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()
def plot_omb(date_D, a_D, b_D,
picPath, title,
date_s, date_e):
"""
画偏差时序彩色图
"""
plt.style.use(os.path.join(DV_PATH, 'dv_pub_timeseries.mplstyle'))
if (np.isnan(a_D)).all():
Log.error('Everything is NaN: %s' % picPath)
return
ylim_min, ylim_max = 210, 330
y_res = 0.2
x_size = (date_e - date_s).days
yy = np.arange(ylim_min, ylim_max, y_res) + y_res / 2. # 一列的y值
grid = np.ones(len(date_D)) * yy.reshape(-1, 1)
aa = a_D * np.ones((len(grid), 1))
bb = b_D * np.ones((len(grid), 1))
grid = grid - np.divide((grid - bb), aa)
# zz = np.zeros((len(yy), x_size)) # 2D， 要画的值
zz = np.full((len(yy), x_size), -65535) # 将值填充为 - ，以前填充0
zz = np.ma.masked_where(zz == -65535, zz)
j = 0
xx = [] # 一行的x值
for i in xrange(x_size): # 补充缺失数据的天
date_i = date_s + relativedelta(days=i)
xx.append(date_i)
if j < len(date_D) and date_D[j] == date_i:
zz[:, i] = grid[:, j]
j = j + 1
fig = plt.figure(figsize=(6, 4))
ax = fig.add_subplot(111)
norm = mpl.colors.Normalize(vmin=-4.0, vmax=4.0)
xx = np.array(xx)
plt.pcolormesh(xx, yy, zz, cmap='jet', norm=norm, shading='flat', zorder=0)
plt.grid(True, zorder=10)
xlim_min = date_s
xlim_max = date_e
plt.xlim(xlim_min, xlim_max)
plt.ylim(ylim_min, ylim_max)
plt.ylabel('TB($K$)', fontsize=11, fontproperties=FONT0)
# format the ticks
setXLocator(ax, xlim_min, xlim_max)
set_tick_font(ax)
# title
plt.title(title, fontsize=12, fontproperties=FONT0)
plt.tight_layout()
# --------------------
fig.subplots_adjust(bottom=0.25)
# -------add colorbar ---------
fig.canvas.draw()
point_bl = ax.get_position().get_points()[0] # 左下
point_tr = ax.get_position().get_points()[1] # 右上
cbar_height = 0.05
colorbar_ax = fig.add_axes([point_bl[0] - 0.05, 0.05,
(point_tr[0] - point_bl[0]) / 2.2, cbar_height])
mpl.colorbar.ColorbarBase(colorbar_ax, cmap='jet',
norm=norm,
orientation='horizontal')
# ---font of colorbar-----------
for l in colorbar_ax.xaxis.get_ticklabels():
l.set_fontproperties(FONT0)
l.set_fontsize(9)
# ------Time and ORG_NAME----------------
ymd_s, ymd_e = date_s.strftime('%Y%m%d'), date_e.strftime('%Y%m%d')
if ymd_s != ymd_e:
fig.text(0.52, 0.05, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.52, 0.05, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.82, 0.05, ORG_NAME, fontproperties=FONT0)
# ---------------
pb_io.make_sure_path_exists(os.path.dirname(picPath))
plt.savefig(picPath)
print picPath
fig.clear()
plt.close()