, "media")
STATICFILES_DIRS = (
pth(BASE_DIR, "static"),
)
TEMPLATE_DIRS = (
pth(BASE_DIR, "templates"),
)
def plot(self,
fig_path='.',
start=None,
final=None,
mean_freq=None,
tick_freq='6h'):
from matplotlib.colors import LogNorm
from matplotlib.pyplot import subplots, close, show
## plot time series
## make picture dir
save_path = pth(fig_path, self.nam)
mkdir(save_path) if not exists(save_path) else None
## get data
dt = self.get_data(start, final, mean_freq)
## plot (T, Td), pressure, uv wind
## parameter
fs = 13.
x_tick = dt.asfreq(tick_freq).index
x_tick_lab = x_tick.strftime('%Y-%m-%d%n%X')
setting = self.meta
## plot
fig, ax = subplots(figsize=(12, 6), dpi=150.)
pm = ax.pcolormesh(dt.index,
dt.keys(),
dt.T,
cmap='jet',
norm=LogNorm(vmin=setting['vmin'],
vmax=setting['vmax']),
shading='auto')
ax.hlines(2.5, dt.index[0], dt.index[-1], color='#000000', ls='--')
ax.hlines(10., dt.index[0], dt.index[-1], color='#000000', ls='--')
box = ax.get_position()
ax.set_position([box.x0, box.y0 + 0.02, box.width, box.height])
cax = fig.add_axes([.92, box.y0 + 0.02, .015, box.height])
cb = fig.colorbar(pm, cax=cax)
ax.tick_params(which='major', length=6., labelsize=fs - 2.)
ax.tick_params(which='minor', length=3.5)
cb.ax.tick_params(which='major', length=5., labelsize=fs - 2.)
cb.ax.tick_params(which='minor', length=2.5)
ax.set(xticks=x_tick, yscale='log')
ax.set_xticklabels(x_tick_lab)
ax.set_xlabel('Time', fontsize=fs)
ax.set_ylabel('Diameter ($\mu m$)', fontsize=fs)
cb.ax.set_title('number conc.\n(#/$m^3$/$\Delta log D_p$)',
fontsize=fs - 2.)
fig.suptitle(f'GRIMM data ({mean_freq} ave.)',
fontsize=fs + 2.,
style='italic')
# show()
fig.savefig(
pth(
save_path,
f'GRIMM_{dt.index[0].strftime("%Y%m%d%H%M")}-{dt.index[-1].strftime("%Y%m%d%H%M")}.png'
))
close()
return dt
def run():
res = 0
fig_pth = pth('..', 'picture')
## NDU
# '''
start_dtm = dtm(2021, 4, 1, 0, 0, 0)
final_dtm = dtm(2021, 4, 6, 0, 0, 0)
path = pth('..', 'data', 'Lidar_NDU', 'use')
reader = NDU.reader(path, start_dtm, final_dtm, reset=res)
# dt = reader.get_data(dtm(2021,4,2,0,0,0),dtm(2021,4,5,0,0,0))
dt = reader.get_data()
plot.plot_all(dt, fig_pth, tick_freq='24h')
# '''
## SSC
# '''
start_dtm = dtm(2021, 4, 1)
final_dtm = dtm(2021, 4, 6)
path = pth('..', 'data', 'Lidar_SSC')
reader = SSC.reader(path, start_dtm, final_dtm, reset=res)
# dt = reader.get_data(dtm(2021,4,2,0,0,0),dtm(2021,4,5,0,0,0))
dt = reader.get_data()
plot.plot_all(dt, fig_pth, tick_freq='24h')
# '''
## RCEC
# '''
start_dtm = dtm(2021, 4, 1)
final_dtm = dtm(2021, 4, 6)
path = pth('..', 'data', 'Lidar_RCEC')
reader = RCEC.reader(path, start_dtm, final_dtm, reset=res)
# dt = reader.get_data(dtm(2021,4,2,0,0,0),dtm(2021,4,5,0,0,0))
dt = reader.get_data()
plot.plot_all(dt, fig_pth, tick_freq='24h')
# '''
## TORI
# '''
start_dtm = dtm(2021, 4, 1)
final_dtm = dtm(2021, 4, 6)
path = pth('..', 'data', 'Lidar_TORI')
reader = TORI.reader(path, start_dtm, final_dtm, reset=res)
# dt = reader.get_data(dtm(2021,4,2,0,0,0),dtm(2021,4,5,0,0,0))
dt = reader.get_data()
plot.plot_all(dt, fig_pth, tick_freq='24h')
# '''
## GRIMM
'''
start_dtm = dtm(2021,4,1)
final_dtm = dtm(2021,4,4)
path = pth('..','data','GRIMM')
grimm = GRIMM.reader(path,start_dtm,final_dtm,reset=False)
# dt = grimm.get_data()
# dt = grimm.plot(pth('..','picture'),dtm(2021,4,2,6),dtm(2021,4,2,18),tick_freq='2h',mean_freq='6T')
dt = grimm.plot(pth('..','picture'),start_dtm,final_dtm,tick_freq='12h',mean_freq='30T')
# '''
## WXT
'''
start_dtm = dtm(2021,4,1)
final_dtm = dtm(2021,4,5)
path = pth('..','data','WXT')
wxt = WXT.reader(path,start_dtm,final_dtm,reset=False)
dt = wxt.get_data()
dt = wxt.plot(pth('..','picture'),dtm(2021,4,3,12),dtm(2021,4,4,16),tick_freq='6h',mean_freq='30T')
# '''
return dt
