def write_bias(channel, part1, part2, xname, ymd, dict_bias, day_or_night):
"""
生成 RMD 日数据文件和月数据文件
:param channel:
:param part1:
:param part2:
:param xname:
:param ymd:
:param dict_bias:
:param day_or_night:
:return:
"""
if not (xname in ["ref", "tbb"]):
return
for chan in channel:
o_path = os.path.join(ABR_DIR, "%s_%s" % (part1, part2), "BIAS")
file_name_monthly = os.path.join(
o_path, "%s_%s_%s_%s_%s_Monthly.txt" %
(part1, part2, xname.upper(), chan, day_or_night))
file_name_daily = os.path.join(
o_path, "%s_%s_%s_%s_%s_Daily.txt" %
(part1, part2, xname.upper(), chan, day_or_night))
# 写入日数据
bias = dict_bias[xname][chan][0]
md = dict_bias[xname][chan][1]
title_daily = ("{:15} " * 3).format("YMD", "Bias", "MD") + "\n"
data_daily = ("{:15} " * 3).format(ymd, bias, md) + "\n"
day_data_write(title_daily, data_daily, file_name_daily)
# 写入月数据
title_monthly = ("{:15} " * 5).format("YMD", "Bias", "Bias_STD",
"MD", "MD_STD") + "\n"
bias_data = get_bias_data(file_name_daily)
date_data = bias_data["date"]
bias_day_data = np.vstack(bias_data["bias"])
md_day_data = np.vstack(bias_data["md"])
if len(date_data) <= 2: # 如果小于2天的数据，不计算月平均
continue
bias_md_data = np.concatenate((bias_day_data, md_day_data), axis=1)
data_monthly = month_average(date_data, bias_md_data) # 计算月平均和STD
for data in data_monthly:
ymd_monthly = data[0]
bias_mean_monthly = data[1]
md_mean_monthly = data[2]
bias_std_monthly = data[3]
md_std_monthly = data[4]
data_str = ("{:15} " * 5).format(
ymd_monthly, bias_mean_monthly, bias_std_monthly,
md_mean_monthly, md_std_monthly) + "\n"
day_data_write(title_monthly, data_str, file_name_monthly)
print file_name_daily
print file_name_monthly
def write_cabr(channel, part1, part2, o_name, ymd,
dict_cabr, day_or_night):
"""
生成 CABR 数据文件
:param channel:
:param part1:
:param part2:
:param o_name:
:param ymd:
:param dict_cabr:
:param day_or_night:
:return:
"""
for chan in channel:
o_path = os.path.join(ABR_DIR, "%s_%s" % (part1, part2), "CABR")
file_name_monthly = os.path.join(
o_path, "%s_%s_%s_%s_%s_Monthly.txt" % (
part1, part2, o_name, chan, day_or_night))
file_name_daily = os.path.join(
o_path, "%s_%s_%s_%s_%s_Daily.txt" % (
part1, part2, o_name, chan, day_or_night))
# 写入日数据
title_daily = ("{:15} " * 5).format("YMD", "Count", "Slope", "Intercept",
"RSquared") + "\n"
c, a, b, r = dict_cabr[o_name][chan]
data_daily = ("{:15} " * 5).format(ymd, c, a, b, r) + "\n"
day_data_write(title_daily, data_daily, file_name_daily)
# 写入月数据
cabr_data = get_cabr_data(file_name_daily)
title_monthly = ("{:15} " * 8).format("YMD", "Count", "Slope", "Slope_STD",
"Intercept", "Intercept_STD", "RSquared",
"RSquared_STD") + "\n"
date_data = cabr_data["date"]
if len(date_data) <= 2: # 如果小于2天的数据，不计算月平均
continue
c_data = cabr_data["count"]
s_data = np.vstack(cabr_data["slope"])
i_data = np.vstack(cabr_data["intercept"])
r_data = np.vstack(cabr_data["rsquared"])
abr_data = np.concatenate((s_data, i_data, r_data), axis=1) # 合并 a b c 数据
count_monthly = month_count(date_data, c_data) # 计算月总数
abr_monthly = month_average(date_data, abr_data)[:, 1:] # 计算月平均和STD
data_monthly = np.concatenate((count_monthly, abr_monthly), axis=1)
for data in data_monthly:
ymd_monthly = data[0]
count_monthly = data[1]
slope_mean_monthly = data[2]
intercept_mean_monthly = data[3]
rsquared_mean_monthly = data[4]
slope_std_monthly = data[5]
intercept_std_monthly = data[6]
rsquared_std_monthly = data[7]
data_str = ("{:15} " * 8).format(ymd_monthly, count_monthly, slope_mean_monthly,
slope_std_monthly,
intercept_mean_monthly, intercept_std_monthly,
rsquared_mean_monthly, rsquared_std_monthly) + "\n"
day_data_write(title_monthly, data_str, file_name_monthly)
print file_name_daily
print file_name_monthly