def plot(x, y, weight, o_file, num_file, part1, part2, chan, ymd,
xname, xname_l, xunit, xmin, xmax, yname, yname_l, yunit, ymin, ymax,
is_diagonal, is_monthly):
plt.style.use(os.path.join(DV_PATH, "dv_pub_regression.mplstyle"))
# 过滤 正负 delta+8 倍 std 的杂点 ------------------------
w = 1.0 / weight if weight is not None else None
RadCompare = G_reg1d(x, y, w)
reg_line = x * RadCompare[0] + RadCompare[1]
delta = np.abs(y - reg_line)
mean_delta = np.mean(delta)
std_delta = np.std(delta)
max_y = reg_line + mean_delta + std_delta * 8
min_y = reg_line - mean_delta - std_delta * 8
idx = np.logical_and(y < max_y, y > min_y)
x = x[idx]
y = y[idx]
w = w[idx] if weight is not None else None
# -----------------------------------------
RadCompare = G_reg1d(x, y, w)
length_rad = len(x)
bias_and_md = [] # 当 bias 没有被计算的时候，不输出 bias
if not is_monthly and is_diagonal:
# return [len(x), RadCompare[0], RadCompare[1], RadCompare[4]]
fig = plt.figure(figsize=(14, 4.5))
# fig.subplots_adjust(top=0.90)
ax1 = plt.subplot2grid((1, 3), (0, 0))
ax2 = plt.subplot2grid((1, 3), (0, 1))
ax3 = plt.subplot2grid((1, 3), (0, 2))
# 图片 Title
titleName = "%s-%s" % (xname.upper(), yname.upper())
title = "{} Regression {} Days {}_{} {} {}".format(
titleName, num_file, part1, part2, chan, ymd)
# 画回归图 -----------------------------------------------
print "draw regression"
regress_xmin = xmin
regress_xmax = xmax
regress_ymin = ymin
regress_ymax = ymax
regress_axislimit = {
"xlimit": (regress_xmin, regress_xmax),
"ylimit": (regress_ymin, regress_ymax),
}
if xunit != "":
xlabel = "{} {} (${}$)".format(part1, xname_l, xunit)
else:
xlabel = "{} {}".format(part1, xname_l)
if yunit != "":
ylabel = "{} {} (${}$)".format(part2, yname_l, yunit)
else:
ylabel = "{} {}".format(part2, yname_l)
regress_label = {
"xlabel": xlabel, "ylabel": ylabel, "fontsize": 14,
}
if xname == "tbb":
regress_locator = {"locator_x": (None, None),
"locator_y": (None, 5)}
elif xname == "ref":
regress_locator = {"locator_x": (None, None),
"locator_y": (None, 5)}
else:
regress_locator = None
regress_annotate = {
"left": ["{:10}: {:7.4f}".format("Slope", RadCompare[0]),
"{:10}: {:7.4f}".format("Intercept", RadCompare[1]),
"{:10}: {:7.4f}".format("Cor-Coef", RadCompare[4]),
"{:10}: {:7d}".format("Number", length_rad)],
"fontsize": 14,
}
regress_tick = {"fontsize": 14, }
regress_diagonal = {"line_color": "#808080", "line_width": 1.2}
regress_regressline = {"line_color": "r", "line_width": 1.2}
scatter_point = {"scatter_alpha": 0.8}
dv_pub_3d.draw_regression(
ax1, x, y, label=regress_label, ax_annotate=regress_annotate, tick=regress_tick,
axislimit=regress_axislimit, locator=regress_locator,
diagonal=regress_diagonal, regressline=regress_regressline,
scatter_point=scatter_point,
)
# 画偏差分布图 ---------------------------------------------
print "draw distribution"
distri_xmin = xmin
distri_xmax = xmax
if xname == "tbb":
distri_ymin = -4
distri_ymax = 4
elif xname == "ref":
distri_ymin = -0.08
distri_ymax = 0.08
else:
distri_ymin = None
distri_ymax = None
distri_limit = {
"xlimit": (distri_xmin, distri_xmax),
"ylimit": (distri_ymin, distri_ymax),
}
# x y 轴标签
xlabel = "{}".format(xname_l)
if xname == "tbb":
ylabel = "{} bias {}_{} ".format(xname.upper(), part1, part2, )
elif xname == "ref":
ylabel = "{} bias {}_{} ".format(xname.capitalize(), part1, part2, )
else:
ylabel = "{} bias {}_{} ".format(xname, part1, part2, )
distri_label = {
"xlabel": xlabel, "ylabel": ylabel, "fontsize": 14,
}
# 获取 MeanBias 信息
bias_range = 0.15
boundary = xmin + (xmax - xmin) * 0.15
bias_info = bias_information(x, y, boundary, bias_range)
# 格式化 MeanBias 信息
info_lower = "MeanBias(<={:d}%Range)=\n {:.4f}±{:.4f}@{:.4f}".format(
int(bias_range * 100), bias_info.get("md_lower"),
bias_info.get("std_lower"), bias_info.get("mt_lower"))
info_greater = "MeanBias(>{:d}%Range)=\n {:.4f}±{:.4f}@{:.4f}".format(
int(bias_range * 100), bias_info.get("md_greater"),
bias_info.get("std_greater"), bias_info.get("mt_greater"))
# 绝对偏差和相对偏差信息 TBB=250K REF=0.25
ab = RadCompare
a = ab[0]
b = ab[1]
if xname == "tbb":
bias = 250 - (250 * a + b)
bias_info_md = "TBB Bias(250K):{:.4f}K".format(bias)
elif xname == "ref":
bias = (0.25 - (0.25 * a + b)) / 0.25 * 100
bias_info_md = "Relative Bias(REF0.25):{:.4f}%".format(bias)
else:
bias = np.NaN # RMD or TBB bias
bias_info_md = ""
bias_and_md.append(bias)
# Range Mean : 偏差图的 MD 信息
if xname == "tbb":
md_greater = bias_info.get("md_greater", np.NaN)
md = md_greater
elif xname == "ref":
md_greater = bias_info.get("md_greater")
mt_greater = bias_info.get("mt_greater")
if md_greater is not None and mt_greater is not None:
md = (md_greater / mt_greater) * 100
else:
md = np.NaN
else:
md = np.NaN
bias_and_md.append(md)
# 添加注释信息
distri_annotate = {"left": [], "leftbottom": [], "right": [], "fontsize": 14, }
distri_annotate.get("left").append(bias_info_md)
distri_annotate.get("leftbottom").append(info_lower)
distri_annotate.get("leftbottom").append(info_greater)
# 添加 tick 信息
distri_tick = {"fontsize": 14, }
# 添加间隔数量
if xname == "tbb":
distri_locator = {"locator_x": (None, None), "locator_y": (8, 5)}
elif xname == "ref":
distri_locator = {"locator_x": (None, None), "locator_y": (8, 5)}
else:
distri_locator = None
# y=0 线配置
zeroline = {"line_color": "#808080", "line_width": 1.0}
# 偏差点配置
scatter_delta = {
"scatter_marker": "o", "scatter_size": 5, "scatter_alpha": 0.8,
"scatter_linewidth": 0, "scatter_zorder": 100,
"scatter_color": BLUE,
}
# 偏差回归线配置
regressline = {"line_color": "r", "line_width": 1.2}
dv_pub_3d.draw_distribution(ax2, x, y, label=distri_label,
ax_annotate=distri_annotate,
tick=distri_tick,
axislimit=distri_limit,
locator=distri_locator,
zeroline=zeroline,
scatter_delta=scatter_delta,
regressline=regressline,
)
# 画直方图 --------------------------------------------------
print "draw histogram"
histogram_xmin = xmin
histogram_xmax = xmax
histogram_axislimit = {
"xlimit": (histogram_xmin, histogram_xmax),
}
histogram_xlabel = "{}".format(xname_l)
histogram_ylabel = "match point numbers"
histogram_label = {
"xlabel": histogram_xlabel, "ylabel": histogram_ylabel, "fontsize": 14,
}
# 添加间隔数量
if xname == "tbb":
histogram_locator = {"locator_x": (None, None),
"locator_y": (None, 5)}
elif xname == "ref":
histogram_locator = {"locator_x": (None, None),
"locator_y": (None, 5)}
else:
histogram_locator = None
histogram_x = {
"label": part1, "color": "red", "alpha": 0.4, "bins": 100, "fontsize": 14,
}
histogram_y = {
"label": part2, "color": "blue", "alpha": 0.4, "bins": 100,"fontsize": 14,
}
histogram_tick = {"fontsize": 14, }
dv_pub_3d.draw_histogram(
ax3, x, label=histogram_label, locator=histogram_locator, tick=histogram_tick,
axislimit=histogram_axislimit, histogram=histogram_x,
)
dv_pub_3d.draw_histogram(
ax3, y, label=histogram_label, locator=histogram_locator, tick=histogram_tick,
axislimit=histogram_axislimit, histogram=histogram_y,
)
elif not is_monthly and not is_diagonal:
fig = plt.figure(figsize=(4.5, 4.5))
# fig.subplots_adjust(bottom=0.12, top=0.86)
ax1 = plt.subplot2grid((1, 1), (0, 0))
# 图片 Title
titleName = "%s-%s" % (xname.upper(), yname.upper())
title = "{} Regression {} Days\n{}_{} {} {}".format(
titleName, num_file, part1, part2, chan, ymd)
# 画回归图 ----------------------------------------------------
print "draw regression"
regress_xmin = xmin
regress_xmax = xmax
regress_ymin = ymin
regress_ymax = ymax
regress_axislimit = {
"xlimit": (regress_xmin, regress_xmax),
"ylimit": (regress_ymin, regress_ymax),
}
if xunit != "":
xlabel = "{} {} (${}$)".format(part1, xname_l, xunit)
else:
xlabel = "{} {}".format(part1, xname_l)
if yunit != "":
ylabel = "{} {} (${}$)".format(part2, yname_l, yunit)
else:
ylabel = "{} {}".format(part2, yname_l)
regress_label = {
"xlabel": xlabel, "ylabel": ylabel, "fontsize": 14,
}
if xname == "tbb":
regress_locator = {"locator_x": (5, None), "locator_y": (5, 5)}
elif xname == "ref":
regress_locator = {"locator_x": (None, None),
"locator_y": (None, 5)}
elif xname == "dn":
regress_locator = {"locator_x": (5, None),
"locator_y": (None, 5)}
else:
regress_locator = None
regress_annotate = {
"left": ["{:10}: {:7.4f}".format("Slope", RadCompare[0]),
"{:10}: {:7.4f}".format("Intercept", RadCompare[1]),
"{:10}: {:7.4f}".format("Cor-Coef", RadCompare[4]),
"{:10}: {:7d}".format("Number", length_rad)],
"fontsize": 14,
}
regress_tick = {"fontsize": 14, }
regress_diagonal = {"line_color": "#808080", "line_width": 1.2}
regress_regressline = {"line_color": "r", "line_width": 1.2}
scatter_point = {"scatter_alpha": 0.8}
dv_pub_3d.draw_regression(
ax1, x, y, label=regress_label, ax_annotate=regress_annotate, tick=regress_tick,
axislimit=regress_axislimit, locator=regress_locator,
diagonal=regress_diagonal, regressline=regress_regressline,
scatter_point=scatter_point,
)
elif is_monthly:
o_file = o_file + "_density"
fig = plt.figure(figsize=(4.5, 4.5))
# fig.subplots_adjust(bottom=0.12, top=0.86)
ax1 = plt.subplot2grid((1, 1), (0, 0))
# 图片 Title Label
titleName = "%s-%s" % (xname.upper(), yname.upper())
title = "{} Regression {} Days\n{}_{} {} {}".format(
titleName, num_file, part1, part2, chan, ymd)
# 画密度图 -----------------------------------------------------
print "draw density"
density_xmin = xmin
density_xmax = xmax
density_ymin = ymin
density_ymax = ymax
density_axislimit = {
"xlimit": (density_xmin, density_xmax),
"ylimit": (density_ymin, density_ymax),
}
if xunit != "":
xlabel = "{} {} (${}$)".format(part1, xname_l, xunit)
else:
xlabel = "{} {}".format(part1, xname_l)
if yunit != "":
ylabel = "{} {} (${}$)".format(part2, yname_l, yunit)
else:
ylabel = "{} {}".format(part2, yname_l)
density_label = {
"xlabel": xlabel, "ylabel": ylabel, "fontsize": 14,
}
if xname == "tbb":
density_locator = {"locator_x": (5, None), "locator_y": (5, 5)}
elif xname == "ref":
density_locator = {"locator_x": (None, None),
"locator_y": (None, 5)}
elif xname == "dn":
density_locator = {"locator_x": (5, None),
"locator_y": (None, 5)}
else:
density_locator = None
density_annotate = {
"left": ["{:10}: {:7.4f}".format("Slope", RadCompare[0]),
"{:10}: {:7.4f}".format("Intercept", RadCompare[1]),
"{:10}: {:7.4f}".format("Cor-Coef", RadCompare[4]),
"{:10}: {:7d}".format("Number", length_rad)],
"fontsize": 14,
}
density_tick = {"fontsize": 14, }
density_diagonal = {"line_color": "#808080", "line_width": 1.2}
density_regressline = {"line_color": "r", "line_width": 1.2}
density = {
"size": 5, "marker": "o", "alpha": 1
}
dv_pub_3d.draw_regression(
ax1, x, y, label=density_label, ax_annotate=density_annotate, tick=density_tick,
axislimit=density_axislimit, locator=density_locator,
diagonal=density_diagonal, regressline=density_regressline,
density=density,
)
else:
print "::::::No output Pic {} : ".format(ymd)
return
# 自动调整子图间距
plt.tight_layout()
if not is_monthly and is_diagonal:
fig.subplots_adjust(top=0.90)
elif not is_monthly and not is_diagonal:
fig.subplots_adjust(bottom=0.12, top=0.86)
elif is_monthly:
fig.subplots_adjust(bottom=0.12, top=0.86)
FONT1.set_size(14)
fig.suptitle(title, fontsize=14, fontproperties=FONT1)
pb_io.make_sure_path_exists(os.path.dirname(o_file))
fig.savefig(o_file, dpi=100)
print o_file + ".png"
print "-" * 100
fig.clear()
plt.close()
return [len(x), RadCompare[0], RadCompare[1],
RadCompare[4]], bias_and_md # num, a, b, r, bias and md
def plot(x, y, weight, picPath,
part1, part2, chan, ym, DayOrNight, reference_list,
xname, xname_l, xunit, xmin, xmax):
"""
x: 参考卫星传感器数据
y: FY数据
"""
plt.style.use(os.path.join(DV_PATH, 'dv_pub_regression.mplstyle'))
if xname_l == "TBB":
xname_l = "TB"
# 过滤 正负 delta+8倍std 的杂点 ------------
w = 1.0 / weight if weight is not None else None
RadCompare = G_reg1d(x, y, w)
reg_line = x * RadCompare[0] + RadCompare[1]
delta = np.abs(y - reg_line)
mean_delta = np.mean(delta)
std_delta = np.std(delta)
max_y = reg_line + mean_delta + std_delta * 8
min_y = reg_line - mean_delta - std_delta * 8
idx = np.logical_and(y < max_y, y > min_y)
x = x[idx]
y = y[idx]
w = w[idx] if weight is not None else None
# -----------------------------------------
if xname == "tbb":
step = 5
else:
step = 0.1
# 计算回归信息： 斜率，截距，R
RadCompare = G_reg1d(x, y, w)
# 开始绘图
fig = plt.figure(figsize=(6, 5))
ax1 = plt.subplot2grid((2, 1), (0, 0))
ax2 = plt.subplot2grid((2, 1), (1, 0))
# 图片 Title
title = '%s Bias Monthly Statistics\n%s Minus %s %s %s' % \
(xname_l, part1, part2, chan, DayOrNight)
# plot 偏差分布图 -------------------------------------------------
# x y 轴范围
distri_xmin = xmin
distri_xmax = xmax
if xname == "tbb":
distri_ymin = -4
distri_ymax = 4
elif xname == "ref":
distri_ymin = -0.08
distri_ymax = 0.08
else:
distri_ymin = None
distri_ymax = None
distri_limit = {
"xlimit": (distri_xmin, distri_xmax),
"ylimit": (distri_ymin, distri_ymax),
}
distri_locator = {
"locator_x": (None, None), "locator_y": (8, 5)
}
# Distri label
distri_label = {}
if xunit != "":
ylabel = 'D{}({})'.format(xname_l, xunit)
else:
ylabel = "D{}".format(xname_l)
distri_label["ylabel"] = ylabel
ref_temp = reference_list[0] # 获取拟合系数
# 获取 MeanBias 信息
bias_range = 0.15
boundary = xmin + (xmax - xmin) * 0.15
bias_info = bias_information(x, y, boundary, bias_range)
# 绝对偏差和相对偏差信息 TBB=250K REF=0.25
ab = RadCompare
a = ab[0]
b = ab[1]
if xname == 'tbb':
bias_info_md = "TBB Bias ({} K) : {:.4f} K".format(
ref_temp, ref_temp - (ref_temp * a + b))
elif xname == 'ref':
bias_info_md = "Relative Bias (REF {}) : {:.4f} %".format(
ref_temp, (ref_temp - (ref_temp * a + b)) / ref_temp * 100)
else:
bias_info_md = ""
# 配置注释信息
distri_annotate = {"left": [bias_info.get("info_lower"),
bias_info.get("info_greater"),
bias_info_md]}
# 注释线配置
if xname == "tbb":
avxline = {
'line_x': ref_temp, 'line_color': '#4cd964', 'line_width': 0.7,
'word': str(ref_temp) + xunit, 'word_color': EDGE_GRAY,
'word_size': 6, 'word_location': (ref_temp, -3.5)
}
elif xname == "ref":
avxline = {
'line_x': ref_temp, 'line_color': '#4cd964', 'line_width': 0.7,
'word': str(ref_temp) + xunit, 'word_color': EDGE_GRAY,
'word_size': 6, 'word_location': (ref_temp, -0.07)
}
else:
avxline = None
distri_annotate = None
# y=0 线配置
zeroline = {"line_color": '#808080', "line_width": 1.0}
# 偏差点配置
scatter_delta = {
"scatter_marker": 'o', "scatter_size": 1.5, "scatter_alpha": 0.5,
"scatter_linewidth": 0, "scatter_zorder": 100, "scatter_color": BLUE,
}
# 偏差 fill 配置
background_fill = {
"fill_marker": 'o-', "fill_size": 6, "fill_alpha": 0.5,
"fill_linewidth": 0.6, "fill_zorder": 50, "fill_color": RED,
"fill_step": step,
}
dv_pub_3d.draw_distribution(ax1, x, y, label=distri_label, ax_annotate=distri_annotate,
axislimit=distri_limit, locator=distri_locator,
zeroline=zeroline,
scatter_delta=scatter_delta,
avxline=avxline,
background_fill=background_fill,
)
# 绘制 Bar 图 -------------------------------------------------
bar_xmin = distri_xmin
bar_xmax = distri_xmax
bar_ymin = 0
bar_ymax = 7
bar_limit = {
"xlimit": (bar_xmin, bar_xmax),
"ylimit": (bar_ymin, bar_ymax),
}
if xname == "tbb":
bar_locator = {
"locator_x": (None, None), "locator_y": (7, 5)
}
elif xname == "ref":
bar_locator = {
"locator_x": (None, None), "locator_y": (7, 5)
}
else:
bar_locator = None
# bar 的宽度
if xname == "tbb":
width = 3
elif xname == "ref":
width = 0.07
else:
width = 1
# bar 配置
bar = {
"bar_width": width, "bar_color": BLUE, "bar_linewidth": 0,
"text_size": 6, "text_font": FONT_MONO, "bar_step": step,
}
bar_annotate = {
"left": ['Total Number: %7d' % len(x)]
}
bar_label = {
"xlabel": '%s %s' % (part2, xname_l) + (
'($%s$)' % xunit if xunit != "" else ""),
"ylabel": 'Number of sample points\nlog (base = 10)'
}
dv_pub_3d.draw_bar(ax2, x, y, label=bar_label, ax_annotate=bar_annotate,
axislimit=bar_limit, locator=bar_locator,
bar=bar,
)
# ---------------
plt.tight_layout()
# 将 ax1 的 xticklabels 设置为不可见
plt.setp(ax1.get_xticklabels(), visible=False)
# 子图的底间距
fig.subplots_adjust(bottom=0.16, top=0.90)
FONT1.set_size(11)
fig.suptitle(title, fontsize=11, fontproperties=FONT1)
fig.text(0.6, 0.02, '%s' % ym, fontsize=11, fontproperties=FONT0)
fig.text(0.8, 0.02, ORG_NAME, fontsize=11, fontproperties=FONT0)
# ---------------
pb_io.make_sure_path_exists(os.path.dirname(picPath))
fig.savefig(picPath)
print picPath + '.png'
plt.close()
fig.clear()
def plot_rmd(date_d, data_d, date_m, data_m, std_m, pic_path, date_s, date_e,
sat_name, pair, chan, day_or_night, ref_temp,
xname, xname_l, xunit,
yname, yname_l, yunit,
):
if (np.isnan(data_d)).all():
Log.error('Everything is NaN: %s' % pic_path)
return
plt.style.use(os.path.join(DV_PATH, 'dv_pub_timeseries.mplstyle'))
fig = plt.figure(figsize=(6, 4))
# fig.subplots_adjust(top=0.88, bottom=0.11, left=0.12, right=0.97)
ax1 = plt.subplot2grid((1, 1), (0, 0))
# 设置 title 参数
part1, part2 = pair.split('_')
title = 'Time Series of REF Relative Bias \n{} Minus {} {} {} REF={}'.format(
part1, part2, chan, day_or_night, ref_temp)
# plot timeseries --------------------------------------------------------
timeseries_xmin = pb_time.ymd2date(
'%04d%02d01' % (date_s.year, date_s.month))
timeseries_xmax = date_e
if "FY2" in sat_name:
timeseries_ymin = -20
timeseries_ymax = 20
elif "FY3" in sat_name:
timeseries_ymin = -20
timeseries_ymax = 20
elif "FY4" in sat_name:
timeseries_ymin = -20
timeseries_ymax = 20
else:
timeseries_ymin = None
timeseries_ymax = None
timeseries_axislimit = {
"xlimit": (timeseries_xmin, timeseries_xmax),
"ylimit": (timeseries_ymin, timeseries_ymax),
}
# x y 轴标签
timeseries_label = {}
if xunit != "":
ylabel = 'Relative Bias {}'.format(xunit)
else:
ylabel = "Relative Bias %"
timeseries_label["ylabel"] = ylabel
# x, y 轴大刻度的数量，和小刻度的数量
timeseries_locator = {"locator_x": (None, None), "locator_y": (8, 2)}
# y=0 线配置
timeseries_zeroline = {"line_color": '#808080', "line_width": 1.0}
timeseries_daily = {
"marker": 'x', "color": BLUE, "linewidth": None,
"markersize": 6, "markerfacecolor": None, "markeredgecolor": BLUE,
"alpha": 0.8, "markeredgewidth": 0.3, "label": "Daily",
}
dv_pub_3d.draw_timeseries(
ax1, date_d, data_d, label=timeseries_label,
axislimit=timeseries_axislimit, locator=timeseries_locator,
zeroline=timeseries_zeroline, timeseries=timeseries_daily,
)
timeseries_monthly = {
"marker": 'o-', "color": RED, "linewidth": 0.6,
"markersize": 5, "markerfacecolor": None, "markeredgecolor": RED,
"alpha": 0.8, "markeredgewidth": 0, "label": "Monthly",
}
background_fill_timeseries = {
'x': date_m, 'y': data_m - std_m, 'y1': data_m + std_m, "color": RED,
}
dv_pub_3d.draw_timeseries(
ax1, date_m, data_m, label=timeseries_label,
axislimit=timeseries_axislimit, locator=timeseries_locator,
zeroline=timeseries_zeroline, timeseries=timeseries_monthly,
background_fill=background_fill_timeseries,
)
# --------------------
plt.tight_layout()
fig.suptitle(title, fontsize=11, fontproperties=FONT0)
fig.subplots_adjust(bottom=0.2, top=0.88)
circle1 = mpatches.Circle((74, 15), 6, color=BLUE, ec=EDGE_GRAY, lw=0)
circle2 = mpatches.Circle((164, 15), 6, color=RED, ec=EDGE_GRAY, lw=0)
fig.patches.extend([circle1, circle2])
fig.text(0.15, 0.02, 'Daily', color=BLUE, fontproperties=FONT0)
fig.text(0.3, 0.02, 'Monthly', color=RED, fontproperties=FONT0)
ymd_s, ymd_e = date_s.strftime('%Y%m%d'), date_e.strftime('%Y%m%d')
if ymd_s != ymd_e:
fig.text(0.50, 0.02, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.50, 0.02, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.8, 0.02, ORG_NAME, fontproperties=FONT0)
# ---------------
pb_io.make_sure_path_exists(os.path.dirname(pic_path))
plt.savefig(pic_path)
print pic_path
fig.clear()
plt.close()