def run(pair, ymd, is_monthly):
"""
pair: sat1+sensor1_sat2+sensor2
ymd: str YYYYMMDD
"""
# 提取参数中的卫星信息和传感器信息
part1, part2 = pair.split("_")
sat1, sensor1 = part1.split("+")
sat2, sensor2 = part2.split("+")
# 判断是静止卫星还是动态卫星
if "FY2" in part1 or "FY4" in part1:
Type = "GEOLEO"
elif "FY3" in part1:
Type = "LEOLEO"
else:
Log.error("Cant distinguish the satellite type")
return
# 加载绘图配置文件
plt_cfg_file = os.path.join(MainPath, "%s_%s_3d.yaml" % (sensor1, sensor2))
plt_cfg = loadYamlCfg(plt_cfg_file)
if plt_cfg is None:
Log.error("Not find the config file: {}".format(plt_cfg_file))
return
Log.info(u"----- Start Drawing Regression-Pic, PAIR: {}, YMD: {} -----".format(pair, ymd))
for each in plt_cfg["regression"]:
dict_cabr = {}
dict_cabr_d = {}
dict_cabr_n = {}
dict_bias = {}
dict_bias_d = {}
dict_bias_n = {}
# 需要回滚的天数
if is_monthly:
PERIOD = calendar.monthrange(int(ymd[:4]), int(ymd[4:6]))[1] # 当月天数
ymd = ymd[:6] + "%02d" % PERIOD # 当月最后一天
else:
PERIOD = plt_cfg[each]["days"]
# must be in "all", "day", "night"
Day_Night = ["all", "day", "night"]
if "time" in plt_cfg[each].keys():
Day_Night = plt_cfg[each]["time"]
for t in Day_Night:
if t not in ["all", "day", "night"]:
Day_Night.remove(t)
for idx, chan in enumerate(plt_cfg[each]["chan"]):
Log.info(u"Start Drawing {} Channel {}".format(each, chan))
oneHDF5 = ReadHDF5()
num_file = PERIOD
for daydelta in xrange(PERIOD):
cur_ymd = pb_time.ymd_plus(ymd, -daydelta)
hdf5_name = "COLLOC+%sIR,%s_C_BABJ_%s.hdf5" % (Type, pair, cur_ymd)
filefullpath = os.path.join(MATCH_DIR, pair, hdf5_name)
if not os.path.isfile(filefullpath):
Log.info(u"File not found: {}".format(filefullpath))
num_file -= 1
continue
if not oneHDF5.LoadData(filefullpath, chan):
Log.error("Error occur when reading %s of %s" % (chan, filefullpath))
if num_file == 0:
Log.error(u"No file found.")
continue
elif num_file != PERIOD:
Log.error(u"{} of {} file(s) found.".format(num_file, PERIOD))
if is_monthly:
str_time = ymd[:6]
cur_path = os.path.join(MRA_DIR, pair, str_time)
else:
str_time = ymd
cur_path = os.path.join(DRA_DIR, pair, str_time)
# delete 0 in std
if len(oneHDF5.rad1_std) > 0.0001: # TODO: 有些极小的std可能是异常值，而导致权重极大，所以 std>0 改成 std>0.0001
deletezeros = np.where(oneHDF5.rad1_std > 0.0001)
oneHDF5.rad1_std = oneHDF5.rad1_std[deletezeros]
oneHDF5.rad1 = oneHDF5.rad1[deletezeros] if len(
oneHDF5.rad1) > 0 else oneHDF5.rad1
oneHDF5.rad2 = oneHDF5.rad2[deletezeros] if len(
oneHDF5.rad2) > 0 else oneHDF5.rad2
oneHDF5.tbb1 = oneHDF5.tbb1[deletezeros] if len(
oneHDF5.tbb1) > 0 else oneHDF5.tbb1
oneHDF5.tbb2 = oneHDF5.tbb2[deletezeros] if len(
oneHDF5.tbb2) > 0 else oneHDF5.tbb2
oneHDF5.time = oneHDF5.time[deletezeros] if len(
oneHDF5.time) > 0 else oneHDF5.time
oneHDF5.lon1 = oneHDF5.lon1[deletezeros] if len(
oneHDF5.lon1) > 0 else oneHDF5.lon1
oneHDF5.lon2 = oneHDF5.lon2[deletezeros] if len(
oneHDF5.lon2) > 0 else oneHDF5.lon2
if len(oneHDF5.ref1_std) > 0.0001:
deletezeros = np.where(oneHDF5.ref1_std > 0.0001)
oneHDF5.ref1_std = oneHDF5.ref1_std[deletezeros]
oneHDF5.ref1 = oneHDF5.ref1[deletezeros] if len(
oneHDF5.ref1) > 0 else oneHDF5.ref1
oneHDF5.ref2 = oneHDF5.ref2[deletezeros] if len(
oneHDF5.ref2) > 0 else oneHDF5.ref2
oneHDF5.dn1 = oneHDF5.dn1[deletezeros] if len(
oneHDF5.dn1) > 0 else oneHDF5.dn1
oneHDF5.dn2 = oneHDF5.dn1[deletezeros] if len(
oneHDF5.dn2) > 0 else oneHDF5.dn2
oneHDF5.time = oneHDF5.time[deletezeros] if len(
oneHDF5.time) > 0 else oneHDF5.time
oneHDF5.lon1 = oneHDF5.lon1[deletezeros] if len(
oneHDF5.lon1) > 0 else oneHDF5.lon1
oneHDF5.lon2 = oneHDF5.lon2[deletezeros] if len(
oneHDF5.lon2) > 0 else oneHDF5.lon2
# find out day and night
if ("day" in Day_Night or "night" in Day_Night) and len(oneHDF5.time) > 0:
vect_is_day = np.vectorize(is_day_timestamp_and_lon)
day_index = vect_is_day(oneHDF5.time, oneHDF5.lon1)
night_index = np.logical_not(day_index)
else:
day_index = None
night_index = None
# 将每个对通用的属性值放到对循环，每个通道用到的属性值放到通道循环
# get threhold, unit, names...
xname, yname = each.split("-")
xname_l = plt_cfg[each]["x_name"]
xunit = plt_cfg[each]["x_unit"]
xlimit = plt_cfg[each]["x_range"][idx]
xmin, xmax = xlimit.split("-")
xmin = float(xmin)
xmax = float(xmax)
yname_l = plt_cfg[each]["y_name"]
yunit = plt_cfg[each]["y_unit"]
ylimit = plt_cfg[each]["y_range"][idx]
ymin, ymax = ylimit.split("-")
ymin = float(ymin)
ymax = float(ymax)
weight = None
if "rad" in xname:
x = oneHDF5.rad1
elif "tbb" in xname:
x = oneHDF5.tbb1
elif "ref" in xname:
x = oneHDF5.ref1
elif "dn" in xname:
x = oneHDF5.dn1
else:
Log.error("Can't plot %s" % each)
continue
if "rad" in yname:
y = oneHDF5.rad2
elif "tbb" in yname:
y = oneHDF5.tbb2
elif "ref" in yname:
y = oneHDF5.ref2
else:
Log.error("Can't plot %s" % each)
continue
if "rad" in xname and "rad" in yname:
if len(oneHDF5.rad1_std) > 0:
weight = oneHDF5.rad1_std
o_name = "RadCalCoeff"
elif "tbb" in xname and "tbb" in yname:
o_name = "TBBCalCoeff"
elif "ref" in xname and "ref" in yname:
if len(oneHDF5.ref1_std) > 0:
weight = oneHDF5.ref1_std
o_name = "CorrcCoeff"
elif "dn" in xname and "ref" in yname:
o_name = "CalCoeff"
# 画对角线
if xname == yname:
diagonal = True
else:
diagonal = False
if "all" in Day_Night and o_name not in dict_cabr:
dict_cabr[o_name] = {}
dict_bias[xname] = {}
if "day" in Day_Night and o_name not in dict_cabr_d:
dict_cabr_d[o_name] = {}
dict_bias_d[xname] = {}
if "night" in Day_Night and o_name not in dict_cabr_n:
dict_cabr_n[o_name] = {}
dict_bias_n[xname] = {}
# 对样本点数量进行判断，如果样本点少于 100 个，则不进行绘制
if x.size < 100:
Log.error("Not enough match point to draw: {}, {}".format(each, chan))
if "all" in Day_Night:
dict_cabr[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
dict_bias[xname][chan] = [np.NaN, np.NaN]
if "day" in Day_Night:
dict_cabr_d[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
dict_bias_d[xname][chan] = [np.NaN, np.NaN]
if "night" in Day_Night:
dict_cabr_n[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
dict_bias_n[xname][chan] = [np.NaN, np.NaN]
continue
# regression starts
if "all" in Day_Night:
o_file = os.path.join(cur_path,
"%s_%s_%s_ALL_%s" % (
pair, o_name, chan, str_time))
print("x_all, y_all", len(x), len(y))
abr, bias = plot(x, y, weight, o_file,
num_file, part1, part2, chan, str_time,
xname, xname_l, xunit, xmin, xmax,
yname, yname_l, yunit, ymin, ymax,
diagonal, is_monthly)
if abr:
dict_cabr[o_name][chan] = abr
else:
dict_cabr[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
if bias:
dict_bias[xname][chan] = bias
else:
dict_bias[xname][chan] = [np.NaN, np.NaN]
# ------- day ----------
if "day" in Day_Night:
if day_index is not None and np.where(day_index)[0].size > 10:
o_file = os.path.join(cur_path,
"%s_%s_%s_Day_%s" % (
pair, o_name, chan, str_time))
x_d = x[day_index]
y_d = y[day_index]
w_d = weight[day_index] if weight is not None else None
print("x_all, y_all", len(x), len(y))
print("x_day, y_day", len(x_d), len(y_d))
abr, bias = plot(x_d, y_d, w_d, o_file,
num_file, part1, part2, chan, str_time,
xname, xname_l, xunit, xmin, xmax,
yname, yname_l, yunit, ymin, ymax,
diagonal, is_monthly)
if abr:
dict_cabr_d[o_name][chan] = abr
else:
dict_cabr_d[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
if bias:
dict_bias_d[xname][chan] = bias
else:
dict_bias_d[xname][chan] = [np.NaN, np.NaN]
else:
dict_cabr_d[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
dict_bias_d[xname][chan] = [np.NaN, np.NaN]
# ---------night ------------
if "night" in Day_Night:
if night_index is not None and np.where(night_index)[0].size > 10:
o_file = os.path.join(cur_path, "%s_%s_%s_Night_%s" % (
pair, o_name, chan, str_time))
x_n = x[night_index]
y_n = y[night_index]
w_n = weight[night_index] if weight is not None else None
print("x_all, y_all", len(x), len(y))
print("x_night, y_night", len(x_n), len(y_n))
abr, bias = plot(x_n, y_n, w_n, o_file,
num_file, part1, part2, chan, str_time,
xname, xname_l, xunit, xmin, xmax,
yname, yname_l, yunit, ymin, ymax,
diagonal, is_monthly)
if abr:
dict_cabr_n[o_name][chan] = abr
else:
dict_cabr_n[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
if bias:
dict_bias_n[xname][chan] = bias
else:
dict_bias_n[xname][chan] = [np.NaN, np.NaN]
else:
dict_cabr_n[o_name][chan] = [0, np.NaN, np.NaN, np.NaN]
dict_bias_n[xname][chan] = [np.NaN, np.NaN]
oneHDF5.clear()
# write txt
lock.acquire()
channel = plt_cfg[each]["chan"]
if "all" in Day_Night:
for o_name in dict_cabr:
write_bias(channel, part1, part2, xname, ymd,
dict_bias, "ALL")
write_cabr(channel, part1, part2, o_name, ymd,
dict_cabr, "ALL")
if "day" in Day_Night:
for o_name in dict_cabr_d:
write_bias(channel, part1, part2, xname, ymd,
dict_bias_d, "Day")
write_cabr(channel, part1, part2, o_name, ymd,
dict_cabr_d, "Day")
if "night" in Day_Night:
for o_name in dict_cabr_n:
write_bias(channel, part1, part2, xname, ymd,
dict_bias_n, "Night")
write_cabr(channel, part1, part2, o_name, ymd,
dict_cabr_n, "Night")
lock.release()
def run(pair, ymd):
"""
pair: sat1+sensor1_sat2+sensor2
ymd: str YYYYMMDD
"""
# 提取参数中的卫星信息和传感器信息
part1, part2 = pair.split("_")
sat1, sensor1 = part1.split("+")
sat2, sensor2 = part2.split("+")
# 判断是静止卫星还是动态卫星
if "FY2" in part1 or "FY4" in part1:
Type = "GEOLEO"
elif "FY3" in part1:
Type = "LEOLEO"
else:
LOG.error("Cant distinguish the satellite type")
return
# 加载绘图配置文件
plt_cfg_file = os.path.join(MAIN_PATH, "cfg", "%s.plt" % pair)
plt_cfg = loadYamlCfg(plt_cfg_file)
if plt_cfg is None:
LOG.error("Not find the config file: {}".format(plt_cfg_file))
return
PERIOD = calendar.monthrange(int(ymd[:4]), int(ymd[4:6]))[1] # 当月天数
ym = ymd[:6]
ymd = ym + '%02d' % PERIOD # 当月最后一天
LOG.info(u"----- Start Drawing Monthly TBBias Analysis Pic, PAIR: {}, YMD: {} -----".format(pair, ymd))
for each in plt_cfg['monthly_staistics']:
# Day_Night must be in 'all', 'day', 'night'
Day_Night = ['all', 'day', 'night'] # default
if 'time' in plt_cfg[each].keys():
Day_Night = plt_cfg[each]['time']
for i in Day_Night:
if i not in ['all', 'day', 'night']:
Day_Night.remove(i)
for idx, chan in enumerate(plt_cfg[each]['chan']):
LOG.info(u"Start Drawing {} Channel {}".format(each, chan))
oneHDF5 = ReadHDF5()
# load Matched HDF5
num_file = PERIOD
for daydelta in xrange(PERIOD):
cur_ymd = pb_time.ymd_plus(ymd, -daydelta)
nc_name = 'COLLOC+%sIR,%s_C_BABJ_%s.hdf5' % (Type, pair, cur_ymd)
filefullpath = os.path.join(MATCH_DIR, pair, nc_name)
if not os.path.isfile(filefullpath):
LOG.info(u"HDF5 not found: {}".format(filefullpath))
num_file -= 1
continue
if not oneHDF5.LoadData(filefullpath, chan):
LOG.error('Error occur when reading %s of %s' % (chan, filefullpath))
if num_file == 0:
LOG.error(u"No file found.")
continue
elif num_file != PERIOD:
LOG.info(u"{} of {} file(s) found.".format(num_file, PERIOD))
# 输出目录
cur_path = os.path.join(MBA_DIR, pair, ymd[:6])
# get threhold, unit, names...
xname, yname = each.split('-')
bias = xname
xname_l = xname.upper()
xunit = plt_cfg[each]['x_unit']
xlimit = plt_cfg[each]['x_range'][idx]
xmin, xmax = xlimit.split('-')
xmin = float(xmin)
xmax = float(xmax)
# delete 0 in std
if len(oneHDF5.rad1_std) > 0.0001: # TODO: 有些极小的std可能是异常值，而导致权重极大，所以 std>0 改成 std>0.0001
deletezeros = np.where(oneHDF5.rad1_std > 0.0001)
oneHDF5.rad1_std = oneHDF5.rad1_std[deletezeros]
oneHDF5.rad1 = oneHDF5.rad1[deletezeros] if len(
oneHDF5.rad1) > 0 else oneHDF5.rad1
oneHDF5.rad2 = oneHDF5.rad2[deletezeros] if len(
oneHDF5.rad2) > 0 else oneHDF5.rad2
oneHDF5.tbb1 = oneHDF5.tbb1[deletezeros] if len(
oneHDF5.tbb1) > 0 else oneHDF5.tbb1
oneHDF5.tbb2 = oneHDF5.tbb2[deletezeros] if len(
oneHDF5.tbb2) > 0 else oneHDF5.tbb2
oneHDF5.time = oneHDF5.time[deletezeros] if len(
oneHDF5.time) > 0 else oneHDF5.time
oneHDF5.lon1 = oneHDF5.lon1[deletezeros] if len(
oneHDF5.lon1) > 0 else oneHDF5.lon1
oneHDF5.lon2 = oneHDF5.lon2[deletezeros] if len(
oneHDF5.lon2) > 0 else oneHDF5.lon2
if len(oneHDF5.ref1_std) > 0.0001:
deletezeros = np.where(oneHDF5.ref1_std > 0.0001)
oneHDF5.ref1_std = oneHDF5.ref1_std[deletezeros]
oneHDF5.ref1 = oneHDF5.ref1[deletezeros] if len(
oneHDF5.ref1) > 0 else oneHDF5.ref1
oneHDF5.ref2 = oneHDF5.ref2[deletezeros] if len(
oneHDF5.ref2) > 0 else oneHDF5.ref2
oneHDF5.dn1 = oneHDF5.dn1[deletezeros] if len(
oneHDF5.dn1) > 0 else oneHDF5.dn1
oneHDF5.dn2 = oneHDF5.dn1[deletezeros] if len(
oneHDF5.dn2) > 0 else oneHDF5.dn2
oneHDF5.time = oneHDF5.time[deletezeros] if len(
oneHDF5.time) > 0 else oneHDF5.time
oneHDF5.lon1 = oneHDF5.lon1[deletezeros] if len(
oneHDF5.lon1) > 0 else oneHDF5.lon1
oneHDF5.lon2 = oneHDF5.lon2[deletezeros] if len(
oneHDF5.lon2) > 0 else oneHDF5.lon2
# find out day and night
if ('day' in Day_Night or 'night' in Day_Night) and len(oneHDF5.time) > 0:
vect_is_day = np.vectorize(is_day_timestamp_and_lon)
day_index = vect_is_day(oneHDF5.time, oneHDF5.lon1)
night_index = np.logical_not(day_index)
else:
day_index = None
night_index = None
# get x
dset_name = xname + "1"
if hasattr(oneHDF5, dset_name):
x = getattr(oneHDF5, dset_name)
else:
LOG.error("Can't plot, no %s in HDF5 class" % dset_name)
continue
# get y
dset_name = yname + "2"
if hasattr(oneHDF5, dset_name):
y = getattr(oneHDF5, dset_name)
else:
LOG.error("Can't plot, no %s in HDF5 class" % dset_name)
continue
if 'rad' == bias:
o_name = 'RadBiasMonthStats'
elif 'tbb' == bias:
o_name = 'TBBiasMonthStats'
elif 'ref' == bias:
o_name = 'RefBiasMonthStats'
else:
o_name = 'DUMMY'
if x.size < 10:
LOG.error("Not enough match point to draw.")
continue
# 获取 std
weight = None
if 'rad' in xname and 'rad' in yname:
if len(oneHDF5.rad1_std) > 0:
weight = oneHDF5.rad1_std
elif 'tbb' in xname and 'tbb' in yname:
weight = None
elif 'ref' in xname and 'ref' in yname:
if len(oneHDF5.ref1_std) > 0:
weight = oneHDF5.ref1_std
elif 'dn' in xname and 'ref' in yname:
weight = None
# rad-specified regression starts
reference_list = []
if 'reference' in plt_cfg[each]:
reference_list = plt_cfg[each]['reference'][idx]
if 'all' in Day_Night:
o_file = os.path.join(cur_path,
'%s_%s_%s_ALL_%s' % (pair, o_name, chan, ym))
print("x_all, y_all", len(x), len(y))
plot(x, y, weight, o_file,
part1, part2, chan, ym, 'ALL', reference_list,
xname, xname_l, xunit, xmin, xmax)
# ------- day ----------
if 'day' in Day_Night:
if day_index is not None and np.where(day_index)[0].size > 10:
# rad-specified
o_file = os.path.join(cur_path,
'%s_%s_%s_Day_%s' % (pair, o_name, chan, ym))
x_d = x[day_index]
y_d = y[day_index]
w_d = weight[day_index] if weight is not None else None
print("x_all, y_all", len(x), len(y))
print("x_day, y_day", len(x_d), len(y_d))
plot(x_d, y_d, w_d, o_file,
part1, part2, chan, ym, 'Day', reference_list,
xname, xname_l, xunit, xmin, xmax)
if 'night' in Day_Night:
# ---------night ------------
if night_index is not None and np.where(night_index)[0].size > 10:
# rad-specified
o_file = os.path.join(cur_path,
'%s_%s_%s_Night_%s' % (pair, o_name, chan, ym))
x_n = x[night_index]
y_n = y[night_index]
w_n = weight[day_index] if weight is not None else None
print("x_all, y_all", len(x), len(y))
print("x_night, y_night", len(x_n), len(y_n))
plot(x_n, y_n, w_n, o_file,
part1, part2, chan, ym, 'Night', reference_list,
xname, xname_l, xunit, xmin, xmax)
def run(pair, date_s, date_e):
"""
pair: sat1+sensor1_sat2+sensor2
date_s: datetime of start date
None 处理 从发星 到 有数据的最后一天
date_e: datetime of end date
None 处理 从发星 到 有数据的最后一天
"""
# 提取参数中的卫星信息和传感器信息
part1, part2 = pair.split('_')
sat1, sensor1 = part1.split('+')
sat2, sensor2 = part2.split('+')
# 判断是否从发星开始处理
if date_s is None or date_e is None:
isLaunch = True
elif date_s is not None and date_e is not None:
isLaunch = False
else:
Log.error('Wrong date argument')
return
# 加载绘图配置文件
plt_cfg_file = os.path.join(MAIN_PATH, "cfg", "%s.plt" % pair)
plt_cfg = loadYamlCfg(plt_cfg_file)
if plt_cfg is None:
Log.error("Not find the config file: {}".format(plt_cfg_file))
return
# 设置开始时间和结束时间
if isLaunch:
if sat1 in GLOBAL_CONFIG['LUANCH_DATE']:
date_s = pb_time.ymd2date(str(GLOBAL_CONFIG['LUANCH_DATE'][sat1]))
date_e = datetime.utcnow()
else:
Log.error('%s not in LUANCH_DATE of Cfg, use the first day in txt instead.')
return
ymd_s, ymd_e = date_s.strftime('%Y%m%d'), date_e.strftime('%Y%m%d')
Log.info(u"----- Start Drawing Regression-Pic, PAIR: {} -----".format(pair))
for each in plt_cfg['time_series']:
# must be in 'all', 'day', 'night'
Day_Night = ['all', 'day', 'night']
if 'time' in plt_cfg[each].keys():
Day_Night = plt_cfg[each]['time']
for i in Day_Night:
if i not in ['all', 'day', 'night']:
Day_Night.remove(i)
# 将每个对通用的属性值放到对循环，每个通道用到的属性值放到通道循环
xname, yname = each.split('-')
xname_l = plt_cfg[each]['x_name']
xunit = plt_cfg[each]['x_unit']
yname_l = plt_cfg[each]['y_name']
yunit = plt_cfg[each]['y_unit']
if 'tbb' in xname and 'tbb' in yname:
o_name = 'TBBCalCoeff'
elif 'ref' in xname and 'ref' in yname:
o_name = 'CorrcCoeff'
elif 'dn' in xname and 'ref' in yname:
o_name = 'CalCoeff'
else:
continue
for DayOrNight in Day_Night:
if DayOrNight == 'all':
DayOrNight = DayOrNight.upper() # all -> ALL
else:
DayOrNight = DayOrNight[0].upper() + DayOrNight[1:]
for i, chan in enumerate(plt_cfg[each]['chan']):
x_range = plt_cfg[each]['x_range'][i]
y_range = plt_cfg[each]['y_range'][i]
# plot slope Intercept count ------------------------
print "plot abc : {} {} {}".format(each, DayOrNight, chan)
abc_path = os.path.join(ABR_DIR, '%s_%s' % (part1, part2),
"CABR")
abc_daily_file = os.path.join(abc_path, '%s_%s_%s_%s_%s_Daily.txt' % (
part1, part2, o_name, chan, DayOrNight))
abc_monthly_file = os.path.join(abc_path, '%s_%s_%s_%s_%s_Monthly.txt' % (
part1, part2, o_name, chan, DayOrNight))
abc_day_data = get_cabr_data(abc_daily_file)
abc_month_data = get_cabr_data(abc_monthly_file)
date_D = abc_day_data["date"]
a_D = abc_day_data["slope"]
b_D = abc_day_data["intercept"]
c_D = np.log10(abc_day_data["count"])
date_M = abc_month_data["date"] + relativedelta(days=14)
a_M = abc_month_data["slope"]
b_M = abc_month_data["intercept"]
c_M = np.log10(abc_month_data["count"])
idx_D = np.where(np.logical_and(date_D >= date_s, date_D <= date_e))
idx_M = np.where(np.logical_and(date_M >= pb_time.ymd2date(ymd_s[:6] + '01'), date_M <= date_e))
title = 'Time Series of Slope Intercept & Counts %s %s\n(%s = Slope * %s + Intercept)' % \
(chan, DayOrNight,
part1.replace('+', '_'),
part2.replace('+', '_'))
if isLaunch:
picPath = os.path.join(ABC_DIR, pair,
'%s_%s_ABC_%s_%s_Launch.png' % (pair, o_name, chan, DayOrNight))
else:
picPath = os.path.join(ABC_DIR, pair, ymd_e,
'%s_%s_ABC_%s_%s_Year_%s.png' % (pair, o_name, chan, DayOrNight, ymd_e))
# 系数坐标范围
slope_range = plt_cfg[each]['slope_range'][i]
slope_min, slope_max = slope_range.split('-')
slope_min = float(slope_min)
slope_max = float(slope_max)
plot_abc(date_D[idx_D], a_D[idx_D], b_D[idx_D], c_D[idx_D],
date_M[idx_M], a_M[idx_M], b_M[idx_M], c_M[idx_M],
picPath, title, date_s, date_e, slope_min, slope_max,
each)
# plot MD ----------------------------------------------------
if xname == "ref" or xname == "tbb":
print "plot MD : {} {} {}".format(each, DayOrNight, chan)
# 从 BIAS 文件中读取数据
bias_ref_path = os.path.join(ABR_DIR, '%s_%s' % (part1, part2),
"BIAS")
file_name_monthly = os.path.join(
bias_ref_path, '%s_%s_%s_%s_%s_Monthly.txt' % (
part1, part2, xname.upper(), chan, DayOrNight))
file_name_daily = os.path.join(
bias_ref_path, '%s_%s_%s_%s_%s_Daily.txt' % (
part1, part2, xname.upper(), chan, DayOrNight))
bias_d = get_bias_data(file_name_daily)
bias_m = get_bias_data(file_name_monthly)
date_md_d = bias_d["date"]
data_md_d = bias_d["md"]
date_md_m = bias_m["date"] + relativedelta(days=14)
data_md_m = bias_m["md"]
std_md_m = bias_m["md_std"]
idx_d = np.where(np.logical_and(date_md_d >= date_s, date_md_d <= date_e))
idx_m = np.where(
np.logical_and(date_md_m >= pb_time.ymd2date(ymd_s[:6] + '01'), date_md_m <= date_e))
# 根据拟合系数进行绘制
reference_list = plt_cfg[each]['reference'][i]
for ref_temp in reference_list:
if isLaunch:
pic_path = os.path.join(
OMB_DIR, pair, '%s_%s_MD_%s_%s_Launch.png' % (
pair, xname.upper(), chan, DayOrNight))
else:
# plot latest year
pic_path = os.path.join(
OMB_DIR, pair, ymd_e,
'%s_%s_MD_%s_%s_Year_%s.png' % (
pair, xname.upper(), chan, DayOrNight, ymd_e,
))
plot_md(
date_md_d[idx_d], data_md_d[idx_d],
date_md_m[idx_m], data_md_m[idx_m],
std_md_m[idx_m],
pic_path, date_s, date_e,
sat1, pair, chan, DayOrNight, ref_temp,
xname, xname_l, xunit, x_range,
yname, yname_l, yunit, y_range,
)
# plot RMD ----------------------------------------------------
if xname == "ref" and yname == "ref":
print "plot RMD : {} {} {}".format(each, DayOrNight, chan)
# 从 BIAS 文件中读取数据
bias_ref_path = os.path.join(ABR_DIR, '%s_%s' % (part1, part2),
"BIAS")
file_name_monthly = os.path.join(
bias_ref_path, '%s_%s_%s_%s_%s_Monthly.txt' % (
part1, part2, xname.upper(), chan, DayOrNight))
file_name_daily = os.path.join(
bias_ref_path, '%s_%s_%s_%s_%s_Daily.txt' % (
part1, part2, xname.upper(), chan, DayOrNight))
bias_d = get_bias_data(file_name_daily)
bias_m = get_bias_data(file_name_monthly)
date_rmd_d = bias_d["date"]
data_rmd_d = bias_d["bias"]
date_rmd_m = bias_m["date"] + relativedelta(days=14)
data_rmd_m = bias_m["bias"]
std_rmd_m = bias_m["bias_std"]
idx_d = np.where(np.logical_and(date_rmd_d >= date_s, date_rmd_d <= date_e))
idx_m = np.where(np.logical_and(date_rmd_m >= pb_time.ymd2date(ymd_s[:6] + '01'), date_rmd_m <= date_e))
# 根据拟合系数进行绘制
reference_list = plt_cfg[each]['reference'][i]
for ref_temp in reference_list:
# ref_temp_f = float(ref_temp)
if isLaunch:
pic_path = os.path.join(
OMB_DIR, pair, '%s_RMD_%s_%s_Launch_%d.png' % (
pair, chan, DayOrNight, ref_temp * 100))
else:
# plot latest year
pic_path = os.path.join(
OMB_DIR, pair, ymd_e,
'%s_RMD_%s_%s_Year_%s_%d.png' % (
pair, chan, DayOrNight, ymd_e,
ref_temp * 100))
plot_rmd(
date_rmd_d[idx_d], data_rmd_d[idx_d],
date_rmd_m[idx_m], data_rmd_m[idx_m],
std_rmd_m[idx_m],
pic_path, date_s, date_e,
sat1, pair, chan, DayOrNight, ref_temp,
xname, xname_l, xunit,
yname, yname_l, yunit,
)
# plot TBBias ------------------------
if xname == 'tbb' and yname == 'tbb':
print "plot TBBias : {} {} {}".format(each, DayOrNight, chan)
# 从 BIAS 文件中读取数据
bias_tbb_path = os.path.join(ABR_DIR, '%s_%s' % (part1, part2),
"BIAS")
file_name_monthly = os.path.join(
bias_tbb_path, '%s_%s_%s_%s_%s_Monthly.txt' % (
part1, part2, xname.upper(), chan, DayOrNight))
file_name_daily = os.path.join(
bias_tbb_path, '%s_%s_%s_%s_%s_Daily.txt' % (
part1, part2, xname.upper(), chan, DayOrNight))
tbbias_d = get_bias_data(file_name_daily)
tbbias_m = get_bias_data(file_name_monthly)
date_tbbias_d = tbbias_d["date"]
data_tbbias_d = tbbias_d["bias"]
date_tbbias_m = tbbias_m["date"]
date_tbbias_m = date_tbbias_m + relativedelta(days=14)
data_tbbias_m = tbbias_m["bias"]
std_tbbias_m = tbbias_m["bias_std"]
idx_d = np.where(np.logical_and(date_tbbias_d >= date_s, date_tbbias_d <= date_e))
idx_m = np.where(np.logical_and(date_tbbias_m >= pb_time.ymd2date(ymd_s[:6] + '01'), date_tbbias_m <= date_e))
# 根据拟合系数进行绘制
reference_list = plt_cfg[each]['reference'][i]
for ref_temp in reference_list:
if isLaunch:
pic_path = os.path.join(
OMB_DIR, pair, '%s_TBBias_%s_%s_Launch_%dK.png' % (
pair, chan, DayOrNight, ref_temp))
else:
# plot latest year
pic_path = os.path.join(
OMB_DIR, pair, ymd_e,
'%s_TBBias_%s_%s_Year_%s_%dK.png' % (
pair, chan, DayOrNight, ymd_e,
ref_temp))
plot_tbbias(
date_tbbias_d[idx_d], data_tbbias_d[idx_d],
date_tbbias_m[idx_m], data_tbbias_m[idx_m],
std_tbbias_m[idx_m],
pic_path, date_s, date_e,
sat1, pair, chan, DayOrNight, ref_temp,
xname, xname_l, xunit,
yname, yname_l, yunit,
)
# plot interpolated TBBias img (obs minus backgroud) -------------
print "plot OMB : {} {} {}".format(each, DayOrNight, chan)
title = 'Brightness Temperature Correction\n%s %s %s' % \
(pair, chan, DayOrNight)
if isLaunch:
picPath = os.path.join(OMB_DIR, pair,
'%s_TBBOMB_%s_%s_Launch.png' % (
pair, chan, DayOrNight))
else:
picPath = os.path.join(OMB_DIR, pair, ymd_e,
'%s_TBBOMB_%s_%s_Year_%s.png' % (
pair, chan, DayOrNight, ymd_e))
plot_omb(date_D[idx_D], a_D[idx_D], b_D[idx_D],
picPath, title, date_s, date_e)
def run(pair1, pair2, date_s, date_e):
"""
pair: sat1+sensor1_sat2+sensor2
date_s: datetime of start date
None 处理 从发星 到 有数据的最后一天
date_e: datetime of end date
None 处理 从发星 到 有数据的最后一天
"""
Log.info(u'开始运行双差统计图绘制程序%s %s-----------------' % (pair1, pair2))
isLaunch = False
if date_s is None or date_e is None:
isLaunch = True
satsen11, satsen12 = pair1.split("_")
satsen21, satsen22 = pair2.split("_")
if satsen11 != satsen21:
Log.error("%s and %s not the same, can't do double bias" % (satsen11, satsen21))
return
# 读取传感器对的配置文件
sat11, sen11 = satsen11.split('+')
sat12, sen12 = satsen12.split('+')
plt_cfg_file = os.path.join(MAIN_PATH, "cfg", '%s_%s_3d.yaml' % (sen11, sen12))
plt_cfg = loadYamlCfg(plt_cfg_file)
chans = plt_cfg["rad-rad"]["chan"]
# change sensor Name
if "VISSR" in satsen11: # VISSR -> SVISSR
satsen11 = satsen11.replace("VISSR", "SVISSR")
satsen21 = satsen21.replace("VISSR", "SVISSR")
if "METOP-" in satsen12: # METOP -> MetOp
satsen12 = satsen12.replace("METOP-", "MetOp")
if "METOP-" in satsen22: # METOP -> MetOp
satsen22 = satsen22.replace("METOP-", "MetOp")
flst = [e for e in os.listdir(StdNC_DIR) if os.path.isfile(os.path.join(StdNC_DIR, e))]
nc1_path = nc2_path = None
for each in flst:
if satsen11 in each and satsen12 in each:
nc1_path = os.path.join(StdNC_DIR, each)
if satsen21 in each and satsen22 in each:
nc2_path = os.path.join(StdNC_DIR, each)
nc1 = stdNC()
if not nc1.LoadData(nc1_path):
return
nc2 = stdNC()
if not nc2.LoadData(nc2_path):
return
time1 = nc1.time[:, 0]
time2 = nc2.time[:, 0]
tbbias1 = nc1.tbbias
tbbias2 = nc2.tbbias
reftmp = nc1.reftmp
if date_s is None: # TODO:
# timestamp_s = max(time1[0], time2[0])
# date_s = datetime.fromtimestamp(timestamp_s, tz=pytz.utc)
sat1, sen1 = satsen11.split("+")
date_s = pb_time.ymd2date(GLOBAL_CONFIG["LUANCH_DATE"][sat1])
date_s = pytz.utc.localize(date_s)
timestamp_s = calendar.timegm(date_s.timetuple())
if date_e is None:
timestamp_e = min(time1[-1], time2[-1])
date_e = datetime.fromtimestamp(timestamp_e, tz=pytz.utc)
else:
date_e = pytz.utc.localize(date_e)
timestamp_e = calendar.timegm(date_e.timetuple())
days1 = tbbias1.shape[0]
days2 = tbbias2.shape[0]
index1 = []
index2 = []
date_D = []
for i in xrange(days1):
if time1[i] < timestamp_s or time1[i] > timestamp_e:
continue
idxs2 = np.where(time2 == time1[i])[0]
if len(idxs2) != 1:
continue
date_D.append(datetime.fromtimestamp(time1[i]))
index1.append(i)
index2.append(idxs2[0])
if len(date_D) == 0:
return
for k, ch in enumerate(chans):
ch = chans[k]
ref_temp = reftmp[k]
tb1 = tbbias1[index1, k]
tb2 = tbbias2[index2, k]
bias_D = tb1 - tb2
idx = np.logical_or(tb1 < -998, tb2 < -998)
bias_D[idx] = None
date_M, bias_M = month_mean(date_D, bias_D)
title = 'Time Series of Double Bias \n%s_%s Miuns %s_%s %s %dK' % \
(satsen11, satsen12, satsen11, satsen22, ch, ref_temp)
if isLaunch:
picPath = os.path.join(DBB_DIR, '%s_%s' % (pair1, satsen22),
'%s_%s_DoubleBias_%s_Launch_%dK.png' % (pair1, satsen22, ch, ref_temp))
else:
# plot latest year
ymd_s = date_s.strftime("%Y%m%d")
ymd_e = date_e.strftime("%Y%m%d")
picPath = os.path.join(DBB_DIR, '%s_%s' % (pair1, satsen22), ymd_s,
'%s_%s_DoubleBias_%s_Year_%s_%dK.png' % (pair1, satsen22, ch, ymd_s, ref_temp))
plot_tbbias(date_D, bias_D, date_M, bias_M, picPath, title, date_s, date_e)
Log.info(u'Success')
def run(pair, ymd):
"""
pair: sat1+sensor1_sat2+sensor2
ymd: YYYYMMDD
"""
# 提取参数中的卫星信息和传感器信息
part1, part2 = pair.split("_")
sat1, sensor1 = part1.split("+")
sat2, sensor2 = part2.split("+")
# 判断是静止卫星还是动态卫星
if "FY2" in part1 or "FY4" in part1:
Type = "GEOLEO"
elif "FY3" in part1:
Type = "LEOLEO"
else:
Log.error("Cant distinguish the satellite type")
return
# 加载绘图配置文件
plt_cfg_file = os.path.join(MainPath, "%s_%s_3d.yaml" % (sensor1, sensor2))
plt_cfg = loadYamlCfg(plt_cfg_file)
if plt_cfg is None:
Log.error("Not find the config file: {}".format(plt_cfg_file))
return
# 读取配置文件的信息
PERIOD = plt_cfg['collocation_map']['days'] # 回滚天数
chans = plt_cfg['collocation_map']['chan'] # 通道
maptype = plt_cfg['collocation_map']['maptype'] # 需要绘制的类型
if 'area' in maptype: # 区域块视图
area = plt_cfg['collocation_map']['area']
else:
area = None
if 'polar' in maptype: # 两极视图
polar = plt_cfg['collocation_map']['polar']
else:
polar = None
# 读取范围配置
if not area and not polar:
return
else:
map_range = (polar, area)
Log.info(
u"----- Start Drawing Matched Map-Pic, PAIR: {}, YMD: {}, PERIOD: {} -----"
.format(pair, ymd, PERIOD))
# 读取 HDF5 文件数据
oneHDF5 = ReadHDF5()
num_file = PERIOD
cur_ymd = pb_time.ymd_plus(ymd, 1) # 回滚天数，现在为 1
for daydelta in xrange(PERIOD):
cur_ymd = pb_time.ymd_plus(ymd, -daydelta)
filename = "COLLOC+%sIR,%s+%s_%s+%s_C_BABJ_%s.hdf5" % (
Type, sat1, sensor1, sat2, sensor2, cur_ymd)
filefullpath = os.path.join(MATCH_DIR, pair, filename)
if not os.path.isfile(filefullpath):
Log.info(u"File not found: {}".format(filefullpath))
num_file -= 1
continue
if not oneHDF5.LoadData(filefullpath, chans):
Log.error('Error occur when reading %s of %s' %
(chans, filefullpath))
if num_file == 0:
Log.error(u"No file found.")
return
elif num_file != PERIOD:
Log.error(u"{} of {} file(s) found.".format(num_file, PERIOD))
cur_path = os.path.join(DMS_DIR, pair, ymd)
o_file = os.path.join(cur_path,
'%s_%s_MatchedPoints_ALL_%s' % (part1, part2, ymd))
# find out day and night
vect_is_day = np.vectorize(is_day_timestamp_and_lon)
day_index = vect_is_day(oneHDF5.time, oneHDF5.lon1)
night_index = np.logical_not(day_index)
x = oneHDF5.lon1 # 经度数据
y = oneHDF5.lat1 # 维度数据
print 'date: {}, x_all: {} y_all: {} '.format(ymd, len(x), len(y))
draw_butterfly(part1, part2, cur_ymd, ymd, x, y, o_file, map_range)
# ------- day ----------
if np.where(day_index)[0].size > 0:
o_file = os.path.join(
cur_path, '%s-%s_MatchedPoints_Day_%s' % (part1, part2, ymd))
x_d = x[day_index]
y_d = y[day_index]
print 'date: {}, x_day: {} y_day: {} '.format(ymd, len(x_d), len(y_d))
draw_butterfly(part1, part2, cur_ymd, ymd, x_d, y_d, o_file, map_range)
# ---------night ------------
if np.where(night_index)[0].size > 0:
o_file = os.path.join(
cur_path, '%s-%s_MatchedPoints_Night_%s' % (part1, part2, ymd))
x_n = x[night_index]
y_n = y[night_index]
print 'date: {}, x_night: {} y_night: {} '.format(
ymd, len(x_n), len(y_n))
draw_butterfly(part1, part2, cur_ymd, ymd, x_n, y_n, o_file, map_range)
Log.info(u"Success")