def write(self, data, data_name, out_file):
if self.error:
return
pb_io.make_sure_path_exists(os.path.dirname(self.ofile))
# 写入 HDF5 文件
with h5py.File(out_file, 'a') as h5:
if data_name == "Longitude" or data_name == "Latitude":
h5.create_dataset(data_name,
dtype='f4',
data=data,
compression='gzip',
compression_opts=5,
shuffle=True)
elif data_name == "Ocean_Flag":
h5.create_dataset(data_name,
dtype='i4',
data=data,
compression='gzip',
compression_opts=5,
shuffle=True)
else:
h5.create_dataset(data_name,
dtype='i2',
data=data,
compression='gzip',
compression_opts=5,
shuffle=True)
# 复制属性
attrs = self.attrs[data_name]
for key, value in attrs.items():
h5[data_name].attrs[key] = value
def write(self):
if self.error:
return
pb_io.make_sure_path_exists(os.path.dirname(self.ofile))
# 写入 HDF5 文件
with h5py.File(self.ofile, 'w') as h5:
for k in self.out_data:
# 创建数据集
if k == "Longitude" or k == "Latitude":
h5.create_dataset(k,
dtype='f4',
data=self.out_data[k],
compression='gzip',
compression_opts=5,
shuffle=True)
elif k == "Ocean_Flag":
continue
else:
h5.create_dataset(k,
dtype='i2',
data=self.out_data[k],
compression='gzip',
compression_opts=5,
shuffle=True)
# 复制属性
if k == "Longitude" or k == "Latitude" or k == "Ocean_Flag":
continue
attrs = self.attrs[k]
for key, value in attrs.items():
h5[k].attrs[key] = value
def plot(self):
"""
绘制快视图
:return:
"""
if self.error:
return
try:
# 图片大小
self._get_picture_size(self.picture_width)
fig = plt.figure(figsize=(self.wight, self.height))
ax1 = plt.subplot2grid((1, 1), (0, 0))
# 背景色
fig.set_facecolor(self.facecolor)
# 增加经纬度线
if self.lat_lon_line is not None:
self._get_lat_lon_line()
if self.error:
return
ax1.scatter(self.line_lon, self.line_lat, s=0.001, alpha=1)
# 添加经纬度的文字
if self.text is not None:
self.add_text(ax1)
# 绘主图
plt.imshow(self.data,
cmap=plt.get_cmap(self.cmap),
vmin=self.vmin,
vmax=self.vmax)
ax1.axis('off')
plt.tight_layout()
fig.subplots_adjust(bottom=0, top=1, left=0, right=1)
colorbar_position = fig.add_axes([0.2, 0.95, 0.6, 0.03])
cb = plt.colorbar(cax=colorbar_position, orientation='horizontal')
cb.ax.tick_params(labelsize=8)
if self.main_view is not None and "colorbar_ticks" in self.main_view:
self.colorbar_ticks = self.main_view["colorbar_ticks"]
cb.set_ticks(self.colorbar_ticks)
if self.main_view is not None and "colorbar_tick_label" in self.main_view:
self.colorbar_tick_label = self.main_view[
"colorbar_tick_label"]
cb.set_ticklabels(self.colorbar_tick_label)
pb_io.make_sure_path_exists(os.path.dirname(self.out_picture))
fig.savefig(self.out_picture, dpi=200)
fig.clear()
plt.close()
except Exception as why:
print "plot: {}".format(why)
self.error = True
return
def plot_map(lats, lons, values, out_file, box=None):
p = dv_map_oc.dv_map()
p.show_bg_color = False
p.colorbar_fmt = "%0.2f"
title = ''
# 是否绘制某个区域
if box:
lat_s = float(box[0])
lat_n = float(box[1])
lon_w = float(box[2])
lon_e = float(box[3])
box = [lat_s, lat_n, lon_w, lon_e]
else:
box = [90, -90, -180, 180]
# 绘制经纬度线
p.delat = 10
p.delon = 10
if box[0] - box[1] > 90:
p.delat = 30 # 30
elif box[2] - box[3] > 180:
p.delon = 30 # 30
p.show_line_of_latlon = True
# 是否设置 colorbar 范围
if len(values) == 0:
return
vmin = np.min(values)
vmax = np.max(values)
# # 是否填写 colorbar title
# p.colorbar_label = colorbar_label
#
# p.colorbar_ticks = legend["ticks"]
#
# p.colorbar_tick_labels = legend["tick_labels"]
p.title = title
with time_block("plot combine map", switch=TIME_TEST):
p.easyplot(lats,
lons,
values,
ptype=None,
vmin=vmin,
vmax=vmax,
box=box,
markersize=2,
marker='o')
# p.easyplot(lats, lons, value, ptype="pcolormesh", vmin=vmin, vmax=vmax, box=box)
pb_io.make_sure_path_exists(os.path.dirname(out_file))
p.savefig(out_file, dpi=300)
print '>>> {}'.format(out_file)
def draw_butterfly(sat1Nm, sat2Nm, ymd_s, ymd_e, fy2_lon, fy2_lat, lons, lats, days, out_fig_file):
'''
画 FY2X 匹配蝴蝶图
'''
fig = plt.figure(figsize=(8, 6), dpi=100) # china
ax = subplot(111)
plt.subplots_adjust(left=0.11, right=0.91, bottom=0.12, top=0.92)
m = drawFig_map(fig)
maxday = np.max(days)
COLORS = ['#d65856', '#d4d656', '#4cd964', '#1abc9c', '#5ac8fa', '#007aff', '#5856d6']
if len(COLORS) < maxday:
Log.error('defined COLORS not enough.')
return
for i in xrange(1, maxday + 1):
idx = days == i
lons_1day = lons[idx]
lats_1day = lats[idx]
plot_matchpoint(m, lons_1day, lats_1day, COLORS[i - 1])
x, y = m(fy2_lon, fy2_lat)
m.plot(x, y, marker='o', linewidth=0, markerfacecolor=RED, markeredgecolor=EDGE_GRAY,
markersize=8, mew=0.2)
# ---------legend-----------
circle1 = mpatches.Circle((58, 36), 6, color=RED, ec=EDGE_GRAY, lw=0.3)
circle_lst = [circle1]
for i in xrange(maxday):
circle_lst.append(mpatches.Circle((219 + i * 7, 36), 6, color=COLORS[i], ec=EDGE_GRAY, lw=0.3))
fig.patches.extend(circle_lst)
TEXT_Y = 0.05
fig.text(0.1, TEXT_Y, '%s' % sat1Nm, color=RED, fontproperties=FONT0)
fig.text(0.34, TEXT_Y, '%s' % sat2Nm, color=BLUE, fontproperties=FONT0)
if ymd_s != ymd_e:
fig.text(0.55, TEXT_Y, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.55, TEXT_Y, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.83, TEXT_Y, ORG_NAME, fontproperties=FONT0)
# 设定Map边框粗细
spines = ax.spines
for eachspine in spines:
spines[eachspine].set_linewidth(0)
pb_io.make_sure_path_exists(os.path.dirname(out_fig_file))
fig.savefig(out_fig_file, dpi=100)
fig.clear()
plt.close()
def plot_bias_map(lat=None,
lon=None,
data=None,
out_file=None,
title=None,
vmin=None,
vmax=None):
if title:
title = title
else:
title = "Map"
# 绘制偏差的全球分布图，保持0值永远在bar的中心
if vmin is not None and vmax is not None:
vmin = vmin
vmax = vmax
else:
datamax = np.max(data)
if datamax >= 0:
vmin = -1.0 * datamax
vmax = datamax
else:
vmin = datamax
vmax = -1.0 * datamax
p = dv_map.dv_map()
p.colorbar_fmt = '%0.3f'
color_list = [
'#000081', '#0000C8', '#1414FF', '#A3A3FF', '#FFA3A3', '#FF1414',
'#C70000', '#810000'
]
cmap = colors.ListedColormap(color_list, 'indexed')
p.easyplot(lat,
lon,
data,
vmin=vmin,
vmax=vmax,
ptype=None,
markersize=0.05,
marker='s',
colormap=cmap)
p.title = title
make_sure_path_exists(os.path.dirname(out_file))
p.savefig(out_file)
print '>>> {}'.format(out_file)
def draw_butterfly(sat1Nm, sat2Nm, ymd_s, ymd_e, lons, lats, out_fig_file):
'''
画 FY3X 匹配蝴蝶图
'''
# COLORS = ['#4cd964', '#1abc9c', '#5ac8fa', '#007aff', '#5856d6']
COLORS = [RED]
fig = plt.figure(figsize=(8, 5), dpi=100) # china
# plt.subplots_adjust(left=0.11, right=0.91, bottom=0.12, top=0.92)
plt.subplots_adjust(left=0.09, right=0.93, bottom=0.12, top=0.94)
ax = subplot(121)
m1 = drawFig_map(ax, "north")
plot_matchpoint(m1, lons, lats, COLORS[0])
ax = subplot(122)
m2 = drawFig_map(ax, "south")
plot_matchpoint(m2, lons, lats, COLORS[0])
# ---------legend-----------
circle1 = mpatches.Circle((58, 36), 6, color=RED, ec=EDGE_GRAY, lw=0.3)
# circle_lst = [circle1]
# for i in xrange(1):
# circle_lst.append(mpatches.Circle((219 + i * 7, 36), 6, color=COLORS[i], ec=EDGE_GRAY, lw=0.3))
# fig.patches.extend(circle_lst)
TEXT_Y = 0.05
fig.text(0.1, TEXT_Y, '%s' % sat1Nm, color=RED, fontproperties=FONT0)
# fig.text(0.34, TEXT_Y, '%s' % sat2Nm, color=BLUE, fontproperties=FONT0)
if ymd_s != ymd_e:
fig.text(0.55, TEXT_Y, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.55, TEXT_Y, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.83, TEXT_Y, ORG_NAME, fontproperties=FONT0)
# 设定Map边框粗细
spines = ax.spines
for eachspine in spines:
spines[eachspine].set_linewidth(0)
pb_io.make_sure_path_exists(os.path.dirname(out_fig_file))
fig.savefig(out_fig_file, dpi=100)
fig.clear()
plt.close()
def _write(self, out_file):
if self.error:
return
pb_io.make_sure_path_exists(os.path.dirname(out_file))
# 写入 HDF5 文件
with h5py.File(out_file, 'w') as h5:
# 创建数据集
h5.create_dataset("data_ii", dtype='i2',
data=self.data_ii,
compression='gzip', compression_opts=1,
shuffle=True)
h5.create_dataset("data_jj", dtype='i2',
data=self.data_jj,
compression='gzip', compression_opts=1,
shuffle=True)
h5.create_dataset("lut_ii", dtype='i2',
data=self.lut_ii,
compression='gzip', compression_opts=1,
shuffle=True)
h5.create_dataset("lut_jj", dtype='i2',
data=self.lut_jj,
compression='gzip', compression_opts=1,
shuffle=True)
def plot_regression(
data_x=None,
data_y=None,
out_file=None,
title=None,
x_label=None,
y_label=None,
annotate=None,
ymd_start=None,
ymd_end=None,
ymd=None,
point_color=True,
plot_slope=True,
plot_zero=True,
):
main_path = os.path.dirname(os.path.dirname(__file__))
style_file = os.path.join(main_path, "cfg", 'histogram.mplstyle')
plt.style.use(style_file)
fig = plt.figure(figsize=(6, 6))
# fig.subplots_adjust(top=0.88, bottom=0.11, left=0.12, right=0.97)
ax1 = plt.subplot2grid((1, 1), (0, 0))
annotate_new = {'left_top': []}
if plot_slope:
# 绘制回归线
max_value = np.nanmax(data_x)
min_value = np.nanmin(data_x)
color_regression = '#ff0000'
width_regression = 1.0
ab = np.polyfit(data_x, data_y, 1)
p1 = np.poly1d(ab)
p1_max = p1(max_value)
p1_min = p1(min_value)
ax1.plot([min_value, max_value], [p1_min, p1_max],
color=color_regression,
linewidth=width_regression,
zorder=100)
annotate_new = {
'left_top':
['Slope={:.4f}'.format(ab[0]), 'Offset={:.4f}'.format(ab[1])]
}
# 绘制对角线
if plot_zero:
color_diagonal = '#888888'
width_diagonal = 1.0
max_value = abs(np.nanmax(np.concatenate((data_x, data_y))))
min_value = -1 * max_value
ax1.plot([min_value, max_value], [min_value, max_value],
color=color_diagonal,
linewidth=width_diagonal,
zorder=80)
ax = Scatter(ax1)
x_min_value = np.min(data_x)
x_max_value = np.max(data_x)
y_min_value = np.min(data_y)
y_max_value = np.max(data_y)
ax.set_x_axis_range(x_min_value, x_max_value)
ax.set_y_axis_range(y_min_value, y_max_value)
if x_label:
ax.set_x_label(x_label)
if y_label:
ax.set_y_label(y_label)
if annotate:
if plot_slope:
annotate_new['left_top'].extend(annotate['left_top'])
else:
annotate_new = annotate
ax.set_annotate(annotate=annotate_new)
size = 1
alpha = 0.8 # 透明度
marker = "o" # 形状
color = "b" # 颜色
ax.set_scatter(size=size, alpha=alpha, marker=marker, color=color)
# kde 是否绘制密度点颜色
if point_color:
ax.plot_scatter(data_x=data_x, data_y=data_y, kde=True)
plt.colorbar(ax.plot_result)
else:
ax.plot_scatter(data_x=data_x, data_y=data_y, kde=False)
# --------------------
plt.tight_layout()
fig.suptitle(title, fontsize=11, fontproperties=FONT0)
fig.subplots_adjust(bottom=0.13, top=0.88)
if ymd_start and ymd_end:
fig.text(0.50,
0.02,
'%s-%s' % (ymd_start, ymd_end),
fontproperties=FONT0)
elif ymd:
fig.text(0.50, 0.02, '%s' % ymd, fontproperties=FONT0)
fig.text(0.8, 0.02, 'OCC', fontproperties=FONT0)
# ---------------
make_sure_path_exists(os.path.dirname(out_file))
fig.savefig(out_file)
fig.clear()
plt.close()
print '>>> {}'.format(out_file)
def plot_scatter(
data_x=None,
data_y=None,
out_file=None,
title=None,
x_range=None,
y_range=None,
x_label=None,
y_label=None,
annotate=None,
ymd_start=None,
ymd_end=None,
ymd=None,
):
main_path = os.path.dirname(os.path.dirname(__file__))
style_file = os.path.join(main_path, "cfg", 'histogram.mplstyle')
plt.style.use(style_file)
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))
ax = Scatter(ax1)
if x_range:
ax.set_x_axis_range(x_range[0], x_range[1])
if y_range:
ax.set_y_axis_range(y_range[0], y_range[1])
if x_label:
ax.set_x_label(x_label)
if y_label:
ax.set_y_label(y_label)
if annotate:
ax.set_annotate(annotate=annotate)
size = 1
alpha = 0.8 # 透明度
marker = "o" # 形状
color = "b" # 颜色
ax.set_scatter(size=size, alpha=alpha, marker=marker, color=color)
ax.plot_scatter(data_x=data_x, data_y=data_y)
# --------------------
plt.tight_layout()
fig.suptitle(title, fontsize=11, fontproperties=FONT0)
fig.subplots_adjust(bottom=0.2, top=0.88)
if ymd_start and ymd_end:
fig.text(0.50,
0.02,
'%s-%s' % (ymd_start, ymd_end),
fontproperties=FONT0)
elif ymd:
fig.text(0.50, 0.02, '%s' % ymd, fontproperties=FONT0)
fig.text(0.8, 0.02, 'OCC', fontproperties=FONT0)
# ---------------
make_sure_path_exists(os.path.dirname(out_file))
fig.savefig(out_file)
fig.clear()
plt.close()
print '>>> {}'.format(out_file)
def draw_combine(self):
"""
通过日合成文件，画数据集的全球分布图
文件中需要有 Latitude 和Longitude 两个数据集
:return:
"""
try:
with h5py.File(self.in_file, 'r') as h5:
dataset = h5.get(self.dataset_name)
value = dataset[:]
slope = dataset.attrs["Slope"]
intercept = dataset.attrs["Intercept"]
value = value * slope + intercept
lats = h5.get("Latitude")[:]
lons = h5.get("Longitude")[:]
except Exception as why:
print why
return
# 过滤有效范围外的值
idx = np.where(value > 0) # 不计算小于 0 的无效值
if len(idx[0]) == 0:
print "Don't have enough valid value： {} {}".format(
self.dataset_name, len(idx[0]))
return
else:
print "{} valid value count: {}".format(self.dataset_name,
len(idx[0]))
value = value[idx]
lats = lats[idx]
lons = lons[idx]
# ############对特殊数据集的值进行处理
# 有一些数据集的数据在绘图时需要取对数，否则无法区分
if self.map is not None and "log10" in self.map:
if self.map["log10"]:
value = np.log10(value)
# 有一些数据按照原来的 slope 不对，需要乘 10
if "Rw" in self.dataset_name:
value = value * 10
# #################################
if DEBUG:
print "-" * 100
print self.dataset_name
d = np.histogram(value, bins=[x * 0.05 for x in xrange(-40, 80)])
for i in xrange(len(d[0])):
print "{:10} :: {:10}".format(d[1][i], d[0][i])
print value.min()
print value.max()
print "-" * 100
p = dv_map_oc.dv_map()
p.show_bg_color = True
p.colorbar_fmt = "%0.2f"
if self.map is not None and "title" in self.map:
title = self.map["title"]
else:
title = self.dataset_name
# 绘制经纬度线
if self.map is not None and "lat_lon_line" in self.map:
lat_lon_line = self.map["lat_lon_line"]
delat = lat_lon_line["delat"]
delon = lat_lon_line["delon"]
p.delat = delat # 30
p.delon = delon # 30
p.show_line_of_latlon = True
else:
p.show_line_of_latlon = False
# 是否绘制某个区域
if self.map is not None and "area_range" in self.map:
area_range = self.map["area_range"]
lat_s = float(area_range.get("lat_s"))
lat_n = float(area_range.get("lat_n"))
lon_w = float(area_range.get("lon_w"))
lon_e = float(area_range.get("lon_e"))
box = [lat_s, lat_n, lon_w, lon_e]
else:
box = None
# 是否设置 colorbar 范围
if self.map is not None and "legend" in self.map:
legend = self.map["legend"]
vmin = legend["vmin"]
vmax = legend["vmax"]
# 是否填写 colorbar title
if "label" in legend:
colorbar_label = legend["label"]
p.colorbar_label = colorbar_label
if "ticks" in legend:
p.colorbar_ticks = legend["ticks"]
if "tick_labels" in legend:
p.colorbar_tick_labels = legend["tick_labels"]
else:
vmin = vmax = None
p.title = title
with time_block("plot combine map", switch=TIME_TEST):
p.easyplot(lats,
lons,
value,
ptype=None,
vmin=vmin,
vmax=vmax,
box=box,
markersize=0.1,
marker='o')
# p.easyplot(lats, lons, value, ptype="pcolormesh", vmin=vmin, vmax=vmax, box=box)
pb_io.make_sure_path_exists(os.path.dirname(self.out_file))
p.savefig(self.out_file, dpi=300)
def write(self):
"""
将处理后的数据写入 HDF5 文件
"""
# 创建生成输出目录
pb_io.make_sure_path_exists(os.path.dirname(self.out_file))
# 写入数据
with h5py.File(self.out_file, 'w') as hdf5:
with h5py.File(self.l1_1000m, 'r') as m1000:
with h5py.File(self.obc_1000m, 'r') as obc:
# 创建输出文件的数据集
for i in xrange(0, 20):
channel_name = 'CH_{:02}'.format(i + 1)
name = '{}/Dn'.format(channel_name)
k = i
if k != 4:
data = self.Dn[channel_name].astype('u2')
else:
data = m1000.get('EV_250_Aggr.1KM_Emissive')[:]
dtype = 'u2'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/Ref'.format(channel_name)
k = i
if k < 4:
data = self.ev_250m_ref[k]
elif k == 4:
data = m1000.get('EV_250_Aggr.1KM_Emissive')[:]
else:
k = k - 5
data = self.ev_1000m_ref[k]
dtype = 'u2'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/SV'.format(channel_name)
k = i
if k < 4:
data = self.sv_extract_obc[k]
elif k == 4:
data = obc.get('SV_250m_EMIS')[:]
else:
k = k - 1
data = self.sv_extract_obc[k]
dtype = 'u2'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/CalCoeff'.format(channel_name)
k = i
if k < 4:
data = self.coeff[k].reshape(-1, 1)
elif k == 4:
data = np.array([1., 0., 0.]).reshape(-1, 1)
else:
k = k - 1
data = self.coeff[k].reshape(-1, 1)
dtype = 'f4'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/BB'.format(channel_name)
data = m1000.get('BB_DN_average')[i].reshape(-1, 1)
dtype = 'f4'
self._create_dataset(name, data, dtype, hdf5)
name = 'Height'
dtype = 'i2'
data = m1000.get('Height')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'LandCover'
dtype = 'u1'
data = m1000.get('LandCover')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'LandSeaMask'
dtype = 'u1'
data = m1000.get('LandSeaMask')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'Latitude'
dtype = 'f4'
data = m1000.get('Latitude')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'Longitude'
dtype = 'f4'
data = m1000.get('Longitude')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'SolarZenith'
dtype = 'i2'
data = m1000.get('SolarZenith')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'SolarAzimuth'
dtype = 'i2'
data = m1000.get('SolarAzimuth')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'SensorZenith'
dtype = 'i2'
data = m1000.get('SensorZenith')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'SensorAzimuth'
dtype = 'i2'
data = m1000.get('SensorAzimuth')[:]
self._create_dataset(name, data, dtype, hdf5)
name = 'Times'
dtype = 'i4'
data = self.Time
self._create_dataset(name, data, dtype, hdf5)
# 复制文件属性
pb_io.copy_attrs_h5py(m1000, hdf5)
# 添加文件属性
hdf5.attrs['dsl'] = self.dsl
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_abc(date_D, a_D, b_D, c_D, date_M, a_M, b_M, c_M, picPath, title,
date_s, date_e, var):
fig = plt.figure(figsize=(6, 6))
ax1 = plt.subplot(311)
ax2 = plt.subplot(312, sharex=ax1)
ax3 = plt.subplot(313, sharex=ax1)
# format the Xticks
xlim_min = pb_time.ymd2date('%04d%02d01' % (date_s.year, date_s.month))
xlim_max = date_e
ax1.set_xlim(xlim_min, xlim_max)
# format the Yticks\
if var == "tbb-tbb" or var == "ref-ref":
ax1.set_ylim(0, 2)
ax1.yaxis.set_major_locator(MultipleLocator(0.5))
ax1.yaxis.set_minor_locator(MultipleLocator(0.1))
elif var == "dn-ref":
ax1.set_ylim(0.0002, 0.0007)
ax1.yaxis.set_major_locator(MultipleLocator(0.0001))
ax1.yaxis.set_minor_locator(MultipleLocator(0.00005))
if var == "tbb-tbb":
ax2.set_ylim(-30, 30)
ax2.yaxis.set_major_locator(MultipleLocator(10))
ax2.yaxis.set_minor_locator(MultipleLocator(2))
elif var == "ref-ref":
ax2.set_ylim(-0.1, 0.1)
ax2.yaxis.set_major_locator(MultipleLocator(0.02))
ax2.yaxis.set_minor_locator(MultipleLocator(0.01))
elif var == "dn-ref":
ax2.set_ylim(-0.08, 0.08)
ax2.yaxis.set_major_locator(MultipleLocator(0.02))
ax2.yaxis.set_minor_locator(MultipleLocator(0.01))
ax3.set_ylim(0, 7)
ax3.yaxis.set_major_locator(MultipleLocator(1))
ax3.yaxis.set_minor_locator(MultipleLocator(0.2))
# plot ax1 -------------------------------------------------
plt.sca(ax1)
plt.plot(date_D,
a_D,
'x',
ms=5,
markerfacecolor=None,
markeredgecolor=BLUE,
alpha=0.8,
mew=0.3,
label='Daily')
plt.plot(date_M, a_M, 'o-', ms=4, lw=0.6, c=RED, mew=0, label='Monthly')
plt.ylabel('Slope', fontsize=11, fontproperties=FONT0)
plt.grid(True)
plt.title(title, fontsize=12, fontproperties=FONT0)
set_tick_font(ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
# plot ax2 -------------------------------------------------
plt.sca(ax2)
plt.plot(date_D,
b_D,
'x',
ms=5,
markerfacecolor=None,
markeredgecolor=BLUE,
alpha=0.8,
mew=0.3,
label='Daily')
plt.plot(date_M, b_M, 'o-', ms=4, lw=0.6, c=RED, mew=0, label='Monthly')
plt.ylabel('Intercept', fontsize=11, fontproperties=FONT0)
plt.grid(True)
set_tick_font(ax2)
plt.setp(ax2.get_xticklabels(), visible=False)
# point number -------------------------------------------------
plt.sca(ax3)
plt.fill_between(date_D, 0, c_D, edgecolor=BLUE, facecolor=BLUE, alpha=0.6)
# plt.fill_between(date_M, 0, c_M,
# edgecolor=RED, facecolor=RED, alpha=0.5)
# plt.bar(date_M, c_M, width=1, align='edge', # "center",
# color=RED, linewidth=0)
plt.plot(date_M, c_M, 'o-', ms=4, lw=0.6, c=RED, mew=0, label='Monthly')
plt.ylabel('Number of sample points\nlog (base = 10)',
fontsize=11,
fontproperties=FONT0)
plt.grid(True)
set_tick_font(ax3)
setXLocator(ax3, xlim_min, xlim_max)
# circle1 = mpatches.Circle((430, 563), 5, color=BLUE, ec=EDGE_GRAY, lw=0)
# circle2 = mpatches.Circle((508, 563), 5, color=RED, ec=EDGE_GRAY, lw=0)
# fig.patches.extend([circle1, circle2])
#
# fig.text(0.74, 0.93, 'Daily', color=BLUE, fontproperties=FONT0)
# fig.text(0.86, 0.93, 'Monthly', color=RED, fontproperties=FONT0)
#---------------
plt.tight_layout()
fig.subplots_adjust(bottom=0.14)
circle1 = mpatches.Circle((74, 18), 6, color=BLUE, ec=EDGE_GRAY, lw=0)
circle2 = mpatches.Circle((164, 18), 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(picPath))
fig.savefig(picPath)
plt.close()
fig.clear
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(dvPath, "dv_pub_regression_dev.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_dev.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_dev.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_dev.draw_histogram(
ax3, x, label=histogram_label, locator=histogram_locator, tick=histogram_tick,
axislimit=histogram_axislimit, histogram=histogram_x,
)
dv_pub_3d_dev.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_dev.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_dev.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 writeTxt(plt_cfg, part1, part2, o_name, ymd, dict_cabr, DayOrNight, isMonthly):
'''
生成abr文件
ymd: YYYYMMDD or YYYYMM
'''
if len(ymd) == 6:
ymd = ymd + '01'
if isMonthly:
FileName = os.path.join(ABR_DIR, '%s_%s' % (part1, part2), '%s_%s_%s_%s_Monthly.txt' % (part1, part2, o_name, DayOrNight))
else:
FileName = os.path.join(ABR_DIR, '%s_%s' % (part1, part2), '%s_%s_%s_%s_%s.txt' % (part1, part2, o_name, DayOrNight, ymd[:4]))
title_line = 'YMD '
newline = ''
for chan in plt_cfg['chan']:
title_line = title_line + ' Count(%s) Slope(%s) Intercept(%s) RSquared(%s)' % (chan, chan, chan, chan)
newline = newline + ' %10d %-10.6f %-10.6f %-10.6f' % (tuple(dict_cabr[o_name][chan]))
newline = newline + '\n' # don't forget to end with \n
allLines = []
titleLines = []
DICT_TXT = {}
pb_io.make_sure_path_exists(os.path.dirname(FileName))
# 写十行头信息
titleLines.append('Sat1: %s\n' % part1)
titleLines.append('Sat2: %s\n' % part2)
if isMonthly:
titleLines.append('TimeRange: since launch\n')
titleLines.append(' Monthly\n')
else:
titleLines.append('TimeRange: %s\n' % ymd[:4])
titleLines.append(' Daily\n')
titleLines.append('Day or Night: %s\n' % DayOrNight)
titleLines.append('Calc time : %s\n' % get_local_time().strftime('%Y.%m.%d %H:%M:%S'))
titleLines.append('\n')
titleLines.append('\n')
titleLines.append(title_line + '\n')
titleLines.append('-' * len(title_line) + '\n')
#
if os.path.isfile(FileName) and os.path.getsize(FileName) != 0:
fp = open(FileName, 'r')
for i in xrange(10):
fp.readline() # 跳过头十行
Lines = fp.readlines()
fp.close()
# 使用字典特性，保证时间唯一，读取数据
for Line in Lines:
DICT_TXT[Line[:8]] = Line[8:]
# 添加或更改数据
DICT_TXT[ymd] = newline
# 按照时间排序
newLines = sorted(DICT_TXT.iteritems(), key=lambda d:d[0], reverse=False)
for i in xrange(len(newLines)):
allLines.append(str(newLines[i][0]) + str(newLines[i][1]))
else:
allLines.append(ymd + newline)
fp = open(FileName, 'w')
fp.writelines(titleLines)
fp.writelines(allLines)
fp.close()
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 main(sat_sensor, in_file):
"""
对L3数据进行合成
:param sat_sensor: 卫星+传感器
:param in_file: yaml 文件
:return:
"""
# ######################## 初始化 ###########################
# 获取程序所在位置，拼接配置文件
app = InitApp(sat_sensor)
if app.error:
print "Load config file error."
return
gc = app.global_config
sc = app.sat_config
yc = Config(in_file)
log = LogServer(gc.path_out_log)
# 加载全局配置信息
sat_sensor1 = sat_sensor.split('_')[1]
sat_sensor2 = sat_sensor.split('_')[0]
sat1, sensor1 = sat_sensor1.split('+')
sat2, sensor2 = sat_sensor2.split('+')
# 加载卫星配置信息
s_channel1 = sc.name
s_channel2 = sc.name
timseries_channels_config = sc.timeseries_l2_channels
# 加载业务配置信息
# ######################## 开始处理 ###########################
print "-" * 100
print "Start plot verify result."
if not os.path.isfile(in_file):
log.error("File is not exist: {}".format(in_file))
return
print "<<< {}".format(in_file)
all_files = yc.path_ipath
# 加载数据
data_absolute = dict()
data_relative = dict()
date = dict()
ref_s1_all = dict()
ref_s2_all = dict()
amount_all = dict()
date_start = ymd2date(yc.info_ymd_s)
date_end = ymd2date(yc.info_ymd_e)
point_count_min = 2
result = dict()
while date_start <= date_end:
ymd_now = date_start.strftime('%Y%m%d')
in_files = get_one_day_files(all_files=all_files, ymd=ymd_now, ext='.h5',
pattern_ymd=r'.*_(\d{8})')
cross_data = ReadCrossDataL2()
cross_data.read_cross_data(in_files=in_files)
# 循环通道数据
for channel in cross_data.data:
if channel not in data_absolute:
data_absolute[channel] = list()
if channel not in data_relative:
data_relative[channel] = list()
if channel not in date:
date[channel] = list()
if channel not in ref_s1_all:
ref_s1_all[channel] = list()
if channel not in ref_s2_all:
ref_s2_all[channel] = list()
if channel not in amount_all:
amount_all[channel] = list()
if channel not in result:
result[channel] = dict()
if not isinstance(cross_data.data[channel], dict):
continue
ref_s2 = cross_data.data[channel]['MERSI_FovMean']
fine_count = len(ref_s2)
print '---INFO--- {} Points: {}'.format(channel, fine_count)
if fine_count < point_count_min:
print '***WARNING***Dont have enough point to plot: < {}'.format(point_count_min)
continue
ref_s1 = cross_data.data[channel]['MODIS_FovMean']
# 过滤 3 倍std之外的点
mean_ref_s2 = np.nanmean(ref_s2)
std_ref_s2 = np.nanstd(ref_s2)
min_ref_s2 = mean_ref_s2 - 3 * std_ref_s2
max_ref_s2 = mean_ref_s2 + 3 * std_ref_s2
idx = np.logical_and(ref_s2 >= min_ref_s2, ref_s2 <= max_ref_s2)
ref_s2 = ref_s2[idx]
ref_s1 = ref_s1[idx]
# 计算相对偏差和绝对偏差
bias = Bias()
absolute_bias = bias.absolute_deviation(ref_s1, ref_s2)
relative_bias = bias.relative_deviation(ref_s1, ref_s2)
data_absolute[channel].append(np.mean(absolute_bias))
data_relative[channel].append(np.mean(relative_bias))
date[channel].append(date_start)
mean_absolute = np.nanmean(absolute_bias)
std_absolute = np.nanstd(absolute_bias)
amount_absolute = len(absolute_bias)
median_absolute = np.nanmedian(absolute_bias)
rms_absolute = rms(absolute_bias)
mean_relative = np.nanmean(relative_bias)
std_relative = np.nanstd(relative_bias)
amount_relative = len(relative_bias)
median_relative = np.nanmedian(relative_bias)
rms_relative = rms(relative_bias)
mean_ref_s1 = np.nanmean(ref_s1)
std_ref_s1 = np.nanstd(ref_s1)
amount_ref_s1 = len(ref_s1)
median_ref_s1 = np.nanmedian(ref_s1)
rms_ref_s1 = rms(ref_s1)
ref_s1_all[channel].append(mean_ref_s1)
mean_ref_s2 = np.nanmean(ref_s2)
std_ref_s2 = np.nanstd(ref_s2)
amount_ref_s2 = len(ref_s2)
median_ref_s2 = np.nanmedian(ref_s2)
rms_ref_s2 = rms(ref_s2)
ref_s2_all[channel].append(mean_ref_s2)
amount_all[channel].append(amount_ref_s1)
fix_point = sc.plot_scatter_fix_ref
f025_absolute, f025_relative = get_dif_pdif(ref_s1, ref_s2, fix_point)
result_names = ['Dif_mean', 'Dif_std', 'Dif_median', 'Dif_count',
'Dif_rms', 'Dif_025',
'PDif_mean', 'PDif_std', 'PDif_median', 'PDif_count',
'PDif_rms', 'PDif_025',
'{}_MODIS_mean'.format(channel), '{}_MODIS_std'.format(channel), '{}_MODIS_median'.format(channel), '{}_MODIS_count'.format(channel),
'{}_MODIS_rms'.format(channel),
'{}_MERSI_mean'.format(channel), '{}_MERSI_std'.format(channel), '{}_MERSI_median'.format(channel), '{}_MERSI_count'.format(channel),
'{}_MERSI_rms'.format(channel),
'Date']
datas = [mean_absolute, std_absolute, median_absolute, amount_absolute,
rms_absolute, f025_absolute,
mean_relative, std_relative, median_relative, amount_relative,
rms_relative, f025_relative,
mean_ref_s1, std_ref_s1, median_ref_s1, amount_ref_s1,
rms_ref_s1,
mean_ref_s2, std_ref_s2, median_ref_s2, amount_ref_s2,
rms_ref_s2,
ymd_now]
for result_name, data in zip(result_names, datas):
if result_name not in result[channel]:
result[channel][result_name] = list()
else:
result[channel][result_name].append(data)
date_start = date_start + relativedelta(days=1)
for channel in data_absolute:
plot_config = timseries_channels_config[channel]
dif_y_range = plot_config.get('dif_y_range')
pdif_y_range = plot_config.get('pdif_y_range')
ref_s1_y_range = plot_config.get('ref_s1_y_range')
ref_s2_y_range = plot_config.get('ref_s2_y_range')
count_y_range = plot_config.get('count_y_range')
absolute_bias = data_absolute[channel]
if len(absolute_bias) == 0:
print 'Dont have enough point to plot, is 0: {}'.format(channel)
continue
relative_bias = data_relative[channel]
date_channel = date[channel]
ref_s1_channel = ref_s1_all[channel]
ref_s2_channel = ref_s2_all[channel]
amount_channel = amount_all[channel]
# 绘制时间序列图
channel1 = channel
index_channel1 = s_channel1.index(channel1)
channel2 = s_channel2[index_channel1]
title_series = '{}_{} {}_{} Time Series'.format(sat_sensor1, channel1, sat_sensor2,
channel2)
title_ref_s1 = '{}_{} {} Time Series'.format(channel1, sat_sensor1, channel1)
title_ref_s2 = '{}_{} {} Time Series'.format(channel2, sat_sensor2, channel2)
title_amount = '{}_{} {}_{} Matched Points Count Time Series'.format(
sat_sensor1, channel1, sat_sensor2, channel2)
y_label_series_absolute = 'Dif {}-{}'.format(sensor1, sensor2)
y_label_series_relative = 'PDif ({}/{})-1'.format(sensor1, sensor2)
y_label_ref_s1 = '{}'.format(channel1)
y_label_ref_s2 = '{}'.format(channel2)
y_label_amount = 'Count'
picture_path = yc.path_opath
# 孙凌添加,出两张图,限制Y轴坐标的图和不限制Y轴坐标的图,这里是限制Y轴坐标的图
picture_name_absolute = 'Time_Series_Dif_{}_{}_{}_{}.png'.format(
sat_sensor1, channel1, sat_sensor2, channel2)
picture_name_relative = 'Time_Series_PDif_{}_{}_{}_{}.png'.format(
sat_sensor1, channel1, sat_sensor2, channel2)
picture_name_ref_s1 = 'Time_Series_{}_{}.png'.format(sat_sensor1, channel1)
picture_name_ref_s2 = 'Time_Series_{}_{}.png'.format(sat_sensor2, channel2)
picture_name_amount = 'Time_Series_Count_{}_{}_{}_{}.png'.format(
sat_sensor1, channel1, sat_sensor2, channel2)
picture_file_absolute = os.path.join(picture_path, picture_name_absolute)
picture_file_relative = os.path.join(picture_path, picture_name_relative)
picture_file_ref_s1 = os.path.join(picture_path, picture_name_ref_s1)
picture_file_ref_s2 = os.path.join(picture_path, picture_name_ref_s2)
picture_file_amount = os.path.join(picture_path, picture_name_amount)
plot_time_series(day_data_x=date_channel, day_data_y=absolute_bias,
y_range=dif_y_range,
out_file=picture_file_absolute,
title=title_series, y_label=y_label_series_absolute,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e, )
plot_time_series(day_data_x=date_channel, day_data_y=relative_bias,
y_range=pdif_y_range,
out_file=picture_file_relative,
title=title_series, y_label=y_label_series_relative,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e, )
plot_time_series(day_data_x=date_channel, day_data_y=ref_s1_channel,
y_range=ref_s1_y_range,
out_file=picture_file_ref_s1,
title=title_ref_s1, y_label=y_label_ref_s1,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e,
zero_line=False)
plot_time_series(day_data_x=date_channel, day_data_y=ref_s2_channel,
y_range=ref_s2_y_range,
out_file=picture_file_ref_s2,
title=title_ref_s2, y_label=y_label_ref_s2,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e,
zero_line=False)
plot_time_series(day_data_x=date_channel, day_data_y=amount_channel,
y_range=count_y_range,
out_file=picture_file_amount,
title=title_amount, y_label=y_label_amount,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e,
plot_background=False)
# 孙凌添加,出两张图,限制Y轴坐标的图和不限制Y轴坐标的图,这里是不限制Y轴坐标的图
picture_name_absolute = 'Time_Series_Dif_{}_{}_{}_{}_NL.png'.format(
sat_sensor1, channel1, sat_sensor2, channel2)
picture_name_relative = 'Time_Series_PDif_{}_{}_{}_{}_NL.png'.format(
sat_sensor1, channel1, sat_sensor2, channel2)
picture_name_ref_s1 = 'Time_Series_{}_{}_NL.png'.format(sat_sensor1, channel1)
picture_name_ref_s2 = 'Time_Series_{}_{}_NL.png'.format(sat_sensor2, channel2)
picture_name_amount = 'Time_Series_Count_{}_{}_{}_{}_NL.png'.format(
sat_sensor1, channel1, sat_sensor2, channel2)
picture_file_absolute = os.path.join(picture_path, picture_name_absolute)
picture_file_relative = os.path.join(picture_path, picture_name_relative)
picture_file_ref_s1 = os.path.join(picture_path, picture_name_ref_s1)
picture_file_ref_s2 = os.path.join(picture_path, picture_name_ref_s2)
picture_file_amount = os.path.join(picture_path, picture_name_amount)
plot_time_series(day_data_x=date_channel, day_data_y=absolute_bias,
out_file=picture_file_absolute,
title=title_series, y_label=y_label_series_absolute,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e, )
plot_time_series(day_data_x=date_channel, day_data_y=relative_bias,
out_file=picture_file_relative,
title=title_series, y_label=y_label_series_relative,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e, )
plot_time_series(day_data_x=date_channel, day_data_y=ref_s1_channel,
out_file=picture_file_ref_s1,
title=title_ref_s1, y_label=y_label_ref_s1,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e, )
plot_time_series(day_data_x=date_channel, day_data_y=ref_s2_channel,
out_file=picture_file_ref_s2,
title=title_ref_s2, y_label=y_label_ref_s2,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e, )
plot_time_series(day_data_x=date_channel, day_data_y=amount_channel,
out_file=picture_file_amount,
title=title_amount, y_label=y_label_amount,
ymd_start=yc.info_ymd_s, ymd_end=yc.info_ymd_e,
plot_background=False)
# 输出HDF5
hdf5_name = '{}_{}_Dif_PDif_Daily.HDF'.format(sat_sensor1, sat_sensor2)
out_path = yc.path_opath
out_file_hdf5 = os.path.join(out_path, hdf5_name)
make_sure_path_exists(out_path)
write_hdf5(out_file_hdf5, result)
keys = amount_all.keys()
keys.sort()
for channel in keys:
print 'CHANNEL: {} POINT: {}'.format(channel, np.sum(amount_all[channel]))
print '-' * 100
# 输出月的HDF5
# 加载数据
data_absolute = dict()
data_relative = dict()
date = dict()
ref_s1_all = dict()
ref_s2_all = dict()
amount_all = dict()
date_start = ymd2date(yc.info_ymd_s)
date_end = ymd2date(yc.info_ymd_e)
result = dict()
while date_start <= date_end:
ymd_now = date_start.strftime('%Y%m')
in_files = get_one_day_files(all_files=all_files, ymd=ymd_now, ext='.h5',
pattern_ymd=r'.*_(\d{6})')
cross_data = ReadCrossDataL2()
cross_data.read_cross_data(in_files=in_files)
# 循环通道数据
for channel in cross_data.data:
if channel not in data_absolute:
data_absolute[channel] = list()
if channel not in data_relative:
data_relative[channel] = list()
if channel not in date:
date[channel] = list()
if channel not in ref_s1_all:
ref_s1_all[channel] = list()
if channel not in ref_s2_all:
ref_s2_all[channel] = list()
if channel not in amount_all:
amount_all[channel] = list()
if channel not in result:
result[channel] = dict()
ref_s1 = cross_data.data[channel]['MERSI_FovMean']
if len(ref_s1) == 0:
print '{} {} : Dont have enough point, is 0'.format(ymd_now, channel)
continue
ref_s2 = cross_data.data[channel]['MODIS_FovMean']
# 过滤 3 倍std之外的点
mean_ref_s1 = np.nanmean(ref_s1)
std_ref_s1 = np.nanstd(ref_s1)
min_ref_s1 = mean_ref_s1 - 3 * std_ref_s1
max_ref_s1 = mean_ref_s1 + 3 * std_ref_s1
idx = np.logical_and(ref_s1 >= min_ref_s1, ref_s1 <= max_ref_s1)
ref_s1 = ref_s1[idx]
ref_s2 = ref_s2[idx]
# 计算相对偏差和绝对偏差
bias = Bias()
absolute_bias = bias.absolute_deviation(ref_s1, ref_s2)
relative_bias = bias.relative_deviation(ref_s1, ref_s2)
data_absolute[channel].append(np.mean(absolute_bias))
data_relative[channel].append(np.mean(relative_bias))
date[channel].append(date_start)
mean_absolute = np.nanmean(absolute_bias)
std_absolute = np.nanstd(absolute_bias)
amount_absolute = len(absolute_bias)
median_absolute = np.nanmedian(absolute_bias)
rms_absolute = rms(absolute_bias)
mean_relative = np.nanmean(relative_bias)
std_relative = np.nanstd(relative_bias)
amount_relative = len(relative_bias)
median_relative = np.nanmedian(relative_bias)
rms_relative = rms(relative_bias)
mean_ref_s1 = np.nanmean(ref_s1)
std_ref_s1 = np.nanstd(ref_s1)
amount_ref_s1 = len(ref_s1)
median_ref_s1 = np.nanmedian(ref_s1)
rms_ref_s1 = rms(ref_s1)
ref_s1_all[channel].append(mean_ref_s1)
mean_ref_s2 = np.nanmean(ref_s2)
std_ref_s2 = np.nanstd(ref_s2)
amount_ref_s2 = len(ref_s2)
median_ref_s2 = np.nanmedian(ref_s2)
rms_ref_s2 = rms(ref_s2)
ref_s2_all[channel].append(mean_ref_s2)
amount_all[channel].append(amount_ref_s1)
fix_point = sc.plot_scatter_fix_ref
f025_absolute, f025_relative = get_dif_pdif(ref_s1, ref_s2, fix_point)
result_names = ['Dif_mean', 'Dif_std', 'Dif_median', 'Dif_count',
'Dif_rms', 'Dif_025',
'PDif_mean', 'PDif_std', 'PDif_median', 'PDif_count',
'PDif_rms', 'PDif_025',
'{}_MODIS_mean'.format(channel), '{}_MODIS_std'.format(channel), '{}_MODIS_median'.format(channel), '{}_MODIS_count'.format(channel),
'{}_MODIS_rms'.format(channel),
'{}_MERSI_mean'.format(channel), '{}_MERSI_std'.format(channel), '{}_MERSI_median'.format(channel), '{}_MERSI_count'.format(channel),
'{}_MERSI_rms'.format(channel),
'Date']
datas = [mean_absolute, std_absolute, median_absolute, amount_absolute,
rms_absolute, f025_absolute,
mean_relative, std_relative, median_relative, amount_relative,
rms_relative, f025_relative,
mean_ref_s1, std_ref_s1, median_ref_s1, amount_ref_s1,
rms_ref_s1,
mean_ref_s2, std_ref_s2, median_ref_s2, amount_ref_s2,
rms_ref_s2,
ymd_now]
for result_name, data in zip(result_names, datas):
if result_name not in result[channel]:
result[channel][result_name] = list()
else:
result[channel][result_name].append(data)
date_start = date_start + relativedelta(months=1)
# 输出HDF5
hdf5_name = '{}_{}_Dif_PDif_Monthly.HDF'.format(sat_sensor1, sat_sensor2)
out_path = yc.path_opath
out_file_hdf5 = os.path.join(out_path, hdf5_name)
make_sure_path_exists(out_path)
write_hdf5(out_file_hdf5, result)
print '-' * 100
def draw_butterfly(sat1Nm, sat2Nm, ymd_s, ymd_e, lons, lats, out_fig_file,
map_range):
"""
画 FY3X 匹配蝴蝶图
"""
if len(lons) == 0:
return
plt.style.use(os.path.join(dvPath, 'dv_pub_map.mplstyle'))
# COLORS = ['#4cd964', '#1abc9c', '#5ac8fa', '#007aff', '#5856d6']
COLORS = [RED]
if map_range[0] and map_range[1]:
fig = plt.figure(figsize=(8, 10), dpi=100) # china
plt.subplots_adjust(left=0.09, right=0.93, bottom=0.12, top=0.94)
ax1 = plt.subplot2grid((2, 2), (0, 0))
ax2 = plt.subplot2grid((2, 2), (0, 1))
ax3 = plt.subplot2grid((2, 2), (1, 0), colspan=2)
# 画两极
polar_range = map_range[0] # 两极范围
m = drawFig_map(ax1, "north", polar_range)
plot_matchpoint(m, lons, lats, COLORS[0])
m = drawFig_map(ax2, "south", polar_range)
plot_matchpoint(m, lons, lats, COLORS[0])
# 画区域
area_range = map_range[1] # 区域范围
m = drawFig_map(ax3, "area", area_range)
plot_matchpoint(m, lons, lats, COLORS[0])
elif map_range[0]:
fig = plt.figure(figsize=(8, 5), dpi=100) # china
plt.subplots_adjust(left=0.09, right=0.93, bottom=0.12, top=0.94)
ax1 = plt.subplot2grid((1, 2), (0, 0))
ax2 = plt.subplot2grid((1, 2), (0, 1))
# 画两极
polar_range = map_range[0] # 两极范围
m = drawFig_map(ax1, "north", polar_range)
plot_matchpoint(m, lons, lats, COLORS[0])
m = drawFig_map(ax2, "south", polar_range)
plot_matchpoint(m, lons, lats, COLORS[0])
elif map_range[1]:
fig = plt.figure(figsize=(8, 5), dpi=100) # china
plt.subplots_adjust(left=0.09, right=0.93, bottom=0.12, top=0.94)
ax3 = plt.subplot2grid((1, 2), (0, 0), colspan=2)
# 画区域
area_range = map_range[1] # 区域范围
m = drawFig_map(ax3, "area", area_range)
plot_matchpoint(m, lons, lats, COLORS[0])
# ---------legend-----------
# 对整张图片添加文字
TEXT_Y = 0.05
fig.text(0.1, TEXT_Y, '%s' % sat1Nm, color=RED, fontproperties=FONT0)
if ymd_s != ymd_e:
fig.text(0.55, TEXT_Y, '%s-%s' % (ymd_s, ymd_e), fontproperties=FONT0)
else:
fig.text(0.55, TEXT_Y, '%s' % ymd_s, fontproperties=FONT0)
fig.text(0.83, TEXT_Y, ORG_NAME, fontproperties=FONT0)
pb_io.make_sure_path_exists(os.path.dirname(out_fig_file))
fig.savefig(out_fig_file, dpi=100)
print out_fig_file
print '-' * 50
plt.close()
fig.clear()
def plot_histogram(data=None,
out_file=None,
title=None,
x_label=None,
y_label=None,
bins_count=200,
x_range=None,
hist_label=None,
annotate=None,
ymd_start=None,
ymd_end=None,
ymd=None):
"""
:param ymd_end:
:param ymd_start:
:param out_file: str
:param data: np.ndarray
:param title: str
:param x_label: str
:param y_label: str
:param bins_count: int
:param x_range: (int or float, int or float)
:param hist_label: str
:param annotate:
:return:
"""
main_path = os.path.dirname(os.path.dirname(__file__))
style_file = os.path.join(main_path, "cfg", 'histogram.mplstyle')
plt.style.use(style_file)
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))
ax = Histogram(ax1)
if x_range:
ax.set_x_axis_range(x_range[0], x_range[1])
if x_label:
ax.set_x_label(x_label)
if y_label:
ax.set_y_label(y_label)
if annotate:
ax.set_annotate(annotate=annotate)
ax.set_histogram(bins_count=bins_count)
ax.set_histogram(label=hist_label)
ax.plot_histogram(data)
# --------------------
plt.tight_layout()
fig.suptitle(title, fontsize=11, fontproperties=FONT0)
fig.subplots_adjust(bottom=0.2, top=0.88)
if ymd_start and ymd_end:
fig.text(0.50,
0.02,
'%s-%s' % (ymd_start, ymd_end),
fontproperties=FONT0)
elif ymd:
fig.text(0.50, 0.02, '%s' % ymd, fontproperties=FONT0)
fig.text(0.8, 0.02, 'OCC', fontproperties=FONT0)
# ---------------
make_sure_path_exists(os.path.dirname(out_file))
plt.savefig(out_file)
fig.clear()
plt.close()
print '>>> {}'.format(out_file)
def draw(self, in_file, proj_file, dataset_name, vmin=None, vmax=None):
if self.error:
return
# 加载 Proj 数据
if os.path.isfile(proj_file):
try:
with h5py.File(proj_file, 'r') as h5:
lut_ii = h5.get("lut_ii")[:]
lut_jj = h5.get("lut_jj")[:]
data_ii = h5.get("data_ii")[:]
data_jj = h5.get("data_jj")[:]
except Exception as why:
print why
print "Can't open file: {}".format(proj_file)
return
else:
print "File does not exist: {}".format(proj_file)
return
with time_block("Draw load", switch=TIME_TEST):
# 加载产品数据
if os.path.isfile(in_file):
try:
with h5py.File(in_file, 'r') as h5:
proj_value = h5.get(dataset_name)[:][data_ii, data_jj]
except Exception as why:
print why
print "Can't open file: {}".format(in_file)
return
else:
print "File does not exist: {}".format(in_file)
return
if vmin is not None:
vmin = vmin
if vmax is not None:
vmax = vmax
p = dv_map.dv_map()
p.title = "{} {}".format(dataset_name, self.ymd)
# 增加省边界
# p1.show_china_province = True
p.delat = 30
p.delon = 30
p.show_line_of_latlon = False
# p.colormap = 'gist_rainbow'
# p.colormap = 'viridis'
# p.colormap = 'brg'
# 创建查找表
lookup_table = prj_core(
self.cmd, self.res, unit="deg", row=self.row, col=self.col)
lookup_table.grid_lonslats()
lons = lookup_table.lons
lats = lookup_table.lats
# 创建完整的数据投影
value = np.full((self.row, self.col), self.fill_value, dtype='f4')
value[lut_ii, lut_jj] = proj_value
value = np.ma.masked_less_equal(value, 0) # 掩掉 <=0 的数据
# 乘数据的系数，水色产品为 0.001
slope = 0.001
value = value * slope
p.easyplot(lats, lons, value, ptype=None, vmin=vmin,
vmax=vmax, markersize=0.1, marker='o')
out_png_path = os.path.dirname(in_file)
out_png = os.path.join(out_png_path, '{}.png'.format(dataset_name))
pb_io.make_sure_path_exists(os.path.dirname(out_png))
p.savefig(out_png, dpi=300)
def plot_time_series(day_data_x=None,
day_data_y=None,
out_file=None,
title=None,
x_range=None,
y_range=None,
y_label=None,
y_major_count=None,
y_minor_count=None,
ymd_start=None,
ymd_end=None,
ymd=None,
zero_line=True,
plot_background=True):
main_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
style_file = os.path.join(main_path, "cfg", 'time_series.mplstyle')
plt.style.use(style_file)
fig = plt.figure(figsize=(6, 4))
ax1 = plt.subplot2grid((1, 1), (0, 0))
ax = TimeSeries(ax1)
# 配置属性
if x_range is None:
x_range = [np.min(day_data_x), np.max(day_data_x)]
ax.set_x_axis_range(axis_min=x_range[0], axis_max=x_range[1])
if y_range is not None:
ax.set_y_axis_range(axis_min=y_range[0], axis_max=y_range[1])
if y_label is not None:
ax.set_y_label(y_label)
if y_major_count is not None:
ax.set_y_major_count(y_major_count)
if y_minor_count is not None:
ax.set_y_minor_count(y_minor_count)
# 绘制日数据长时间序列
ax.set_time_series(maker='o',
color=BLUE,
line_width=None,
marker_size=3,
marker_facecolor=None,
marker_edgecolor=BLUE,
marker_edgewidth=0.3,
alpha=0.8,
label="Daily")
ax.plot_time_series(day_data_x, day_data_y)
# 绘制背景填充
if plot_background:
month_data_x, month_data_y, month_data_std = get_month_avg_std(
day_data_x, day_data_y)
ax1.errorbar(month_data_x,
month_data_y,
yerr=month_data_std,
fmt='-o',
color=RED,
alpha=0.3)
# ax.set_time_series(maker='o-', color=RED, line_width=0.6, marker_size=3,
# marker_facecolor=None,
# marker_edgecolor=RED, marker_edgewidth=0, alpha=0.8,
# label="Monthly")
# ax.plot_time_series(month_data_x, month_data_y)
# ax.set_background_fill(x=month_data_x,
# y1=month_data_y - month_data_std,
# y2=month_data_y + month_data_std,
# color=RED,
# alpha=0.1,
# )
# ax.plot_background_fill()
# 绘制 y=0 线配置，在绘制之间设置x轴范围
if zero_line:
ax.plot_zero_line()
# 格式化 ax
ax.set_ax()
# --------------------
plt.tight_layout()
fig.suptitle(title, fontsize=11, fontproperties=FONT0)
fig.subplots_adjust(bottom=0.2, top=0.88)
if ymd_start and ymd_end:
fig.text(0.50,
0.02,
'%s-%s' % (ymd_start, ymd_end),
fontproperties=FONT0)
elif ymd:
fig.text(0.50, 0.02, '%s' % ymd, fontproperties=FONT0)
fig.text(0.8, 0.02, 'OCC', fontproperties=FONT0)
# ---------------
make_sure_path_exists(os.path.dirname(out_file))
fig.savefig(out_file)
fig.clear()
plt.close()
print '>>> {}'.format(out_file)
def plot(x, y, 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
'''
if xname_l == "TBB": xname_l = "TB"
xlim_min = xmin
xlim_max = xmax
delta = y - x
if xname == "tbb":
step = 5
else:
step = 0.1
T_seg, mean_seg, std_seg, sampleNums = get_bar_data(
x, delta, xlim_min, xlim_max, step)
RadCompare = G_reg1d(x, y)
a, b = RadCompare[0], RadCompare[1]
fig = plt.figure(figsize=(6, 5))
ax1 = plt.subplot(211)
ax2 = plt.subplot(212, sharex=ax1)
# format the Xticks
ax1.set_xlim(xlim_min, xlim_max)
# format the Yticks
if xname == "tbb":
ax1.set_ylim(-10, 10)
ax1.yaxis.set_major_locator(MultipleLocator(5))
ax1.yaxis.set_minor_locator(MultipleLocator(1))
elif xname == "ref":
ax1.set_ylim(-0.3, 0.3)
ax1.yaxis.set_major_locator(MultipleLocator(0.1))
ax1.yaxis.set_minor_locator(MultipleLocator(0.02))
ax2.set_ylim(0, 7)
ax2.yaxis.set_major_locator(MultipleLocator(1))
ax2.yaxis.set_minor_locator(MultipleLocator(0.2))
title = '%s Bias Monthly Statistics\n%s Minus %s %s %s' % \
(xname_l, part2, part1, chan, DayOrNight)
# plot ax1 -------------------------------------------------
plt.sca(ax1)
strlist = [[]]
for ref_temp in reference_list:
plt.axvline(x=ref_temp, color='#4cd964', lw=0.7)
ax1.annotate(str(ref_temp) + xunit, (ref_temp, -7.5),
va="top",
ha="center",
color=EDGE_GRAY,
size=6,
fontproperties=FONT_MONO)
strlist[0].append(
"%s Bias %s: %6.3f" %
(xname_l, str(ref_temp) + xunit, ref_temp * a + b - ref_temp))
strlist[0].append('Total Number: %7d' % len(x))
plt.plot(x,
delta,
'o',
ms=1.5,
markerfacecolor=BLUE,
alpha=0.5,
mew=0,
zorder=10)
plt.plot(T_seg, mean_seg, 'o-', ms=6, lw=0.6, c=RED, mew=0, zorder=50)
plt.fill_between(T_seg,
mean_seg - std_seg,
mean_seg + std_seg,
facecolor=RED,
edgecolor=RED,
interpolate=True,
alpha=0.4,
zorder=100)
ylabel = 'D%s' % (xname_l) + ('($%s$)' % xunit if xunit != "" else "")
plt.ylabel(ylabel, fontsize=11, fontproperties=FONT0)
plt.grid(True)
plt.title(title, fontsize=12, fontproperties=FONT0)
set_tick_font(ax1)
plt.setp(ax1.get_xticklabels(), visible=False)
# point number -------------------------------------------------
plt.sca(ax2)
if xname == "tbb":
width = 3
elif xname == "ref":
width = 0.07
plt.bar(T_seg,
np.log10(sampleNums),
width=width,
align="center",
color=BLUE,
linewidth=0)
for i, T in enumerate(T_seg):
if sampleNums[i] > 0:
plt.text(T,
np.log10(sampleNums[i]) + 0.2,
'%d' % int(sampleNums[i]),
ha="center",
fontsize=6,
fontproperties=FONT_MONO)
add_annotate(ax2, strlist, 'left', EDGE_GRAY, 9)
plt.ylabel('Number of sample points\nlog (base = 10)',
fontsize=11,
fontproperties=FONT0)
xlabel = '%s %s' % (part2, xname_l) + ('($%s$)' %
xunit if xunit != "" else "")
plt.xlabel(xlabel, fontsize=11, fontproperties=FONT0)
plt.grid(True)
set_tick_font(ax2)
#---------------
plt.tight_layout()
fig.subplots_adjust(bottom=0.16)
# circle1 = mpatches.Circle((74, 18), 5, color=BLUE, ec=EDGE_GRAY, lw=0)
# circle2 = mpatches.Circle((164, 18), 5, 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)
fig.text(0.6, 0.02, '%s' % ym, fontproperties=FONT0)
fig.text(0.8, 0.02, ORG_NAME, fontproperties=FONT0)
#---------------
pb_io.make_sure_path_exists(os.path.dirname(picPath))
fig.savefig(picPath)
plt.close()
fig.clear
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, diagonal):
plt.style.use(os.path.join(dvPath, 'dv_pub_legacy.mplstyle'))
titleName = '%s-%s' % (xname.upper(), yname.upper())
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)
DictTitle_rad = {
'xlabel': xlabel,
'ylabel': ylabel,
'title': '{} Regression {} Days {}_{} {} {}'.format(
titleName, num_file, part1, part2, chan, ymd)}
w = 1.0 / weight if weight is not None else None
RadCompare = G_reg1d(x, y, w)
# 过滤 正负 delta+4倍std 的杂点 ------------------------
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 * 4
min_y = reg_line - mean_delta - std_delta * 4
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)
pb_io.make_sure_path_exists(os.path.dirname(o_file))
if diagonal:
dv_pub_legacy.draw_Scatter_Bar(x, y,
o_file, DictTitle_rad,
[['{:15}: {:7.4f}'.format('Slope', RadCompare[0]),
'{:15}: {:7.4f}'.format('Intercept', RadCompare[1]),
'{:15}: {:7.4f}'.format('Cor-Coef', RadCompare[4]),
'{:15}: {:7d}'.format('Number', length_rad)]], '',
part1, part2, xname, xname_l,
xmin, xmax, ymin, ymax)
else:
dv_pub_legacy.draw_Scatter(x, y,
o_file, DictTitle_rad,
[['{:15}: {:7.4f}'.format('Slope', RadCompare[0]),
'{:15}: {:7.4f}'.format('Intercept', RadCompare[1]),
'{:15}: {:7.4f}'.format('Cor-Coef', RadCompare[4]),
'{:15}: {:7d}'.format('Number', length_rad)]], '',
xmin, xmax, ymin, ymax, diagonal)
return [len(x), RadCompare[0], RadCompare[1], RadCompare[4]] # num, a, b, r
def write(self, out_file):
"""
:return:
"""
# 创建生成输出目录
pb_io.make_sure_path_exists(os.path.dirname(out_file))
# 将现在的第5通道数据复制一份到20通道 2018/07/27
self.Dn['CH_20'] = self.Dn['CH_05']
self.Ref['CH_20'] = self.Ref['CH_05']
self.SV['CH_20'] = self.SV['CH_05']
self.cal_coeff1['CH_20'] = self.cal_coeff1['CH_05']
self.BB['CH_20'] = self.BB['CH_05']
# 写入数据
with h5py.File(out_file, 'w') as hdf5:
for i in xrange(0, 20):
channel_name = 'CH_{:02}'.format(i + 1)
name = '{}/Dn'.format(channel_name)
data = self.Dn[channel_name].astype('u2')
dtype = 'u2'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/Ref'.format(channel_name)
data = self.Ref[channel_name].astype('u2')
dtype = 'u2'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/SV'.format(channel_name)
data = self.SV[channel_name].astype('u2')
dtype = 'u2'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/CalCoeff'.format(channel_name)
data = self.cal_coeff1[channel_name]
dtype = 'f4'
self._create_dataset(name, data, dtype, hdf5)
name = '{}/BB'.format(channel_name)
data = self.BB[channel_name]
dtype = 'f4'
self._create_dataset(name, data, dtype, hdf5)
name = 'Height'
dtype = 'i2'
data = self.ary_height
self._create_dataset(name, data, dtype, hdf5)
name = 'LandCover'
dtype = 'u1'
data = self.ary_land_cover
self._create_dataset(name, data, dtype, hdf5)
name = 'LandSeaMask'
dtype = 'u1'
data = self.ary_land_sea_mask
self._create_dataset(name, data, dtype, hdf5)
name = 'Latitude'
dtype = 'f4'
data = self.ary_lat
self._create_dataset(name, data, dtype, hdf5)
name = 'Longitude'
dtype = 'f4'
data = self.ary_lon
self._create_dataset(name, data, dtype, hdf5)
name = 'SolarZenith'
dtype = 'i2'
data = self.ary_sunz
self._create_dataset(name, data, dtype, hdf5)
name = 'SolarAzimuth'
dtype = 'i2'
data = self.ary_suna
self._create_dataset(name, data, dtype, hdf5)
name = 'SensorZenith'
dtype = 'i2'
data = self.ary_satz
self._create_dataset(name, data, dtype, hdf5)
name = 'SensorAzimuth'
dtype = 'i2'
data = self.ary_satz
self._create_dataset(name, data, dtype, hdf5)
name = 'Times'
dtype = 'i4'
data = self.Time
self._create_dataset(name, data, dtype, hdf5)
# 复制文件属性
with h5py.File(self.in_file, 'r') as hdf5_in_file:
pb_io.copy_attrs_h5py(hdf5_in_file, hdf5)
def plot_tbbias(date_D, bias_D, date_M, bias_M, picPath, title, date_s, date_e,
satName):
'''
画偏差时序折线图
'''
fig = plt.figure(figsize=(6, 4))
# plt.subplots_adjust(left=0.13, right=0.95, bottom=0.11, top=0.97)
if (np.isnan(bias_D)).all():
Log.error('Everything is NaN: %s' % picPath)
return
plt.plot(date_D,
bias_D,
'x',
ms=6,
markerfacecolor=None,
markeredgecolor=BLUE,
alpha=0.8,
mew=0.3,
label='Daily')
plt.plot(date_M, bias_M, 'o-', ms=5, lw=0.6, c=RED, mew=0, label='Monthly')
plt.grid(True)
plt.ylabel('DTB($K$)', fontsize=11, fontproperties=FONT0)
xlim_min = pb_time.ymd2date('%04d%02d01' % (date_s.year, date_s.month))
xlim_max = date_e
plt.xlim(xlim_min, xlim_max)
if "FY2" in satName:
plt.ylim(-8, 8)
elif "FY3" in satName:
plt.ylim(-8, 8)
elif "FY4" in satName:
plt.ylim(-2, 2)
ax = plt.gca()
# 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=0.2)
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(picPath))
plt.savefig(picPath)
fig.clear()
plt.close()
def plot_tbbias(date_D, bias_D, date_M, bias_M, picPath, title, date_s, date_e):
"""
画偏差时序折线图
"""
plt.style.use(os.path.join(DV_PATH, 'dv_pub_timeseries.mplstyle'))
fig = plt.figure(figsize=(6, 4))
# plt.subplots_adjust(left=0.13, right=0.95, bottom=0.11, top=0.97)
if (np.isnan(bias_D)).all():
Log.error('Everything is NaN: %s' % picPath)
return
plt.plot(date_D, bias_D, 'x', ms=6,
markerfacecolor=None, markeredgecolor=BLUE, alpha=0.8,
mew=0.3, label='Daily')
plt.plot(date_M, bias_M, 'o-', ms=5, lw=0.6, c=RED,
mew=0, label='Monthly')
plt.grid(True)
plt.ylabel('DTB($K$)', fontsize=11, fontproperties=FONT0)
xlim_min = date_s
xlim_max = date_e
plt.xlim(xlim_min, xlim_max)
plt.ylim(-1, 1)
# 画 y=0 线
plt.plot([xlim_min, xlim_max], [0, 0], color='#808080',
linewidth=1.0)
ax = plt.gca()
# format the ticks
setXLocator(ax, xlim_min, xlim_max)
set_tick_font(ax)
ax.yaxis.set_major_locator(MultipleLocator(0.25))
ax.yaxis.set_minor_locator(MultipleLocator(0.125))
# title
plt.title(title, fontsize=12, fontproperties=FONT0)
plt.tight_layout()
#--------------------
fig.subplots_adjust(bottom=0.2)
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(picPath))
plt.savefig(picPath)
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
'''
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
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=-10.0, vmax=10.0)
xx = np.array(xx)
plt.pcolormesh(xx,
yy,
zz,
cmap='jet',
norm=norm,
shading='gouraud',
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)
fig.clear()
plt.close()
def plot_bias(date_D, bias_D, date_M, bias_M, picPath, title, date_s, date_e,
each, date_type, ylim_min, ylim_max):
"""
画偏差时序折线图
"""
plt.style.use(os.path.join(dvPath, 'dv_pub_timeseries.mplstyle'))
fig = plt.figure(figsize=(6, 4))
# plt.subplots_adjust(left=0.13, right=0.95, bottom=0.11, top=0.97)
plt.plot(date_D,
bias_D,
'x',
ms=6,
markerfacecolor=None,
markeredgecolor=BLUE,
alpha=0.8,
mew=0.3,
label='Daily')
plt.plot(date_M, bias_M, 'o-', ms=5, lw=0.6, c=RED, mew=0, label='Monthly')
plt.grid(True)
plt.ylabel('%s %s' % (each, date_type), fontsize=11, fontproperties=FONT0)
xlim_min = pb_time.ymd2date('%04d%02d01' % (date_s.year, date_s.month))
xlim_max = date_e
plt.plot([xlim_min, xlim_max], [0, 0], '#808080') # 在 y=0 绘制一条深灰色直线
plt.xlim(xlim_min, xlim_max)
plt.ylim(ylim_min, ylim_max)
ax = plt.gca()
# format the ticks
interval = (ylim_max - ylim_min) / 8 # 8 个间隔
minibar = interval / 2.
setXLocator(ax, xlim_min, xlim_max)
set_tick_font(ax)
# 如果范围为浮点数，需要进行一次格式化，否则图像不会显示最后一个刻度
if isinstance(interval, float):
interval = float('%.5f' % interval)
minibar = float('%.5f' % minibar)
ax.yaxis.set_major_locator(MultipleLocator(interval))
ax.yaxis.set_minor_locator(MultipleLocator(minibar))
# title
plt.title(title, fontsize=12, fontproperties=FONT0)
plt.tight_layout()
# --------------------
fig.subplots_adjust(bottom=0.2)
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(picPath))
plt.savefig(picPath)
fig.clear()
plt.close()
